diff --git a/.github/workflows/run-gigaspeech-2022-05-13.yml b/.github/workflows/run-gigaspeech-2022-05-13.yml index d250b72b0..dc33751d3 100644 --- a/.github/workflows/run-gigaspeech-2022-05-13.yml +++ b/.github/workflows/run-gigaspeech-2022-05-13.yml @@ -59,6 +59,8 @@ jobs: - name: Install Python dependencies run: | grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install + pip uninstall -y protobuf + pip install --no-binary protobuf protobuf - name: Cache kaldifeat id: my-cache diff --git a/.github/workflows/run-librispeech-2022-03-12.yml b/.github/workflows/run-librispeech-2022-03-12.yml index b18b84378..291f2bc71 100644 --- a/.github/workflows/run-librispeech-2022-03-12.yml +++ b/.github/workflows/run-librispeech-2022-03-12.yml @@ -59,6 +59,8 @@ jobs: - name: Install Python dependencies run: | grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install + pip uninstall -y protobuf + pip install --no-binary protobuf protobuf - name: Cache kaldifeat id: my-cache @@ -99,7 +101,7 @@ jobs: with: path: | ~/tmp/fbank-libri - key: cache-libri-fbank-test-clean-and-test-other + key: cache-libri-fbank-test-clean-and-test-other-v2 - name: Compute fbank for LibriSpeech test-clean and test-other if: steps.libri-test-clean-and-test-other-fbank.outputs.cache-hit != 'true' diff --git a/.github/workflows/run-librispeech-2022-04-29.yml b/.github/workflows/run-librispeech-2022-04-29.yml index 6c8188b48..b04718f86 100644 --- a/.github/workflows/run-librispeech-2022-04-29.yml +++ b/.github/workflows/run-librispeech-2022-04-29.yml @@ -59,6 +59,8 @@ jobs: - name: Install Python dependencies run: | grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install + pip uninstall -y protobuf + pip install --no-binary protobuf protobuf - name: Cache kaldifeat id: my-cache @@ -99,7 +101,7 @@ jobs: with: path: | ~/tmp/fbank-libri - key: cache-libri-fbank-test-clean-and-test-other + key: cache-libri-fbank-test-clean-and-test-other-v2 - name: Compute fbank for LibriSpeech test-clean and test-other if: steps.libri-test-clean-and-test-other-fbank.outputs.cache-hit != 'true' diff --git a/.github/workflows/run-librispeech-2022-05-13.yml b/.github/workflows/run-librispeech-2022-05-13.yml index 2290e18d4..bb3d74e55 100644 --- a/.github/workflows/run-librispeech-2022-05-13.yml +++ b/.github/workflows/run-librispeech-2022-05-13.yml @@ -59,6 +59,8 @@ jobs: - name: Install Python dependencies run: | grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install + pip uninstall -y protobuf + pip install --no-binary protobuf protobuf - name: Cache kaldifeat id: my-cache @@ -99,7 +101,7 @@ jobs: with: path: | ~/tmp/fbank-libri - key: cache-libri-fbank-test-clean-and-test-other + key: cache-libri-fbank-test-clean-and-test-other-v2 - name: Compute fbank for LibriSpeech test-clean and test-other if: steps.libri-test-clean-and-test-other-fbank.outputs.cache-hit != 'true' diff --git a/.github/workflows/run-librispeech-pruned-transducer-stateless3-2022-05-13.yml b/.github/workflows/run-librispeech-pruned-transducer-stateless3-2022-05-13.yml index 512f1b334..47976fc2c 100644 --- a/.github/workflows/run-librispeech-pruned-transducer-stateless3-2022-05-13.yml +++ b/.github/workflows/run-librispeech-pruned-transducer-stateless3-2022-05-13.yml @@ -59,6 +59,8 @@ jobs: - name: Install Python dependencies run: | grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install + pip uninstall -y protobuf + pip install --no-binary protobuf protobuf - name: Cache kaldifeat id: my-cache @@ -99,7 +101,7 @@ jobs: with: path: | ~/tmp/fbank-libri - key: cache-libri-fbank-test-clean-and-test-other + key: cache-libri-fbank-test-clean-and-test-other-v2 - name: Compute fbank for LibriSpeech test-clean and test-other if: steps.libri-test-clean-and-test-other-fbank.outputs.cache-hit != 'true' diff --git a/.github/workflows/run-librispeech-transducer-stateless2-2022-04-19.yml b/.github/workflows/run-librispeech-transducer-stateless2-2022-04-19.yml index 3864f4aa3..e05b04bee 100644 --- a/.github/workflows/run-librispeech-transducer-stateless2-2022-04-19.yml +++ b/.github/workflows/run-librispeech-transducer-stateless2-2022-04-19.yml @@ -59,6 +59,8 @@ jobs: - name: Install Python dependencies run: | grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install + pip uninstall -y protobuf + pip install --no-binary protobuf protobuf - name: Cache kaldifeat id: my-cache @@ -99,7 +101,7 @@ jobs: with: path: | ~/tmp/fbank-libri - key: cache-libri-fbank-test-clean-and-test-other + key: cache-libri-fbank-test-clean-and-test-other-v2 - name: Compute fbank for LibriSpeech test-clean and test-other if: steps.libri-test-clean-and-test-other-fbank.outputs.cache-hit != 'true' diff --git a/.github/workflows/run-pretrained-conformer-ctc.yml b/.github/workflows/run-pretrained-conformer-ctc.yml index 69f15060b..f4c6bf507 100644 --- a/.github/workflows/run-pretrained-conformer-ctc.yml +++ b/.github/workflows/run-pretrained-conformer-ctc.yml @@ -49,6 +49,8 @@ jobs: - name: Install Python dependencies run: | grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install + pip uninstall -y protobuf + pip install --no-binary protobuf protobuf - name: Cache kaldifeat id: my-cache diff --git a/.github/workflows/run-pretrained-transducer-stateless-librispeech-100h.yml b/.github/workflows/run-pretrained-transducer-stateless-librispeech-100h.yml index f77d9e658..348a68095 100644 --- a/.github/workflows/run-pretrained-transducer-stateless-librispeech-100h.yml +++ b/.github/workflows/run-pretrained-transducer-stateless-librispeech-100h.yml @@ -58,6 +58,8 @@ jobs: - name: Install Python dependencies run: | grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install + pip uninstall -y protobuf + pip install --no-binary protobuf protobuf - name: Cache kaldifeat id: my-cache @@ -98,7 +100,7 @@ jobs: with: path: | ~/tmp/fbank-libri - key: cache-libri-fbank-test-clean-and-test-other + key: cache-libri-fbank-test-clean-and-test-other-v2 - name: Compute fbank for LibriSpeech test-clean and test-other if: steps.libri-test-clean-and-test-other-fbank.outputs.cache-hit != 'true' diff --git a/.github/workflows/run-pretrained-transducer-stateless-librispeech-multi-datasets.yml b/.github/workflows/run-pretrained-transducer-stateless-librispeech-multi-datasets.yml index ddfa62073..d1369c2b1 100644 --- a/.github/workflows/run-pretrained-transducer-stateless-librispeech-multi-datasets.yml +++ b/.github/workflows/run-pretrained-transducer-stateless-librispeech-multi-datasets.yml @@ -58,6 +58,8 @@ jobs: - name: Install Python dependencies run: | grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install + pip uninstall -y protobuf + pip install --no-binary protobuf protobuf - name: Cache kaldifeat id: my-cache @@ -98,7 +100,7 @@ jobs: with: path: | ~/tmp/fbank-libri - key: cache-libri-fbank-test-clean-and-test-other + key: cache-libri-fbank-test-clean-and-test-other-v2 - name: Compute fbank for LibriSpeech test-clean and test-other if: steps.libri-test-clean-and-test-other-fbank.outputs.cache-hit != 'true' diff --git a/.github/workflows/run-pretrained-transducer-stateless-modified-2-aishell.yml b/.github/workflows/run-pretrained-transducer-stateless-modified-2-aishell.yml index 659dbc9da..9d095a0aa 100644 --- a/.github/workflows/run-pretrained-transducer-stateless-modified-2-aishell.yml +++ b/.github/workflows/run-pretrained-transducer-stateless-modified-2-aishell.yml @@ -49,6 +49,8 @@ jobs: - name: Install Python dependencies run: | grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install + pip uninstall -y protobuf + pip install --no-binary protobuf protobuf - name: Cache kaldifeat id: my-cache diff --git a/.github/workflows/run-pretrained-transducer-stateless-modified-aishell.yml b/.github/workflows/run-pretrained-transducer-stateless-modified-aishell.yml index f4e56bd6c..868fe6fbe 100644 --- a/.github/workflows/run-pretrained-transducer-stateless-modified-aishell.yml +++ b/.github/workflows/run-pretrained-transducer-stateless-modified-aishell.yml @@ -49,6 +49,8 @@ jobs: - name: Install Python dependencies run: | grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install + pip uninstall -y protobuf + pip install --no-binary protobuf protobuf - name: Cache kaldifeat id: my-cache diff --git a/.github/workflows/run-pretrained-transducer-stateless.yml b/.github/workflows/run-pretrained-transducer-stateless.yml index cdea78a88..78c1ca059 100644 --- a/.github/workflows/run-pretrained-transducer-stateless.yml +++ b/.github/workflows/run-pretrained-transducer-stateless.yml @@ -58,6 +58,8 @@ jobs: - name: Install Python dependencies run: | grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install + pip uninstall -y protobuf + pip install --no-binary protobuf protobuf - name: Cache kaldifeat id: my-cache @@ -98,7 +100,7 @@ jobs: with: path: | ~/tmp/fbank-libri - key: cache-libri-fbank-test-clean-and-test-other + key: cache-libri-fbank-test-clean-and-test-other-v2 - name: Compute fbank for LibriSpeech test-clean and test-other if: steps.libri-test-clean-and-test-other-fbank.outputs.cache-hit != 'true' diff --git a/.github/workflows/run-pretrained-transducer.yml b/.github/workflows/run-pretrained-transducer.yml index f1b051047..959e57278 100644 --- a/.github/workflows/run-pretrained-transducer.yml +++ b/.github/workflows/run-pretrained-transducer.yml @@ -49,6 +49,8 @@ jobs: - name: Install Python dependencies run: | grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install + pip uninstall -y protobuf + pip install --no-binary protobuf protobuf - name: Cache kaldifeat id: my-cache diff --git a/.github/workflows/run-yesno-recipe.yml b/.github/workflows/run-yesno-recipe.yml index 38c36a7c6..ce77c47df 100644 --- a/.github/workflows/run-yesno-recipe.yml +++ b/.github/workflows/run-yesno-recipe.yml @@ -62,6 +62,8 @@ jobs: - name: Install Python dependencies run: | grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install + pip uninstall -y protobuf + pip install --no-binary protobuf protobuf - name: Run yesno recipe shell: bash diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index fce14c460..f9dab7afe 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -76,6 +76,9 @@ jobs: pip install k2==${{ matrix.k2-version }}+cpu.torch${{ matrix.torch }} -f https://k2-fsa.org/nightly/ pip install git+https://github.com/lhotse-speech/lhotse # icefall requirements + pip uninstall -y protobuf + pip install --no-binary protobuf protobuf + pip install -r requirements.txt - name: Install graphviz diff --git a/README.md b/README.md index d88ed7aac..2096681ea 100644 --- a/README.md +++ b/README.md @@ -2,6 +2,14 @@ +## Introduction + +icefall contains ASR recipes for various datasets +using . + +You can use to deploy models +trained with icefall. + ## Installation Please refer to @@ -12,7 +20,7 @@ for installation. Please refer to for more information. -We provide 6 recipes at present: +We provide the following recipes: - [yesno][yesno] - [LibriSpeech][librispeech] @@ -22,6 +30,7 @@ We provide 6 recipes at present: - [GigaSpeech][gigaspeech] - [Aidatatang_200zh][aidatatang_200zh] - [WenetSpeech][wenetspeech] + - [Alimeeting][alimeeting] ### yesno @@ -126,7 +135,7 @@ The best CER we currently have is: | CER | 4.26 | -We provide a Colab notebook to run a pre-trained conformer CTC model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1WnG17io5HEZ0Gn_cnh_VzK5QYOoiiklC?usp=sharing) +We provide a Colab notebook to run a pre-trained conformer CTC model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg](https://colab.research.google.com/drive/1WnG17io5HEZ0Gn_cnh_VzK5QYOoiiklC?usp=sharing) #### Transducer Stateless Model @@ -247,6 +256,20 @@ We provide one model for this recipe: [Pruned stateless RNN-T: Conformer encoder We provide a Colab notebook to run a pre-trained Pruned Transducer Stateless model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1EV4e1CHa1GZgEF-bZgizqI9RyFFehIiN?usp=sharing) +### Alimeeting + +We provide one model for this recipe: [Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss][Alimeeting_pruned_transducer_stateless2]. + +#### Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss (trained with far subset) + +| | Eval | Test-Net | +|----------------------|--------|----------| +| greedy search | 31.77 | 34.66 | +| fast beam search | 31.39 | 33.02 | +| modified beam search | 30.38 | 34.25 | + +We provide a Colab notebook to run a pre-trained Pruned Transducer Stateless model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1tKr3f0mL17uO_ljdHGKtR7HOmthYHwJG?usp=sharing) + ## Deployment with C++ Once you have trained a model in icefall, you may want to deploy it with C++, @@ -274,6 +297,7 @@ Please see: [![Open In Colab](https://colab.research.google.com/assets/colab-bad [GigaSpeech_pruned_transducer_stateless2]: egs/gigaspeech/ASR/pruned_transducer_stateless2 [Aidatatang_200zh_pruned_transducer_stateless2]: egs/aidatatang_200zh/ASR/pruned_transducer_stateless2 [WenetSpeech_pruned_transducer_stateless2]: egs/wenetspeech/ASR/pruned_transducer_stateless2 +[Alimeeting_pruned_transducer_stateless2]: egs/alimeeting/ASR/pruned_transducer_stateless2 [yesno]: egs/yesno/ASR [librispeech]: egs/librispeech/ASR [aishell]: egs/aishell/ASR @@ -282,4 +306,6 @@ Please see: [![Open In Colab](https://colab.research.google.com/assets/colab-bad [gigaspeech]: egs/gigaspeech/ASR [aidatatang_200zh]: egs/aidatatang_200zh/ASR [wenetspeech]: egs/wenetspeech/ASR +[alimeeting]: egs/alimeeting/ASR [k2]: https://github.com/k2-fsa/k2 +) diff --git a/egs/aidatatang_200zh/ASR/local/compute_fbank_aidatatang_200zh.py b/egs/aidatatang_200zh/ASR/local/compute_fbank_aidatatang_200zh.py index 3c4cfc7f8..9850cf251 100755 --- a/egs/aidatatang_200zh/ASR/local/compute_fbank_aidatatang_200zh.py +++ b/egs/aidatatang_200zh/ASR/local/compute_fbank_aidatatang_200zh.py @@ -29,7 +29,7 @@ import os from pathlib import Path import torch -from lhotse import CutSet, Fbank, FbankConfig, LilcomHdf5Writer +from lhotse import CutSet, Fbank, FbankConfig, LilcomChunkyWriter from lhotse.recipes.utils import read_manifests_if_cached from icefall.utils import get_executor @@ -43,7 +43,7 @@ torch.set_num_interop_threads(1) def compute_fbank_aidatatang_200zh(num_mel_bins: int = 80): - src_dir = Path("data/manifests/aidatatang_200zh") + src_dir = Path("data/manifests") output_dir = Path("data/fbank") num_jobs = min(15, os.cpu_count()) @@ -52,8 +52,13 @@ def compute_fbank_aidatatang_200zh(num_mel_bins: int = 80): "dev", "test", ) + prefix = "aidatatang" + suffix = "jsonl.gz" manifests = read_manifests_if_cached( - dataset_parts=dataset_parts, output_dir=src_dir + dataset_parts=dataset_parts, + output_dir=src_dir, + prefix=prefix, + suffix=suffix, ) assert manifests is not None @@ -61,10 +66,14 @@ def compute_fbank_aidatatang_200zh(num_mel_bins: int = 80): with get_executor() as ex: # Initialize the executor only once. for partition, m in manifests.items(): - if (output_dir / f"cuts_{partition}.json.gz").is_file(): + if (output_dir / f"{prefix}_cuts_{partition}.{suffix}").is_file(): logging.info(f"{partition} already exists - skipping.") continue logging.info(f"Processing {partition}") + + for sup in m["supervisions"]: + sup.custom = {"origin": "aidatatang_200zh"} + cut_set = CutSet.from_manifests( recordings=m["recordings"], supervisions=m["supervisions"], @@ -77,13 +86,14 @@ def compute_fbank_aidatatang_200zh(num_mel_bins: int = 80): ) cut_set = cut_set.compute_and_store_features( extractor=extractor, - storage_path=f"{output_dir}/feats_{partition}", + storage_path=f"{output_dir}/{prefix}_feats_{partition}", # when an executor is specified, make more partitions num_jobs=num_jobs if ex is None else 80, executor=ex, - storage_type=LilcomHdf5Writer, + storage_type=LilcomChunkyWriter, ) - cut_set.to_json(output_dir / f"cuts_{partition}.json.gz") + + cut_set.to_file(output_dir / f"{prefix}_cuts_{partition}.{suffix}") def get_args(): diff --git a/egs/aidatatang_200zh/ASR/local/display_manifest_statistics.py b/egs/aidatatang_200zh/ASR/local/display_manifest_statistics.py index 2352785ac..d66e5cfca 100644 --- a/egs/aidatatang_200zh/ASR/local/display_manifest_statistics.py +++ b/egs/aidatatang_200zh/ASR/local/display_manifest_statistics.py @@ -25,19 +25,19 @@ for usage. """ -from lhotse import load_manifest +from lhotse import load_manifest_lazy def main(): paths = [ - "./data/fbank/cuts_train.json.gz", - "./data/fbank/cuts_dev.json.gz", - "./data/fbank/cuts_test.json.gz", + "./data/fbank/aidatatang_cuts_train.jsonl.gz", + "./data/fbank/aidatatang_cuts_dev.jsonl.gz", + "./data/fbank/aidatatang_cuts_test.jsonl.gz", ] for path in paths: print(f"Starting display the statistics for {path}") - cuts = load_manifest(path) + cuts = load_manifest_lazy(path) cuts.describe() @@ -45,7 +45,7 @@ if __name__ == "__main__": main() """ -Starting display the statistics for ./data/fbank/cuts_train.json.gz +Starting display the statistics for ./data/fbank/aidatatang_cuts_train.jsonl.gz Cuts count: 494715 Total duration (hours): 422.6 Speech duration (hours): 422.6 (100.0%) @@ -61,7 +61,7 @@ min 1.0 99.5% 8.0 99.9% 9.5 max 18.1 -Starting display the statistics for ./data/fbank/cuts_dev.json.gz +Starting display the statistics for ./data/fbank/aidatatang_cuts_dev.jsonl.gz Cuts count: 24216 Total duration (hours): 20.2 Speech duration (hours): 20.2 (100.0%) @@ -77,7 +77,7 @@ min 1.2 99.5% 7.3 99.9% 8.8 max 11.3 -Starting display the statistics for ./data/fbank/cuts_test.json.gz +Starting display the statistics for ./data/fbank/aidatatang_cuts_test.jsonl.gz Cuts count: 48144 Total duration (hours): 40.2 Speech duration (hours): 40.2 (100.0%) diff --git a/egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/asr_datamodule.py b/egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/asr_datamodule.py index 447a011cb..6a5b57e24 100644 --- a/egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/asr_datamodule.py +++ b/egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/asr_datamodule.py @@ -28,10 +28,10 @@ from lhotse import ( Fbank, FbankConfig, load_manifest, + load_manifest_lazy, set_caching_enabled, ) from lhotse.dataset import ( - BucketingSampler, CutConcatenate, CutMix, DynamicBucketingSampler, @@ -206,7 +206,7 @@ class Aidatatang_200zhAsrDataModule: """ logging.info("About to get Musan cuts") cuts_musan = load_manifest( - self.args.manifest_dir / "cuts_musan.json.gz" + self.args.manifest_dir / "musan_cuts.jsonl.gz" ) transforms = [] @@ -290,13 +290,12 @@ class Aidatatang_200zhAsrDataModule: ) if self.args.bucketing_sampler: - logging.info("Using BucketingSampler.") - train_sampler = BucketingSampler( + logging.info("Using DynamicBucketingSampler.") + train_sampler = DynamicBucketingSampler( cuts_train, max_duration=self.args.max_duration, shuffle=self.args.shuffle, num_buckets=self.args.num_buckets, - bucket_method="equal_duration", drop_last=True, ) else: @@ -402,14 +401,20 @@ class Aidatatang_200zhAsrDataModule: @lru_cache() def train_cuts(self) -> CutSet: logging.info("About to get train cuts") - return load_manifest(self.args.manifest_dir / "cuts_train.json.gz") + return load_manifest_lazy( + self.args.manifest_dir / "aidatatang_cuts_train.jsonl.gz" + ) @lru_cache() def valid_cuts(self) -> CutSet: logging.info("About to get dev cuts") - return load_manifest(self.args.manifest_dir / "cuts_dev.json.gz") + return load_manifest_lazy( + self.args.manifest_dir / "aidatatang_cuts_dev.jsonl.gz" + ) @lru_cache() def test_cuts(self) -> List[CutSet]: logging.info("About to get test cuts") - return load_manifest(self.args.manifest_dir / "cuts_test.json.gz") + return load_manifest_lazy( + self.args.manifest_dir / "aidatatang_cuts_test.jsonl.gz" + ) diff --git a/egs/aishell/ASR/conformer_ctc/train.py b/egs/aishell/ASR/conformer_ctc/train.py index 369ad310f..a228cc1fe 100755 --- a/egs/aishell/ASR/conformer_ctc/train.py +++ b/egs/aishell/ASR/conformer_ctc/train.py @@ -195,9 +195,9 @@ def get_params() -> AttributeDict: "best_train_epoch": -1, "best_valid_epoch": -1, "batch_idx_train": 0, - "log_interval": 10, + "log_interval": 50, "reset_interval": 200, - "valid_interval": 3000, + "valid_interval": 2000, # parameters for k2.ctc_loss "beam_size": 10, "reduction": "sum", diff --git a/egs/aishell/ASR/local/compute_fbank_aidatatang_200zh.py b/egs/aishell/ASR/local/compute_fbank_aidatatang_200zh.py new file mode 100755 index 000000000..8cdfad71f --- /dev/null +++ b/egs/aishell/ASR/local/compute_fbank_aidatatang_200zh.py @@ -0,0 +1,119 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This file computes fbank features of the aidatatang_200zh dataset. +It looks for manifests in the directory data/manifests. + +The generated fbank features are saved in data/fbank. +""" + +import argparse +import logging +import os +from pathlib import Path + +import torch +from lhotse import CutSet, Fbank, FbankConfig, LilcomChunkyWriter +from lhotse.recipes.utils import read_manifests_if_cached + +from icefall.utils import get_executor + +# Torch's multithreaded behavior needs to be disabled or +# it wastes a lot of CPU and slow things down. +# Do this outside of main() in case it needs to take effect +# even when we are not invoking the main (e.g. when spawning subprocesses). +torch.set_num_threads(1) +torch.set_num_interop_threads(1) + + +def compute_fbank_aidatatang_200zh(num_mel_bins: int = 80): + src_dir = Path("data/manifests") + output_dir = Path("data/fbank") + num_jobs = min(15, os.cpu_count()) + + dataset_parts = ( + "train", + "test", + "dev", + ) + prefix = "aidatatang" + suffix = "jsonl.gz" + manifests = read_manifests_if_cached( + dataset_parts=dataset_parts, + output_dir=src_dir, + prefix=prefix, + suffix=suffix, + ) + assert manifests is not None + + extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins)) + + with get_executor() as ex: # Initialize the executor only once. + for partition, m in manifests.items(): + if (output_dir / f"{prefix}_cuts_{partition}.{suffix}").is_file(): + logging.info(f"{partition} already exists - skipping.") + continue + logging.info(f"Processing {partition}") + + for sup in m["supervisions"]: + sup.custom = {"origin": "aidatatang_200zh"} + + cut_set = CutSet.from_manifests( + recordings=m["recordings"], + supervisions=m["supervisions"], + ) + if "train" in partition: + cut_set = ( + cut_set + + cut_set.perturb_speed(0.9) + + cut_set.perturb_speed(1.1) + ) + cut_set = cut_set.compute_and_store_features( + extractor=extractor, + storage_path=f"{output_dir}/{prefix}_feats_{partition}", + # when an executor is specified, make more partitions + num_jobs=num_jobs if ex is None else 80, + executor=ex, + storage_type=LilcomChunkyWriter, + ) + + cut_set.to_file(output_dir / f"{prefix}_cuts_{partition}.{suffix}") + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--num-mel-bins", + type=int, + default=80, + help="""The number of mel bins for Fbank""", + ) + + return parser.parse_args() + + +if __name__ == "__main__": + formatter = ( + "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + ) + + logging.basicConfig(format=formatter, level=logging.INFO) + + args = get_args() + compute_fbank_aidatatang_200zh(num_mel_bins=args.num_mel_bins) diff --git a/egs/aishell/ASR/local/compute_fbank_aishell.py b/egs/aishell/ASR/local/compute_fbank_aishell.py index 70dee81d8..e27e35ec5 100755 --- a/egs/aishell/ASR/local/compute_fbank_aishell.py +++ b/egs/aishell/ASR/local/compute_fbank_aishell.py @@ -29,7 +29,7 @@ import os from pathlib import Path import torch -from lhotse import CutSet, Fbank, FbankConfig, LilcomHdf5Writer +from lhotse import CutSet, Fbank, FbankConfig, LilcomChunkyWriter from lhotse.recipes.utils import read_manifests_if_cached from icefall.utils import get_executor @@ -52,8 +52,13 @@ def compute_fbank_aishell(num_mel_bins: int = 80): "dev", "test", ) + prefix = "aishell" + suffix = "jsonl.gz" manifests = read_manifests_if_cached( - prefix="aishell", dataset_parts=dataset_parts, output_dir=src_dir + dataset_parts=dataset_parts, + output_dir=src_dir, + prefix=prefix, + suffix=suffix, ) assert manifests is not None @@ -61,7 +66,7 @@ def compute_fbank_aishell(num_mel_bins: int = 80): with get_executor() as ex: # Initialize the executor only once. for partition, m in manifests.items(): - if (output_dir / f"cuts_{partition}.json.gz").is_file(): + if (output_dir / f"{prefix}_cuts_{partition}.{suffix}").is_file(): logging.info(f"{partition} already exists - skipping.") continue logging.info(f"Processing {partition}") @@ -77,13 +82,13 @@ def compute_fbank_aishell(num_mel_bins: int = 80): ) cut_set = cut_set.compute_and_store_features( extractor=extractor, - storage_path=f"{output_dir}/feats_{partition}", + storage_path=f"{output_dir}/{prefix}_feats_{partition}", # when an executor is specified, make more partitions num_jobs=num_jobs if ex is None else 80, executor=ex, - storage_type=LilcomHdf5Writer, + storage_type=LilcomChunkyWriter, ) - cut_set.to_json(output_dir / f"cuts_{partition}.json.gz") + cut_set.to_file(output_dir / f"{prefix}_cuts_{partition}.{suffix}") def get_args(): diff --git a/egs/aishell/ASR/local/display_manifest_statistics.py b/egs/aishell/ASR/local/display_manifest_statistics.py index 0ae731a1d..c478f7331 100755 --- a/egs/aishell/ASR/local/display_manifest_statistics.py +++ b/egs/aishell/ASR/local/display_manifest_statistics.py @@ -25,18 +25,18 @@ for usage. """ -from lhotse import load_manifest +from lhotse import load_manifest_lazy def main(): - # path = "./data/fbank/cuts_train.json.gz" - # path = "./data/fbank/cuts_test.json.gz" - # path = "./data/fbank/cuts_dev.json.gz" - # path = "./data/fbank/aidatatang_200zh/cuts_train_raw.jsonl.gz" - # path = "./data/fbank/aidatatang_200zh/cuts_test_raw.jsonl.gz" - path = "./data/fbank/aidatatang_200zh/cuts_dev_raw.jsonl.gz" + # path = "./data/fbank/aishell_cuts_train.jsonl.gz" + # path = "./data/fbank/aishell_cuts_test.jsonl.gz" + path = "./data/fbank/aishell_cuts_dev.jsonl.gz" + # path = "./data/fbank/aidatatang_cuts_train.jsonl.gz" + # path = "./data/fbank/aidatatang_cuts_test.jsonl.gz" + # path = "./data/fbank/aidatatang_cuts_dev.jsonl.gz" - cuts = load_manifest(path) + cuts = load_manifest_lazy(path) cuts.describe() diff --git a/egs/aishell/ASR/local/process_aidatatang_200zh.py b/egs/aishell/ASR/local/process_aidatatang_200zh.py deleted file mode 100755 index ac2b86927..000000000 --- a/egs/aishell/ASR/local/process_aidatatang_200zh.py +++ /dev/null @@ -1,71 +0,0 @@ -#!/usr/bin/env python3 -# Copyright 2022 Xiaomi Corp. (Fangjun Kuang) -# -# See ../../../../LICENSE for clarification regarding multiple authors -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import logging -from pathlib import Path - -from lhotse import CutSet -from lhotse.recipes.utils import read_manifests_if_cached - - -def preprocess_aidatatang_200zh(): - src_dir = Path("data/manifests/aidatatang_200zh") - output_dir = Path("data/fbank/aidatatang_200zh") - output_dir.mkdir(exist_ok=True, parents=True) - - dataset_parts = ( - "train", - "test", - "dev", - ) - - logging.info("Loading manifest") - manifests = read_manifests_if_cached( - dataset_parts=dataset_parts, output_dir=src_dir, prefix="aidatatang" - ) - assert len(manifests) > 0 - - for partition, m in manifests.items(): - logging.info(f"Processing {partition}") - raw_cuts_path = output_dir / f"cuts_{partition}_raw.jsonl.gz" - if raw_cuts_path.is_file(): - logging.info(f"{partition} already exists - skipping") - continue - - for sup in m["supervisions"]: - sup.custom = {"origin": "aidatatang_200zh"} - - cut_set = CutSet.from_manifests( - recordings=m["recordings"], - supervisions=m["supervisions"], - ) - - logging.info(f"Saving to {raw_cuts_path}") - cut_set.to_file(raw_cuts_path) - - -def main(): - formatter = ( - "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" - ) - logging.basicConfig(format=formatter, level=logging.INFO) - - preprocess_aidatatang_200zh() - - -if __name__ == "__main__": - main() diff --git a/egs/aishell/ASR/prepare_aidatatang_200zh.sh b/egs/aishell/ASR/prepare_aidatatang_200zh.sh index 60b2060ec..f1d4d18a7 100755 --- a/egs/aishell/ASR/prepare_aidatatang_200zh.sh +++ b/egs/aishell/ASR/prepare_aidatatang_200zh.sh @@ -42,18 +42,18 @@ if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then log "Stage 1: Prepare manifest" # We assume that you have downloaded the aidatatang_200zh corpus # to $dl_dir/aidatatang_200zh - if [ ! -f data/manifests/aidatatang_200zh/.manifests.done ]; then - mkdir -p data/manifests/aidatatang_200zh - lhotse prepare aidatatang-200zh $dl_dir data/manifests/aidatatang_200zh - touch data/manifests/aidatatang_200zh/.manifests.done + if [ ! -f data/manifests/.aidatatang_200zh_manifests.done ]; then + mkdir -p data/manifests + lhotse prepare aidatatang-200zh $dl_dir data/manifests + touch data/manifests/.aidatatang_200zh_manifests.done fi fi if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then log "Stage 2: Process aidatatang_200zh" - if [ ! -f data/fbank/aidatatang_200zh/.fbank.done ]; then - mkdir -p data/fbank/aidatatang_200zh - lhotse prepare aidatatang-200zh $dl_dir data/manifests/aidatatang_200zh - touch data/fbank/aidatatang_200zh/.fbank.done + if [ ! -f data/fbank/.aidatatang_200zh_fbank.done ]; then + mkdir -p data/fbank + ./local/compute_fbank_aidatatang_200zh.py + touch data/fbank/.aidatatang_200zh_fbank.done fi fi diff --git a/egs/aishell/ASR/tdnn_lstm_ctc/asr_datamodule.py b/egs/aishell/ASR/tdnn_lstm_ctc/asr_datamodule.py index 507db2933..d24ba6bb7 100644 --- a/egs/aishell/ASR/tdnn_lstm_ctc/asr_datamodule.py +++ b/egs/aishell/ASR/tdnn_lstm_ctc/asr_datamodule.py @@ -23,11 +23,11 @@ from functools import lru_cache from pathlib import Path from typing import List -from lhotse import CutSet, Fbank, FbankConfig, load_manifest +from lhotse import CutSet, Fbank, FbankConfig, load_manifest, load_manifest_lazy from lhotse.dataset import ( - BucketingSampler, CutConcatenate, CutMix, + DynamicBucketingSampler, K2SpeechRecognitionDataset, PrecomputedFeatures, SingleCutSampler, @@ -93,7 +93,7 @@ class AishellAsrDataModule: "--num-buckets", type=int, default=30, - help="The number of buckets for the BucketingSampler" + help="The number of buckets for the DynamicBucketingSampler" "(you might want to increase it for larger datasets).", ) group.add_argument( @@ -133,6 +133,12 @@ class AishellAsrDataModule: help="When enabled (=default), the examples will be " "shuffled for each epoch.", ) + group.add_argument( + "--drop-last", + type=str2bool, + default=True, + help="Whether to drop last batch. Used by sampler.", + ) group.add_argument( "--return-cuts", type=str2bool, @@ -178,7 +184,7 @@ class AishellAsrDataModule: def train_dataloaders(self, cuts_train: CutSet) -> DataLoader: logging.info("About to get Musan cuts") cuts_musan = load_manifest( - self.args.manifest_dir / "cuts_musan.json.gz" + self.args.manifest_dir / "musan_cuts.jsonl.gz" ) transforms = [] @@ -262,14 +268,13 @@ class AishellAsrDataModule: ) if self.args.bucketing_sampler: - logging.info("Using BucketingSampler.") - train_sampler = BucketingSampler( + logging.info("Using DynamicBucketingSampler.") + train_sampler = DynamicBucketingSampler( cuts_train, max_duration=self.args.max_duration, shuffle=self.args.shuffle, num_buckets=self.args.num_buckets, - bucket_method="equal_duration", - drop_last=True, + drop_last=self.args.drop_last, ) else: logging.info("Using SingleCutSampler.") @@ -313,7 +318,7 @@ class AishellAsrDataModule: cut_transforms=transforms, return_cuts=self.args.return_cuts, ) - valid_sampler = BucketingSampler( + valid_sampler = DynamicBucketingSampler( cuts_valid, max_duration=self.args.max_duration, shuffle=False, @@ -337,8 +342,10 @@ class AishellAsrDataModule: else PrecomputedFeatures(), return_cuts=self.args.return_cuts, ) - sampler = BucketingSampler( - cuts, max_duration=self.args.max_duration, shuffle=False + sampler = DynamicBucketingSampler( + cuts, + max_duration=self.args.max_duration, + shuffle=False, ) test_dl = DataLoader( test, @@ -351,17 +358,21 @@ class AishellAsrDataModule: @lru_cache() def train_cuts(self) -> CutSet: logging.info("About to get train cuts") - cuts_train = load_manifest( - self.args.manifest_dir / "cuts_train.json.gz" + cuts_train = load_manifest_lazy( + self.args.manifest_dir / "aishell_cuts_train.jsonl.gz" ) return cuts_train @lru_cache() def valid_cuts(self) -> CutSet: logging.info("About to get dev cuts") - return load_manifest(self.args.manifest_dir / "cuts_dev.json.gz") + return load_manifest_lazy( + self.args.manifest_dir / "aishell_cuts_dev.jsonl.gz" + ) @lru_cache() def test_cuts(self) -> List[CutSet]: logging.info("About to get test cuts") - return load_manifest(self.args.manifest_dir / "cuts_test.json.gz") + return load_manifest_lazy( + self.args.manifest_dir / "aishell_cuts_test.jsonl.gz" + ) diff --git a/egs/aishell/ASR/tdnn_lstm_ctc/train.py b/egs/aishell/ASR/tdnn_lstm_ctc/train.py index 3327cdb79..7619b0551 100755 --- a/egs/aishell/ASR/tdnn_lstm_ctc/train.py +++ b/egs/aishell/ASR/tdnn_lstm_ctc/train.py @@ -15,6 +15,14 @@ # See the License for the specific language governing permissions and # limitations under the License. +""" +Usage + export CUDA_VISIBLE_DEVICES="0,1,2,3" + ./tdnn_lstm_ctc/train.py \ + --world-size 4 \ + --num-epochs 20 \ + --max-duration 300 +""" import argparse import logging diff --git a/egs/aishell/ASR/transducer_stateless/conformer.py b/egs/aishell/ASR/transducer_stateless/conformer.py index 149df92ab..7e8dc4ace 100644 --- a/egs/aishell/ASR/transducer_stateless/conformer.py +++ b/egs/aishell/ASR/transducer_stateless/conformer.py @@ -110,9 +110,7 @@ class Conformer(Transformer): x = x.permute(1, 0, 2) # (N, T, C) -> (T, N, C) # Caution: We assume the subsampling factor is 4! - with warnings.catch_warnings(): - warnings.simplefilter("ignore") - lengths = ((x_lens - 1) // 2 - 1) // 2 + lengths = (((x_lens - 1) >> 1) - 1) >> 1 assert x.size(0) == lengths.max().item() mask = make_pad_mask(lengths) diff --git a/egs/aishell/ASR/transducer_stateless/train.py b/egs/aishell/ASR/transducer_stateless/train.py index f615c78f4..d54157709 100755 --- a/egs/aishell/ASR/transducer_stateless/train.py +++ b/egs/aishell/ASR/transducer_stateless/train.py @@ -21,6 +21,7 @@ import argparse import logging +import warnings from pathlib import Path from shutil import copyfile from typing import Optional, Tuple @@ -386,7 +387,11 @@ def compute_loss( assert loss.requires_grad == is_training info = MetricsTracker() - info["frames"] = (feature_lens // params.subsampling_factor).sum().item() + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + info["frames"] = ( + (feature_lens // params.subsampling_factor).sum().item() + ) # Note: We use reduction=sum while computing the loss. info["loss"] = loss.detach().cpu().item() @@ -599,21 +604,18 @@ def run(rank, world_size, args): train_cuts = aishell.train_cuts() def remove_short_and_long_utt(c: Cut): - # Keep only utterances with duration between 1 second and 20 seconds - return 1.0 <= c.duration <= 20.0 - - num_in_total = len(train_cuts) + # Keep only utterances with duration between 1 second and 12 seconds + # + # Caution: There is a reason to select 12.0 here. Please see + # ../local/display_manifest_statistics.py + # + # You should use ../local/display_manifest_statistics.py to get + # an utterance duration distribution for your dataset to select + # the threshold + return 1.0 <= c.duration <= 12.0 train_cuts = train_cuts.filter(remove_short_and_long_utt) - num_left = len(train_cuts) - num_removed = num_in_total - num_left - removed_percent = num_removed / num_in_total * 100 - - logging.info(f"Before removing short and long utterances: {num_in_total}") - logging.info(f"After removing short and long utterances: {num_left}") - logging.info(f"Removed {num_removed} utterances ({removed_percent:.5f}%)") - train_dl = aishell.train_dataloaders(train_cuts) valid_dl = aishell.valid_dataloaders(aishell.valid_cuts()) diff --git a/egs/aishell/ASR/transducer_stateless_modified-2/aidatatang_200zh.py b/egs/aishell/ASR/transducer_stateless_modified-2/aidatatang_200zh.py index 84ca64c89..26d4ee111 100644 --- a/egs/aishell/ASR/transducer_stateless_modified-2/aidatatang_200zh.py +++ b/egs/aishell/ASR/transducer_stateless_modified-2/aidatatang_200zh.py @@ -18,7 +18,7 @@ import logging from pathlib import Path -from lhotse import CutSet, load_manifest +from lhotse import CutSet, load_manifest_lazy class AIDatatang200zh: @@ -28,26 +28,26 @@ class AIDatatang200zh: manifest_dir: It is expected to contain the following files:: - - cuts_dev_raw.jsonl.gz - - cuts_train_raw.jsonl.gz - - cuts_test_raw.jsonl.gz + - aidatatang_cuts_dev.jsonl.gz + - aidatatang_cuts_train.jsonl.gz + - aidatatang_cuts_test.jsonl.gz """ self.manifest_dir = Path(manifest_dir) def train_cuts(self) -> CutSet: - f = self.manifest_dir / "cuts_train_raw.jsonl.gz" + f = self.manifest_dir / "aidatatang_cuts_train.jsonl.gz" logging.info(f"About to get train cuts from {f}") - cuts_train = load_manifest(f) + cuts_train = load_manifest_lazy(f) return cuts_train def valid_cuts(self) -> CutSet: - f = self.manifest_dir / "cuts_valid_raw.jsonl.gz" + f = self.manifest_dir / "aidatatang_cuts_valid.jsonl.gz" logging.info(f"About to get valid cuts from {f}") - cuts_valid = load_manifest(f) + cuts_valid = load_manifest_lazy(f) return cuts_valid def test_cuts(self) -> CutSet: - f = self.manifest_dir / "cuts_test_raw.jsonl.gz" + f = self.manifest_dir / "aidatatang_cuts_test.jsonl.gz" logging.info(f"About to get test cuts from {f}") - cuts_test = load_manifest(f) + cuts_test = load_manifest_lazy(f) return cuts_test diff --git a/egs/aishell/ASR/transducer_stateless_modified-2/aishell.py b/egs/aishell/ASR/transducer_stateless_modified-2/aishell.py index 94d1da066..ddeca4d88 100644 --- a/egs/aishell/ASR/transducer_stateless_modified-2/aishell.py +++ b/egs/aishell/ASR/transducer_stateless_modified-2/aishell.py @@ -18,7 +18,7 @@ import logging from pathlib import Path -from lhotse import CutSet, load_manifest +from lhotse import CutSet, load_manifest_lazy class AIShell: @@ -28,26 +28,26 @@ class AIShell: manifest_dir: It is expected to contain the following files:: - - cuts_dev.json.gz - - cuts_train.json.gz - - cuts_test.json.gz + - aishell_cuts_dev.jsonl.gz + - aishell_cuts_train.jsonl.gz + - aishell_cuts_test.jsonl.gz """ self.manifest_dir = Path(manifest_dir) def train_cuts(self) -> CutSet: - f = self.manifest_dir / "cuts_train.json.gz" + f = self.manifest_dir / "aishell_cuts_train.jsonl.gz" logging.info(f"About to get train cuts from {f}") - cuts_train = load_manifest(f) + cuts_train = load_manifest_lazy(f) return cuts_train def valid_cuts(self) -> CutSet: - f = self.manifest_dir / "cuts_dev.json.gz" + f = self.manifest_dir / "aishell_cuts_dev.jsonl.gz" logging.info(f"About to get valid cuts from {f}") - cuts_valid = load_manifest(f) + cuts_valid = load_manifest_lazy(f) return cuts_valid def test_cuts(self) -> CutSet: - f = self.manifest_dir / "cuts_test.json.gz" + f = self.manifest_dir / "aishell_cuts_test.jsonl.gz" logging.info(f"About to get test cuts from {f}") - cuts_test = load_manifest(f) + cuts_test = load_manifest_lazy(f) return cuts_test diff --git a/egs/aishell/ASR/transducer_stateless_modified-2/asr_datamodule.py b/egs/aishell/ASR/transducer_stateless_modified-2/asr_datamodule.py index 20eb8155c..838e53658 100644 --- a/egs/aishell/ASR/transducer_stateless_modified-2/asr_datamodule.py +++ b/egs/aishell/ASR/transducer_stateless_modified-2/asr_datamodule.py @@ -24,7 +24,6 @@ from typing import Optional from lhotse import CutSet, Fbank, FbankConfig from lhotse.dataset import ( - BucketingSampler, CutMix, DynamicBucketingSampler, K2SpeechRecognitionDataset, @@ -73,8 +72,7 @@ class AsrDataModule: "--num-buckets", type=int, default=30, - help="The number of buckets for the BucketingSampler " - "and DynamicBucketingSampler." + help="The number of buckets for the DynamicBucketingSampler " "(you might want to increase it for larger datasets).", ) @@ -147,7 +145,6 @@ class AsrDataModule: def train_dataloaders( self, cuts_train: CutSet, - dynamic_bucketing: bool, on_the_fly_feats: bool, cuts_musan: Optional[CutSet] = None, ) -> DataLoader: @@ -157,9 +154,6 @@ class AsrDataModule: Cuts for training. cuts_musan: If not None, it is the cuts for mixing. - dynamic_bucketing: - True to use DynamicBucketingSampler; - False to use BucketingSampler. on_the_fly_feats: True to use OnTheFlyFeatures; False to use PrecomputedFeatures. @@ -232,25 +226,14 @@ class AsrDataModule: return_cuts=self.args.return_cuts, ) - if dynamic_bucketing: - logging.info("Using DynamicBucketingSampler.") - train_sampler = DynamicBucketingSampler( - cuts_train, - max_duration=self.args.max_duration, - shuffle=self.args.shuffle, - num_buckets=self.args.num_buckets, - drop_last=True, - ) - else: - logging.info("Using BucketingSampler.") - train_sampler = BucketingSampler( - cuts_train, - max_duration=self.args.max_duration, - shuffle=self.args.shuffle, - num_buckets=self.args.num_buckets, - bucket_method="equal_duration", - drop_last=True, - ) + logging.info("Using DynamicBucketingSampler.") + train_sampler = DynamicBucketingSampler( + cuts_train, + max_duration=self.args.max_duration, + shuffle=self.args.shuffle, + num_buckets=self.args.num_buckets, + drop_last=True, + ) logging.info("About to create train dataloader") train_dl = DataLoader( @@ -279,7 +262,7 @@ class AsrDataModule: cut_transforms=transforms, return_cuts=self.args.return_cuts, ) - valid_sampler = BucketingSampler( + valid_sampler = DynamicBucketingSampler( cuts_valid, max_duration=self.args.max_duration, shuffle=False, @@ -303,8 +286,10 @@ class AsrDataModule: else PrecomputedFeatures(), return_cuts=self.args.return_cuts, ) - sampler = BucketingSampler( - cuts, max_duration=self.args.max_duration, shuffle=False + sampler = DynamicBucketingSampler( + cuts, + max_duration=self.args.max_duration, + shuffle=False, ) logging.debug("About to create test dataloader") test_dl = DataLoader( diff --git a/egs/aishell/ASR/transducer_stateless_modified-2/train.py b/egs/aishell/ASR/transducer_stateless_modified-2/train.py index 53d4e455f..962fffdf5 100755 --- a/egs/aishell/ASR/transducer_stateless_modified-2/train.py +++ b/egs/aishell/ASR/transducer_stateless_modified-2/train.py @@ -41,6 +41,7 @@ export CUDA_VISIBLE_DEVICES="0,1,2" import argparse import logging import random +import warnings from pathlib import Path from shutil import copyfile from typing import Optional, Tuple @@ -446,7 +447,11 @@ def compute_loss( assert loss.requires_grad == is_training info = MetricsTracker() - info["frames"] = (feature_lens // params.subsampling_factor).sum().item() + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + info["frames"] = ( + (feature_lens // params.subsampling_factor).sum().item() + ) # Note: We use reduction=sum while computing the loss. info["loss"] = loss.detach().cpu().item() @@ -636,19 +641,15 @@ def train_one_epoch( def filter_short_and_long_utterances(cuts: CutSet) -> CutSet: def remove_short_and_long_utt(c: Cut): # Keep only utterances with duration between 1 second and 12 seconds + # + # Caution: There is a reason to select 12.0 here. Please see + # ../local/display_manifest_statistics.py + # + # You should use ../local/display_manifest_statistics.py to get + # an utterance duration distribution for your dataset to select + # the threshold return 1.0 <= c.duration <= 12.0 - num_in_total = len(cuts) - cuts = cuts.filter(remove_short_and_long_utt) - - num_left = len(cuts) - num_removed = num_in_total - num_left - removed_percent = num_removed / num_in_total * 100 - - logging.info(f"Before removing short and long utterances: {num_in_total}") - logging.info(f"After removing short and long utterances: {num_left}") - logging.info(f"Removed {num_removed} utterances ({removed_percent:.5f}%)") - return cuts @@ -728,15 +729,14 @@ def run(rank, world_size, args): train_cuts = aishell.train_cuts() train_cuts = filter_short_and_long_utterances(train_cuts) - datatang = AIDatatang200zh( - manifest_dir=f"{args.manifest_dir}/aidatatang_200zh" - ) + datatang = AIDatatang200zh(manifest_dir=args.manifest_dir) train_datatang_cuts = datatang.train_cuts() train_datatang_cuts = filter_short_and_long_utterances(train_datatang_cuts) + train_datatang_cuts = train_datatang_cuts.repeat(times=None) if args.enable_musan: cuts_musan = load_manifest( - Path(args.manifest_dir) / "cuts_musan.json.gz" + Path(args.manifest_dir) / "musan_cuts.jsonl.gz" ) else: cuts_musan = None @@ -745,22 +745,23 @@ def run(rank, world_size, args): train_dl = asr_datamodule.train_dataloaders( train_cuts, - dynamic_bucketing=False, on_the_fly_feats=False, cuts_musan=cuts_musan, ) datatang_train_dl = asr_datamodule.train_dataloaders( train_datatang_cuts, - dynamic_bucketing=True, - on_the_fly_feats=True, + on_the_fly_feats=False, cuts_musan=cuts_musan, ) valid_cuts = aishell.valid_cuts() valid_dl = asr_datamodule.valid_dataloaders(valid_cuts) - for dl in [train_dl, datatang_train_dl]: + for dl in [ + train_dl, + # datatang_train_dl + ]: scan_pessimistic_batches_for_oom( model=model, train_dl=dl, diff --git a/egs/aishell/ASR/transducer_stateless_modified/train.py b/egs/aishell/ASR/transducer_stateless_modified/train.py index 524854b73..d3ffccafa 100755 --- a/egs/aishell/ASR/transducer_stateless_modified/train.py +++ b/egs/aishell/ASR/transducer_stateless_modified/train.py @@ -37,6 +37,7 @@ export CUDA_VISIBLE_DEVICES="0,1,2" import argparse import logging +import warnings from pathlib import Path from shutil import copyfile from typing import Optional, Tuple @@ -411,7 +412,11 @@ def compute_loss( assert loss.requires_grad == is_training info = MetricsTracker() - info["frames"] = (feature_lens // params.subsampling_factor).sum().item() + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + info["frames"] = ( + (feature_lens // params.subsampling_factor).sum().item() + ) # Note: We use reduction=sum while computing the loss. info["loss"] = loss.detach().cpu().item() @@ -625,20 +630,17 @@ def run(rank, world_size, args): def remove_short_and_long_utt(c: Cut): # Keep only utterances with duration between 1 second and 12 seconds + # + # Caution: There is a reason to select 12.0 here. Please see + # ../local/display_manifest_statistics.py + # + # You should use ../local/display_manifest_statistics.py to get + # an utterance duration distribution for your dataset to select + # the threshold return 1.0 <= c.duration <= 12.0 - num_in_total = len(train_cuts) - train_cuts = train_cuts.filter(remove_short_and_long_utt) - num_left = len(train_cuts) - num_removed = num_in_total - num_left - removed_percent = num_removed / num_in_total * 100 - - logging.info(f"Before removing short and long utterances: {num_in_total}") - logging.info(f"After removing short and long utterances: {num_left}") - logging.info(f"Removed {num_removed} utterances ({removed_percent:.5f}%)") - train_dl = aishell.train_dataloaders(train_cuts) valid_dl = aishell.valid_dataloaders(aishell.valid_cuts()) diff --git a/egs/alimeeting/ASR/README.md b/egs/alimeeting/ASR/README.md new file mode 100644 index 000000000..257fe38d5 --- /dev/null +++ b/egs/alimeeting/ASR/README.md @@ -0,0 +1,19 @@ + +# Introduction + +This recipe includes some different ASR models trained with Alimeeting (far). + +[./RESULTS.md](./RESULTS.md) contains the latest results. + +# Transducers + +There are various folders containing the name `transducer` in this folder. +The following table lists the differences among them. + +| | Encoder | Decoder | Comment | +|---------------------------------------|---------------------|--------------------|-----------------------------| +| `pruned_transducer_stateless2` | Conformer(modified) | Embedding + Conv1d | Using k2 pruned RNN-T loss | | + +The decoder in `transducer_stateless` is modified from the paper +[Rnn-Transducer with Stateless Prediction Network](https://ieeexplore.ieee.org/document/9054419/). +We place an additional Conv1d layer right after the input embedding layer. diff --git a/egs/alimeeting/ASR/RESULTS.md b/egs/alimeeting/ASR/RESULTS.md new file mode 100644 index 000000000..745795a20 --- /dev/null +++ b/egs/alimeeting/ASR/RESULTS.md @@ -0,0 +1,71 @@ +## Results + +### Alimeeting Char training results (Pruned Transducer Stateless2) + +#### 2022-06-01 + +Using the codes from this PR https://github.com/k2-fsa/icefall/pull/378. + +The WERs are +| | eval | test | comment | +|------------------------------------|------------|------------|------------------------------------------| +| greedy search | 31.77 | 34.66 | --epoch 29, --avg 18, --max-duration 100 | +| modified beam search (beam size 4) | 30.38 | 33.02 | --epoch 29, --avg 18, --max-duration 100 | +| fast beam search (set as default) | 31.39 | 34.25 | --epoch 29, --avg 18, --max-duration 1500| + +The training command for reproducing is given below: + +``` +export CUDA_VISIBLE_DEVICES="0,1,2,3" + +./pruned_transducer_stateless2/train.py \ + --world-size 4 \ + --num-epochs 30 \ + --start-epoch 0 \ + --exp-dir pruned_transducer_stateless2/exp \ + --lang-dir data/lang_char \ + --max-duration 220 \ + --save-every-n 1000 + +``` + +The tensorboard training log can be found at +https://tensorboard.dev/experiment/AoqgSvZKTZCJhJbOuG3W6g/#scalars + +The decoding command is: +``` +epoch=29 +avg=18 + +## greedy search +./pruned_transducer_stateless2/decode.py \ + --epoch $epoch \ + --avg $avg \ + --exp-dir pruned_transducer_stateless2/exp \ + --lang-dir ./data/lang_char \ + --max-duration 100 + +## modified beam search +./pruned_transducer_stateless2/decode.py \ + --epoch $epoch \ + --avg $avg \ + --exp-dir pruned_transducer_stateless2/exp \ + --lang-dir ./data/lang_char \ + --max-duration 100 \ + --decoding-method modified_beam_search \ + --beam-size 4 + +## fast beam search +./pruned_transducer_stateless2/decode.py \ + --epoch $epoch \ + --avg $avg \ + --exp-dir ./pruned_transducer_stateless2/exp \ + --lang-dir ./data/lang_char \ + --max-duration 1500 \ + --decoding-method fast_beam_search \ + --beam 4 \ + --max-contexts 4 \ + --max-states 8 +``` + +A pre-trained model and decoding logs can be found at diff --git a/egs/alimeeting/ASR/local/__init__.py b/egs/alimeeting/ASR/local/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/egs/alimeeting/ASR/local/compute_fbank_alimeeting.py b/egs/alimeeting/ASR/local/compute_fbank_alimeeting.py new file mode 100755 index 000000000..2ff473c60 --- /dev/null +++ b/egs/alimeeting/ASR/local/compute_fbank_alimeeting.py @@ -0,0 +1,124 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This file computes fbank features of the aishell dataset. +It looks for manifests in the directory data/manifests. + +The generated fbank features are saved in data/fbank. +""" + +import argparse +import logging +import os +from pathlib import Path + +import torch +from lhotse import CutSet, Fbank, FbankConfig, LilcomChunkyWriter +from lhotse.recipes.utils import read_manifests_if_cached + +from icefall.utils import get_executor + +# Torch's multithreaded behavior needs to be disabled or +# it wastes a lot of CPU and slow things down. +# Do this outside of main() in case it needs to take effect +# even when we are not invoking the main (e.g. when spawning subprocesses). +torch.set_num_threads(1) +torch.set_num_interop_threads(1) + + +def compute_fbank_alimeeting(num_mel_bins: int = 80): + src_dir = Path("data/manifests") + output_dir = Path("data/fbank") + num_jobs = min(15, os.cpu_count()) + + dataset_parts = ( + "train", + "eval", + "test", + ) + + prefix = "alimeeting" + suffix = "jsonl.gz" + manifests = read_manifests_if_cached( + dataset_parts=dataset_parts, + output_dir=src_dir, + prefix=prefix, + suffix=suffix, + ) + assert manifests is not None + + extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins)) + + with get_executor() as ex: # Initialize the executor only once. + for partition, m in manifests.items(): + if (output_dir / f"{prefix}_cuts_{partition}.{suffix}").is_file(): + logging.info(f"{partition} already exists - skipping.") + continue + logging.info(f"Processing {partition}") + cut_set = CutSet.from_manifests( + recordings=m["recordings"], + supervisions=m["supervisions"], + ) + if "train" in partition: + cut_set = ( + cut_set + + cut_set.perturb_speed(0.9) + + cut_set.perturb_speed(1.1) + ) + cur_num_jobs = num_jobs if ex is None else 80 + cur_num_jobs = min(cur_num_jobs, len(cut_set)) + + cut_set = cut_set.compute_and_store_features( + extractor=extractor, + storage_path=f"{output_dir}/{prefix}_feats_{partition}", + # when an executor is specified, make more partitions + num_jobs=cur_num_jobs, + executor=ex, + storage_type=LilcomChunkyWriter, + ) + + logging.info("About splitting cuts into smaller chunks") + cut_set = cut_set.trim_to_supervisions( + keep_overlapping=False, + min_duration=None, + ) + cut_set.to_file(output_dir / f"{prefix}_cuts_{partition}.