From 19c4214958768f79dd13c89f1f05e52d35b5dce2 Mon Sep 17 00:00:00 2001 From: pkufool Date: Mon, 23 Aug 2021 10:43:59 +0800 Subject: [PATCH 01/10] Fix code style and add copyright. (#18) * Fix style and add copyright * Minor fix * Remove duplicate lines * Reformat conformer.py by black * Reformat code style with black. * Fix github workflows * Fix lhotse installation * Install icefall requirements * Update k2 version, remove lhotse from test workflow --- .flake8 | 3 +++ .github/workflows/test.yml | 14 ++++------- .../ASR/conformer_ctc/conformer.py | 19 ++++++++++++--- egs/librispeech/ASR/conformer_ctc/decode.py | 16 +++++++++++-- .../ASR/conformer_ctc/pretrained.py | 24 +++++++++++++++---- .../ASR/conformer_ctc/subsampling.py | 17 +++++++++++++ .../ASR/conformer_ctc/test_subsampling.py | 16 +++++++++++++ .../ASR/conformer_ctc/test_transformer.py | 16 +++++++++++++ egs/librispeech/ASR/conformer_ctc/train.py | 16 ++++++++++++- .../ASR/conformer_ctc/transformer.py | 24 +++++++++++++++---- egs/librispeech/ASR/local/compile_hlg.py | 16 +++++++++++++ .../ASR/local/compute_fbank_librispeech.py | 24 ++++++++++++++++--- .../ASR/local/compute_fbank_musan.py | 21 ++++++++++++++-- egs/librispeech/ASR/local/download_lm.py | 17 ++++++++++++- egs/librispeech/ASR/local/prepare_lang.py | 16 ++++++++++++- egs/librispeech/ASR/local/prepare_lang_bpe.py | 16 +++++++++++++ .../ASR/local/test_prepare_lang.py | 16 +++++++++++++ egs/librispeech/ASR/local/train_bpe_model.py | 16 +++++++++++++ .../ASR/tdnn_lstm_ctc/asr_datamodule.py | 17 +++++++++++++ egs/librispeech/ASR/tdnn_lstm_ctc/decode.py | 15 ++++++++++++ egs/librispeech/ASR/tdnn_lstm_ctc/model.py | 17 +++++++++++++ egs/librispeech/ASR/tdnn_lstm_ctc/train.py | 16 +++++++++++++ icefall/bpe_graph_compiler.py | 21 +++++++++++++++- icefall/checkpoint.py | 17 +++++++++++++ icefall/dataset/datamodule.py | 17 +++++++++++++ icefall/decode.py | 16 +++++++++++++ icefall/dist.py | 17 +++++++++++++ icefall/graph_compiler.py | 22 ++++++++++++++++- icefall/lexicon.py | 17 +++++++++++++ icefall/utils.py | 17 +++++++++++++ test/test_bpe_graph_compiler.py | 18 ++++++++++++-- test/test_checkpoint.py | 16 +++++++++++++ test/test_graph_compiler.py | 16 ++++++++++++- test/test_lexicon.py | 16 +++++++++++++ test/test_utils.py | 17 +++++++++++++ 35 files changed, 563 insertions(+), 36 deletions(-) diff --git a/.flake8 b/.flake8 index 090e97971..3f1227b9b 100644 --- a/.flake8 +++ b/.flake8 @@ -2,6 +2,9 @@ show-source=true statistics=true max-line-length = 80 +per-file-ignores = + # line too long + egs/librispeech/ASR/conformer_ctc/conformer.py: E501, exclude = .git, diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 9a298877a..9110e7db4 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -32,7 +32,7 @@ jobs: os: [ubuntu-18.04, macos-10.15] python-version: [3.6, 3.7, 3.8, 3.9] torch: ["1.8.1"] - k2-version: ["1.2.dev20210724"] + k2-version: ["1.4.dev20210822"] fail-fast: false steps: @@ -47,16 +47,10 @@ jobs: - name: Install Python dependencies run: | - python3 -m pip install --upgrade pip pytest kaldialign + python3 -m pip install --upgrade pip pytest pip install k2==${{ matrix.k2-version }}+cpu.torch${{ matrix.torch }} -f https://k2-fsa.org/nightly/ - - # Don't use: pip install lhotse - # since it installs a version of PyTorch that is not predictable - git clone --depth 1 https://github.com/lhotse-speech/lhotse - cd lhotse - sed -i.bak "/torch/d" requirements.txt - pip install -r ./requirements.txt - + # icefall requirements + pip install -r requirements.txt - name: Run tests if: startsWith(matrix.os, 'ubuntu') diff --git a/egs/librispeech/ASR/conformer_ctc/conformer.py b/egs/librispeech/ASR/conformer_ctc/conformer.py index a00664a99..08287d686 100644 --- a/egs/librispeech/ASR/conformer_ctc/conformer.py +++ b/egs/librispeech/ASR/conformer_ctc/conformer.py @@ -1,7 +1,20 @@ #!/usr/bin/env python3 - # Copyright (c) 2021 University of Chinese Academy of Sciences (author: Han Zhu) -# Apache 2.0 +# +# 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 import warnings @@ -396,7 +409,7 @@ class RelPositionalEncoding(torch.nn.Module): :, self.pe.size(1) // 2 - x.size(1) - + 1 : self.pe.size(1) // 2 + + 1 : self.pe.size(1) // 2 # noqa E203 + x.size(1), ] return self.dropout(x), self.dropout(pos_emb) diff --git a/egs/librispeech/ASR/conformer_ctc/decode.py b/egs/librispeech/ASR/conformer_ctc/decode.py index c540b1ea1..6abcf3385 100755 --- a/egs/librispeech/ASR/conformer_ctc/decode.py +++ b/egs/librispeech/ASR/conformer_ctc/decode.py @@ -1,8 +1,20 @@ #!/usr/bin/env python3 - # Copyright 2021 Xiaomi Corporation (Author: Liyong Guo, 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. -# (still working in progress) import argparse import logging diff --git a/egs/librispeech/ASR/conformer_ctc/pretrained.py b/egs/librispeech/ASR/conformer_ctc/pretrained.py index c63616d28..95029fadb 100755 --- a/egs/librispeech/ASR/conformer_ctc/pretrained.py +++ b/egs/librispeech/ASR/conformer_ctc/pretrained.py @@ -1,4 +1,20 @@ #!/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. + import argparse import logging @@ -59,7 +75,7 @@ def get_parser(): decoding lattice and then use 1best to decode the rescored lattice. We call it HLG decoding + n-gram LM rescoring. - (3) attention-decoder - Extract n paths from he rescored + (3) attention-decoder - Extract n paths from the rescored lattice and use the transformer attention decoder for rescoring. We call it HLG decoding + n-gram LM rescoring + attention @@ -245,9 +261,9 @@ def main(): if params.method in ["whole-lattice-rescoring", "attention-decoder"]: logging.info(f"Loading G from {params.G}") G = k2.Fsa.from_dict(torch.load(params.G, map_location="cpu")) - G = G.to(device) # Add epsilon self-loops to G as we will compose # it with the whole lattice later + G = G.to(device) G = k2.add_epsilon_self_loops(G) G = k2.arc_sort(G) G.lm_scores = G.scores.clone() @@ -268,7 +284,7 @@ def main(): ) waves = [w.to(device) for w in waves] - logging.info(f"Decoding started") + logging.info("Decoding started") features = fbank(waves) features = pad_sequence( @@ -338,7 +354,7 @@ def main(): s += f"{filename}:\n{words}\n\n" logging.info(s) - logging.info(f"Decoding Done") + logging.info("Decoding Done") if __name__ == "__main__": diff --git a/egs/librispeech/ASR/conformer_ctc/subsampling.py b/egs/librispeech/ASR/conformer_ctc/subsampling.py index 5c3e1222e..720ed6c22 100644 --- a/egs/librispeech/ASR/conformer_ctc/subsampling.py +++ b/egs/librispeech/ASR/conformer_ctc/subsampling.py @@ -1,3 +1,20 @@ +# 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. + + import torch import torch.nn as nn diff --git a/egs/librispeech/ASR/conformer_ctc/test_subsampling.py b/egs/librispeech/ASR/conformer_ctc/test_subsampling.py index 937845d77..e3361d0c9 100755 --- a/egs/librispeech/ASR/conformer_ctc/test_subsampling.py +++ b/egs/librispeech/ASR/conformer_ctc/test_subsampling.py @@ -1,4 +1,20 @@ #!/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. + from subsampling import Conv2dSubsampling from subsampling import VggSubsampling diff --git a/egs/librispeech/ASR/conformer_ctc/test_transformer.py b/egs/librispeech/ASR/conformer_ctc/test_transformer.py index 08e680607..b90215274 100644 --- a/egs/librispeech/ASR/conformer_ctc/test_transformer.py +++ b/egs/librispeech/ASR/conformer_ctc/test_transformer.py @@ -1,4 +1,20 @@ #!/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. + import torch from transformer import ( diff --git a/egs/librispeech/ASR/conformer_ctc/train.py b/egs/librispeech/ASR/conformer_ctc/train.py index d17ee6164..df9637c34 100755 --- a/egs/librispeech/ASR/conformer_ctc/train.py +++ b/egs/librispeech/ASR/conformer_ctc/train.py @@ -1,6 +1,20 @@ #!/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 is just at the very beginning ... import argparse import logging diff --git a/egs/librispeech/ASR/conformer_ctc/transformer.py b/egs/librispeech/ASR/conformer_ctc/transformer.py index 51c77b220..191d2d612 100644 --- a/egs/librispeech/ASR/conformer_ctc/transformer.py +++ b/egs/librispeech/ASR/conformer_ctc/transformer.py @@ -1,5 +1,19 @@ -# Copyright (c) 2021 University of Chinese Academy of Sciences (author: Han Zhu) -# Apache 2.0 +# 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 math from typing import Dict, List, Optional, Tuple @@ -779,7 +793,8 @@ class Noam(object): class LabelSmoothingLoss(nn.Module): """ - Label-smoothing loss. KL-divergence between q_{smoothed ground truth prob.}(w) + Label-smoothing loss. KL-divergence between + q_{smoothed ground truth prob.}(w) and p_{prob. computed by model}(w) is minimized. Modified from https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/transformer/label_smoothing_loss.py # noqa @@ -864,7 +879,8 @@ def encoder_padding_mask( frames, before subsampling) Returns: - Tensor: Mask tensor of dimension (batch_size, input_length), True denote the masked indices. + Tensor: Mask tensor of dimension (batch_size, input_length), + True denote the masked indices. """ if supervisions is None: return None diff --git a/egs/librispeech/ASR/local/compile_hlg.py b/egs/librispeech/ASR/local/compile_hlg.py index 9f28bb74d..19a1ddd23 100755 --- a/egs/librispeech/ASR/local/compile_hlg.py +++ b/egs/librispeech/ASR/local/compile_hlg.py @@ -1,4 +1,20 @@ #!/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 lang_dir and generates HLG from diff --git a/egs/librispeech/ASR/local/compute_fbank_librispeech.py b/egs/librispeech/ASR/local/compute_fbank_librispeech.py index d81096070..b89d2dfc2 100755 --- a/egs/librispeech/ASR/local/compute_fbank_librispeech.py +++ b/egs/librispeech/ASR/local/compute_fbank_librispeech.py @@ -1,4 +1,20 @@ #!/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 LibriSpeech dataset. @@ -17,8 +33,9 @@ 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 +# 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) @@ -53,7 +70,8 @@ def compute_fbank_librispeech(): continue logging.info(f"Processing {partition}") cut_set = CutSet.from_manifests( - recordings=m["recordings"], supervisions=m["supervisions"], + recordings=m["recordings"], + supervisions=m["supervisions"], ) if "train" in partition: cut_set = ( diff --git a/egs/librispeech/ASR/local/compute_fbank_musan.py b/egs/librispeech/ASR/local/compute_fbank_musan.py index 0fc515d8c..e10a664b2 100755 --- a/egs/librispeech/ASR/local/compute_fbank_musan.py +++ b/egs/librispeech/ASR/local/compute_fbank_musan.py @@ -1,4 +1,20 @@ #!/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 musan dataset. @@ -17,8 +33,9 @@ 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 +# 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) diff --git a/egs/librispeech/ASR/local/download_lm.py b/egs/librispeech/ASR/local/download_lm.py index 5c9e2a675..94d23afed 100755 --- a/egs/librispeech/ASR/local/download_lm.py +++ b/egs/librispeech/ASR/local/download_lm.py @@ -1,6 +1,21 @@ #!/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) """ This file downloads the following LibriSpeech LM files: diff --git a/egs/librispeech/ASR/local/prepare_lang.py b/egs/librispeech/ASR/local/prepare_lang.py index f7fde7796..0880019b3 100755 --- a/egs/librispeech/ASR/local/prepare_lang.py +++ b/egs/librispeech/ASR/local/prepare_lang.py @@ -1,6 +1,20 @@ #!/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) """ This script takes as input a lexicon file "data/lang_phone/lexicon.txt" diff --git a/egs/librispeech/ASR/local/prepare_lang_bpe.py b/egs/librispeech/ASR/local/prepare_lang_bpe.py index 68b8db966..39d347661 100755 --- a/egs/librispeech/ASR/local/prepare_lang_bpe.py +++ b/egs/librispeech/ASR/local/prepare_lang_bpe.py @@ -1,4 +1,20 @@ #!/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) diff --git a/egs/librispeech/ASR/local/test_prepare_lang.py b/egs/librispeech/ASR/local/test_prepare_lang.py index 23ab53c7d..d4cf62bba 100755 --- a/egs/librispeech/ASR/local/test_prepare_lang.py +++ b/egs/librispeech/ASR/local/test_prepare_lang.py @@ -1,4 +1,20 @@ #!/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) diff --git a/egs/librispeech/ASR/local/train_bpe_model.py b/egs/librispeech/ASR/local/train_bpe_model.py index 9872a7c6a..9ceb181e1 100755 --- a/egs/librispeech/ASR/local/train_bpe_model.py +++ b/egs/librispeech/ASR/local/train_bpe_model.py @@ -1,4 +1,20 @@ #!/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. + # You can install sentencepiece via: # diff --git a/egs/librispeech/ASR/tdnn_lstm_ctc/asr_datamodule.py b/egs/librispeech/ASR/tdnn_lstm_ctc/asr_datamodule.py index 8d8c7a366..2c3c7252a 100644 --- a/egs/librispeech/ASR/tdnn_lstm_ctc/asr_datamodule.py +++ b/egs/librispeech/ASR/tdnn_lstm_ctc/asr_datamodule.py @@ -1,3 +1,20 @@ +# 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. + + import argparse import logging from functools import lru_cache diff --git a/egs/librispeech/ASR/tdnn_lstm_ctc/decode.py b/egs/librispeech/ASR/tdnn_lstm_ctc/decode.py index 72f39ef40..130fc16a2 100755 --- a/egs/librispeech/ASR/tdnn_lstm_ctc/decode.py +++ b/egs/librispeech/ASR/tdnn_lstm_ctc/decode.py @@ -1,4 +1,19 @@ #!