{suffix}") + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--num-mel-bins", + type=int, + default=80, + help="""The number of mel bins for Fbank""", + ) + + return parser.parse_args() + + +if __name__ == "__main__": + formatter = ( + "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + ) + + logging.basicConfig(format=formatter, level=logging.INFO) + + args = get_args() + compute_fbank_alimeeting(num_mel_bins=args.num_mel_bins) diff --git a/egs/alimeeting/ASR/local/compute_fbank_musan.py b/egs/alimeeting/ASR/local/compute_fbank_musan.py new file mode 120000 index 000000000..5833f2484 --- /dev/null +++ b/egs/alimeeting/ASR/local/compute_fbank_musan.py @@ -0,0 +1 @@ +../../../librispeech/ASR/local/compute_fbank_musan.py \ No newline at end of file diff --git a/egs/alimeeting/ASR/local/display_manifest_statistics.py b/egs/alimeeting/ASR/local/display_manifest_statistics.py new file mode 100644 index 000000000..16cdecc91 --- /dev/null +++ b/egs/alimeeting/ASR/local/display_manifest_statistics.py @@ -0,0 +1,96 @@ +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang +# Mingshuang Luo) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This file displays duration statistics of utterances in a manifest. +You can use the displayed value to choose minimum/maximum duration +to remove short and long utterances during the training. +See the function `remove_short_and_long_utt()` +in ../../../librispeech/ASR/transducer/train.py +for usage. +""" + + +from lhotse import load_manifest_lazy + + +def main(): + paths = [ + "./data/fbank/alimeeting_cuts_train.jsonl.gz", + "./data/fbank/alimeeting_cuts_eval.jsonl.gz", + "./data/fbank/alimeeting_cuts_test.jsonl.gz", + ] + + for path in paths: + print(f"Starting display the statistics for {path}") + cuts = load_manifest_lazy(path) + cuts.describe() + + +if __name__ == "__main__": + main() + +""" +Starting display the statistics for ./data/fbank/alimeeting_cuts_train.jsonl.gz +Cuts count: 559092 +Total duration (hours): 424.6 +Speech duration (hours): 424.6 (100.0%) +*** +Duration statistics (seconds): +mean 2.7 +std 3.0 +min 0.0 +25% 0.7 +50% 1.7 +75% 3.6 +99% 13.6 +99.5% 14.7 +99.9% 16.2 +max 284.3 +Starting display the statistics for ./data/fbank/alimeeting_cuts_eval.jsonl.gz +Cuts count: 6457 +Total duration (hours): 4.9 +Speech duration (hours): 4.9 (100.0%) +*** +Duration statistics (seconds): +mean 2.7 +std 3.1 +min 0.1 +25% 0.6 +50% 1.6 +75% 3.5 +99% 13.6 +99.5% 14.1 +99.9% 14.7 +max 15.8 +Starting display the statistics for ./data/fbank/alimeeting_cuts_test.jsonl.gz +Cuts count: 16358 +Total duration (hours): 12.5 +Speech duration (hours): 12.5 (100.0%) +*** +Duration statistics (seconds): +mean 2.7 +std 2.9 +min 0.1 +25% 0.7 +50% 1.7 +75% 3.5 +99% 13.7 +99.5% 14.2 +99.9% 14.8 +max 15.7 +""" diff --git a/egs/alimeeting/ASR/local/prepare_char.py b/egs/alimeeting/ASR/local/prepare_char.py new file mode 100755 index 000000000..d9e47d17a --- /dev/null +++ b/egs/alimeeting/ASR/local/prepare_char.py @@ -0,0 +1,248 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang, +# Wei Kang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" + +This script takes as input `lang_dir`, which should contain:: + + - lang_dir/text, + - lang_dir/words.txt + +and generates the following files in the directory `lang_dir`: + + - lexicon.txt + - lexicon_disambig.txt + - L.pt + - L_disambig.pt + - tokens.txt +""" + +import re +from pathlib import Path +from typing import Dict, List + +import k2 +import torch +from prepare_lang import ( + Lexicon, + add_disambig_symbols, + add_self_loops, + write_lexicon, + write_mapping, +) + + +def lexicon_to_fst_no_sil( + lexicon: Lexicon, + token2id: Dict[str, int], + word2id: Dict[str, int], + need_self_loops: bool = False, +) -> k2.Fsa: + """Convert a lexicon to an FST (in k2 format). + + Args: + lexicon: + The input lexicon. See also :func:`read_lexicon` + token2id: + A dict mapping tokens to IDs. + word2id: + A dict mapping words to IDs. + need_self_loops: + If True, add self-loop to states with non-epsilon output symbols + on at least one arc out of the state. The input label for this + self loop is `token2id["#0"]` and the output label is `word2id["#0"]`. + Returns: + Return an instance of `k2.Fsa` representing the given lexicon. + """ + loop_state = 0 # words enter and leave from here + next_state = 1 # the next un-allocated state, will be incremented as we go + + arcs = [] + + # The blank symbol is defined in local/train_bpe_model.py + assert token2id[""] == 0 + assert word2id[""] == 0 + + eps = 0 + + for word, pieces in lexicon: + assert len(pieces) > 0, f"{word} has no pronunciations" + cur_state = loop_state + + word = word2id[word] + pieces = [ + token2id[i] if i in token2id else token2id[""] for i in pieces + ] + + for i in range(len(pieces) - 1): + w = word if i == 0 else eps + arcs.append([cur_state, next_state, pieces[i], w, 0]) + + cur_state = next_state + next_state += 1 + + # now for the last piece of this word + i = len(pieces) - 1 + w = word if i == 0 else eps + arcs.append([cur_state, loop_state, pieces[i], w, 0]) + + if need_self_loops: + disambig_token = token2id["#0"] + disambig_word = word2id["#0"] + arcs = add_self_loops( + arcs, + disambig_token=disambig_token, + disambig_word=disambig_word, + ) + + final_state = next_state + arcs.append([loop_state, final_state, -1, -1, 0]) + arcs.append([final_state]) + + arcs = sorted(arcs, key=lambda arc: arc[0]) + arcs = [[str(i) for i in arc] for arc in arcs] + arcs = [" ".join(arc) for arc in arcs] + arcs = "\n".join(arcs) + + fsa = k2.Fsa.from_str(arcs, acceptor=False) + return fsa + + +def contain_oov(token_sym_table: Dict[str, int], tokens: List[str]) -> bool: + """Check if all the given tokens are in token symbol table. + + Args: + token_sym_table: + Token symbol table that contains all the valid tokens. + tokens: + A list of tokens. + Returns: + Return True if there is any token not in the token_sym_table, + otherwise False. + """ + for tok in tokens: + if tok not in token_sym_table: + return True + return False + + +def generate_lexicon( + token_sym_table: Dict[str, int], words: List[str] +) -> Lexicon: + """Generate a lexicon from a word list and token_sym_table. + + Args: + token_sym_table: + Token symbol table that mapping token to token ids. + words: + A list of strings representing words. + Returns: + Return a dict whose keys are words and values are the corresponding + tokens. + """ + lexicon = [] + for word in words: + chars = list(word.strip(" \t")) + if contain_oov(token_sym_table, chars): + continue + lexicon.append((word, chars)) + + # The OOV word is + lexicon.append(("", [""])) + return lexicon + + +def generate_tokens(text_file: str) -> Dict[str, int]: + """Generate tokens from the given text file. + + Args: + text_file: + A file that contains text lines to generate tokens. + Returns: + Return a dict whose keys are tokens and values are token ids ranged + from 0 to len(keys) - 1. + """ + tokens: Dict[str, int] = dict() + tokens[""] = 0 + tokens[""] = 1 + tokens[""] = 2 + whitespace = re.compile(r"([ \t\r\n]+)") + with open(text_file, "r", encoding="utf-8") as f: + for line in f: + line = re.sub(whitespace, "", line) + chars = list(line) + for char in chars: + if char not in tokens: + tokens[char] = len(tokens) + return tokens + + +def main(): + lang_dir = Path("data/lang_char") + text_file = lang_dir / "text" + + word_sym_table = k2.SymbolTable.from_file(lang_dir / "words.txt") + + words = word_sym_table.symbols + + excluded = ["", "!SIL", "", "", "#0", "", ""] + for w in excluded: + if w in words: + words.remove(w) + + token_sym_table = generate_tokens(text_file) + + lexicon = generate_lexicon(token_sym_table, words) + + lexicon_disambig, max_disambig = add_disambig_symbols(lexicon) + + next_token_id = max(token_sym_table.values()) + 1 + for i in range(max_disambig + 1): + disambig = f"#{i}" + assert disambig not in token_sym_table + token_sym_table[disambig] = next_token_id + next_token_id += 1 + + word_sym_table.add("#0") + word_sym_table.add("") + word_sym_table.add("") + + write_mapping(lang_dir / "tokens.txt", token_sym_table) + + write_lexicon(lang_dir / "lexicon.txt", lexicon) + write_lexicon(lang_dir / "lexicon_disambig.txt", lexicon_disambig) + + L = lexicon_to_fst_no_sil( + lexicon, + token2id=token_sym_table, + word2id=word_sym_table, + ) + + L_disambig = lexicon_to_fst_no_sil( + lexicon_disambig, + token2id=token_sym_table, + word2id=word_sym_table, + need_self_loops=True, + ) + torch.save(L.as_dict(), lang_dir / "L.pt") + torch.save(L_disambig.as_dict(), lang_dir / "L_disambig.pt") + + +if __name__ == "__main__": + main() diff --git a/egs/alimeeting/ASR/local/prepare_lang.py b/egs/alimeeting/ASR/local/prepare_lang.py new file mode 100755 index 000000000..e5ae89ec4 --- /dev/null +++ b/egs/alimeeting/ASR/local/prepare_lang.py @@ -0,0 +1,390 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This script takes as input a lexicon file "data/lang_phone/lexicon.txt" +consisting of words and tokens (i.e., phones) and does the following: + +1. Add disambiguation symbols to the lexicon and generate lexicon_disambig.txt + +2. Generate tokens.txt, the token table mapping a token to a unique integer. + +3. Generate words.txt, the word table mapping a word to a unique integer. + +4. Generate L.pt, in k2 format. It can be loaded by + + d = torch.load("L.pt") + lexicon = k2.Fsa.from_dict(d) + +5. Generate L_disambig.pt, in k2 format. +""" +import argparse +import math +from collections import defaultdict +from pathlib import Path +from typing import Any, Dict, List, Tuple + +import k2 +import torch + +from icefall.lexicon import read_lexicon, write_lexicon + +Lexicon = List[Tuple[str, List[str]]] + + +def write_mapping(filename: str, sym2id: Dict[str, int]) -> None: + """Write a symbol to ID mapping to a file. + + Note: + No need to implement `read_mapping` as it can be done + through :func:`k2.SymbolTable.from_file`. + + Args: + filename: + Filename to save the mapping. + sym2id: + A dict mapping symbols to IDs. + Returns: + Return None. + """ + with open(filename, "w", encoding="utf-8") as f: + for sym, i in sym2id.items(): + f.write(f"{sym} {i}\n") + + +def get_tokens(lexicon: Lexicon) -> List[str]: + """Get tokens from a lexicon. + + Args: + lexicon: + It is the return value of :func:`read_lexicon`. + Returns: + Return a list of unique tokens. + """ + ans = set() + for _, tokens in lexicon: + ans.update(tokens) + sorted_ans = sorted(list(ans)) + return sorted_ans + + +def get_words(lexicon: Lexicon) -> List[str]: + """Get words from a lexicon. + + Args: + lexicon: + It is the return value of :func:`read_lexicon`. + Returns: + Return a list of unique words. + """ + ans = set() + for word, _ in lexicon: + ans.add(word) + sorted_ans = sorted(list(ans)) + return sorted_ans + + +def add_disambig_symbols(lexicon: Lexicon) -> Tuple[Lexicon, int]: + """It adds pseudo-token disambiguation symbols #1, #2 and so on + at the ends of tokens to ensure that all pronunciations are different, + and that none is a prefix of another. + + See also add_lex_disambig.pl from kaldi. + + Args: + lexicon: + It is returned by :func:`read_lexicon`. + Returns: + Return a tuple with two elements: + + - The output lexicon with disambiguation symbols + - The ID of the max disambiguation symbol that appears + in the lexicon + """ + + # (1) Work out the count of each token-sequence in the + # lexicon. + count = defaultdict(int) + for _, tokens in lexicon: + count[" ".join(tokens)] += 1 + + # (2) For each left sub-sequence of each token-sequence, note down + # that it exists (for identifying prefixes of longer strings). + issubseq = defaultdict(int) + for _, tokens in lexicon: + tokens = tokens.copy() + tokens.pop() + while tokens: + issubseq[" ".join(tokens)] = 1 + tokens.pop() + + # (3) For each entry in the lexicon: + # if the token sequence is unique and is not a + # prefix of another word, no disambig symbol. + # Else output #1, or #2, #3, ... if the same token-seq + # has already been assigned a disambig symbol. + ans = [] + + # We start with #1 since #0 has its own purpose + first_allowed_disambig = 1 + max_disambig = first_allowed_disambig - 1 + last_used_disambig_symbol_of = defaultdict(int) + + for word, tokens in lexicon: + tokenseq = " ".join(tokens) + assert tokenseq != "" + if issubseq[tokenseq] == 0 and count[tokenseq] == 1: + ans.append((word, tokens)) + continue + + cur_disambig = last_used_disambig_symbol_of[tokenseq] + if cur_disambig == 0: + cur_disambig = first_allowed_disambig + else: + cur_disambig += 1 + + if cur_disambig > max_disambig: + max_disambig = cur_disambig + last_used_disambig_symbol_of[tokenseq] = cur_disambig + tokenseq += f" #{cur_disambig}" + ans.append((word, tokenseq.split())) + return ans, max_disambig + + +def generate_id_map(symbols: List[str]) -> Dict[str, int]: + """Generate ID maps, i.e., map a symbol to a unique ID. + + Args: + symbols: + A list of unique symbols. + Returns: + A dict containing the mapping between symbols and IDs. + """ + return {sym: i for i, sym in enumerate(symbols)} + + +def add_self_loops( + arcs: List[List[Any]], disambig_token: int, disambig_word: int +) -> List[List[Any]]: + """Adds self-loops to states of an FST to propagate disambiguation symbols + through it. They are added on each state with non-epsilon output symbols + on at least one arc out of the state. + + See also fstaddselfloops.pl from Kaldi. One difference is that + Kaldi uses OpenFst style FSTs and it has multiple final states. + This function uses k2 style FSTs and it does not need to add self-loops + to the final state. + + The input label of a self-loop is `disambig_token`, while the output + label is `disambig_word`. + + Args: + arcs: + A list-of-list. The sublist contains + `[src_state, dest_state, label, aux_label, score]` + disambig_token: + It is the token ID of the symbol `#0`. + disambig_word: + It is the word ID of the symbol `#0`. + + Return: + Return new `arcs` containing self-loops. + """ + states_needs_self_loops = set() + for arc in arcs: + src, dst, ilabel, olabel, score = arc + if olabel != 0: + states_needs_self_loops.add(src) + + ans = [] + for s in states_needs_self_loops: + ans.append([s, s, disambig_token, disambig_word, 0]) + + return arcs + ans + + +def lexicon_to_fst( + lexicon: Lexicon, + token2id: Dict[str, int], + word2id: Dict[str, int], + sil_token: str = "SIL", + sil_prob: float = 0.5, + need_self_loops: bool = False, +) -> k2.Fsa: + """Convert a lexicon to an FST (in k2 format) with optional silence at + the beginning and end of each word. + + Args: + lexicon: + The input lexicon. See also :func:`read_lexicon` + token2id: + A dict mapping tokens to IDs. + word2id: + A dict mapping words to IDs. + sil_token: + The silence token. + sil_prob: + The probability for adding a silence at the beginning and end + of the word. + need_self_loops: + If True, add self-loop to states with non-epsilon output symbols + on at least one arc out of the state. The input label for this + self loop is `token2id["#0"]` and the output label is `word2id["#0"]`. + Returns: + Return an instance of `k2.Fsa` representing the given lexicon. + """ + assert sil_prob > 0.0 and sil_prob < 1.0 + # CAUTION: we use score, i.e, negative cost. + sil_score = math.log(sil_prob) + no_sil_score = math.log(1.0 - sil_prob) + + start_state = 0 + loop_state = 1 # words enter and leave from here + sil_state = 2 # words terminate here when followed by silence; this state + # has a silence transition to loop_state. + next_state = 3 # the next un-allocated state, will be incremented as we go. + arcs = [] + + assert token2id[""] == 0 + assert word2id[""] == 0 + + eps = 0 + + sil_token = token2id[sil_token] + + arcs.append([start_state, loop_state, eps, eps, no_sil_score]) + arcs.append([start_state, sil_state, eps, eps, sil_score]) + arcs.append([sil_state, loop_state, sil_token, eps, 0]) + + for word, tokens in lexicon: + assert len(tokens) > 0, f"{word} has no pronunciations" + cur_state = loop_state + + word = word2id[word] + tokens = [token2id[i] for i in tokens] + + for i in range(len(tokens) - 1): + w = word if i == 0 else eps + arcs.append([cur_state, next_state, tokens[i], w, 0]) + + cur_state = next_state + next_state += 1 + + # now for the last token of this word + # It has two out-going arcs, one to the loop state, + # the other one to the sil_state. + i = len(tokens) - 1 + w = word if i == 0 else eps + arcs.append([cur_state, loop_state, tokens[i], w, no_sil_score]) + arcs.append([cur_state, sil_state, tokens[i], w, sil_score]) + + if need_self_loops: + disambig_token = token2id["#0"] + disambig_word = word2id["#0"] + arcs = add_self_loops( + arcs, + disambig_token=disambig_token, + disambig_word=disambig_word, + ) + + final_state = next_state + arcs.append([loop_state, final_state, -1, -1, 0]) + arcs.append([final_state]) + + arcs = sorted(arcs, key=lambda arc: arc[0]) + arcs = [[str(i) for i in arc] for arc in arcs] + arcs = [" ".join(arc) for arc in arcs] + arcs = "\n".join(arcs) + + fsa = k2.Fsa.from_str(arcs, acceptor=False) + return fsa + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--lang-dir", type=str, help="The lang dir, data/lang_phone" + ) + return parser.parse_args() + + +def main(): + out_dir = Path(get_args().lang_dir) + lexicon_filename = out_dir / "lexicon.txt" + sil_token = "SIL" + sil_prob = 0.5 + + lexicon = read_lexicon(lexicon_filename) + tokens = get_tokens(lexicon) + words = get_words(lexicon) + + lexicon_disambig, max_disambig = add_disambig_symbols(lexicon) + + for i in range(max_disambig + 1): + disambig = f"#{i}" + assert disambig not in tokens + tokens.append(f"#{i}") + + assert "" not in tokens + tokens = [""] + tokens + + assert "" not in words + assert "#0" not in words + assert "" not in words + assert "" not in words + + words = [""] + words + ["#0", "", ""] + + token2id = generate_id_map(tokens) + word2id = generate_id_map(words) + + write_mapping(out_dir / "tokens.txt", token2id) + write_mapping(out_dir / "words.txt", word2id) + write_lexicon(out_dir / "lexicon_disambig.txt", lexicon_disambig) + + L = lexicon_to_fst( + lexicon, + token2id=token2id, + word2id=word2id, + sil_token=sil_token, + sil_prob=sil_prob, + ) + + L_disambig = lexicon_to_fst( + lexicon_disambig, + token2id=token2id, + word2id=word2id, + sil_token=sil_token, + sil_prob=sil_prob, + need_self_loops=True, + ) + torch.save(L.as_dict(), out_dir / "L.pt") + torch.save(L_disambig.as_dict(), out_dir / "L_disambig.pt") + + if False: + # Just for debugging, will remove it + L.labels_sym = k2.SymbolTable.from_file(out_dir / "tokens.txt") + L.aux_labels_sym = k2.SymbolTable.from_file(out_dir / "words.txt") + L_disambig.labels_sym = L.labels_sym + L_disambig.aux_labels_sym = L.aux_labels_sym + L.draw(out_dir / "L.png", title="L") + L_disambig.draw(out_dir / "L_disambig.png", title="L_disambig") + + +if __name__ == "__main__": + main() diff --git a/egs/alimeeting/ASR/local/prepare_words.py b/egs/alimeeting/ASR/local/prepare_words.py new file mode 100755 index 000000000..65aca2983 --- /dev/null +++ b/egs/alimeeting/ASR/local/prepare_words.py @@ -0,0 +1,84 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +# Copyright 2021 Xiaomi Corp. (authors: Mingshuang Luo) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This script takes as input words.txt without ids: + - words_no_ids.txt +and generates the new words.txt with related ids. + - words.txt +""" + + +import argparse +import logging + +from tqdm import tqdm + + +def get_parser(): + parser = argparse.ArgumentParser( + description="Prepare words.txt", + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + ) + parser.add_argument( + "--input-file", + default="data/lang_char/words_no_ids.txt", + type=str, + help="the words file without ids for WenetSpeech", + ) + parser.add_argument( + "--output-file", + default="data/lang_char/words.txt", + type=str, + help="the words file with ids for WenetSpeech", + ) + + return parser + + +def main(): + parser = get_parser() + args = parser.parse_args() + + input_file = args.input_file + output_file = args.output_file + + f = open(input_file, "r", encoding="utf-8") + lines = f.readlines() + new_lines = [] + add_words = [" 0", "!SIL 1", " 2", " 3"] + new_lines.extend(add_words) + + logging.info("Starting reading the input file") + for i in tqdm(range(len(lines))): + x = lines[i] + idx = 4 + i + new_line = str(x.strip("\n")) + " " + str(idx) + new_lines.append(new_line) + + logging.info("Starting writing the words.txt") + f_out = open(output_file, "w", encoding="utf-8") + for line in new_lines: + f_out.write(line) + f_out.write("\n") + + +if __name__ == "__main__": + main() diff --git a/egs/alimeeting/ASR/local/test_prepare_lang.py b/egs/alimeeting/ASR/local/test_prepare_lang.py new file mode 100755 index 000000000..d4cf62bba --- /dev/null +++ b/egs/alimeeting/ASR/local/test_prepare_lang.py @@ -0,0 +1,106 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +# Copyright (c) 2021 Xiaomi Corporation (authors: Fangjun Kuang) + +import os +import tempfile + +import k2 +from prepare_lang import ( + add_disambig_symbols, + generate_id_map, + get_phones, + get_words, + lexicon_to_fst, + read_lexicon, + write_lexicon, + write_mapping, +) + + +def generate_lexicon_file() -> str: + fd, filename = tempfile.mkstemp() + os.close(fd) + s = """ + !SIL SIL + SPN + SPN + f f + a a + foo f o o + bar b a r + bark b a r k + food f o o d + food2 f o o d + fo f o + """.strip() + with open(filename, "w") as f: + f.write(s) + return filename + + +def test_read_lexicon(filename: str): + lexicon = read_lexicon(filename) + phones = get_phones(lexicon) + words = get_words(lexicon) + print(lexicon) + print(phones) + print(words) + lexicon_disambig, max_disambig = add_disambig_symbols(lexicon) + print(lexicon_disambig) + print("max disambig:", f"#{max_disambig}") + + phones = ["", "SIL", "SPN"] + phones + for i in range(max_disambig + 1): + phones.append(f"#{i}") + words = [""] + words + + phone2id = generate_id_map(phones) + word2id = generate_id_map(words) + + print(phone2id) + print(word2id) + + write_mapping("phones.txt", phone2id) + write_mapping("words.txt", word2id) + + write_lexicon("a.txt", lexicon) + write_lexicon("a_disambig.txt", lexicon_disambig) + + fsa = lexicon_to_fst(lexicon, phone2id=phone2id, word2id=word2id) + fsa.labels_sym = k2.SymbolTable.from_file("phones.txt") + fsa.aux_labels_sym = k2.SymbolTable.from_file("words.txt") + fsa.draw("L.pdf", title="L") + + fsa_disambig = lexicon_to_fst( + lexicon_disambig, phone2id=phone2id, word2id=word2id + ) + fsa_disambig.labels_sym = k2.SymbolTable.from_file("phones.txt") + fsa_disambig.aux_labels_sym = k2.SymbolTable.from_file("words.txt") + fsa_disambig.draw("L_disambig.pdf", title="L_disambig") + + +def main(): + filename = generate_lexicon_file() + test_read_lexicon(filename) + os.remove(filename) + + +if __name__ == "__main__": + main() diff --git a/egs/alimeeting/ASR/local/text2segments.py b/egs/alimeeting/ASR/local/text2segments.py new file mode 100644 index 000000000..3df727c67 --- /dev/null +++ b/egs/alimeeting/ASR/local/text2segments.py @@ -0,0 +1,83 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +# Copyright 2021 Xiaomi Corp. (authors: Mingshuang Luo) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This script takes as input "text", which refers to the transcript file for +WenetSpeech: + - text +and generates the output file text_word_segmentation which is implemented +with word segmenting: + - text_words_segmentation +""" + + +import argparse + +import jieba +from tqdm import tqdm + +jieba.enable_paddle() + + +def get_parser(): + parser = argparse.ArgumentParser( + description="Chinese Word Segmentation for text", + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + ) + parser.add_argument( + "--input-file", + default="data/lang_char/text", + type=str, + help="the input text file for WenetSpeech", + ) + parser.add_argument( + "--output-file", + default="data/lang_char/text_words_segmentation", + type=str, + help="the text implemented with words segmenting for WenetSpeech", + ) + + return parser + + +def main(): + parser = get_parser() + args = parser.parse_args() + + input_file = args.input_file + output_file = args.output_file + + f = open(input_file, "r", encoding="utf-8") + lines = f.readlines() + new_lines = [] + for i in tqdm(range(len(lines))): + x = lines[i].rstrip() + seg_list = jieba.cut(x, use_paddle=True) + new_line = " ".join(seg_list) + new_lines.append(new_line) + + f_new = open(output_file, "w", encoding="utf-8") + for line in new_lines: + f_new.write(line) + f_new.write("\n") + + +if __name__ == "__main__": + main() diff --git a/egs/alimeeting/ASR/local/text2token.py b/egs/alimeeting/ASR/local/text2token.py new file mode 100755 index 000000000..71be2a613 --- /dev/null +++ b/egs/alimeeting/ASR/local/text2token.py @@ -0,0 +1,195 @@ +#!/usr/bin/env python3 +# Copyright 2017 Johns Hopkins University (authors: Shinji Watanabe) +# 2022 Xiaomi Corp. (authors: Mingshuang Luo) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import argparse +import codecs +import re +import sys +from typing import List + +from pypinyin import lazy_pinyin, pinyin + +is_python2 = sys.version_info[0] == 2 + + +def exist_or_not(i, match_pos): + start_pos = None + end_pos = None + for pos in match_pos: + if pos[0] <= i < pos[1]: + start_pos = pos[0] + end_pos = pos[1] + break + + return start_pos, end_pos + + +def get_parser(): + parser = argparse.ArgumentParser( + description="convert raw text to tokenized text", + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + ) + parser.add_argument( + "--nchar", + "-n", + default=1, + type=int, + help="number of characters to split, i.e., \ + aabb -> a a b b with -n 1 and aa bb with -n 2", + ) + parser.add_argument( + "--skip-ncols", "-s", default=0, type=int, help="skip first n columns" + ) + parser.add_argument( + "--space", default="", type=str, help="space symbol" + ) + parser.add_argument( + "--non-lang-syms", + "-l", + default=None, + type=str, + help="list of non-linguistic symobles, e.g., etc.", + ) + parser.add_argument( + "text", type=str, default=False, nargs="?", help="input text" + ) + parser.add_argument( + "--trans_type", + "-t", + type=str, + default="char", + choices=["char", "pinyin", "lazy_pinyin"], + help="""Transcript type. char/pinyin/lazy_pinyin""", + ) + return parser + + +def token2id( + texts, token_table, token_type: str = "lazy_pinyin", oov: str = "" +) -> List[List[int]]: + """Convert token to id. + Args: + texts: + The input texts, it refers to the chinese text here. + token_table: + The token table is built based on "data/lang_xxx/token.txt" + token_type: + The type of token, such as "pinyin" and "lazy_pinyin". + oov: + Out of vocabulary token. When a word(token) in the transcript + does not exist in the token list, it is replaced with `oov`. + + Returns: + The list of ids for the input texts. + """ + if texts is None: + raise ValueError("texts can't be None!") + else: + oov_id = token_table[oov] + ids: List[List[int]] = [] + for text in texts: + chars_list = list(str(text)) + if token_type == "lazy_pinyin": + text = lazy_pinyin(chars_list) + sub_ids = [ + token_table[txt] if txt in token_table else oov_id + for txt in text + ] + ids.append(sub_ids) + else: # token_type = "pinyin" + text = pinyin(chars_list) + sub_ids = [ + token_table[txt[0]] if txt[0] in token_table else oov_id + for txt in text + ] + ids.append(sub_ids) + return ids + + +def main(): + parser = get_parser() + args = parser.parse_args() + + rs = [] + if args.non_lang_syms is not None: + with codecs.open(args.non_lang_syms, "r", encoding="utf-8") as f: + nls = [x.rstrip() for x in f.readlines()] + rs = [re.compile(re.escape(x)) for x in nls] + + if args.text: + f = codecs.open(args.text, encoding="utf-8") + else: + f = codecs.getreader("utf-8")( + sys.stdin if is_python2 else sys.stdin.buffer + ) + + sys.stdout = codecs.getwriter("utf-8")( + sys.stdout if is_python2 else sys.stdout.buffer + ) + line = f.readline() + n = args.nchar + while line: + x = line.split() + print(" ".join(x[: args.skip_ncols]), end=" ") + a = " ".join(x[args.skip_ncols :]) # noqa E203 + + # get all matched positions + match_pos = [] + for r in rs: + i = 0 + while i >= 0: + m = r.search(a, i) + if m: + match_pos.append([m.start(), m.end()]) + i = m.end() + else: + break + if len(match_pos) > 0: + chars = [] + i = 0 + while i < len(a): + start_pos, end_pos = exist_or_not(i, match_pos) + if start_pos is not None: + chars.append(a[start_pos:end_pos]) + i = end_pos + else: + chars.append(a[i]) + i += 1 + a = chars + + if args.trans_type == "pinyin": + a = pinyin(list(str(a))) + a = [one[0] for one in a] + + if args.trans_type == "lazy_pinyin": + a = lazy_pinyin(list(str(a))) + + a = [a[j : j + n] for j in range(0, len(a), n)] # noqa E203 + + a_flat = [] + for z in a: + a_flat.append("".join(z)) + + a_chars = "".join(a_flat) + print(a_chars) + line = f.readline() + + +if __name__ == "__main__": + main() diff --git a/egs/alimeeting/ASR/prepare.sh b/egs/alimeeting/ASR/prepare.sh new file mode 100755 index 000000000..eb2ac697d --- /dev/null +++ b/egs/alimeeting/ASR/prepare.sh @@ -0,0 +1,133 @@ +#!/usr/bin/env bash + +set -eou pipefail + +stage=-1 +stop_stage=100 + +# We assume dl_dir (download dir) contains the following +# directories and files. If not, they will be downloaded +# by this script automatically. +# +# - $dl_dir/alimeeting +# This directory contains the following files downloaded from +# https://openslr.org/62/ +# +# - Train_Ali_far.tar.gz +# - Train_Ali_near.tar.gz +# - Test_Ali.tar.gz +# - Eval_Ali.tar.gz +# +# - $dl_dir/musan +# This directory contains the following directories downloaded from +# http://www.openslr.org/17/ +# +# - music +# - noise +# - speech + +dl_dir=$PWD/download + +. shared/parse_options.sh || exit 1 + +# All files generated by this script are saved in "data". +# You can safely remove "data" and rerun this script to regenerate it. +mkdir -p data + +log() { + # This function is from espnet + local fname=${BASH_SOURCE[1]##*/} + echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*" +} + +log "dl_dir: $dl_dir" + +if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then + log "Stage 0: Download data" + + if [ ! -f $dl_dir/alimeeting/Train_Ali_far.tar.gz ]; then + lhotse download ali-meeting $dl_dir/alimeeting + fi +fi + +if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then + log "Stage 1: Prepare alimeeting manifest" + # We assume that you have downloaded the alimeeting corpus + # to $dl_dir/alimeeting + if [ ! -f data/manifests/alimeeting/.manifests.done ]; then + mkdir -p data/manifests/alimeeting + lhotse prepare ali-meeting $dl_dir/alimeeting data/manifests/alimeeting + touch data/manifests/alimeeting/.manifests.done + fi +fi + +if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then + log "Stage 2: Process alimeeting" + if [ ! -f data/fbank/alimeeting/.fbank.done ]; then + mkdir -p data/fbank/alimeeting + lhotse prepare ali-meeting $dl_dir/alimeeting data/manifests/alimeeting + fi +fi + +if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then + log "Stage 3: Prepare musan manifest" + # We assume that you have downloaded the musan corpus + # to data/musan + if [ ! -f data/manifests/.musan_manifests.done ]; then + log "It may take 6 minutes" + mkdir -p data/manifests + lhotse prepare musan $dl_dir/musan data/manifests + touch data/manifests/.musan_manifests.done + fi +fi + +if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then + log "Stage 4: Compute fbank for musan" + if [ ! -f data/fbank/.msuan.done ]; then + mkdir -p data/fbank + ./local/compute_fbank_musan.py + touch data/fbank/.msuan.done + fi +fi + +if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then + log "Stage 5: Compute fbank for alimeeting" + if [ ! -f data/fbank/.alimeeting.done ]; then + mkdir -p data/fbank + ./local/compute_fbank_alimeeting.py + touch data/fbank/.alimeeting.done + fi +fi + +if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then + log "Stage 6: Prepare char based lang" + lang_char_dir=data/lang_char + mkdir -p $lang_char_dir + + # Prepare text. + # Note: in Linux, you can install jq with the following command: + # wget -O jq https://github.com/stedolan/jq/releases/download/jq-1.6/jq-linux64 + gunzip -c data/manifests/alimeeting/supervisions_train.jsonl.gz \ + | jq ".text" | sed 's/"//g' \ + | ./local/text2token.py -t "char" > $lang_char_dir/text + + # Prepare words segments + python ./local/text2segments.py \ + --input $lang_char_dir/text \ + --output $lang_char_dir/text_words_segmentation + + cat $lang_char_dir/text_words_segmentation | sed "s/ /\n/g" \ + | sort -u | sed "/^$/d" \ + | uniq > $lang_char_dir/words_no_ids.txt + + # Prepare words.txt + if [ ! -f $lang_char_dir/words.txt ]; then + ./local/prepare_words.py \ + --input-file $lang_char_dir/words_no_ids.txt \ + --output-file $lang_char_dir/words.txt + fi + + if [ ! -f $lang_char_dir/L_disambig.pt ]; then + ./local/prepare_char.py + fi +fi diff --git a/egs/alimeeting/ASR/pruned_transducer_stateless2/__init__.py b/egs/alimeeting/ASR/pruned_transducer_stateless2/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/egs/alimeeting/ASR/pruned_transducer_stateless2/asr_datamodule.py b/egs/alimeeting/ASR/pruned_transducer_stateless2/asr_datamodule.py new file mode 100644 index 000000000..bf6faad7a --- /dev/null +++ b/egs/alimeeting/ASR/pruned_transducer_stateless2/asr_datamodule.py @@ -0,0 +1,421 @@ +# Copyright 2021 Piotr Żelasko +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import argparse +import inspect +import logging +from functools import lru_cache +from pathlib import Path +from typing import Any, Dict, List, Optional + +import torch +from lhotse import ( + CutSet, + Fbank, + FbankConfig, + load_manifest, + load_manifest_lazy, + set_caching_enabled, +) +from lhotse.dataset import ( + CutConcatenate, + CutMix, + DynamicBucketingSampler, + K2SpeechRecognitionDataset, + PrecomputedFeatures, + SingleCutSampler, + SpecAugment, +) +from lhotse.dataset.input_strategies import OnTheFlyFeatures +from lhotse.utils import fix_random_seed +from torch.utils.data import DataLoader + +from icefall.utils import str2bool + +set_caching_enabled(False) +torch.set_num_threads(1) + + +class _SeedWorkers: + def __init__(self, seed: int): + self.seed = seed + + def __call__(self, worker_id: int): + fix_random_seed(self.seed + worker_id) + + +class AlimeetingAsrDataModule: + """ + DataModule for k2 ASR experiments. + It assumes there is always one train and valid dataloader, + but there can be multiple test dataloaders (e.g. LibriSpeech test-clean + and test-other). + It contains all the common data pipeline modules used in ASR + experiments, e.g.: + - dynamic batch size, + - bucketing samplers, + - cut concatenation, + - augmentation, + - on-the-fly feature extraction + This class should be derived for specific corpora used in ASR tasks. + """ + + def __init__(self, args: argparse.Namespace): + self.args = args + + @classmethod + def add_arguments(cls, parser: argparse.ArgumentParser): + group = parser.add_argument_group( + title="ASR data related options", + description="These options are used for the preparation of " + "PyTorch DataLoaders from Lhotse CutSet's -- they control the " + "effective batch sizes, sampling strategies, applied data " + "augmentations, etc.", + ) + group.add_argument( + "--manifest-dir", + type=Path, + default=Path("data/fbank"), + help="Path to directory with train/dev/test cuts.", + ) + group.add_argument( + "--max-duration", + type=int, + default=200.0, + help="Maximum pooled recordings duration (seconds) in a " + "single batch. You can reduce it if it causes CUDA OOM.", + ) + group.add_argument( + "--bucketing-sampler", + type=str2bool, + default=True, + help="When enabled, the batches will come from buckets of " + "similar duration (saves padding frames).", + ) + group.add_argument( + "--num-buckets", + type=int, + default=300, + help="The number of buckets for the DynamicBucketingSampler" + "(you might want to increase it for larger datasets).", + ) + group.add_argument( + "--concatenate-cuts", + type=str2bool, + default=False, + help="When enabled, utterances (cuts) will be concatenated " + "to minimize the amount of padding.", + ) + group.add_argument( + "--duration-factor", + type=float, + default=1.0, + help="Determines the maximum duration of a concatenated cut " + "relative to the duration of the longest cut in a batch.", + ) + group.add_argument( + "--gap", + type=float, + default=1.0, + help="The amount of padding (in seconds) inserted between " + "concatenated cuts. This padding is filled with noise when " + "noise augmentation is used.", + ) + group.add_argument( + "--on-the-fly-feats", + type=str2bool, + default=False, + help="When enabled, use on-the-fly cut mixing and feature " + "extraction. Will drop existing precomputed feature manifests " + "if available.", + ) + group.add_argument( + "--shuffle", + type=str2bool, + default=True, + help="When enabled (=default), the examples will be " + "shuffled for each epoch.", + ) + group.add_argument( + "--return-cuts", + type=str2bool, + default=True, + help="When enabled, each batch will have the " + "field: batch['supervisions']['cut'] with the cuts that " + "were used to construct it.", + ) + + group.add_argument( + "--num-workers", + type=int, + default=2, + help="The number of training dataloader workers that " + "collect the batches.", + ) + + group.add_argument( + "--enable-spec-aug", + type=str2bool, + default=True, + help="When enabled, use SpecAugment for training dataset.", + ) + + group.add_argument( + "--spec-aug-time-warp-factor", + type=int, + default=80, + help="Used only when --enable-spec-aug is True. " + "It specifies the factor for time warping in SpecAugment. " + "Larger values mean more warping. " + "A value less than 1 means to disable time warp.", + ) + + group.add_argument( + "--enable-musan", + type=str2bool, + default=True, + help="When enabled, select noise from MUSAN and mix it" + "with training dataset. ", + ) + + def train_dataloaders( + self, + cuts_train: CutSet, + sampler_state_dict: Optional[Dict[str, Any]] = None, + ) -> DataLoader: + """ + Args: + cuts_train: + CutSet for training. + sampler_state_dict: + The state dict for the training sampler. + """ + logging.info("About to get Musan cuts") + cuts_musan = load_manifest( + self.args.manifest_dir / "musan_cuts.jsonl.gz" + ) + + transforms = [] + if self.args.enable_musan: + logging.info("Enable MUSAN") + transforms.append( + CutMix( + cuts=cuts_musan, prob=0.5, snr=(10, 20), preserve_id=True + ) + ) + else: + logging.info("Disable MUSAN") + + if self.args.concatenate_cuts: + logging.info( + f"Using cut concatenation with duration factor " + f"{self.args.duration_factor} and gap {self.args.gap}." + ) + # Cut concatenation should be the first transform in the list, + # so that if we e.g. mix noise in, it will fill the gaps between + # different utterances. + transforms = [ + CutConcatenate( + duration_factor=self.args.duration_factor, gap=self.args.gap + ) + ] + transforms + + input_transforms = [] + if self.args.enable_spec_aug: + logging.info("Enable SpecAugment") + logging.info( + f"Time warp factor: {self.args.spec_aug_time_warp_factor}" + ) + # Set the value of num_frame_masks according to Lhotse's version. + # In different Lhotse's versions, the default of num_frame_masks is + # different. + num_frame_masks = 10 + num_frame_masks_parameter = inspect.signature( + SpecAugment.__init__ + ).parameters["num_frame_masks"] + if num_frame_masks_parameter.default == 1: + num_frame_masks = 2 + logging.info(f"Num frame mask: {num_frame_masks}") + input_transforms.append( + SpecAugment( + time_warp_factor=self.args.spec_aug_time_warp_factor, + num_frame_masks=num_frame_masks, + features_mask_size=27, + num_feature_masks=2, + frames_mask_size=100, + ) + ) + else: + logging.info("Disable SpecAugment") + + logging.info("About to create train dataset") + train = K2SpeechRecognitionDataset( + cut_transforms=transforms, + input_transforms=input_transforms, + return_cuts=self.args.return_cuts, + ) + + if self.args.on_the_fly_feats: + # NOTE: the PerturbSpeed transform should be added only if we + # remove it from data prep stage. + # Add on-the-fly speed perturbation; since originally it would + # have increased epoch size by 3, we will apply prob 2/3 and use + # 3x more epochs. + # Speed perturbation probably should come first before + # concatenation, but in principle the transforms order doesn't have + # to be strict (e.g. could be randomized) + # transforms = [PerturbSpeed(factors=[0.9, 1.1], p=2/3)] + transforms # noqa + # Drop feats to be on the safe side. + train = K2SpeechRecognitionDataset( + cut_transforms=transforms, + input_strategy=OnTheFlyFeatures( + Fbank(FbankConfig(num_mel_bins=80)) + ), + input_transforms=input_transforms, + return_cuts=self.args.return_cuts, + ) + + if self.args.bucketing_sampler: + logging.info("Using DynamicBucketingSampler.") + train_sampler = DynamicBucketingSampler( + cuts_train, + max_duration=self.args.max_duration, + shuffle=self.args.shuffle, + num_buckets=self.args.num_buckets, + buffer_size=30000, + drop_last=True, + ) + else: + logging.info("Using SingleCutSampler.") + train_sampler = SingleCutSampler( + cuts_train, + max_duration=self.args.max_duration, + shuffle=self.args.shuffle, + ) + logging.info("About to create train dataloader") + + # 'seed' is derived from the current random state, which will have + # previously been set in the main process. + seed = torch.randint(0, 100000, ()).item() + worker_init_fn = _SeedWorkers(seed) + + train_dl = DataLoader( + train, + sampler=train_sampler, + batch_size=None, + num_workers=self.args.num_workers, + persistent_workers=False, + worker_init_fn=worker_init_fn, + ) + + if sampler_state_dict is not None: + logging.info("Loading sampler state dict") + train_dl.sampler.load_state_dict(sampler_state_dict) + + return train_dl + + def valid_dataloaders(self, cuts_valid: CutSet) -> DataLoader: + transforms = [] + if self.args.concatenate_cuts: + transforms = [ + CutConcatenate( + duration_factor=self.args.duration_factor, gap=self.args.gap + ) + ] + transforms + + logging.info("About to create dev dataset") + if self.args.on_the_fly_feats: + validate = K2SpeechRecognitionDataset( + cut_transforms=transforms, + input_strategy=OnTheFlyFeatures( + Fbank(FbankConfig(num_mel_bins=80)) + ), + return_cuts=self.args.return_cuts, + ) + else: + validate = K2SpeechRecognitionDataset( + cut_transforms=transforms, + return_cuts=self.args.return_cuts, + ) + valid_sampler = DynamicBucketingSampler( + cuts_valid, + max_duration=self.args.max_duration, + shuffle=False, + ) + logging.info("About to create dev dataloader") + + from lhotse.dataset.iterable_dataset import IterableDatasetWrapper + + dev_iter_dataset = IterableDatasetWrapper( + dataset=validate, + sampler=valid_sampler, + ) + valid_dl = DataLoader( + dev_iter_dataset, + batch_size=None, + num_workers=self.args.num_workers, + persistent_workers=False, + ) + + return valid_dl + + def test_dataloaders(self, cuts: CutSet) -> DataLoader: + logging.debug("About to create test dataset") + test = K2SpeechRecognitionDataset( + input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))) + if self.args.on_the_fly_feats + else PrecomputedFeatures(), + return_cuts=self.args.return_cuts, + ) + sampler = DynamicBucketingSampler( + cuts, + max_duration=self.args.max_duration, + shuffle=False, + ) + from lhotse.dataset.iterable_dataset import IterableDatasetWrapper + + test_iter_dataset = IterableDatasetWrapper( + dataset=test, + sampler=sampler, + ) + test_dl = DataLoader( + test_iter_dataset, + batch_size=None, + num_workers=self.args.num_workers, + ) + return test_dl + + @lru_cache() + def train_cuts(self) -> CutSet: + logging.info("About to get train cuts") + return load_manifest_lazy( + self.args.manifest_dir / "alimeeting_cuts_train.jsonl.gz" + ) + + @lru_cache() + def valid_cuts(self) -> CutSet: + logging.info("About to get dev cuts") + return load_manifest_lazy( + self.args.manifest_dir / "alimeeting_cuts_eval.jsonl.gz" + ) + + @lru_cache() + def test_cuts(self) -> List[CutSet]: + logging.info("About to get test cuts") + return load_manifest_lazy( + self.args.manifest_dir / "alimeeting_cuts_test.jsonl.gz" + ) diff --git a/egs/alimeeting/ASR/pruned_transducer_stateless2/beam_search.py b/egs/alimeeting/ASR/pruned_transducer_stateless2/beam_search.py new file mode 120000 index 000000000..e24eca39f --- /dev/null +++ b/egs/alimeeting/ASR/pruned_transducer_stateless2/beam_search.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless2/beam_search.py \ No newline at end of file diff --git a/egs/alimeeting/ASR/pruned_transducer_stateless2/conformer.py b/egs/alimeeting/ASR/pruned_transducer_stateless2/conformer.py new file mode 120000 index 000000000..a65957180 --- /dev/null +++ b/egs/alimeeting/ASR/pruned_transducer_stateless2/conformer.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless2/conformer.py \ No newline at end of file diff --git a/egs/alimeeting/ASR/pruned_transducer_stateless2/decode.py b/egs/alimeeting/ASR/pruned_transducer_stateless2/decode.py new file mode 100755 index 000000000..cb455838e --- /dev/null +++ b/egs/alimeeting/ASR/pruned_transducer_stateless2/decode.py @@ -0,0 +1,615 @@ +#!/usr/bin/env python3 +# +# Copyright 2021 Xiaomi Corporation (Author: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +When training with the far data, usage: +(1) greedy search +./pruned_transducer_stateless2/decode.py \ + --epoch 29 \ + --avg 18 \ + --exp-dir ./pruned_transducer_stateless2/exp \ + --lang-dir data/lang_char \ + --max-duration 100 \ + --decoding-method greedy_search + +(2) modified beam search +./pruned_transducer_stateless2/decode.py \ + --epoch 29 \ + --avg 18 \ + --exp-dir ./pruned_transducer_stateless2/exp \ + --lang-dir data/lang_char \ + --max-duration 100 \ + --decoding-method modified_beam_search \ + --beam-size 4 + +(3) fast beam search +./pruned_transducer_stateless2/decode.py \ + --epoch 29 \ + --avg 18 \ + --exp-dir ./pruned_transducer_stateless2/exp \ + --lang-dir data/lang_char \ + --max-duration 1500 \ + --decoding-method fast_beam_search \ + --beam 4 \ + --max-contexts 4 \ + --max-states 8 +""" + + +import argparse +import logging +from collections import defaultdict +from pathlib import Path +from typing import Dict, List, Optional, Tuple + +import k2 +import torch +import torch.nn as nn +from asr_datamodule import AlimeetingAsrDataModule +from beam_search import ( + beam_search, + fast_beam_search_one_best, + greedy_search, + greedy_search_batch, + modified_beam_search, +) +from lhotse.cut import Cut +from train import get_params, get_transducer_model + +from icefall.checkpoint import ( + average_checkpoints, + find_checkpoints, + load_checkpoint, +) +from icefall.lexicon import Lexicon +from icefall.utils import ( + AttributeDict, + setup_logger, + store_transcripts, + write_error_stats, +) + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--epoch", + type=int, + default=28, + help="It specifies the checkpoint to use for decoding." + "Note: Epoch counts from 0.", + ) + + parser.add_argument( + "--batch", + type=int, + default=None, + help="It specifies the batch checkpoint to use for decoding." + "Note: Epoch counts from 0.", + ) + + parser.add_argument( + "--avg", + type=int, + default=15, + help="Number of checkpoints to average. Automatically select " + "consecutive checkpoints before the checkpoint specified by " + "'--epoch'. ", + ) + + parser.add_argument( + "--avg-last-n", + type=int, + default=0, + help="""If positive, --epoch and --avg are ignored and it + will use the last n checkpoints exp_dir/checkpoint-xxx.pt + where xxx is the number of processed batches while + saving that checkpoint. + """, + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="pruned_transducer_stateless2/exp", + help="The experiment dir", + ) + + parser.add_argument( + "--lang-dir", + type=str, + default="data/lang_char", + help="""The lang dir + It contains language related input files such as + "lexicon.txt" + """, + ) + + parser.add_argument( + "--decoding-method", + type=str, + default="greedy_search", + help="""Possible values are: + - greedy_search + - beam_search + - modified_beam_search + - fast_beam_search + """, + ) + + parser.add_argument( + "--beam-size", + type=int, + default=4, + help="""An interger indicating how many candidates we will keep for each + frame. Used only when --decoding-method is beam_search or + modified_beam_search.""", + ) + + parser.add_argument( + "--beam", + type=float, + default=4, + help="""A floating point value to calculate the cutoff score during beam + search (i.e., `cutoff = max-score - beam`), which is the same as the + `beam` in Kaldi. + Used only when --decoding-method is fast_beam_search""", + ) + + parser.add_argument( + "--max-contexts", + type=int, + default=4, + help="""Used only when --decoding-method is + fast_beam_search""", + ) + + parser.add_argument( + "--max-states", + type=int, + default=8, + help="""Used only when --decoding-method is + fast_beam_search""", + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; " + "2 means tri-gram", + ) + parser.add_argument( + "--max-sym-per-frame", + type=int, + default=1, + help="""Maximum number of symbols per frame. + Used only when --decoding_method is greedy_search""", + ) + + return parser + + +def decode_one_batch( + params: AttributeDict, + model: nn.Module, + lexicon: Lexicon, + batch: dict, + decoding_graph: Optional[k2.Fsa] = None, +) -> Dict[str, List[List[str]]]: + """Decode one batch and return the result in a dict. The dict has the + following format: + + - key: It indicates the setting used for decoding. For example, + if greedy_search is used, it would be "greedy_search" + If beam search with a beam size of 7 is used, it would be + "beam_7" + - value: It contains the decoding result. `len(value)` equals to + batch size. `value[i]` is the decoding result for the i-th + utterance in the given batch. + Args: + params: + It's the return value of :func:`get_params`. + model: + The neural model. + batch: + It is the return value from iterating + `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation + for the format of the `batch`. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used + only when --decoding_method is fast_beam_search. + Returns: + Return the decoding result. See above description for the format of + the returned dict. + """ + device = model.device + feature = batch["inputs"] + assert feature.ndim == 3 + + feature = feature.to(device) + # at entry, feature is (N, T, C) + + supervisions = batch["supervisions"] + feature_lens = supervisions["num_frames"].to(device) + encoder_out, encoder_out_lens = model.encoder( + x=feature, x_lens=feature_lens + ) + hyps = [] + + if params.decoding_method == "fast_beam_search": + hyp_tokens = fast_beam_search_one_best( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + ) + for i in range(encoder_out.size(0)): + hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]]) + elif ( + params.decoding_method == "greedy_search" + and params.max_sym_per_frame == 1 + ): + hyp_tokens = greedy_search_batch( + model=model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + ) + for i in range(encoder_out.size(0)): + hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]]) + elif params.decoding_method == "modified_beam_search": + hyp_tokens = modified_beam_search( + model=model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam_size, + ) + for i in range(encoder_out.size(0)): + hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]]) + else: + batch_size = encoder_out.size(0) + + for i in range(batch_size): + # fmt: off + encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]] + # fmt: on + if params.decoding_method == "greedy_search": + hyp = greedy_search( + model=model, + encoder_out=encoder_out_i, + max_sym_per_frame=params.max_sym_per_frame, + ) + elif params.decoding_method == "beam_search": + hyp = beam_search( + model=model, + encoder_out=encoder_out_i, + beam=params.beam_size, + ) + else: + raise ValueError( + f"Unsupported decoding method: {params.decoding_method}" + ) + hyps.append([lexicon.token_table[idx] for idx in hyp]) + + if params.decoding_method == "greedy_search": + return {"greedy_search": hyps} + elif params.decoding_method == "fast_beam_search": + return { + ( + f"beam_{params.beam}_" + f"max_contexts_{params.max_contexts}_" + f"max_states_{params.max_states}" + ): hyps + } + else: + return {f"beam_size_{params.beam_size}": hyps} + + +def decode_dataset( + dl: torch.utils.data.DataLoader, + params: AttributeDict, + model: nn.Module, + lexicon: Lexicon, + decoding_graph: Optional[k2.Fsa] = None, +) -> Dict[str, List[Tuple[List[str], List[str]]]]: + """Decode dataset. + + Args: + dl: + PyTorch's dataloader containing the dataset to decode. + params: + It is returned by :func:`get_params`. + model: + The neural model. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used + only when --decoding_method is fast_beam_search. + Returns: + Return a dict, whose key may be "greedy_search" if greedy search + is used, or it may be "beam_7" if beam size of 7 is used. + Its value is a list of tuples. Each tuple contains two elements: + The first is the reference transcript, and the second is the + predicted result. + """ + num_cuts = 0 + + try: + num_batches = len(dl) + except TypeError: + num_batches = "?" + + if params.decoding_method == "greedy_search": + log_interval = 100 + else: + log_interval = 50 + + results = defaultdict(list) + for batch_idx, batch in enumerate(dl): + texts = batch["supervisions"]["text"] + texts = [list(str(text).replace(" ", "")) for text in texts] + + hyps_dict = decode_one_batch( + params=params, + model=model, + lexicon=lexicon, + decoding_graph=decoding_graph, + batch=batch, + ) + + for name, hyps in hyps_dict.items(): + this_batch = [] + assert len(hyps) == len(texts) + for hyp_words, ref_text in zip(hyps, texts): + this_batch.append((ref_text, hyp_words)) + + results[name].extend(this_batch) + + num_cuts += len(texts) + + if batch_idx % log_interval == 0: + batch_str = f"{batch_idx}/{num_batches}" + + logging.info( + f"batch {batch_str}, cuts processed until now is {num_cuts}" + ) + return results + + +def save_results( + params: AttributeDict, + test_set_name: str, + results_dict: Dict[str, List[Tuple[List[int], List[int]]]], +): + test_set_wers = dict() + for key, results in results_dict.items(): + recog_path = ( + params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt" + ) + store_transcripts(filename=recog_path, texts=results) + logging.info(f"The transcripts are stored in {recog_path}") + + # The following prints out WERs, per-word error statistics and aligned + # ref/hyp pairs. + errs_filename = ( + params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt" + ) + with open(errs_filename, "w") as f: + wer = write_error_stats( + f, f"{test_set_name}-{key}", results, enable_log=True + ) + test_set_wers[key] = wer + + logging.info("Wrote detailed error stats to {}".format(errs_filename)) + + test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1]) + errs_info = ( + params.res_dir + / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt" + ) + with open(errs_info, "w") as f: + print("settings\tWER", file=f) + for key, val in test_set_wers: + print("{}\t{}".format(key, val), file=f) + + s = "\nFor {}, WER of different settings are:\n".format(test_set_name) + note = "\tbest for {}".format(test_set_name) + for key, val in test_set_wers: + s += "{}\t{}{}\n".format(key, val, note) + note = "" + logging.info(s) + + +@torch.no_grad() +def main(): + parser = get_parser() + AlimeetingAsrDataModule.add_arguments(parser) + args = parser.parse_args() + args.exp_dir = Path(args.exp_dir) + + params = get_params() + params.update(vars(args)) + + assert params.decoding_method in ( + "greedy_search", + "beam_search", + "fast_beam_search", + "modified_beam_search", + ) + params.res_dir = params.exp_dir / params.decoding_method + + params.suffix = f"epoch-{params.epoch}-avg-{params.avg}" + if "fast_beam_search" in params.decoding_method: + params.suffix += f"-beam-{params.beam}" + params.suffix += f"-max-contexts-{params.max_contexts}" + params.suffix += f"-max-states-{params.max_states}" + elif "beam_search" in params.decoding_method: + params.suffix += f"-beam-{params.beam_size}" + else: + params.suffix += f"-context-{params.context_size}" + params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}" + + setup_logger(f"{params.res_dir}/log-decode-{params.suffix}") + logging.info("Decoding started") + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"Device: {device}") + + lexicon = Lexicon(params.lang_dir) + params.blank_id = lexicon.token_table[""] + params.vocab_size = max(lexicon.tokens) + 1 + + logging.info(params) + + logging.info("About to create model") + model = get_transducer_model(params) + + if params.avg_last_n > 0: + filenames = find_checkpoints(params.exp_dir)[: params.avg_last_n] + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + elif params.avg == 1: + load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) + elif params.batch is not None: + filenames = f"{params.exp_dir}/checkpoint-{params.batch}.pt" + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints([filenames], device=device)) + else: + start = params.epoch - params.avg + 1 + filenames = [] + for i in range(start, params.epoch + 1): + if start >= 0: + filenames.append(f"{params.exp_dir}/epoch-{i}.pt") + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + + average = average_checkpoints(filenames, device=device) + checkpoint = {"model": average} + torch.save( + checkpoint, + "pruned_transducer_stateless2/exp/pretrained_epoch_29_avg_18.pt", + ) + + model.to(device) + model.eval() + model.device = device + + if params.decoding_method == "fast_beam_search": + decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device) + else: + decoding_graph = None + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + # Note: Please use "pip install webdataset==0.1.103" + # for installing the webdataset. + import glob + import os + + from lhotse import CutSet + from lhotse.dataset.webdataset import export_to_webdataset + + alimeeting = AlimeetingAsrDataModule(args) + + dev = "eval" + test = "test" + + if not os.path.exists(f"{dev}/shared-0.tar"): + os.makedirs(dev) + dev_cuts = alimeeting.valid_cuts() + export_to_webdataset( + dev_cuts, + output_path=f"{dev}/shared-%d.tar", + shard_size=300, + ) + + if not os.path.exists(f"{test}/shared-0.tar"): + os.makedirs(test) + test_cuts = alimeeting.test_cuts() + export_to_webdataset( + test_cuts, + output_path=f"{test}/shared-%d.tar", + shard_size=300, + ) + + dev_shards = [ + str(path) + for path in sorted(glob.glob(os.path.join(dev, "shared-*.tar"))) + ] + cuts_dev_webdataset = CutSet.from_webdataset( + dev_shards, + split_by_worker=True, + split_by_node=True, + shuffle_shards=True, + ) + + test_shards = [ + str(path) + for path in sorted(glob.glob(os.path.join(test, "shared-*.tar"))) + ] + cuts_test_webdataset = CutSet.from_webdataset( + test_shards, + split_by_worker=True, + split_by_node=True, + shuffle_shards=True, + ) + + def remove_short_and_long_utt(c: Cut): + return 1.0 <= c.duration + + cuts_dev_webdataset = cuts_dev_webdataset.filter(remove_short_and_long_utt) + cuts_test_webdataset = cuts_test_webdataset.filter( + remove_short_and_long_utt + ) + + dev_dl = alimeeting.valid_dataloaders(cuts_dev_webdataset) + test_dl = alimeeting.test_dataloaders(cuts_test_webdataset) + + test_sets = ["dev", "test"] + test_dl = [dev_dl, test_dl] + + for test_set, test_dl in zip(test_sets, test_dl): + results_dict = decode_dataset( + dl=test_dl, + params=params, + model=model, + lexicon=lexicon, + decoding_graph=decoding_graph, + ) + save_results( + params=params, + test_set_name=test_set, + results_dict=results_dict, + ) + + logging.info("Done!") + + +if __name__ == "__main__": + main() diff --git a/egs/alimeeting/ASR/pruned_transducer_stateless2/decoder.py b/egs/alimeeting/ASR/pruned_transducer_stateless2/decoder.py new file mode 120000 index 000000000..722e1c894 --- /dev/null +++ b/egs/alimeeting/ASR/pruned_transducer_stateless2/decoder.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless2/decoder.py \ No newline at end of file diff --git a/egs/alimeeting/ASR/pruned_transducer_stateless2/encoder_interface.py b/egs/alimeeting/ASR/pruned_transducer_stateless2/encoder_interface.py new file mode 120000 index 000000000..653c5b09a --- /dev/null +++ b/egs/alimeeting/ASR/pruned_transducer_stateless2/encoder_interface.py @@ -0,0 +1 @@ +../../../librispeech/ASR/transducer_stateless/encoder_interface.py \ No newline at end of file diff --git a/egs/alimeeting/ASR/pruned_transducer_stateless2/export.py b/egs/alimeeting/ASR/pruned_transducer_stateless2/export.py new file mode 100644 index 000000000..0a69e0a57 --- /dev/null +++ b/egs/alimeeting/ASR/pruned_transducer_stateless2/export.py @@ -0,0 +1,178 @@ +# Copyright 2021 Xiaomi Corporation (Author: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# This script converts several saved checkpoints +# to a single one using model averaging. +""" +Usage: +./pruned_transducer_stateless2/export.py \ + --exp-dir ./pruned_transducer_stateless2/exp \ + --lang-dir data/lang_char \ + --epoch 29 \ + --avg 18 + +It will generate a file exp_dir/pretrained.pt + +To use the generated file with `pruned_transducer_stateless2/decode.py`, +you can do: + + cd /path/to/exp_dir + ln -s pretrained.pt epoch-9999.pt + + cd /path/to/egs/alimeeting/ASR + ./pruned_transducer_stateless2/decode.py \ + --exp-dir ./pruned_transducer_stateless2/exp \ + --epoch 9999 \ + --avg 1 \ + --max-duration 100 \ + --lang-dir data/lang_char +""" + +import argparse +import logging +from pathlib import Path + +import torch +from train import get_params, get_transducer_model + +from icefall.checkpoint import average_checkpoints, load_checkpoint +from icefall.lexicon import Lexicon +from icefall.utils import str2bool + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--epoch", + type=int, + default=28, + help="It specifies the checkpoint to use for decoding." + "Note: Epoch counts from 0.", + ) + + parser.add_argument( + "--avg", + type=int, + default=15, + help="Number of checkpoints to average. Automatically select " + "consecutive checkpoints before the checkpoint specified by " + "'--epoch'. ", + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="pruned_transducer_stateless2/exp", + help="""It specifies the directory where all training related + files, e.g., checkpoints, log, etc, are saved + """, + ) + + parser.add_argument( + "--lang-dir", + type=str, + default="data/lang_char", + help="The lang dir", + ) + + parser.add_argument( + "--jit", + type=str2bool, + default=False, + help="""True to save a model after applying torch.jit.script. + """, + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; " + "2 means tri-gram", + ) + + return parser + + +def main(): + args = get_parser().parse_args() + args.exp_dir = Path(args.exp_dir) + + assert args.jit is False, "Support torchscript will be added later" + + params = get_params() + params.update(vars(args)) + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"device: {device}") + + lexicon = Lexicon(params.lang_dir) + + params.blank_id = 0 + params.vocab_size = max(lexicon.tokens) + 1 + + logging.info(params) + + logging.info("About to create model") + model = get_transducer_model(params) + + model.to(device) + + if params.avg == 1: + load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) + else: + start = params.epoch - params.avg + 1 + filenames = [] + for i in range(start, params.epoch + 1): + if start >= 0: + filenames.append(f"{params.exp_dir}/epoch-{i}.pt") + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + + model.eval() + + model.to("cpu") + model.eval() + + if params.jit: + logging.info("Using torch.jit.script") + model = torch.jit.script(model) + filename = params.exp_dir / "cpu_jit.pt" + model.save(str(filename)) + logging.info(f"Saved to {filename}") + else: + logging.info("Not using torch.jit.script") + # Save it using a format so that it can be loaded + # by :func:`load_checkpoint` + filename = params.exp_dir / "pretrained.pt" + torch.save({"model": model.state_dict()}, str(filename)) + logging.info(f"Saved to {filename}") + + +if __name__ == "__main__": + formatter = ( + "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + ) + + logging.basicConfig(format=formatter, level=logging.INFO) + main() diff --git a/egs/alimeeting/ASR/pruned_transducer_stateless2/joiner.py b/egs/alimeeting/ASR/pruned_transducer_stateless2/joiner.py new file mode 120000 index 000000000..9052f3cbb --- /dev/null +++ b/egs/alimeeting/ASR/pruned_transducer_stateless2/joiner.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless2/joiner.py \ No newline at end of file diff --git a/egs/alimeeting/ASR/pruned_transducer_stateless2/model.py b/egs/alimeeting/ASR/pruned_transducer_stateless2/model.py new file mode 120000 index 000000000..a99e74334 --- /dev/null +++ b/egs/alimeeting/ASR/pruned_transducer_stateless2/model.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless2/model.py \ No newline at end of file diff --git a/egs/alimeeting/ASR/pruned_transducer_stateless2/optim.py b/egs/alimeeting/ASR/pruned_transducer_stateless2/optim.py new file mode 120000 index 000000000..0a2f285aa --- /dev/null +++ b/egs/alimeeting/ASR/pruned_transducer_stateless2/optim.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless2/optim.py \ No newline at end of file diff --git a/egs/alimeeting/ASR/pruned_transducer_stateless2/pretrained.py b/egs/alimeeting/ASR/pruned_transducer_stateless2/pretrained.py new file mode 100644 index 000000000..93b1e1f57 --- /dev/null +++ b/egs/alimeeting/ASR/pruned_transducer_stateless2/pretrained.py @@ -0,0 +1,347 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang) +# 2022 Xiaomi Crop. (authors: Mingshuang Luo) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Here, the far data is used for training, usage: + +(1) greedy search +./pruned_transducer_stateless2/pretrained.py \ + --checkpoint ./pruned_transducer_stateless2/exp/pretrained.pt \ + --lang-dir ./data/lang_char \ + --decoding-method greedy_search \ + --max-sym-per-frame 1 \ + /path/to/foo.wav \ + /path/to/bar.wav + +(2) modified beam search +./pruned_transducer_stateless2/pretrained.py \ + --checkpoint ./pruned_transducer_stateless2/exp/pretrained.pt \ + --lang-dir ./data/lang_char \ + --decoding-method modified_beam_search \ + --beam-size 4 \ + /path/to/foo.wav \ + /path/to/bar.wav + +(3) fast beam search +./pruned_transducer_stateless2/pretrained.py \ + --checkpoint ./pruned_transducer_stateless/exp/pretrained.pt \ + --lang-dir ./data/lang_char \ + --decoding-method fast_beam_search \ + --beam 4 \ + --max-contexts 4 \ + --max-states 8 \ + /path/to/foo.wav \ + /path/to/bar.wav + +You can also use `./pruned_transducer_stateless2/exp/epoch-xx.pt`. + +Note: ./pruned_transducer_stateless2/exp/pretrained.pt is generated by +./pruned_transducer_stateless2/export.py +""" + + +import argparse +import logging +import math +from typing import List + +import k2 +import kaldifeat +import torch +import torchaudio +from beam_search import ( + beam_search, + fast_beam_search_one_best, + greedy_search, + greedy_search_batch, + modified_beam_search, +) +from torch.nn.utils.rnn import pad_sequence +from train import get_params, get_transducer_model + +from icefall.lexicon import Lexicon + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--checkpoint", + type=str, + required=True, + help="Path to the checkpoint. " + "The checkpoint is assumed to be saved by " + "icefall.checkpoint.save_checkpoint().", + ) + + parser.add_argument( + "--lang-dir", + type=str, + help="""Path to lang. + """, + ) + + parser.add_argument( + "--decoding-method", + type=str, + default="greedy_search", + help="""Possible values are: + - greedy_search + - modified_beam_search + - fast_beam_search + """, + ) + + parser.add_argument( + "sound_files", + type=str, + nargs="+", + help="The input sound file(s) to transcribe. " + "Supported formats are those supported by torchaudio.load(). " + "For example, wav and flac are supported. " + "The sample rate has to be 16kHz.", + ) + + parser.add_argument( + "--sample-rate", + type=int, + default=16000, + help="The sample rate of the input sound file", + ) + + parser.add_argument( + "--beam-size", + type=int, + default=4, + help="Used only when --method is beam_search and modified_beam_search ", + ) + + parser.add_argument( + "--beam", + type=float, + default=4, + help="""A floating point value to calculate the cutoff score during beam + search (i.e., `cutoff = max-score - beam`), which is the same as the + `beam` in Kaldi. + Used only when --decoding-method is fast_beam_search""", + ) + + parser.add_argument( + "--max-contexts", + type=int, + default=4, + help="""Used only when --decoding-method is + fast_beam_search""", + ) + + parser.add_argument( + "--max-states", + type=int, + default=8, + help="""Used only when --decoding-method is + fast_beam_search""", + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; " + "2 means tri-gram", + ) + + parser.add_argument( + "--max-sym-per-frame", + type=int, + default=1, + help="""Maximum number of symbols per frame. Used only when + --method is greedy_search. + """, + ) + + return parser + + +def read_sound_files( + filenames: List[str], expected_sample_rate: float +) -> List[torch.Tensor]: + """Read a list of sound files into a list 1-D float32 torch tensors. + Args: + filenames: + A list of sound filenames. + expected_sample_rate: + The expected sample rate of the sound files. + Returns: + Return a list of 1-D float32 torch tensors. + """ + ans = [] + for f in filenames: + wave, sample_rate = torchaudio.load(f) + assert sample_rate == expected_sample_rate, ( + f"expected sample rate: {expected_sample_rate}. " + f"Given: {sample_rate}" + ) + # We use only the first channel + ans.append(wave[0]) + return ans + + +@torch.no_grad() +def main(): + parser = get_parser() + args = parser.parse_args() + + params = get_params() + + params.update(vars(args)) + + lexicon = Lexicon(params.lang_dir) + params.blank_id = lexicon.token_table[""] + params.vocab_size = max(lexicon.tokens) + 1 + + logging.info(f"{params}") + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"device: {device}") + + logging.info("Creating model") + model = get_transducer_model(params) + + checkpoint = torch.load(args.checkpoint, map_location="cpu") + model.load_state_dict(checkpoint["model"], strict=False) + model.to(device) + model.eval() + model.device = device + + if params.decoding_method == "fast_beam_search": + decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device) + else: + decoding_graph = None + + logging.info("Constructing Fbank computer") + opts = kaldifeat.FbankOptions() + opts.device = device + opts.frame_opts.dither = 0 + opts.frame_opts.snip_edges = False + opts.frame_opts.samp_freq = params.sample_rate + opts.mel_opts.num_bins = params.feature_dim + + fbank = kaldifeat.Fbank(opts) + + logging.info(f"Reading sound files: {params.sound_files}") + waves = read_sound_files( + filenames=params.sound_files, expected_sample_rate=params.sample_rate + ) + waves = [w.to(device) for w in waves] + + logging.info("Decoding started") + features = fbank(waves) + feature_lengths = [f.size(0) for f in features] + + features = pad_sequence( + features, batch_first=True, padding_value=math.log(1e-10) + ) + + feature_lengths = torch.tensor(feature_lengths, device=device) + + with torch.no_grad(): + encoder_out, encoder_out_lens = model.encoder( + x=features, x_lens=feature_lengths + ) + + hyps = [] + msg = f"Using {params.decoding_method}" + logging.info(msg) + + if params.decoding_method == "fast_beam_search": + hyp_tokens = fast_beam_search_one_best( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + ) + for i in range(encoder_out.size(0)): + hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]]) + elif ( + params.decoding_method == "greedy_search" + and params.max_sym_per_frame == 1 + ): + hyp_tokens = greedy_search_batch( + model=model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + ) + for i in range(encoder_out.size(0)): + hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]]) + elif params.decoding_method == "modified_beam_search": + hyp_tokens = modified_beam_search( + model=model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam_size, + ) + for i in range(encoder_out.size(0)): + hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]]) + else: + batch_size = encoder_out.size(0) + + for i in range(batch_size): + # fmt: off + encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]] + # fmt: on + if params.decoding_method == "greedy_search": + hyp = greedy_search( + model=model, + encoder_out=encoder_out_i, + max_sym_per_frame=params.max_sym_per_frame, + ) + elif params.decoding_method == "beam_search": + hyp = beam_search( + model=model, + encoder_out=encoder_out_i, + beam=params.beam_size, + ) + else: + raise ValueError( + f"Unsupported decoding method: {params.decoding_method}" + ) + hyps.append([lexicon.token_table[idx] for idx in hyp]) + + s = "\n" + for filename, hyp in zip(params.sound_files, hyps): + words = " ".join(hyp) + s += f"{filename}:\n{words}\n\n" + logging.info(s) + + logging.info("Decoding Done") + + +if __name__ == "__main__": + formatter = ( + "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + ) + + logging.basicConfig(format=formatter, level=logging.INFO) + main() diff --git a/egs/alimeeting/ASR/pruned_transducer_stateless2/scaling.py b/egs/alimeeting/ASR/pruned_transducer_stateless2/scaling.py new file mode 120000 index 000000000..c10cdfe12 --- /dev/null +++ b/egs/alimeeting/ASR/pruned_transducer_stateless2/scaling.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless2/scaling.py \ No newline at end of file diff --git a/egs/alimeeting/ASR/pruned_transducer_stateless2/train.py b/egs/alimeeting/ASR/pruned_transducer_stateless2/train.py new file mode 100644 index 000000000..81a0ede7f --- /dev/null +++ b/egs/alimeeting/ASR/pruned_transducer_stateless2/train.py @@ -0,0 +1,972 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang, +# Wei Kang +# Mingshuang Luo) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Usage: + +export CUDA_VISIBLE_DEVICES="0,1,2,3" + +./pruned_transducer_stateless2/train.py \ + --world-size 4 \ + --num-epochs 30 \ + --start-epoch 0 \ + --exp-dir pruned_transducer_stateless2/exp \ + --lang-dir data/lang_char \ + --max-duration 220 \ + --save-every-n 1000 + +# For mix precision training: + +./pruned_transducer_stateless2/train.py \ + --world-size 4 \ + --num-epochs 30 \ + --start-epoch 0 \ + --exp-dir pruned_transducer_stateless2/exp \ + --lang-dir data/lang_char \ + --max-duration 220 \ + --save-every-n 1000 + --use-fp16 True + +""" + +import argparse +import logging +import os +import warnings +from pathlib import Path +from shutil import copyfile +from typing import Any, Dict, Optional, Tuple, Union + +import k2 +import optim +import torch +import torch.multiprocessing as mp +import torch.nn as nn +from asr_datamodule import AlimeetingAsrDataModule +from conformer import Conformer +from decoder import Decoder +from joiner import Joiner +from lhotse.cut import Cut +from lhotse.dataset.sampling.base import CutSampler +from lhotse.utils import fix_random_seed +from model import Transducer +from optim import Eden, Eve +from torch import Tensor +from torch.cuda.amp import GradScaler +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.utils.tensorboard import SummaryWriter + +from icefall import diagnostics +from icefall.char_graph_compiler import CharCtcTrainingGraphCompiler +from icefall.checkpoint import load_checkpoint, remove_checkpoints +from icefall.checkpoint import save_checkpoint as save_checkpoint_impl +from icefall.checkpoint import save_checkpoint_with_global_batch_idx +from icefall.dist import cleanup_dist, setup_dist +from icefall.env import get_env_info +from icefall.lexicon import Lexicon +from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool + +LRSchedulerType = Union[ + torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler +] + +os.environ["CUDA_LAUNCH_BLOCKING"] = "1" + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--world-size", + type=int, + default=1, + help="Number of GPUs for DDP training.", + ) + + parser.add_argument( + "--master-port", + type=int, + default=12359, + help="Master port to use for DDP training.", + ) + + parser.add_argument( + "--tensorboard", + type=str2bool, + default=True, + help="Should various information be logged in tensorboard.", + ) + + parser.add_argument( + "--num-epochs", + type=int, + default=30, + help="Number of epochs to train.", + ) + + parser.add_argument( + "--start-epoch", + type=int, + default=0, + help="""Resume training from from this epoch. + If it is positive, it will load checkpoint from + transducer_stateless2/exp/epoch-{start_epoch-1}.pt + """, + ) + + parser.add_argument( + "--start-batch", + type=int, + default=0, + help="""If positive, --start-epoch is ignored and + it loads the checkpoint from exp-dir/checkpoint-{start_batch}.pt + """, + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="pruned_transducer_stateless2/exp", + help="""The experiment dir. + It specifies the directory where all training related + files, e.g., checkpoints, log, etc, are saved + """, + ) + + parser.add_argument( + "--lang-dir", + type=str, + default="data/lang_char", + help="""The lang dir + It contains language related input files such as + "lexicon.txt" + """, + ) + + parser.add_argument( + "--initial-lr", + type=float, + default=0.003, + help="The initial learning rate. This value should not need to be changed.", + ) + + parser.add_argument( + "--lr-batches", + type=float, + default=5000, + help="""Number of steps that affects how rapidly the learning rate decreases. + We suggest not to change this.""", + ) + + parser.add_argument( + "--lr-epochs", + type=float, + default=6, + help="""Number of epochs that affects how rapidly the learning rate decreases. + """, + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; " + "2 means tri-gram", + ) + + parser.add_argument( + "--prune-range", + type=int, + default=5, + help="The prune range for rnnt loss, it means how many symbols(context)" + "we are using to compute the loss", + ) + + parser.add_argument( + "--lm-scale", + type=float, + default=0.25, + help="The scale to smooth the loss with lm " + "(output of prediction network) part.", + ) + + parser.add_argument( + "--am-scale", + type=float, + default=0.0, + help="The scale to smooth the loss with am (output of encoder network)" + "part.", + ) + + parser.add_argument( + "--simple-loss-scale", + type=float, + default=0.5, + help="To get pruning ranges, we will calculate a simple version" + "loss(joiner is just addition), this simple loss also uses for" + "training (as a regularization item). We will scale the simple loss" + "with this parameter before adding to the final loss.", + ) + + parser.add_argument( + "--seed", + type=int, + default=42, + help="The seed for random generators intended for reproducibility", + ) + + parser.add_argument( + "--print-diagnostics", + type=str2bool, + default=False, + help="Accumulate stats on activations, print them and exit.", + ) + + parser.add_argument( + "--save-every-n", + type=int, + default=8000, + help="""Save checkpoint after processing this number of batches" + periodically. We save checkpoint to exp-dir/ whenever + params.batch_idx_train % save_every_n == 0. The checkpoint filename + has the form: f'exp-dir/checkpoint-{params.batch_idx_train}.pt' + Note: It also saves checkpoint to `exp-dir/epoch-xxx.pt` at the + end of each epoch where `xxx` is the epoch number counting from 0. + """, + ) + + parser.add_argument( + "--keep-last-k", + type=int, + default=20, + help="""Only keep this number of checkpoints on disk. + For instance, if it is 3, there are only 3 checkpoints + in the exp-dir with filenames `checkpoint-xxx.pt`. + It does not affect checkpoints with name `epoch-xxx.pt`. + """, + ) + + parser.add_argument( + "--use-fp16", + type=str2bool, + default=False, + help="Whether to use half precision training.", + ) + + return parser + + +def get_params() -> AttributeDict: + """Return a dict containing training parameters. + All training related parameters that are not passed from the commandline + are saved in the variable `params`. + Commandline options are merged into `params` after they are parsed, so + you can also access them via `params`. + Explanation of options saved in `params`: + - best_train_loss: Best training loss so far. It is used to select + the model that has the lowest training loss. It is + updated during the training. + - best_valid_loss: Best validation loss so far. It is used to select + the model that has the lowest validation loss. It is + updated during the training. + - best_train_epoch: It is the epoch that has the best training loss. + - best_valid_epoch: It is the epoch that has the best validation loss. + - batch_idx_train: Used to writing statistics to tensorboard. It + contains number of batches trained so far across + epochs. + - log_interval: Print training loss if batch_idx % log_interval` is 0 + - reset_interval: Reset statistics if batch_idx % reset_interval is 0 + - valid_interval: Run validation if batch_idx % valid_interval is 0 + - feature_dim: The model input dim. It has to match the one used + in computing features. + - subsampling_factor: The subsampling factor for the model. + - encoder_dim: Hidden dim for multi-head attention model. + - num_decoder_layers: Number of decoder layer of transformer decoder. + - warm_step: The warm_step for Noam optimizer. + """ + params = AttributeDict( + { + "best_train_loss": float("inf"), + "best_valid_loss": float("inf"), + "best_train_epoch": -1, + "best_valid_epoch": -1, + "batch_idx_train": 10, + "log_interval": 1, + "reset_interval": 200, + "valid_interval": 400, + # parameters for conformer + "feature_dim": 80, + "subsampling_factor": 4, + "encoder_dim": 512, + "nhead": 8, + "dim_feedforward": 2048, + "num_encoder_layers": 12, + # parameters for decoder + "decoder_dim": 512, + # parameters for joiner + "joiner_dim": 512, + # parameters for Noam + "model_warm_step": 200, + "env_info": get_env_info(), + } + ) + + return params + + +def get_encoder_model(params: AttributeDict) -> nn.Module: + # TODO: We can add an option to switch between Conformer and Transformer + encoder = Conformer( + num_features=params.feature_dim, + subsampling_factor=params.subsampling_factor, + d_model=params.encoder_dim, + nhead=params.nhead, + dim_feedforward=params.dim_feedforward, + num_encoder_layers=params.num_encoder_layers, + ) + return encoder + + +def get_decoder_model(params: AttributeDict) -> nn.Module: + decoder = Decoder( + vocab_size=params.vocab_size, + decoder_dim=params.decoder_dim, + blank_id=params.blank_id, + context_size=params.context_size, + ) + return decoder + + +def get_joiner_model(params: AttributeDict) -> nn.Module: + joiner = Joiner( + encoder_dim=params.encoder_dim, + decoder_dim=params.decoder_dim, + joiner_dim=params.joiner_dim, + vocab_size=params.vocab_size, + ) + return joiner + + +def get_transducer_model(params: AttributeDict) -> nn.Module: + encoder = get_encoder_model(params) + decoder = get_decoder_model(params) + joiner = get_joiner_model(params) + + model = Transducer( + encoder=encoder, + decoder=decoder, + joiner=joiner, + encoder_dim=params.encoder_dim, + decoder_dim=params.decoder_dim, + joiner_dim=params.joiner_dim, + vocab_size=params.vocab_size, + ) + return model + + +def load_checkpoint_if_available( + params: AttributeDict, + model: nn.Module, + optimizer: Optional[torch.optim.Optimizer] = None, + scheduler: Optional[LRSchedulerType] = None, +) -> Optional[Dict[str, Any]]: + """Load checkpoint from file. + If params.start_batch is positive, it will load the checkpoint from + `params.exp_dir/checkpoint-{params.start_batch}.pt`. Otherwise, if + params.start_epoch is positive, it will load the checkpoint from + `params.start_epoch - 1`. + Apart from loading state dict for `model` and `optimizer` it also updates + `best_train_epoch`, `best_train_loss`, `best_valid_epoch`, + and `best_valid_loss` in `params`. + Args: + params: + The return value of :func:`get_params`. + model: + The training model. + optimizer: + The optimizer that we are using. + scheduler: + The scheduler that we are using. + Returns: + Return a dict containing previously saved training info. + """ + if params.start_batch > 0: + filename = params.exp_dir / f"checkpoint-{params.start_batch}.pt" + elif params.start_epoch > 0: + filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt" + else: + return None + + assert filename.is_file(), f"{filename} does not exist!" + + saved_params = load_checkpoint( + filename, + model=model, + optimizer=optimizer, + scheduler=scheduler, + ) + + keys = [ + "best_train_epoch", + "best_valid_epoch", + "batch_idx_train", + "best_train_loss", + "best_valid_loss", + ] + for k in keys: + params[k] = saved_params[k] + + if params.start_batch > 0: + if "cur_epoch" in saved_params: + params["start_epoch"] = saved_params["cur_epoch"] + + return saved_params + + +def save_checkpoint( + params: AttributeDict, + model: nn.Module, + optimizer: Optional[torch.optim.Optimizer] = None, + scheduler: Optional[LRSchedulerType] = None, + sampler: Optional[CutSampler] = None, + scaler: Optional[GradScaler] = None, + rank: int = 0, +) -> None: + """Save model, optimizer, scheduler and training stats to file. + Args: + params: + It is returned by :func:`get_params`. + model: + The training model. + optimizer: + The optimizer used in the training. + sampler: + The sampler for the training dataset. + scaler: + The scaler used for mix precision training. + """ + if rank != 0: + return + filename = params.exp_dir / f"epoch-{params.cur_epoch}.pt" + save_checkpoint_impl( + filename=filename, + model=model, + params=params, + optimizer=optimizer, + scheduler=scheduler, + sampler=sampler, + scaler=scaler, + rank=rank, + ) + + if params.best_train_epoch == params.cur_epoch: + best_train_filename = params.exp_dir / "best-train-loss.pt" + copyfile(src=filename, dst=best_train_filename) + + if params.best_valid_epoch == params.cur_epoch: + best_valid_filename = params.exp_dir / "best-valid-loss.pt" + copyfile(src=filename, dst=best_valid_filename) + + +def compute_loss( + params: AttributeDict, + model: nn.Module, + graph_compiler: CharCtcTrainingGraphCompiler, + batch: dict, + is_training: bool, + warmup: float = 1.0, +) -> Tuple[Tensor, MetricsTracker]: + """ + Compute CTC loss given the model and its inputs. + Args: + params: + Parameters for training. See :func:`get_params`. + model: + The model for training. It is an instance of Conformer in our case. + batch: + A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()` + for the content in it. + is_training: + True for training. False for validation. When it is True, this + function enables autograd during computation; when it is False, it + disables autograd. + warmup: a floating point value which increases throughout training; + values >= 1.0 are fully warmed up and have all modules present. + """ + device = model.device + feature = batch["inputs"] + # at entry, feature is (N, T, C) + assert feature.ndim == 3 + feature = feature.to(device) + + supervisions = batch["supervisions"] + feature_lens = supervisions["num_frames"].to(device) + + texts = batch["supervisions"]["text"] + + y = graph_compiler.texts_to_ids(texts) + if type(y) == list: + y = k2.RaggedTensor(y).to(device) + else: + y = y.to(device) + + with torch.set_grad_enabled(is_training): + simple_loss, pruned_loss = model( + x=feature, + x_lens=feature_lens, + y=y, + prune_range=params.prune_range, + am_scale=params.am_scale, + lm_scale=params.lm_scale, + warmup=warmup, + ) + # after the main warmup step, we keep pruned_loss_scale small + # for the same amount of time (model_warm_step), to avoid + # overwhelming the simple_loss and causing it to diverge, + # in case it had not fully learned the alignment yet. + pruned_loss_scale = ( + 0.0 + if warmup < 1.0 + else (0.1 if warmup > 1.0 and warmup < 2.0 else 1.0) + ) + loss = ( + params.simple_loss_scale * simple_loss + + pruned_loss_scale * pruned_loss + ) + assert loss.requires_grad == is_training + + info = MetricsTracker() + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + info["frames"] = ( + (feature_lens // params.subsampling_factor).sum().item() + ) + + # Note: We use reduction=sum while computing the loss. + info["loss"] = loss.detach().cpu().item() + info["simple_loss"] = simple_loss.detach().cpu().item() + info["pruned_loss"] = pruned_loss.detach().cpu().item() + + return loss, info + + +def compute_validation_loss( + params: AttributeDict, + model: nn.Module, + graph_compiler: CharCtcTrainingGraphCompiler, + valid_dl: torch.utils.data.DataLoader, + world_size: int = 1, +) -> MetricsTracker: + """Run the validation process.""" + model.eval() + + tot_loss = MetricsTracker() + + for batch_idx, batch in enumerate(valid_dl): + loss, loss_info = compute_loss( + params=params, + model=model, + graph_compiler=graph_compiler, + batch=batch, + is_training=False, + ) + assert loss.requires_grad is False + tot_loss = tot_loss + loss_info + + if world_size > 1: + tot_loss.reduce(loss.device) + + loss_value = tot_loss["loss"] / tot_loss["frames"] + if loss_value < params.best_valid_loss: + params.best_valid_epoch = params.cur_epoch + params.best_valid_loss = loss_value + + return tot_loss + + +def train_one_epoch( + params: AttributeDict, + model: nn.Module, + optimizer: torch.optim.Optimizer, + scheduler: LRSchedulerType, + graph_compiler: CharCtcTrainingGraphCompiler, + train_dl: torch.utils.data.DataLoader, + valid_dl: torch.utils.data.DataLoader, + scaler: GradScaler, + tb_writer: Optional[SummaryWriter] = None, + world_size: int = 1, + rank: int = 0, +) -> None: + """Train the model for one epoch. + The training loss from the mean of all frames is saved in + `params.train_loss`. It runs the validation process every + `params.valid_interval` batches. + Args: + params: + It is returned by :func:`get_params`. + model: + The model for training. + optimizer: + The optimizer we are using. + scheduler: + The learning rate scheduler, we call step() every step. + train_dl: + Dataloader for the training dataset. + valid_dl: + Dataloader for the validation dataset. + scaler: + The scaler used for mix precision training. + tb_writer: + Writer to write log messages to tensorboard. + world_size: + Number of nodes in DDP training. If it is 1, DDP is disabled. + rank: + The rank of the node in DDP training. If no DDP is used, it should + be set to 0. + """ + model.train() + + tot_loss = MetricsTracker() + + for batch_idx, batch in enumerate(train_dl): + + params.batch_idx_train += 1 + batch_size = len(batch["supervisions"]["text"]) + + with torch.cuda.amp.autocast(enabled=params.use_fp16): + loss, loss_info = compute_loss( + params=params, + model=model, + graph_compiler=graph_compiler, + batch=batch, + is_training=True, + warmup=(params.batch_idx_train / params.model_warm_step), + ) + # summary stats + tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info + + # NOTE: We use reduction==sum and loss is computed over utterances + # in the batch and there is no normalization to it so far. + scaler.scale(loss).backward() + scheduler.step_batch(params.batch_idx_train) + scaler.step(optimizer) + scaler.update() + optimizer.zero_grad() + + if params.print_diagnostics and batch_idx == 5: + return + + if ( + params.batch_idx_train > 0 + and params.batch_idx_train % params.save_every_n == 0 + ): + save_checkpoint_with_global_batch_idx( + out_dir=params.exp_dir, + global_batch_idx=params.batch_idx_train, + model=model, + params=params, + optimizer=optimizer, + scheduler=scheduler, + sampler=train_dl.sampler, + scaler=scaler, + rank=rank, + ) + remove_checkpoints( + out_dir=params.exp_dir, + topk=params.keep_last_k, + rank=rank, + ) + + if batch_idx % params.log_interval == 0: + cur_lr = scheduler.get_last_lr()[0] + logging.info( + f"Epoch {params.cur_epoch}, " + f"batch {batch_idx}, loss[{loss_info}], " + f"tot_loss[{tot_loss}], batch size: {batch_size}, " + f"lr: {cur_lr:.2e}" + ) + + if tb_writer is not None: + tb_writer.add_scalar( + "train/learning_rate", cur_lr, params.batch_idx_train + ) + + loss_info.write_summary( + tb_writer, "train/current_", params.batch_idx_train + ) + tot_loss.write_summary( + tb_writer, "train/tot_", params.batch_idx_train + ) + + if batch_idx > 0 and batch_idx % params.valid_interval == 0: + logging.info("Computing validation loss") + valid_info = compute_validation_loss( + params=params, + model=model, + graph_compiler=graph_compiler, + valid_dl=valid_dl, + world_size=world_size, + ) + model.train() + logging.info(f"Epoch {params.cur_epoch}, validation: {valid_info}") + if tb_writer is not None: + valid_info.write_summary( + tb_writer, "train/valid_", params.batch_idx_train + ) + + loss_value = tot_loss["loss"] / tot_loss["frames"] + params.train_loss = loss_value + if params.train_loss < params.best_train_loss: + params.best_train_epoch = params.cur_epoch + params.best_train_loss = params.train_loss + + +def run(rank, world_size, args): + """ + Args: + rank: + It is a value between 0 and `world_size-1`, which is + passed automatically by `mp.spawn()` in :func:`main`. + The node with rank 0 is responsible for saving checkpoint. + world_size: + Number of GPUs for DDP training. + args: + The return value of get_parser().parse_args() + """ + params = get_params() + params.update(vars(args)) + + fix_random_seed(params.seed) + if world_size > 1: + setup_dist(rank, world_size, params.master_port) + + setup_logger(f"{params.exp_dir}/log/log-train") + logging.info("Training started") + + if args.tensorboard and rank == 0: + tb_writer = SummaryWriter(log_dir=f"{params.exp_dir}/tensorboard") + else: + tb_writer = None + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", rank) + logging.info(f"Device: {device}") + + lexicon = Lexicon(params.lang_dir) + graph_compiler = CharCtcTrainingGraphCompiler( + lexicon=lexicon, + device=device, + ) + + params.blank_id = lexicon.token_table[""] + params.vocab_size = max(lexicon.tokens) + 1 + + logging.info(params) + + logging.info("About to create model") + model = get_transducer_model(params) + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + checkpoints = load_checkpoint_if_available(params=params, model=model) + + model.to(device) + if world_size > 1: + logging.info("Using DDP") + model = DDP(model, device_ids=[rank]) + model.device = device + + optimizer = Eve(model.parameters(), lr=params.initial_lr) + + scheduler = Eden(optimizer, params.lr_batches, params.lr_epochs) + + if checkpoints and "optimizer" in checkpoints: + logging.info("Loading optimizer state dict") + optimizer.load_state_dict(checkpoints["optimizer"]) + + if ( + checkpoints + and "scheduler" in checkpoints + and checkpoints["scheduler"] is not None + ): + logging.info("Loading scheduler state dict") + scheduler.load_state_dict(checkpoints["scheduler"]) + + if params.print_diagnostics: + opts = diagnostics.TensorDiagnosticOptions( + 2 ** 22 + ) # allow 4 megabytes per sub-module + diagnostic = diagnostics.attach_diagnostics(model, opts) + + alimeeting = AlimeetingAsrDataModule(args) + + train_cuts = alimeeting.train_cuts() + valid_cuts = alimeeting.valid_cuts() + + def remove_short_and_long_utt(c: Cut): + # Keep only utterances with duration between 1 second and 15.0 seconds + # + # Caution: There is a reason to select 10.0 here. Please see + # ../local/display_manifest_statistics.py + # + # You should use ../local/display_manifest_statistics.py to get + # an utterance duration distribution for your dataset to select + # the threshold + return 1.0 <= c.duration <= 15.0 + + train_cuts = train_cuts.filter(remove_short_and_long_utt) + + valid_dl = alimeeting.valid_dataloaders(valid_cuts) + + if params.start_batch > 0 and checkpoints and "sampler" in checkpoints: + # We only load the sampler's state dict when it loads a checkpoint + # saved in the middle of an epoch + sampler_state_dict = checkpoints["sampler"] + else: + sampler_state_dict = None + + train_dl = alimeeting.train_dataloaders( + train_cuts, sampler_state_dict=sampler_state_dict + ) + + if not params.print_diagnostics and params.start_batch == 0: + scan_pessimistic_batches_for_oom( + model=model, + train_dl=train_dl, + optimizer=optimizer, + graph_compiler=graph_compiler, + params=params, + ) + + scaler = GradScaler(enabled=params.use_fp16) + if checkpoints and "grad_scaler" in checkpoints: + logging.info("Loading grad scaler state dict") + scaler.load_state_dict(checkpoints["grad_scaler"]) + + for epoch in range(params.start_epoch, params.num_epochs): + scheduler.step_epoch(epoch) + fix_random_seed(params.seed + epoch) + train_dl.sampler.set_epoch(epoch) + + if tb_writer is not None: + tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train) + + params.cur_epoch = epoch + + train_one_epoch( + params=params, + model=model, + optimizer=optimizer, + scheduler=scheduler, + graph_compiler=graph_compiler, + train_dl=train_dl, + valid_dl=valid_dl, + scaler=scaler, + tb_writer=tb_writer, + world_size=world_size, + rank=rank, + ) + + if params.print_diagnostics: + diagnostic.print_diagnostics() + break + + save_checkpoint( + params=params, + model=model, + optimizer=optimizer, + scheduler=scheduler, + sampler=train_dl.sampler, + scaler=scaler, + rank=rank, + ) + + logging.info("Done!") + + if world_size > 1: + torch.distributed.barrier() + cleanup_dist() + + +def scan_pessimistic_batches_for_oom( + model: nn.Module, + train_dl: torch.utils.data.DataLoader, + optimizer: torch.optim.Optimizer, + graph_compiler: CharCtcTrainingGraphCompiler, + params: AttributeDict, +): + from lhotse.dataset import find_pessimistic_batches + + logging.info( + "Sanity check -- see if any of the batches in epoch 0 would cause OOM." + ) + batches, crit_values = find_pessimistic_batches(train_dl.sampler) + for criterion, cuts in batches.items(): + batch = train_dl.dataset[cuts] + try: + # warmup = 0.0 is so that the derivs for the pruned loss stay zero + # (i.e. are not remembered by the decaying-average in adam), because + # we want to avoid these params being subject to shrinkage in adam. + with torch.cuda.amp.autocast(enabled=params.use_fp16): + loss, _ = compute_loss( + params=params, + model=model, + graph_compiler=graph_compiler, + batch=batch, + is_training=True, + warmup=0.0, + ) + loss.backward() + optimizer.step() + optimizer.zero_grad() + except RuntimeError as e: + if "CUDA out of memory" in str(e): + logging.error( + "Your GPU ran out of memory with the current " + "max_duration setting. We recommend decreasing " + "max_duration and trying again.\n" + f"Failing criterion: {criterion} " + f"(={crit_values[criterion]}) ..." + ) + raise + + +def main(): + parser = get_parser() + AlimeetingAsrDataModule.add_arguments(parser) + args = parser.parse_args() + args.lang_dir = Path(args.lang_dir) + args.exp_dir = Path(args.exp_dir) + + world_size = args.world_size + assert world_size >= 1 + if world_size > 1: + mp.spawn(run, args=(world_size, args), nprocs=world_size, join=True) + else: + run(rank=0, world_size=1, args=args) + + +torch.set_num_threads(1) +torch.set_num_interop_threads(1) + +if __name__ == "__main__": + main() diff --git a/egs/alimeeting/ASR/shared b/egs/alimeeting/ASR/shared new file mode 120000 index 000000000..3a3b28f96 --- /dev/null +++ b/egs/alimeeting/ASR/shared @@ -0,0 +1 @@ +../../../egs/aishell/ASR/shared \ No newline at end of file diff --git a/egs/gigaspeech/ASR/conformer_ctc/asr_datamodule.py b/egs/gigaspeech/ASR/conformer_ctc/asr_datamodule.py index ab958fa68..d78e26240 100644 --- a/egs/gigaspeech/ASR/conformer_ctc/asr_datamodule.py +++ b/egs/gigaspeech/ASR/conformer_ctc/asr_datamodule.py @@ -20,9 +20,8 @@ import logging from functools import lru_cache from pathlib import Path -from lhotse import CutSet, Fbank, FbankConfig, load_manifest +from lhotse import CutSet, Fbank, FbankConfig, load_manifest, load_manifest_lazy from lhotse.dataset import ( - BucketingSampler, CutConcatenate, CutMix, DynamicBucketingSampler, @@ -191,7 +190,7 @@ class GigaSpeechAsrDataModule: def train_dataloaders(self, cuts_train: CutSet) -> DataLoader: logging.info("About to get Musan cuts") cuts_musan = load_manifest( - self.args.manifest_dir / "cuts_musan.json.gz" + self.args.manifest_dir / "musan_cuts.jsonl.gz" ) transforms = [] @@ -315,7 +314,7 @@ class GigaSpeechAsrDataModule: cut_transforms=transforms, return_cuts=self.args.return_cuts, ) - valid_sampler = BucketingSampler( + valid_sampler = DynamicBucketingSampler( cuts_valid, max_duration=self.args.max_duration, shuffle=False, @@ -339,8 +338,10 @@ class GigaSpeechAsrDataModule: else PrecomputedFeatures(), return_cuts=self.args.return_cuts, ) - sampler = BucketingSampler( - cuts, max_duration=self.args.max_duration, shuffle=False + sampler = DynamicBucketingSampler( + cuts, + max_duration=self.args.max_duration, + shuffle=False, ) logging.debug("About to create test dataloader") test_dl = DataLoader( @@ -361,7 +362,9 @@ class GigaSpeechAsrDataModule: @lru_cache() def dev_cuts(self) -> CutSet: logging.info("About to get dev cuts") - cuts_valid = load_manifest(self.args.manifest_dir / "cuts_DEV.jsonl.gz") + cuts_valid = load_manifest_lazy( + self.args.manifest_dir / "cuts_DEV.jsonl.gz" + ) if self.args.small_dev: return cuts_valid.subset(first=1000) else: @@ -370,4 +373,4 @@ class GigaSpeechAsrDataModule: @lru_cache() def test_cuts(self) -> CutSet: logging.info("About to get test cuts") - return load_manifest(self.args.manifest_dir / "cuts_TEST.jsonl.gz") + return load_manifest_lazy(self.args.manifest_dir / "cuts_TEST.jsonl.gz") diff --git a/egs/gigaspeech/ASR/local/compute_fbank_musan.py b/egs/gigaspeech/ASR/local/compute_fbank_musan.py deleted file mode 100755 index 562872993..000000000 --- a/egs/gigaspeech/ASR/local/compute_fbank_musan.py +++ /dev/null @@ -1,103 +0,0 @@ -#!/usr/bin/env python3 -# Copyright 2021 Johns Hopkins University (Piotr Żelasko) -# Copyright 2021 Xiaomi Corp. (Fangjun Kuang) -# -# See ../../../../LICENSE for clarification regarding multiple authors -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import logging -from pathlib import Path - -import torch -from lhotse import ( - CutSet, - KaldifeatFbank, - KaldifeatFbankConfig, - combine, -) -from lhotse.recipes.utils import read_manifests_if_cached - -# Torch's multithreaded behavior needs to be disabled or -# it wastes a lot of CPU and slow things down. -# Do this outside of main() in case it needs to take effect -# even when we are not invoking the main (e.g. when spawning subprocesses). -torch.set_num_threads(1) -torch.set_num_interop_threads(1) - - -def compute_fbank_musan(): - src_dir = Path("data/manifests") - output_dir = Path("data/fbank") - - # number of workers in dataloader - num_workers = 10 - - # number of seconds in a batch - batch_duration = 600 - - dataset_parts = ( - "music", - "speech", - "noise", - ) - - manifests = read_manifests_if_cached( - prefix="musan", dataset_parts=dataset_parts, output_dir=src_dir - ) - assert manifests is not None - - musan_cuts_path = output_dir / "cuts_musan.json.gz" - - if musan_cuts_path.is_file(): - logging.info(f"{musan_cuts_path} already exists - skipping") - return - - logging.info("Extracting features for Musan") - - device = torch.device("cpu") - if torch.cuda.is_available(): - device = torch.device("cuda", 0) - extractor = KaldifeatFbank(KaldifeatFbankConfig(device=device)) - - logging.info(f"device: {device}") - - musan_cuts = ( - CutSet.from_manifests( - recordings=combine( - part["recordings"] for part in manifests.values() - ) - ) - .cut_into_windows(10.0) - .filter(lambda c: c.duration > 5) - .compute_and_store_features_batch( - extractor=extractor, - storage_path=f"{output_dir}/feats_musan", - num_workers=num_workers, - batch_duration=batch_duration, - ) - ) - musan_cuts.to_json(musan_cuts_path) - - -def main(): - formatter = ( - "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" - ) - logging.basicConfig(format=formatter, level=logging.INFO) - - compute_fbank_musan() - - -if __name__ == "__main__": - main() diff --git a/egs/gigaspeech/ASR/local/compute_fbank_musan.py b/egs/gigaspeech/ASR/local/compute_fbank_musan.py new file mode 120000 index 000000000..5833f2484 --- /dev/null +++ b/egs/gigaspeech/ASR/local/compute_fbank_musan.py @@ -0,0 +1 @@ +../../../librispeech/ASR/local/compute_fbank_musan.py \ No newline at end of file diff --git a/egs/gigaspeech/ASR/pruned_transducer_stateless2/asr_datamodule.py b/egs/gigaspeech/ASR/pruned_transducer_stateless2/asr_datamodule.py index ff3d3b07a..c87686e1e 100644 --- a/egs/gigaspeech/ASR/pruned_transducer_stateless2/asr_datamodule.py +++ b/egs/gigaspeech/ASR/pruned_transducer_stateless2/asr_datamodule.py @@ -23,9 +23,8 @@ from pathlib import Path from typing import Any, Dict, Optional import torch -from lhotse import CutSet, Fbank, FbankConfig, load_manifest +from lhotse import CutSet, Fbank, FbankConfig, load_manifest, load_manifest_lazy from lhotse.dataset import ( - BucketingSampler, CutConcatenate, CutMix, DynamicBucketingSampler, @@ -218,7 +217,7 @@ class GigaSpeechAsrDataModule: logging.info("Enable MUSAN") logging.info("About to get Musan cuts") cuts_musan = load_manifest( - self.args.manifest_dir / "cuts_musan.json.gz" + self.args.manifest_dir / "musan_cuts.jsonl.gz" ) transforms.append( CutMix( @@ -358,7 +357,7 @@ class GigaSpeechAsrDataModule: cut_transforms=transforms, return_cuts=self.args.return_cuts, ) - valid_sampler = BucketingSampler( + valid_sampler = DynamicBucketingSampler( cuts_valid, max_duration=self.args.max_duration, shuffle=False, @@ -382,8 +381,10 @@ class GigaSpeechAsrDataModule: else PrecomputedFeatures(), return_cuts=self.args.return_cuts, ) - sampler = BucketingSampler( - cuts, max_duration=self.args.max_duration, shuffle=False + sampler = DynamicBucketingSampler( + cuts, + max_duration=self.args.max_duration, + shuffle=False, ) logging.debug("About to create test dataloader") test_dl = DataLoader( @@ -404,7 +405,9 @@ class GigaSpeechAsrDataModule: @lru_cache() def dev_cuts(self) -> CutSet: logging.info("About to get dev cuts") - cuts_valid = load_manifest(self.args.manifest_dir / "cuts_DEV.jsonl.gz") + cuts_valid = load_manifest_lazy( + self.args.manifest_dir / "cuts_DEV.jsonl.gz" + ) if self.args.small_dev: return cuts_valid.subset(first=1000) else: @@ -413,4 +416,4 @@ class GigaSpeechAsrDataModule: @lru_cache() def test_cuts(self) -> CutSet: logging.info("About to get test cuts") - return load_manifest(self.args.manifest_dir / "cuts_TEST.jsonl.gz") + return load_manifest_lazy(self.args.manifest_dir / "cuts_TEST.jsonl.gz") diff --git a/egs/librispeech/ASR/README.md b/egs/librispeech/ASR/README.md index 6ccf2fcc6..e2aaa9d7e 100644 --- a/egs/librispeech/ASR/README.md +++ b/egs/librispeech/ASR/README.md @@ -21,6 +21,8 @@ The following table lists the differences among them. | `pruned_transducer_stateless3` | Conformer(modified) | Embedding + Conv1d | Using k2 pruned RNN-T loss + using GigaSpeech as extra training data | | `pruned_transducer_stateless4` | Conformer(modified) | Embedding + Conv1d | same as pruned_transducer_stateless2 + save averaged models periodically during training | | `pruned_transducer_stateless5` | Conformer(modified) | Embedding + Conv1d | same as pruned_transducer_stateless4 + more layers + random combiner| +| `pruned_transducer_stateless6` | Conformer(modified) | Embedding + Conv1d | same as pruned_transducer_stateless4 + distillation with hubert| +| `pruned_stateless_emformer_rnnt2` | Emformer(from torchaudio) | Embedding + Conv1d | Using Emformer from torchaudio for streaming ASR| The decoder in `transducer_stateless` is modified from the paper diff --git a/egs/librispeech/ASR/RESULTS-100hours.md b/egs/librispeech/ASR/RESULTS-100hours.md index 2e1bbd687..3a064e69d 100644 --- a/egs/librispeech/ASR/RESULTS-100hours.md +++ b/egs/librispeech/ASR/RESULTS-100hours.md @@ -3,6 +3,31 @@ This page shows the WERs for test-clean/test-other using only train-clean-100 subset as training data. +## Distillation with hubert +### 2022-05-27 +Related models/log/tensorboard: +https://huggingface.co/GuoLiyong/stateless6_baseline_vs_disstillation + +Following results are obtained by ./distillation_with_hubert.sh + +The only differences is in pruned_transducer_stateless6/train.py. + +For baseline: set enable_distillation=False + +For distillation: set enable_distillation=True (the default) + +Decoding method is modified beam search. +| | test-clean | test-other | comment | +|-------------------------------------|------------|------------|------------------------------------------| +| baseline no vq distillation | 7.09 | 18.88 | --epoch 20, --avg 10, --max-duration 200 | +| baseline no vq distillation | 6.83 | 18.19 | --epoch 30, --avg 10, --max-duration 200 | +| baseline no vq distillation | 6.73 | 17.79 | --epoch 40, --avg 10, --max-duration 200 | +| baseline no vq distillation | 6.75 | 17.68 | --epoch 50, --avg 10, --max-duration 200 | +| distillation with hubert | 5.82 | 15.98 | --epoch 20, --avg 10, --max-duration 200 | +| distillation with hubert | 5.52 | 15.15 | --epoch 30, --avg 10, --max-duration 200 | +| distillation with hubert | 5.45 | 14.94 | --epoch 40, --avg 10, --max-duration 200 | +| distillation with hubert | 5.50 | 14.77 | --epoch 50, --avg 10, --max-duration 200 | + ## Conformer encoder + embedding decoder ### 2022-02-21 diff --git a/egs/librispeech/ASR/RESULTS.md b/egs/librispeech/ASR/RESULTS.md index 453751ba5..66410ef40 100644 --- a/egs/librispeech/ASR/RESULTS.md +++ b/egs/librispeech/ASR/RESULTS.md @@ -1,5 +1,73 @@ ## Results +### LibriSpeech BPE training results (Pruned Stateless Emformer RNN-T) + +[pruned_stateless_emformer_rnnt2](./pruned_stateless_emformer_rnnt2) + +Use . + +Use [Emformer](https://arxiv.org/abs/2010.10759) from [torchaudio](https://github.com/pytorch/audio) +for streaming ASR. The Emformer model is imported from torchaudio without modifications. + +You can use to deploy it. + +| | test-clean | test-other | comment | +|-------------------------------------|------------|------------|----------------------------------------| +| greedy search (max sym per frame 1) | 4.28 | 11.42 | --epoch 39 --avg 6 --max-duration 600 | +| modified beam search | 4.22 | 11.16 | --epoch 39 --avg 6 --max-duration 600 | +| fast beam search | 4.29 | 11.26 | --epoch 39 --avg 6 --max-duration 600 | + + +The training commands are: +```bash +export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7" + +./pruned_stateless_emformer_rnnt2/train.py \ + --world-size 8 \ + --num-epochs 40 \ + --start-epoch 1 \ + --exp-dir pruned_stateless_emformer_rnnt2/exp-full \ + --full-libri 1 \ + --use-fp16 0 \ + --max-duration 200 \ + --prune-range 5 \ + --lm-scale 0.25 \ + --master-port 12358 \ + --num-encoder-layers 18 \ + --left-context-length 128 \ + --segment-length 8 \ + --right-context-length 4 +``` + +The tensorboard log can be found at + + +The decoding commands are: +```bash +for m in greedy_search fast_beam_search modified_beam_search; do + for epoch in 39; do + for avg in 6; do + ./pruned_stateless_emformer_rnnt2/decode.py \ + --epoch $epoch \ + --avg $avg \ + --use-averaged-model 1 \ + --exp-dir pruned_stateless_emformer_rnnt2/exp-full \ + --max-duration 50 \ + --decoding-method $m \ + --num-encoder-layers 18 \ + --left-context-length 128 \ + --segment-length 8 \ + --right-context-length 4 + done + done +done +``` + +You can find a pretrained model, training logs, decoding logs, and decoding +results at: + + + ### LibriSpeech BPE training results (Pruned Stateless Transducer 5) [pruned_transducer_stateless5](./pruned_transducer_stateless5) @@ -193,6 +261,126 @@ You can find a pretrained model, training logs, decoding logs, and decoding results at: + +### LibriSpeech BPE training results (Pruned Stateless Transducer 4) + +[pruned_transducer_stateless4](./pruned_transducer_stateless4) + +This version saves averaged model during training, and decodes with averaged model. + +See for details about the idea of model averaging. + +#### Training on full librispeech + +See + +Using commit `ec0b0e92297cc03fdb09f48cd235e84d2c04156b`. + +The WERs are: + +| | test-clean | test-other | comment | +|-------------------------------------|------------|------------|-------------------------------------------------------------------------------| +| greedy search (max sym per frame 1) | 2.75 | 6.74 | --epoch 30 --avg 6 --use_averaged_model False | +| greedy search (max sym per frame 1) | 2.69 | 6.64 | --epoch 30 --avg 6 --use_averaged_model True | +| fast beam search | 2.72 | 6.67 | --epoch 30 --avg 6 --use_averaged_model False | +| fast beam search | 2.66 | 6.6 | --epoch 30 --avg 6 --use_averaged_model True | +| modified beam search | 2.67 | 6.68 | --epoch 30 --avg 6 --use_averaged_model False | +| modified beam search | 2.62 | 6.57 | --epoch 30 --avg 6 --use_averaged_model True | + +The training command is: + +```bash +./pruned_transducer_stateless4/train.py \ + --world-size 6 \ + --num-epochs 30 \ + --start-epoch 1 \ + --exp-dir pruned_transducer_stateless4/exp \ + --full-libri 1 \ + --max-duration 300 \ + --save-every-n 8000 \ + --keep-last-k 20 \ + --average-period 100 +``` + +The tensorboard log can be found at + + +The decoding command using greedy search is: +```bash +./pruned_transducer_stateless4/decode.py \ + --epoch 30 \ + --avg 6 \ + --exp-dir pruned_transducer_stateless4/exp \ + --max-duration 300 \ + --decoding-method greedy_search \ + --use-averaged-model True +``` + +The decoding command using fast beam search is: +```bash +./pruned_transducer_stateless4/decode.py \ + --epoch 30 \ + --avg 6 \ + --exp-dir pruned_transducer_stateless4/exp \ + --max-duration 300 \ + --decoding-method fast_beam_search \ + --use-averaged-model True \ + --beam 4 \ + --max-contexts 4 \ + --max-states 8 +``` + +The decoding command using modified beam search is: +```bash +./pruned_transducer_stateless4/decode.py \ + --epoch 30 \ + --avg 6 \ + --exp-dir pruned_transducer_stateless4/exp \ + --max-duration 300 \ + --decoding-method modified_beam_search \ + --use-averaged-model True \ + --beam-size 4 +``` + +Pretrained models, training logs, decoding logs, and decoding results +are available at + + +#### Training on train-clean-100 + +See + +Using commit `ec0b0e92297cc03fdb09f48cd235e84d2c04156b`. + +The WERs are: + +| | test-clean | test-other | comment | +|-------------------------------------|------------|------------|-------------------------------------------------------------------------------| +| greedy search (max sym per frame 1) | 7.0 | 18.95 | --epoch 30 --avg 10 --use_averaged_model False | +| greedy search (max sym per frame 1) | 6.92 | 18.65 | --epoch 30 --avg 10 --use_averaged_model True | +| fast beam search | 6.82 | 18.47 | --epoch 30 --avg 10 --use_averaged_model False | +| fast beam search | 6.74 | 18.2 | --epoch 30 --avg 10 --use_averaged_model True | +| modified beam search | 6.74 | 18.39 | --epoch 30 --avg 10 --use_averaged_model False | +| modified beam search | 6.74 | 18.12 | --epoch 30 --avg 10 --use_averaged_model True | + +The training command is: + +```bash +./pruned_transducer_stateless4/train.py \ + --world-size 3 \ + --num-epochs 30 \ + --start-epoch 1 \ + --exp-dir pruned_transducer_stateless4/exp \ + --full-libri 0 \ + --max-duration 300 \ + --save-every-n 8000 \ + --keep-last-k 20 \ + --average-period 100 +``` + +The tensorboard log can be found at + + ### LibriSpeech BPE training results (Pruned Stateless Transducer 3, 2022-04-29) [pruned_transducer_stateless3](./pruned_transducer_stateless3) diff --git a/egs/librispeech/ASR/conformer_ctc/ali.py b/egs/librispeech/ASR/conformer_ctc/ali.py index 42fa2308e..2828e309e 100755 --- a/egs/librispeech/ASR/conformer_ctc/ali.py +++ b/egs/librispeech/ASR/conformer_ctc/ali.py @@ -96,14 +96,14 @@ def get_parser(): - labels_xxx.h5 - aux_labels_xxx.h5 - - cuts_xxx.json.gz + - librispeech_cuts_xxx.jsonl.gz where xxx is the value of `--dataset`. For instance, if `--dataset` is `train-clean-100`, it will contain 3 files: - `labels_train-clean-100.h5` - `aux_labels_train-clean-100.h5` - - `cuts_train-clean-100.json.gz` + - `librispeech_cuts_train-clean-100.jsonl.gz` Note: Both labels_xxx.h5 and aux_labels_xxx.h5 contain framewise alignment. The difference is that labels_xxx.h5 contains repeats. @@ -289,7 +289,9 @@ def main(): out_labels_ali_filename = out_dir / f"labels_{params.dataset}.h5" out_aux_labels_ali_filename = out_dir / f"aux_labels_{params.dataset}.h5" - out_manifest_filename = out_dir / f"cuts_{params.dataset}.json.gz" + out_manifest_filename = ( + out_dir / f"librispeech_cuts_{params.dataset}.jsonl.gz" + ) for f in ( out_labels_ali_filename, diff --git a/egs/librispeech/ASR/conformer_ctc/train.py b/egs/librispeech/ASR/conformer_ctc/train.py index b81bd6330..fc8fc8863 100755 --- a/egs/librispeech/ASR/conformer_ctc/train.py +++ b/egs/librispeech/ASR/conformer_ctc/train.py @@ -17,6 +17,17 @@ # See the License for the specific language governing permissions and # limitations under the License. +""" +Usage: + export CUDA_VISIBLE_DEVICES="0,1,2,3" + ./conformer_ctc/train.py \ + --exp-dir ./conformer_ctc/exp \ + --world-size 4 \ + --full-libri 1 \ + --max-duration 200 \ + --num-epochs 20 +""" + import argparse import logging from pathlib import Path @@ -29,6 +40,7 @@ import torch.multiprocessing as mp import torch.nn as nn from asr_datamodule import LibriSpeechAsrDataModule from conformer import Conformer +from lhotse.cut import Cut from lhotse.utils import fix_random_seed from torch import Tensor from torch.nn.parallel import DistributedDataParallel as DDP @@ -676,6 +688,20 @@ def run(rank, world_size, args): if params.full_libri: train_cuts += librispeech.train_clean_360_cuts() train_cuts += librispeech.train_other_500_cuts() + + def remove_short_and_long_utt(c: Cut): + # Keep only utterances with duration between 1 second and 20 seconds + # + # Caution: There is a reason to select 20.0 here. Please see + # ../local/display_manifest_statistics.py + # + # You should use ../local/display_manifest_statistics.py to get + # an utterance duration distribution for your dataset to select + # the threshold + return 1.0 <= c.duration <= 20.0 + + train_cuts = train_cuts.filter(remove_short_and_long_utt) + train_dl = librispeech.train_dataloaders(train_cuts) valid_cuts = librispeech.dev_clean_cuts() diff --git a/egs/librispeech/ASR/distillation_with_hubert.sh b/egs/librispeech/ASR/distillation_with_hubert.sh new file mode 100644 index 000000000..e18ba8f55 --- /dev/null +++ b/egs/librispeech/ASR/distillation_with_hubert.sh @@ -0,0 +1,144 @@ +# A short introduction about distillation framework. +# +# A typical traditional distillation method is +# Loss(teacher embedding, student embedding). +# +# Comparing to these, the proposed distillation framework contains two mainly steps: +# codebook indexes = quantizer.encode(teacher embedding) +# Loss(codebook indexes, student embedding) +# +# Things worth to meantion: +# 1. The float type teacher embedding is quantized into a sequence of +# 8-bit integer codebook indexes. +# 2. a middle layer 36(1-based) out of total 48 layers is used to extract +# teacher embeddings. +# 3. a middle layer 6(1-based) out of total 6 layers is used to extract +# student embeddings. + +# This is an example to do distillation with librispeech clean-100 subset. +# run with command: +# bash distillation_with_hubert.sh [0|1|2|3|4] +# +# For example command +# bash distillation_with_hubert.sh 0 +# will download hubert model. +stage=$1 + +# Set the GPUs available. +# This script requires at least one GPU. +# You MUST set environment variable "CUDA_VISIBLE_DEVICES", +# even you only have ONE GPU. It needed by CodebookIndexExtractor to determine numbert of jobs to extract codebook indexes parallelly. + +# Suppose only one GPU exists: +# export CUDA_VISIBLE_DEVICES="0" +# +# Suppose GPU 2,3,4,5 are available. +export CUDA_VISIBLE_DEVICES="2,3,4,5" + + +if [ $stage -eq 0 ]; then + # Preparation stage. + + # Install fairseq according to: + # https://github.com/pytorch/fairseq + # when testing this code: + # commit 806855bf660ea748ed7ffb42fe8dcc881ca3aca0 is used. + has_fairseq=$(python3 -c "import importlib; print(importlib.util.find_spec('fairseq') is not None)") + if [ $has_fairseq == 'False' ]; then + echo "Please install fairseq before running following stages" + exit 1 + fi + + # Install quantization toolkit: + # pip install git+https://github.com/danpovey/quantization.git@master + # when testing this code: + # commit c17ffe67aa2e6ca6b6855c50fde812f2eed7870b is used. + + has_quantization=$(python3 -c "import importlib; print(importlib.util.find_spec('quantization') is not None)") + if [ $has_quantization == 'False' ]; then + echo "Please install quantization before running following stages" + exit 1 + fi + + echo "Download hubert model." + # Parameters about model. + exp_dir=./pruned_transducer_stateless6/exp/ + model_id=hubert_xtralarge_ll60k_finetune_ls960 + hubert_model_dir=${exp_dir}/hubert_models + hubert_model=${hubert_model_dir}/${model_id}.pt + mkdir -p ${hubert_model_dir} + # For more models refer to: https://github.com/pytorch/fairseq/tree/main/examples/hubert + if [ -f ${hubert_model} ]; then + echo "hubert model alread exists." + else + wget -c https://dl.fbaipublicfiles.com/hubert/${model_id} -P ${hubert_model} + wget -c wget https://dl.fbaipublicfiles.com/fairseq/wav2vec/dict.ltr.txt -P ${hubert_model_dir} + fi +fi + +if [ ! -d ./data/fbank ]; then + echo "This script assumes ./data/fbank is already generated by prepare.sh" + exit 1 +fi + +if [ $stage -eq 1 ]; then + # This stage is not directly used by codebook indexes extraction. + # It is a method to "prove" that the downloaed hubert model + # is inferenced in an correct way if WERs look like normal. + # Expect WERs: + # [test-clean-ctc_greedy_search] %WER 2.04% [1075 / 52576, 92 ins, 104 del, 879 sub ] + # [test-other-ctc_greedy_search] %WER 3.71% [1942 / 52343, 152 ins, 126 del, 1664 sub ] + ./pruned_transducer_stateless6/hubert_decode.py +fi + +if [ $stage -eq 2 ]; then + # Analysis of disk usage: + # With num_codebooks==8, each teacher embedding is quantized into + # a sequence of eight 8-bit integers, i.e. only eight bytes are needed. + # Training dataset including clean-100h with speed perturb 0.9 and 1.1 has 300 hours. + # The output frame rates of Hubert is 50 per second. + # Theoretically, 412M = 300 * 3600 * 50 * 8 / 1024 / 1024 is needed. + # The actual size of all "*.h5" files storaging codebook index is 450M. + # I think the extra "48M" usage is some meta information. + + # Time consumption analysis: + # For quantizer training data(teacher embedding) extraction, only 1000 utts from clean-100 are used. + # Together with quantizer training, no more than 20 minutes will be used. + # + # For codebook indexes extraction, + # with two pieces of NVIDIA A100 gpus, around three hours needed to process 300 hours training data, + # i.e. clean-100 with speed purteb 0.9 and 1.1. + + # GPU usage: + # During quantizer's training data(teacher embedding) and it's training, + # only the first ONE GPU is used. + # During codebook indexes extraction, ALL GPUs set by CUDA_VISIBLE_DEVICES are used. + ./pruned_transducer_stateless6/extract_codebook_index.py \ + --full-libri False +fi + +if [ $stage -eq 3 ]; then + # Example training script. + # Note: it's better to set spec-aug-time-warpi-factor=-1 + WORLD_SIZE=$(echo ${CUDA_VISIBLE_DEVICES} | awk '{n=split($1, _, ","); print n}') + ./pruned_transducer_stateless6/train.py \ + --manifest-dir ./data/vq_fbank \ + --master-port 12359 \ + --full-libri False \ + --spec-aug-time-warp-factor -1 \ + --max-duration 300 \ + --world-size ${WORLD_SIZE} \ + --num-epochs 20 +fi + +if [ $stage -eq 4 ]; then + # Results should be similar to: + # errs-test-clean-beam_size_4-epoch-20-avg-10-beam-4.txt:%WER = 5.67 + # errs-test-other-beam_size_4-epoch-20-avg-10-beam-4.txt:%WER = 15.60 + ./pruned_transducer_stateless6/decode.py \ + --decoding-method "modified_beam_search" \ + --epoch 20 \ + --avg 10 \ + --max-duration 200 \ + --exp-dir ./pruned_transducer_stateless6/exp +fi diff --git a/egs/librispeech/ASR/local/compute_fbank_gigaspeech_dev_test.py b/egs/librispeech/ASR/local/compute_fbank_gigaspeech_dev_test.py index 9f1039893..68d93d2c5 100644 --- a/egs/librispeech/ASR/local/compute_fbank_gigaspeech_dev_test.py +++ b/egs/librispeech/ASR/local/compute_fbank_gigaspeech_dev_test.py @@ -20,11 +20,7 @@ import logging from pathlib import Path import torch -from lhotse import ( - CutSet, - KaldifeatFbank, - KaldifeatFbankConfig, -) +from lhotse import CutSet, KaldifeatFbank, KaldifeatFbankConfig # Torch's multithreaded behavior needs to be disabled or # it wastes a lot of CPU and slow things down. @@ -51,13 +47,16 @@ def compute_fbank_gigaspeech_dev_test(): logging.info(f"device: {device}") + prefix = "gigaspeech" + suffix = "jsonl.gz" + for partition in subsets: - cuts_path = in_out_dir / f"cuts_{partition}.jsonl.gz" + cuts_path = in_out_dir / f"{prefix}_cuts_{partition}.{suffix}" if cuts_path.is_file(): logging.info(f"{cuts_path} exists - skipping") continue - raw_cuts_path = in_out_dir / f"cuts_{partition}_raw.jsonl.gz" + raw_cuts_path = in_out_dir / f"{prefix}_cuts_{partition}_raw.{suffix}" logging.info(f"Loading {raw_cuts_path}") cut_set = CutSet.from_file(raw_cuts_path) @@ -66,7 +65,7 @@ def compute_fbank_gigaspeech_dev_test(): cut_set = cut_set.compute_and_store_features_batch( extractor=extractor, - storage_path=f"{in_out_dir}/feats_{partition}", + storage_path=f"{in_out_dir}/{prefix}_feats_{partition}", num_workers=num_workers, batch_duration=batch_duration, ) diff --git a/egs/librispeech/ASR/local/compute_fbank_gigaspeech_splits.py b/egs/librispeech/ASR/local/compute_fbank_gigaspeech_splits.py index a7ed2467d..f826f064e 100644 --- a/egs/librispeech/ASR/local/compute_fbank_gigaspeech_splits.py +++ b/egs/librispeech/ASR/local/compute_fbank_gigaspeech_splits.py @@ -77,7 +77,7 @@ def get_parser(): def compute_fbank_gigaspeech_splits(args): num_splits = args.num_splits - output_dir = f"data/fbank/XL_split_{num_splits}" + output_dir = f"data/fbank/gigaspeech_XL_split_{num_splits}" output_dir = Path(output_dir) assert output_dir.exists(), f"{output_dir} does not exist!" @@ -96,17 +96,19 @@ def compute_fbank_gigaspeech_splits(args): extractor = KaldifeatFbank(KaldifeatFbankConfig(device=device)) logging.info(f"device: {device}") + prefix = "gigaspeech" + num_digits = 8 # num_digits is fixed by lhotse split-lazy for i in range(start, stop): idx = f"{i + 1}".zfill(num_digits) logging.info(f"Processing {idx}/{num_splits}") - cuts_path = output_dir / f"cuts_XL.{idx}.jsonl.gz" + cuts_path = output_dir / f"{prefix}_cuts_XL.{idx}.jsonl.gz" if cuts_path.is_file(): logging.info(f"{cuts_path} exists - skipping") continue - raw_cuts_path = output_dir / f"cuts_XL_raw.{idx}.jsonl.gz" + raw_cuts_path = output_dir / f"{prefix}_cuts_XL_raw.{idx}.jsonl.gz" if not raw_cuts_path.is_file(): logging.info(f"{raw_cuts_path} does not exist - skipping it") continue @@ -115,13 +117,13 @@ def compute_fbank_gigaspeech_splits(args): cut_set = CutSet.from_file(raw_cuts_path) logging.info("Computing features") - if (output_dir / f"feats_XL_{idx}.lca").exists(): - logging.info(f"Removing {output_dir}/feats_XL_{idx}.lca") - os.remove(output_dir / f"feats_XL_{idx}.lca") + if (output_dir / f"{prefix}_feats_XL_{idx}.lca").exists(): + logging.info(f"Removing {output_dir}/{prefix}_feats_XL_{idx}.lca") + os.remove(output_dir / f"{prefix}_feats_XL_{idx}.lca") cut_set = cut_set.compute_and_store_features_batch( extractor=extractor, - storage_path=f"{output_dir}/feats_XL_{idx}", + storage_path=f"{output_dir}/{prefix}_feats_XL_{idx}", num_workers=args.num_workers, batch_duration=args.batch_duration, ) diff --git a/egs/librispeech/ASR/local/compute_fbank_librispeech.py b/egs/librispeech/ASR/local/compute_fbank_librispeech.py index 92f4f6ab7..642d9fd32 100755 --- a/egs/librispeech/ASR/local/compute_fbank_librispeech.py +++ b/egs/librispeech/ASR/local/compute_fbank_librispeech.py @@ -28,7 +28,7 @@ import os from pathlib import Path import torch -from lhotse import ChunkedLilcomHdf5Writer, CutSet, Fbank, FbankConfig +from lhotse import CutSet, Fbank, FbankConfig, LilcomChunkyWriter from lhotse.recipes.utils import read_manifests_if_cached from icefall.utils import get_executor @@ -56,8 +56,13 @@ def compute_fbank_librispeech(): "train-clean-360", "train-other-500", ) + prefix = "librispeech" + suffix = "jsonl.gz" manifests = read_manifests_if_cached( - prefix="librispeech", dataset_parts=dataset_parts, output_dir=src_dir + dataset_parts=dataset_parts, + output_dir=src_dir, + prefix=prefix, + suffix=suffix, ) assert manifests is not None @@ -65,7 +70,8 @@ def compute_fbank_librispeech(): with get_executor() as ex: # Initialize the executor only once. for partition, m in manifests.items(): - if (output_dir / f"cuts_{partition}.json.gz").is_file(): + cuts_filename = f"{prefix}_cuts_{partition}.{suffix}" + if (output_dir / cuts_filename).is_file(): logging.info(f"{partition} already exists - skipping.") continue logging.info(f"Processing {partition}") @@ -81,13 +87,13 @@ def compute_fbank_librispeech(): ) cut_set = cut_set.compute_and_store_features( extractor=extractor, - storage_path=f"{output_dir}/feats_{partition}", + storage_path=f"{output_dir}/{prefix}_feats_{partition}", # when an executor is specified, make more partitions num_jobs=num_jobs if ex is None else 80, executor=ex, - storage_type=ChunkedLilcomHdf5Writer, + storage_type=LilcomChunkyWriter, ) - cut_set.to_json(output_dir / f"cuts_{partition}.json.gz") + cut_set.to_file(output_dir / cuts_filename) if __name__ == "__main__": diff --git a/egs/librispeech/ASR/local/compute_fbank_musan.py b/egs/librispeech/ASR/local/compute_fbank_musan.py index 368bea4e8..fef372129 100755 --- a/egs/librispeech/ASR/local/compute_fbank_musan.py +++ b/egs/librispeech/ASR/local/compute_fbank_musan.py @@ -28,7 +28,7 @@ import os from pathlib import Path import torch -from lhotse import ChunkedLilcomHdf5Writer, CutSet, Fbank, FbankConfig, combine +from lhotse import CutSet, Fbank, FbankConfig, LilcomChunkyWriter, combine from lhotse.recipes.utils import read_manifests_if_cached from icefall.utils import get_executor @@ -52,12 +52,22 @@ def compute_fbank_musan(): "speech", "noise", ) + prefix = "musan" + suffix = "jsonl.gz" manifests = read_manifests_if_cached( - prefix="musan", dataset_parts=dataset_parts, output_dir=src_dir + dataset_parts=dataset_parts, + output_dir=src_dir, + prefix=prefix, + suffix=suffix, ) assert manifests is not None - musan_cuts_path = output_dir / "cuts_musan.json.gz" + assert len(manifests) == len(dataset_parts), ( + len(manifests), + len(dataset_parts), + ) + + musan_cuts_path = output_dir / "musan_cuts.jsonl.gz" if musan_cuts_path.is_file(): logging.info(f"{musan_cuts_path} already exists - skipping") @@ -79,13 +89,13 @@ def compute_fbank_musan(): .filter(lambda c: c.duration > 5) .compute_and_store_features( extractor=extractor, - storage_path=f"{output_dir}/feats_musan", + storage_path=f"{output_dir}/musan_feats", num_jobs=num_jobs if ex is None else 80, executor=ex, - storage_type=ChunkedLilcomHdf5Writer, + storage_type=LilcomChunkyWriter, ) ) - musan_cuts.to_json(musan_cuts_path) + musan_cuts.to_file(musan_cuts_path) if __name__ == "__main__": diff --git a/egs/librispeech/ASR/local/display_manifest_statistics.py b/egs/librispeech/ASR/local/display_manifest_statistics.py index 15bd206fa..c3c684235 100755 --- a/egs/librispeech/ASR/local/display_manifest_statistics.py +++ b/egs/librispeech/ASR/local/display_manifest_statistics.py @@ -25,19 +25,19 @@ for usage. """ -from lhotse import load_manifest +from lhotse import load_manifest_lazy def main(): - path = "./data/fbank/cuts_train-clean-100.json.gz" - path = "./data/fbank/cuts_train-clean-360.json.gz" - path = "./data/fbank/cuts_train-other-500.json.gz" - path = "./data/fbank/cuts_dev-clean.json.gz" - path = "./data/fbank/cuts_dev-other.json.gz" - path = "./data/fbank/cuts_test-clean.json.gz" - path = "./data/fbank/cuts_test-other.json.gz" + # path = "./data/fbank/librispeech_cuts_train-clean-100.jsonl.gz" + # path = "./data/fbank/librispeech_cuts_train-clean-360.jsonl.gz" + # path = "./data/fbank/librispeech_cuts_train-other-500.jsonl.gz" + # path = "./data/fbank/librispeech_cuts_dev-clean.jsonl.gz" + # path = "./data/fbank/librispeech_cuts_dev-other.jsonl.gz" + # path = "./data/fbank/librispeech_cuts_test-clean.jsonl.gz" + path = "./data/fbank/librispeech_cuts_test-other.jsonl.gz" - cuts = load_manifest(path) + cuts = load_manifest_lazy(path) cuts.describe() diff --git a/egs/librispeech/ASR/local/preprocess_gigaspeech.py b/egs/librispeech/ASR/local/preprocess_gigaspeech.py index cd1345904..0f4ae820b 100644 --- a/egs/librispeech/ASR/local/preprocess_gigaspeech.py +++ b/egs/librispeech/ASR/local/preprocess_gigaspeech.py @@ -58,17 +58,19 @@ def preprocess_giga_speech(): ) logging.info("Loading manifest (may take 4 minutes)") + prefix = "gigaspeech" + suffix = "jsonl.gz" manifests = read_manifests_if_cached( dataset_parts=dataset_parts, output_dir=src_dir, - prefix="gigaspeech", - suffix="jsonl.gz", + prefix=prefix, + suffix=suffix, ) assert manifests is not None for partition, m in manifests.items(): logging.info(f"Processing {partition}") - raw_cuts_path = output_dir / f"cuts_{partition}_raw.jsonl.gz" + raw_cuts_path = output_dir / f"{prefix}_cuts_{partition}_raw.{suffix}" if raw_cuts_path.is_file(): logging.info(f"{partition} already exists - skipping") continue diff --git a/egs/librispeech/ASR/local/validate_manifest.py b/egs/librispeech/ASR/local/validate_manifest.py index 8d3d4c7ce..7c57d629a 100755 --- a/egs/librispeech/ASR/local/validate_manifest.py +++ b/egs/librispeech/ASR/local/validate_manifest.py @@ -25,7 +25,7 @@ We will add more checks later if needed. Usage example: python3 ./local/validate_manifest.py \ - ./data/fbank/cuts_train-clean-100.json.gz + ./data/fbank/librispeech_cuts_train-clean-100.jsonl.gz """ @@ -33,7 +33,7 @@ import argparse import logging from pathlib import Path -from lhotse import load_manifest, CutSet +from lhotse import CutSet, load_manifest_lazy from lhotse.cut import Cut @@ -76,7 +76,7 @@ def main(): logging.info(f"Validating {manifest}") assert manifest.is_file(), f"{manifest} does not exist" - cut_set = load_manifest(manifest) + cut_set = load_manifest_lazy(manifest) assert isinstance(cut_set, CutSet) for c in cut_set: diff --git a/egs/librispeech/ASR/prepare.sh b/egs/librispeech/ASR/prepare.sh index 8cfb046c8..17a638502 100755 --- a/egs/librispeech/ASR/prepare.sh +++ b/egs/librispeech/ASR/prepare.sh @@ -40,9 +40,9 @@ dl_dir=$PWD/download # It will generate data/lang_bpe_xxx, # data/lang_bpe_yyy if the array contains xxx, yyy vocab_sizes=( - 5000 - 2000 - 1000 + # 5000 + # 2000 + # 1000 500 ) @@ -132,7 +132,7 @@ if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then ) for part in ${parts[@]}; do python3 ./local/validate_manifest.py \ - data/fbank/cuts_${part}.json.gz + data/fbank/librispeech_cuts_${part}.jsonl.gz done touch data/fbank/.librispeech-validated.done fi diff --git a/egs/librispeech/ASR/prepare_giga_speech.sh b/egs/librispeech/ASR/prepare_giga_speech.sh index 26b921eab..6f85ddc29 100755 --- a/egs/librispeech/ASR/prepare_giga_speech.sh +++ b/egs/librispeech/ASR/prepare_giga_speech.sh @@ -124,9 +124,9 @@ fi if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then log "Stage 4: Split XL subset into ${num_splits} pieces" - split_dir=data/fbank/XL_split_${num_splits} + split_dir=data/fbank/gigaspeech_XL_split_${num_splits} if [ ! -f $split_dir/.split_completed ]; then - lhotse split-lazy ./data/fbank/cuts_XL_raw.jsonl.gz $split_dir $chunk_size + lhotse split-lazy ./data/fbank/gigaspeech_cuts_XL_raw.jsonl.gz $split_dir $chunk_size touch $split_dir/.split_completed fi fi diff --git a/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/asr_datamodule.py b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/asr_datamodule.py new file mode 120000 index 000000000..b4e5427e0 --- /dev/null +++ b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/asr_datamodule.py @@ -0,0 +1 @@ +../pruned_transducer_stateless/asr_datamodule.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/beam_search.py b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/beam_search.py new file mode 120000 index 000000000..227d2247c --- /dev/null +++ b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/beam_search.py @@ -0,0 +1 @@ +../pruned_transducer_stateless/beam_search.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/decode.py b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/decode.py new file mode 100755 index 000000000..e9989579b --- /dev/null +++ b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/decode.py @@ -0,0 +1,645 @@ +#!/usr/bin/env python3 +# +# Copyright 2021-2022 Xiaomi Corporation (Author: Fangjun Kuang, +# Zengwei Yao) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Usage: +(1) greedy search +./pruned_stateless_emformer_rnnt/decode.py \ + --epoch 30 \ + --avg 15 \ + --exp-dir ./pruned_stateless_emformer_rnnt/exp \ + --max-duration 600 \ + --decoding-method greedy_search + +(2) beam search (not recommended) +./pruned_stateless_emformer_rnnt/decode.py \ + --epoch 30 \ + --avg 15 \ + --exp-dir ./pruned_stateless_emformer_rnnt/exp \ + --max-duration 600 \ + --decoding-method beam_search \ + --beam-size 4 + +(3) modified beam search +./pruned_stateless_emformer_rnnt/decode.py \ + --epoch 30 \ + --avg 15 \ + --exp-dir ./pruned_stateless_emformer_rnnt/exp \ + --max-duration 600 \ + --decoding-method modified_beam_search \ + --beam-size 4 + +(4) fast beam search +./pruned_stateless_emformer_rnnt/decode.py \ + --epoch 30 \ + --avg 15 \ + --exp-dir ./pruned_stateless_emformer_rnnt/exp \ + --max-duration 600 \ + --decoding-method fast_beam_search \ + --beam 4 \ + --max-contexts 4 \ + --max-states 8 +""" + + +import argparse +import logging +import math +from collections import defaultdict +from pathlib import Path +from typing import Dict, List, Optional, Tuple + +import k2 +import sentencepiece as spm +import torch +import torch.nn as nn +from asr_datamodule import LibriSpeechAsrDataModule +from beam_search import ( + beam_search, + fast_beam_search_one_best, + greedy_search, + greedy_search_batch, + modified_beam_search, +) +from train import add_model_arguments, get_params, get_transducer_model + +from icefall.checkpoint import ( + average_checkpoints, + average_checkpoints_with_averaged_model, + find_checkpoints, + load_checkpoint, +) +from icefall.utils import ( + AttributeDict, + setup_logger, + store_transcripts, + str2bool, + write_error_stats, +) + +LOG_EPS = math.log(1e-10) + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--epoch", + type=int, + default=30, + help="""It specifies the checkpoint to use for decoding. + Note: Epoch counts from 1. + You can specify --avg to use more checkpoints for model averaging.""", + ) + + parser.add_argument( + "--iter", + type=int, + default=0, + help="""If positive, --epoch is ignored and it + will use the checkpoint exp_dir/checkpoint-iter.pt. + You can specify --avg to use more checkpoints for model averaging. + """, + ) + + parser.add_argument( + "--avg", + type=int, + default=15, + help="Number of checkpoints to average. Automatically select " + "consecutive checkpoints before the checkpoint specified by " + "'--epoch' and '--iter'", + ) + + parser.add_argument( + "--use-averaged-model", + type=str2bool, + default=False, + help="Whether to load averaged model. Currently it only supports " + "using --epoch. If True, it would decode with the averaged model " + "over the epoch range from `epoch-avg` (excluded) to `epoch`." + "Actually only the models with epoch number of `epoch-avg` and " + "`epoch` are loaded for averaging. ", + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="pruned_stateless_emformer_rnnt/exp", + help="The experiment dir", + ) + + parser.add_argument( + "--bpe-model", + type=str, + default="data/lang_bpe_500/bpe.model", + help="Path to the BPE model", + ) + + parser.add_argument( + "--decoding-method", + type=str, + default="greedy_search", + help="""Possible values are: + - greedy_search + - beam_search + - modified_beam_search + - fast_beam_search + """, + ) + + parser.add_argument( + "--beam-size", + type=int, + default=4, + help="""An integer indicating how many candidates we will keep for each + frame. Used only when --decoding-method is beam_search or + modified_beam_search.""", + ) + + parser.add_argument( + "--beam", + type=float, + default=4, + help="""A floating point value to calculate the cutoff score during beam + search (i.e., `cutoff = max-score - beam`), which is the same as the + `beam` in Kaldi. + Used only when --decoding-method is fast_beam_search""", + ) + + parser.add_argument( + "--max-contexts", + type=int, + default=4, + help="""Used only when --decoding-method is + fast_beam_search""", + ) + + parser.add_argument( + "--max-states", + type=int, + default=8, + help="""Used only when --decoding-method is + fast_beam_search""", + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; " + "2 means tri-gram", + ) + parser.add_argument( + "--max-sym-per-frame", + type=int, + default=1, + help="""Maximum number of symbols per frame. + Used only when --decoding_method is greedy_search""", + ) + + add_model_arguments(parser) + + return parser + + +def decode_one_batch( + params: AttributeDict, + model: nn.Module, + sp: spm.SentencePieceProcessor, + batch: dict, + decoding_graph: Optional[k2.Fsa] = None, +) -> Dict[str, List[List[str]]]: + """Decode one batch and return the result in a dict. The dict has the + following format: + + - key: It indicates the setting used for decoding. For example, + if greedy_search is used, it would be "greedy_search" + If beam search with a beam size of 7 is used, it would be + "beam_7" + - value: It contains the decoding result. `len(value)` equals to + batch size. `value[i]` is the decoding result for the i-th + utterance in the given batch. + Args: + params: + It's the return value of :func:`get_params`. + model: + The neural model. + sp: + The BPE model. + batch: + It is the return value from iterating + `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation + for the format of the `batch`. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used + only when --decoding_method is fast_beam_search. + Returns: + Return the decoding result. See above description for the format of + the returned dict. + """ + device = next(model.parameters()).device + feature = batch["inputs"] + assert feature.ndim == 3 + + feature = feature.to(device) + # at entry, feature is (N, T, C) + + supervisions = batch["supervisions"] + feature_lens = supervisions["num_frames"].to(device) + + feature_lens += params.left_context_length + feature = torch.nn.functional.pad( + feature, + pad=(0, 0, 0, params.left_context_length), + value=LOG_EPS, + ) + + encoder_out, encoder_out_lens = model.encoder( + x=feature, x_lens=feature_lens + ) + hyps = [] + + if params.decoding_method == "fast_beam_search": + hyp_tokens = fast_beam_search_one_best( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) + elif ( + params.decoding_method == "greedy_search" + and params.max_sym_per_frame == 1 + ): + hyp_tokens = greedy_search_batch( + model=model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) + elif params.decoding_method == "modified_beam_search": + hyp_tokens = modified_beam_search( + model=model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam_size, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) + else: + batch_size = encoder_out.size(0) + + for i in range(batch_size): + # fmt: off + encoder_out_i = encoder_out[i:i + 1, :encoder_out_lens[i]] + # fmt: on + if params.decoding_method == "greedy_search": + hyp = greedy_search( + model=model, + encoder_out=encoder_out_i, + max_sym_per_frame=params.max_sym_per_frame, + ) + elif params.decoding_method == "beam_search": + hyp = beam_search( + model=model, + encoder_out=encoder_out_i, + beam=params.beam_size, + ) + else: + raise ValueError( + f"Unsupported decoding method: {params.decoding_method}" + ) + hyps.append(sp.decode(hyp).split()) + + if params.decoding_method == "greedy_search": + return {"greedy_search": hyps} + elif params.decoding_method == "fast_beam_search": + return { + ( + f"beam_{params.beam}_" + f"max_contexts_{params.max_contexts}_" + f"max_states_{params.max_states}" + ): hyps + } + else: + return {f"beam_size_{params.beam_size}": hyps} + + +def decode_dataset( + dl: torch.utils.data.DataLoader, + params: AttributeDict, + model: nn.Module, + sp: spm.SentencePieceProcessor, + decoding_graph: Optional[k2.Fsa] = None, +) -> Dict[str, List[Tuple[List[str], List[str]]]]: + """Decode dataset. + + Args: + dl: + PyTorch's dataloader containing the dataset to decode. + params: + It is returned by :func:`get_params`. + model: + The neural model. + sp: + The BPE model. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used + only when --decoding_method is fast_beam_search. + Returns: + Return a dict, whose key may be "greedy_search" if greedy search + is used, or it may be "beam_7" if beam size of 7 is used. + Its value is a list of tuples. Each tuple contains two elements: + The first is the reference transcript, and the second is the + predicted result. + """ + num_cuts = 0 + + try: + num_batches = len(dl) + except TypeError: + num_batches = "?" + + if params.decoding_method == "greedy_search": + log_interval = 50 + else: + log_interval = 10 + + results = defaultdict(list) + for batch_idx, batch in enumerate(dl): + texts = batch["supervisions"]["text"] + + hyps_dict = decode_one_batch( + params=params, + model=model, + sp=sp, + decoding_graph=decoding_graph, + batch=batch, + ) + + for name, hyps in hyps_dict.items(): + this_batch = [] + assert len(hyps) == len(texts) + for hyp_words, ref_text in zip(hyps, texts): + ref_words = ref_text.split() + this_batch.append((ref_words, hyp_words)) + + results[name].extend(this_batch) + + num_cuts += len(texts) + + if batch_idx % log_interval == 0: + batch_str = f"{batch_idx}/{num_batches}" + + logging.info( + f"batch {batch_str}, cuts processed until now is {num_cuts}" + ) + return results + + +def save_results( + params: AttributeDict, + test_set_name: str, + results_dict: Dict[str, List[Tuple[List[int], List[int]]]], +): + test_set_wers = dict() + for key, results in results_dict.items(): + recog_path = ( + params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt" + ) + store_transcripts(filename=recog_path, texts=results) + logging.info(f"The transcripts are stored in {recog_path}") + + # The following prints out WERs, per-word error statistics and aligned + # ref/hyp pairs. + errs_filename = ( + params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt" + ) + with open(errs_filename, "w") as f: + wer = write_error_stats( + f, f"{test_set_name}-{key}", results, enable_log=True + ) + test_set_wers[key] = wer + + logging.info("Wrote detailed error stats to {}".format(errs_filename)) + + test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1]) + errs_info = ( + params.res_dir + / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt" + ) + with open(errs_info, "w") as f: + print("settings\tWER", file=f) + for key, val in test_set_wers: + print("{}\t{}".format(key, val), file=f) + + s = "\nFor {}, WER of different settings are:\n".format(test_set_name) + note = "\tbest for {}".format(test_set_name) + for key, val in test_set_wers: + s += "{}\t{}{}\n".format(key, val, note) + note = "" + logging.info(s) + + +@torch.no_grad() +def main(): + parser = get_parser() + LibriSpeechAsrDataModule.add_arguments(parser) + args = parser.parse_args() + args.exp_dir = Path(args.exp_dir) + + params = get_params() + params.update(vars(args)) + + assert params.decoding_method in ( + "greedy_search", + "beam_search", + "fast_beam_search", + "modified_beam_search", + ) + params.res_dir = params.exp_dir / params.decoding_method + + if params.iter > 0: + params.suffix = f"iter-{params.iter}-avg-{params.avg}" + else: + params.suffix = f"epoch-{params.epoch}-avg-{params.avg}" + + if "fast_beam_search" in params.decoding_method: + params.suffix += f"-beam-{params.beam}" + params.suffix += f"-max-contexts-{params.max_contexts}" + params.suffix += f"-max-states-{params.max_states}" + elif "beam_search" in params.decoding_method: + params.suffix += ( + f"-{params.decoding_method}-beam-size-{params.beam_size}" + ) + else: + params.suffix += f"-context-{params.context_size}" + params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}" + + if params.use_averaged_model: + params.suffix += "-use-averaged-model" + + setup_logger(f"{params.res_dir}/log-decode-{params.suffix}") + logging.info("Decoding started") + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"Device: {device}") + + sp = spm.SentencePieceProcessor() + sp.load(params.bpe_model) + + # and are defined in local/train_bpe_model.py + params.blank_id = sp.piece_to_id("") + params.unk_id = sp.piece_to_id("") + params.vocab_size = sp.get_piece_size() + + logging.info(params) + + logging.info("About to create model") + model = get_transducer_model(params) + + if not params.use_averaged_model: + if params.iter > 0: + filenames = find_checkpoints( + params.exp_dir, iteration=-params.iter + )[: params.avg] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + elif params.avg == 1: + load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) + else: + start = params.epoch - params.avg + 1 + filenames = [] + for i in range(start, params.epoch + 1): + if i >= 1: + filenames.append(f"{params.exp_dir}/epoch-{i}.pt") + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + else: + if params.iter > 0: + filenames = find_checkpoints( + params.exp_dir, iteration=-params.iter + )[: params.avg + 1] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg + 1: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + filename_start = filenames[-1] + filename_end = filenames[0] + logging.info( + "Calculating the averaged model over iteration checkpoints" + f" from {filename_start} (excluded) to {filename_end}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + else: + assert params.avg > 0, params.avg + start = params.epoch - params.avg + assert start >= 1, start + filename_start = f"{params.exp_dir}/epoch-{start}.pt" + filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt" + logging.info( + f"Calculating the averaged model over epoch range from " + f"{start} (excluded) to {params.epoch}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + + model.to(device) + model.eval() + + if params.decoding_method == "fast_beam_search": + decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device) + else: + decoding_graph = None + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + librispeech = LibriSpeechAsrDataModule(args) + + test_clean_cuts = librispeech.test_clean_cuts() + test_other_cuts = librispeech.test_other_cuts() + + test_clean_dl = librispeech.test_dataloaders(test_clean_cuts) + test_other_dl = librispeech.test_dataloaders(test_other_cuts) + + test_sets = ["test-clean", "test-other"] + test_dl = [test_clean_dl, test_other_dl] + + for test_set, test_dl in zip(test_sets, test_dl): + results_dict = decode_dataset( + dl=test_dl, + params=params, + model=model, + sp=sp, + decoding_graph=decoding_graph, + ) + + save_results( + params=params, + test_set_name=test_set, + results_dict=results_dict, + ) + + logging.info("Done!") + + +if __name__ == "__main__": + main() diff --git a/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/decoder.py b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/decoder.py new file mode 120000 index 000000000..0d5f10dc0 --- /dev/null +++ b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/decoder.py @@ -0,0 +1 @@ +../pruned_transducer_stateless/decoder.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/emformer.py b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/emformer.py new file mode 100644 index 000000000..318cd5094 --- /dev/null +++ b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/emformer.py @@ -0,0 +1,315 @@ +# Copyright 2022 Xiaomi Corporation (Author: Mingshuang Luo) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from typing import List, Optional, Tuple + +import torch +import torch.nn as nn +from encoder_interface import EncoderInterface +from subsampling import Conv2dSubsampling, VggSubsampling + +try: + from torchaudio.models import Emformer as _Emformer +except ImportError: + import torchaudio + + print( + "Please install torchaudio >= 0.11.0. " + f"Current version: {torchaudio.__version__}" + ) + raise + + +def unstack_states( + states: List[List[torch.Tensor]], +) -> List[List[List[torch.Tensor]]]: + """Unstack the emformer state corresponding to a batch of utterances + into a list of states, were the i-th entry is the state from the i-th + utterance in the batch. + + Args: + states: + A list-of-list of tensors. ``len(states)`` equals to number of + layers in the emformer. ``states[i]]`` contains the states for + the i-th layer. ``states[i][k]`` is either a 3-D tensor of shape + ``(T, N, C)`` or a 2-D tensor of shape ``(C, N)`` + """ + batch_size = states[0][0].size(1) + num_layers = len(states) + + ans = [None] * batch_size + for i in range(batch_size): + ans[i] = [[] for _ in range(num_layers)] + + for li, layer in enumerate(states): + for s in layer: + s_list = s.unbind(dim=1) + # We will use stack(dim=1) later in stack_states() + for bi, b in enumerate(ans): + b[li].append(s_list[bi]) + return ans + + +def stack_states( + state_list: List[List[List[torch.Tensor]]], +) -> List[List[torch.Tensor]]: + """Stack list of emformer states that correspond to separate utterances + into a single emformer state so that it can be used as an input for + emformer when those utterances are formed into a batch. + + Note: + It is the inverse of :func:`unstack_states`. + + Args: + state_list: + Each element in state_list corresponding to the internal state + of the emformer model for a single utterance. + Returns: + Return a new state corresponding to a batch of utterances. + See the input argument of :func:`unstack_states` for the meaning + of the returned tensor. + """ + batch_size = len(state_list) + ans = [] + for layer in state_list[0]: + # layer is a list of tensors + if batch_size > 1: + ans.append([[s] for s in layer]) + # Note: We will stack ans[layer][s][] later to get ans[layer][s] + else: + ans.append([s.unsqueeze(1) for s in layer]) + + for b, states in enumerate(state_list[1:], 1): + for li, layer in enumerate(states): + for si, s in enumerate(layer): + ans[li][si].append(s) + if b == batch_size - 1: + ans[li][si] = torch.stack(ans[li][si], dim=1) + # We will use unbind(dim=1) later in unstack_states() + return ans + + +class Emformer(EncoderInterface): + """This is just a simple wrapper around torchaudio.models.Emformer. + We may replace it with our own implementation some time later. + """ + + def __init__( + self, + num_features: int, + output_dim: int, + d_model: int, + nhead: int, + dim_feedforward: int, + num_encoder_layers: int, + segment_length: int, + left_context_length: int, + right_context_length: int, + max_memory_size: int = 0, + dropout: float = 0.1, + subsampling_factor: int = 4, + vgg_frontend: bool = False, + ) -> None: + """ + Args: + num_features: + The input dimension of the model. + output_dim: + The output dimension of the model. + d_model: + Attention dimension. + nhead: + Number of heads in multi-head attention. + dim_feedforward: + The output dimension of the feedforward layers in encoder. + num_encoder_layers: + Number of encoder layers. + segment_length: + Number of frames per segment before subsampling. + left_context_length: + Number of frames in the left context before subsampling. + right_context_length: + Number of frames in the right context before subsampling. + max_memory_size: + TODO. + dropout: + Dropout in encoder. + subsampling_factor: + Number of output frames is num_in_frames // subsampling_factor. + Currently, subsampling_factor MUST be 4. + vgg_frontend: + True to use vgg style frontend for subsampling. + """ + super().__init__() + + self.subsampling_factor = subsampling_factor + if subsampling_factor != 4: + raise NotImplementedError("Support only 'subsampling_factor=4'.") + + # self.encoder_embed converts the input of shape (N, T, num_features) + # to the shape (N, T//subsampling_factor, d_model). + # That is, it does two things simultaneously: + # (1) subsampling: T -> T//subsampling_factor + # (2) embedding: num_features -> d_model + if vgg_frontend: + self.encoder_embed = VggSubsampling(num_features, d_model) + else: + self.encoder_embed = Conv2dSubsampling(num_features, d_model) + + self.segment_length = segment_length # before subsampling + self.right_context_length = right_context_length + + assert right_context_length % subsampling_factor == 0 + assert segment_length % subsampling_factor == 0 + assert left_context_length % subsampling_factor == 0 + + left_context_length = left_context_length // subsampling_factor + right_context_length = right_context_length // subsampling_factor + segment_length = segment_length // subsampling_factor + + self.model = _Emformer( + input_dim=d_model, + num_heads=nhead, + ffn_dim=dim_feedforward, + num_layers=num_encoder_layers, + segment_length=segment_length, + dropout=dropout, + activation="relu", + left_context_length=left_context_length, + right_context_length=right_context_length, + max_memory_size=max_memory_size, + weight_init_scale_strategy="depthwise", + tanh_on_mem=False, + negative_inf=-1e8, + ) + + self.encoder_output_layer = nn.Sequential( + nn.Dropout(p=dropout), nn.Linear(d_model, output_dim) + ) + self.log_eps = math.log(1e-10) + + self._has_init_state = False + self._init_state = torch.jit.Attribute([], List[List[torch.Tensor]]) + + def forward( + self, + x: torch.Tensor, + x_lens: torch.Tensor, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Args: + x: + Input features of shape (N, T, C). + x_lens: + A int32 tensor of shape (N,) containing valid frames in `x` before + padding. We have `x.size(1) == x_lens.max()` + Returns: + Return a tuple containing two tensors: + + - encoder_out, a tensor of shape (N, T', C) + - encoder_out_lens, a int32 tensor of shape (N,) containing the + valid frames in `encoder_out` before padding + """ + x = nn.functional.pad( + x, + # (left, right, top, bottom) + # left/right are for the channel dimension, i.e., axis 2 + # top/bottom are for the time dimension, i.e., axis 1 + (0, 0, 0, self.right_context_length), + value=self.log_eps, + ) # (N, T, C) -> (N, T+right_context_length, C) + + x = self.encoder_embed(x) + + # Caution: We assume the subsampling factor is 4! + x_lens = (((x_lens - 1) >> 1) - 1) >> 1 + + emformer_out, emformer_out_lens = self.model(x, x_lens) + logits = self.encoder_output_layer(emformer_out) + + return logits, emformer_out_lens + + @torch.jit.export + def streaming_forward( + self, + x: torch.Tensor, + x_lens: torch.Tensor, + states: Optional[List[List[torch.Tensor]]] = None, + ): + """ + Args: + x: + A 3-D tensor of shape (N, T, C). Note: x also contains right + context frames. + x_lens: + A 2-D tensor of shap containing the number of valid frames for each + element in `x` before padding. Note: It also counts right context + frames. + states: + Internal states of the model. + Returns: + Return a tuple containing 3 tensors: + - encoder_out, a 3-D tensor of shape (N, T, C) + - encoder_out_lens: a 1-D tensor of shape (N,) + - next_state, internal model states for the next invocation + """ + x = self.encoder_embed(x) + + # Caution: We assume the subsampling factor is 4! + x_lens = (((x_lens - 1) >> 1) - 1) >> 1 + + emformer_out, emformer_out_lens, states = self.model.infer( + x, x_lens, states + ) + + if x.size(1) != ( + self.model.segment_length + self.model.right_context_length + ): + raise ValueError( + "Incorrect input shape." + f"{x.size(1)} vs {self.model.segment_length} + " + f"{self.model.right_context_length}" + ) + + logits = self.encoder_output_layer(emformer_out) + + return logits, emformer_out_lens, states + + @torch.jit.export + def get_init_state(self, device: torch.device) -> List[List[torch.Tensor]]: + """Return the initial state of each layer. + + Returns: + Return the initial state of each layer. NOTE: the returned + tensors are on the given device. `len(ans) == num_emformer_layers`. + """ + if self._has_init_state: + # Note(fangjun): It is OK to share the init state as it is + # not going to be modified by the model + return self._init_state + + batch_size = 1 + + ans: List[List[torch.Tensor]] = [] + for layer in self.model.emformer_layers: + s = layer._init_state(batch_size=batch_size, device=device) + ans.append(s) + + self._has_init_state = True + self._init_state = ans + + return ans diff --git a/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/encoder_interface.py b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/encoder_interface.py new file mode 120000 index 000000000..a478f2351 --- /dev/null +++ b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/encoder_interface.py @@ -0,0 +1 @@ +../pruned_transducer_stateless/encoder_interface.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/export.py b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/export.py new file mode 100755 index 000000000..2375f5001 --- /dev/null +++ b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/export.py @@ -0,0 +1,281 @@ +#!/usr/bin/env python3 +# +# Copyright 2021 Xiaomi Corporation (Author: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# This script converts several saved checkpoints +# to a single one using model averaging. +""" +Usage: +./prunted_stateless_emformer_rnnt/export.py \ + --exp-dir ./prunted_stateless_emformer_rnnt/exp \ + --bpe-model data/lang_bpe_500/bpe.model \ + --epoch 20 \ + --avg 10 + +It will generate a file exp_dir/pretrained.pt + +To use the generated file with `prunted_stateless_emformer_rnnt/decode.py`, +you can do: + + cd /path/to/exp_dir + ln -s pretrained.pt epoch-9999.pt + + cd /path/to/egs/librispeech/ASR + ./prunted_stateless_emformer_rnnt/decode.py \ + --exp-dir ./prunted_stateless_emformer_rnnt/exp \ + --epoch 9999 \ + --avg 1 \ + --max-duration 600 \ + --decoding-method greedy_search \ + --bpe-model data/lang_bpe_500/bpe.model +""" + +import argparse +import logging +from pathlib import Path + +import sentencepiece as spm +import torch +from train import add_model_arguments, get_params, get_transducer_model + +from icefall.checkpoint import ( + average_checkpoints, + average_checkpoints_with_averaged_model, + find_checkpoints, + load_checkpoint, +) +from icefall.utils import str2bool + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--epoch", + type=int, + default=28, + help="""It specifies the checkpoint to use for averaging. + Note: Epoch counts from 1. + You can specify --avg to use more checkpoints for model averaging.""", + ) + + parser.add_argument( + "--iter", + type=int, + default=0, + help="""If positive, --epoch is ignored and it + will use the checkpoint exp_dir/checkpoint-iter.pt. + You can specify --avg to use more checkpoints for model averaging. + """, + ) + + parser.add_argument( + "--avg", + type=int, + default=15, + help="Number of checkpoints to average. Automatically select " + "consecutive checkpoints before the checkpoint specified by " + "'--epoch' and '--iter'", + ) + + parser.add_argument( + "--use-averaged-model", + type=str2bool, + default=False, + help="Whether to load averaged model. Currently it only supports " + "using --epoch. If True, it would decode with the averaged model " + "over the epoch range from `epoch-avg` (excluded) to `epoch`." + "Actually only the models with epoch number of `epoch-avg` and " + "`epoch` are loaded for averaging. ", + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="prunted_stateless_emformer_rnnt/exp", + help="""It specifies the directory where all training related + files, e.g., checkpoints, log, etc, are saved + """, + ) + + parser.add_argument( + "--bpe-model", + type=str, + default="data/lang_bpe_500/bpe.model", + help="Path to the BPE model", + ) + + parser.add_argument( + "--jit", + type=str2bool, + default=False, + help="""True to save a model after applying torch.jit.script. + """, + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; " + "2 means tri-gram", + ) + + add_model_arguments(parser) + + return parser + + +def main(): + args = get_parser().parse_args() + args.exp_dir = Path(args.exp_dir) + + params = get_params() + params.update(vars(args)) + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"device: {device}") + + sp = spm.SentencePieceProcessor() + sp.load(params.bpe_model) + + # and are defined in local/train_bpe_model.py + params.blank_id = sp.piece_to_id("") + params.unk_id = sp.piece_to_id("") + params.vocab_size = sp.get_piece_size() + + logging.info(params) + + logging.info("About to create model") + model = get_transducer_model(params) + + if not params.use_averaged_model: + if params.iter > 0: + filenames = find_checkpoints( + params.exp_dir, iteration=-params.iter + )[: params.avg] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + elif params.avg == 1: + load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) + else: + start = params.epoch - params.avg + 1 + filenames = [] + for i in range(start, params.epoch + 1): + if i >= 1: + filenames.append(f"{params.exp_dir}/epoch-{i}.pt") + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + else: + if params.iter > 0: + filenames = find_checkpoints( + params.exp_dir, iteration=-params.iter + )[: params.avg + 1] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg + 1: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + filename_start = filenames[-1] + filename_end = filenames[0] + logging.info( + "Calculating the averaged model over iteration checkpoints" + f" from {filename_start} (excluded) to {filename_end}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + else: + assert params.avg > 0, params.avg + start = params.epoch - params.avg + assert start >= 1, start + filename_start = f"{params.exp_dir}/epoch-{start}.pt" + filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt" + logging.info( + f"Calculating the averaged model over epoch range from " + f"{start} (excluded) to {params.epoch}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + + model.eval() + + model.to("cpu") + model.eval() + for p in model.parameters(): + p.requires_grad_(False) + + if params.jit: + # We won't use the forward() method of the model in C++, so just ignore + # it here. + # Otherwise, one of its arguments is a ragged tensor and is not + # torch scriptabe. + model.__class__.forward = torch.jit.ignore(model.__class__.forward) + logging.info("Using torch.jit.script") + model = torch.jit.script(model) + filename = params.exp_dir / "cpu_jit.pt" + model.save(str(filename)) + logging.info(f"Saved to {filename}") + else: + logging.info("Not using torch.jit.script") + # Save it using a format so that it can be loaded + # by :func:`load_checkpoint` + filename = params.exp_dir / "pretrained.pt" + torch.save({"model": model.state_dict()}, str(filename)) + logging.info(f"Saved to {filename}") + + +if __name__ == "__main__": + formatter = ( + "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + ) + + logging.basicConfig(format=formatter, level=logging.INFO) + main() diff --git a/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/joiner.py b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/joiner.py new file mode 120000 index 000000000..81ad47c55 --- /dev/null +++ b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/joiner.py @@ -0,0 +1 @@ +../pruned_transducer_stateless/joiner.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/model.py b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/model.py new file mode 100644 index 000000000..2f019bcdb --- /dev/null +++ b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/model.py @@ -0,0 +1,169 @@ +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang, Wei Kang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import k2 +import torch +import torch.nn as nn +from encoder_interface import EncoderInterface + +from icefall.utils import add_sos + + +class Transducer(nn.Module): + """It implements https://arxiv.org/pdf/1211.3711.pdf + "Sequence Transduction with Recurrent Neural Networks" + """ + + def __init__( + self, + encoder: EncoderInterface, + decoder: nn.Module, + joiner: nn.Module, + ): + """ + Args: + encoder: + It is the transcription network in the paper. Its accepts + two inputs: `x` of (N, T, C) and `x_lens` of shape (N,). + It returns two tensors: `logits` of shape (N, T, C) and + `logit_lens` of shape (N,). + decoder: + It is the prediction network in the paper. Its input shape + is (N, U) and its output shape is (N, U, C). It should contain + one attribute: `blank_id`. + joiner: + It has two inputs with shapes: (N, T, C) and (N, U, C). Its + output shape is (N, T, U, C). Note that its output contains + unnormalized probs, i.e., not processed by log-softmax. + """ + super().