/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. import argparse diff --git a/egs/librispeech/ASR/tdnn_lstm_ctc/model.py b/egs/librispeech/ASR/tdnn_lstm_ctc/model.py index 0dc4228dc..5e04c11b4 100644 --- a/egs/librispeech/ASR/tdnn_lstm_ctc/model.py +++ b/egs/librispeech/ASR/tdnn_lstm_ctc/model.py @@ -1,3 +1,20 @@ +# 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. + + import torch import torch.nn as nn diff --git a/egs/librispeech/ASR/tdnn_lstm_ctc/train.py b/egs/librispeech/ASR/tdnn_lstm_ctc/train.py index 4adb988a0..23e224f76 100755 --- a/egs/librispeech/ASR/tdnn_lstm_ctc/train.py +++ b/egs/librispeech/ASR/tdnn_lstm_ctc/train.py @@ -1,4 +1,20 @@ #!/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. + import argparse import logging diff --git a/icefall/bpe_graph_compiler.py b/icefall/bpe_graph_compiler.py index e22cf4edc..e76b7ea32 100644 --- a/icefall/bpe_graph_compiler.py +++ b/icefall/bpe_graph_compiler.py @@ -1,3 +1,20 @@ +# 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. + + from pathlib import Path from typing import List, Union @@ -57,7 +74,9 @@ class BpeCtcTrainingGraphCompiler(object): return self.sp.encode(texts, out_type=int) def compile( - self, piece_ids: List[List[int]], modified: bool = False, + self, + piece_ids: List[List[int]], + modified: bool = False, ) -> k2.Fsa: """Build a ctc graph from a list-of-list piece IDs. diff --git a/icefall/checkpoint.py b/icefall/checkpoint.py index a64ecfcf6..b8a628f4e 100644 --- a/icefall/checkpoint.py +++ b/icefall/checkpoint.py @@ -1,3 +1,20 @@ +# 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. + + import logging from pathlib import Path from typing import Any, Dict, List, Optional, Union diff --git a/icefall/dataset/datamodule.py b/icefall/dataset/datamodule.py index 8560c5db0..97767ef9d 100644 --- a/icefall/dataset/datamodule.py +++ b/icefall/dataset/datamodule.py @@ -1,3 +1,20 @@ +# 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. + + import argparse from typing import List, Union diff --git a/icefall/decode.py b/icefall/decode.py index 49d642f1c..bcc869e99 100644 --- a/icefall/decode.py +++ b/icefall/decode.py @@ -1,3 +1,19 @@ +# 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. + import logging from typing import Dict, List, Optional, Tuple, Union diff --git a/icefall/dist.py b/icefall/dist.py index d314d2a43..203c7c563 100644 --- a/icefall/dist.py +++ b/icefall/dist.py @@ -1,3 +1,20 @@ +# 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. + + import os import torch diff --git a/icefall/graph_compiler.py b/icefall/graph_compiler.py index f7ba3cdaf..23ac247e8 100644 --- a/icefall/graph_compiler.py +++ b/icefall/graph_compiler.py @@ -1,3 +1,20 @@ +# 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. + + from typing import List import k2 @@ -8,7 +25,10 @@ from icefall.lexicon import Lexicon class CtcTrainingGraphCompiler(object): def __init__( - self, lexicon: Lexicon, device: torch.device, oov: str = "", + self, + lexicon: Lexicon, + device: torch.device, + oov: str = "", ): """ Args: diff --git a/icefall/lexicon.py b/icefall/lexicon.py index 89747b11b..f1127c7cf 100644 --- a/icefall/lexicon.py +++ b/icefall/lexicon.py @@ -1,3 +1,20 @@ +# 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. + + import logging import re import sys diff --git a/icefall/utils.py b/icefall/utils.py index 3d48badfe..2994c2d47 100644 --- a/icefall/utils.py +++ b/icefall/utils.py @@ -1,3 +1,20 @@ +# 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. + + import argparse import logging import os diff --git a/test/test_bpe_graph_compiler.py b/test/test_bpe_graph_compiler.py index 7b941e5a7..67d300b7d 100755 --- a/test/test_bpe_graph_compiler.py +++ b/test/test_bpe_graph_compiler.py @@ -1,6 +1,20 @@ #!/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) from icefall.bpe_graph_compiler import BpeCtcTrainingGraphCompiler from icefall.lexicon import BpeLexicon @@ -15,7 +29,7 @@ def test(): compiler = BpeCtcTrainingGraphCompiler(lang_dir) ids = compiler.texts_to_ids(["HELLO", "WORLD ZZZ"]) - fsa = compiler.compile(ids) + compiler.compile(ids) lexicon = BpeLexicon(lang_dir) ids0 = lexicon.words_to_piece_ids(["HELLO"]) diff --git a/test/test_checkpoint.py b/test/test_checkpoint.py index 343768957..511a11c23 100644 --- a/test/test_checkpoint.py +++ b/test/test_checkpoint.py @@ -1,4 +1,20 @@ #!/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. + import pytest import torch diff --git a/test/test_graph_compiler.py b/test/test_graph_compiler.py index 4083d79ac..ccfb57d49 100644 --- a/test/test_graph_compiler.py +++ b/test/test_graph_compiler.py @@ -1,6 +1,20 @@ #!/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 re diff --git a/test/test_lexicon.py b/test/test_lexicon.py index b1284d98a..6801b3a89 100644 --- a/test/test_lexicon.py +++ b/test/test_lexicon.py @@ -1,4 +1,20 @@ #!/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. + from pathlib import Path diff --git a/test/test_utils.py b/test/test_utils.py index 27b1ac203..2dd79689f 100644 --- a/test/test_utils.py +++ b/test/test_utils.py @@ -1,4 +1,21 @@ #!/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. + + import k2 import pytest import torch From 6c2c9b9d744b3f78a5a49aeb118f277242c5dcc9 Mon Sep 17 00:00:00 2001 From: Fangjun Kuang Date: Mon, 23 Aug 2021 11:36:29 +0800 Subject: [PATCH 02/10] Add recipe for the yes_no dataset. (#16) * Add recipe for the yes_no dataset. * Refactoring: Remove unused code. * Add Colab notebook for the yesno dataset. * Add GitHub actions to run yesno. * Fix a typo. * Minor fixes. * Train more epochs for GitHub actions. * Minor fixes. * Minor fixes. * Fix style issues. --- .github/workflows/run-yesno-recipe.yml | 89 +++ README.md | 19 +- .../ASR/local/compute_fbank_librispeech.py | 2 +- .../ASR/local/compute_fbank_musan.py | 2 +- .../ASR/tdnn_lstm_ctc/asr_datamodule.py | 4 +- egs/librispeech/ASR/tdnn_lstm_ctc/decode.py | 2 +- egs/yesno/ASR/README.md | 15 + egs/yesno/ASR/local/compile_hlg.py | 134 +++++ egs/yesno/ASR/local/compute_fbank_yesno.py | 81 +++ egs/yesno/ASR/local/prepare_lang.py | 367 ++++++++++++ egs/yesno/ASR/prepare.sh | 93 +++ egs/yesno/ASR/shared | 1 + egs/yesno/ASR/tdnn/asr_datamodule.py | 260 ++++++++ egs/yesno/ASR/tdnn/decode.py | 308 ++++++++++ egs/yesno/ASR/tdnn/model.py | 84 +++ egs/yesno/ASR/tdnn/train.py | 558 ++++++++++++++++++ icefall/dataset/datamodule.py | 2 +- 17 files changed, 2012 insertions(+), 9 deletions(-) create mode 100644 .github/workflows/run-yesno-recipe.yml create mode 100644 egs/yesno/ASR/README.md create mode 100755 egs/yesno/ASR/local/compile_hlg.py create mode 100755 egs/yesno/ASR/local/compute_fbank_yesno.py create mode 100755 egs/yesno/ASR/local/prepare_lang.py create mode 100755 egs/yesno/ASR/prepare.sh create mode 120000 egs/yesno/ASR/shared create mode 100644 egs/yesno/ASR/tdnn/asr_datamodule.py create mode 100755 egs/yesno/ASR/tdnn/decode.py create mode 100755 egs/yesno/ASR/tdnn/model.py create mode 100755 egs/yesno/ASR/tdnn/train.py diff --git a/.github/workflows/run-yesno-recipe.yml b/.github/workflows/run-yesno-recipe.yml new file mode 100644 index 000000000..7e36139a3 --- /dev/null +++ b/.github/workflows/run-yesno-recipe.yml @@ -0,0 +1,89 @@ +# Copyright 2021 Fangjun Kuang (csukuangfj@gmail.com) + +# 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. + +name: run-yesno-recipe + +on: + push: + branches: + - master + pull_request: + branches: + - master + +jobs: + run-yesno-recipe: + runs-on: ${{ matrix.os }} + strategy: + matrix: + # os: [ubuntu-18.04, macos-10.15] + # TODO: enable macOS for CPU testing + os: [ubuntu-18.04] + python-version: [3.8] + fail-fast: false + + steps: + - uses: actions/checkout@v2 + with: + fetch-depth: 0 + + - name: Setup Python ${{ matrix.python-version }} + uses: actions/setup-python@v1 + with: + python-version: ${{ matrix.python-version }} + + - name: Install libnsdfile and libsox + if: startsWith(matrix.os, 'ubuntu') + run: | + sudo apt update + sudo apt install -q -y libsndfile1-dev libsndfile1 ffmpeg + sudo apt install -q -y --fix-missing sox libsox-dev libsox-fmt-all + + - name: Install Python dependencies + run: | + python3 -m pip install --upgrade pip black flake8 + python3 -m pip install -U pip + python3 -m pip install k2==1.4.dev20210822+cpu.torch1.7.1 -f https://k2-fsa.org/nightly/ + python3 -m pip install torchaudio==0.7.2 + python3 -m pip install git+https://github.com/lhotse-speech/lhotse + + # We are in ./icefall and there is a file: requirements.txt in it + python3 -m pip install -r requirements.txt + + - name: Run yesno recipe + shell: bash + working-directory: ${{github.workspace}} + run: | + export PYTHONPATH=$PWD:$PYTHONPATH + echo $PYTHONPATH + ls -lh + + # The following three lines are for macOS + lib_path=$(python -c "from distutils.sysconfig import get_python_lib; print(get_python_lib())") + echo "lib_path: $lib_path" + export DYLD_LIBRARY_PATH=$lib_path:$DYLD_LIBRARY_PATH + ls -lh $lib_path + + cd egs/yesno/ASR + ./prepare.sh + python3 ./tdnn/train.py --num-epochs 100 + python3 ./tdnn/decode.py --epoch 99 + python3 ./tdnn/decode.py --epoch 95 + python3 ./tdnn/decode.py --epoch 90 + python3 ./tdnn/decode.py --epoch 80 + python3 ./tdnn/decode.py --epoch 70 + python3 ./tdnn/decode.py --epoch 60 + # TODO: Check that the WER is less than some value diff --git a/README.md b/README.md index b49a7f04c..0a9b657b3 100644 --- a/README.md +++ b/README.md @@ -48,10 +48,22 @@ python3 -c "import icefall; print(icefall.__file__)" It should print the path to `icefall`. -## Run recipes +## Recipes -At present, only LibriSpeech recipe is provided. Please -follow [egs/librispeech/ASR/README.md][LibriSpeech] to run it. +At present, two recipes are provided: + + - [LibriSpeech][LibriSpeech] + - [yesno][yesno] [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1tIjjzaJc3IvGyKiMCDWO-TSnBgkcuN3B?usp=sharing) + +### Yesno + +For the yesno recipe, training with 50 epochs takes less than 2 minutes using **CPU**. + +The WER is + +``` +[test_set] %WER 0.42% [1 / 240, 0 ins, 1 del, 0 sub ] +``` ## Use Pre-trained models @@ -60,6 +72,7 @@ for how to use pre-trained models. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1huyupXAcHsUrKaWfI83iMEJ6J0Nh0213?usp=sharing) +[yesno]: egs/yesno/ASR/README.md [LibriSpeech]: egs/librispeech/ASR/README.md [k2-install]: https://k2.readthedocs.io/en/latest/installation/index.html# [k2]: https://github.com/k2-fsa/k2 diff --git a/egs/librispeech/ASR/local/compute_fbank_librispeech.py b/egs/librispeech/ASR/local/compute_fbank_librispeech.py index b89d2dfc2..b26034eb2 100755 --- a/egs/librispeech/ASR/local/compute_fbank_librispeech.py +++ b/egs/librispeech/ASR/local/compute_fbank_librispeech.py @@ -18,7 +18,7 @@ """ This file computes fbank features of the LibriSpeech dataset. -Its looks for manifests in the directory data/manifests. +It looks for manifests in the directory data/manifests. The generated fbank features are saved in data/fbank. """ diff --git a/egs/librispeech/ASR/local/compute_fbank_musan.py b/egs/librispeech/ASR/local/compute_fbank_musan.py index e10a664b2..d44524e70 100755 --- a/egs/librispeech/ASR/local/compute_fbank_musan.py +++ b/egs/librispeech/ASR/local/compute_fbank_musan.py @@ -18,7 +18,7 @@ """ This file computes fbank features of the musan dataset. -Its looks for manifests in the directory data/manifests. +It looks for manifests in the directory data/manifests. The generated fbank features are saved in data/fbank. """ diff --git a/egs/librispeech/ASR/tdnn_lstm_ctc/asr_datamodule.py b/egs/librispeech/ASR/tdnn_lstm_ctc/asr_datamodule.py index 2c3c7252a..91c1d6a96 100644 --- a/egs/librispeech/ASR/tdnn_lstm_ctc/asr_datamodule.py +++ b/egs/librispeech/ASR/tdnn_lstm_ctc/asr_datamodule.py @@ -1,4 +1,4 @@ -# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang) +# Copyright 2021 Piotr Żelasko # # See ../../../../LICENSE for clarification regarding multiple authors # @@ -40,7 +40,7 @@ from icefall.utils import str2bool class LibriSpeechAsrDataModule(DataModule): """ - DataModule for K2 ASR experiments. + 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). diff --git a/egs/librispeech/ASR/tdnn_lstm_ctc/decode.py b/egs/librispeech/ASR/tdnn_lstm_ctc/decode.py index 130fc16a2..27e0b9643 100755 --- a/egs/librispeech/ASR/tdnn_lstm_ctc/decode.py +++ b/egs/librispeech/ASR/tdnn_lstm_ctc/decode.py @@ -348,7 +348,7 @@ def main(): logging.info(f"device: {device}") HLG = k2.Fsa.from_dict( - torch.load("data/lang_phone/HLG.pt", map_location="cpu") + torch.load(f"{params.lang_dir}/HLG.pt", map_location="cpu") ) HLG = HLG.to(device) assert HLG.requires_grad is False diff --git a/egs/yesno/ASR/README.md b/egs/yesno/ASR/README.md new file mode 100644 index 000000000..653c576fa --- /dev/null +++ b/egs/yesno/ASR/README.md @@ -0,0 +1,15 @@ +## Yesno recipe + +You can run the recipe with **CPU**. + + +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1tIjjzaJc3IvGyKiMCDWO-TSnBgkcuN3B?usp=sharing) + +The above Colab notebook finishes the training using **CPU** +within two minutes (50 epochs in total). + +The WER is + +``` +[test_set] %WER 0.42% [1 / 240, 0 ins, 1 del, 0 sub ] +``` diff --git a/egs/yesno/ASR/local/compile_hlg.py b/egs/yesno/ASR/local/compile_hlg.py new file mode 100755 index 000000000..f2fafd013 --- /dev/null +++ b/egs/yesno/ASR/local/compile_hlg.py @@ -0,0 +1,134 @@ +#!