__init__() + assert isinstance(encoder, EncoderInterface), type(encoder) + assert hasattr(decoder, "blank_id") + + self.encoder = encoder + self.decoder = decoder + self.joiner = joiner + + def forward( + self, + x: torch.Tensor, + x_lens: torch.Tensor, + y: k2.RaggedTensor, + prune_range: int = 5, + am_scale: float = 0.0, + lm_scale: float = 0.0, + ) -> torch.Tensor: + """ + Args: + x: + A 3-D tensor of shape (N, T, C). + x_lens: + A 1-D tensor of shape (N,). It contains the number of frames in `x` + before padding. + y: + A ragged tensor with 2 axes [utt][label]. It contains labels of each + utterance. + prune_range: + The prune range for rnnt loss, it means how many symbols(context) + we are considering for each frame to compute the loss. + am_scale: + The scale to smooth the loss with am (output of encoder network) + part + lm_scale: + The scale to smooth the loss with lm (output of predictor network) + part + Returns: + Return the transducer loss. + + Note: + Regarding am_scale & lm_scale, it will make the loss-function one of + the form: + lm_scale * lm_probs + am_scale * am_probs + + (1-lm_scale-am_scale) * combined_probs + """ + assert x.ndim == 3, x.shape + assert x_lens.ndim == 1, x_lens.shape + assert y.num_axes == 2, y.num_axes + + assert x.size(0) == x_lens.size(0) == y.dim0 + + encoder_out, x_lens = self.encoder(x, x_lens) + assert torch.all(x_lens > 0) + + # Now for the decoder, i.e., the prediction network + row_splits = y.shape.row_splits(1) + y_lens = row_splits[1:] - row_splits[:-1] + + blank_id = self.decoder.blank_id + sos_y = add_sos(y, sos_id=blank_id) + + # sos_y_padded: [B, S + 1], start with SOS. + sos_y_padded = sos_y.pad(mode="constant", padding_value=blank_id) + + # decoder_out: [B, S + 1, C] + decoder_out = self.decoder(sos_y_padded) + + # Note: y does not start with SOS + # y_padded : [B, S] + y_padded = y.pad(mode="constant", padding_value=0) + + y_padded = y_padded.to(torch.int64) + boundary = torch.zeros( + (x.size(0), 4), dtype=torch.int64, device=x.device + ) + boundary[:, 2] = y_lens + boundary[:, 3] = x_lens + + simple_loss, (px_grad, py_grad) = k2.rnnt_loss_smoothed( + lm=decoder_out, + am=encoder_out, + symbols=y_padded, + termination_symbol=blank_id, + lm_only_scale=lm_scale, + am_only_scale=am_scale, + boundary=boundary, + reduction="sum", + return_grad=True, + ) + + # ranges : [B, T, prune_range] + ranges = k2.get_rnnt_prune_ranges( + px_grad=px_grad, + py_grad=py_grad, + boundary=boundary, + s_range=prune_range, + ) + + # am_pruned : [B, T, prune_range, C] + # lm_pruned : [B, T, prune_range, C] + am_pruned, lm_pruned = k2.do_rnnt_pruning( + am=encoder_out, lm=decoder_out, ranges=ranges + ) + + # logits : [B, T, prune_range, C] + logits = self.joiner(am_pruned, lm_pruned) + + pruned_loss = k2.rnnt_loss_pruned( + logits=logits, + symbols=y_padded, + ranges=ranges, + termination_symbol=blank_id, + boundary=boundary, + reduction="sum", + ) + + return (simple_loss, pruned_loss) diff --git a/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/noam.py b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/noam.py new file mode 100644 index 000000000..e46bf35fb --- /dev/null +++ b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/noam.py @@ -0,0 +1,104 @@ +# Copyright 2021 University of Chinese Academy of Sciences (author: Han Zhu) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch + + +class Noam(object): + """ + Implements Noam optimizer. + + Proposed in + "Attention Is All You Need", https://arxiv.org/pdf/1706.03762.pdf + + Modified from + https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/transformer/optimizer.py # noqa + + Args: + params: + iterable of parameters to optimize or dicts defining parameter groups + model_size: + attention dimension of the transformer model + factor: + learning rate factor + warm_step: + warmup steps + """ + + def __init__( + self, + params, + model_size: int = 256, + factor: float = 10.0, + warm_step: int = 25000, + weight_decay=0, + ) -> None: + """Construct an Noam object.""" + self.optimizer = torch.optim.Adam( + params, lr=0, betas=(0.9, 0.98), eps=1e-9, weight_decay=weight_decay + ) + self._step = 0 + self.warmup = warm_step + self.factor = factor + self.model_size = model_size + self._rate = 0 + + @property + def param_groups(self): + """Return param_groups.""" + return self.optimizer.param_groups + + def step(self): + """Update parameters and rate.""" + self._step += 1 + rate = self.rate() + for p in self.optimizer.param_groups: + p["lr"] = rate + self._rate = rate + self.optimizer.step() + + def rate(self, step=None): + """Implement `lrate` above.""" + if step is None: + step = self._step + return ( + self.factor + * self.model_size ** (-0.5) + * min(step ** (-0.5), step * self.warmup ** (-1.5)) + ) + + def zero_grad(self): + """Reset gradient.""" + self.optimizer.zero_grad() + + def state_dict(self): + """Return state_dict.""" + return { + "_step": self._step, + "warmup": self.warmup, + "factor": self.factor, + "model_size": self.model_size, + "_rate": self._rate, + "optimizer": self.optimizer.state_dict(), + } + + def load_state_dict(self, state_dict): + """Load state_dict.""" + for key, value in state_dict.items(): + if key == "optimizer": + self.optimizer.load_state_dict(state_dict["optimizer"]) + else: + setattr(self, key, value) diff --git a/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/subsampling.py b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/subsampling.py new file mode 120000 index 000000000..6fee09e58 --- /dev/null +++ b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/subsampling.py @@ -0,0 +1 @@ +../conformer_ctc/subsampling.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/test_emformer.py b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/test_emformer.py new file mode 100755 index 000000000..aef506e81 --- /dev/null +++ b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/test_emformer.py @@ -0,0 +1,142 @@ +#!/usr/bin/env python3 +# Copyright 2022 Xiaomi Corp. (authors: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +To run this file, do: + + cd icefall/egs/librispeech/ASR + python ./pruned_stateless_emformer_rnnt/test_emformer.py +""" + +import torch +from emformer import Emformer, stack_states, unstack_states + + +def test_emformer(): + N = 3 + T = 300 + C = 80 + + output_dim = 500 + + encoder = Emformer( + num_features=C, + output_dim=output_dim, + d_model=512, + nhead=8, + dim_feedforward=2048, + num_encoder_layers=20, + segment_length=16, + left_context_length=120, + right_context_length=4, + vgg_frontend=False, + ) + + x = torch.rand(N, T, C) + x_lens = torch.randint(100, T, (N,)) + x_lens[0] = T + + y, y_lens = encoder(x, x_lens) + + y_lens = (((x_lens - 1) >> 1) - 1) >> 1 + assert x.size(0) == x.size(0) + assert y.size(1) == max(y_lens) + assert y.size(2) == output_dim + + num_param = sum([p.numel() for p in encoder.parameters()]) + print(f"Number of encoder parameters: {num_param}") + + +def test_emformer_streaming_forward(): + N = 3 + C = 80 + + output_dim = 500 + + encoder = Emformer( + num_features=C, + output_dim=output_dim, + d_model=512, + nhead=8, + dim_feedforward=2048, + num_encoder_layers=20, + segment_length=16, + left_context_length=120, + right_context_length=4, + vgg_frontend=False, + ) + + x = torch.rand(N, 23, C) + x_lens = torch.full((N,), 23) + y, y_lens, states = encoder.streaming_forward(x=x, x_lens=x_lens) + + state_list = unstack_states(states) + states2 = stack_states(state_list) + + for ss, ss2 in zip(states, states2): + for s, s2 in zip(ss, ss2): + assert torch.allclose(s, s2), f"{s.sum()}, {s2.sum()}" + + +def test_emformer_init_state(): + num_encoder_layers = 20 + d_model = 512 + encoder = Emformer( + num_features=80, + output_dim=500, + d_model=512, + nhead=8, + dim_feedforward=2048, + num_encoder_layers=num_encoder_layers, + segment_length=16, + left_context_length=120, + right_context_length=4, + vgg_frontend=False, + ) + init_state = encoder.get_init_state() + assert len(init_state) == num_encoder_layers + layer0_state = init_state[0] + assert len(layer0_state) == 4 + + assert layer0_state[0].shape == ( + 0, # max_memory_size + 1, # batch_size + d_model, # input_dim + ) + + assert layer0_state[1].shape == ( + encoder.model.left_context_length, + 1, # batch_size + d_model, # input_dim + ) + assert layer0_state[2].shape == layer0_state[1].shape + assert layer0_state[3].shape == ( + 1, # always 1 + 1, # batch_size + ) + + +@torch.no_grad() +def main(): + test_emformer() + test_emformer_streaming_forward() + test_emformer_init_state() + + +if __name__ == "__main__": + torch.manual_seed(20220329) + main() diff --git a/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/test_model.py b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/test_model.py new file mode 100755 index 000000000..573817b85 --- /dev/null +++ b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/test_model.py @@ -0,0 +1,59 @@ +#!/usr/bin/env python3 +# Copyright 2022 Xiaomi Corp. (authors: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +To run this file, do: + + cd icefall/egs/librispeech/ASR + python ./pruned_stateless_emformer_rnnt/test_model.py +""" + +import torch +from train import get_params, get_transducer_model + + +def test_model(): + params = get_params() + params.vocab_size = 500 + params.blank_id = 0 + params.context_size = 2 + params.unk_id = 2 + + params.attention_dim = 512 + params.nhead = 8 + params.dim_feedforward = 2048 + params.num_encoder_layers = 18 + params.left_context_length = 128 + params.segment_length = 8 + params.right_context_length = 4 + params.memory_size = 0 + + model = get_transducer_model(params) + + num_param = sum([p.numel() for p in model.parameters()]) + print(f"Number of model parameters: {num_param}") + model.__class__.forward = torch.jit.ignore(model.__class__.forward) + torch.jit.script(model) + + +def main(): + test_model() + + +if __name__ == "__main__": + main() diff --git a/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/train.py b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/train.py new file mode 100755 index 000000000..cd62787fa --- /dev/null +++ b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/train.py @@ -0,0 +1,1034 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang, +# Wei Kang, +# Mingshuang Luo,) +# Zengwei Yao) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Usage: + +export CUDA_VISIBLE_DEVICES="0,1,2,3" + +./pruned_stateless_emformer_rnnt/train.py \ + --world-size 4 \ + --num-epochs 30 \ + --start-epoch 1 \ + --exp-dir pruned_stateless_emformer_rnnt/exp \ + --full-libri 1 \ + --max-duration 300 +""" + + +import argparse +import copy +import logging +import warnings +from pathlib import Path +from shutil import copyfile +from typing import Any, Dict, Optional, Tuple, Union + +import k2 +import sentencepiece as spm +import torch +import torch.multiprocessing as mp +import torch.nn as nn +from asr_datamodule import LibriSpeechAsrDataModule +from decoder import Decoder +from emformer import Emformer +from joiner import Joiner +from lhotse.cut import Cut +from lhotse.dataset.sampling.base import CutSampler +from lhotse.utils import fix_random_seed +from model import Transducer +from noam import Noam +from torch import Tensor +from torch.cuda.amp import GradScaler +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.utils.tensorboard import SummaryWriter + +from icefall import diagnostics +from icefall.checkpoint import load_checkpoint, remove_checkpoints +from icefall.checkpoint import save_checkpoint as save_checkpoint_impl +from icefall.checkpoint import ( + save_checkpoint_with_global_batch_idx, + update_averaged_model, +) +from icefall.dist import cleanup_dist, setup_dist +from icefall.env import get_env_info +from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool + + +def add_model_arguments(parser: argparse.ArgumentParser): + parser.add_argument( + "--attention-dim", + type=int, + default=512, + help="Attention dim for the Emformer", + ) + + parser.add_argument( + "--nhead", + type=int, + default=8, + help="Number of attention heads for the Emformer", + ) + + parser.add_argument( + "--dim-feedforward", + type=int, + default=2048, + help="Feed-forward dimension for the Emformer", + ) + + parser.add_argument( + "--num-encoder-layers", + type=int, + default=18, + help="Number of encoder layers for the Emformer", + ) + + parser.add_argument( + "--left-context-length", + type=int, + default=128, + help="Number of frames for the left context in the Emformer", + ) + + parser.add_argument( + "--segment-length", + type=int, + default=8, + help="Number of frames for each segment in the Emformer", + ) + + parser.add_argument( + "--right-context-length", + type=int, + default=4, + help="Number of frames for right context in the Emformer", + ) + + parser.add_argument( + "--memory-size", + type=int, + default=0, + help="Number of entries in the memory for the Emformer", + ) + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--world-size", + type=int, + default=1, + help="Number of GPUs for DDP training.", + ) + + parser.add_argument( + "--master-port", + type=int, + default=12354, + help="Master port to use for DDP training.", + ) + + parser.add_argument( + "--tensorboard", + type=str2bool, + default=True, + help="Should various information be logged in tensorboard.", + ) + + parser.add_argument( + "--num-epochs", + type=int, + default=30, + help="Number of epochs to train.", + ) + + parser.add_argument( + "--start-epoch", + type=int, + default=1, + help="""Resume training from this epoch. It should be positive. + If larger than 1, it will load checkpoint from + exp-dir/epoch-{start_epoch-1}.pt + """, + ) + + parser.add_argument( + "--start-batch", + type=int, + default=0, + help="""If positive, --start-epoch is ignored and + it loads the checkpoint from exp-dir/checkpoint-{start_batch}.pt + """, + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="pruned_stateless_emformer_rnnt/exp", + help="""The experiment dir. + It specifies the directory where all training related + files, e.g., checkpoints, log, etc, are saved + """, + ) + + parser.add_argument( + "--bpe-model", + type=str, + default="data/lang_bpe_500/bpe.model", + help="Path to the BPE model", + ) + + parser.add_argument( + "--lr-factor", + type=float, + default=5.0, + help="The lr_factor for Noam optimizer", + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; " + "2 means tri-gram", + ) + + parser.add_argument( + "--prune-range", + type=int, + default=5, + help="The prune range for rnnt loss, it means how many symbols(context)" + "we are using to compute the loss", + ) + + parser.add_argument( + "--lm-scale", + type=float, + default=0.25, + help="The scale to smooth the loss with lm " + "(output of prediction network) part.", + ) + + parser.add_argument( + "--am-scale", + type=float, + default=0.0, + help="The scale to smooth the loss with am (output of encoder network)" + "part.", + ) + + parser.add_argument( + "--simple-loss-scale", + type=float, + default=0.5, + help="To get pruning ranges, we will calculate a simple version" + "loss(joiner is just addition), this simple loss also uses for" + "training (as a regularization item). We will scale the simple loss" + "with this parameter before adding to the final loss.", + ) + + parser.add_argument( + "--seed", + type=int, + default=42, + help="The seed for random generators intended for reproducibility", + ) + + parser.add_argument( + "--print-diagnostics", + type=str2bool, + default=False, + help="Accumulate stats on activations, print them and exit.", + ) + + parser.add_argument( + "--save-every-n", + type=int, + default=8000, + help="""Save checkpoint after processing this number of batches" + periodically. We save checkpoint to exp-dir/ whenever + params.batch_idx_train % save_every_n == 0. The checkpoint filename + has the form: f'exp-dir/checkpoint-{params.batch_idx_train}.pt' + Note: It also saves checkpoint to `exp-dir/epoch-xxx.pt` at the + end of each epoch where `xxx` is the epoch number counting from 0. + """, + ) + + parser.add_argument( + "--keep-last-k", + type=int, + default=20, + help="""Only keep this number of checkpoints on disk. + For instance, if it is 3, there are only 3 checkpoints + in the exp-dir with filenames `checkpoint-xxx.pt`. + It does not affect checkpoints with name `epoch-xxx.pt`. + """, + ) + + parser.add_argument( + "--average-period", + type=int, + default=100, + help="""Update the averaged model, namely `model_avg`, after processing + this number of batches. `model_avg` is a separate version of model, + in which each floating-point parameter is the average of all the + parameters from the start of training. Each time we take the average, + we do: `model_avg = model * (average_period / batch_idx_train) + + model_avg * ((batch_idx_train - average_period) / batch_idx_train)`. + """, + ) + + parser.add_argument( + "--use-fp16", + type=str2bool, + default=False, + help="Whether to use half precision training.", + ) + + add_model_arguments(parser) + + return parser + + +def get_params() -> AttributeDict: + """Return a dict containing training parameters. + + All training related parameters that are not passed from the commandline + are saved in the variable `params`. + + Commandline options are merged into `params` after they are parsed, so + you can also access them via `params`. + + Explanation of options saved in `params`: + + - best_train_loss: Best training loss so far. It is used to select + the model that has the lowest training loss. It is + updated during the training. + + - best_valid_loss: Best validation loss so far. It is used to select + the model that has the lowest validation loss. It is + updated during the training. + + - best_train_epoch: It is the epoch that has the best training loss. + + - best_valid_epoch: It is the epoch that has the best validation loss. + + - batch_idx_train: Used to writing statistics to tensorboard. It + contains number of batches trained so far across + epochs. + + - log_interval: Print training loss if batch_idx % log_interval` is 0 + + - reset_interval: Reset statistics if batch_idx % reset_interval is 0 + + - valid_interval: Run validation if batch_idx % valid_interval is 0 + + - feature_dim: The model input dim. It has to match the one used + in computing features. + + - subsampling_factor: The subsampling factor for the model. + + - attention_dim: Hidden dim for multi-head attention model. + + - num_decoder_layers: Number of decoder layer of transformer decoder. + + - warm_step: The warm_step for Noam optimizer. + """ + params = AttributeDict( + { + "best_train_loss": float("inf"), + "best_valid_loss": float("inf"), + "best_train_epoch": -1, + "best_valid_epoch": -1, + "batch_idx_train": 0, + "log_interval": 50, + "reset_interval": 200, + "valid_interval": 3000, # For the 100h subset, use 800 + # parameters for Emformer + "feature_dim": 80, + "subsampling_factor": 4, + "vgg_frontend": False, + # parameters for decoder + "embedding_dim": 512, + # parameters for Noam + "warm_step": 80000, # For the 100h subset, use 20000 + "env_info": get_env_info(), + } + ) + + return params + + +def get_encoder_model(params: AttributeDict) -> nn.Module: + encoder = Emformer( + num_features=params.feature_dim, + output_dim=params.vocab_size, + subsampling_factor=params.subsampling_factor, + d_model=params.attention_dim, + nhead=params.nhead, + dim_feedforward=params.dim_feedforward, + num_encoder_layers=params.num_encoder_layers, + vgg_frontend=params.vgg_frontend, + left_context_length=params.left_context_length, + segment_length=params.segment_length, + right_context_length=params.right_context_length, + max_memory_size=params.memory_size, + ) + return encoder + + +def get_decoder_model(params: AttributeDict) -> nn.Module: + decoder = Decoder( + vocab_size=params.vocab_size, + embedding_dim=params.embedding_dim, + blank_id=params.blank_id, + unk_id=params.unk_id, + context_size=params.context_size, + ) + return decoder + + +def get_joiner_model(params: AttributeDict) -> nn.Module: + joiner = Joiner( + input_dim=params.vocab_size, + inner_dim=params.embedding_dim, + output_dim=params.vocab_size, + ) + return joiner + + +def get_transducer_model(params: AttributeDict) -> nn.Module: + encoder = get_encoder_model(params) + decoder = get_decoder_model(params) + joiner = get_joiner_model(params) + + model = Transducer( + encoder=encoder, + decoder=decoder, + joiner=joiner, + ) + return model + + +def load_checkpoint_if_available( + params: AttributeDict, + model: nn.Module, + model_avg: Optional[nn.Module] = None, + optimizer: Optional[torch.optim.Optimizer] = None, +) -> Optional[Dict[str, Any]]: + """Load checkpoint from file. + + If params.start_batch is positive, it will load the checkpoint from + `params.exp_dir/checkpoint-{params.start_batch}.pt`. Otherwise, if + params.start_epoch is larger than 1, it will load the checkpoint from + `params.start_epoch - 1`. + + Apart from loading state dict for `model` and `optimizer` it also updates + `best_train_epoch`, `best_train_loss`, `best_valid_epoch`, + and `best_valid_loss` in `params`. + + Args: + params: + The return value of :func:`get_params`. + model: + The training model. + model_avg: + The stored model averaged from the start of training. + optimizer: + The optimizer that we are using. + Returns: + Return a dict containing previously saved training info. + """ + if params.start_batch > 0: + filename = params.exp_dir / f"checkpoint-{params.start_batch}.pt" + elif params.start_epoch > 1: + filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt" + else: + return None + + assert filename.is_file(), f"{filename} does not exist!" + + saved_params = load_checkpoint( + filename, + model=model, + model_avg=model_avg, + optimizer=optimizer, + ) + + keys = [ + "best_train_epoch", + "best_valid_epoch", + "batch_idx_train", + "best_train_loss", + "best_valid_loss", + ] + for k in keys: + params[k] = saved_params[k] + + if params.start_batch > 0: + if "cur_epoch" in saved_params: + params["start_epoch"] = saved_params["cur_epoch"] + + if "cur_batch_idx" in saved_params: + params["cur_batch_idx"] = saved_params["cur_batch_idx"] + + return saved_params + + +def save_checkpoint( + params: AttributeDict, + model: Union[nn.Module, DDP], + model_avg: Optional[nn.Module] = None, + optimizer: Optional[torch.optim.Optimizer] = None, + sampler: Optional[CutSampler] = None, + scaler: Optional[GradScaler] = None, + rank: int = 0, +) -> None: + """Save model, optimizer, and training stats to file. + + Args: + params: + It is returned by :func:`get_params`. + model: + The training model. + model_avg: + The stored model averaged from the start of training. + optimizer: + The optimizer used in the training. + sampler: + The sampler for the training dataset. + scaler: + The scaler used for mix precision training. + """ + if rank != 0: + return + filename = params.exp_dir / f"epoch-{params.cur_epoch}.pt" + save_checkpoint_impl( + filename=filename, + model=model, + model_avg=model_avg, + params=params, + optimizer=optimizer, + sampler=sampler, + scaler=scaler, + rank=rank, + ) + + if params.best_train_epoch == params.cur_epoch: + best_train_filename = params.exp_dir / "best-train-loss.pt" + copyfile(src=filename, dst=best_train_filename) + + if params.best_valid_epoch == params.cur_epoch: + best_valid_filename = params.exp_dir / "best-valid-loss.pt" + copyfile(src=filename, dst=best_valid_filename) + + +def compute_loss( + params: AttributeDict, + model: Union[nn.Module, DDP], + sp: spm.SentencePieceProcessor, + batch: dict, + is_training: bool, +) -> Tuple[Tensor, MetricsTracker]: + """ + Compute pruned RNN-T loss given the model and its inputs. + + Args: + params: + Parameters for training. See :func:`get_params`. + model: + The model for training. It is an instance of Emformer in our case. + batch: + A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()` + for the content in it. + is_training: + True for training. False for validation. When it is True, this + function enables autograd during computation; when it is False, it + disables autograd. + """ + device = ( + model.device + if isinstance(model, DDP) + else next(model.parameters()).device + ) + feature = batch["inputs"] + # at entry, feature is (N, T, C) + assert feature.ndim == 3 + feature = feature.to(device) + + supervisions = batch["supervisions"] + feature_lens = supervisions["num_frames"].to(device) + + texts = batch["supervisions"]["text"] + y = sp.encode(texts, out_type=int) + y = k2.RaggedTensor(y).to(device) + + with torch.set_grad_enabled(is_training): + simple_loss, pruned_loss = model( + x=feature, + x_lens=feature_lens, + y=y, + prune_range=params.prune_range, + am_scale=params.am_scale, + lm_scale=params.lm_scale, + ) + loss = params.simple_loss_scale * simple_loss + pruned_loss + + assert loss.requires_grad == is_training + + info = MetricsTracker() + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + info["frames"] = ( + (feature_lens // params.subsampling_factor).sum().item() + ) + + # Note: We use reduction=sum while computing the loss. + info["loss"] = loss.detach().cpu().item() + info["simple_loss"] = simple_loss.detach().cpu().item() + info["pruned_loss"] = pruned_loss.detach().cpu().item() + + return loss, info + + +def compute_validation_loss( + params: AttributeDict, + model: Union[nn.Module, DDP], + sp: spm.SentencePieceProcessor, + valid_dl: torch.utils.data.DataLoader, + world_size: int = 1, +) -> MetricsTracker: + """Run the validation process.""" + model.eval() + + tot_loss = MetricsTracker() + + for batch_idx, batch in enumerate(valid_dl): + loss, loss_info = compute_loss( + params=params, + model=model, + sp=sp, + batch=batch, + is_training=False, + ) + assert loss.requires_grad is False + tot_loss = tot_loss + loss_info + + if world_size > 1: + tot_loss.reduce(loss.device) + + loss_value = tot_loss["loss"] / tot_loss["frames"] + if loss_value < params.best_valid_loss: + params.best_valid_epoch = params.cur_epoch + params.best_valid_loss = loss_value + + return tot_loss + + +def train_one_epoch( + params: AttributeDict, + model: Union[nn.Module, DDP], + optimizer: torch.optim.Optimizer, + sp: spm.SentencePieceProcessor, + train_dl: torch.utils.data.DataLoader, + valid_dl: torch.utils.data.DataLoader, + scaler: GradScaler, + model_avg: Optional[nn.Module] = None, + tb_writer: Optional[SummaryWriter] = None, + world_size: int = 1, + rank: int = 0, +) -> None: + """Train the model for one epoch. + + The training loss from the mean of all frames is saved in + `params.train_loss`. It runs the validation process every + `params.valid_interval` batches. + + Args: + params: + It is returned by :func:`get_params`. + model: + The model for training. + optimizer: + The optimizer we are using. + train_dl: + Dataloader for the training dataset. + valid_dl: + Dataloader for the validation dataset. + scaler: + The scaler used for mix precision training. + model_avg: + The stored model averaged from the start of training. + tb_writer: + Writer to write log messages to tensorboard. + world_size: + Number of nodes in DDP training. If it is 1, DDP is disabled. + rank: + The rank of the node in DDP training. If no DDP is used, it should + be set to 0. + """ + model.train() + + tot_loss = MetricsTracker() + + cur_batch_idx = params.get("cur_batch_idx", 0) + + for batch_idx, batch in enumerate(train_dl): + if batch_idx < cur_batch_idx: + continue + cur_batch_idx = batch_idx + + params.batch_idx_train += 1 + batch_size = len(batch["supervisions"]["text"]) + + with torch.cuda.amp.autocast(enabled=params.use_fp16): + loss, loss_info = compute_loss( + params=params, + model=model, + sp=sp, + batch=batch, + is_training=True, + ) + # summary stats + tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info + + # NOTE: We use reduction==sum and loss is computed over utterances + # in the batch and there is no normalization to it so far. + scaler.scale(loss).backward() + scaler.step(optimizer) + scaler.update() + optimizer.zero_grad() + + if params.print_diagnostics and batch_idx == 30: + return + + if ( + rank == 0 + and params.batch_idx_train > 0 + and params.batch_idx_train % params.average_period == 0 + ): + update_averaged_model( + params=params, + model_cur=model, + model_avg=model_avg, + ) + + if ( + params.batch_idx_train > 0 + and params.batch_idx_train % params.save_every_n == 0 + ): + params.cur_batch_idx = batch_idx + save_checkpoint_with_global_batch_idx( + out_dir=params.exp_dir, + global_batch_idx=params.batch_idx_train, + model=model, + model_avg=model_avg, + params=params, + optimizer=optimizer, + sampler=train_dl.sampler, + scaler=scaler, + rank=rank, + ) + del params.cur_batch_idx + remove_checkpoints( + out_dir=params.exp_dir, + topk=params.keep_last_k, + rank=rank, + ) + + if batch_idx % params.log_interval == 0: + cur_lr = optimizer.rate() + logging.info( + f"Epoch {params.cur_epoch}, " + f"batch {batch_idx}, loss[{loss_info}], " + f"tot_loss[{tot_loss}], batch size: {batch_size}, " + f"lr: {cur_lr:.2e}" + ) + + if tb_writer is not None: + tb_writer.add_scalar( + "train/learning_rate", cur_lr, params.batch_idx_train + ) + + loss_info.write_summary( + tb_writer, "train/current_", params.batch_idx_train + ) + tot_loss.write_summary( + tb_writer, "train/tot_", params.batch_idx_train + ) + + if batch_idx > 0 and batch_idx % params.valid_interval == 0: + logging.info("Computing validation loss") + valid_info = compute_validation_loss( + params=params, + model=model, + sp=sp, + valid_dl=valid_dl, + world_size=world_size, + ) + model.train() + logging.info(f"Epoch {params.cur_epoch}, validation: {valid_info}") + if tb_writer is not None: + valid_info.write_summary( + tb_writer, "train/valid_", params.batch_idx_train + ) + + loss_value = tot_loss["loss"] / tot_loss["frames"] + params.train_loss = loss_value + if params.train_loss < params.best_train_loss: + params.best_train_epoch = params.cur_epoch + params.best_train_loss = params.train_loss + + +def run(rank, world_size, args): + """ + Args: + rank: + It is a value between 0 and `world_size-1`, which is + passed automatically by `mp.spawn()` in :func:`main`. + The node with rank 0 is responsible for saving checkpoint. + world_size: + Number of GPUs for DDP training. + args: + The return value of get_parser().parse_args() + """ + params = get_params() + params.update(vars(args)) + if params.full_libri is False: + params.valid_interval = 800 + params.warm_step = 20000 + + fix_random_seed(params.seed) + if world_size > 1: + setup_dist(rank, world_size, params.master_port) + + setup_logger(f"{params.exp_dir}/log/log-train") + logging.info("Training started") + + if args.tensorboard and rank == 0: + tb_writer = SummaryWriter(log_dir=f"{params.exp_dir}/tensorboard") + else: + tb_writer = None + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", rank) + logging.info(f"Device: {device}") + + sp = spm.SentencePieceProcessor() + sp.load(params.bpe_model) + + # and are defined in local/train_bpe_model.py + params.blank_id = sp.piece_to_id("") + params.unk_id = sp.piece_to_id("") + params.vocab_size = sp.get_piece_size() + + logging.info(params) + + logging.info("About to create model") + model = get_transducer_model(params) + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + assert params.save_every_n >= params.average_period + model_avg: Optional[nn.Module] = None + if rank == 0: + # model_avg is only used with rank 0 + model_avg = copy.deepcopy(model) + + assert params.start_epoch > 0, params.start_epoch + checkpoints = load_checkpoint_if_available( + params=params, model=model, model_avg=model_avg + ) + + model.to(device) + if world_size > 1: + logging.info("Using DDP") + model = DDP(model, device_ids=[rank]) + + optimizer = Noam( + model.parameters(), + model_size=params.attention_dim, + factor=params.lr_factor, + warm_step=params.warm_step, + ) + + if checkpoints and "optimizer" in checkpoints: + logging.info("Loading optimizer state dict") + optimizer.load_state_dict(checkpoints["optimizer"]) + + if params.print_diagnostics: + diagnostic = diagnostics.attach_diagnostics(model) + + librispeech = LibriSpeechAsrDataModule(args) + + train_cuts = librispeech.train_clean_100_cuts() + if params.full_libri: + train_cuts += librispeech.train_clean_360_cuts() + train_cuts += librispeech.train_other_500_cuts() + + def remove_short_and_long_utt(c: Cut): + # Keep only utterances with duration between 1 second and 20 seconds + # + # Caution: There is a reason to select 20.0 here. Please see + # ../local/display_manifest_statistics.py + # + # You should use ../local/display_manifest_statistics.py to get + # an utterance duration distribution for your dataset to select + # the threshold + return 1.0 <= c.duration <= 20.0 + + train_cuts = train_cuts.filter(remove_short_and_long_utt) + + if params.start_batch > 0 and checkpoints and "sampler" in checkpoints: + # We only load the sampler's state dict when it loads a checkpoint + # saved in the middle of an epoch + sampler_state_dict = checkpoints["sampler"] + else: + sampler_state_dict = None + + train_dl = librispeech.train_dataloaders( + train_cuts, sampler_state_dict=sampler_state_dict + ) + + valid_cuts = librispeech.dev_clean_cuts() + valid_cuts += librispeech.dev_other_cuts() + valid_dl = librispeech.valid_dataloaders(valid_cuts) + + if not params.print_diagnostics: + scan_pessimistic_batches_for_oom( + model=model, + train_dl=train_dl, + optimizer=optimizer, + sp=sp, + params=params, + ) + + scaler = GradScaler(enabled=params.use_fp16) + if checkpoints and "grad_scaler" in checkpoints: + logging.info("Loading grad scaler state dict") + scaler.load_state_dict(checkpoints["grad_scaler"]) + + for epoch in range(params.start_epoch, params.num_epochs + 1): + fix_random_seed(params.seed + epoch - 1) + train_dl.sampler.set_epoch(epoch - 1) + + if tb_writer is not None: + tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train) + + params.cur_epoch = epoch + + train_one_epoch( + params=params, + model=model, + model_avg=model_avg, + optimizer=optimizer, + sp=sp, + train_dl=train_dl, + valid_dl=valid_dl, + scaler=scaler, + tb_writer=tb_writer, + world_size=world_size, + rank=rank, + ) + + if params.print_diagnostics: + diagnostic.print_diagnostics() + break + + save_checkpoint( + params=params, + model=model, + model_avg=model_avg, + optimizer=optimizer, + sampler=train_dl.sampler, + scaler=scaler, + rank=rank, + ) + + logging.info("Done!") + + if world_size > 1: + torch.distributed.barrier() + cleanup_dist() + + +def scan_pessimistic_batches_for_oom( + model: Union[nn.Module, DDP], + train_dl: torch.utils.data.DataLoader, + optimizer: torch.optim.Optimizer, + sp: spm.SentencePieceProcessor, + params: AttributeDict, +): + from lhotse.dataset import find_pessimistic_batches + + logging.info( + "Sanity check -- see if any of the batches in epoch 1 would cause OOM." + ) + batches, crit_values = find_pessimistic_batches(train_dl.sampler) + for criterion, cuts in batches.items(): + batch = train_dl.dataset[cuts] + try: + with torch.cuda.amp.autocast(enabled=params.use_fp16): + loss, _ = compute_loss( + params=params, + model=model, + sp=sp, + batch=batch, + is_training=True, + ) + loss.backward() + optimizer.step() + optimizer.zero_grad() + except RuntimeError as e: + if "CUDA out of memory" in str(e): + logging.error( + "Your GPU ran out of memory with the current " + "max_duration setting. We recommend decreasing " + "max_duration and trying again.\n" + f"Failing criterion: {criterion} " + f"(={crit_values[criterion]}) ..." + ) + raise + + +def main(): + parser = get_parser() + LibriSpeechAsrDataModule.add_arguments(parser) + args = parser.parse_args() + args.exp_dir = Path(args.exp_dir) + + world_size = args.world_size + assert world_size >= 1 + if world_size > 1: + mp.spawn(run, args=(world_size, args), nprocs=world_size, join=True) + else: + run(rank=0, world_size=1, args=args) + + +torch.set_num_threads(1) +torch.set_num_interop_threads(1) + +if __name__ == "__main__": + main() diff --git a/egs/librispeech/ASR/pruned_transducer_stateless/joiner.py b/egs/librispeech/ASR/pruned_transducer_stateless/joiner.py index 7c5a93a86..c9522df8a 100644 --- a/egs/librispeech/ASR/pruned_transducer_stateless/joiner.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless/joiner.py @@ -32,13 +32,15 @@ class Joiner(nn.Module): """ Args: encoder_out: - Output from the encoder. Its shape is (N, T, s_range, C). + Output from the encoder. Its shape is (N, T, s_range, C) during + training or (N, C) in case of streaming decoding. decoder_out: - Output from the decoder. Its shape is (N, T, s_range, C). - Returns: + Output from the decoder. Its shape is (N, T, s_range, C) during + training or (N, C) in case of streaming decoding. Return a tensor of shape (N, T, s_range, C). """ - assert encoder_out.ndim == decoder_out.ndim == 4 + assert encoder_out.ndim == decoder_out.ndim + assert encoder_out.ndim in (2, 4) assert encoder_out.shape == decoder_out.shape logit = encoder_out + decoder_out diff --git a/egs/librispeech/ASR/pruned_transducer_stateless/train.py b/egs/librispeech/ASR/pruned_transducer_stateless/train.py index c360d025a..448419759 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless/train.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless/train.py @@ -456,7 +456,7 @@ def compute_loss( is_training: bool, ) -> Tuple[Tensor, MetricsTracker]: """ - Compute CTC loss given the model and its inputs. + Compute RNN-T loss given the model and its inputs. Args: params: @@ -807,28 +807,8 @@ def run(rank, world_size, args): # the threshold return 1.0 <= c.duration <= 20.0 - num_in_total = len(train_cuts) - train_cuts = train_cuts.filter(remove_short_and_long_utt) - try: - num_left = len(train_cuts) - num_removed = num_in_total - num_left - removed_percent = num_removed / num_in_total * 100 - - logging.info( - f"Before removing short and long utterances: {num_in_total}" - ) - logging.info(f"After removing short and long utterances: {num_left}") - logging.info( - f"Removed {num_removed} utterances ({removed_percent:.5f}%)" - ) - except TypeError as e: - # You can ignore this error as previous versions of Lhotse work fine - # for the above code. In recent versions of Lhotse, it uses - # lazy filter, producing cutsets that don't have the __len__ method - logging.info(str(e)) - if params.start_batch > 0 and checkpoints and "sampler" in checkpoints: # We only load the sampler's state dict when it loads a checkpoint # saved in the middle of an epoch diff --git a/egs/librispeech/ASR/pruned_transducer_stateless2/train.py b/egs/librispeech/ASR/pruned_transducer_stateless2/train.py index eed2df755..36ee7ca74 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless2/train.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless2/train.py @@ -510,7 +510,7 @@ def compute_loss( warmup: float = 1.0, ) -> Tuple[Tensor, MetricsTracker]: """ - Compute CTC loss given the model and its inputs. + Compute RNN-T loss given the model and its inputs. Args: params: diff --git a/egs/librispeech/ASR/pruned_transducer_stateless3/asr_datamodule.py b/egs/librispeech/ASR/pruned_transducer_stateless3/asr_datamodule.py index df1e52202..b54d1aa39 100644 --- a/egs/librispeech/ASR/pruned_transducer_stateless3/asr_datamodule.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless3/asr_datamodule.py @@ -22,7 +22,6 @@ from typing import Optional from lhotse import CutSet, Fbank, FbankConfig from lhotse.dataset import ( - BucketingSampler, CutMix, DynamicBucketingSampler, K2SpeechRecognitionDataset, @@ -71,8 +70,7 @@ class AsrDataModule: "--num-buckets", type=int, default=30, - help="The number of buckets for the BucketingSampler " - "and DynamicBucketingSampler." + help="The number of buckets for the DynamicBucketingSampler. " "(you might want to increase it for larger datasets).", ) @@ -152,7 +150,6 @@ class AsrDataModule: def train_dataloaders( self, cuts_train: CutSet, - dynamic_bucketing: bool, on_the_fly_feats: bool, cuts_musan: Optional[CutSet] = None, ) -> DataLoader: @@ -162,9 +159,6 @@ class AsrDataModule: Cuts for training. cuts_musan: If not None, it is the cuts for mixing. - dynamic_bucketing: - True to use DynamicBucketingSampler; - False to use BucketingSampler. on_the_fly_feats: True to use OnTheFlyFeatures; False to use PrecomputedFeatures. @@ -230,25 +224,14 @@ class AsrDataModule: return_cuts=self.args.return_cuts, ) - if dynamic_bucketing: - logging.info("Using DynamicBucketingSampler.") - train_sampler = DynamicBucketingSampler( - cuts_train, - max_duration=self.args.max_duration, - shuffle=self.args.shuffle, - num_buckets=self.args.num_buckets, - drop_last=True, - ) - else: - logging.info("Using BucketingSampler.") - train_sampler = BucketingSampler( - cuts_train, - max_duration=self.args.max_duration, - shuffle=self.args.shuffle, - num_buckets=self.args.num_buckets, - bucket_method="equal_duration", - drop_last=True, - ) + logging.info("Using DynamicBucketingSampler.") + train_sampler = DynamicBucketingSampler( + cuts_train, + max_duration=self.args.max_duration, + shuffle=self.args.shuffle, + num_buckets=self.args.num_buckets, + drop_last=True, + ) logging.info("About to create train dataloader") train_dl = DataLoader( @@ -277,10 +260,12 @@ class AsrDataModule: cut_transforms=transforms, return_cuts=self.args.return_cuts, ) - valid_sampler = BucketingSampler( + valid_sampler = DynamicBucketingSampler( cuts_valid, max_duration=self.args.max_duration, shuffle=False, + num_buckets=self.args.num_buckets, + drop_last=False, ) logging.info("About to create dev dataloader") valid_dl = DataLoader( @@ -301,8 +286,12 @@ class AsrDataModule: else PrecomputedFeatures(), return_cuts=self.args.return_cuts, ) - sampler = BucketingSampler( - cuts, max_duration=self.args.max_duration, shuffle=False + sampler = DynamicBucketingSampler( + cuts, + max_duration=self.args.max_duration, + shuffle=False, + num_buckets=self.args.num_buckets, + drop_last=True, ) logging.debug("About to create test dataloader") test_dl = DataLoader( diff --git a/egs/librispeech/ASR/pruned_transducer_stateless3/gigaspeech.py b/egs/librispeech/ASR/pruned_transducer_stateless3/gigaspeech.py index 3f8bf3ba9..36f32c6b3 100644 --- a/egs/librispeech/ASR/pruned_transducer_stateless3/gigaspeech.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless3/gigaspeech.py @@ -22,7 +22,7 @@ import re from pathlib import Path import lhotse -from lhotse import CutSet, load_manifest +from lhotse import CutSet, load_manifest_lazy class GigaSpeech: @@ -32,13 +32,13 @@ class GigaSpeech: manifest_dir: It is expected to contain the following files:: - - XL_split_2000/cuts_XL.*.jsonl.gz - - cuts_L_raw.jsonl.gz - - cuts_M_raw.jsonl.gz - - cuts_S_raw.jsonl.gz - - cuts_XS_raw.jsonl.gz - - cuts_DEV_raw.jsonl.gz - - cuts_TEST_raw.jsonl.gz + - gigaspeech_XL_split_2000/gigaspeech_cuts_XL.*.jsonl.gz + - gigaspeech_cuts_L_raw.jsonl.gz + - gigaspeech_cuts_M_raw.jsonl.gz + - gigaspeech_cuts_S_raw.jsonl.gz + - gigaspeech_cuts_XS_raw.jsonl.gz + - gigaspeech_cuts_DEV_raw.jsonl.gz + - gigaspeech_cuts_TEST_raw.jsonl.gz """ self.manifest_dir = Path(manifest_dir) @@ -46,10 +46,12 @@ class GigaSpeech: logging.info("About to get train-XL cuts") filenames = list( - glob.glob(f"{self.manifest_dir}/XL_split_2000/cuts_XL.*.jsonl.gz") + glob.glob( + f"{self.manifest_dir}/gigaspeech_XL_split_2000/gigaspeech_cuts_XL.*.jsonl.gz" # noqa + ) ) - pattern = re.compile(r"cuts_XL.([0-9]+).jsonl.gz") + pattern = re.compile(r"gigaspeech_cuts_XL.([0-9]+).jsonl.gz") idx_filenames = [ (int(pattern.search(f).group(1)), f) for f in filenames ] @@ -64,31 +66,31 @@ class GigaSpeech: ) def train_L_cuts(self) -> CutSet: - f = self.manifest_dir / "cuts_L_raw.jsonl.gz" + f = self.manifest_dir / "gigaspeech_cuts_L_raw.jsonl.gz" logging.info(f"About to get train-L cuts from {f}") return CutSet.from_jsonl_lazy(f) def train_M_cuts(self) -> CutSet: - f = self.manifest_dir / "cuts_M_raw.jsonl.gz" + f = self.manifest_dir / "gigaspeech_cuts_M_raw.jsonl.gz" logging.info(f"About to get train-M cuts from {f}") return CutSet.from_jsonl_lazy(f) def train_S_cuts(self) -> CutSet: - f = self.manifest_dir / "cuts_S_raw.jsonl.gz" + f = self.manifest_dir / "gigaspeech_cuts_S_raw.jsonl.gz" logging.info(f"About to get train-S cuts from {f}") return CutSet.from_jsonl_lazy(f) def train_XS_cuts(self) -> CutSet: - f = self.manifest_dir / "cuts_XS_raw.jsonl.gz" + f = self.manifest_dir / "gigaspeech_cuts_XS_raw.jsonl.gz" logging.info(f"About to get train-XS cuts from {f}") return CutSet.from_jsonl_lazy(f) def test_cuts(self) -> CutSet: - f = self.manifest_dir / "cuts_TEST.jsonl.gz" + f = self.manifest_dir / "gigaspeech_cuts_TEST.jsonl.gz" logging.info(f"About to get TEST cuts from {f}") - return load_manifest(f) + return load_manifest_lazy(f) def dev_cuts(self) -> CutSet: - f = self.manifest_dir / "cuts_DEV.jsonl.gz" + f = self.manifest_dir / "gigaspeech_cuts_DEV.jsonl.gz" logging.info(f"About to get DEV cuts from {f}") - return load_manifest(f) + return load_manifest_lazy(f) diff --git a/egs/librispeech/ASR/pruned_transducer_stateless3/librispeech.py b/egs/librispeech/ASR/pruned_transducer_stateless3/librispeech.py index 00b7c8334..6dba8e9fe 100644 --- a/egs/librispeech/ASR/pruned_transducer_stateless3/librispeech.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless3/librispeech.py @@ -18,7 +18,7 @@ import logging from pathlib import Path -from lhotse import CutSet, load_manifest +from lhotse import CutSet, load_manifest_lazy class LibriSpeech: @@ -28,47 +28,47 @@ class LibriSpeech: manifest_dir: It is expected to contain the following files:: - - cuts_dev-clean.json.gz - - cuts_dev-other.json.gz - - cuts_test-clean.json.gz - - cuts_test-other.json.gz - - cuts_train-clean-100.json.gz - - cuts_train-clean-360.json.gz - - cuts_train-other-500.json.gz + - librispeech_cuts_dev-clean.jsonl.gz + - librispeech_cuts_dev-other.jsonl.gz + - librispeech_cuts_test-clean.jsonl.gz + - librispeech_cuts_test-other.jsonl.gz + - librispeech_cuts_train-clean-100.jsonl.gz + - librispeech_cuts_train-clean-360.jsonl.gz + - librispeech_cuts_train-other-500.jsonl.gz """ self.manifest_dir = Path(manifest_dir) def train_clean_100_cuts(self) -> CutSet: - f = self.manifest_dir / "cuts_train-clean-100.json.gz" + f = self.manifest_dir / "librispeech_cuts_train-clean-100.jsonl.gz" logging.info(f"About to get train-clean-100 cuts from {f}") - return load_manifest(f) + return load_manifest_lazy(f) def train_clean_360_cuts(self) -> CutSet: - f = self.manifest_dir / "cuts_train-clean-360.json.gz" + f = self.manifest_dir / "librispeech_cuts_train-clean-360.jsonl.gz" logging.info(f"About to get train-clean-360 cuts from {f}") - return load_manifest(f) + return load_manifest_lazy(f) def train_other_500_cuts(self) -> CutSet: - f = self.manifest_dir / "cuts_train-other-500.json.gz" + f = self.manifest_dir / "librispeech_cuts_train-other-500.jsonl.gz" logging.info(f"About to get train-other-500 cuts from {f}") - return load_manifest(f) + return load_manifest_lazy(f) def test_clean_cuts(self) -> CutSet: - f = self.manifest_dir / "cuts_test-clean.json.gz" + f = self.manifest_dir / "librispeech_cuts_test-clean.jsonl.gz" logging.info(f"About to get test-clean cuts from {f}") - return load_manifest(f) + return load_manifest_lazy(f) def test_other_cuts(self) -> CutSet: - f = self.manifest_dir / "cuts_test-other.json.gz" + f = self.manifest_dir / "librispeech_cuts_test-other.jsonl.gz" logging.info(f"About to get test-other cuts from {f}") - return load_manifest(f) + return load_manifest_lazy(f) def dev_clean_cuts(self) -> CutSet: - f = self.manifest_dir / "cuts_dev-clean.json.gz" + f = self.manifest_dir / "librispeech_cuts_dev-clean.jsonl.gz" logging.info(f"About to get dev-clean cuts from {f}") - return load_manifest(f) + return load_manifest_lazy(f) def dev_other_cuts(self) -> CutSet: - f = self.manifest_dir / "cuts_dev-other.json.gz" + f = self.manifest_dir / "librispeech_cuts_dev-other.jsonl.gz" logging.info(f"About to get dev-other cuts from {f}") - return load_manifest(f) + return load_manifest_lazy(f) diff --git a/egs/librispeech/ASR/pruned_transducer_stateless3/train.py b/egs/librispeech/ASR/pruned_transducer_stateless3/train.py index f5a25a226..92eae78d1 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless3/train.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless3/train.py @@ -546,7 +546,7 @@ def compute_loss( warmup: float = 1.0, ) -> Tuple[Tensor, MetricsTracker]: """ - Compute CTC loss given the model and its inputs. + Compute RNN-T loss given the model and its inputs. Args: params: @@ -799,7 +799,7 @@ def train_one_epoch( f"tot_loss[{tot_loss}], " f"libri_tot_loss[{libri_tot_loss}], " f"giga_tot_loss[{giga_tot_loss}], " - f"batch size: {batch_size}" + f"batch size: {batch_size}, " f"lr: {cur_lr:.2e}" ) @@ -969,7 +969,7 @@ def run(rank, world_size, args): if args.enable_musan: cuts_musan = load_manifest( - Path(args.manifest_dir) / "cuts_musan.json.gz" + Path(args.manifest_dir) / "musan_cuts.jsonl.gz" ) else: cuts_musan = None @@ -978,14 +978,12 @@ def run(rank, world_size, args): train_dl = asr_datamodule.train_dataloaders( train_cuts, - dynamic_bucketing=False, on_the_fly_feats=False, cuts_musan=cuts_musan, ) giga_train_dl = asr_datamodule.train_dataloaders( train_giga_cuts, - dynamic_bucketing=True, on_the_fly_feats=False, cuts_musan=cuts_musan, ) diff --git a/egs/librispeech/ASR/pruned_transducer_stateless4/decode.py b/egs/librispeech/ASR/pruned_transducer_stateless4/decode.py index d1af63aaa..70afc3ea3 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless4/decode.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless4/decode.py @@ -128,7 +128,7 @@ def get_parser(): parser.add_argument( "--use-averaged-model", type=str2bool, - default=False, + default=True, help="Whether to load averaged model. Currently it only supports " "using --epoch. If True, it would decode with the averaged model " "over the epoch range from `epoch-avg` (excluded) to `epoch`." diff --git a/egs/librispeech/ASR/pruned_transducer_stateless4/export.py b/egs/librispeech/ASR/pruned_transducer_stateless4/export.py deleted file mode 120000 index 19c56a722..000000000 --- a/egs/librispeech/ASR/pruned_transducer_stateless4/export.py +++ /dev/null @@ -1 +0,0 @@ -../pruned_transducer_stateless2/export.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless4/export.py b/egs/librispeech/ASR/pruned_transducer_stateless4/export.py new file mode 100755 index 000000000..8f64b5d64 --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless4/export.py @@ -0,0 +1,273 @@ +#!/usr/bin/env python3 +# +# Copyright 2021 Xiaomi Corporation (Author: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# This script converts several saved checkpoints +# to a single one using model averaging. +""" +Usage: +./pruned_transducer_stateless4/export.py \ + --exp-dir ./pruned_transducer_stateless4/exp \ + --bpe-model data/lang_bpe_500/bpe.model \ + --epoch 20 \ + --avg 10 + +It will generate a file exp_dir/pretrained.pt + +To use the generated file with `pruned_transducer_stateless4/decode.py`, +you can do: + + cd /path/to/exp_dir + ln -s pretrained.pt epoch-9999.pt + + cd /path/to/egs/librispeech/ASR + ./pruned_transducer_stateless4/decode.py \ + --exp-dir ./pruned_transducer_stateless4/exp \ + --epoch 9999 \ + --avg 1 \ + --max-duration 100 \ + --bpe-model data/lang_bpe_500/bpe.model \ + --use-averaged-model False +""" + +import argparse +import logging +from pathlib import Path + +import sentencepiece as spm +import torch +from train import get_params, get_transducer_model + +from icefall.checkpoint import ( + average_checkpoints, + average_checkpoints_with_averaged_model, + find_checkpoints, + load_checkpoint, +) +from icefall.utils import str2bool + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--epoch", + type=int, + default=28, + help="""It specifies the checkpoint to use for averaging. + Note: Epoch counts from 0. + You can specify --avg to use more checkpoints for model averaging.""", + ) + + parser.add_argument( + "--iter", + type=int, + default=0, + help="""If positive, --epoch is ignored and it + will use the checkpoint exp_dir/checkpoint-iter.pt. + You can specify --avg to use more checkpoints for model averaging. + """, + ) + + parser.add_argument( + "--avg", + type=int, + default=15, + help="Number of checkpoints to average. Automatically select " + "consecutive checkpoints before the checkpoint specified by " + "'--epoch' and '--iter'", + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="pruned_transducer_stateless2/exp", + help="""It specifies the directory where all training related + files, e.g., checkpoints, log, etc, are saved + """, + ) + + parser.add_argument( + "--bpe-model", + type=str, + default="data/lang_bpe_500/bpe.model", + help="Path to the BPE model", + ) + + parser.add_argument( + "--jit", + type=str2bool, + default=False, + help="""True to save a model after applying torch.jit.script. + """, + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; " + "2 means tri-gram", + ) + + parser.add_argument( + "--use-averaged-model", + type=str2bool, + default=True, + help="Whether to load averaged model. Currently it only supports " + "using --epoch. If True, it would decode with the averaged model " + "over the epoch range from `epoch-avg` (excluded) to `epoch`." + "Actually only the models with epoch number of `epoch-avg` and " + "`epoch` are loaded for averaging. ", + ) + + return parser + + +def main(): + args = get_parser().parse_args() + args.exp_dir = Path(args.exp_dir) + + params = get_params() + params.update(vars(args)) + + device = torch.device("cpu") + + logging.info(f"device: {device}") + + sp = spm.SentencePieceProcessor() + sp.load(params.bpe_model) + + # is defined in local/train_bpe_model.py + params.blank_id = sp.piece_to_id("") + params.vocab_size = sp.get_piece_size() + + logging.info(params) + + logging.info("About to create model") + model = get_transducer_model(params) + + model.to(device) + + if not params.use_averaged_model: + if params.iter > 0: + filenames = find_checkpoints( + params.exp_dir, iteration=-params.iter + )[: params.avg] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + elif params.avg == 1: + load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) + else: + start = params.epoch - params.avg + 1 + filenames = [] + for i in range(start, params.epoch + 1): + if i >= 1: + filenames.append(f"{params.exp_dir}/epoch-{i}.pt") + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + else: + if params.iter > 0: + filenames = find_checkpoints( + params.exp_dir, iteration=-params.iter + )[: params.avg + 1] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg + 1: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + filename_start = filenames[-1] + filename_end = filenames[0] + logging.info( + "Calculating the averaged model over iteration checkpoints" + f" from {filename_start} (excluded) to {filename_end}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + else: + assert params.avg > 0, params.avg + start = params.epoch - params.avg + assert start >= 1, start + filename_start = f"{params.exp_dir}/epoch-{start}.pt" + filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt" + logging.info( + f"Calculating the averaged model over epoch range from " + f"{start} (excluded) to {params.epoch}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + + model.eval() + + if params.jit: + # We won't use the forward() method of the model in C++, so just ignore + # it here. + # Otherwise, one of its arguments is a ragged tensor and is not + # torch scriptabe. + model.