/usr/bin/env python3 + +""" +This script takes as input lang_dir and generates HLG from + + - H, the ctc topology, built from tokens contained in lang_dir/lexicon.txt + - L, the lexicon, built from lang_dir/L_disambig.pt + + Caution: We use a lexicon that contains disambiguation symbols + + - G, the LM, built from data/lm/G.fst.txt + +The generated HLG is saved in $lang_dir/HLG.pt +""" +import argparse +import logging +from pathlib import Path + +import k2 +import torch + +from icefall.lexicon import Lexicon + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--lang-dir", + type=str, + help="""Input and output directory. + """, + ) + + return parser.parse_args() + + +def compile_HLG(lang_dir: str) -> k2.Fsa: + """ + Args: + lang_dir: + The language directory, e.g., data/lang_phone or data/lang_bpe_5000. + + Return: + An FSA representing HLG. + """ + lexicon = Lexicon(lang_dir) + max_token_id = max(lexicon.tokens) + logging.info(f"Building ctc_topo. max_token_id: {max_token_id}") + H = k2.ctc_topo(max_token_id) + L = k2.Fsa.from_dict(torch.load(f"{lang_dir}/L_disambig.pt")) + + logging.info("Loading G.fst.txt") + with open("data/lm/G.fst.txt") as f: + G = k2.Fsa.from_openfst(f.read(), acceptor=False) + + first_token_disambig_id = lexicon.token_table["#0"] + first_word_disambig_id = lexicon.word_table["#0"] + + L = k2.arc_sort(L) + G = k2.arc_sort(G) + + logging.info("Intersecting L and G") + LG = k2.compose(L, G) + logging.info(f"LG shape: {LG.shape}") + + logging.info("Connecting LG") + LG = k2.connect(LG) + logging.info(f"LG shape after k2.connect: {LG.shape}") + + logging.info(type(LG.aux_labels)) + logging.info("Determinizing LG") + + LG = k2.determinize(LG) + logging.info(type(LG.aux_labels)) + + logging.info("Connecting LG after k2.determinize") + LG = k2.connect(LG) + + logging.info("Removing disambiguation symbols on LG") + + LG.labels[LG.labels >= first_token_disambig_id] = 0 + + assert isinstance(LG.aux_labels, k2.RaggedInt) + LG.aux_labels.values()[LG.aux_labels.values() >= first_word_disambig_id] = 0 + + LG = k2.remove_epsilon(LG) + logging.info(f"LG shape after k2.remove_epsilon: {LG.shape}") + + LG = k2.connect(LG) + LG.aux_labels = k2.ragged.remove_values_eq(LG.aux_labels, 0) + + logging.info("Arc sorting LG") + LG = k2.arc_sort(LG) + + logging.info("Composing H and LG") + # CAUTION: The name of the inner_labels is fixed + # to `tokens`. If you want to change it, please + # also change other places in icefall that are using + # it. + HLG = k2.compose(H, LG, inner_labels="tokens") + + logging.info("Connecting LG") + HLG = k2.connect(HLG) + + logging.info("Arc sorting LG") + HLG = k2.arc_sort(HLG) + logging.info(f"HLG.shape: {HLG.shape}") + + return HLG + + +def main(): + args = get_args() + lang_dir = Path(args.lang_dir) + + if (lang_dir / "HLG.pt").is_file(): + logging.info(f"{lang_dir}/HLG.pt already exists - skipping") + return + + logging.info(f"Processing {lang_dir}") + + HLG = compile_HLG(lang_dir) + logging.info(f"Saving HLG.pt to {lang_dir}") + torch.save(HLG.as_dict(), f"{lang_dir}/HLG.pt") + + +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/yesno/ASR/local/compute_fbank_yesno.py b/egs/yesno/ASR/local/compute_fbank_yesno.py new file mode 100755 index 000000000..dad7319fd --- /dev/null +++ b/egs/yesno/ASR/local/compute_fbank_yesno.py @@ -0,0 +1,81 @@ +#!/usr/bin/env python3 + +""" +This file computes fbank features of the yesno dataset. +It looks for manifests in the directory data/manifests. + +The generated fbank features are saved in data/fbank. +""" + +import logging +import os +from pathlib import Path + +import torch +from lhotse import CutSet, Fbank, FbankConfig, LilcomHdf5Writer +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_yesno(): + src_dir = Path("data/manifests") + output_dir = Path("data/fbank") + + # This dataset is rather small, so we use only one job + num_jobs = min(1, os.cpu_count()) + num_mel_bins = 23 + + dataset_parts = ( + "train", + "test", + ) + manifests = read_manifests_if_cached( + dataset_parts=dataset_parts, output_dir=src_dir + ) + 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"cuts_{partition}.json.gz").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) + ) + cut_set = cut_set.compute_and_store_features( + extractor=extractor, + storage_path=f"{output_dir}/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, + ) + cut_set.to_json(output_dir / f"cuts_{partition}.json.gz") + + +if __name__ == "__main__": + formatter = ( + "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + ) + + logging.basicConfig(format=formatter, level=logging.INFO) + + compute_fbank_yesno() diff --git a/egs/yesno/ASR/local/prepare_lang.py b/egs/yesno/ASR/local/prepare_lang.py new file mode 100755 index 000000000..f7fde7796 --- /dev/null +++ b/egs/yesno/ASR/local/prepare_lang.py @@ -0,0 +1,367 @@ +#!/usr/bin/env python3 + +# Copyright (c) 2021 Xiaomi Corporation (authors: Fangjun Kuang) + +""" +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 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 main(): + out_dir = Path("data/lang_phone") + 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/yesno/ASR/prepare.sh b/egs/yesno/ASR/prepare.sh new file mode 100755 index 000000000..9a0cc48bb --- /dev/null +++ b/egs/yesno/ASR/prepare.sh @@ -0,0 +1,93 @@ +#!/usr/bin/env bash + +set -eou pipefail + +stage=-1 +stop_stage=100 + +dl_dir=$PWD/download + +lang_dir=data/lang_phone +lm_dir=data/lm + +. shared/parse_options.sh || exit 1 + +mkdir -p $lang_dir +mkdir -p $lm_dir + +log() { + # This function is from espnet + local fname=${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" + mkdir -p $dl_dir + + if [ ! -f $dl_dir/waves_yesno/.completed ]; then + lhotse download yesno $dl_dir + fi +fi + +if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then + log "Stage 1: Prepare yesno manifest" + mkdir -p data/manifests + lhotse prepare yesno $dl_dir/waves_yesno data/manifests +fi + +if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then + log "Stage 2: Compute fbank for yesno" + mkdir -p data/fbank + ./local/compute_fbank_yesno.py +fi + +if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then + log "Stage 3: Prepare lang" + # NOTE: " SIL" is added for implementation convenience + # as the graph compiler code requires that there is a OOV word + # in the lexicon. + ( + echo " SIL" + echo "YES Y" + echo "NO N" + echo " SIL" + ) > $lang_dir/lexicon.txt + + ./local/prepare_lang.py +fi + +if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then + log "Stage 4: Prepare G" + # We use a unigram G + cat < $lm_dir/G.arpa + +\data\\ +ngram 1=4 + +\1-grams: +-1 NO +-1 YES +-99 +-1 + +\end\\ + +EOF + + if [ ! -f $lm_dir/G.fst.txt ]; then + python3 -m kaldilm \ + --read-symbol-table="$lang_dir/words.txt" \ + --disambig-symbol='#0' \ + $lm_dir/G.arpa > $lm_dir/G.fst.txt + fi +fi + +if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then + log "Stage 5: Compile HLG" + if [ ! -f $lang_dir/HLG.pt ]; then + ./local/compile_hlg.py --lang-dir $lang_dir + fi +fi diff --git a/egs/yesno/ASR/shared b/egs/yesno/ASR/shared new file mode 120000 index 000000000..4c5e91438 --- /dev/null +++ b/egs/yesno/ASR/shared @@ -0,0 +1 @@ +../../../icefall/shared/ \ No newline at end of file diff --git a/egs/yesno/ASR/tdnn/asr_datamodule.py b/egs/yesno/ASR/tdnn/asr_datamodule.py new file mode 100644 index 000000000..8b2b44c8a --- /dev/null +++ b/egs/yesno/ASR/tdnn/asr_datamodule.py @@ -0,0 +1,260 @@ +# Copyright 2021 Piotr Żelasko +# 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. +import argparse +import logging +from functools import lru_cache +from pathlib import Path +from typing import List + +from lhotse import CutSet, Fbank, FbankConfig, load_manifest +from lhotse.dataset import ( + BucketingSampler, + CutConcatenate, + K2SpeechRecognitionDataset, + PrecomputedFeatures, + SingleCutSampler, + SpecAugment, +) +from lhotse.dataset.input_strategies import OnTheFlyFeatures +from torch.utils.data import DataLoader + +from icefall.dataset.datamodule import DataModule +from icefall.utils import str2bool + + +class YesNoAsrDataModule(DataModule): + """ + DataModule for k2 ASR experiments. + It assumes there is always one train 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 + """ + + @classmethod + def add_arguments(cls, parser: argparse.ArgumentParser): + super().add_arguments(parser) + 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( + "--feature-dir", + type=Path, + default=Path("data/fbank"), + help="Path to directory with train/test cuts.", + ) + group.add_argument( + "--max-duration", + type=int, + default=30.0, + 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=False, + help="When enabled, the batches will come from buckets of " + "similar duration (saves padding frames).", + ) + group.add_argument( + "--num-buckets", + type=int, + default=10, + help="The number of buckets for the BucketingSampler" + "(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.", + ) + + def train_dataloaders(self) -> DataLoader: + logging.info("About to get train cuts") + cuts_train = self.train_cuts() + + logging.info("About to create train dataset") + transforms = [] + 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 = [ + SpecAugment( + num_frame_masks=2, + features_mask_size=27, + num_feature_masks=2, + frames_mask_size=100, + ) + ] + + 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=23)) + ), + input_transforms=input_transforms, + return_cuts=self.args.return_cuts, + ) + + if self.args.bucketing_sampler: + 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, + ) + 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") + + train_dl = DataLoader( + train, + sampler=train_sampler, + batch_size=None, + num_workers=self.args.num_workers, + persistent_workers=False, + ) + + return train_dl + + def test_dataloaders(self) -> DataLoader: + logging.info("About to get test cuts") + cuts_test = self.test_cuts() + + logging.debug("About to create test dataset") + test = K2SpeechRecognitionDataset( + input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=23))) + if self.args.on_the_fly_feats + else PrecomputedFeatures(), + return_cuts=self.args.return_cuts, + ) + sampler = SingleCutSampler( + cuts_test, max_duration=self.args.max_duration + ) + logging.debug("About to create test dataloader") + test_dl = DataLoader( + test, batch_size=None, sampler=sampler, num_workers=1 + ) + return test_dl + + @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") + 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") + return cuts_test diff --git a/egs/yesno/ASR/tdnn/decode.py b/egs/yesno/ASR/tdnn/decode.py new file mode 100755 index 000000000..a87219010 --- /dev/null +++ b/egs/yesno/ASR/tdnn/decode.py @@ -0,0 +1,308 @@ +#!