__class__.forward = torch.jit.ignore(model.__class__.forward) + logging.info("Using torch.jit.script") + model = torch.jit.script(model) + filename = params.exp_dir / "cpu_jit.pt" + model.save(str(filename)) + logging.info(f"Saved to {filename}") + else: + logging.info("Not using torch.jit.script") + # Save it using a format so that it can be loaded + # by :func:`load_checkpoint` + filename = params.exp_dir / "pretrained.pt" + torch.save({"model": model.state_dict()}, str(filename)) + logging.info(f"Saved to {filename}") + + +if __name__ == "__main__": + formatter = ( + "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + ) + + logging.basicConfig(format=formatter, level=logging.INFO) + main() diff --git a/egs/librispeech/ASR/pruned_transducer_stateless4/train.py b/egs/librispeech/ASR/pruned_transducer_stateless4/train.py index ca7207122..48c0e683d 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless4/train.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless4/train.py @@ -536,7 +536,7 @@ def compute_loss( warmup: float = 1.0, ) -> Tuple[Tensor, MetricsTracker]: """ - Compute CTC loss given the model and its inputs. + Compute RNN-T loss given the model and its inputs. Args: params: diff --git a/egs/librispeech/ASR/pruned_transducer_stateless5/train.py b/egs/librispeech/ASR/pruned_transducer_stateless5/train.py index 7252ee436..e77eb19ff 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless5/train.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless5/train.py @@ -577,7 +577,7 @@ def compute_loss( warmup: float = 1.0, ) -> Tuple[Tensor, MetricsTracker]: """ - Compute CTC loss given the model and its inputs. + Compute RNN-T loss given the model and its inputs. Args: params: diff --git a/egs/librispeech/ASR/pruned_transducer_stateless6/__init__.py b/egs/librispeech/ASR/pruned_transducer_stateless6/__init__.py new file mode 120000 index 000000000..b24e5e357 --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless6/__init__.py @@ -0,0 +1 @@ +../pruned_transducer_stateless2/__init__.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless6/asr_datamodule.py b/egs/librispeech/ASR/pruned_transducer_stateless6/asr_datamodule.py new file mode 120000 index 000000000..a074d6085 --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless6/asr_datamodule.py @@ -0,0 +1 @@ +../pruned_transducer_stateless2/asr_datamodule.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless6/beam_search.py b/egs/librispeech/ASR/pruned_transducer_stateless6/beam_search.py new file mode 120000 index 000000000..8554e44cc --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless6/beam_search.py @@ -0,0 +1 @@ +../pruned_transducer_stateless2/beam_search.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless6/conformer.py b/egs/librispeech/ASR/pruned_transducer_stateless6/conformer.py new file mode 100644 index 000000000..a0781da1f --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless6/conformer.py @@ -0,0 +1,1064 @@ +#!/usr/bin/env python3 +# Copyright (c) 2021 University of Chinese Academy of Sciences (author: Han Zhu) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy +import math +import warnings +from typing import List, Optional, Tuple + +import torch +from encoder_interface import EncoderInterface +from scaling import ( + ActivationBalancer, + BasicNorm, + DoubleSwish, + ScaledConv1d, + ScaledConv2d, + ScaledLinear, +) +from torch import Tensor, nn + +from icefall.utils import make_pad_mask + + +class Conformer(EncoderInterface): + """ + Args: + num_features (int): Number of input features + subsampling_factor (int): subsampling factor of encoder (the convolution layers before transformers) + d_model (int): attention dimension, also the output dimension + nhead (int): number of head + dim_feedforward (int): feedforward dimention + num_encoder_layers (int): number of encoder layers + dropout (float): dropout rate + layer_dropout (float): layer-dropout rate. + cnn_module_kernel (int): Kernel size of convolution module + vgg_frontend (bool): whether to use vgg frontend. + """ + + def __init__( + self, + num_features: int, + subsampling_factor: int = 4, + d_model: int = 256, + nhead: int = 4, + dim_feedforward: int = 2048, + num_encoder_layers: int = 12, + dropout: float = 0.1, + layer_dropout: float = 0.075, + cnn_module_kernel: int = 31, + middle_output_layer: int = None, # 0-based layer index + ) -> None: + super(Conformer, self).__init__() + + self.num_features = num_features + self.subsampling_factor = subsampling_factor + if subsampling_factor != 4: + raise NotImplementedError("Support only 'subsampling_factor=4'.") + + # self.encoder_embed converts the input of shape (N, T, num_features) + # to the shape (N, T//subsampling_factor, d_model). + # That is, it does two things simultaneously: + # (1) subsampling: T -> T//subsampling_factor + # (2) embedding: num_features -> d_model + self.encoder_embed = Conv2dSubsampling(num_features, d_model) + + self.encoder_pos = RelPositionalEncoding(d_model, dropout) + + encoder_layer = ConformerEncoderLayer( + d_model, + nhead, + dim_feedforward, + dropout, + layer_dropout, + cnn_module_kernel, + ) + + output_layers = [] + if middle_output_layer is not None: + assert ( + middle_output_layer >= 0 + and middle_output_layer < num_encoder_layers + ) + output_layers.append(middle_output_layer) + + # The last layer is always needed. + output_layers.append(num_encoder_layers - 1) + + self.encoder = ConformerEncoder( + encoder_layer, num_encoder_layers, output_layers=output_layers + ) + + def forward( + self, x: torch.Tensor, x_lens: torch.Tensor, warmup: float = 1.0 + ) -> Tuple[List[torch.Tensor], torch.Tensor]: + """ + Args: + x: + The input tensor. Its shape is (batch_size, seq_len, feature_dim). + x_lens: + A tensor of shape (batch_size,) containing the number of frames in + `x` before padding. + warmup: + A floating point value that gradually increases from 0 throughout + training; when it is >= 1.0 we are "fully warmed up". It is used + to turn modules on sequentially. + Returns: + Return a tuple containing 2 tensors: + - embeddings: its shape is (batch_size, output_seq_len, d_model) + - lengths, a tensor of shape (batch_size,) containing the number + of frames in `embeddings` before padding. + """ + x = self.encoder_embed(x) + x, pos_emb = self.encoder_pos(x) + x = x.permute(1, 0, 2) # (N, T, C) -> (T, N, C) + + # Caution: We assume the subsampling factor is 4! + + # lengths = ((x_lens - 1) // 2 - 1) // 2 # issue an warning + # + # Note: rounding_mode in torch.div() is available only in torch >= 1.8.0 + lengths = (((x_lens - 1) >> 1) - 1) >> 1 + + assert x.size(0) == lengths.max().item() + mask = make_pad_mask(lengths) + + layer_results = self.encoder( + x, pos_emb, src_key_padding_mask=mask, warmup=warmup + ) # (T, N, C) + + return layer_results, lengths + + +class ConformerEncoderLayer(nn.Module): + """ + ConformerEncoderLayer is made up of self-attn, feedforward and convolution networks. + See: "Conformer: Convolution-augmented Transformer for Speech Recognition" + + Args: + d_model: the number of expected features in the input (required). + nhead: the number of heads in the multiheadattention models (required). + dim_feedforward: the dimension of the feedforward network model (default=2048). + dropout: the dropout value (default=0.1). + cnn_module_kernel (int): Kernel size of convolution module. + + Examples:: + >>> encoder_layer = ConformerEncoderLayer(d_model=512, nhead=8) + >>> src = torch.rand(10, 32, 512) + >>> pos_emb = torch.rand(32, 19, 512) + >>> out = encoder_layer(src, pos_emb) + """ + + def __init__( + self, + d_model: int, + nhead: int, + dim_feedforward: int = 2048, + dropout: float = 0.1, + layer_dropout: float = 0.075, + cnn_module_kernel: int = 31, + ) -> None: + super(ConformerEncoderLayer, self).__init__() + + self.layer_dropout = layer_dropout + + self.d_model = d_model + + self.self_attn = RelPositionMultiheadAttention( + d_model, nhead, dropout=0.0 + ) + + self.feed_forward = nn.Sequential( + ScaledLinear(d_model, dim_feedforward), + ActivationBalancer(channel_dim=-1), + DoubleSwish(), + nn.Dropout(dropout), + ScaledLinear(dim_feedforward, d_model, initial_scale=0.25), + ) + + self.feed_forward_macaron = nn.Sequential( + ScaledLinear(d_model, dim_feedforward), + ActivationBalancer(channel_dim=-1), + DoubleSwish(), + nn.Dropout(dropout), + ScaledLinear(dim_feedforward, d_model, initial_scale=0.25), + ) + + self.conv_module = ConvolutionModule(d_model, cnn_module_kernel) + + self.norm_final = BasicNorm(d_model) + + # try to ensure the output is close to zero-mean (or at least, zero-median). + self.balancer = ActivationBalancer( + channel_dim=-1, min_positive=0.45, max_positive=0.55, max_abs=6.0 + ) + + self.dropout = nn.Dropout(dropout) + + def forward( + self, + src: Tensor, + pos_emb: Tensor, + src_mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + warmup: float = 1.0, + ) -> Tensor: + """ + Pass the input through the encoder layer. + + Args: + src: the sequence to the encoder layer (required). + pos_emb: Positional embedding tensor (required). + src_mask: the mask for the src sequence (optional). + src_key_padding_mask: the mask for the src keys per batch (optional). + warmup: controls selective bypass of of layers; if < 1.0, we will + bypass layers more frequently. + + Shape: + src: (S, N, E). + pos_emb: (N, 2*S-1, E) + src_mask: (S, S). + src_key_padding_mask: (N, S). + S is the source sequence length, N is the batch size, E is the feature number + """ + src_orig = src + + warmup_scale = min(0.1 + warmup, 1.0) + # alpha = 1.0 means fully use this encoder layer, 0.0 would mean + # completely bypass it. + if self.training: + alpha = ( + warmup_scale + if torch.rand(()).item() <= (1.0 - self.layer_dropout) + else 0.1 + ) + else: + alpha = 1.0 + + # macaron style feed forward module + src = src + self.dropout(self.feed_forward_macaron(src)) + + # multi-headed self-attention module + src_att = self.self_attn( + src, + src, + src, + pos_emb=pos_emb, + attn_mask=src_mask, + key_padding_mask=src_key_padding_mask, + )[0] + src = src + self.dropout(src_att) + + # convolution module + src = src + self.dropout(self.conv_module(src)) + + # feed forward module + src = src + self.dropout(self.feed_forward(src)) + + src = self.norm_final(self.balancer(src)) + + if alpha != 1.0: + src = alpha * src + (1 - alpha) * src_orig + + return src + + +class ConformerEncoder(nn.Module): + r"""ConformerEncoder is a stack of N encoder layers + + Args: + encoder_layer: an instance of the ConformerEncoderLayer() class (required). + num_layers: the number of sub-encoder-layers in the encoder (required). + + Examples:: + >>> encoder_layer = ConformerEncoderLayer(d_model=512, nhead=8) + >>> conformer_encoder = ConformerEncoder(encoder_layer, num_layers=6) + >>> src = torch.rand(10, 32, 512) + >>> pos_emb = torch.rand(32, 19, 512) + >>> out = conformer_encoder(src, pos_emb) + """ + + def __init__( + self, + encoder_layer: nn.Module, + num_layers: int, + output_layers: List[int], + ) -> None: + super().__init__() + self.layers = nn.ModuleList( + [copy.deepcopy(encoder_layer) for i in range(num_layers)] + ) + self.num_layers = num_layers + self.output_layers = output_layers + + def forward( + self, + src: Tensor, + pos_emb: Tensor, + mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + warmup: float = 1.0, + ) -> List[Tensor]: + r"""Pass the input through the encoder layers in turn. + + Args: + src: the sequence to the encoder (required). + pos_emb: Positional embedding tensor (required). + mask: the mask for the src sequence (optional). + src_key_padding_mask: the mask for the src keys per batch (optional). + + Shape: + src: (S, N, E). + pos_emb: (N, 2*S-1, E) + mask: (S, S). + src_key_padding_mask: (N, S). + S is the source sequence length, T is the target sequence length, N is the batch size, E is the feature number + + """ + output = src + + layer_results = [] + for i, mod in enumerate(self.layers): + output = mod( + output, + pos_emb, + src_mask=mask, + src_key_padding_mask=src_key_padding_mask, + warmup=warmup, + ) + if i in self.output_layers: + # (T, N, C) --> (N, T, C) + layer_results.append(output.permute(1, 0, 2)) + + return layer_results + + +class RelPositionalEncoding(torch.nn.Module): + """Relative positional encoding module. + + See : Appendix B in "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context" + Modified from https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/transformer/embedding.py + + Args: + d_model: Embedding dimension. + dropout_rate: Dropout rate. + max_len: Maximum input length. + + """ + + def __init__( + self, d_model: int, dropout_rate: float, max_len: int = 5000 + ) -> None: + """Construct an PositionalEncoding object.""" + super(RelPositionalEncoding, self).__init__() + self.d_model = d_model + self.dropout = torch.nn.Dropout(p=dropout_rate) + self.pe = None + self.extend_pe(torch.tensor(0.0).expand(1, max_len)) + + def extend_pe(self, x: Tensor) -> None: + """Reset the positional encodings.""" + if self.pe is not None: + # self.pe contains both positive and negative parts + # the length of self.pe is 2 * input_len - 1 + if self.pe.size(1) >= x.size(1) * 2 - 1: + # Note: TorchScript doesn't implement operator== for torch.Device + if self.pe.dtype != x.dtype or str(self.pe.device) != str( + x.device + ): + self.pe = self.pe.to(dtype=x.dtype, device=x.device) + return + # Suppose `i` means to the position of query vecotr and `j` means the + # position of key vector. We use position relative positions when keys + # are to the left (i>j) and negative relative positions otherwise (i Tuple[Tensor, Tensor]: + """Add positional encoding. + + Args: + x (torch.Tensor): Input tensor (batch, time, `*`). + + Returns: + torch.Tensor: Encoded tensor (batch, time, `*`). + torch.Tensor: Encoded tensor (batch, 2*time-1, `*`). + + """ + self.extend_pe(x) + pos_emb = self.pe[ + :, + self.pe.size(1) // 2 + - x.size(1) + + 1 : self.pe.size(1) // 2 # noqa E203 + + x.size(1), + ] + return self.dropout(x), self.dropout(pos_emb) + + +class RelPositionMultiheadAttention(nn.Module): + r"""Multi-Head Attention layer with relative position encoding + + See reference: "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context" + + Args: + embed_dim: total dimension of the model. + num_heads: parallel attention heads. + dropout: a Dropout layer on attn_output_weights. Default: 0.0. + + Examples:: + + >>> rel_pos_multihead_attn = RelPositionMultiheadAttention(embed_dim, num_heads) + >>> attn_output, attn_output_weights = multihead_attn(query, key, value, pos_emb) + """ + + def __init__( + self, + embed_dim: int, + num_heads: int, + dropout: float = 0.0, + ) -> None: + super(RelPositionMultiheadAttention, self).__init__() + self.embed_dim = embed_dim + self.num_heads = num_heads + self.dropout = dropout + self.head_dim = embed_dim // num_heads + assert ( + self.head_dim * num_heads == self.embed_dim + ), "embed_dim must be divisible by num_heads" + + self.in_proj = ScaledLinear(embed_dim, 3 * embed_dim, bias=True) + self.out_proj = ScaledLinear( + embed_dim, embed_dim, bias=True, initial_scale=0.25 + ) + + # linear transformation for positional encoding. + self.linear_pos = ScaledLinear(embed_dim, embed_dim, bias=False) + # these two learnable bias are used in matrix c and matrix d + # as described in "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context" Section 3.3 + self.pos_bias_u = nn.Parameter(torch.Tensor(num_heads, self.head_dim)) + self.pos_bias_v = nn.Parameter(torch.Tensor(num_heads, self.head_dim)) + self.pos_bias_u_scale = nn.Parameter(torch.zeros(()).detach()) + self.pos_bias_v_scale = nn.Parameter(torch.zeros(()).detach()) + self._reset_parameters() + + def _pos_bias_u(self): + return self.pos_bias_u * self.pos_bias_u_scale.exp() + + def _pos_bias_v(self): + return self.pos_bias_v * self.pos_bias_v_scale.exp() + + def _reset_parameters(self) -> None: + nn.init.normal_(self.pos_bias_u, std=0.01) + nn.init.normal_(self.pos_bias_v, std=0.01) + + def forward( + self, + query: Tensor, + key: Tensor, + value: Tensor, + pos_emb: Tensor, + key_padding_mask: Optional[Tensor] = None, + need_weights: bool = True, + attn_mask: Optional[Tensor] = None, + ) -> Tuple[Tensor, Optional[Tensor]]: + r""" + Args: + query, key, value: map a query and a set of key-value pairs to an output. + pos_emb: Positional embedding tensor + key_padding_mask: if provided, specified padding elements in the key will + be ignored by the attention. When given a binary mask and a value is True, + the corresponding value on the attention layer will be ignored. When given + a byte mask and a value is non-zero, the corresponding value on the attention + layer will be ignored + need_weights: output attn_output_weights. + attn_mask: 2D or 3D mask that prevents attention to certain positions. A 2D mask will be broadcasted for all + the batches while a 3D mask allows to specify a different mask for the entries of each batch. + + Shape: + - Inputs: + - query: :math:`(L, N, E)` where L is the target sequence length, N is the batch size, E is + the embedding dimension. + - key: :math:`(S, N, E)`, where S is the source sequence length, N is the batch size, E is + the embedding dimension. + - value: :math:`(S, N, E)` where S is the source sequence length, N is the batch size, E is + the embedding dimension. + - pos_emb: :math:`(N, 2*L-1, E)` where L is the target sequence length, N is the batch size, E is + the embedding dimension. + - key_padding_mask: :math:`(N, S)` where N is the batch size, S is the source sequence length. + If a ByteTensor is provided, the non-zero positions will be ignored while the position + with the zero positions will be unchanged. If a BoolTensor is provided, the positions with the + value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged. + - attn_mask: 2D mask :math:`(L, S)` where L is the target sequence length, S is the source sequence length. + 3D mask :math:`(N*num_heads, L, S)` where N is the batch size, L is the target sequence length, + S is the source sequence length. attn_mask ensure that position i is allowed to attend the unmasked + positions. If a ByteTensor is provided, the non-zero positions are not allowed to attend + while the zero positions will be unchanged. If a BoolTensor is provided, positions with ``True`` + is not allowed to attend while ``False`` values will be unchanged. If a FloatTensor + is provided, it will be added to the attention weight. + + - Outputs: + - attn_output: :math:`(L, N, E)` where L is the target sequence length, N is the batch size, + E is the embedding dimension. + - attn_output_weights: :math:`(N, L, S)` where N is the batch size, + L is the target sequence length, S is the source sequence length. + """ + return self.multi_head_attention_forward( + query, + key, + value, + pos_emb, + self.embed_dim, + self.num_heads, + self.in_proj.get_weight(), + self.in_proj.get_bias(), + self.dropout, + self.out_proj.get_weight(), + self.out_proj.get_bias(), + training=self.training, + key_padding_mask=key_padding_mask, + need_weights=need_weights, + attn_mask=attn_mask, + ) + + def rel_shift(self, x: Tensor) -> Tensor: + """Compute relative positional encoding. + + Args: + x: Input tensor (batch, head, time1, 2*time1-1). + time1 means the length of query vector. + + Returns: + Tensor: tensor of shape (batch, head, time1, time2) + (note: time2 has the same value as time1, but it is for + the key, while time1 is for the query). + """ + (batch_size, num_heads, time1, n) = x.shape + assert n == 2 * time1 - 1 + # Note: TorchScript requires explicit arg for stride() + batch_stride = x.stride(0) + head_stride = x.stride(1) + time1_stride = x.stride(2) + n_stride = x.stride(3) + return x.as_strided( + (batch_size, num_heads, time1, time1), + (batch_stride, head_stride, time1_stride - n_stride, n_stride), + storage_offset=n_stride * (time1 - 1), + ) + + def multi_head_attention_forward( + self, + query: Tensor, + key: Tensor, + value: Tensor, + pos_emb: Tensor, + embed_dim_to_check: int, + num_heads: int, + in_proj_weight: Tensor, + in_proj_bias: Tensor, + dropout_p: float, + out_proj_weight: Tensor, + out_proj_bias: Tensor, + training: bool = True, + key_padding_mask: Optional[Tensor] = None, + need_weights: bool = True, + attn_mask: Optional[Tensor] = None, + ) -> Tuple[Tensor, Optional[Tensor]]: + r""" + Args: + query, key, value: map a query and a set of key-value pairs to an output. + pos_emb: Positional embedding tensor + embed_dim_to_check: total dimension of the model. + num_heads: parallel attention heads. + in_proj_weight, in_proj_bias: input projection weight and bias. + dropout_p: probability of an element to be zeroed. + out_proj_weight, out_proj_bias: the output projection weight and bias. + training: apply dropout if is ``True``. + key_padding_mask: if provided, specified padding elements in the key will + be ignored by the attention. This is an binary mask. When the value is True, + the corresponding value on the attention layer will be filled with -inf. + need_weights: output attn_output_weights. + attn_mask: 2D or 3D mask that prevents attention to certain positions. A 2D mask will be broadcasted for all + the batches while a 3D mask allows to specify a different mask for the entries of each batch. + + Shape: + Inputs: + - query: :math:`(L, N, E)` where L is the target sequence length, N is the batch size, E is + the embedding dimension. + - key: :math:`(S, N, E)`, where S is the source sequence length, N is the batch size, E is + the embedding dimension. + - value: :math:`(S, N, E)` where S is the source sequence length, N is the batch size, E is + the embedding dimension. + - pos_emb: :math:`(N, 2*L-1, E)` or :math:`(1, 2*L-1, E)` where L is the target sequence + length, N is the batch size, E is the embedding dimension. + - key_padding_mask: :math:`(N, S)` where N is the batch size, S is the source sequence length. + If a ByteTensor is provided, the non-zero positions will be ignored while the zero positions + will be unchanged. If a BoolTensor is provided, the positions with the + value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged. + - attn_mask: 2D mask :math:`(L, S)` where L is the target sequence length, S is the source sequence length. + 3D mask :math:`(N*num_heads, L, S)` where N is the batch size, L is the target sequence length, + S is the source sequence length. attn_mask ensures that position i is allowed to attend the unmasked + positions. If a ByteTensor is provided, the non-zero positions are not allowed to attend + while the zero positions will be unchanged. If a BoolTensor is provided, positions with ``True`` + are not allowed to attend while ``False`` values will be unchanged. If a FloatTensor + is provided, it will be added to the attention weight. + + Outputs: + - attn_output: :math:`(L, N, E)` where L is the target sequence length, N is the batch size, + E is the embedding dimension. + - attn_output_weights: :math:`(N, L, S)` where N is the batch size, + L is the target sequence length, S is the source sequence length. + """ + + tgt_len, bsz, embed_dim = query.size() + assert embed_dim == embed_dim_to_check + assert key.size(0) == value.size(0) and key.size(1) == value.size(1) + + head_dim = embed_dim // num_heads + assert ( + head_dim * num_heads == embed_dim + ), "embed_dim must be divisible by num_heads" + + scaling = float(head_dim) ** -0.5 + + if torch.equal(query, key) and torch.equal(key, value): + # self-attention + q, k, v = nn.functional.linear( + query, in_proj_weight, in_proj_bias + ).chunk(3, dim=-1) + + elif torch.equal(key, value): + # encoder-decoder attention + # This is inline in_proj function with in_proj_weight and in_proj_bias + _b = in_proj_bias + _start = 0 + _end = embed_dim + _w = in_proj_weight[_start:_end, :] + if _b is not None: + _b = _b[_start:_end] + q = nn.functional.linear(query, _w, _b) + + # This is inline in_proj function with in_proj_weight and in_proj_bias + _b = in_proj_bias + _start = embed_dim + _end = None + _w = in_proj_weight[_start:, :] + if _b is not None: + _b = _b[_start:] + k, v = nn.functional.linear(key, _w, _b).chunk(2, dim=-1) + + else: + # This is inline in_proj function with in_proj_weight and in_proj_bias + _b = in_proj_bias + _start = 0 + _end = embed_dim + _w = in_proj_weight[_start:_end, :] + if _b is not None: + _b = _b[_start:_end] + q = nn.functional.linear(query, _w, _b) + + # This is inline in_proj function with in_proj_weight and in_proj_bias + _b = in_proj_bias + _start = embed_dim + _end = embed_dim * 2 + _w = in_proj_weight[_start:_end, :] + if _b is not None: + _b = _b[_start:_end] + k = nn.functional.linear(key, _w, _b) + + # This is inline in_proj function with in_proj_weight and in_proj_bias + _b = in_proj_bias + _start = embed_dim * 2 + _end = None + _w = in_proj_weight[_start:, :] + if _b is not None: + _b = _b[_start:] + v = nn.functional.linear(value, _w, _b) + + if attn_mask is not None: + assert ( + attn_mask.dtype == torch.float32 + or attn_mask.dtype == torch.float64 + or attn_mask.dtype == torch.float16 + or attn_mask.dtype == torch.uint8 + or attn_mask.dtype == torch.bool + ), "Only float, byte, and bool types are supported for attn_mask, not {}".format( + attn_mask.dtype + ) + if attn_mask.dtype == torch.uint8: + warnings.warn( + "Byte tensor for attn_mask is deprecated. Use bool tensor instead." + ) + attn_mask = attn_mask.to(torch.bool) + + if attn_mask.dim() == 2: + attn_mask = attn_mask.unsqueeze(0) + if list(attn_mask.size()) != [1, query.size(0), key.size(0)]: + raise RuntimeError( + "The size of the 2D attn_mask is not correct." + ) + elif attn_mask.dim() == 3: + if list(attn_mask.size()) != [ + bsz * num_heads, + query.size(0), + key.size(0), + ]: + raise RuntimeError( + "The size of the 3D attn_mask is not correct." + ) + else: + raise RuntimeError( + "attn_mask's dimension {} is not supported".format( + attn_mask.dim() + ) + ) + # attn_mask's dim is 3 now. + + # convert ByteTensor key_padding_mask to bool + if ( + key_padding_mask is not None + and key_padding_mask.dtype == torch.uint8 + ): + warnings.warn( + "Byte tensor for key_padding_mask is deprecated. Use bool tensor instead." + ) + key_padding_mask = key_padding_mask.to(torch.bool) + + q = (q * scaling).contiguous().view(tgt_len, bsz, num_heads, head_dim) + k = k.contiguous().view(-1, bsz, num_heads, head_dim) + v = v.contiguous().view(-1, bsz * num_heads, head_dim).transpose(0, 1) + + src_len = k.size(0) + + if key_padding_mask is not None: + assert key_padding_mask.size(0) == bsz, "{} == {}".format( + key_padding_mask.size(0), bsz + ) + assert key_padding_mask.size(1) == src_len, "{} == {}".format( + key_padding_mask.size(1), src_len + ) + + q = q.transpose(0, 1) # (batch, time1, head, d_k) + + pos_emb_bsz = pos_emb.size(0) + assert pos_emb_bsz in (1, bsz) # actually it is 1 + p = self.linear_pos(pos_emb).view(pos_emb_bsz, -1, num_heads, head_dim) + p = p.transpose(1, 2) # (batch, head, 2*time1-1, d_k) + + q_with_bias_u = (q + self._pos_bias_u()).transpose( + 1, 2 + ) # (batch, head, time1, d_k) + + q_with_bias_v = (q + self._pos_bias_v()).transpose( + 1, 2 + ) # (batch, head, time1, d_k) + + # compute attention score + # first compute matrix a and matrix c + # as described in "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context" Section 3.3 + k = k.permute(1, 2, 3, 0) # (batch, head, d_k, time2) + matrix_ac = torch.matmul( + q_with_bias_u, k + ) # (batch, head, time1, time2) + + # compute matrix b and matrix d + matrix_bd = torch.matmul( + q_with_bias_v, p.transpose(-2, -1) + ) # (batch, head, time1, 2*time1-1) + matrix_bd = self.rel_shift(matrix_bd) + + attn_output_weights = ( + matrix_ac + matrix_bd + ) # (batch, head, time1, time2) + + attn_output_weights = attn_output_weights.view( + bsz * num_heads, tgt_len, -1 + ) + + assert list(attn_output_weights.size()) == [ + bsz * num_heads, + tgt_len, + src_len, + ] + + if attn_mask is not None: + if attn_mask.dtype == torch.bool: + attn_output_weights.masked_fill_(attn_mask, float("-inf")) + else: + attn_output_weights += attn_mask + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights.view( + bsz, num_heads, tgt_len, src_len + ) + attn_output_weights = attn_output_weights.masked_fill( + key_padding_mask.unsqueeze(1).unsqueeze(2), + float("-inf"), + ) + attn_output_weights = attn_output_weights.view( + bsz * num_heads, tgt_len, src_len + ) + + attn_output_weights = nn.functional.softmax(attn_output_weights, dim=-1) + attn_output_weights = nn.functional.dropout( + attn_output_weights, p=dropout_p, training=training + ) + + attn_output = torch.bmm(attn_output_weights, v) + assert list(attn_output.size()) == [bsz * num_heads, tgt_len, head_dim] + attn_output = ( + attn_output.transpose(0, 1) + .contiguous() + .view(tgt_len, bsz, embed_dim) + ) + attn_output = nn.functional.linear( + attn_output, out_proj_weight, out_proj_bias + ) + + if need_weights: + # average attention weights over heads + attn_output_weights = attn_output_weights.view( + bsz, num_heads, tgt_len, src_len + ) + return attn_output, attn_output_weights.sum(dim=1) / num_heads + else: + return attn_output, None + + +class ConvolutionModule(nn.Module): + """ConvolutionModule in Conformer model. + Modified from https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/conformer/convolution.py + + Args: + channels (int): The number of channels of conv layers. + kernel_size (int): Kernerl size of conv layers. + bias (bool): Whether to use bias in conv layers (default=True). + + """ + + def __init__( + self, channels: int, kernel_size: int, bias: bool = True + ) -> None: + """Construct an ConvolutionModule object.""" + super(ConvolutionModule, self).__init__() + # kernerl_size should be a odd number for 'SAME' padding + assert (kernel_size - 1) % 2 == 0 + + self.pointwise_conv1 = ScaledConv1d( + channels, + 2 * channels, + kernel_size=1, + stride=1, + padding=0, + bias=bias, + ) + + # after pointwise_conv1 we put x through a gated linear unit (nn.functional.glu). + # For most layers the normal rms value of channels of x seems to be in the range 1 to 4, + # but sometimes, for some reason, for layer 0 the rms ends up being very large, + # between 50 and 100 for different channels. This will cause very peaky and + # sparse derivatives for the sigmoid gating function, which will tend to make + # the loss function not learn effectively. (for most layers the average absolute values + # are in the range 0.5..9.0, and the average p(x>0), i.e. positive proportion, + # at the output of pointwise_conv1.output is around 0.35 to 0.45 for different + # layers, which likely breaks down as 0.5 for the "linear" half and + # 0.2 to 0.3 for the part that goes into the sigmoid. The idea is that if we + # constrain the rms values to a reasonable range via a constraint of max_abs=10.0, + # it will be in a better position to start learning something, i.e. to latch onto + # the correct range. + self.deriv_balancer1 = ActivationBalancer( + channel_dim=1, max_abs=10.0, min_positive=0.05, max_positive=1.0 + ) + + self.depthwise_conv = ScaledConv1d( + channels, + channels, + kernel_size, + stride=1, + padding=(kernel_size - 1) // 2, + groups=channels, + bias=bias, + ) + + self.deriv_balancer2 = ActivationBalancer( + channel_dim=1, min_positive=0.05, max_positive=1.0 + ) + + self.activation = DoubleSwish() + + self.pointwise_conv2 = ScaledConv1d( + channels, + channels, + kernel_size=1, + stride=1, + padding=0, + bias=bias, + initial_scale=0.25, + ) + + def forward(self, x: Tensor) -> Tensor: + """Compute convolution module. + + Args: + x: Input tensor (#time, batch, channels). + + Returns: + Tensor: Output tensor (#time, batch, channels). + + """ + # exchange the temporal dimension and the feature dimension + x = x.permute(1, 2, 0) # (#batch, channels, time). + + # GLU mechanism + x = self.pointwise_conv1(x) # (batch, 2*channels, time) + + x = self.deriv_balancer1(x) + x = nn.functional.glu(x, dim=1) # (batch, channels, time) + + # 1D Depthwise Conv + x = self.depthwise_conv(x) + + x = self.deriv_balancer2(x) + x = self.activation(x) + + x = self.pointwise_conv2(x) # (batch, channel, time) + + return x.permute(2, 0, 1) + + +class Conv2dSubsampling(nn.Module): + """Convolutional 2D subsampling (to 1/4 length). + + Convert an input of shape (N, T, idim) to an output + with shape (N, T', odim), where + T' = ((T-1)//2 - 1)//2, which approximates T' == T//4 + + It is based on + https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/transformer/subsampling.py # noqa + """ + + def __init__( + self, + in_channels: int, + out_channels: int, + layer1_channels: int = 8, + layer2_channels: int = 32, + layer3_channels: int = 128, + ) -> None: + """ + Args: + in_channels: + Number of channels in. The input shape is (N, T, in_channels). + Caution: It requires: T >=7, in_channels >=7 + out_channels + Output dim. The output shape is (N, ((T-1)//2 - 1)//2, out_channels) + layer1_channels: + Number of channels in layer1 + layer1_channels: + Number of channels in layer2 + """ + assert in_channels >= 7 + super().__init__() + + self.conv = nn.Sequential( + ScaledConv2d( + in_channels=1, + out_channels=layer1_channels, + kernel_size=3, + padding=1, + ), + ActivationBalancer(channel_dim=1), + DoubleSwish(), + ScaledConv2d( + in_channels=layer1_channels, + out_channels=layer2_channels, + kernel_size=3, + stride=2, + ), + ActivationBalancer(channel_dim=1), + DoubleSwish(), + ScaledConv2d( + in_channels=layer2_channels, + out_channels=layer3_channels, + kernel_size=3, + stride=2, + ), + ActivationBalancer(channel_dim=1), + DoubleSwish(), + ) + self.out = ScaledLinear( + layer3_channels * (((in_channels - 1) // 2 - 1) // 2), out_channels + ) + # set learn_eps=False because out_norm is preceded by `out`, and `out` + # itself has learned scale, so the extra degree of freedom is not + # needed. + self.out_norm = BasicNorm(out_channels, learn_eps=False) + # constrain median of output to be close to zero. + self.out_balancer = ActivationBalancer( + channel_dim=-1, min_positive=0.45, max_positive=0.55 + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Subsample x. + + Args: + x: + Its shape is (N, T, idim). + + Returns: + Return a tensor of shape (N, ((T-1)//2 - 1)//2, odim) + """ + # On entry, x is (N, T, idim) + x = x.unsqueeze(1) # (N, T, idim) -> (N, 1, T, idim) i.e., (N, C, H, W) + x = self.conv(x) + # Now x is of shape (N, odim, ((T-1)//2 - 1)//2, ((idim-1)//2 - 1)//2) + b, c, t, f = x.size() + x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f)) + # Now x is of shape (N, ((T-1)//2 - 1))//2, odim) + x = self.out_norm(x) + x = self.out_balancer(x) + return x + + +if __name__ == "__main__": + feature_dim = 50 + c = Conformer(num_features=feature_dim, d_model=128, nhead=4) + batch_size = 5 + seq_len = 20 + # Just make sure the forward pass runs. + f = c( + torch.randn(batch_size, seq_len, feature_dim), + torch.full((batch_size,), seq_len, dtype=torch.int64), + warmup=0.5, + ) diff --git a/egs/librispeech/ASR/pruned_transducer_stateless6/decode.py b/egs/librispeech/ASR/pruned_transducer_stateless6/decode.py new file mode 100755 index 000000000..4739a6526 --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless6/decode.py @@ -0,0 +1,634 @@ +#!/usr/bin/env python3 +# +# Copyright 2021-2022 Xiaomi Corporation (Author: Fangjun Kuang, +# Zengwei Yao) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Usage: +(1) greedy search +./pruned_transducer_stateless6/decode.py \ + --epoch 30 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless6/exp \ + --max-duration 600 \ + --decoding-method greedy_search + +(2) beam search (not recommended) +./pruned_transducer_stateless6/decode.py \ + --epoch 30 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless6/exp \ + --max-duration 600 \ + --decoding-method beam_search \ + --beam-size 4 + +(3) modified beam search +./pruned_transducer_stateless6/decode.py \ + --epoch 30 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless6/exp \ + --max-duration 600 \ + --decoding-method modified_beam_search \ + --beam-size 4 + +(4) fast beam search +./pruned_transducer_stateless6/decode.py \ + --epoch 30 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless6/exp \ + --max-duration 600 \ + --decoding-method fast_beam_search \ + --beam 4 \ + --max-contexts 4 \ + --max-states 8 +""" + + +import argparse +import logging +from collections import defaultdict +from pathlib import Path +from typing import Dict, List, Optional, Tuple + +import k2 +import sentencepiece as spm +import torch +import torch.nn as nn +from asr_datamodule import LibriSpeechAsrDataModule +from beam_search import ( + beam_search, + fast_beam_search_one_best, + greedy_search, + greedy_search_batch, + modified_beam_search, +) +from train import get_params, get_transducer_model + +from icefall.checkpoint import ( + average_checkpoints, + average_checkpoints_with_averaged_model, + find_checkpoints, + load_checkpoint, +) +from icefall.utils import ( + AttributeDict, + setup_logger, + store_transcripts, + str2bool, + write_error_stats, +) + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--epoch", + type=int, + default=30, + help="""It specifies the checkpoint to use for decoding. + Note: Epoch counts from 1. + You can specify --avg to use more checkpoints for model averaging.""", + ) + + parser.add_argument( + "--iter", + type=int, + default=0, + help="""If positive, --epoch is ignored and it + will use the checkpoint exp_dir/checkpoint-iter.pt. + You can specify --avg to use more checkpoints for model averaging. + """, + ) + + parser.add_argument( + "--avg", + type=int, + default=15, + help="Number of checkpoints to average. Automatically select " + "consecutive checkpoints before the checkpoint specified by " + "'--epoch' and '--iter'", + ) + + parser.add_argument( + "--use-averaged-model", + type=str2bool, + default=False, + help="Whether to load averaged model. Currently it only supports " + "using --epoch. If True, it would decode with the averaged model " + "over the epoch range from `epoch-avg` (excluded) to `epoch`." + "Actually only the models with epoch number of `epoch-avg` and " + "`epoch` are loaded for averaging. ", + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="pruned_transducer_stateless6/exp", + help="The experiment dir", + ) + + parser.add_argument( + "--bpe-model", + type=str, + default="data/lang_bpe_500/bpe.model", + help="Path to the BPE model", + ) + + parser.add_argument( + "--decoding-method", + type=str, + default="greedy_search", + help="""Possible values are: + - greedy_search + - beam_search + - modified_beam_search + - fast_beam_search + """, + ) + + parser.add_argument( + "--beam-size", + type=int, + default=4, + help="""An integer indicating how many candidates we will keep for each + frame. Used only when --decoding-method is beam_search or + modified_beam_search.""", + ) + + parser.add_argument( + "--beam", + type=float, + default=4, + help="""A floating point value to calculate the cutoff score during beam + search (i.e., `cutoff = max-score - beam`), which is the same as the + `beam` in Kaldi. + Used only when --decoding-method is fast_beam_search""", + ) + + parser.add_argument( + "--max-contexts", + type=int, + default=4, + help="""Used only when --decoding-method is + fast_beam_search""", + ) + + parser.add_argument( + "--max-states", + type=int, + default=8, + help="""Used only when --decoding-method is + fast_beam_search""", + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; " + "2 means tri-gram", + ) + parser.add_argument( + "--max-sym-per-frame", + type=int, + default=1, + help="""Maximum number of symbols per frame. + Used only when --decoding_method is greedy_search""", + ) + + return parser + + +def decode_one_batch( + params: AttributeDict, + model: nn.Module, + sp: spm.SentencePieceProcessor, + batch: dict, + decoding_graph: Optional[k2.Fsa] = None, +) -> Dict[str, List[List[str]]]: + """Decode one batch and return the result in a dict. The dict has the + following format: + + - key: It indicates the setting used for decoding. For example, + if greedy_search is used, it would be "greedy_search" + If beam search with a beam size of 7 is used, it would be + "beam_7" + - value: It contains the decoding result. `len(value)` equals to + batch size. `value[i]` is the decoding result for the i-th + utterance in the given batch. + Args: + params: + It's the return value of :func:`get_params`. + model: + The neural model. + sp: + The BPE model. + batch: + It is the return value from iterating + `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation + for the format of the `batch`. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used + only when --decoding_method is fast_beam_search. + Returns: + Return the decoding result. See above description for the format of + the returned dict. + """ + device = next(model.parameters()).device + feature = batch["inputs"] + assert feature.ndim == 3 + + feature = feature.to(device) + # at entry, feature is (N, T, C) + + supervisions = batch["supervisions"] + feature_lens = supervisions["num_frames"].to(device) + + layer_results, encoder_out_lens = model.encoder( + x=feature, x_lens=feature_lens + ) + encoder_out = layer_results[-1] + hyps = [] + + if params.decoding_method == "fast_beam_search": + hyp_tokens = fast_beam_search_one_best( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) + elif ( + params.decoding_method == "greedy_search" + and params.max_sym_per_frame == 1 + ): + hyp_tokens = greedy_search_batch( + model=model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) + elif params.decoding_method == "modified_beam_search": + hyp_tokens = modified_beam_search( + model=model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam_size, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) + else: + batch_size = encoder_out.size(0) + + for i in range(batch_size): + # fmt: off + encoder_out_i = encoder_out[i:i + 1, :encoder_out_lens[i]] + # fmt: on + if params.decoding_method == "greedy_search": + hyp = greedy_search( + model=model, + encoder_out=encoder_out_i, + max_sym_per_frame=params.max_sym_per_frame, + ) + elif params.decoding_method == "beam_search": + hyp = beam_search( + model=model, + encoder_out=encoder_out_i, + beam=params.beam_size, + ) + else: + raise ValueError( + f"Unsupported decoding method: {params.decoding_method}" + ) + hyps.append(sp.decode(hyp).split()) + + if params.decoding_method == "greedy_search": + return {"greedy_search": hyps} + elif params.decoding_method == "fast_beam_search": + return { + ( + f"beam_{params.beam}_" + f"max_contexts_{params.max_contexts}_" + f"max_states_{params.max_states}" + ): hyps + } + else: + return {f"beam_size_{params.beam_size}": hyps} + + +def decode_dataset( + dl: torch.utils.data.DataLoader, + params: AttributeDict, + model: nn.Module, + sp: spm.SentencePieceProcessor, + decoding_graph: Optional[k2.Fsa] = None, +) -> Dict[str, List[Tuple[List[str], List[str]]]]: + """Decode dataset. + + Args: + dl: + PyTorch's dataloader containing the dataset to decode. + params: + It is returned by :func:`get_params`. + model: + The neural model. + sp: + The BPE model. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used + only when --decoding_method is fast_beam_search. + Returns: + Return a dict, whose key may be "greedy_search" if greedy search + is used, or it may be "beam_7" if beam size of 7 is used. + Its value is a list of tuples. Each tuple contains two elements: + The first is the reference transcript, and the second is the + predicted result. + """ + num_cuts = 0 + + try: + num_batches = len(dl) + except TypeError: + num_batches = "?" + + if params.decoding_method == "greedy_search": + log_interval = 50 + else: + log_interval = 10 + + results = defaultdict(list) + for batch_idx, batch in enumerate(dl): + texts = batch["supervisions"]["text"] + + hyps_dict = decode_one_batch( + params=params, + model=model, + sp=sp, + decoding_graph=decoding_graph, + batch=batch, + ) + + for name, hyps in hyps_dict.items(): + this_batch = [] + assert len(hyps) == len(texts) + for hyp_words, ref_text in zip(hyps, texts): + ref_words = ref_text.split() + this_batch.append((ref_words, hyp_words)) + + results[name].extend(this_batch) + + num_cuts += len(texts) + + if batch_idx % log_interval == 0: + batch_str = f"{batch_idx}/{num_batches}" + + logging.info( + f"batch {batch_str}, cuts processed until now is {num_cuts}" + ) + return results + + +def save_results( + params: AttributeDict, + test_set_name: str, + results_dict: Dict[str, List[Tuple[List[int], List[int]]]], +): + test_set_wers = dict() + for key, results in results_dict.items(): + recog_path = ( + params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt" + ) + store_transcripts(filename=recog_path, texts=results) + logging.info(f"The transcripts are stored in {recog_path}") + + # The following prints out WERs, per-word error statistics and aligned + # ref/hyp pairs. + errs_filename = ( + params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt" + ) + with open(errs_filename, "w") as f: + wer = write_error_stats( + f, f"{test_set_name}-{key}", results, enable_log=True + ) + test_set_wers[key] = wer + + logging.info("Wrote detailed error stats to {}".format(errs_filename)) + + test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1]) + errs_info = ( + params.res_dir + / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt" + ) + with open(errs_info, "w") as f: + print("settings\tWER", file=f) + for key, val in test_set_wers: + print("{}\t{}".format(key, val), file=f) + + s = "\nFor {}, WER of different settings are:\n".format(test_set_name) + note = "\tbest for {}".format(test_set_name) + for key, val in test_set_wers: + s += "{}\t{}{}\n".format(key, val, note) + note = "" + logging.info(s) + + +@torch.no_grad() +def main(): + parser = get_parser() + LibriSpeechAsrDataModule.add_arguments(parser) + args = parser.parse_args() + args.exp_dir = Path(args.exp_dir) + + params = get_params() + params.update(vars(args)) + + assert params.decoding_method in ( + "greedy_search", + "beam_search", + "fast_beam_search", + "modified_beam_search", + ) + params.res_dir = params.exp_dir / params.decoding_method + + if params.iter > 0: + params.suffix = f"iter-{params.iter}-avg-{params.avg}" + else: + params.suffix = f"epoch-{params.epoch}-avg-{params.avg}" + + if "fast_beam_search" in params.decoding_method: + params.suffix += f"-beam-{params.beam}" + params.suffix += f"-max-contexts-{params.max_contexts}" + params.suffix += f"-max-states-{params.max_states}" + elif "beam_search" in params.decoding_method: + params.suffix += ( + f"-{params.decoding_method}-beam-size-{params.beam_size}" + ) + else: + params.suffix += f"-context-{params.context_size}" + params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}" + + if params.use_averaged_model: + params.suffix += "-use-averaged-model" + + setup_logger(f"{params.res_dir}/log-decode-{params.suffix}") + logging.info("Decoding started") + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"Device: {device}") + + sp = spm.SentencePieceProcessor() + sp.load(params.bpe_model) + + # and are defined in local/train_bpe_model.py + params.blank_id = sp.piece_to_id("") + params.unk_id = sp.piece_to_id("") + params.vocab_size = sp.get_piece_size() + + logging.info(params) + + logging.info("About to create model") + model = get_transducer_model(params) + + if not params.use_averaged_model: + if params.iter > 0: + filenames = find_checkpoints( + params.exp_dir, iteration=-params.iter + )[: params.avg] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + elif params.avg == 1: + load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) + else: + start = params.epoch - params.avg + 1 + filenames = [] + for i in range(start, params.epoch + 1): + if i >= 1: + filenames.append(f"{params.exp_dir}/epoch-{i}.pt") + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + else: + if params.iter > 0: + filenames = find_checkpoints( + params.exp_dir, iteration=-params.iter + )[: params.avg + 1] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg + 1: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + filename_start = filenames[-1] + filename_end = filenames[0] + logging.info( + "Calculating the averaged model over iteration checkpoints" + f" from {filename_start} (excluded) to {filename_end}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + else: + assert params.avg > 0, params.avg + start = params.epoch - params.avg + assert start >= 1, start + filename_start = f"{params.exp_dir}/epoch-{start}.pt" + filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt" + logging.info( + f"Calculating the averaged model over epoch range from " + f"{start} (excluded) to {params.epoch}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + + model.to(device) + model.eval() + + if params.decoding_method == "fast_beam_search": + decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device) + else: + decoding_graph = None + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + librispeech = LibriSpeechAsrDataModule(args) + + test_clean_cuts = librispeech.test_clean_cuts() + test_other_cuts = librispeech.test_other_cuts() + + test_clean_dl = librispeech.test_dataloaders(test_clean_cuts) + test_other_dl = librispeech.test_dataloaders(test_other_cuts) + + test_sets = ["test-clean", "test-other"] + test_dl = [test_clean_dl, test_other_dl] + + for test_set, test_dl in zip(test_sets, test_dl): + results_dict = decode_dataset( + dl=test_dl, + params=params, + model=model, + sp=sp, + decoding_graph=decoding_graph, + ) + + save_results( + params=params, + test_set_name=test_set, + results_dict=results_dict, + ) + + logging.info("Done!") + + +if __name__ == "__main__": + main() diff --git a/egs/librispeech/ASR/pruned_transducer_stateless6/decoder.py b/egs/librispeech/ASR/pruned_transducer_stateless6/decoder.py new file mode 120000 index 000000000..0793c5709 --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless6/decoder.py @@ -0,0 +1 @@ +../pruned_transducer_stateless2/decoder.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless6/encoder_interface.py b/egs/librispeech/ASR/pruned_transducer_stateless6/encoder_interface.py new file mode 120000 index 000000000..b9aa0ae08 --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless6/encoder_interface.py @@ -0,0 +1 @@ +../pruned_transducer_stateless2/encoder_interface.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless6/export.py b/egs/librispeech/ASR/pruned_transducer_stateless6/export.py new file mode 100755 index 000000000..cff9c7377 --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless6/export.py @@ -0,0 +1,217 @@ +#!/usr/bin/env python3 +# +# Copyright 2021 Xiaomi Corporation (Author: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# This script converts several saved checkpoints +# to a single one using model averaging. +""" +Usage: +./pruned_transducer_stateless2/export.py \ + --exp-dir ./pruned_transducer_stateless2/exp \ + --bpe-model data/lang_bpe_500/bpe.model \ + --epoch 20 \ + --avg 10 + +It will generate a file exp_dir/pretrained.pt + +To use the generated file with `pruned_transducer_stateless2/decode.py`, +you can do: + + cd /path/to/exp_dir + ln -s pretrained.pt epoch-9999.pt + + cd /path/to/egs/librispeech/ASR + ./pruned_transducer_stateless2/decode.py \ + --exp-dir ./pruned_transducer_stateless2/exp \ + --epoch 9999 \ + --avg 1 \ + --max-duration 100 \ + --bpe-model data/lang_bpe_500/bpe.model +""" + +import argparse +import logging +from pathlib import Path + +import sentencepiece as spm +import torch +from train import get_params, get_transducer_model + +from icefall.checkpoint import ( + average_checkpoints, + find_checkpoints, + load_checkpoint, +) +from icefall.utils import str2bool + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--epoch", + type=int, + default=28, + help="""It specifies the checkpoint to use for averaging. + Note: Epoch counts from 0. + You can specify --avg to use more checkpoints for model averaging.""", + ) + + parser.add_argument( + "--iter", + type=int, + default=0, + help="""If positive, --epoch is ignored and it + will use the checkpoint exp_dir/checkpoint-iter.pt. + You can specify --avg to use more checkpoints for model averaging. + """, + ) + + parser.add_argument( + "--avg", + type=int, + default=15, + help="Number of checkpoints to average. Automatically select " + "consecutive checkpoints before the checkpoint specified by " + "'--epoch' and '--iter'", + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="pruned_transducer_stateless2/exp", + help="""It specifies the directory where all training related + files, e.g., checkpoints, log, etc, are saved + """, + ) + + parser.add_argument( + "--bpe-model", + type=str, + default="data/lang_bpe_500/bpe.model", + help="Path to the BPE model", + ) + + parser.add_argument( + "--jit", + type=str2bool, + default=False, + help="""True to save a model after applying torch.jit.script. + """, + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; " + "2 means tri-gram", + ) + + return parser + + +def main(): + args = get_parser().parse_args() + args.exp_dir = Path(args.exp_dir) + + params = get_params() + params.update(vars(args)) + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"device: {device}") + + sp = spm.SentencePieceProcessor() + sp.load(params.bpe_model) + + # is defined in local/train_bpe_model.py + params.blank_id = sp.piece_to_id("") + params.vocab_size = sp.get_piece_size() + + logging.info(params) + + logging.info("About to create model") + model = get_transducer_model(params) + + model.to(device) + + if params.iter > 0: + filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[ + : params.avg + ] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + elif params.avg == 1: + load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) + else: + start = params.epoch - params.avg + 1 + filenames = [] + for i in range(start, params.epoch + 1): + if start >= 0: + filenames.append(f"{params.exp_dir}/epoch-{i}.pt") + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + + model.eval() + + model.to("cpu") + model.eval() + + if params.jit: + # We won't use the forward() method of the model in C++, so just ignore + # it here. + # Otherwise, one of its arguments is a ragged tensor and is not + # torch scriptabe. + model.__class__.forward = torch.jit.ignore(model.