/usr/bin/env python3 + + +import argparse +import logging +from pathlib import Path +from typing import List, Tuple + +import k2 +import torch +import torch.nn as nn +from asr_datamodule import YesNoAsrDataModule +from model import Tdnn + +from icefall.checkpoint import average_checkpoints, load_checkpoint +from icefall.decode import get_lattice, one_best_decoding +from icefall.lexicon import Lexicon +from icefall.utils import ( + AttributeDict, + get_texts, + setup_logger, + store_transcripts, + write_error_stats, +) + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--epoch", + type=int, + default=9, + 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'. ", + ) + return parser + + +def get_params() -> AttributeDict: + params = AttributeDict( + { + "exp_dir": Path("tdnn/exp/"), + "lang_dir": Path("data/lang_phone"), + "lm_dir": Path("data/lm"), + "feature_dim": 23, + "search_beam": 20, + "output_beam": 8, + "min_active_states": 30, + "max_active_states": 10000, + "use_double_scores": True, + } + ) + return params + + +def decode_one_batch( + params: AttributeDict, + model: nn.Module, + HLG: k2.Fsa, + batch: dict, + word_table: k2.SymbolTable, +) -> List[List[int]]: + """Decode one batch and return the result in a list-of-list. + Each sub list contains the word IDs for an utterance in the batch. + + Args: + params: + It's the return value of :func:`get_params`. + + - params.method is "1best", it uses 1best decoding. + - params.method is "nbest", it uses nbest decoding. + + model: + The neural model. + HLG: + The decoding graph. + batch: + It is the return value from iterating + `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation + for the format of the `batch`. + (https://github.com/lhotse-speech/lhotse/blob/master/lhotse/dataset/speech_recognition.py) + word_table: + It is the word symbol table. + Returns: + Return the decoding result. `len(ans)` == batch size. + """ + device = HLG.device + feature = batch["inputs"] + assert feature.ndim == 3 + feature = feature.to(device) + # at entry, feature is [N, T, C] + + nnet_output = model(feature) + # nnet_output is [N, T, C] + + supervisions = batch["supervisions"] + + supervision_segments = torch.stack( + ( + supervisions["sequence_idx"], + supervisions["start_frame"], + supervisions["num_frames"], + ), + 1, + ).to(torch.int32) + + lattice = get_lattice( + nnet_output=nnet_output, + HLG=HLG, + supervision_segments=supervision_segments, + search_beam=params.search_beam, + output_beam=params.output_beam, + min_active_states=params.min_active_states, + max_active_states=params.max_active_states, + ) + + best_path = one_best_decoding( + lattice=lattice, use_double_scores=params.use_double_scores + ) + hyps = get_texts(best_path) + hyps = [[word_table[i] for i in ids] for ids in hyps] + return hyps + + +def decode_dataset( + dl: torch.utils.data.DataLoader, + params: AttributeDict, + model: nn.Module, + HLG: k2.Fsa, + word_table: k2.SymbolTable, +) -> List[Tuple[List[int], List[int]]]: + """Decode dataset. + + Args: + dl: + PyTorch's dataloader containing the dataset to decode. + params: + It is returned by :func:`get_params`. + model: + The neural model. + HLG: + The decoding graph. + word_table: + It is word symbol table. + Returns: + Return a tuple contains two elements (ref_text, hyp_text): + 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 = [] + for batch_idx, batch in enumerate(dl): + texts = batch["supervisions"]["text"] + + hyps = decode_one_batch( + params=params, + model=model, + HLG=HLG, + batch=batch, + word_table=word_table, + ) + + 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.extend(this_batch) + + num_cuts += len(batch["supervisions"]["text"]) + + if batch_idx % 100 == 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( + exp_dir: Path, + test_set_name: str, + results: List[Tuple[List[int], List[int]]], +) -> None: + """Save results to `exp_dir`. + Args: + exp_dir: + The output directory. This function create the following files inside + this directory: + + - recogs-{test_set_name}.text + + It contains the reference and hypothesis results, like below:: + + ref=['NO', 'NO', 'NO', 'YES', 'NO', 'NO', 'NO', 'YES'] + hyp=['NO', 'NO', 'NO', 'YES', 'NO', 'NO', 'NO', 'YES'] + ref=['NO', 'NO', 'YES', 'NO', 'YES', 'NO', 'NO', 'YES'] + hyp=['NO', 'NO', 'YES', 'NO', 'YES', 'NO', 'NO', 'YES'] + + - errs-{test_set_name}.txt + + It contains the detailed WER. + test_set_name: + The name of the test set, which will be part of the result filename. + results: + A list of tuples, each of which contains (ref_words, hyp_words). + Returns: + Return None. + """ + recog_path = exp_dir / f"recogs-{test_set_name}.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 = exp_dir / f"errs-{test_set_name}.txt" + with open(errs_filename, "w") as f: + write_error_stats(f, f"{test_set_name}", results) + + logging.info("Wrote detailed error stats to {}".format(errs_filename)) + + +@torch.no_grad() +def main(): + parser = get_parser() + YesNoAsrDataModule.add_arguments(parser) + args = parser.parse_args() + + params = get_params() + params.update(vars(args)) + + setup_logger(f"{params.exp_dir}/log/log-decode") + logging.info("Decoding started") + logging.info(params) + + lexicon = Lexicon(params.lang_dir) + max_token_id = max(lexicon.tokens) + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"device: {device}") + + HLG = k2.Fsa.from_dict( + torch.load(f"{params.lang_dir}/HLG.pt", map_location="cpu") + ) + HLG = HLG.to(device) + assert HLG.requires_grad is False + + model = Tdnn( + num_features=params.feature_dim, + num_classes=max_token_id + 1, # +1 for the blank symbol + ) + 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.load_state_dict(average_checkpoints(filenames)) + + model.to(device) + model.eval() + + yes_no = YesNoAsrDataModule(args) + test_dl = yes_no.test_dataloaders() + results = decode_dataset( + dl=test_dl, + params=params, + model=model, + HLG=HLG, + word_table=lexicon.word_table, + ) + + save_results( + exp_dir=params.exp_dir, test_set_name="test_set", results=results + ) + + logging.info("Done!") + + +if __name__ == "__main__": + main() diff --git a/egs/yesno/ASR/tdnn/model.py b/egs/yesno/ASR/tdnn/model.py new file mode 100755 index 000000000..df0aa246d --- /dev/null +++ b/egs/yesno/ASR/tdnn/model.py @@ -0,0 +1,84 @@ +#!/usr/bin/env python3 + +# Copyright (c) 2021 Xiaomi Corp. (author: Fangjun Kuang) + + +import torch +import torch.nn as nn + + +class Tdnn(nn.Module): + def __init__(self, num_features: int, num_classes: int): + """ + Args: + num_features: + Model input dimension. + num_classes: + Model output dimension + """ + super().__init__() + + self.tdnn = nn.Sequential( + nn.Conv1d( + in_channels=num_features, + out_channels=32, + kernel_size=3, + padding=1, + ), + nn.ReLU(inplace=True), + nn.BatchNorm1d(num_features=32, affine=False), + nn.Conv1d( + in_channels=32, + out_channels=32, + kernel_size=5, + padding=4, + dilation=2, + ), + nn.ReLU(inplace=True), + nn.BatchNorm1d(num_features=32, affine=False), + nn.Conv1d( + in_channels=32, + out_channels=32, + kernel_size=5, + padding=8, + dilation=4, + ), + nn.ReLU(inplace=True), + nn.BatchNorm1d(num_features=32, affine=False), + ) + self.output_linear = nn.Linear(in_features=32, out_features=num_classes) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Args: + x: + The input tensor with shape [N, T, C] + + Returns: + The output tensor has shape [N, T, C] + """ + x = x.permute(0, 2, 1) # [N, T, C] -> [N, C, T] + x = self.tdnn(x) + x = x.permute(0, 2, 1) # [N, C, T] -> [N, T, C] + x = self.output_linear(x) + x = nn.functional.log_softmax(x, dim=-1) + return x + + +def test_tdnn(): + num_features = 23 + num_classes = 4 + model = Tdnn(num_features=num_features, num_classes=num_classes) + num_param = sum([p.numel() for p in model.parameters()]) + print(f"Number of model parameters: {num_param}") + N = 2 + T = 100 + C = num_features + x = torch.randn(N, T, C) + y = model(x) + print(x.shape) + print(y.shape) + + +if __name__ == "__main__": + test_tdnn() diff --git a/egs/yesno/ASR/tdnn/train.py b/egs/yesno/ASR/tdnn/train.py new file mode 100755 index 000000000..a5a248c9c --- /dev/null +++ b/egs/yesno/ASR/tdnn/train.py @@ -0,0 +1,558 @@ +#!/usr/bin/env python3 + +import argparse +import logging +from pathlib import Path +from shutil import copyfile +from typing import Optional + +import k2 +import torch +import torch.distributed as dist +import torch.multiprocessing as mp +import torch.nn as nn +import torch.optim as optim +from asr_datamodule import YesNoAsrDataModule +from lhotse.utils import fix_random_seed +from model import Tdnn +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.nn.utils import clip_grad_norm_ +from torch.utils.tensorboard import SummaryWriter + +from icefall.checkpoint import load_checkpoint +from icefall.checkpoint import save_checkpoint as save_checkpoint_impl +from icefall.dist import cleanup_dist, setup_dist +from icefall.graph_compiler import CtcTrainingGraphCompiler +from icefall.lexicon import Lexicon +from icefall.utils import ( + AttributeDict, + encode_supervisions, + setup_logger, + str2bool, +) + + +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=50, + help="Number of epochs to train.", + ) + + return parser + + +def get_params() -> AttributeDict: + """Return a dict containing training parameters. + + All training related parameters that are not passed from the commandline + is 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`: + + - exp_dir: It specifies the directory where all training related + files, e.g., checkpoints, log, etc, are saved + + - lang_dir: It contains language related input files such as + "lexicon.txt" + + - lr: It specifies the initial learning rate + + - feature_dim: The model input dim. It has to match the one used + in computing features. + + - weight_decay: The weight_decay for the optimizer. + + - subsampling_factor: The subsampling factor for the model. + + - start_epoch: If it is not zero, load checkpoint `start_epoch-1` + and continue training from that checkpoint. + + - num_epochs: Number of epochs to train. + + - 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 + + - valid_interval: Run validation if batch_idx % valid_interval` is 0 + + - beam_size: It is used in k2.ctc_loss + + - reduction: It is used in k2.ctc_loss + + - use_double_scores: It is used in k2.ctc_loss + """ + params = AttributeDict( + { + "exp_dir": Path("tdnn/exp"), + "lang_dir": Path("data/lang_phone"), + "lr": 1e-3, + "feature_dim": 23, + "weight_decay": 1e-6, + "start_epoch": 0, + "num_epochs": 50, + "best_train_loss": float("inf"), + "best_valid_loss": float("inf"), + "best_train_epoch": -1, + "best_valid_epoch": -1, + "batch_idx_train": 0, + "log_interval": 10, + "valid_interval": 10, + "beam_size": 10, + "reduction": "sum", + "use_double_scores": True, + } + ) + + return params + + +def load_checkpoint_if_available( + params: AttributeDict, + model: nn.Module, + optimizer: Optional[torch.optim.Optimizer] = None, + scheduler: Optional[torch.optim.lr_scheduler._LRScheduler] = None, +) -> None: + """Load checkpoint from file. + + If params.start_epoch is positive, it will load the checkpoint from + `params.start_epoch - 1`. Otherwise, this function does nothing. + + Apart from loading state dict for `model`, `optimizer` and `scheduler`, + 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 learning rate scheduler we are using. + Returns: + Return None. + """ + if params.start_epoch <= 0: + return + + filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt" + 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] + + return saved_params + + +def save_checkpoint( + params: AttributeDict, + model: nn.Module, + optimizer: torch.optim.Optimizer, + scheduler: torch.optim.lr_scheduler._LRScheduler, + 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. + """ + 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, + 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, + batch: dict, + graph_compiler: CtcTrainingGraphCompiler, + is_training: bool, +): + """ + 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 Tdnn in our case. + batch: + A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()` + for the content in it. + graph_compiler: + It is used to build a decoding graph from a ctc topo and training + transcript. The training transcript is contained in the given `batch`, + while the ctc topo is built when this compiler is instantiated. + 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 = graph_compiler.device + feature = batch["inputs"] + # at entry, feature is [N, T, C] + assert feature.ndim == 3 + feature = feature.to(device) + + with torch.set_grad_enabled(is_training): + nnet_output = model(feature) + # nnet_output is [N, T, C] + + # NOTE: We need `encode_supervisions` to sort sequences with + # different duration in decreasing order, required by + # `k2.intersect_dense` called in `k2.ctc_loss` + supervisions = batch["supervisions"] + supervision_segments, texts = encode_supervisions( + supervisions, subsampling_factor=1 + ) + decoding_graph = graph_compiler.compile(texts) + + dense_fsa_vec = k2.DenseFsaVec( + nnet_output, + supervision_segments, + ) + + loss = k2.ctc_loss( + decoding_graph=decoding_graph, + dense_fsa_vec=dense_fsa_vec, + output_beam=params.beam_size, + reduction=params.reduction, + use_double_scores=params.use_double_scores, + ) + + assert loss.requires_grad == is_training + + # train_frames and valid_frames are used for printing. + if is_training: + params.train_frames = supervision_segments[:, 2].sum().item() + else: + params.valid_frames = supervision_segments[:, 2].sum().item() + + return loss + + +def compute_validation_loss( + params: AttributeDict, + model: nn.Module, + graph_compiler: CtcTrainingGraphCompiler, + valid_dl: torch.utils.data.DataLoader, + world_size: int = 1, +) -> None: + """Run the validation process. The validation loss + is saved in `params.valid_loss`. + """ + model.eval() + + tot_loss = 0.0 + tot_frames = 0.0 + for batch_idx, batch in enumerate(valid_dl): + loss = compute_loss( + params=params, + model=model, + batch=batch, + graph_compiler=graph_compiler, + is_training=False, + ) + assert loss.requires_grad is False + + loss_cpu = loss.detach().cpu().item() + tot_loss += loss_cpu + tot_frames += params.valid_frames + + if world_size > 1: + s = torch.tensor([tot_loss, tot_frames], device=loss.device) + dist.all_reduce(s, op=dist.ReduceOp.SUM) + s = s.cpu().tolist() + tot_loss = s[0] + tot_frames = s[1] + + params.valid_loss = tot_loss / tot_frames + + if params.valid_loss < params.best_valid_loss: + params.best_valid_epoch = params.cur_epoch + params.best_valid_loss = params.valid_loss + + +def train_one_epoch( + params: AttributeDict, + model: nn.Module, + optimizer: torch.optim.Optimizer, + graph_compiler: CtcTrainingGraphCompiler, + train_dl: torch.utils.data.DataLoader, + valid_dl: torch.utils.data.DataLoader, + tb_writer: Optional[SummaryWriter] = None, + world_size: int = 1, +) -> 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. + graph_compiler: + It is used to convert transcripts to FSAs. + train_dl: + Dataloader for the training dataset. + valid_dl: + Dataloader for the validation dataset. + tb_writer: + Writer to write log messages to tensorboard. + world_size: + Number of nodes in DDP training. If it is 1, DDP is disabled. + """ + model.train() + + tot_loss = 0.0 # sum of losses over all batches + tot_frames = 0.0 # sum of frames over all batches + for batch_idx, batch in enumerate(train_dl): + params.batch_idx_train += 1 + batch_size = len(batch["supervisions"]["text"]) + + loss = compute_loss( + params=params, + model=model, + batch=batch, + graph_compiler=graph_compiler, + is_training=True, + ) + + # NOTE: We use reduction==sum and loss is computed over utterances + # in the batch and there is no normalization to it so far. + + optimizer.zero_grad() + loss.backward() + clip_grad_norm_(model.parameters(), 5.0, 2.0) + optimizer.step() + + loss_cpu = loss.detach().cpu().item() + + tot_frames += params.train_frames + tot_loss += loss_cpu + tot_avg_loss = tot_loss / tot_frames + + if batch_idx % params.log_interval == 0: + logging.info( + f"Epoch {params.cur_epoch}, batch {batch_idx}, " + f"batch avg loss {loss_cpu/params.train_frames:.4f}, " + f"total avg loss: {tot_avg_loss:.4f}, " + f"batch size: {batch_size}" + ) + + if batch_idx > 0 and batch_idx % params.valid_interval == 0: + 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}, valid loss {params.valid_loss:.4f}," + f" best valid loss: {params.best_valid_loss:.4f} " + f"best valid epoch: {params.best_valid_epoch}" + ) + + params.train_loss = tot_loss / tot_frames + + 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(42) + 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") + logging.info(params) + + if args.tensorboard and rank == 0: + tb_writer = SummaryWriter(log_dir=f"{params.exp_dir}/tensorboard") + else: + tb_writer = None + + lexicon = Lexicon(params.lang_dir) + max_phone_id = max(lexicon.tokens) + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", rank) + + graph_compiler = CtcTrainingGraphCompiler(lexicon=lexicon, device=device) + + model = Tdnn( + num_features=params.feature_dim, + num_classes=max_phone_id + 1, # +1 for the blank symbol + ) + + checkpoints = load_checkpoint_if_available(params=params, model=model) + + model.to(device) + if world_size > 1: + model = DDP(model, device_ids=[rank]) + + optimizer = optim.AdamW( + model.parameters(), + lr=params.lr, + weight_decay=params.weight_decay, + ) + + if checkpoints: + optimizer.load_state_dict(checkpoints["optimizer"]) + + yes_no = YesNoAsrDataModule(args) + train_dl = yes_no.train_dataloaders() + + # There are only 60 waves: 30 files are used for training + # and the remaining 30 files are used for testing. + # We use test data as validation. + valid_dl = yes_no.test_dataloaders() + + for epoch in range(params.start_epoch, params.num_epochs): + 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, + graph_compiler=graph_compiler, + train_dl=train_dl, + valid_dl=valid_dl, + tb_writer=tb_writer, + world_size=world_size, + ) + + save_checkpoint( + params=params, + model=model, + optimizer=optimizer, + scheduler=None, + rank=rank, + ) + + logging.info("Done!") + if world_size > 1: + torch.distributed.barrier() + cleanup_dist() + + +def main(): + parser = get_parser() + YesNoAsrDataModule.add_arguments(parser) + args = parser.parse_args() + + 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) + + +if __name__ == "__main__": + main() diff --git a/icefall/dataset/datamodule.py b/icefall/dataset/datamodule.py index 97767ef9d..97918ffd8 100644 --- a/icefall/dataset/datamodule.py +++ b/icefall/dataset/datamodule.py @@ -1,4 +1,4 @@ -# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang) +# Copyright 2021 Piotr Żelasko # # See ../../LICENSE for clarification regarding multiple authors # From 57cb61166571f1747a8995515d5afd560c0943b7 Mon Sep 17 00:00:00 2001 From: Fangjun Kuang Date: Mon, 23 Aug 2021 15:59:36 +0800 Subject: [PATCH 03/10] [yesno] Remove padding in TDNN (#21) * Disable SpecAug for yesno. Also replace Adam with SGD. * Remove padding in the model to make the results reproducible. --- .github/workflows/run-yesno-recipe.yml | 15 ++------------- egs/yesno/ASR/tdnn/asr_datamodule.py | 12 ------------ egs/yesno/ASR/tdnn/decode.py | 19 +++++++------------ egs/yesno/ASR/tdnn/model.py | 3 --- egs/yesno/ASR/tdnn/train.py | 23 +++++++++++------------ 5 files changed, 20 insertions(+), 52 deletions(-) diff --git a/.github/workflows/run-yesno-recipe.yml b/.github/workflows/run-yesno-recipe.yml index 7e36139a3..39a6a0e80 100644 --- a/.github/workflows/run-yesno-recipe.yml +++ b/.github/workflows/run-yesno-recipe.yml @@ -69,21 +69,10 @@ jobs: run: | export PYTHONPATH=$PWD:$PYTHONPATH echo $PYTHONPATH - ls -lh - # The following three lines are for macOS - lib_path=$(python -c "from distutils.sysconfig import get_python_lib; print(get_python_lib())") - echo "lib_path: $lib_path" - export DYLD_LIBRARY_PATH=$lib_path:$DYLD_LIBRARY_PATH - ls -lh $lib_path cd egs/yesno/ASR ./prepare.sh - python3 ./tdnn/train.py --num-epochs 100 - python3 ./tdnn/decode.py --epoch 99 - python3 ./tdnn/decode.py --epoch 95 - python3 ./tdnn/decode.py --epoch 90 - python3 ./tdnn/decode.py --epoch 80 - python3 ./tdnn/decode.py --epoch 70 - python3 ./tdnn/decode.py --epoch 60 + python3 ./tdnn/train.py + python3 ./tdnn/decode.py # TODO: Check that the WER is less than some value diff --git a/egs/yesno/ASR/tdnn/asr_datamodule.py b/egs/yesno/ASR/tdnn/asr_datamodule.py index 8b2b44c8a..e6614e3ce 100644 --- a/egs/yesno/ASR/tdnn/asr_datamodule.py +++ b/egs/yesno/ASR/tdnn/asr_datamodule.py @@ -27,7 +27,6 @@ from lhotse.dataset import ( K2SpeechRecognitionDataset, PrecomputedFeatures, SingleCutSampler, - SpecAugment, ) from lhotse.dataset.input_strategies import OnTheFlyFeatures from torch.utils.data import DataLoader @@ -163,18 +162,8 @@ class YesNoAsrDataModule(DataModule): ) ] + transforms - input_transforms = [ - SpecAugment( - num_frame_masks=2, - features_mask_size=27, - num_feature_masks=2, - frames_mask_size=100, - ) - ] - train = K2SpeechRecognitionDataset( cut_transforms=transforms, - input_transforms=input_transforms, return_cuts=self.args.return_cuts, ) @@ -194,7 +183,6 @@ class YesNoAsrDataModule(DataModule): input_strategy=OnTheFlyFeatures( Fbank(FbankConfig(num_mel_bins=23)) ), - input_transforms=input_transforms, return_cuts=self.args.return_cuts, ) diff --git a/egs/yesno/ASR/tdnn/decode.py b/egs/yesno/ASR/tdnn/decode.py index a87219010..b600c182c 100755 --- a/egs/yesno/ASR/tdnn/decode.py +++ b/egs/yesno/ASR/tdnn/decode.py @@ -32,14 +32,14 @@ def get_parser(): parser.add_argument( "--epoch", type=int, - default=9, + default=14, help="It specifies the checkpoint to use for decoding." "Note: Epoch counts from 0.", ) parser.add_argument( "--avg", type=int, - default=15, + default=2, help="Number of checkpoints to average. Automatically select " "consecutive checkpoints before the checkpoint specified by " "'--epoch'. ", @@ -104,16 +104,11 @@ def decode_one_batch( nnet_output = model(feature) # nnet_output is [N, T, C] - supervisions = batch["supervisions"] - - supervision_segments = torch.stack( - ( - supervisions["sequence_idx"], - supervisions["start_frame"], - supervisions["num_frames"], - ), - 1, - ).to(torch.int32) + batch_size = nnet_output.shape[0] + supervision_segments = torch.tensor( + [[i, 0, nnet_output.shape[1]] for i in range(batch_size)], + dtype=torch.int32, + ) lattice = get_lattice( nnet_output=nnet_output, diff --git a/egs/yesno/ASR/tdnn/model.py b/egs/yesno/ASR/tdnn/model.py index df0aa246d..52cff37e0 100755 --- a/egs/yesno/ASR/tdnn/model.py +++ b/egs/yesno/ASR/tdnn/model.py @@ -23,7 +23,6 @@ class Tdnn(nn.Module): in_channels=num_features, out_channels=32, kernel_size=3, - padding=1, ), nn.ReLU(inplace=True), nn.BatchNorm1d(num_features=32, affine=False), @@ -31,7 +30,6 @@ class Tdnn(nn.Module): in_channels=32, out_channels=32, kernel_size=5, - padding=4, dilation=2, ), nn.ReLU(inplace=True), @@ -40,7 +38,6 @@ class Tdnn(nn.Module): in_channels=32, out_channels=32, kernel_size=5, - padding=8, dilation=4, ), nn.ReLU(inplace=True), diff --git a/egs/yesno/ASR/tdnn/train.py b/egs/yesno/ASR/tdnn/train.py index a5a248c9c..04e1ab698 100755 --- a/egs/yesno/ASR/tdnn/train.py +++ b/egs/yesno/ASR/tdnn/train.py @@ -24,12 +24,7 @@ from icefall.checkpoint import save_checkpoint as save_checkpoint_impl from icefall.dist import cleanup_dist, setup_dist from icefall.graph_compiler import CtcTrainingGraphCompiler from icefall.lexicon import Lexicon -from icefall.utils import ( - AttributeDict, - encode_supervisions, - setup_logger, - str2bool, -) +from icefall.utils import AttributeDict, setup_logger, str2bool def get_parser(): @@ -61,7 +56,7 @@ def get_parser(): parser.add_argument( "--num-epochs", type=int, - default=50, + default=15, help="Number of epochs to train.", ) @@ -129,11 +124,10 @@ def get_params() -> AttributeDict: { "exp_dir": Path("tdnn/exp"), "lang_dir": Path("data/lang_phone"), - "lr": 1e-3, + "lr": 1e-2, "feature_dim": 23, "weight_decay": 1e-6, "start_epoch": 0, - "num_epochs": 50, "best_train_loss": float("inf"), "best_valid_loss": float("inf"), "best_train_epoch": -1, @@ -278,9 +272,14 @@ def compute_loss( # different duration in decreasing order, required by # `k2.intersect_dense` called in `k2.ctc_loss` supervisions = batch["supervisions"] - supervision_segments, texts = encode_supervisions( - supervisions, subsampling_factor=1 + texts = supervisions["text"] + + batch_size = nnet_output.shape[0] + supervision_segments = torch.tensor( + [[i, 0, nnet_output.shape[1]] for i in range(batch_size)], + dtype=torch.int32, ) + decoding_graph = graph_compiler.compile(texts) dense_fsa_vec = k2.DenseFsaVec( @@ -491,7 +490,7 @@ def run(rank, world_size, args): if world_size > 1: model = DDP(model, device_ids=[rank]) - optimizer = optim.AdamW( + optimizer = optim.SGD( model.parameters(), lr=params.lr, weight_decay=params.weight_decay, From 01da00dca0a83d5b39f2930d7948a25e9dc727b5 Mon Sep 17 00:00:00 2001 From: Fangjun Kuang Date: Tue, 24 Aug 2021 14:28:08 +0800 Subject: [PATCH 04/10] WIP: Add documentation. (#22) * Begin to add documentation. * WIP: Add documentation. * Fix a typo. * Add more doc for the recipe yesno. * Add more doc for the yesno recipe. --- docs/.gitignore | 1 + docs/Makefile | 20 + docs/make.bat | 35 ++ docs/requirements.txt | 1 + docs/source/_static/logo.png | Bin 0 -> 682367 bytes docs/source/conf.py | 77 +++ docs/source/index.rst | 24 + .../images/device-CPU_CUDA-orange.svg | 1 + .../images/os-Linux_macOS-ff69b4.svg | 1 + .../images/python-3.6_3.7_3.8_3.9-blue.svg | 1 + ....0_1.7.0_1.7.1_1.8.0_1.8.1_1.9.0-green.svg | 1 + docs/source/installation/index.rst | 469 ++++++++++++++++++ .../images/yesno-tdnn-tensorboard-log.png | Bin 0 -> 124174 bytes docs/source/recipes/index.rst | 18 + docs/source/recipes/librispeech.rst | 2 + docs/source/recipes/yesno.rst | 445 +++++++++++++++++ egs/yesno/ASR/tdnn/decode.py | 18 + egs/yesno/ASR/tdnn/pretrained.py | 209 ++++++++ egs/yesno/ASR/tdnn/train.py | 31 +- 19 files changed, 1352 insertions(+), 2 deletions(-) create mode 100644 docs/.gitignore create mode 100644 docs/Makefile create mode 100644 docs/make.bat create mode 100644 docs/requirements.txt create mode 100644 docs/source/_static/logo.png create mode 100644 docs/source/conf.py create mode 100644 docs/source/index.rst create mode 100644 docs/source/installation/images/device-CPU_CUDA-orange.svg create mode 100644 docs/source/installation/images/os-Linux_macOS-ff69b4.svg create mode 100644 docs/source/installation/images/python-3.6_3.7_3.8_3.9-blue.svg create mode 100644 docs/source/installation/images/torch-1.6.0_1.7.0_1.7.1_1.8.0_1.8.1_1.9.0-green.svg create mode 100644 docs/source/installation/index.rst create mode 100644 docs/source/recipes/images/yesno-tdnn-tensorboard-log.png create mode 100644 docs/source/recipes/index.rst create mode 100644 docs/source/recipes/librispeech.rst create mode 100644 docs/source/recipes/yesno.rst create mode 100755 egs/yesno/ASR/tdnn/pretrained.py diff --git a/docs/.gitignore b/docs/.gitignore new file mode 100644 index 000000000..567609b12 --- /dev/null +++ b/docs/.gitignore @@ -0,0 +1 @@ +build/ diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 000000000..d0c3cbf10 --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = source +BUILDDIR = build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/make.bat b/docs/make.bat new file mode 100644 index 000000000..6247f7e23 --- /dev/null +++ b/docs/make.bat @@ -0,0 +1,35 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=source +set BUILDDIR=build + +if "%1" == "" goto help + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.http://sphinx-doc.org/ + exit /b 1 +) + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% + +:end +popd diff --git a/docs/requirements.txt b/docs/requirements.txt new file mode 100644 index 000000000..483a4e960 --- /dev/null +++ b/docs/requirements.txt @@ -0,0 +1 @@ +sphinx_rtd_theme diff --git a/docs/source/_static/logo.png b/docs/source/_static/logo.png new file mode 100644 index 0000000000000000000000000000000000000000..84d42568c182b1f6f237d97b7a5966b0c158b48e GIT binary patch literal 682367 zcmcdygpi2k@ zg7?NkOiWQ)OpMmf0cvVtZ32Nv1x3YRYes62`EGJQm)86b=Le2QP}xmddVdlTVXhCa zrLNti@9p9=}bjsWFF`La>v5ic^-a`Z(fRsxgFDk#_MG z9iu31_!-|3FifG&ON>R*z(c$i5+sp>3WtBCqgpl=1f`x z-iiP!YmU;F&-J5(3T8taa+RKDiEKoewzuV3;vXi7=M?cn9;+3E$SO~!XI;BNHbTfo 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HcmV?d00001 diff --git a/docs/source/conf.py b/docs/source/conf.py new file mode 100644 index 000000000..f97f72d54 --- /dev/null +++ b/docs/source/conf.py @@ -0,0 +1,77 @@ +# Configuration file for the Sphinx documentation builder. +# +# This file only contains a selection of the most common options. For a full +# list see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Path setup -------------------------------------------------------------- + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +# +# import os +# import sys +# sys.path.insert(0, os.path.abspath('.')) + +import sphinx_rtd_theme + + +# -- Project information ----------------------------------------------------- + +project = "icefall" +copyright = "2021, icefall development team" +author = "icefall development team" + +# The full version, including alpha/beta/rc tags +release = "0.1" + + +# -- General configuration --------------------------------------------------- + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ + "sphinx_rtd_theme", +] + +# Add any paths that contain templates here, relative to this directory. +templates_path = ["_templates"] + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This pattern also affects html_static_path and html_extra_path. +exclude_patterns = [] + +source_suffix = { + ".rst": "restructuredtext", +} +master_doc = "index" + + +# -- Options for HTML output ------------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +# +html_theme = "sphinx_rtd_theme" +html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] +html_show_sourcelink = True + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ["_static", "installation/images"] + +pygments_style = "sphinx" + +numfig = True + +html_context = { + "display_github": True, + "github_user": "k2-fsa", + "github_repo": "icefall", + "github_version": "master", + "conf_py_path": "/icefall/docs/source/", +} diff --git a/docs/source/index.rst b/docs/source/index.rst new file mode 100644 index 000000000..c5cd2e832 --- /dev/null +++ b/docs/source/index.rst @@ -0,0 +1,24 @@ +.. icefall documentation master file, created by + sphinx-quickstart on Mon Aug 23 16:07:39 2021. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +icefall +======= + +.. image:: _static/logo.png + :alt: icefall logo + :width: 168px + :align: center + :target: https://github.com/k2-fsa/icefall + + +Documentation for `icefall `_, containing +speech recognition recipes using `k2 `_. + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + installation/index + recipes/index diff --git a/docs/source/installation/images/device-CPU_CUDA-orange.svg b/docs/source/installation/images/device-CPU_CUDA-orange.svg new file mode 100644 index 000000000..b760102e3 --- /dev/null +++ b/docs/source/installation/images/device-CPU_CUDA-orange.svg @@ -0,0 +1 @@ +device: CPU | CUDAdeviceCPU | CUDA \ No newline at end of file diff --git a/docs/source/installation/images/os-Linux_macOS-ff69b4.svg b/docs/source/installation/images/os-Linux_macOS-ff69b4.svg new file mode 100644 index 000000000..44c112747 --- /dev/null +++ b/docs/source/installation/images/os-Linux_macOS-ff69b4.svg @@ -0,0 +1 @@ +os: Linux | macOSosLinux | macOS \ No newline at end of file diff --git a/docs/source/installation/images/python-3.6_3.7_3.8_3.9-blue.svg b/docs/source/installation/images/python-3.6_3.7_3.8_3.9-blue.svg new file mode 100644 index 000000000..676feba2c --- /dev/null +++ b/docs/source/installation/images/python-3.6_3.7_3.8_3.9-blue.svg @@ -0,0 +1 @@ +python: 3.6 | 3.7 | 3.8 | 3.9python3.6 | 3.7 | 3.8 | 3.9 \ No newline at end of file diff --git a/docs/source/installation/images/torch-1.6.0_1.7.0_1.7.1_1.8.0_1.8.1_1.9.0-green.svg b/docs/source/installation/images/torch-1.6.0_1.7.0_1.7.1_1.8.0_1.8.1_1.9.0-green.svg new file mode 100644 index 000000000..d9b0efe1a --- /dev/null +++ b/docs/source/installation/images/torch-1.6.0_1.7.0_1.7.1_1.8.0_1.8.1_1.9.0-green.svg @@ -0,0 +1 @@ +torch: 1.6.0 | 1.7.0 | 1.7.1 | 1.8.0 | 1.8.1 | 1.9.0torch1.6.0 | 1.7.0 | 1.7.1 | 1.8.0 | 1.8.1 | 1.9.0 \ No newline at end of file diff --git a/docs/source/installation/index.rst b/docs/source/installation/index.rst new file mode 100644 index 000000000..6edea12d8 --- /dev/null +++ b/docs/source/installation/index.rst @@ -0,0 +1,469 @@ +.. _install icefall: + +Installation +============ + +- |os| +- |device| +- |python_versions| +- |torch_versions| + +.. |os| image:: ./images/os-Linux_macOS-ff69b4.svg + :alt: Supported operating systems + +.. |device| image:: ./images/device-CPU_CUDA-orange.svg + :alt: Supported devices + +.. |python_versions| image:: ./images/python-3.6_3.7_3.8_3.9-blue.svg + :alt: Supported python versions + +.. |torch_versions| image:: ./images/torch-1.6.0_1.7.0_1.7.1_1.8.0_1.8.1_1.9.0-green.svg + :alt: Supported PyTorch versions + +icefall depends on `k2 `_ and +`lhotse `_. + +We recommend you to install ``k2`` first, as ``k2`` is bound to +a specific version of PyTorch after compilation. Install ``k2`` also +installs its dependency PyTorch, which can be reused by ``lhotse``. + + +(1) Install k2 +-------------- + +Please refer to ``_ +to install `k2`. + +.. HINT:: + + If you have already installed PyTorch and don't want to replace it, + please install a version of k2 that is compiled against the version + of PyTorch you are using. + +(2) Install lhotse +------------------ + +Please refer to ``_ +to install ``lhotse``. + +.. HINT:: + + Install ``lhotse`` also installs its dependency `torchaudio `_. + +(3) Download icefall +-------------------- + +icefall is a collection of Python scripts, so you don't need to install it +and we don't provide a ``setup.py`` to install it. + +What you need is to download it and set the environment variable ``PYTHONPATH`` +to point to it. + +Assume you want to place ``icefall`` in the folder ``/tmp``. The +following commands show you how to setup ``icefall``: + + +.. code-block:: bash + + cd /tmp + git clone https://github.com/k2-fsa/icefall + cd icefall + pip install -r requirements.txt + export PYTHONPATH=/tmp/icefall:$PYTHONPATH + +.. HINT:: + + You can put several versions of ``icefall`` in the same virtual environment. + To switch among different versions of ``icefall``, just set ``PYTHONPATH`` + to point to the version you want. + + +Installation example +-------------------- + +The following shows an example about setting up the environment. + + +(1) Create a virtual environment +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code-block:: bash + + $ virtualenv -p python3.8 test-icefall + + created virtual environment CPython3.8.6.final.0-64 in 1540ms + creator CPython3Posix(dest=/ceph-fj/fangjun/test-icefall, clear=False, no_vcs_ignore=False, global=False) + seeder FromAppData(download=False, pip=bundle, setuptools=bundle, wheel=bundle, via=copy, app_data_dir=/root/fangjun/.local/share/v + irtualenv) + added seed packages: pip==21.1.3, setuptools==57.4.0, wheel==0.36.2 + activators BashActivator,CShellActivator,FishActivator,PowerShellActivator,PythonActivator,XonshActivator + + +(2) Activate your virtual environment +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code-block:: bash + + $ source test-icefall/bin/activate + +(3) Install k2 +~~~~~~~~~~~~~~ + +.. code-block:: bash + + $ pip install k2==1.4.dev20210822+cpu.torch1.9.0 -f https://k2-fsa.org/nightly/index.html + + Looking in links: https://k2-fsa.org/nightly/index.html + Collecting k2==1.4.dev20210822+cpu.torch1.9.0 + Downloading https://k2-fsa.org/nightly/whl/k2-1.4.dev20210822%2Bcpu.torch1.9.0-cp38-cp38-linux_x86_64.whl (1.6 MB) + |________________________________| 1.6 MB 185 kB/s + Collecting graphviz + Downloading graphviz-0.17-py3-none-any.whl (18 kB) + Collecting torch==1.9.0 + Using cached torch-1.9.0-cp38-cp38-manylinux1_x86_64.whl (831.4 MB) + Collecting typing-extensions + Using cached typing_extensions-3.10.0.0-py3-none-any.whl (26 kB) + Installing collected packages: typing-extensions, torch, graphviz, k2 + Successfully installed graphviz-0.17 k2-1.4.dev20210822+cpu.torch1.9.0 torch-1.9.0 typing-extensions-3.10.0.0 + +.. WARNING:: + + We choose to install a CPU version of k2 for testing. You would probably want to install + a CUDA version of k2. + + +(4) Install lhotse +~~~~~~~~~~~~~~~~~~ + +.. code-block:: + + $ pip install git+https://github.com/lhotse-speech/lhotse + + Collecting git+https://github.com/lhotse-speech/lhotse + Cloning https://github.com/lhotse-speech/lhotse to /tmp/pip-req-build-7b1b76ge + Running command git clone -q https://github.com/lhotse-speech/lhotse /tmp/pip-req-build-7b1b76ge + Collecting audioread>=2.1.9 + Using cached audioread-2.1.9-py3-none-any.whl + Collecting SoundFile>=0.10 + Using cached SoundFile-0.10.3.post1-py2.py3-none-any.whl (21 kB) + Collecting click>=7.1.1 + Using cached click-8.0.1-py3-none-any.whl (97 kB) + Collecting cytoolz>=0.10.1 + Using cached cytoolz-0.11.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.9 MB) + Collecting dataclasses + Using cached dataclasses-0.6-py3-none-any.whl (14 kB) + Collecting h5py>=2.10.0 + Downloading h5py-3.4.0-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (4.5 MB) + |________________________________| 4.5 MB 684 kB/s + Collecting intervaltree>=3.1.0 + Using cached intervaltree-3.1.0-py2.py3-none-any.whl + Collecting lilcom>=1.1.0 + Using cached lilcom-1.1.1-cp38-cp38-linux_x86_64.whl + Collecting numpy>=1.18.1 + Using cached numpy-1.21.2-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (15.8 MB) + Collecting packaging + Using cached packaging-21.0-py3-none-any.whl (40 kB) + Collecting pyyaml>=5.3.1 + Using cached PyYAML-5.4.1-cp38-cp38-manylinux1_x86_64.whl (662 kB) + Collecting tqdm + Downloading tqdm-4.62.1-py2.py3-none-any.whl (76 kB) + |________________________________| 76 kB 2.7 MB/s + Collecting torchaudio==0.9.0 + Downloading torchaudio-0.9.0-cp38-cp38-manylinux1_x86_64.whl (1.9 MB) + |________________________________| 1.9 MB 73.1 MB/s + Requirement already satisfied: torch==1.9.0 in ./test-icefall/lib/python3.8/site-packages (from torchaudio==0.9.0->lhotse===0.8.0.dev + -2a1410b-clean) (1.9.0) + Requirement already satisfied: typing-extensions in ./test-icefall/lib/python3.8/site-packages (from torch==1.9.0->torchaudio==0.9.0- + >lhotse===0.8.0.dev-2a1410b-clean) (3.10.0.0) + Collecting toolz>=0.8.0 + Using cached toolz-0.11.1-py3-none-any.whl (55 kB) + Collecting sortedcontainers<3.0,>=2.0 + Using cached sortedcontainers-2.4.0-py2.py3-none-any.whl (29 kB) + Collecting cffi>=1.0 + Using cached cffi-1.14.6-cp38-cp38-manylinux1_x86_64.whl (411 kB) + Collecting pycparser + Using cached pycparser-2.20-py2.py3-none-any.whl (112 kB) + Collecting pyparsing>=2.0.2 + Using cached pyparsing-2.4.7-py2.py3-none-any.whl (67 kB) + Building wheels for collected packages: lhotse + Building wheel for lhotse (setup.py) ... done + Created wheel for lhotse: filename=lhotse-0.8.0.dev_2a1410b_clean-py3-none-any.whl size=342242 sha256=f683444afa4dc0881133206b4646a + 9d0f774224cc84000f55d0a67f6e4a37997 + Stored in directory: /tmp/pip-ephem-wheel-cache-ftu0qysz/wheels/7f/7a/8e/a0bf241336e2e3cb573e1e21e5600952d49f5162454f2e612f + WARNING: Built wheel for lhotse is invalid: Metadata 1.2 mandates PEP 440 version, but '0.8.0.dev-2a1410b-clean' is not + Failed to build lhotse + Installing collected packages: pycparser, toolz, sortedcontainers, pyparsing, numpy, cffi, tqdm, torchaudio, SoundFile, pyyaml, packa + ging, lilcom, intervaltree, h5py, dataclasses, cytoolz, click, audioread, lhotse + Running setup.py install for lhotse ... done + DEPRECATION: lhotse was installed using the legacy 'setup.py install' method, because a wheel could not be built for it. A possible + replacement is to fix the wheel build issue reported above. You can find discussion regarding this at https://github.com/pypa/pip/is + sues/8368. + Successfully installed SoundFile-0.10.3.post1 audioread-2.1.9 cffi-1.14.6 click-8.0.1 cytoolz-0.11.0 dataclasses-0.6 h5py-3.4.0 inter + valtree-3.1.0 lhotse-0.8.0.dev-2a1410b-clean lilcom-1.1.1 numpy-1.21.2 packaging-21.0 pycparser-2.20 pyparsing-2.4.7 pyyaml-5.4.1 sor + tedcontainers-2.4.0 toolz-0.11.1 torchaudio-0.9.0 tqdm-4.62.1 + +**NOTE**: After installing ``lhotse``, you will encounter the following error: + +.. code-block:: + + $ lhotse download --help + -bash: /ceph-fj/fangjun/test-icefall/bin/lhotse: python: bad interpreter: No such file or directory + +The correct fix is: + +.. code-block:: + + echo '#!