__class__.forward) + logging.info("Using torch.jit.script") + model = torch.jit.script(model) + filename = params.exp_dir / "cpu_jit.pt" + model.save(str(filename)) + logging.info(f"Saved to {filename}") + else: + logging.info("Not using torch.jit.script") + # Save it using a format so that it can be loaded + # by :func:`load_checkpoint` + filename = params.exp_dir / "pretrained.pt" + torch.save({"model": model.state_dict()}, str(filename)) + logging.info(f"Saved to {filename}") + + +if __name__ == "__main__": + formatter = ( + "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + ) + + logging.basicConfig(format=formatter, level=logging.INFO) + main() diff --git a/egs/librispeech/ASR/pruned_transducer_stateless6/extract_codebook_index.py b/egs/librispeech/ASR/pruned_transducer_stateless6/extract_codebook_index.py new file mode 100755 index 000000000..c5c172ff2 --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless6/extract_codebook_index.py @@ -0,0 +1,80 @@ +#!/usr/bin/env python3 +# Copyright 2022 Xiaomi Corporation (Author: Liyong Guo) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import argparse +import os +from pathlib import Path + +import torch +from vq_utils import CodebookIndexExtractor +from asr_datamodule import LibriSpeechAsrDataModule +from hubert_xlarge import HubertXlargeFineTuned +from icefall.utils import AttributeDict + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + parser.add_argument( + "--exp-dir", + type=Path, + default="pruned_transducer_stateless6/exp/", + help="The experiment dir", + ) + + return parser + + +def get_world_size(): + warn_message = ( + "It's better to use GPU to extrac codebook indices" + "Please set with commonds like: export CUDA_VISIBLE_DEVICES=0,1,2,3" + ) + assert ( + torch.cuda.is_available() and "CUDA_VISIBLE_DEVICES" in os.environ + ), warn_message + world_size = len(os.environ["CUDA_VISIBLE_DEVICES"].split(",")) + assert world_size > 0, warn_message + return world_size + + +def main(): + world_size = get_world_size() + parser = get_parser() + LibriSpeechAsrDataModule.add_arguments(parser) + HubertXlargeFineTuned.add_arguments(parser) + CodebookIndexExtractor.add_arguments(parser) + + args = parser.parse_args() + params = AttributeDict() + params.update(vars(args)) + + # reset some parameters needed by hubert. + params.update(HubertXlargeFineTuned.get_params()) + params.device = torch.device("cuda", 0) + params.world_size = world_size + + extractor = CodebookIndexExtractor(params=params) + extractor.extract_and_save_embedding() + extractor.train_quantizer() + extractor.extract_codebook_indexes() + + +if __name__ == "__main__": + main() diff --git a/egs/librispeech/ASR/pruned_transducer_stateless6/hubert_decode.py b/egs/librispeech/ASR/pruned_transducer_stateless6/hubert_decode.py new file mode 100755 index 000000000..10b0e5edc --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless6/hubert_decode.py @@ -0,0 +1,205 @@ +#!/usr/bin/env python3 +# Copyright 2022 Xiaomi Corporation (Author: Liyong Guo) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import argparse +import logging +from collections import defaultdict +from pathlib import Path +from typing import Dict, List, Tuple + +import torch + +from asr_datamodule import LibriSpeechAsrDataModule +from hubert_xlarge import HubertXlargeFineTuned + +from icefall.utils import ( + AttributeDict, + setup_logger, + store_transcripts, + write_error_stats, +) + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--exp-dir", + type=Path, + default="pruned_transducer_stateless6/exp/", + help="The experiment dir", + ) + + return parser + + +def decode_dataset( + dl: torch.utils.data.DataLoader, + hubert_model: HubertXlargeFineTuned, + params: AttributeDict, +) -> Dict[str, List[Tuple[List[str], List[str]]]]: + """Decode dataset. + + Args: + dl: + PyTorch's dataloader containing the dataset to decode. + model: + The neural model. + + Returns: + Return a dict, whose key is decoding method "ctc_greedy_search". + Its value is a list of tuples. + Each tuple contains two elements: + The first is the reference transcript, and the second is the + predicted result. + """ + results = [] + + num_cuts = 0 + + try: + num_batches = len(dl) + except TypeError: + num_batches = "?" + + results = defaultdict(list) + for batch_idx, batch in enumerate(dl): + + hyps = hubert_model.ctc_greedy_search(batch) + + texts = batch["supervisions"]["text"] + assert len(hyps) == len(texts) + this_batch = [] + + for hyp_text, ref_text in zip(hyps, texts): + ref_words = ref_text.split() + hyp_words = hyp_text.split() + this_batch.append((ref_words, hyp_words)) + + results["ctc_greedy_search"].extend(this_batch) + + num_cuts += len(texts) + + if batch_idx % 20 == 0: + batch_str = f"{batch_idx}/{num_batches}" + + logging.info( + f"batch {batch_str}, cuts processed until now is {num_cuts}" + ) + return results + + +def save_results( + params: AttributeDict, + test_set_name: str, + results_dict: Dict[str, List[Tuple[List[int], List[int]]]], +): + test_set_wers = dict() + for key, results in results_dict.items(): + recog_path = params.res_dir / f"recogs-{test_set_name}-{key}.txt" + store_transcripts(filename=recog_path, texts=results) + + # The following prints out WERs, per-word error statistics and aligned + # ref/hyp pairs. + errs_filename = params.res_dir / f"errs-{test_set_name}-{key}.txt" + with open(errs_filename, "w") as f: + wer = write_error_stats( + f, f"{test_set_name}-{key}", results, enable_log=True + ) + test_set_wers[key] = wer + + logging.info( + "Wrote detailed error stats to {}".format(errs_filename) + ) + + test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1]) + errs_info = params.res_dir / f"wer-summary-{test_set_name}.txt" + with open(errs_info, "w") as f: + print("settings\tWER", file=f) + for key, val in test_set_wers: + print("{}\t{}".format(key, val), file=f) + + s = "\nFor {}, WER of different settings are:\n".format(test_set_name) + note = "\tbest for {}".format(test_set_name) + for key, val in test_set_wers: + s += "{}\t{}{}\n".format(key, val, note) + note = "" + logging.info(s) + + +@torch.no_grad() +def main(): + parser = get_parser() + LibriSpeechAsrDataModule.add_arguments(parser) + HubertXlargeFineTuned.add_arguments(parser) + args = parser.parse_args() + + params = AttributeDict() + params.update(vars(args)) + # reset some parameters needed by hubert. + params.update(HubertXlargeFineTuned.get_params()) + + params.res_dir = ( + params.exp_dir / f"ctc_greedy_search-{params.teacher_model_id}" + ) + + setup_logger(f"{params.res_dir}/log/log-ctc_greedy_search") + logging.info("Decoding started") + logging.info(params) + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"device: {device}") + params.device = device + + hubert_model = HubertXlargeFineTuned(params) + + librispeech = LibriSpeechAsrDataModule(params) + + test_clean_cuts = librispeech.test_clean_cuts() + test_other_cuts = librispeech.test_other_cuts() + + test_clean_dl = librispeech.test_dataloaders(test_clean_cuts) + test_other_dl = librispeech.test_dataloaders(test_other_cuts) + + test_sets = ["test-clean", "test-other"] + test_dl = [test_clean_dl, test_other_dl] + + for test_set, test_dl in zip(test_sets, test_dl): + results_dict = decode_dataset( + dl=test_dl, + hubert_model=hubert_model, + params=params, + ) + + save_results( + params=params, test_set_name=test_set, results_dict=results_dict + ) + + logging.info("Done!") + + +torch.set_num_threads(1) +torch.set_num_interop_threads(1) + +if __name__ == "__main__": + main() diff --git a/egs/librispeech/ASR/pruned_transducer_stateless6/hubert_xlarge.py b/egs/librispeech/ASR/pruned_transducer_stateless6/hubert_xlarge.py new file mode 100644 index 000000000..55ce7b00d --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless6/hubert_xlarge.py @@ -0,0 +1,220 @@ +#!/usr/bin/env python3 +# Copyright 2022 Xiaomi Corporation (Author: Liyong Guo) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import argparse +import logging +from pathlib import Path +from typing import Dict, List, Tuple + +import torch +from fairseq import ( + checkpoint_utils, + tasks, + utils, +) +from fairseq.data.data_utils import post_process +from omegaconf import OmegaConf + +from icefall.utils import AttributeDict + + +def _load_hubert_model(params: AttributeDict): + """ + Load the hubert model. + + The model loaded is specified by params.hubert_model_dir + and params.teacher_model_id. + + Returned model carries hubert, + while processor is responsible to map model's output to human readable transcripts. + """ + cfg_task = OmegaConf.create( + { + "_name": "hubert_pretraining", + "single_target": True, + "fine_tuning": True, + "data": str(params.hubert_model_dir), + } + ) + model_path = Path(params.hubert_model_dir) / ( + params.teacher_model_id + ".pt" + ) + task = tasks.setup_task(cfg_task) + processor = task.target_dictionary + models, saved_cfg = checkpoint_utils.load_model_ensemble( + utils.split_paths(str(model_path), separator="\\"), + arg_overrides={}, + strict=True, + suffix="", + num_shards=1, + ) + model = models[0] + model.to(params.device) + model.eval() + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + return model, processor + + +class HubertXlargeFineTuned: + """ + A wrapper of hubert extra large fine-tuned model. + + A teacher model is responsible for: + 1. load teacher model + 2. extracting embeddings to train quantizer. + 3. extract codebook indices + 4. verify its performance with ctc_greedy_search method. + """ + + def __init__(self, params: AttributeDict): + self.model, self.processor = _load_hubert_model(params) + self.w2v_model = self.model.w2v_encoder.w2v_model + self.params = params + + @staticmethod + def get_params() -> AttributeDict: + """Return a dict containing parameters defined in other modules. + + Their default value conflits to hubert's requirements so they are reset as following. + """ + params = AttributeDict( + { + # parameters defined in asr_datamodule.py + "input_strategy": "AudioSamples", + "enable_musan": False, + "enable_spec_aug": False, + "return_cuts": True, + "drop_last": False, + # parameters used by quantizer + "embedding_dim": 1280, + } + ) + return params + + @classmethod + def add_arguments(cls, parser: argparse.ArgumentParser): + # Options about model loading. + parser.add_argument( + "--hubert-model-dir", + type=Path, + default="./pruned_transducer_stateless6/exp/hubert_models/", + help="path to save downloaded hubert models.", + ) + + parser.add_argument( + "--teacher-model-id", + type=str, + default="hubert_xtralarge_ll60k_finetune_ls960", + help="""could be one of: + [ + "hubert_xtralarge_ll60k_finetune_ls960", # fine-tuned model. + "hubert_xtralarge_ll60k.pt", # pretrained model without fintuing. + ]""", + ) + parser.add_argument( + "--total-layers", + type=int, + default=48, + ) + + # Modified from HubertModel.forward to extract all middle layers output + def extract_layers_result( + self, + batch: Dict, + ) -> List[torch.Tensor]: + """ + Extract activations from all layers. + """ + features = batch["inputs"] + + # corresponding task.normalize in fairseq + features = torch.nn.functional.layer_norm(features, features.shape) + + supervisions = batch["supervisions"] + num_samples = supervisions["num_samples"] + B, T = features.shape + padding_mask = torch.arange(0, T).expand(B, T) > num_samples.reshape( + [-1, 1] + ) + + padding_mask = padding_mask.to(self.params.device) + features = features.to(self.params.device) + + features = self.w2v_model.forward_features(features) + + features = features.transpose(1, 2) + features = self.w2v_model.layer_norm(features) + + padding_mask = self.w2v_model.forward_padding_mask( + features, padding_mask + ) + + if self.w2v_model.post_extract_proj is not None: + features = self.w2v_model.post_extract_proj(features) + + _, layer_results = self.w2v_model.encoder( + features, + padding_mask=padding_mask, + ) + return layer_results + + def extract_embedding(self, batch) -> Tuple[torch.tensor, List[int]]: + """ + Eextract embeddings specified by self.params.embedding_layer. + + These embeddings could be used to train quantizer + or to extract codebook indexes. + + The returned List[int] is valid length of each embedding. + We only want to store codebook indexes related to + these valid embeddings. + """ + supervisions = batch["supervisions"] + cut_list = supervisions["cut"] + assert all(c.start == 0 for c in cut_list) + layer_results = self.extract_layers_result(batch) + embeddings = layer_results[self.params.embedding_layer - 1][0] + encoder_embedding = embeddings.transpose(0, 1) # N, T, C + N = encoder_embedding.shape[0] + assert len(cut_list) == N + # 320 is from: 16,000 / 50 = sample_rate / hbuert output frame rate + num_frames = (supervisions["num_samples"] // 320).tolist() + return encoder_embedding, num_frames + + def ctc_greedy_search(self, batch): + """ + Mainly used to verify hubert model is used correctly. + """ + layer_results = self.extract_layers_result(batch=batch) + encoder_out = self.w2v_model.encoder.layer_norm( + layer_results[self.params.total_layers - 1][0] + ) + encoder_out = self.model.w2v_encoder.proj(encoder_out.transpose(0, 1)) + + toks = encoder_out.argmax(dim=-1) + blank = 0 + toks = [tok.unique_consecutive() for tok in toks] + hyps = [ + self.processor.string(tok[tok != blank].int().cpu()) for tok in toks + ] + hyps = [post_process(hyp, "letter") for hyp in hyps] + + return hyps diff --git a/egs/librispeech/ASR/pruned_transducer_stateless6/joiner.py b/egs/librispeech/ASR/pruned_transducer_stateless6/joiner.py new file mode 120000 index 000000000..815fd4bb6 --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless6/joiner.py @@ -0,0 +1 @@ +../pruned_transducer_stateless2/joiner.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless6/model.py b/egs/librispeech/ASR/pruned_transducer_stateless6/model.py new file mode 100644 index 000000000..66bb33e8d --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless6/model.py @@ -0,0 +1,249 @@ +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang, Wei Kang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import k2 +import torch +import torch.nn as nn +from encoder_interface import EncoderInterface +from scaling import ScaledLinear + +from icefall.utils import add_sos + +from quantization.prediction import JointCodebookLoss + + +class Transducer(nn.Module): + """It implements https://arxiv.org/pdf/1211.3711.pdf + "Sequence Transduction with Recurrent Neural Networks" + """ + + def __init__( + self, + encoder: EncoderInterface, + decoder: nn.Module, + joiner: nn.Module, + encoder_dim: int, + decoder_dim: int, + joiner_dim: int, + vocab_size: int, + num_codebooks: int = 0, + ): + """ + Args: + encoder: + It is the transcription network in the paper. Its accepts + two inputs: `x` of (N, T, encoder_dim) and `x_lens` of shape (N,). + It returns two tensors: `logits` of shape (N, T, encoder_dm) and + `logit_lens` of shape (N,). + decoder: + It is the prediction network in the paper. Its input shape + is (N, U) and its output shape is (N, U, decoder_dim). + It should contain one attribute: `blank_id`. + joiner: + It has two inputs with shapes: (N, T, encoder_dim) and + (N, U, decoder_dim). + Its output shape is (N, T, U, vocab_size). Note that its output + contains unnormalized probs, i.e., not processed by log-softmax. + num_codebooks: + Used by distillation loss. + """ + super().__init__() + assert isinstance(encoder, EncoderInterface), type(encoder) + assert hasattr(decoder, "blank_id") + + self.encoder = encoder + self.decoder = decoder + self.joiner = joiner + + self.simple_am_proj = ScaledLinear( + encoder_dim, vocab_size, initial_speed=0.5 + ) + self.simple_lm_proj = ScaledLinear(decoder_dim, vocab_size) + if num_codebooks > 0: + self.codebook_loss_net = JointCodebookLoss( + predictor_channels=encoder_dim, num_codebooks=num_codebooks + ) + + def forward( + self, + x: torch.Tensor, + x_lens: torch.Tensor, + y: k2.RaggedTensor, + prune_range: int = 5, + am_scale: float = 0.0, + lm_scale: float = 0.0, + warmup: float = 1.0, + codebook_indexes: torch.Tensor = None, + ) -> torch.Tensor: + """ + Args: + x: + A 3-D tensor of shape (N, T, C). + x_lens: + A 1-D tensor of shape (N,). It contains the number of frames in `x` + before padding. + y: + A ragged tensor with 2 axes [utt][label]. It contains labels of each + utterance. + prune_range: + The prune range for rnnt loss, it means how many symbols(context) + we are considering for each frame to compute the loss. + am_scale: + The scale to smooth the loss with am (output of encoder network) + part + lm_scale: + The scale to smooth the loss with lm (output of predictor network) + part + warmup: + A value warmup >= 0 that determines which modules are active, values + warmup > 1 "are fully warmed up" and all modules will be active. + codebook_indexes: + codebook_indexes extracted from a teacher model. + Returns: + Return the transducer loss. + + Note: + Regarding am_scale & lm_scale, it will make the loss-function one of + the form: + lm_scale * lm_probs + am_scale * am_probs + + (1-lm_scale-am_scale) * combined_probs + """ + assert x.ndim == 3, x.shape + assert x_lens.ndim == 1, x_lens.shape + assert y.num_axes == 2, y.num_axes + + assert x.size(0) == x_lens.size(0) == y.dim0 + + layer_results, x_lens = self.encoder(x, x_lens, warmup=warmup) + encoder_out = layer_results[-1] + middle_layer_output = layer_results[0] + if self.training and codebook_indexes is not None: + assert hasattr(self, "codebook_loss_net") + if codebook_indexes.shape[1] != middle_layer_output.shape[1]: + codebook_indexes = self.concat_successive_codebook_indexes( + middle_layer_output, codebook_indexes + ) + codebook_loss = self.codebook_loss_net( + middle_layer_output, codebook_indexes + ) + else: + # when codebook index is not available. + codebook_loss = None + + assert torch.all(x_lens > 0) + + # Now for the decoder, i.e., the prediction network + row_splits = y.shape.row_splits(1) + y_lens = row_splits[1:] - row_splits[:-1] + + blank_id = self.decoder.blank_id + sos_y = add_sos(y, sos_id=blank_id) + + # sos_y_padded: [B, S + 1], start with SOS. + sos_y_padded = sos_y.pad(mode="constant", padding_value=blank_id) + + # decoder_out: [B, S + 1, decoder_dim] + decoder_out = self.decoder(sos_y_padded) + + # Note: y does not start with SOS + # y_padded : [B, S] + y_padded = y.pad(mode="constant", padding_value=0) + + y_padded = y_padded.to(torch.int64) + boundary = torch.zeros( + (x.size(0), 4), dtype=torch.int64, device=x.device + ) + boundary[:, 2] = y_lens + boundary[:, 3] = x_lens + + lm = self.simple_lm_proj(decoder_out) + am = self.simple_am_proj(encoder_out) + + with torch.cuda.amp.autocast(enabled=False): + simple_loss, (px_grad, py_grad) = k2.rnnt_loss_smoothed( + lm=lm.float(), + am=am.float(), + symbols=y_padded, + termination_symbol=blank_id, + lm_only_scale=lm_scale, + am_only_scale=am_scale, + boundary=boundary, + reduction="sum", + return_grad=True, + ) + + # ranges : [B, T, prune_range] + ranges = k2.get_rnnt_prune_ranges( + px_grad=px_grad, + py_grad=py_grad, + boundary=boundary, + s_range=prune_range, + ) + + # am_pruned : [B, T, prune_range, encoder_dim] + # lm_pruned : [B, T, prune_range, decoder_dim] + am_pruned, lm_pruned = k2.do_rnnt_pruning( + am=self.joiner.encoder_proj(encoder_out), + lm=self.joiner.decoder_proj(decoder_out), + ranges=ranges, + ) + + # logits : [B, T, prune_range, vocab_size] + + # project_input=False since we applied the decoder's input projections + # prior to do_rnnt_pruning (this is an optimization for speed). + logits = self.joiner(am_pruned, lm_pruned, project_input=False) + + with torch.cuda.amp.autocast(enabled=False): + pruned_loss = k2.rnnt_loss_pruned( + logits=logits.float(), + symbols=y_padded, + ranges=ranges, + termination_symbol=blank_id, + boundary=boundary, + reduction="sum", + ) + + return (simple_loss, pruned_loss, codebook_loss) + + @staticmethod + def concat_successive_codebook_indexes( + middle_layer_output, codebook_indexes + ): + # Output rate of hubert is 50 frames per second, + # while that of current encoder is 25. + # Following code handling two issues: + # 1. + # Roughly speaking, to generate another frame output, + # hubert needes extra two frames, + # while current encoder needs extra four frames. + # Suppose there are only extra three frames provided, + # hubert will generate another frame while current encoder does nothing. + # 2. + # codebook loss is a frame-wise loss, to enalbe 25 frames studnet output + # learns from 50 frames teacher output, two successive frames of teacher model + # output is concatenated together. + t_expected = middle_layer_output.shape[1] + N, T, C = codebook_indexes.shape + + # Handling issue 1. + if T >= t_expected * 2: + codebook_indexes = codebook_indexes[:, : t_expected * 2, :] + # Handling issue 2. + codebook_indexes = codebook_indexes.reshape(N, t_expected, C * 2) + assert middle_layer_output.shape[1] == codebook_indexes.shape[1] + return codebook_indexes diff --git a/egs/librispeech/ASR/pruned_transducer_stateless6/optim.py b/egs/librispeech/ASR/pruned_transducer_stateless6/optim.py new file mode 120000 index 000000000..e2deb4492 --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless6/optim.py @@ -0,0 +1 @@ +../pruned_transducer_stateless2/optim.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless6/scaling.py b/egs/librispeech/ASR/pruned_transducer_stateless6/scaling.py new file mode 120000 index 000000000..09d802cc4 --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless6/scaling.py @@ -0,0 +1 @@ +../pruned_transducer_stateless2/scaling.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless6/test_model.py b/egs/librispeech/ASR/pruned_transducer_stateless6/test_model.py new file mode 100755 index 000000000..9bd75ba21 --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless6/test_model.py @@ -0,0 +1,51 @@ +#!/usr/bin/env python3 +# Copyright 2022 Xiaomi Corp. (authors: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +To run this file, do: + + cd icefall/egs/librispeech/ASR + python ./pruned_transducer_stateless6/test_model.py +""" + +import torch +from train import get_params, get_transducer_model + + +def test_model(): + params = get_params() + params.vocab_size = 500 + params.blank_id = 0 + params.context_size = 2 + params.unk_id = 2 + params.enable_distiallation = False + + model = get_transducer_model(params) + + num_param = sum([p.numel() for p in model.parameters()]) + print(f"Number of model parameters: {num_param}") + model.__class__.forward = torch.jit.ignore(model.__class__.forward) + torch.jit.script(model) + + +def main(): + test_model() + + +if __name__ == "__main__": + main() diff --git a/egs/librispeech/ASR/pruned_transducer_stateless6/train.py b/egs/librispeech/ASR/pruned_transducer_stateless6/train.py new file mode 100755 index 000000000..315c01c8e --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless6/train.py @@ -0,0 +1,1106 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang, +# Wei Kang, +# Mingshuang Luo,) +# Zengwei Yao) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Usage: + +export CUDA_VISIBLE_DEVICES="0,1,2,3" + +./pruned_transducer_stateless6/train.py \ + --world-size 4 \ + --num-epochs 30 \ + --start-epoch 1 \ + --exp-dir pruned_transducer_stateless6/exp \ + --full-libri 1 \ + --max-duration 300 + +# For mix precision training: + +./pruned_transducer_stateless6/train.py \ + --world-size 4 \ + --num-epochs 30 \ + --start-epoch 1 \ + --use-fp16 1 \ + --exp-dir pruned_transducer_stateless6/exp \ + --full-libri 1 \ + --max-duration 550 + +# For distiallation with codebook_indexes: + +./pruned_transducer_stateless6/train.py \ + --manifest-dir ./data/vq_fbank \ + --world-size 4 \ + --num-epochs 30 \ + --start-epoch 1 \ + --exp-dir pruned_transducer_stateless6/exp \ + --full-libri 0 \ + --max-duration 300 + +""" + + +import argparse +import copy +import logging +import warnings +from pathlib import Path +from shutil import copyfile +from typing import Any, Dict, Optional, Tuple, Union + +import k2 +import optim +import sentencepiece as spm +import torch +import torch.multiprocessing as mp +import torch.nn as nn +from asr_datamodule import LibriSpeechAsrDataModule +from conformer import Conformer +from decoder import Decoder +from joiner import Joiner +from lhotse.cut import Cut, MonoCut +from lhotse.dataset.sampling.base import CutSampler +from lhotse.utils import fix_random_seed +from lhotse.dataset.collation import collate_custom_field +from model import Transducer +from optim import Eden, Eve +from torch import Tensor +from torch.cuda.amp import GradScaler +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.utils.tensorboard import SummaryWriter + +from icefall import diagnostics +from icefall.checkpoint import load_checkpoint, remove_checkpoints +from icefall.checkpoint import save_checkpoint as save_checkpoint_impl +from icefall.checkpoint import ( + save_checkpoint_with_global_batch_idx, + update_averaged_model, +) +from icefall.dist import cleanup_dist, setup_dist +from icefall.env import get_env_info +from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool + +LRSchedulerType = Union[ + torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler +] + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--world-size", + type=int, + default=1, + help="Number of GPUs for DDP training.", + ) + + parser.add_argument( + "--master-port", + type=int, + default=12354, + help="Master port to use for DDP training.", + ) + + parser.add_argument( + "--tensorboard", + type=str2bool, + default=True, + help="Should various information be logged in tensorboard.", + ) + + parser.add_argument( + "--num-epochs", + type=int, + default=30, + help="Number of epochs to train.", + ) + + parser.add_argument( + "--start-epoch", + type=int, + default=1, + help="""Resume training from this epoch. It should be positive. + If larger than 1, it will load checkpoint from + exp-dir/epoch-{start_epoch-1}.pt + """, + ) + + parser.add_argument( + "--start-batch", + type=int, + default=0, + help="""If positive, --start-epoch is ignored and + it loads the checkpoint from exp-dir/checkpoint-{start_batch}.pt + """, + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="pruned_transducer_stateless6/exp", + help="""The experiment dir. + It specifies the directory where all training related + files, e.g., checkpoints, log, etc, are saved + """, + ) + + parser.add_argument( + "--bpe-model", + type=str, + default="data/lang_bpe_500/bpe.model", + help="Path to the BPE model", + ) + + parser.add_argument( + "--initial-lr", + type=float, + default=0.003, + help="""The initial learning rate. This value should not need to be + changed.""", + ) + + parser.add_argument( + "--lr-batches", + type=float, + default=5000, + help="""Number of steps that affects how rapidly the learning rate decreases. + We suggest not to change this.""", + ) + + parser.add_argument( + "--lr-epochs", + type=float, + default=6, + help="""Number of epochs that affects how rapidly the learning rate decreases. + """, + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; " + "2 means tri-gram", + ) + + parser.add_argument( + "--prune-range", + type=int, + default=5, + help="The prune range for rnnt loss, it means how many symbols(context)" + "we are using to compute the loss", + ) + + parser.add_argument( + "--lm-scale", + type=float, + default=0.25, + help="The scale to smooth the loss with lm " + "(output of prediction network) part.", + ) + + parser.add_argument( + "--am-scale", + type=float, + default=0.0, + help="The scale to smooth the loss with am (output of encoder network)" + "part.", + ) + + parser.add_argument( + "--simple-loss-scale", + type=float, + default=0.5, + help="To get pruning ranges, we will calculate a simple version" + "loss(joiner is just addition), this simple loss also uses for" + "training (as a regularization item). We will scale the simple loss" + "with this parameter before adding to the final loss.", + ) + + parser.add_argument( + "--codebook-loss-scale", + type=float, + default=0.1, + help="The scale of codebook loss.", + ) + + parser.add_argument( + "--seed", + type=int, + default=42, + help="The seed for random generators intended for reproducibility", + ) + + parser.add_argument( + "--print-diagnostics", + type=str2bool, + default=False, + help="Accumulate stats on activations, print them and exit.", + ) + + parser.add_argument( + "--save-every-n", + type=int, + default=8000, + help="""Save checkpoint after processing this number of batches" + periodically. We save checkpoint to exp-dir/ whenever + params.batch_idx_train % save_every_n == 0. The checkpoint filename + has the form: f'exp-dir/checkpoint-{params.batch_idx_train}.pt' + Note: It also saves checkpoint to `exp-dir/epoch-xxx.pt` at the + end of each epoch where `xxx` is the epoch number counting from 0. + """, + ) + + parser.add_argument( + "--keep-last-k", + type=int, + default=20, + help="""Only keep this number of checkpoints on disk. + For instance, if it is 3, there are only 3 checkpoints + in the exp-dir with filenames `checkpoint-xxx.pt`. + It does not affect checkpoints with name `epoch-xxx.pt`. + """, + ) + + parser.add_argument( + "--average-period", + type=int, + default=100, + help="""Update the averaged model, namely `model_avg`, after processing + this number of batches. `model_avg` is a separate version of model, + in which each floating-point parameter is the average of all the + parameters from the start of training. Each time we take the average, + we do: `model_avg = model * (average_period / batch_idx_train) + + model_avg * ((batch_idx_train - average_period) / batch_idx_train)`. + """, + ) + + parser.add_argument( + "--use-fp16", + type=str2bool, + default=False, + help="Whether to use half precision training.", + ) + + return parser + + +def get_params() -> AttributeDict: + """Return a dict containing training parameters. + + All training related parameters that are not passed from the commandline + are saved in the variable `params`. + + Commandline options are merged into `params` after they are parsed, so + you can also access them via `params`. + + Explanation of options saved in `params`: + + - best_train_loss: Best training loss so far. It is used to select + the model that has the lowest training loss. It is + updated during the training. + + - best_valid_loss: Best validation loss so far. It is used to select + the model that has the lowest validation loss. It is + updated during the training. + + - best_train_epoch: It is the epoch that has the best training loss. + + - best_valid_epoch: It is the epoch that has the best validation loss. + + - batch_idx_train: Used to writing statistics to tensorboard. It + contains number of batches trained so far across + epochs. + + - log_interval: Print training loss if batch_idx % log_interval` is 0 + + - reset_interval: Reset statistics if batch_idx % reset_interval is 0 + + - valid_interval: Run validation if batch_idx % valid_interval is 0 + + - feature_dim: The model input dim. It has to match the one used + in computing features. + + - subsampling_factor: The subsampling factor for the model. + + - encoder_dim: Hidden dim for multi-head attention model. + + - num_decoder_layers: Number of decoder layer of transformer decoder. + + - warm_step: The warm_step for Noam optimizer. + """ + params = AttributeDict( + { + "best_train_loss": float("inf"), + "best_valid_loss": float("inf"), + "best_train_epoch": -1, + "best_valid_epoch": -1, + "batch_idx_train": 0, + "log_interval": 50, + "reset_interval": 200, + "valid_interval": 3000, # For the 100h subset, use 800 + # parameters for conformer + "feature_dim": 80, + "subsampling_factor": 4, + "encoder_dim": 512, + "nhead": 8, + "dim_feedforward": 2048, + "num_encoder_layers": 12, + # parameters for decoder + "decoder_dim": 512, + # parameters for joiner + "joiner_dim": 512, + # parameters for Noam + "model_warm_step": 3000, # arg given to model, not for lrate + "env_info": get_env_info(), + # parameters for distillation with codebook indexes. + "enable_distiallation": True, + "distillation_layer": 5, # 0-based index + # Since output rate of hubert is 50, while that of encoder is 8, + # two successive codebook_index are concatenated together. + # Detailed in function Transducer::concat_sucessive_codebook_indexes. + "num_codebooks": 16, # used to construct distillation loss + } + ) + + return params + + +def get_encoder_model(params: AttributeDict) -> nn.Module: + # TODO: We can add an option to switch between Conformer and Transformer + encoder = Conformer( + num_features=params.feature_dim, + subsampling_factor=params.subsampling_factor, + d_model=params.encoder_dim, + nhead=params.nhead, + dim_feedforward=params.dim_feedforward, + num_encoder_layers=params.num_encoder_layers, + middle_output_layer=params.distillation_layer + if params.enable_distiallation + else None, + ) + return encoder + + +def get_decoder_model(params: AttributeDict) -> nn.Module: + decoder = Decoder( + vocab_size=params.vocab_size, + decoder_dim=params.decoder_dim, + blank_id=params.blank_id, + context_size=params.context_size, + ) + return decoder + + +def get_joiner_model(params: AttributeDict) -> nn.Module: + joiner = Joiner( + encoder_dim=params.encoder_dim, + decoder_dim=params.decoder_dim, + joiner_dim=params.joiner_dim, + vocab_size=params.vocab_size, + ) + return joiner + + +def get_transducer_model(params: AttributeDict) -> nn.Module: + encoder = get_encoder_model(params) + decoder = get_decoder_model(params) + joiner = get_joiner_model(params) + + model = Transducer( + encoder=encoder, + decoder=decoder, + joiner=joiner, + encoder_dim=params.encoder_dim, + decoder_dim=params.decoder_dim, + joiner_dim=params.joiner_dim, + vocab_size=params.vocab_size, + num_codebooks=params.num_codebooks + if params.enable_distiallation + else 0, + ) + return model + + +def load_checkpoint_if_available( + params: AttributeDict, + model: nn.Module, + model_avg: nn.Module = None, + optimizer: Optional[torch.optim.Optimizer] = None, + scheduler: Optional[LRSchedulerType] = None, +) -> Optional[Dict[str, Any]]: + """Load checkpoint from file. + + If params.start_batch is positive, it will load the checkpoint from + `params.exp_dir/checkpoint-{params.start_batch}.pt`. Otherwise, if + params.start_epoch is larger than 1, it will load the checkpoint from + `params.start_epoch - 1`. + + Apart from loading state dict for `model` and `optimizer` it also updates + `best_train_epoch`, `best_train_loss`, `best_valid_epoch`, + and `best_valid_loss` in `params`. + + Args: + params: + The return value of :func:`get_params`. + model: + The training model. + model_avg: + The stored model averaged from the start of training. + optimizer: + The optimizer that we are using. + scheduler: + The scheduler that we are using. + Returns: + Return a dict containing previously saved training info. + """ + if params.start_batch > 0: + filename = params.exp_dir / f"checkpoint-{params.start_batch}.pt" + elif params.start_epoch > 1: + filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt" + else: + return None + + assert filename.is_file(), f"{filename} does not exist!" + + saved_params = load_checkpoint( + filename, + model=model, + model_avg=model_avg, + optimizer=optimizer, + scheduler=scheduler, + ) + + keys = [ + "best_train_epoch", + "best_valid_epoch", + "batch_idx_train", + "best_train_loss", + "best_valid_loss", + ] + for k in keys: + params[k] = saved_params[k] + + if params.start_batch > 0: + if "cur_epoch" in saved_params: + params["start_epoch"] = saved_params["cur_epoch"] + + if "cur_batch_idx" in saved_params: + params["cur_batch_idx"] = saved_params["cur_batch_idx"] + + return saved_params + + +def save_checkpoint( + params: AttributeDict, + model: Union[nn.Module, DDP], + model_avg: Optional[nn.Module] = None, + optimizer: Optional[torch.optim.Optimizer] = None, + scheduler: Optional[LRSchedulerType] = None, + sampler: Optional[CutSampler] = None, + scaler: Optional[GradScaler] = None, + rank: int = 0, +) -> None: + """Save model, optimizer, scheduler and training stats to file. + + Args: + params: + It is returned by :func:`get_params`. + model: + The training model. + model_avg: + The stored model averaged from the start of training. + optimizer: + The optimizer used in the training. + sampler: + The sampler for the training dataset. + scaler: + The scaler used for mix precision training. + """ + if rank != 0: + return + filename = params.exp_dir / f"epoch-{params.cur_epoch}.pt" + save_checkpoint_impl( + filename=filename, + model=model, + model_avg=model_avg, + params=params, + optimizer=optimizer, + scheduler=scheduler, + sampler=sampler, + scaler=scaler, + rank=rank, + ) + + if params.best_train_epoch == params.cur_epoch: + best_train_filename = params.exp_dir / "best-train-loss.pt" + copyfile(src=filename, dst=best_train_filename) + + if params.best_valid_epoch == params.cur_epoch: + best_valid_filename = params.exp_dir / "best-valid-loss.pt" + copyfile(src=filename, dst=best_valid_filename) + + +def extract_codebook_indexes(batch): + cuts = batch["supervisions"]["cut"] + # -100 is identical to ignore_value in CE loss computation. + cuts_pre_mixed = [ + c if isinstance(c, MonoCut) else c.tracks[0].cut for c in cuts + ] + codebook_indexes, codebook_indexes_lens = collate_custom_field( + cuts_pre_mixed, "codebook_indexes", pad_value=-100 + ) + return codebook_indexes, codebook_indexes_lens + + +def compute_loss( + params: AttributeDict, + model: Union[nn.Module, DDP], + sp: spm.SentencePieceProcessor, + batch: dict, + is_training: bool, + warmup: float = 1.0, +) -> Tuple[Tensor, MetricsTracker]: + """ + Compute RNN-T loss given the model and its inputs. + + Args: + params: + Parameters for training. See :func:`get_params`. + model: + The model for training. It is an instance of Conformer in our case. + batch: + A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()` + for the content in it. + is_training: + True for training. False for validation. When it is True, this + function enables autograd during computation; when it is False, it + disables autograd. + warmup: a floating point value which increases throughout training; + values >= 1.0 are fully warmed up and have all modules present. + """ + device = ( + model.device + if isinstance(model, DDP) + else next(model.parameters()).device + ) + feature = batch["inputs"] + # at entry, feature is (N, T, C) + assert feature.ndim == 3 + feature = feature.to(device) + + supervisions = batch["supervisions"] + feature_lens = supervisions["num_frames"].to(device) + + texts = batch["supervisions"]["text"] + y = sp.encode(texts, out_type=int) + y = k2.RaggedTensor(y).to(device) + + info = MetricsTracker() + if is_training and params.enable_distiallation: + codebook_indexes, _ = extract_codebook_indexes(batch) + codebook_indexes = codebook_indexes.to(device) + else: + codebook_indexes = None + + with torch.set_grad_enabled(is_training): + simple_loss, pruned_loss, codebook_loss = model( + x=feature, + x_lens=feature_lens, + y=y, + prune_range=params.prune_range, + am_scale=params.am_scale, + lm_scale=params.lm_scale, + warmup=warmup, + codebook_indexes=codebook_indexes, + ) + # after the main warmup step, we keep pruned_loss_scale small + # for the same amount of time (model_warm_step), to avoid + # overwhelming the simple_loss and causing it to diverge, + # in case it had not fully learned the alignment yet. + pruned_loss_scale = ( + 0.0 + if warmup < 1.0 + else (0.1 if warmup > 1.0 and warmup < 2.0 else 1.0) + ) + loss = ( + params.simple_loss_scale * simple_loss + + pruned_loss_scale * pruned_loss + ) + if is_training and params.enable_distiallation: + assert codebook_loss is not None + loss += params.codebook_loss_scale * codebook_loss + + assert loss.requires_grad == is_training + + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + info["frames"] = ( + (feature_lens // params.subsampling_factor).sum().item() + ) + + # Note: We use reduction=sum while computing the loss. + info["loss"] = loss.detach().cpu().item() + info["simple_loss"] = simple_loss.detach().cpu().item() + info["pruned_loss"] = pruned_loss.detach().cpu().item() + if is_training and params.enable_distiallation: + info["codebook_loss"] = codebook_loss.detach().cpu().item() + + return loss, info + + +def compute_validation_loss( + params: AttributeDict, + model: Union[nn.Module, DDP], + sp: spm.SentencePieceProcessor, + valid_dl: torch.utils.data.DataLoader, + world_size: int = 1, +) -> MetricsTracker: + """Run the validation process.""" + model.eval() + + tot_loss = MetricsTracker() + + for batch_idx, batch in enumerate(valid_dl): + loss, loss_info = compute_loss( + params=params, + model=model, + sp=sp, + batch=batch, + is_training=False, + ) + assert loss.requires_grad is False + tot_loss = tot_loss + loss_info + + if world_size > 1: + tot_loss.reduce(loss.device) + + loss_value = tot_loss["loss"] / tot_loss["frames"] + if loss_value < params.best_valid_loss: + params.best_valid_epoch = params.cur_epoch + params.best_valid_loss = loss_value + + return tot_loss + + +def train_one_epoch( + params: AttributeDict, + model: Union[nn.Module, DDP], + optimizer: torch.optim.Optimizer, + scheduler: LRSchedulerType, + sp: spm.SentencePieceProcessor, + train_dl: torch.utils.data.DataLoader, + valid_dl: torch.utils.data.DataLoader, + scaler: GradScaler, + model_avg: Optional[nn.Module] = None, + tb_writer: Optional[SummaryWriter] = None, + world_size: int = 1, + rank: int = 0, +) -> None: + """Train the model for one epoch. + + The training loss from the mean of all frames is saved in + `params.train_loss`. It runs the validation process every + `params.valid_interval` batches. + + Args: + params: + It is returned by :func:`get_params`. + model: + The model for training. + optimizer: + The optimizer we are using. + scheduler: + The learning rate scheduler, we call step() every step. + train_dl: + Dataloader for the training dataset. + valid_dl: + Dataloader for the validation dataset. + scaler: + The scaler used for mix precision training. + model_avg: + The stored model averaged from the start of training. + tb_writer: + Writer to write log messages to tensorboard. + world_size: + Number of nodes in DDP training. If it is 1, DDP is disabled. + rank: + The rank of the node in DDP training. If no DDP is used, it should + be set to 0. + """ + model.train() + + tot_loss = MetricsTracker() + + cur_batch_idx = params.get("cur_batch_idx", 0) + + for batch_idx, batch in enumerate(train_dl): + if batch_idx < cur_batch_idx: + continue + cur_batch_idx = batch_idx + + params.batch_idx_train += 1 + batch_size = len(batch["supervisions"]["text"]) + + with torch.cuda.amp.autocast(enabled=params.use_fp16): + loss, loss_info = compute_loss( + params=params, + model=model, + sp=sp, + batch=batch, + is_training=True, + warmup=(params.batch_idx_train / params.model_warm_step), + ) + # summary stats + tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info + + # NOTE: We use reduction==sum and loss is computed over utterances + # in the batch and there is no normalization to it so far. + scaler.scale(loss).backward() + scheduler.step_batch(params.batch_idx_train) + scaler.step(optimizer) + scaler.update() + optimizer.zero_grad() + + if params.print_diagnostics and batch_idx == 30: + return + + if ( + rank == 0 + and params.batch_idx_train > 0 + and params.batch_idx_train % params.average_period == 0 + ): + update_averaged_model( + params=params, + model_cur=model, + model_avg=model_avg, + ) + + if ( + params.batch_idx_train > 0 + and params.batch_idx_train % params.save_every_n == 0 + ): + params.cur_batch_idx = batch_idx + save_checkpoint_with_global_batch_idx( + out_dir=params.exp_dir, + global_batch_idx=params.batch_idx_train, + model=model, + model_avg=model_avg, + params=params, + optimizer=optimizer, + scheduler=scheduler, + sampler=train_dl.sampler, + scaler=scaler, + rank=rank, + ) + del params.cur_batch_idx + remove_checkpoints( + out_dir=params.exp_dir, + topk=params.keep_last_k, + rank=rank, + ) + + if batch_idx % params.log_interval == 0: + cur_lr = scheduler.get_last_lr()[0] + logging.info( + f"Epoch {params.cur_epoch}, " + f"batch {batch_idx}, loss[{loss_info}], " + f"tot_loss[{tot_loss}], batch size: {batch_size}, " + f"lr: {cur_lr:.2e}" + ) + + if tb_writer is not None: + tb_writer.add_scalar( + "train/learning_rate", cur_lr, params.batch_idx_train + ) + + loss_info.write_summary( + tb_writer, "train/current_", params.batch_idx_train + ) + tot_loss.write_summary( + tb_writer, "train/tot_", params.batch_idx_train + ) + + if batch_idx > 0 and batch_idx % params.valid_interval == 0: + logging.info("Computing validation loss") + valid_info = compute_validation_loss( + params=params, + model=model, + sp=sp, + valid_dl=valid_dl, + world_size=world_size, + ) + model.train() + logging.info(f"Epoch {params.cur_epoch}, validation: {valid_info}") + if tb_writer is not None: + valid_info.write_summary( + tb_writer, "train/valid_", params.batch_idx_train + ) + + loss_value = tot_loss["loss"] / tot_loss["frames"] + params.train_loss = loss_value + if params.train_loss < params.best_train_loss: + params.best_train_epoch = params.cur_epoch + params.best_train_loss = params.train_loss + + +def run(rank, world_size, args): + """ + Args: + rank: + It is a value between 0 and `world_size-1`, which is + passed automatically by `mp.spawn()` in :func:`main`. + The node with rank 0 is responsible for saving checkpoint. + world_size: + Number of GPUs for DDP training. + args: + The return value of get_parser().parse_args() + """ + params = get_params() + params.update(vars(args)) + if params.full_libri is False: + params.valid_interval = 1600 + + fix_random_seed(params.seed) + if world_size > 1: + setup_dist(rank, world_size, params.master_port) + + setup_logger(f"{params.exp_dir}/log/log-train") + logging.info("Training started") + + if args.tensorboard and rank == 0: + tb_writer = SummaryWriter(log_dir=f"{params.exp_dir}/tensorboard") + else: + tb_writer = None + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", rank) + logging.info(f"Device: {device}") + + sp = spm.SentencePieceProcessor() + sp.load(params.bpe_model) + + # is defined in local/train_bpe_model.py + params.blank_id = sp.piece_to_id("") + params.vocab_size = sp.get_piece_size() + + logging.info(params) + + logging.info("About to create model") + model = get_transducer_model(params) + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + assert params.save_every_n >= params.average_period + model_avg: Optional[nn.Module] = None + if rank == 0: + # model_avg is only used with rank 0 + model_avg = copy.deepcopy(model) + + assert params.start_epoch > 0, params.start_epoch + checkpoints = load_checkpoint_if_available( + params=params, model=model, model_avg=model_avg + ) + + model.to(device) + if world_size > 1: + logging.info("Using DDP") + model = DDP(model, device_ids=[rank]) + + optimizer = Eve(model.parameters(), lr=params.initial_lr) + + scheduler = Eden(optimizer, params.lr_batches, params.lr_epochs) + + if checkpoints and "optimizer" in checkpoints: + logging.info("Loading optimizer state dict") + optimizer.load_state_dict(checkpoints["optimizer"]) + + if ( + checkpoints + and "scheduler" in checkpoints + and checkpoints["scheduler"] is not None + ): + logging.info("Loading scheduler state dict") + scheduler.load_state_dict(checkpoints["scheduler"]) + + if params.print_diagnostics: + diagnostic = diagnostics.attach_diagnostics(model) + + librispeech = LibriSpeechAsrDataModule(args) + + train_cuts = librispeech.train_clean_100_cuts() + if params.full_libri: + train_cuts += librispeech.train_clean_360_cuts() + train_cuts += librispeech.train_other_500_cuts() + + def remove_short_and_long_utt(c: Cut): + # Keep only utterances with duration between 1 second and 20 seconds + # + # Caution: There is a reason to select 20.0 here. Please see + # ../local/display_manifest_statistics.py + # + # You should use ../local/display_manifest_statistics.py to get + # an utterance duration distribution for your dataset to select + # the threshold + return 1.0 <= c.duration <= 20.0 + + train_cuts = train_cuts.filter(remove_short_and_long_utt) + + if params.start_batch > 0 and checkpoints and "sampler" in checkpoints: + # We only load the sampler's state dict when it loads a checkpoint + # saved in the middle of an epoch + sampler_state_dict = checkpoints["sampler"] + else: + sampler_state_dict = None + + train_dl = librispeech.train_dataloaders( + train_cuts, sampler_state_dict=sampler_state_dict + ) + + valid_cuts = librispeech.dev_clean_cuts() + valid_cuts += librispeech.dev_other_cuts() + valid_dl = librispeech.valid_dataloaders(valid_cuts) + + if not params.print_diagnostics: + scan_pessimistic_batches_for_oom( + model=model, + train_dl=train_dl, + optimizer=optimizer, + sp=sp, + params=params, + ) + + scaler = GradScaler(enabled=params.use_fp16) + if checkpoints and "grad_scaler" in checkpoints: + logging.info("Loading grad scaler state dict") + scaler.load_state_dict(checkpoints["grad_scaler"]) + + for epoch in range(params.start_epoch, params.num_epochs + 1): + scheduler.step_epoch(epoch - 1) + fix_random_seed(params.seed + epoch - 1) + train_dl.sampler.set_epoch(epoch - 1) + + if tb_writer is not None: + tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train) + + params.cur_epoch = epoch + + train_one_epoch( + params=params, + model=model, + model_avg=model_avg, + optimizer=optimizer, + scheduler=scheduler, + sp=sp, + train_dl=train_dl, + valid_dl=valid_dl, + scaler=scaler, + tb_writer=tb_writer, + world_size=world_size, + rank=rank, + ) + + if params.print_diagnostics: + diagnostic.print_diagnostics() + break + + save_checkpoint( + params=params, + model=model, + model_avg=model_avg, + optimizer=optimizer, + scheduler=scheduler, + sampler=train_dl.sampler, + scaler=scaler, + rank=rank, + ) + + logging.info("Done!") + + if world_size > 1: + torch.distributed.barrier() + cleanup_dist() + + +def scan_pessimistic_batches_for_oom( + model: Union[nn.Module, DDP], + train_dl: torch.utils.data.DataLoader, + optimizer: torch.optim.Optimizer, + sp: spm.SentencePieceProcessor, + params: AttributeDict, +): + from lhotse.dataset import find_pessimistic_batches + + logging.info( + "Sanity check -- see if any of the batches in epoch 1 would cause OOM." + ) + batches, crit_values = find_pessimistic_batches(train_dl.sampler) + for criterion, cuts in batches.items(): + batch = train_dl.dataset[cuts] + try: + # warmup = 0.0 is so that the derivs for the pruned loss stay zero + # (i.e. are not remembered by the decaying-average in adam), because + # we want to avoid these params being subject to shrinkage in adam. + with torch.cuda.amp.autocast(enabled=params.use_fp16): + loss, _ = compute_loss( + params=params, + model=model, + sp=sp, + batch=batch, + is_training=True, + warmup=0.0, + ) + loss.backward() + optimizer.step() + optimizer.zero_grad() + except RuntimeError as e: + if "CUDA out of memory" in str(e): + logging.error( + "Your GPU ran out of memory with the current " + "max_duration setting. We recommend decreasing " + "max_duration and trying again.\n" + f"Failing criterion: {criterion} " + f"(={crit_values[criterion]}) ..." + ) + raise + + +def main(): + parser = get_parser() + LibriSpeechAsrDataModule.add_arguments(parser) + args = parser.parse_args() + args.exp_dir = Path(args.exp_dir) + + world_size = args.world_size + assert world_size >= 1 + if world_size > 1: + mp.spawn(run, args=(world_size, args), nprocs=world_size, join=True) + else: + run(rank=0, world_size=1, args=args) + + +torch.set_num_threads(1) +torch.set_num_interop_threads(1) + +if __name__ == "__main__": + main() diff --git a/egs/librispeech/ASR/pruned_transducer_stateless6/vq_utils.py b/egs/librispeech/ASR/pruned_transducer_stateless6/vq_utils.py new file mode 100644 index 000000000..c4935f921 --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless6/vq_utils.py @@ -0,0 +1,399 @@ +#!