/usr/bin/env python3' | cat - $(which lhotse) > /tmp/lhotse-bin + chmod +x /tmp/lhotse-bin + mv /tmp/lhotse-bin $(which lhotse) + + +(5) Download icefall +~~~~~~~~~~~~~~~~~~~~ + +.. code-block:: + + $ cd /tmp + $ git clone https://github.com/k2-fsa/icefall + + Cloning into 'icefall'... + remote: Enumerating objects: 500, done. + remote: Counting objects: 100% (500/500), done. + remote: Compressing objects: 100% (308/308), done. + remote: Total 500 (delta 263), reused 307 (delta 102), pack-reused 0 + Receiving objects: 100% (500/500), 172.49 KiB | 385.00 KiB/s, done. + Resolving deltas: 100% (263/263), done. + + $ cd icefall + $ pip install -r requirements.txt + + Collecting kaldilm + Downloading kaldilm-1.8.tar.gz (48 kB) + |________________________________| 48 kB 574 kB/s + Collecting kaldialign + Using cached kaldialign-0.2-cp38-cp38-linux_x86_64.whl + Collecting sentencepiece>=0.1.96 + Using cached sentencepiece-0.1.96-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB) + Collecting tensorboard + Using cached tensorboard-2.6.0-py3-none-any.whl (5.6 MB) + Requirement already satisfied: setuptools>=41.0.0 in /ceph-fj/fangjun/test-icefall/lib/python3.8/site-packages (from tensorboard->-r + requirements.txt (line 4)) (57.4.0) + Collecting absl-py>=0.4 + Using cached absl_py-0.13.0-py3-none-any.whl (132 kB) + Collecting google-auth-oauthlib<0.5,>=0.4.1 + Using cached google_auth_oauthlib-0.4.5-py2.py3-none-any.whl (18 kB) + Collecting grpcio>=1.24.3 + Using cached grpcio-1.39.0-cp38-cp38-manylinux2014_x86_64.whl (4.3 MB) + Requirement already satisfied: wheel>=0.26 in /ceph-fj/fangjun/test-icefall/lib/python3.8/site-packages (from tensorboard->-r require + ments.txt (line 4)) (0.36.2) + Requirement already satisfied: numpy>=1.12.0 in /ceph-fj/fangjun/test-icefall/lib/python3.8/site-packages (from tensorboard->-r requi + rements.txt (line 4)) (1.21.2) + Collecting protobuf>=3.6.0 + Using cached protobuf-3.17.3-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl (1.0 MB) + Collecting werkzeug>=0.11.15 + Using cached Werkzeug-2.0.1-py3-none-any.whl (288 kB) + Collecting tensorboard-data-server<0.7.0,>=0.6.0 + Using cached tensorboard_data_server-0.6.1-py3-none-manylinux2010_x86_64.whl (4.9 MB) + Collecting google-auth<2,>=1.6.3 + Downloading google_auth-1.35.0-py2.py3-none-any.whl (152 kB) + |________________________________| 152 kB 1.4 MB/s + Collecting requests<3,>=2.21.0 + Using cached requests-2.26.0-py2.py3-none-any.whl (62 kB) + Collecting tensorboard-plugin-wit>=1.6.0 + Using cached tensorboard_plugin_wit-1.8.0-py3-none-any.whl (781 kB) + Collecting markdown>=2.6.8 + Using cached Markdown-3.3.4-py3-none-any.whl (97 kB) + Collecting six + Using cached six-1.16.0-py2.py3-none-any.whl (11 kB) + Collecting cachetools<5.0,>=2.0.0 + Using cached cachetools-4.2.2-py3-none-any.whl (11 kB) + Collecting rsa<5,>=3.1.4 + Using cached rsa-4.7.2-py3-none-any.whl (34 kB) + Collecting pyasn1-modules>=0.2.1 + Using cached pyasn1_modules-0.2.8-py2.py3-none-any.whl (155 kB) + Collecting requests-oauthlib>=0.7.0 + Using cached requests_oauthlib-1.3.0-py2.py3-none-any.whl (23 kB) + Collecting pyasn1<0.5.0,>=0.4.6 + Using cached pyasn1-0.4.8-py2.py3-none-any.whl (77 kB) + Collecting urllib3<1.27,>=1.21.1 + Using cached urllib3-1.26.6-py2.py3-none-any.whl (138 kB) + Collecting certifi>=2017.4.17 + Using cached certifi-2021.5.30-py2.py3-none-any.whl (145 kB) + Collecting charset-normalizer~=2.0.0 + Using cached charset_normalizer-2.0.4-py3-none-any.whl (36 kB) + Collecting idna<4,>=2.5 + Using cached idna-3.2-py3-none-any.whl (59 kB) + Collecting oauthlib>=3.0.0 + Using cached oauthlib-3.1.1-py2.py3-none-any.whl (146 kB) + Building wheels for collected packages: kaldilm + Building wheel for kaldilm (setup.py) ... done + Created wheel for kaldilm: filename=kaldilm-1.8-cp38-cp38-linux_x86_64.whl size=897233 sha256=eccb906cafcd45bf9a7e1a1718e4534254bfb + f4c0d0cbc66eee6c88d68a63862 + Stored in directory: /root/fangjun/.cache/pip/wheels/85/7d/63/f2dd586369b8797cb36d213bf3a84a789eeb92db93d2e723c9 + Successfully built kaldilm + Installing collected packages: urllib3, pyasn1, idna, charset-normalizer, certifi, six, rsa, requests, pyasn1-modules, oauthlib, cach + etools, requests-oauthlib, google-auth, werkzeug, tensorboard-plugin-wit, tensorboard-data-server, protobuf, markdown, grpcio, google + -auth-oauthlib, absl-py, tensorboard, sentencepiece, kaldilm, kaldialign + Successfully installed absl-py-0.13.0 cachetools-4.2.2 certifi-2021.5.30 charset-normalizer-2.0.4 google-auth-1.35.0 google-auth-oaut + hlib-0.4.5 grpcio-1.39.0 idna-3.2 kaldialign-0.2 kaldilm-1.8 markdown-3.3.4 oauthlib-3.1.1 protobuf-3.17.3 pyasn1-0.4.8 pyasn1-module + s-0.2.8 requests-2.26.0 requests-oauthlib-1.3.0 rsa-4.7.2 sentencepiece-0.1.96 six-1.16.0 tensorboard-2.6.0 tensorboard-data-server-0 + .6.1 tensorboard-plugin-wit-1.8.0 urllib3-1.26.6 werkzeug-2.0.1 + + +Test Your Installation +---------------------- + +To test that your installation is successful, let us run +the `yesno recipe `_ +on CPU. + +Data preparation +~~~~~~~~~~~~~~~~ + +.. code-block:: bash + + $ export PYTHONPATH=/tmp/icefall:$PYTHONPATH + $ cd /tmp/icefall + $ cd egs/yesno/AS + $ ./prepare.sh + +The log of running ``./prepare.sh`` is: + +.. code-block:: + + 2021-08-23 19:27:26 (prepare.sh:24:main) dl_dir: /tmp/icefall/egs/yesno/ASR/download + 2021-08-23 19:27:26 (prepare.sh:27:main) stage 0: Download data + Downloading waves_yesno.tar.gz: 4.49MB [00:03, 1.39MB/s] + 2021-08-23 19:27:30 (prepare.sh:36:main) Stage 1: Prepare yesno manifest + 2021-08-23 19:27:31 (prepare.sh:42:main) Stage 2: Compute fbank for yesno + 2021-08-23 19:27:32,803 INFO [compute_fbank_yesno.py:52] Processing train + Extracting and storing features: 100%|_______________________________________________________________| 90/90 [00:01<00:00, 80.57it/s] + 2021-08-23 19:27:34,085 INFO [compute_fbank_yesno.py:52] Processing test + Extracting and storing features: 100%|______________________________________________________________| 30/30 [00:00<00:00, 248.21it/s] + 2021-08-23 19:27:34 (prepare.sh:48:main) Stage 3: Prepare lang + 2021-08-23 19:27:35 (prepare.sh:63:main) Stage 4: Prepare G + /tmp/pip-install-fcordre9/kaldilm_6899d26f2d684ad48f21025950cd2866/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Rea + d(std::istream&):79 + [I] Reading \data\ section. + /tmp/pip-install-fcordre9/kaldilm_6899d26f2d684ad48f21025950cd2866/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Rea + d(std::istream&):140 + [I] Reading \1-grams: section. + 2021-08-23 19:27:35 (prepare.sh:89:main) Stage 5: Compile HLG + 2021-08-23 19:27:35,928 INFO [compile_hlg.py:120] Processing data/lang_phone + 2021-08-23 19:27:35,929 INFO [lexicon.py:116] Converting L.pt to Linv.pt + 2021-08-23 19:27:35,931 INFO [compile_hlg.py:48] Building ctc_topo. max_token_id: 3 + 2021-08-23 19:27:35,932 INFO [compile_hlg.py:52] Loading G.fst.txt + 2021-08-23 19:27:35,932 INFO [compile_hlg.py:62] Intersecting L and G + 2021-08-23 19:27:35,933 INFO [compile_hlg.py:64] LG shape: (4, None) + 2021-08-23 19:27:35,933 INFO [compile_hlg.py:66] Connecting LG + 2021-08-23 19:27:35,933 INFO [compile_hlg.py:68] LG shape after k2.connect: (4, None) + 2021-08-23 19:27:35,933 INFO [compile_hlg.py:70] + 2021-08-23 19:27:35,933 INFO [compile_hlg.py:71] Determinizing LG + 2021-08-23 19:27:35,934 INFO [compile_hlg.py:74] + 2021-08-23 19:27:35,934 INFO [compile_hlg.py:76] Connecting LG after k2.determinize + 2021-08-23 19:27:35,934 INFO [compile_hlg.py:79] Removing disambiguation symbols on LG + 2021-08-23 19:27:35,934 INFO [compile_hlg.py:87] LG shape after k2.remove_epsilon: (6, None) + 2021-08-23 19:27:35,935 INFO [compile_hlg.py:92] Arc sorting LG + 2021-08-23 19:27:35,935 INFO [compile_hlg.py:95] Composing H and LG + 2021-08-23 19:27:35,935 INFO [compile_hlg.py:102] Connecting LG + 2021-08-23 19:27:35,935 INFO [compile_hlg.py:105] Arc sorting LG + 2021-08-23 19:27:35,936 INFO [compile_hlg.py:107] HLG.shape: (8, None) + 2021-08-23 19:27:35,936 INFO [compile_hlg.py:123] Saving HLG.pt to data/lang_phone + + +Training +~~~~~~~~ + +Now let us run the training part: + +.. code-block:: + + $ export CUDA_VISIBLE_DEVICES="" + $ ./tdnn/train.py + +.. CAUTION:: + + We use ``export CUDA_VISIBLE_DEVICES=""`` so that icefall uses CPU + even if there are GPUs available. + +The training log is given below: + +.. code-block:: + + 2021-08-23 19:30:31,072 INFO [train.py:465] Training started + 2021-08-23 19:30:31,072 INFO [train.py:466] {'exp_dir': PosixPath('tdnn/exp'), 'lang_dir': PosixPath('data/lang_phone'), 'lr': 0.01, + 'feature_dim': 23, 'weight_decay': 1e-06, 'start_epoch': 0, 'best_train_loss': inf, 'best_valid_loss': inf, 'best_train_epoch': -1, ' + best_valid_epoch': -1, 'batch_idx_train': 0, 'log_interval': 10, 'valid_interval': 10, 'beam_size': 10, 'reduction': 'sum', 'use_doub + le_scores': True, 'world_size': 1, 'master_port': 12354, 'tensorboard': True, 'num_epochs': 15, 'feature_dir': PosixPath('data/fbank' + ), 'max_duration': 30.0, 'bucketing_sampler': False, 'num_buckets': 10, 'concatenate_cuts': False, 'duration_factor': 1.0, 'gap': 1.0 + , 'on_the_fly_feats': False, 'shuffle': True, 'return_cuts': True, 'num_workers': 2} + 2021-08-23 19:30:31,074 INFO [lexicon.py:113] Loading pre-compiled data/lang_phone/Linv.pt + 2021-08-23 19:30:31,098 INFO [asr_datamodule.py:146] About to get train cuts + 2021-08-23 19:30:31,098 INFO [asr_datamodule.py:240] About to get train cuts + 2021-08-23 19:30:31,102 INFO [asr_datamodule.py:149] About to create train dataset + 2021-08-23 19:30:31,102 INFO [asr_datamodule.py:200] Using SingleCutSampler. + 2021-08-23 19:30:31,102 INFO [asr_datamodule.py:206] About to create train dataloader + 2021-08-23 19:30:31,102 INFO [asr_datamodule.py:219] About to get test cuts + 2021-08-23 19:30:31,102 INFO [asr_datamodule.py:246] About to get test cuts + 2021-08-23 19:30:31,357 INFO [train.py:416] Epoch 0, batch 0, batch avg loss 1.0789, total avg loss: 1.0789, batch size: 4 + 2021-08-23 19:30:31,848 INFO [train.py:416] Epoch 0, batch 10, batch avg loss 0.5356, total avg loss: 0.7556, batch size: 4 + 2021-08-23 19:30:32,301 INFO [train.py:432] Epoch 0, valid loss 0.9972, best valid loss: 0.9972 best valid epoch: 0 + 2021-08-23 19:30:32,805 INFO [train.py:416] Epoch 0, batch 20, batch avg loss 0.2436, total avg loss: 0.5717, batch size: 3 + 2021-08-23 19:30:33,109 INFO [train.py:432] Epoch 0, valid loss 0.4167, best valid loss: 0.4167 best valid epoch: 0 + 2021-08-23 19:30:33,121 INFO [checkpoint.py:62] Saving checkpoint to tdnn/exp/epoch-0.pt + 2021-08-23 19:30:33,325 INFO [train.py:416] Epoch 1, batch 0, batch avg loss 0.2214, total avg loss: 0.2214, batch size: 5 + 2021-08-23 19:30:33,798 INFO [train.py:416] Epoch 1, batch 10, batch avg loss 0.0781, total avg loss: 0.1343, batch size: 5 + 2021-08-23 19:30:34,065 INFO [train.py:432] Epoch 1, valid loss 0.0859, best valid loss: 0.0859 best valid epoch: 1 + 2021-08-23 19:30:34,556 INFO [train.py:416] Epoch 1, batch 20, batch avg loss 0.0421, total avg loss: 0.0975, batch size: 3 + 2021-08-23 19:30:34,810 INFO [train.py:432] Epoch 1, valid loss 0.0431, best valid loss: 0.0431 best valid epoch: 1 + 2021-08-23 19:30:34,824 INFO [checkpoint.py:62] Saving checkpoint to tdnn/exp/epoch-1.pt + + ... ... + + 2021-08-23 19:30:49,657 INFO [train.py:416] Epoch 13, batch 0, batch avg loss 0.0109, total avg loss: 0.0109, batch size: 5 + 2021-08-23 19:30:49,984 INFO [train.py:416] Epoch 13, batch 10, batch avg loss 0.0093, total avg loss: 0.0096, batch size: 4 + 2021-08-23 19:30:50,239 INFO [train.py:432] Epoch 13, valid loss 0.0104, best valid loss: 0.0101 best valid epoch: 12 + 2021-08-23 19:30:50,569 INFO [train.py:416] Epoch 13, batch 20, batch avg loss 0.0092, total avg loss: 0.0096, batch size: 2 + 2021-08-23 19:30:50,819 INFO [train.py:432] Epoch 13, valid loss 0.0101, best valid loss: 0.0101 best valid epoch: 13 + 2021-08-23 19:30:50,835 INFO [checkpoint.py:62] Saving checkpoint to tdnn/exp/epoch-13.pt + 2021-08-23 19:30:51,024 INFO [train.py:416] Epoch 14, batch 0, batch avg loss 0.0105, total