/usr/bin/env python3 +# Copyright 2022 Xiaomi Corporation (Author: Liyong Guo) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import argparse +import copy +import glob +import logging +import os +from functools import cached_property +from pathlib import Path +from typing import List, Tuple + +import numpy as np +import torch +import torch.multiprocessing as mp +import quantization + +from asr_datamodule import LibriSpeechAsrDataModule +from hubert_xlarge import HubertXlargeFineTuned +from icefall.utils import ( + AttributeDict, + setup_logger, +) +from lhotse import CutSet, load_manifest +from lhotse.features.io import NumpyHdf5Writer + + +class CodebookIndexExtractor: + """ + A wrapper of quantiation.Quantizer. + + It's responsible for: + 1. extract and save activations from a teacher model. + 2. train quantizer from previous activations. + 3. extract codebook indexes for whole training set. + Normally this step needs multi GPUs. + """ + + def __init__(self, params: AttributeDict): + + self.params = params + params.subsets = ["clean-100"] + if self.params.full_libri: + self.params.subsets += ["clean-360", "other-500"] + + self.init_dirs() + setup_logger(f"{self.vq_dir}/log-vq_extraction") + + def init_dirs(self): + # vq_dir is the root dir for quantizer: + # training data/ quantizer / extracted codebook indexes + self.vq_dir = ( + self.params.exp_dir / f"vq/{self.params.teacher_model_id}/" + ) + self.vq_dir.mkdir(parents=True, exist_ok=True) + + # manifest_dir for : + # splited original manifests, + # extracted codebook indexes and their related manifests + self.manifest_dir = self.vq_dir / f"splits{self.params.world_size}" + self.manifest_dir.mkdir(parents=True, exist_ok=True) + + # It's doesn't matter whether ori_manifest_dir is str or Path. + # Set it to Path to be consistent. + self.ori_manifest_dir = Path("./data/fbank/") + self.dst_manifest_dir = Path("./data/vq_fbank/") + + self.dst_manifest_dir.mkdir(parents=True, exist_ok=True) + + @classmethod + def add_arguments(cls, parser: argparse.ArgumentParser): + # Options about teacher embeddings eatraction. + parser.add_argument( + "--embedding-layer", + type=int, + help="layer to extract teacher embeddings, 1-based.", + default=36, + ) + + parser.add_argument( + "--num-utts", + type=int, + default=1000, + help="num utts to train quantizer", + ) + + parser.add_argument( + "--num-codebooks", + type=int, + default=8, + help="""number of codebooks, + i.e. number of codebook indexes each teacher embedding is compressed. + """, + ) + + @property + def embedding_file_path(self): + """ + The saved embedding is used to train quantizer. + """ + embedding_file_id = ( + f"num_utts_{self.params.num_utts}" + + f"-layer_{self.params.embedding_layer}" + + "-embedding_embeddings.h5" + ) + + embedding_file_path = self.vq_dir / embedding_file_id + return embedding_file_path + + @torch.no_grad() + def extract_and_save_embedding(self): + """ + The extract embedding is used to train quantizer. + """ + if self.embedding_file_path.exists(): + warn_message = ( + f"{self.embedding_file_path} already exists." + + " Skip extracting embeddings from teacher model" + ) + logging.warn(warn_message) + return + + total_cuts = 0 + with NumpyHdf5Writer(self.embedding_file_path) as writer: + for batch_idx, batch in enumerate(self.quantizer_train_dl): + cut_list = batch["supervisions"]["cut"] + ( + encoder_embedding, + num_frames, + ) = self.teacher_model.extract_embedding(batch) + encoder_embedding = encoder_embedding.cpu().numpy() + for idx, cut in enumerate(cut_list): + cut.encoder_embedding = writer.store_array( + key=cut.id, + value=encoder_embedding[idx][: num_frames[idx]], + ) + total_cuts += len(cut_list) + logging.info( + f"Processed {total_cuts} output of {self.params.num_utts} cuts." + ) + + logging.info(f"Processed all {total_cuts} cuts.") + + @property + def quantizer_train_dl(self): + # used to train quantizer. + librispeech = LibriSpeechAsrDataModule(self.params) + quantizer_trian_cuts = librispeech.train_clean_100_cuts().subset( + first=self.params.num_utts + ) + return librispeech.train_dataloaders(quantizer_trian_cuts) + + @cached_property + def quantizer_file_path(self): + quantizer_file_id = ( + f"num_utts-{self.params.num_utts}" + + f"-layer-{self.params.embedding_layer}" + + f"-num_codebooks_{self.params.num_codebooks}" + + "-quantizer.pt" + ) + quantizer_file_path = Path(self.vq_dir) / quantizer_file_id + + return quantizer_file_path + + def train_quantizer(self): + if self.quantizer_file_path.exists(): + warn_message = ( + f"{self.quantizer_file_path} already exists." + + " Skip trainning quantizer." + ) + logging.warn(warn_message) + return + + assert self.embedding_file_path.exists() + trainer = quantization.QuantizerTrainer( + dim=self.params.embedding_dim, + bytes_per_frame=self.params.num_codebooks, + device=self.params.device, + ) + train, valid = quantization.read_hdf5_data(self.embedding_file_path) + B = 512 # Minibatch size, this is very arbitrary, it's close to what we used + # when we tuned this method. + + def minibatch_generator(data: torch.Tensor, repeat: bool): + assert 3 * B < data.shape[0] + cur_offset = 0 + while True if repeat else cur_offset + B <= data.shape[0]: + start = cur_offset % (data.shape[0] + 1 - B) + end = start + B + cur_offset += B + yield data[start:end, :].to(self.params.device).to( + dtype=torch.float + ) + + for x in minibatch_generator(train, repeat=True): + trainer.step(x) + if trainer.done(): + break + + quantizer = trainer.get_quantizer() + torch.save(quantizer.state_dict(), self.quantizer_file_path) + + def split_ori_manifests(self): + """ + When multi gpus are available, split original manifests + and extract codebook indexes in a prallel way. + """ + for subset in self.params.subsets: + logging.info(f"About to split {subset}.") + ori_manifest = f"./data/fbank/cuts_train-{subset}.json.gz" + split_cmd = f"lhotse split {self.params.world_size} {ori_manifest} {self.manifest_dir}" + os.system(f"{split_cmd}") + + def merge_vq_manifests(self): + """ + Merge generated vq included manfiests and storage to self.dst_manifest_dir. + """ + for subset in self.params.subsets: + vq_manifests = f"{self.manifest_dir}/with_codebook_indexes-cuts_train-{subset}*.json.gz" + dst_vq_manifest = ( + self.dst_manifest_dir / f"cuts_train-{subset}.json.gz" + ) + if 1 == self.params.world_size: + merge_cmd = f"cp {vq_manifests} {dst_vq_manifest}" + else: + merge_cmd = f"lhotse combine {vq_manifests} {dst_vq_manifest}" + os.system(f"{merge_cmd}") + + def reuse_manifests(self): + """ + Only train-* subsets are extracted codebook indexes from. + The reset subsets are just a link from ./data/fbank. + """ + + def is_train(manifest: str) -> bool: + for train_subset in ["clean-100", "clean-360", "other-500"]: + if train_subset in manifest: + return True + return False + + # Type of self.ori_nanifest_dir is Path + # and result type of glob.glob is str. + reusable_manifests = [ + manifest + for manifest in glob.glob(f"{self.ori_manifest_dir}/*.gz") + if not is_train(manifest) + ] + for manifest_path in reusable_manifests: + ori_manifest_path = Path(manifest_path).resolve() + # Path cannot used as a parameter of str.replace. + # Cast them to str. + dst_manifest_path = Path( + manifest_path.replace( + str(self.ori_manifest_dir), str(self.dst_manifest_dir) + ) + ).resolve() + if not dst_manifest_path.exists(): + os.symlink(ori_manifest_path, dst_manifest_path) + + def create_vq_fbank(self): + self.reuse_manifests() + self.merge_vq_manifests() + + @cached_property + def teacher_model(self): + return HubertXlargeFineTuned(self.params) + + @cached_property + def quantizer(self): + assert self.quantizer_file_path.exists() + quantizer = quantization.Quantizer( + dim=self.params.embedding_dim, + num_codebooks=self.params.num_codebooks, + codebook_size=256, + ) + quantizer.load_state_dict(torch.load(self.quantizer_file_path)) + quantizer.to(self.params.device) + return quantizer + + def load_ori_dl(self, subset): + if self.params.world_size == 1: + ori_manifest_path = f"./data/fbank/cuts_train-{subset}.json.gz" + else: + ori_manifest_path = ( + self.manifest_dir + / f"cuts_train-{subset}.{self.params.manifest_index}.json.gz" + ) + + cuts = load_manifest(ori_manifest_path) + dl = LibriSpeechAsrDataModule(self.params).train_dataloaders(cuts) + return dl + + def _release_gpu_memory(self): + self.__dict__.pop("teacher_model", None) + self.__dict__.pop("quantizer", None) + torch.cuda.empty_cache() + + def extract_codebook_indexes(self): + if self.params.world_size == 1: + self.extract_codebook_indexes_imp() + else: + # Since a new extractor will be created for each rank in + # compute_codebook_indexes_parallel, it's better to + # release the GPU memory occupied by current extractor. + self._release_gpu_memory() + + # Prepare split manifests for each job. + self.split_ori_manifests() + mp.spawn( + compute_codebook_indexes_parallel, + args=(self.params,), + nprocs=self.params.world_size, + join=True, + ) + self.create_vq_fbank() + + @torch.no_grad() + def extract_codebook_indexes_imp(self): + for subset in self.params.subsets: + num_cuts = 0 + cuts = [] + if self.params.world_size == 1: + manifest_file_id = f"{subset}" + else: + manifest_file_id = f"{subset}-{self.params.manifest_index}" + + manifest_file_path = self.manifest_dir / manifest_file_id + with NumpyHdf5Writer(manifest_file_path) as writer: + for batch_idx, batch in enumerate(self.load_ori_dl(subset)): + ( + encoder_embedding, + num_frames, + ) = self.teacher_model.extract_embedding(batch) + codebook_indexes = self.quantizer.encode(encoder_embedding) + # [N, T, C] + codebook_indexes = codebook_indexes.to("cpu").numpy() + assert np.min(codebook_indexes) >= 0 + assert np.max(codebook_indexes) < 256 + supervisions = batch["supervisions"] + cut_list = supervisions["cut"] + assert len(cut_list) == codebook_indexes.shape[0] + assert all(c.start == 0 for c in supervisions["cut"]) + + for idx, cut in enumerate(cut_list): + cut.codebook_indexes = writer.store_array( + key=cut.id, + value=codebook_indexes[idx][: num_frames[idx]], + frame_shift=0.02, + temporal_dim=0, + start=0, + ) + cuts += cut_list + num_cuts += len(cut_list) + message = f"Processed {num_cuts} cuts from {subset}" + if self.params.world_size > 1: + message += f" by job {self.params.manifest_index}" + logging.info(f"{message}.") + + json_file_path = ( + self.manifest_dir + / f"with_codebook_indexes-cuts_train-{manifest_file_id}.json.gz" + ) + CutSet.from_cuts(cuts).to_json(json_file_path) + + +@torch.no_grad() +def compute_codebook_indexes_parallel( + rank: int, + params, +) -> List[Tuple[str, List[int]]]: + """Create an extractor for each rank and extract codebook indexes parallelly. + + Normally, this function is called by torch.multiprocessing + when multi GPUs are available. + """ + params = copy.deepcopy(params) + device = torch.device("cuda", rank) + params.device = device + + # rank is 0-based while split manifests by "lhotse split" is 1-based. + params.manifest_index = rank + 1 + + extractor = CodebookIndexExtractor(params=params) + extractor.extract_codebook_indexes_imp() diff --git a/egs/librispeech/ASR/tdnn_lstm_ctc/asr_datamodule.py b/egs/librispeech/ASR/tdnn_lstm_ctc/asr_datamodule.py index 8dd1459ca..355ccc99a 100644 --- a/egs/librispeech/ASR/tdnn_lstm_ctc/asr_datamodule.py +++ b/egs/librispeech/ASR/tdnn_lstm_ctc/asr_datamodule.py @@ -24,17 +24,20 @@ from pathlib import Path from typing import Any, Dict, Optional import torch -from lhotse import CutSet, Fbank, FbankConfig, load_manifest -from lhotse.dataset import ( - BucketingSampler, +from lhotse import CutSet, Fbank, FbankConfig, load_manifest, load_manifest_lazy +from lhotse.dataset import ( # noqa F401 for PrecomputedFeatures CutConcatenate, CutMix, + DynamicBucketingSampler, K2SpeechRecognitionDataset, PrecomputedFeatures, SingleCutSampler, SpecAugment, ) -from lhotse.dataset.input_strategies import OnTheFlyFeatures +from lhotse.dataset.input_strategies import ( # noqa F401 For AudioSamples + AudioSamples, + OnTheFlyFeatures, +) from lhotse.utils import fix_random_seed from torch.utils.data import DataLoader @@ -110,7 +113,7 @@ class LibriSpeechAsrDataModule: "--num-buckets", type=int, default=30, - help="The number of buckets for the BucketingSampler" + help="The number of buckets for the DynamicBucketingSampler" "(you might want to increase it for larger datasets).", ) group.add_argument( @@ -150,6 +153,12 @@ class LibriSpeechAsrDataModule: help="When enabled (=default), the examples will be " "shuffled for each epoch.", ) + group.add_argument( + "--drop-last", + type=str2bool, + default=True, + help="Whether to drop last batch. Used by sampler.", + ) group.add_argument( "--return-cuts", type=str2bool, @@ -192,6 +201,13 @@ class LibriSpeechAsrDataModule: "with training dataset. ", ) + group.add_argument( + "--input-strategy", + type=str, + default="PrecomputedFeatures", + help="AudioSamples or PrecomputedFeatures", + ) + def train_dataloaders( self, cuts_train: CutSet, @@ -209,7 +225,7 @@ class LibriSpeechAsrDataModule: logging.info("Enable MUSAN") logging.info("About to get Musan cuts") cuts_musan = load_manifest( - self.args.manifest_dir / "cuts_musan.json.gz" + self.args.manifest_dir / "musan_cuts.jsonl.gz" ) transforms.append( CutMix( @@ -263,6 +279,7 @@ class LibriSpeechAsrDataModule: logging.info("About to create train dataset") train = K2SpeechRecognitionDataset( + input_strategy=eval(self.args.input_strategy)(), cut_transforms=transforms, input_transforms=input_transforms, return_cuts=self.args.return_cuts, @@ -289,14 +306,13 @@ class LibriSpeechAsrDataModule: ) if self.args.bucketing_sampler: - logging.info("Using BucketingSampler.") - train_sampler = BucketingSampler( + logging.info("Using DynamicBucketingSampler.") + train_sampler = DynamicBucketingSampler( cuts_train, max_duration=self.args.max_duration, shuffle=self.args.shuffle, num_buckets=self.args.num_buckets, - bucket_method="equal_duration", - drop_last=True, + drop_last=self.args.drop_last, ) else: logging.info("Using SingleCutSampler.") @@ -350,7 +366,7 @@ class LibriSpeechAsrDataModule: cut_transforms=transforms, return_cuts=self.args.return_cuts, ) - valid_sampler = BucketingSampler( + valid_sampler = DynamicBucketingSampler( cuts_valid, max_duration=self.args.max_duration, shuffle=False, @@ -371,11 +387,13 @@ class LibriSpeechAsrDataModule: test = K2SpeechRecognitionDataset( input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))) if self.args.on_the_fly_feats - else PrecomputedFeatures(), + else eval(self.args.input_strategy)(), return_cuts=self.args.return_cuts, ) - sampler = BucketingSampler( - cuts, max_duration=self.args.max_duration, shuffle=False + sampler = DynamicBucketingSampler( + cuts, + max_duration=self.args.max_duration, + shuffle=False, ) logging.debug("About to create test dataloader") test_dl = DataLoader( @@ -389,40 +407,48 @@ class LibriSpeechAsrDataModule: @lru_cache() def train_clean_100_cuts(self) -> CutSet: logging.info("About to get train-clean-100 cuts") - return load_manifest( - self.args.manifest_dir / "cuts_train-clean-100.json.gz" + return load_manifest_lazy( + self.args.manifest_dir / "librispeech_cuts_train-clean-100.jsonl.gz" ) @lru_cache() def train_clean_360_cuts(self) -> CutSet: logging.info("About to get train-clean-360 cuts") - return load_manifest( - self.args.manifest_dir / "cuts_train-clean-360.json.gz" + return load_manifest_lazy( + self.args.manifest_dir / "librispeech_cuts_train-clean-360.jsonl.gz" ) @lru_cache() def train_other_500_cuts(self) -> CutSet: logging.info("About to get train-other-500 cuts") - return load_manifest( - self.args.manifest_dir / "cuts_train-other-500.json.gz" + return load_manifest_lazy( + self.args.manifest_dir / "librispeech_cuts_train-other-500.jsonl.gz" ) @lru_cache() def dev_clean_cuts(self) -> CutSet: logging.info("About to get dev-clean cuts") - return load_manifest(self.args.manifest_dir / "cuts_dev-clean.json.gz") + return load_manifest_lazy( + self.args.manifest_dir / "librispeech_cuts_dev-clean.jsonl.gz" + ) @lru_cache() def dev_other_cuts(self) -> CutSet: logging.info("About to get dev-other cuts") - return load_manifest(self.args.manifest_dir / "cuts_dev-other.json.gz") + return load_manifest_lazy( + self.args.manifest_dir / "librispeech_cuts_dev-other.jsonl.gz" + ) @lru_cache() def test_clean_cuts(self) -> CutSet: logging.info("About to get test-clean cuts") - return load_manifest(self.args.manifest_dir / "cuts_test-clean.json.gz") + return load_manifest_lazy( + self.args.manifest_dir / "librispeech_cuts_test-clean.jsonl.gz" + ) @lru_cache() def test_other_cuts(self) -> CutSet: logging.info("About to get test-other cuts") - return load_manifest(self.args.manifest_dir / "cuts_test-other.json.gz") + return load_manifest_lazy( + self.args.manifest_dir / "librispeech_cuts_test-other.jsonl.gz" + ) diff --git a/egs/librispeech/ASR/tdnn_lstm_ctc/train.py b/egs/librispeech/ASR/tdnn_lstm_ctc/train.py index 8597525ba..827e3ae1f 100755 --- a/egs/librispeech/ASR/tdnn_lstm_ctc/train.py +++ b/egs/librispeech/ASR/tdnn_lstm_ctc/train.py @@ -16,6 +16,15 @@ # See the License for the specific language governing permissions and # limitations under the License. +""" +Usage: + export CUDA_VISIBLE_DEVICES="0,1,2,3" + ./tdnn_lstm_ctc/train.py \ + --world-size 4 \ + --full-libri 1 \ + --max-duration 300 \ + --num-epochs 20 +""" import argparse import logging @@ -29,6 +38,7 @@ import torch.multiprocessing as mp import torch.nn as nn import torch.optim as optim from asr_datamodule import LibriSpeechAsrDataModule +from lhotse.cut import Cut from lhotse.utils import fix_random_seed from model import TdnnLstm from torch import Tensor @@ -544,10 +554,25 @@ def run(rank, world_size, args): if params.full_libri: train_cuts += librispeech.train_clean_360_cuts() train_cuts += librispeech.train_other_500_cuts() + + def remove_short_and_long_utt(c: Cut): + # Keep only utterances with duration between 1 second and 20 seconds + # + # Caution: There is a reason to select 20.0 here. Please see + # ../local/display_manifest_statistics.py + # + # You should use ../local/display_manifest_statistics.py to get + # an utterance duration distribution for your dataset to select + # the threshold + return 1.0 <= c.duration <= 20.0 + + train_cuts = train_cuts.filter(remove_short_and_long_utt) + train_dl = librispeech.train_dataloaders(train_cuts) valid_cuts = librispeech.dev_clean_cuts() valid_cuts += librispeech.dev_other_cuts() + valid_dl = librispeech.valid_dataloaders(valid_cuts) for epoch in range(params.start_epoch, params.num_epochs): diff --git a/egs/librispeech/ASR/transducer/train.py b/egs/librispeech/ASR/transducer/train.py index cbd9259e0..11c72ae4f 100755 --- a/egs/librispeech/ASR/transducer/train.py +++ b/egs/librispeech/ASR/transducer/train.py @@ -360,7 +360,7 @@ def compute_loss( is_training: bool, ) -> Tuple[Tensor, MetricsTracker]: """ - Compute CTC loss given the model and its inputs. + Compute RNN-T loss given the model and its inputs. Args: params: diff --git a/egs/librispeech/ASR/transducer_lstm/train.py b/egs/librispeech/ASR/transducer_lstm/train.py index eef4d3430..17ba6143c 100755 --- a/egs/librispeech/ASR/transducer_lstm/train.py +++ b/egs/librispeech/ASR/transducer_lstm/train.py @@ -364,7 +364,7 @@ def compute_loss( is_training: bool, ) -> Tuple[Tensor, MetricsTracker]: """ - Compute CTC loss given the model and its inputs. + Compute RNN-T loss given the model and its inputs. Args: params: diff --git a/egs/librispeech/ASR/transducer_stateless/test_compute_ali.py b/egs/librispeech/ASR/transducer_stateless/test_compute_ali.py index 99d5b3788..b00fc34f1 100755 --- a/egs/librispeech/ASR/transducer_stateless/test_compute_ali.py +++ b/egs/librispeech/ASR/transducer_stateless/test_compute_ali.py @@ -44,8 +44,8 @@ from pathlib import Path import sentencepiece as spm import torch from alignment import get_word_starting_frames -from lhotse import CutSet, load_manifest -from lhotse.dataset import K2SpeechRecognitionDataset, SingleCutSampler +from lhotse import CutSet, load_manifest_lazy +from lhotse.dataset import DynamicBucketingSampler, K2SpeechRecognitionDataset from lhotse.dataset.collation import collate_custom_field @@ -93,14 +93,15 @@ def main(): sp = spm.SentencePieceProcessor() sp.load(args.bpe_model) - cuts_json = args.ali_dir / f"cuts_{args.dataset}.json.gz" + cuts_jsonl = args.ali_dir / f"librispeech_cuts_{args.dataset}.jsonl.gz" - logging.info(f"Loading {cuts_json}") - cuts = load_manifest(cuts_json) + logging.info(f"Loading {cuts_jsonl}") + cuts = load_manifest_lazy(cuts_jsonl) - sampler = SingleCutSampler( + sampler = DynamicBucketingSampler( cuts, max_duration=30, + num_buckets=30, shuffle=False, ) diff --git a/egs/librispeech/ASR/transducer_stateless/train.py b/egs/librispeech/ASR/transducer_stateless/train.py index cb7f08a09..837a9de2d 100755 --- a/egs/librispeech/ASR/transducer_stateless/train.py +++ b/egs/librispeech/ASR/transducer_stateless/train.py @@ -381,7 +381,7 @@ def compute_loss( is_training: bool, ) -> Tuple[Tensor, MetricsTracker]: """ - Compute CTC loss given the model and its inputs. + Compute RNN-T loss given the model and its inputs. Args: params: diff --git a/egs/librispeech/ASR/transducer_stateless2/train.py b/egs/librispeech/ASR/transducer_stateless2/train.py index cb13e317c..fe075b073 100755 --- a/egs/librispeech/ASR/transducer_stateless2/train.py +++ b/egs/librispeech/ASR/transducer_stateless2/train.py @@ -370,7 +370,7 @@ def compute_loss( is_training: bool, ) -> Tuple[Tensor, MetricsTracker]: """ - Compute CTC loss given the model and its inputs. + Compute RNN-T loss given the model and its inputs. Args: params: diff --git a/egs/librispeech/ASR/transducer_stateless_multi_datasets/asr_datamodule.py b/egs/librispeech/ASR/transducer_stateless_multi_datasets/asr_datamodule.py deleted file mode 100644 index c6cf739fb..000000000 --- a/egs/librispeech/ASR/transducer_stateless_multi_datasets/asr_datamodule.py +++ /dev/null @@ -1,333 +0,0 @@ -# Copyright 2021 Piotr Żelasko -# 2022 Xiaomi Corp. (authors: Fangjun Kuang -# Mingshuang Luo) -# -# See ../../../../LICENSE for clarification regarding multiple authors -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import argparse -import inspect -import logging -from pathlib import Path -from typing import Optional - -import torch -from lhotse import CutSet, Fbank, FbankConfig -from lhotse.dataset import ( - BucketingSampler, - CutMix, - DynamicBucketingSampler, - K2SpeechRecognitionDataset, - SpecAugment, -) -from lhotse.dataset.input_strategies import ( - OnTheFlyFeatures, - PrecomputedFeatures, -) -from lhotse.utils import fix_random_seed -from torch.utils.data import DataLoader - -from icefall.utils import str2bool - - -class _SeedWorkers: - def __init__(self, seed: int): - self.seed = seed - - def __call__(self, worker_id: int): - fix_random_seed(self.seed + worker_id) - - -class AsrDataModule: - def __init__(self, args: argparse.Namespace): - self.args = args - - @classmethod - def add_arguments(cls, parser: argparse.ArgumentParser): - group = parser.add_argument_group( - title="ASR data related options", - description="These options are used for the preparation of " - "PyTorch DataLoaders from Lhotse CutSet's -- they control the " - "effective batch sizes, sampling strategies, applied data " - "augmentations, etc.", - ) - - group.add_argument( - "--max-duration", - type=int, - default=200.0, - help="Maximum pooled recordings duration (seconds) in a " - "single batch. You can reduce it if it causes CUDA OOM.", - ) - - group.add_argument( - "--bucketing-sampler", - type=str2bool, - default=True, - help="When enabled, the batches will come from buckets of " - "similar duration (saves padding frames).", - ) - - group.add_argument( - "--num-buckets", - type=int, - default=30, - help="The number of buckets for the BucketingSampler " - "and DynamicBucketingSampler." - "(you might want to increase it for larger datasets).", - ) - - group.add_argument( - "--shuffle", - type=str2bool, - default=True, - help="When enabled (=default), the examples will be " - "shuffled for each epoch.", - ) - - group.add_argument( - "--return-cuts", - type=str2bool, - default=True, - help="When enabled, each batch will have the " - "field: batch['supervisions']['cut'] with the cuts that " - "were used to construct it.", - ) - - group.add_argument( - "--num-workers", - type=int, - default=2, - help="The number of training dataloader workers that " - "collect the batches.", - ) - - group.add_argument( - "--enable-spec-aug", - type=str2bool, - default=True, - help="When enabled, use SpecAugment for training dataset.", - ) - - group.add_argument( - "--spec-aug-time-warp-factor", - type=int, - default=80, - help="Used only when --enable-spec-aug is True. " - "It specifies the factor for time warping in SpecAugment. " - "Larger values mean more warping. " - "A value less than 1 means to disable time warp.", - ) - - group.add_argument( - "--enable-musan", - type=str2bool, - default=True, - help="When enabled, select noise from MUSAN and mix it" - "with training dataset. ", - ) - - group.add_argument( - "--manifest-dir", - type=Path, - default=Path("data/fbank"), - help="Path to directory with train/valid/test cuts.", - ) - - group.add_argument( - "--on-the-fly-feats", - type=str2bool, - default=False, - help="When enabled, use on-the-fly cut mixing and feature " - "extraction. Will drop existing precomputed feature manifests " - "if available. Used only in dev/test CutSet", - ) - - def train_dataloaders( - self, - cuts_train: CutSet, - dynamic_bucketing: bool, - on_the_fly_feats: bool, - cuts_musan: Optional[CutSet] = None, - ) -> DataLoader: - """ - Args: - cuts_train: - Cuts for training. - cuts_musan: - If not None, it is the cuts for mixing. - dynamic_bucketing: - True to use DynamicBucketingSampler; - False to use BucketingSampler. - on_the_fly_feats: - True to use OnTheFlyFeatures; - False to use PrecomputedFeatures. - """ - transforms = [] - if cuts_musan is not None: - logging.info("Enable MUSAN") - transforms.append( - CutMix( - cuts=cuts_musan, prob=0.5, snr=(10, 20), preserve_id=True - ) - ) - else: - logging.info("Disable MUSAN") - - input_transforms = [] - - if self.args.enable_spec_aug: - logging.info("Enable SpecAugment") - logging.info( - f"Time warp factor: {self.args.spec_aug_time_warp_factor}" - ) - # Set the value of num_frame_masks according to Lhotse's version. - # In different Lhotse's versions, the default of num_frame_masks is - # different. - num_frame_masks = 10 - num_frame_masks_parameter = inspect.signature( - SpecAugment.__init__ - ).parameters["num_frame_masks"] - if num_frame_masks_parameter.default == 1: - num_frame_masks = 2 - logging.info(f"Num frame mask: {num_frame_masks}") - input_transforms.append( - SpecAugment( - time_warp_factor=self.args.spec_aug_time_warp_factor, - num_frame_masks=num_frame_masks, - features_mask_size=27, - num_feature_masks=2, - frames_mask_size=100, - ) - ) - else: - logging.info("Disable SpecAugment") - - logging.info("About to create train dataset") - train = K2SpeechRecognitionDataset( - cut_transforms=transforms, - input_transforms=input_transforms, - return_cuts=self.args.return_cuts, - ) - - # NOTE: the PerturbSpeed transform should be added only if we - # remove it from data prep stage. - # Add on-the-fly speed perturbation; since originally it would - # have increased epoch size by 3, we will apply prob 2/3 and use - # 3x more epochs. - # Speed perturbation probably should come first before - # concatenation, but in principle the transforms order doesn't have - # to be strict (e.g. could be randomized) - # transforms = [PerturbSpeed(factors=[0.9, 1.1], p=2/3)] + transforms # noqa - # Drop feats to be on the safe side. - train = K2SpeechRecognitionDataset( - cut_transforms=transforms, - input_strategy=( - OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))) - if on_the_fly_feats - else PrecomputedFeatures() - ), - input_transforms=input_transforms, - return_cuts=self.args.return_cuts, - ) - - if dynamic_bucketing: - logging.info("Using DynamicBucketingSampler.") - train_sampler = DynamicBucketingSampler( - cuts_train, - max_duration=self.args.max_duration, - shuffle=self.args.shuffle, - num_buckets=self.args.num_buckets, - drop_last=True, - ) - else: - logging.info("Using BucketingSampler.") - train_sampler = BucketingSampler( - cuts_train, - max_duration=self.args.max_duration, - shuffle=self.args.shuffle, - num_buckets=self.args.num_buckets, - bucket_method="equal_duration", - drop_last=True, - ) - - logging.info("About to create train dataloader") - - # 'seed' is derived from the current random state, which will have - # previously been set in the main process. - seed = torch.randint(0, 100000, ()).item() - worker_init_fn = _SeedWorkers(seed) - - train_dl = DataLoader( - train, - sampler=train_sampler, - batch_size=None, - num_workers=self.args.num_workers, - persistent_workers=False, - worker_init_fn=worker_init_fn, - ) - return train_dl - - def valid_dataloaders(self, cuts_valid: CutSet) -> DataLoader: - transforms = [] - - logging.info("About to create dev dataset") - if self.args.on_the_fly_feats: - validate = K2SpeechRecognitionDataset( - cut_transforms=transforms, - input_strategy=OnTheFlyFeatures( - Fbank(FbankConfig(num_mel_bins=80)) - ), - return_cuts=self.args.return_cuts, - ) - else: - validate = K2SpeechRecognitionDataset( - cut_transforms=transforms, - return_cuts=self.args.return_cuts, - ) - valid_sampler = BucketingSampler( - cuts_valid, - max_duration=self.args.max_duration, - shuffle=False, - ) - logging.info("About to create dev dataloader") - valid_dl = DataLoader( - validate, - sampler=valid_sampler, - batch_size=None, - num_workers=2, - persistent_workers=False, - ) - - return valid_dl - - def test_dataloaders(self, cuts: CutSet) -> DataLoader: - logging.debug("About to create test dataset") - test = K2SpeechRecognitionDataset( - input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))) - if self.args.on_the_fly_feats - else PrecomputedFeatures(), - return_cuts=self.args.return_cuts, - ) - sampler = BucketingSampler( - cuts, max_duration=self.args.max_duration, shuffle=False - ) - logging.debug("About to create test dataloader") - test_dl = DataLoader( - test, - batch_size=None, - sampler=sampler, - num_workers=self.args.num_workers, - ) - return test_dl diff --git a/egs/librispeech/ASR/transducer_stateless_multi_datasets/asr_datamodule.py b/egs/librispeech/ASR/transducer_stateless_multi_datasets/asr_datamodule.py new file mode 120000 index 000000000..3ba9ada4f --- /dev/null +++ b/egs/librispeech/ASR/transducer_stateless_multi_datasets/asr_datamodule.py @@ -0,0 +1 @@ +../pruned_transducer_stateless3/asr_datamodule.py \ No newline at end of file diff --git a/egs/librispeech/ASR/transducer_stateless_multi_datasets/gigaspeech.py b/egs/librispeech/ASR/transducer_stateless_multi_datasets/gigaspeech.py deleted file mode 100644 index 286771d7d..000000000 --- a/egs/librispeech/ASR/transducer_stateless_multi_datasets/gigaspeech.py +++ /dev/null @@ -1,75 +0,0 @@ -# Copyright 2021 Piotr Żelasko -# 2022 Xiaomi Corp. (authors: Fangjun Kuang) -# -# See ../../../../LICENSE for clarification regarding multiple authors -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -import logging -from pathlib import Path - -from lhotse import CutSet, load_manifest - - -class GigaSpeech: - def __init__(self, manifest_dir: str): - """ - Args: - manifest_dir: - It is expected to contain the following files:: - - - cuts_XL_raw.jsonl.gz - - cuts_L_raw.jsonl.gz - - cuts_M_raw.jsonl.gz - - cuts_S_raw.jsonl.gz - - cuts_XS_raw.jsonl.gz - - cuts_DEV_raw.jsonl.gz - - cuts_TEST_raw.jsonl.gz - """ - self.manifest_dir = Path(manifest_dir) - - def train_XL_cuts(self) -> CutSet: - f = self.manifest_dir / "cuts_XL_raw.jsonl.gz" - logging.info(f"About to get train-XL cuts from {f}") - return CutSet.from_jsonl_lazy(f) - - def train_L_cuts(self) -> CutSet: - f = self.manifest_dir / "cuts_L_raw.jsonl.gz" - logging.info(f"About to get train-L cuts from {f}") - return CutSet.from_jsonl_lazy(f) - - def train_M_cuts(self) -> CutSet: - f = self.manifest_dir / "cuts_M_raw.jsonl.gz" - logging.info(f"About to get train-M cuts from {f}") - return CutSet.from_jsonl_lazy(f) - - def train_S_cuts(self) -> CutSet: - f = self.manifest_dir / "cuts_S_raw.jsonl.gz" - logging.info(f"About to get train-S cuts from {f}") - return CutSet.from_jsonl_lazy(f) - - def train_XS_cuts(self) -> CutSet: - f = self.manifest_dir / "cuts_XS_raw.jsonl.gz" - logging.info(f"About to get train-XS cuts from {f}") - return CutSet.from_jsonl_lazy(f) - - def test_cuts(self) -> CutSet: - f = self.manifest_dir / "cuts_TEST.jsonl.gz" - logging.info(f"About to get TEST cuts from {f}") - return load_manifest(f) - - def dev_cuts(self) -> CutSet: - f = self.manifest_dir / "cuts_DEV.jsonl.gz" - logging.info(f"About to get DEV cuts from {f}") - return load_manifest(f) diff --git a/egs/librispeech/ASR/transducer_stateless_multi_datasets/gigaspeech.py b/egs/librispeech/ASR/transducer_stateless_multi_datasets/gigaspeech.py new file mode 120000 index 000000000..5242c652a --- /dev/null +++ b/egs/librispeech/ASR/transducer_stateless_multi_datasets/gigaspeech.py @@ -0,0 +1 @@ +../pruned_transducer_stateless3/gigaspeech.py \ No newline at end of file diff --git a/egs/librispeech/ASR/transducer_stateless_multi_datasets/librispeech.py b/egs/librispeech/ASR/transducer_stateless_multi_datasets/librispeech.py deleted file mode 100644 index 00b7c8334..000000000 --- a/egs/librispeech/ASR/transducer_stateless_multi_datasets/librispeech.py +++ /dev/null @@ -1,74 +0,0 @@ -# Copyright 2021 Piotr Żelasko -# 2022 Xiaomi Corp. (authors: Fangjun Kuang) -# -# See ../../../../LICENSE for clarification regarding multiple authors -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import logging -from pathlib import Path - -from lhotse import CutSet, load_manifest - - -class LibriSpeech: - def __init__(self, manifest_dir: str): - """ - Args: - manifest_dir: - It is expected to contain the following files:: - - - cuts_dev-clean.json.gz - - cuts_dev-other.json.gz - - cuts_test-clean.json.gz - - cuts_test-other.json.gz - - cuts_train-clean-100.json.gz - - cuts_train-clean-360.json.gz - - cuts_train-other-500.json.gz - """ - self.manifest_dir = Path(manifest_dir) - - def train_clean_100_cuts(self) -> CutSet: - f = self.manifest_dir / "cuts_train-clean-100.json.gz" - logging.info(f"About to get train-clean-100 cuts from {f}") - return load_manifest(f) - - def train_clean_360_cuts(self) -> CutSet: - f = self.manifest_dir / "cuts_train-clean-360.json.gz" - logging.info(f"About to get train-clean-360 cuts from {f}") - return load_manifest(f) - - def train_other_500_cuts(self) -> CutSet: - f = self.manifest_dir / "cuts_train-other-500.json.gz" - logging.info(f"About to get train-other-500 cuts from {f}") - return load_manifest(f) - - def test_clean_cuts(self) -> CutSet: - f = self.manifest_dir / "cuts_test-clean.json.gz" - logging.info(f"About to get test-clean cuts from {f}") - return load_manifest(f) - - def test_other_cuts(self) -> CutSet: - f = self.manifest_dir / "cuts_test-other.json.gz" - logging.info(f"About to get test-other cuts from {f}") - return load_manifest(f) - - def dev_clean_cuts(self) -> CutSet: - f = self.manifest_dir / "cuts_dev-clean.json.gz" - logging.info(f"About to get dev-clean cuts from {f}") - return load_manifest(f) - - def dev_other_cuts(self) -> CutSet: - f = self.manifest_dir / "cuts_dev-other.json.gz" - logging.info(f"About to get dev-other cuts from {f}") - return load_manifest(f) diff --git a/egs/librispeech/ASR/transducer_stateless_multi_datasets/librispeech.py b/egs/librispeech/ASR/transducer_stateless_multi_datasets/librispeech.py new file mode 120000 index 000000000..b76723bf5 --- /dev/null +++ b/egs/librispeech/ASR/transducer_stateless_multi_datasets/librispeech.py @@ -0,0 +1 @@ +../pruned_transducer_stateless3/librispeech.py \ No newline at end of file diff --git a/egs/librispeech/ASR/transducer_stateless_multi_datasets/test_asr_datamodule.py b/egs/librispeech/ASR/transducer_stateless_multi_datasets/test_asr_datamodule.py index e1833b841..ef51a7811 100755 --- a/egs/librispeech/ASR/transducer_stateless_multi_datasets/test_asr_datamodule.py +++ b/egs/librispeech/ASR/transducer_stateless_multi_datasets/test_asr_datamodule.py @@ -42,7 +42,7 @@ def test_dataset(): if args.enable_musan: cuts_musan = load_manifest( - Path(args.manifest_dir) / "cuts_musan.json.gz" + Path(args.manifest_dir) / "musan_cuts.jsonl.gz" ) else: cuts_musan = None @@ -57,14 +57,12 @@ def test_dataset(): libri_train_dl = asr_datamodule.train_dataloaders( train_clean_100, - dynamic_bucketing=False, on_the_fly_feats=False, cuts_musan=cuts_musan, ) giga_train_dl = asr_datamodule.train_dataloaders( train_S, - dynamic_bucketing=True, on_the_fly_feats=True, cuts_musan=cuts_musan, ) diff --git a/egs/librispeech/ASR/transducer_stateless_multi_datasets/train.py b/egs/librispeech/ASR/transducer_stateless_multi_datasets/train.py index 5572d3f4c..32ce1032c 100755 --- a/egs/librispeech/ASR/transducer_stateless_multi_datasets/train.py +++ b/egs/librispeech/ASR/transducer_stateless_multi_datasets/train.py @@ -425,7 +425,7 @@ def compute_loss( is_training: bool, ) -> Tuple[Tensor, MetricsTracker]: """ - Compute CTC loss given the model and its inputs. + Compute RNN-T loss given the model and its inputs. Args: params: @@ -662,19 +662,17 @@ def train_one_epoch( def filter_short_and_long_utterances(cuts: CutSet) -> CutSet: def remove_short_and_long_utt(c: Cut): # Keep only utterances with duration between 1 second and 20 seconds + # + # Caution: There is a reason to select 20.0 here. Please see + # ../local/display_manifest_statistics.py + # + # You should use ../local/display_manifest_statistics.py to get + # an utterance duration distribution for your dataset to select + # the threshold return 1.0 <= c.duration <= 20.0 - num_in_total = len(cuts) cuts = cuts.filter(remove_short_and_long_utt) - num_left = len(cuts) - num_removed = num_in_total - num_left - removed_percent = num_removed / num_in_total * 100 - - logging.info(f"Before removing short and long utterances: {num_in_total}") - logging.info(f"After removing short and long utterances: {num_left}") - logging.info(f"Removed {num_removed} utterances ({removed_percent:.5f}%)") - return cuts @@ -767,17 +765,18 @@ def run(rank, world_size, args): # DEV 12 hours # Test 40 hours if params.full_libri: - logging.info("Using the L subset of GigaSpeech (2.5k hours)") - train_giga_cuts = gigaspeech.train_L_cuts() + logging.info("Using the XL subset of GigaSpeech (10k hours)") + train_giga_cuts = gigaspeech.train_XL_cuts() else: logging.info("Using the S subset of GigaSpeech (250 hours)") train_giga_cuts = gigaspeech.train_S_cuts() train_giga_cuts = filter_short_and_long_utterances(train_giga_cuts) + train_giga_cuts = train_giga_cuts.repeat(times=None) if args.enable_musan: cuts_musan = load_manifest( - Path(args.manifest_dir) / "cuts_musan.json.gz" + Path(args.manifest_dir) / "musan_cuts.jsonl.gz" ) else: cuts_musan = None @@ -786,14 +785,12 @@ def run(rank, world_size, args): train_dl = asr_datamodule.train_dataloaders( train_cuts, - dynamic_bucketing=False, on_the_fly_feats=False, cuts_musan=cuts_musan, ) giga_train_dl = asr_datamodule.train_dataloaders( train_giga_cuts, - dynamic_bucketing=True, on_the_fly_feats=True, cuts_musan=cuts_musan, ) diff --git a/egs/tedlium3/ASR/local/compute_fbank_tedlium.py b/egs/tedlium3/ASR/local/compute_fbank_tedlium.py index 14200f34f..e324b5025 100755 --- a/egs/tedlium3/ASR/local/compute_fbank_tedlium.py +++ b/egs/tedlium3/ASR/local/compute_fbank_tedlium.py @@ -27,7 +27,7 @@ import os from pathlib import Path import torch -from lhotse import ChunkedLilcomHdf5Writer, CutSet, Fbank, FbankConfig +from lhotse import CutSet, Fbank, FbankConfig, LilcomChunkyWriter from lhotse.recipes.utils import read_manifests_if_cached from icefall.utils import get_executor @@ -52,8 +52,13 @@ def compute_fbank_tedlium(): "test", ) + prefix = "tedlium" + suffix = "jsonl.gz" manifests = read_manifests_if_cached( - prefix="tedlium", dataset_parts=dataset_parts, output_dir=src_dir + dataset_parts=dataset_parts, + output_dir=src_dir, + prefix=prefix, + suffix=suffix, ) assert manifests is not None @@ -61,7 +66,7 @@ def compute_fbank_tedlium(): with get_executor() as ex: # Initialize the executor only once. for partition, m in manifests.items(): - if (output_dir / f"cuts_{partition}.json.gz").is_file(): + if (output_dir / f"{prefix}_cuts_{partition}.{suffix}").is_file(): logging.info(f"{partition} already exists - skipping.") continue logging.info(f"Processing {partition}") @@ -80,15 +85,15 @@ def compute_fbank_tedlium(): cut_set = cut_set.compute_and_store_features( extractor=extractor, - storage_path=f"{output_dir}/feats_{partition}", + storage_path=f"{output_dir}/{prefix}_feats_{partition}", # when an executor is specified, make more partitions num_jobs=cur_num_jobs, executor=ex, - storage_type=ChunkedLilcomHdf5Writer, + storage_type=LilcomChunkyWriter, ) # Split long cuts into many short and un-overlapping cuts cut_set = cut_set.trim_to_supervisions(keep_overlapping=False) - cut_set.to_json(output_dir / f"cuts_{partition}.json.gz") + cut_set.to_file(output_dir / f"{prefix}_cuts_{partition}.{suffix}") if __name__ == "__main__": diff --git a/egs/tedlium3/ASR/local/display_manifest_statistics.py b/egs/tedlium3/ASR/local/display_manifest_statistics.py index 972d03b12..52e152389 100755 --- a/egs/tedlium3/ASR/local/display_manifest_statistics.py +++ b/egs/tedlium3/ASR/local/display_manifest_statistics.py @@ -27,15 +27,15 @@ for usage. """ -from lhotse import load_manifest +from lhotse import load_manifest_lazy def main(): - path = "./data/fbank/cuts_train.json.gz" - path = "./data/fbank/cuts_dev.json.gz" - path = "./data/fbank/cuts_test.json.gz" + path = "./data/fbank/tedlium_cuts_train.jsonl.gz" + path = "./data/fbank/tedlium_cuts_dev.jsonl.gz" + path = "./data/fbank/tedlium_cuts_test.jsonl.gz" - cuts = load_manifest(path) + cuts = load_manifest_lazy(path) cuts.describe() diff --git a/egs/tedlium3/ASR/transducer_stateless/asr_datamodule.py b/egs/tedlium3/ASR/transducer_stateless/asr_datamodule.py index a6b986a94..51de46ae8 100644 --- a/egs/tedlium3/ASR/transducer_stateless/asr_datamodule.py +++ b/egs/tedlium3/ASR/transducer_stateless/asr_datamodule.py @@ -22,11 +22,11 @@ import logging from functools import lru_cache from pathlib import Path -from lhotse import CutSet, Fbank, FbankConfig, load_manifest +from lhotse import CutSet, Fbank, FbankConfig, load_manifest, load_manifest_lazy from lhotse.dataset import ( - BucketingSampler, CutConcatenate, CutMix, + DynamicBucketingSampler, K2SpeechRecognitionDataset, PrecomputedFeatures, SingleCutSampler, @@ -92,7 +92,7 @@ class TedLiumAsrDataModule: "--num-buckets", type=int, default=30, - help="The number of buckets for the BucketingSampler" + help="The number of buckets for the DynamicBucketingSampler" "(you might want to increase it for larger datasets).", ) group.add_argument( @@ -180,7 +180,7 @@ class TedLiumAsrDataModule: if self.args.enable_musan: logging.info("Enable MUSAN") cuts_musan = load_manifest( - self.args.manifest_dir / "cuts_musan.json.gz" + self.args.manifest_dir / "musan_cuts.jsonl.gz" ) transforms.append( CutMix( @@ -261,13 +261,12 @@ class TedLiumAsrDataModule: ) if self.args.bucketing_sampler: - logging.info("Using BucketingSampler.") - train_sampler = BucketingSampler( + logging.info("Using DynamicBucketingSampler.") + train_sampler = DynamicBucketingSampler( cuts_train, max_duration=self.args.max_duration, shuffle=self.args.shuffle, num_buckets=self.args.num_buckets, - bucket_method="equal_duration", drop_last=True, ) else: @@ -311,7 +310,7 @@ class TedLiumAsrDataModule: cut_transforms=transforms, return_cuts=self.args.return_cuts, ) - valid_sampler = BucketingSampler( + valid_sampler = DynamicBucketingSampler( cuts_valid, max_duration=self.args.max_duration, shuffle=False, @@ -335,8 +334,10 @@ class TedLiumAsrDataModule: else PrecomputedFeatures(), return_cuts=self.args.return_cuts, ) - sampler = BucketingSampler( - cuts, max_duration=self.args.max_duration, shuffle=False + sampler = DynamicBucketingSampler( + cuts, + max_duration=self.args.max_duration, + shuffle=False, ) logging.debug("About to create test dataloader") test_dl = DataLoader( @@ -350,14 +351,20 @@ class TedLiumAsrDataModule: @lru_cache() def train_cuts(self) -> CutSet: logging.info("About to get train cuts") - return load_manifest(self.args.manifest_dir / "cuts_train.json.gz") + return load_manifest_lazy( + self.args.manifest_dir / "tedlium_cuts_train.jsonl.gz" + ) @lru_cache() def dev_cuts(self) -> CutSet: logging.info("About to get dev cuts") - return load_manifest(self.args.manifest_dir / "cuts_dev.json.gz") + return load_manifest_lazy( + self.args.manifest_dir / "tedlium_cuts_dev.jsonl.gz" + ) @lru_cache() def test_cuts(self) -> CutSet: logging.info("About to get test cuts") - return load_manifest(self.args.manifest_dir / "cuts_test.json.gz") + return load_manifest_lazy( + self.args.manifest_dir / "tedlium_cuts_test.jsonl.gz" + ) diff --git a/egs/timit/ASR/local/compute_fbank_timit.py b/egs/timit/ASR/local/compute_fbank_timit.py index 8e3cbac4e..094769c8c 100644 --- a/egs/timit/ASR/local/compute_fbank_timit.py +++ b/egs/timit/ASR/local/compute_fbank_timit.py @@ -29,7 +29,7 @@ import os from pathlib import Path import torch -from lhotse import CutSet, Fbank, FbankConfig, LilcomHdf5Writer +from lhotse import CutSet, Fbank, FbankConfig, LilcomChunkyWriter from lhotse.recipes.utils import read_manifests_if_cached from icefall.utils import get_executor @@ -53,8 +53,13 @@ def compute_fbank_timit(): "DEV", "TEST", ) + prefix = "timit" + suffix = "jsonl.gz" manifests = read_manifests_if_cached( - prefix="timit", dataset_parts=dataset_parts, output_dir=src_dir + dataset_parts=dataset_parts, + output_dir=src_dir, + prefix=prefix, + suffix=suffix, ) assert manifests is not None @@ -62,7 +67,8 @@ def compute_fbank_timit(): with get_executor() as ex: # Initialize the executor only once. for partition, m in manifests.items(): - if (output_dir / f"cuts_{partition}.json.gz").is_file(): + cuts_file = output_dir / f"{prefix}_cuts_{partition}.{suffix}" + if cuts_file.is_file(): logging.info(f"{partition} already exists - skipping.") continue logging.info(f"Processing {partition}") @@ -78,13 +84,13 @@ def compute_fbank_timit(): ) cut_set = cut_set.compute_and_store_features( extractor=extractor, - storage_path=f"{output_dir}/feats_{partition}", + storage_path=f"{output_dir}/{prefix}_feats_{partition}", # when an executor is specified, make more partitions num_jobs=num_jobs if ex is None else 80, executor=ex, - storage_type=LilcomHdf5Writer, + storage_type=LilcomChunkyWriter, ) - cut_set.to_json(output_dir / f"cuts_{partition}.json.gz") + cut_set.to_file(cuts_file) if __name__ == "__main__": diff --git a/egs/timit/ASR/tdnn_lstm_ctc/asr_datamodule.py b/egs/timit/ASR/tdnn_lstm_ctc/asr_datamodule.py index a7029f514..5e2923fb6 100644 --- a/egs/timit/ASR/tdnn_lstm_ctc/asr_datamodule.py +++ b/egs/timit/ASR/tdnn_lstm_ctc/asr_datamodule.py @@ -23,11 +23,11 @@ from functools import lru_cache from pathlib import Path from typing import List, Union -from lhotse import CutSet, Fbank, FbankConfig, load_manifest +from lhotse import CutSet, Fbank, FbankConfig, load_manifest, load_manifest_lazy from lhotse.dataset import ( - BucketingSampler, CutConcatenate, CutMix, + DynamicBucketingSampler, K2SpeechRecognitionDataset, PrecomputedFeatures, SingleCutSampler, @@ -92,7 +92,7 @@ class TimitAsrDataModule(DataModule): "--num-buckets", type=int, default=30, - help="The number of buckets for the BucketingSampler" + help="The number of buckets for the DynamicBucketingSampler" "(you might want to increase it for larger datasets).", ) group.add_argument( @@ -154,7 +154,9 @@ class TimitAsrDataModule(DataModule): cuts_train = self.train_cuts() logging.info("About to get Musan cuts") - cuts_musan = load_manifest(self.args.feature_dir / "cuts_musan.json.gz") + cuts_musan = load_manifest( + self.args.feature_dir / "cuts_musan.jsonl.gz" + ) logging.info("About to create train dataset") transforms = [CutMix(cuts=cuts_musan, prob=0.5, snr=(10, 20))] @@ -218,13 +220,12 @@ class TimitAsrDataModule(DataModule): ) if self.args.bucketing_sampler: - logging.info("Using BucketingSampler.") - train_sampler = BucketingSampler( + logging.info("Using DynamicBucketingSampler.") + train_sampler = DynamicBucketingSampler( cuts_train, max_duration=self.args.max_duration, shuffle=self.args.shuffle, num_buckets=self.args.num_buckets, - bucket_method="equal_duration", drop_last=True, ) else: @@ -322,20 +323,26 @@ class TimitAsrDataModule(DataModule): @lru_cache() def train_cuts(self) -> CutSet: logging.info("About to get train cuts") - cuts_train = load_manifest(self.args.feature_dir / "cuts_TRAIN.json.gz") + cuts_train = load_manifest_lazy( + self.args.feature_dir / "timit_cuts_TRAIN.jsonl.gz" + ) return cuts_train @lru_cache() def valid_cuts(self) -> CutSet: logging.info("About to get dev cuts") - cuts_valid = load_manifest(self.args.feature_dir / "cuts_DEV.json.gz") + cuts_valid = load_manifest_lazy( + self.args.feature_dir / "timit_cuts_DEV.jsonl.gz" + ) return cuts_valid @lru_cache() def test_cuts(self) -> CutSet: logging.debug("About to get test cuts") - cuts_test = load_manifest(self.args.feature_dir / "cuts_TEST.json.gz") + cuts_test = load_manifest_lazy( + self.args.feature_dir / "timit_cuts_TEST.jsonl.gz" + ) return cuts_test diff --git a/egs/wenetspeech/ASR/local/compute_fbank_wenetspeech_splits.py b/egs/wenetspeech/ASR/local/compute_fbank_wenetspeech_splits.py index a828bead9..4622bdb55 100755 --- a/egs/wenetspeech/ASR/local/compute_fbank_wenetspeech_splits.py +++ b/egs/wenetspeech/ASR/local/compute_fbank_wenetspeech_splits.py @@ -23,10 +23,10 @@ from pathlib import Path import torch from lhotse import ( - ChunkedLilcomHdf5Writer, CutSet, KaldifeatFbank, KaldifeatFbankConfig, + LilcomChunkyWriter, set_audio_duration_mismatch_tolerance, set_caching_enabled, ) @@ -135,7 +135,7 @@ def compute_fbank_wenetspeech_splits(args): storage_path=f"{output_dir}/feats_{subset}_{idx}", num_workers=args.num_workers, batch_duration=args.batch_duration, - storage_type=ChunkedLilcomHdf5Writer, + storage_type=LilcomChunkyWriter, ) logging.info("About to split cuts into smaller chunks.") diff --git a/egs/wenetspeech/ASR/local/display_manifest_statistics.py b/egs/wenetspeech/ASR/local/display_manifest_statistics.py index 30dc5a5ec..c41445b8d 100644 --- a/egs/wenetspeech/ASR/local/display_manifest_statistics.py +++ b/egs/wenetspeech/ASR/local/display_manifest_statistics.py @@ -26,7 +26,7 @@ for usage. """ -from lhotse import load_manifest +from lhotse import load_manifest_lazy def main(): @@ -40,7 +40,7 @@ def main(): for path in paths: print(f"Starting display the statistics for {path}") - cuts = load_manifest(path) + cuts = load_manifest_lazy(path) cuts.describe() diff --git a/egs/wenetspeech/ASR/local/text2segments.py b/egs/wenetspeech/ASR/local/text2segments.py index acf6f9698..3df727c67 100644 --- a/egs/wenetspeech/ASR/local/text2segments.py +++ b/egs/wenetspeech/ASR/local/text2segments.py @@ -61,8 +61,8 @@ def main(): parser = get_parser() args = parser.parse_args() - input_file = args.input - output_file = args.output + input_file = args.input_file + output_file = args.output_file f = open(input_file, "r", encoding="utf-8") lines = f.readlines() diff --git a/egs/wenetspeech/ASR/pruned_transducer_stateless2/asr_datamodule.py b/egs/wenetspeech/ASR/pruned_transducer_stateless2/asr_datamodule.py index d2f8d85ce..200a694d6 100644 --- a/egs/wenetspeech/ASR/pruned_transducer_stateless2/asr_datamodule.py +++ b/egs/wenetspeech/ASR/pruned_transducer_stateless2/asr_datamodule.py @@ -28,6 +28,7 @@ from lhotse import ( Fbank, FbankConfig, load_manifest, + load_manifest_lazy, set_caching_enabled, ) from lhotse.dataset import ( @@ -219,7 +220,7 @@ class WenetSpeechAsrDataModule: """ logging.info("About to get Musan cuts") cuts_musan = load_manifest( - self.args.manifest_dir / "cuts_musan.json.gz" + self.args.manifest_dir / "musan_cuts.jsonl.gz" ) transforms = [] @@ -435,16 +436,18 @@ class WenetSpeechAsrDataModule: @lru_cache() def valid_cuts(self) -> CutSet: logging.info("About to get dev cuts") - return load_manifest(self.args.manifest_dir / "cuts_DEV.jsonl.gz") + return load_manifest_lazy(self.args.manifest_dir / "cuts_DEV.jsonl.gz") @lru_cache() def test_net_cuts(self) -> List[CutSet]: logging.info("About to get TEST_NET cuts") - return load_manifest(self.args.manifest_dir / "cuts_TEST_NET.jsonl.gz") + return load_manifest_lazy( + self.args.manifest_dir / "cuts_TEST_NET.jsonl.gz" + ) @lru_cache() def test_meeting_cuts(self) -> List[CutSet]: logging.info("About to get TEST_MEETING cuts") - return load_manifest( + return load_manifest_lazy( self.args.manifest_dir / "cuts_TEST_MEETING.jsonl.gz" ) diff --git a/egs/wenetspeech/ASR/pruned_transducer_stateless2/decode.py b/egs/wenetspeech/ASR/pruned_transducer_stateless2/decode.py index be3d01f6a..41e7a0f44 100755 --- a/egs/wenetspeech/ASR/pruned_transducer_stateless2/decode.py +++ b/egs/wenetspeech/ASR/pruned_transducer_stateless2/decode.py @@ -63,7 +63,7 @@ import torch.nn as nn from asr_datamodule import WenetSpeechAsrDataModule from beam_search import ( beam_search, - fast_beam_search, + fast_beam_search_one_best, greedy_search, greedy_search_batch, modified_beam_search, @@ -256,7 +256,7 @@ def decode_one_batch( hyps = [] if params.decoding_method == "fast_beam_search": - hyp_tokens = fast_beam_search( + hyp_tokens = fast_beam_search_one_best( model=model, decoding_graph=decoding_graph, encoder_out=encoder_out, @@ -274,6 +274,7 @@ def decode_one_batch( hyp_tokens = greedy_search_batch( model=model, encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, ) for i in range(encoder_out.size(0)): hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]]) @@ -282,6 +283,7 @@ def decode_one_batch( model=model, encoder_out=encoder_out, beam=params.beam_size, + encoder_out_lens=encoder_out_lens, ) for i in range(encoder_out.size(0)): hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]]) diff --git a/egs/yesno/ASR/local/compute_fbank_yesno.py b/egs/yesno/ASR/local/compute_fbank_yesno.py index 6922ffe10..fb48b6f8e 100755 --- a/egs/yesno/ASR/local/compute_fbank_yesno.py +++ b/egs/yesno/ASR/local/compute_fbank_yesno.py @@ -12,7 +12,7 @@ import os from pathlib import Path import torch -from lhotse import CutSet, Fbank, FbankConfig, LilcomHdf5Writer +from lhotse import CutSet, Fbank, FbankConfig, LilcomChunkyWriter from lhotse.recipes.utils import read_manifests_if_cached from icefall.utils import get_executor @@ -37,10 +37,13 @@ def compute_fbank_yesno(): "train", "test", ) + prefix = "yesno" + suffix = "jsonl.gz" manifests = read_manifests_if_cached( dataset_parts=dataset_parts, output_dir=src_dir, - prefix="yesno", + prefix=prefix, + suffix=suffix, ) assert manifests is not None @@ -50,7 +53,8 @@ def compute_fbank_yesno(): with get_executor() as ex: # Initialize the executor only once. for partition, m in manifests.items(): - if (output_dir / f"cuts_{partition}.json.gz").is_file(): + cuts_file = output_dir / f"{prefix}_cuts_{partition}.{suffix}" + if cuts_file.is_file(): logging.info(f"{partition} already exists - skipping.") continue logging.info(f"Processing {partition}") @@ -66,13 +70,13 @@ def compute_fbank_yesno(): ) cut_set = cut_set.compute_and_store_features( extractor=extractor, - storage_path=f"{output_dir}/feats_{partition}", + storage_path=f"{output_dir}/{prefix}_feats_{partition}", # when an executor is specified, make more partitions num_jobs=num_jobs if ex is None else 1, # use one job executor=ex, - storage_type=LilcomHdf5Writer, + storage_type=LilcomChunkyWriter, ) - cut_set.to_json(output_dir / f"cuts_{partition}.json.gz") + cut_set.to_file(cuts_file) if __name__ == "__main__": diff --git a/egs/yesno/ASR/tdnn/asr_datamodule.py b/egs/yesno/ASR/tdnn/asr_datamodule.py index 0a5a42089..85e5f1358 100644 --- a/egs/yesno/ASR/tdnn/asr_datamodule.py +++ b/egs/yesno/ASR/tdnn/asr_datamodule.py @@ -20,18 +20,19 @@ from functools import lru_cache from pathlib import Path from typing import List +from lhotse import CutSet, Fbank, FbankConfig, load_manifest_lazy +from lhotse.dataset import ( + CutConcatenate, + DynamicBucketingSampler, + K2SpeechRecognitionDataset, + PrecomputedFeatures, + SingleCutSampler, +) +from lhotse.dataset.input_strategies import OnTheFlyFeatures from torch.utils.data import DataLoader from icefall.dataset.datamodule import DataModule from icefall.utils import str2bool -from lhotse import CutSet, Fbank, FbankConfig, load_manifest -from lhotse.dataset import ( - BucketingSampler, - CutConcatenate, - K2SpeechRecognitionDataset, - PrecomputedFeatures, -) -from lhotse.dataset.input_strategies import OnTheFlyFeatures class YesNoAsrDataModule(DataModule): @@ -84,7 +85,7 @@ class YesNoAsrDataModule(DataModule): "--num-buckets", type=int, default=10, - help="The number of buckets for the BucketingSampler" + help="The number of buckets for the DynamicBucketingSampler" "(you might want to increase it for larger datasets).", ) group.add_argument( @@ -186,18 +187,17 @@ class YesNoAsrDataModule(DataModule): ) if self.args.bucketing_sampler: - logging.info("Using BucketingSampler.") - train_sampler = BucketingSampler( + logging.info("Using DynamicBucketingSampler.") + train_sampler = DynamicBucketingSampler( cuts_train, max_duration=self.args.max_duration, shuffle=self.args.shuffle, num_buckets=self.args.num_buckets, - bucket_method="equal_duration", drop_last=True, ) else: logging.info("Using SingleCutSampler.") - train_sampler = BucketingSampler( + train_sampler = SingleCutSampler( cuts_train, max_duration=self.args.max_duration, shuffle=self.args.shuffle, @@ -225,8 +225,10 @@ class YesNoAsrDataModule(DataModule): else PrecomputedFeatures(), return_cuts=self.args.return_cuts, ) - sampler = BucketingSampler( - cuts_test, max_duration=self.args.max_duration, shuffle=False + sampler = DynamicBucketingSampler( + cuts_test, + max_duration=self.args.max_duration, + shuffle=False, ) logging.debug("About to create test dataloader") test_dl = DataLoader( @@ -240,11 +242,15 @@ class YesNoAsrDataModule(DataModule): @lru_cache() def train_cuts(self) -> CutSet: logging.info("About to get train cuts") - cuts_train = load_manifest(self.args.feature_dir / "cuts_train.json.gz") + cuts_train = load_manifest_lazy( + self.args.feature_dir / "yesno_cuts_train.jsonl.gz" + ) return cuts_train @lru_cache() def test_cuts(self) -> List[CutSet]: logging.info("About to get test cuts") - cuts_test = load_manifest(self.args.feature_dir / "cuts_test.json.gz") + cuts_test = load_manifest_lazy( + self.args.feature_dir / "yesno_cuts_test.jsonl.gz" + ) return cuts_test diff --git a/icefall/utils.py b/icefall/utils.py index daccd4346..b38574f0c 100644 --- a/icefall/utils.py +++ b/icefall/utils.py @@ -127,7 +127,10 @@ def setup_logger( level = logging.CRITICAL logging.basicConfig( - filename=log_filename, format=formatter, level=level, filemode="w" + filename=log_filename, + format=formatter, + level=level, + filemode="w", ) if use_console: console = logging.StreamHandler()