avg loss: 0.0105, batch size: 5 + 2021-08-23 19:30:51,317 INFO [train.py:416] Epoch 14, batch 10, batch avg loss 0.0099, total avg loss: 0.0097, batch size: 4 + 2021-08-23 19:30:51,552 INFO [train.py:432] Epoch 14, valid loss 0.0108, best valid loss: 0.0101 best valid epoch: 13 + 2021-08-23 19:30:51,869 INFO [train.py:416] Epoch 14, batch 20, batch avg loss 0.0096, total avg loss: 0.0097, batch size: 5 + 2021-08-23 19:30:52,107 INFO [train.py:432] Epoch 14, valid loss 0.0102, best valid loss: 0.0101 best valid epoch: 13 + 2021-08-23 19:30:52,126 INFO [checkpoint.py:62] Saving checkpoint to tdnn/exp/epoch-14.pt + 2021-08-23 19:30:52,128 INFO [train.py:537] Done! + +Decoding +~~~~~~~~ + +Let us use the trained model to decode the test set: + +.. code-block:: + + $ ./tdnn/decode.py + +The decoding log is: + +.. code-block:: + + 2021-08-23 19:35:30,192 INFO [decode.py:249] Decoding started + 2021-08-23 19:35:30,192 INFO [decode.py:250] {'exp_dir': PosixPath('tdnn/exp'), 'lang_dir': PosixPath('data/lang_phone'), 'lm_dir': PosixPath('data/lm'), 'feature_dim': 23, 'search_beam': 20, 'output_beam': 8, 'min_active_states': 30, 'max_active_states': 10000, 'use_double_scores': True, 'epoch': 14, 'avg': 2, 'feature_dir': PosixPath('data/fbank'), 'max_duration': 30.0, 'bucketing_sampler': False, 'num_buckets': 10, 'concatenate_cuts': False, 'duration_factor': 1.0, 'gap': 1.0, 'on_the_fly_feats': False, 'shuffle': True, 'return_cuts': True, 'num_workers': 2} + 2021-08-23 19:35:30,193 INFO [lexicon.py:113] Loading pre-compiled data/lang_phone/Linv.pt + 2021-08-23 19:35:30,213 INFO [decode.py:259] device: cpu + 2021-08-23 19:35:30,217 INFO [decode.py:279] averaging ['tdnn/exp/epoch-13.pt', 'tdnn/exp/epoch-14.pt'] + /tmp/icefall/icefall/checkpoint.py:146: UserWarning: floor_divide is deprecated, and will be removed in a future version of pytorch. + It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. + To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (Triggered internally at /pytorch/aten/src/ATen/native/BinaryOps.cpp:450.) + avg[k] //= n + 2021-08-23 19:35:30,220 INFO [asr_datamodule.py:219] About to get test cuts + 2021-08-23 19:35:30,220 INFO [asr_datamodule.py:246] About to get test cuts + 2021-08-23 19:35:30,409 INFO [decode.py:190] batch 0/8, cuts processed until now is 4 + 2021-08-23 19:35:30,571 INFO [decode.py:228] The transcripts are stored in tdnn/exp/recogs-test_set.txt + 2021-08-23 19:35:30,572 INFO [utils.py:317] [test_set] %WER 0.42% [1 / 240, 0 ins, 1 del, 0 sub ] + 2021-08-23 19:35:30,573 INFO [decode.py:236] Wrote detailed error stats to tdnn/exp/errs-test_set.txt + 2021-08-23 19:35:30,573 INFO [decode.py:299] Done! + +**Congratulations!** You have successfully setup the environment and have run the first recipe in ``icefall``. + +Have fun with ``icefall``! diff --git a/docs/source/recipes/images/yesno-tdnn-tensorboard-log.png b/docs/source/recipes/images/yesno-tdnn-tensorboard-log.png new file mode 100644 index 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Other recipes are listed in a alphabetical order. + +.. toctree:: + :maxdepth: 2 + + yesno + + librispeech + diff --git a/docs/source/recipes/librispeech.rst b/docs/source/recipes/librispeech.rst new file mode 100644 index 000000000..5b6ca04d4 --- /dev/null +++ b/docs/source/recipes/librispeech.rst @@ -0,0 +1,2 @@ +LibriSpeech +=========== diff --git a/docs/source/recipes/yesno.rst b/docs/source/recipes/yesno.rst new file mode 100644 index 000000000..e4bcb6f0b --- /dev/null +++ b/docs/source/recipes/yesno.rst @@ -0,0 +1,445 @@ +yesno +===== + +This page shows you how to run the ``yesno`` recipe. It contains: + + - (1) Prepare data for training + - (2) Train a TDNN model + + - (a) View text format logs and visualize TensorBoard logs + - (b) Select device type, i.e., CPU and GPU, for training + - (c) Change training options + - (d) Resume training from a checkpoint + + - (3) Decode with a trained model + + - (a) Select a checkpoint for decoding + - (b) Model averaging + + - (4) Colab notebook + + - (a) It shows you step by step how to setup the environment, how to do training, + and how to do decoding + - (b) How to use a pre-trained model + + - (5) Inference with a pre-trained model + + - (a) Download a pre-trained model, provided by us + - (b) Decode a single sound file with a pre-trained model + - (c) Decode multiple sound files at the same time + +It does **NOT** show you: + + - (1) How to train with multiple GPUs + + The ``yesno`` dataset is so small that CPU is more than enough + for training as well as for decoding. + + - (2) How to use LM rescoring for decoding + + The dataset does not have an LM for rescoring. + +.. HINT:: + + We assume you have read the page :ref:`install icefall` and have setup + the environment for ``icefall``. + +.. HINT:: + + You **don't** need a **GPU** to run this recipe. It can be run on a **CPU**. + The training part takes less than 30 **seconds** on a CPU and you will get + the following WER at the end:: + + [test_set] %WER 0.42% [1 / 240, 0 ins, 1 del, 0 sub ] + +Data preparation +---------------- + +.. code-block:: bash + + $ cd egs/yesno/ASR + $ ./prepare.sh + +The script ``./prepare.sh`` handles the data preparation for you, **automagically**. +All you need to do is to run it. + +The data preparation contains several stages, you can use the following two +options: + + - ``--stage`` + - ``--stop-stage`` + +to control which stage(s) should be run. By default, all stages are executed. + + +For example, + +.. code-block:: bash + + $ cd egs/yesno/ASR + $ ./prepare.sh --stage 0 --stop-stage 0 + +means to run only stage 0. + +To run stage 2 to stage 5, use: + +.. code-block:: bash + + $ ./prepare.sh --stage 2 --stop-stage 5 + + +Training +-------- + +We provide only a TDNN model, contained in +the `tdnn `_ +folder, for ``yesno``. + +The command to run the training part is: + +.. code-block:: bash + + $ cd egs/yesno/ASR + $ export CUDA_VISIBLE_DEVICES="" + $ ./tdnn/train.py + +By default, it will run ``15`` epochs. Training logs and checkpoints are saved +in ``tdnn/exp``. + +In ``tdnn/exp``, you will find the following files: + + - ``epoch-0.pt``, ``epoch-1.pt``, ... + + These are checkpoint files, containing model ``state_dict`` and optimizer ``state_dict``. + To resume training from some checkpoint, say ``epoch-10.pt``, you can use: + + .. code-block:: bash + + $ ./tdnn/train.py --start-epoch 11 + + - ``tensorboard/`` + + This folder contains TensorBoard logs. Training loss, validation loss, learning + rate, etc, are recorded in these logs. You can visualize them by: + + .. code-block:: bash + + $ cd tdnn/exp/tensorboard + $ tensorboard dev upload --logdir . --description "TDNN training for yesno with icefall" + + It will print something like below: + + .. code-block:: + + TensorFlow installation not found - running with reduced feature set. + Upload started and will continue reading any new data as it's added to the logdir. + + To stop uploading, press Ctrl-C. + + New experiment created. View your TensorBoard at: https://tensorboard.dev/experiment/yKUbhb5wRmOSXYkId1z9eg/ + + [2021-08-23T23:49:41] Started scanning logdir. + [2021-08-23T23:49:42] Total uploaded: 135 scalars, 0 tensors, 0 binary objects + Listening for new data in logdir... + + Note there is a URL in the above output, click it and you will see + the following screenshot: + + .. figure:: images/yesno-tdnn-tensorboard-log.png + :width: 600 + :alt: TensorBoard screenshot + :align: center + :target: https://tensorboard.dev/experiment/yKUbhb5wRmOSXYkId1z9eg/ + + TensorBoard screenshot. + + - ``log/log-train-xxxx`` + + It is the detailed training log in text format, same as the one + you saw printed to the console during training. + + + +.. NOTE:: + + By default, ``./tdnn/train.py`` uses GPU 0 for training if GPUs are available. + If you have two GPUs, say, GPU 0 and GPU 1, and you want to use GPU 1 for + training, you can run: + + .. code-block:: bash + + $ export CUDA_VISIBLE_DEVICES="1" + $ ./tdnn/train.py + + Since the ``yesno`` dataset is very small, containing only 30 sound files + for training, and the model in use is also very small, we use: + + .. code-block:: bash + + $ export CUDA_VISIBLE_DEVICES="" + + so that ``./tdnn/train.py`` uses CPU during training. + + If you don't have GPUs, then you don't need to + run ``export CUDA_VISIBLE_DEVICES=""``. + +To see available training options, you can use: + +.. code-block:: bash + + $ ./tdnn/train.py --help + +Other training options, e.g., learning rate, results dir, etc., are +pre-configured in the function ``get_params()`` +in `tdnn/train.py `_. +Normally, you don't need to change them. You can change them by modifying the code, if +you want. + +Decoding +-------- + +The decoding part uses checkpoints saved by the training part, so you have +to run the training part first. + +The command for decoding is: + +.. code-block:: bash + + $ export CUDA_VISIBLE_DEVICES="" + $ ./tdnn/decode.py + +You will see the WER in the output log. + +Decoded results are saved in ``tdnn/exp``. + +.. code-block:: bash + + $ ./tdnn/decode.py --help + +shows you the available decoding options. + +Some commonly used options are: + + - ``--epoch`` + + You can select which checkpoint to be used for decoding. + For instance, ``./tdnn/decode.py --epoch 10`` means to use + ``./tdnn/exp/epoch-10.pt`` for decoding. + + - ``--avg`` + + It's related to model averaging. It specifies number of checkpoints + to be averaged. The averaged model is used for decoding. + For example, the following command: + + .. code-block:: bash + + $ ./tdnn/decode.py --epoch 10 --avg 3 + + uses the average of ``epoch-8.pt``, ``epoch-9.pt`` and ``epoch-10.pt`` + for decoding. + + - ``--export`` + + If it is ``True``, i.e., ``./tdnn/decode.py --export 1``, the code + will save the averaged model to ``tdnn/exp/pretrained.pt``. + See :ref:`yesno use a pre-trained model` for how to use it. + + +.. _yesno use a pre-trained model: + +Pre-trained Model +----------------- + +We have uploaded the pre-trained model to +``_. + +The following shows you how to use the pre-trained model. + +Download the pre-trained model +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code-block:: bash + + $ cd egs/yesno/ASR + $ mkdir tmp + $ cd tmp + $ git lfs install + $ git clone https://huggingface.co/csukuangfj/icefall_asr_yesno_tdnn + +.. CAUTION:: + + You have to use ``git lfs`` to download the pre-trained model. + +After downloading, you will have the following files: + +.. code-block:: bash + + $ cd egs/yesno/ASR + $ tree tmp + +.. code-block:: bash + + tmp/ + `-- icefall_asr_yesno_tdnn + |-- README.md + |-- lang_phone + | |-- HLG.pt + | |-- L.pt + | |-- L_disambig.pt + | |-- Linv.pt + | |-- lexicon.txt + | |-- lexicon_disambig.txt + | |-- tokens.txt + | `-- words.txt + |-- lm + | |-- G.arpa + | `-- G.fst.txt + |-- pretrained.pt + `-- test_waves + |-- 0_0_0_1_0_0_0_1.wav + |-- 0_0_1_0_0_0_1_0.wav + |-- 0_0_1_0_0_1_1_1.wav + |-- 0_0_1_0_1_0_0_1.wav + |-- 0_0_1_1_0_0_0_1.wav + |-- 0_0_1_1_0_1_1_0.wav + |-- 0_0_1_1_1_0_0_0.wav + |-- 0_0_1_1_1_1_0_0.wav + |-- 0_1_0_0_0_1_0_0.wav + |-- 0_1_0_0_1_0_1_0.wav + |-- 0_1_0_1_0_0_0_0.wav + |-- 0_1_0_1_1_1_0_0.wav + |-- 0_1_1_0_0_1_1_1.wav + |-- 0_1_1_1_0_0_1_0.wav + |-- 0_1_1_1_1_0_1_0.wav + |-- 1_0_0_0_0_0_0_0.wav + |-- 1_0_0_0_0_0_1_1.wav + |-- 1_0_0_1_0_1_1_1.wav + |-- 1_0_1_1_0_1_1_1.wav + |-- 1_0_1_1_1_1_0_1.wav + |-- 1_1_0_0_0_1_1_1.wav + |-- 1_1_0_0_1_0_1_1.wav + |-- 1_1_0_1_0_1_0_0.wav + |-- 1_1_0_1_1_0_0_1.wav + |-- 1_1_0_1_1_1_1_0.wav + |-- 1_1_1_0_0_1_0_1.wav + |-- 1_1_1_0_1_0_1_0.wav + |-- 1_1_1_1_0_0_1_0.wav + |-- 1_1_1_1_1_0_0_0.wav + `-- 1_1_1_1_1_1_1_1.wav + + 4 directories, 42 files + +.. code-block:: bash + + $ soxi tmp/icefall_asr_yesno_tdnn/test_waves/0_0_1_0_1_0_0_1.wav + + Input File : 'tmp/icefall_asr_yesno_tdnn/test_waves/0_0_1_0_1_0_0_1.wav' + Channels : 1 + Sample Rate : 8000 + Precision : 16-bit + Duration : 00:00:06.76 = 54080 samples ~ 507 CDDA sectors + File Size : 108k + Bit Rate : 128k + Sample Encoding: 16-bit Signed Integer PCM + +- ``0_0_1_0_1_0_0_1.wav`` + + 0 means No; 1 means Yes. No and Yes are not in English, + but in `Hebrew `_. + So this file contains ``NO NO YES NO YES NO NO YES``. + +Download kaldifeat +~~~~~~~~~~~~~~~~~~ + +`kaldifeat `_ is used for extracting +features from a single or multiple sound files. Please refer to +``_ to install ``kaldifeat`` first. + +Inference with a pre-trained model +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code-block:: bash + + $ cd egs/yesno/ASR + $ ./tdnn/pretrained.py --help + +shows the usage information of ``./tdnn/pretrained.py``. + +To decode a single file, we can use: + +.. code-block:: bash + + ./tdnn/pretrained.py \ + --checkpoint ./tmp/icefall_asr_yesno_tdnn/pretrained.pt \ + --words-file ./tmp/icefall_asr_yesno_tdnn/lang_phone/words.txt \ + --HLG ./tmp/icefall_asr_yesno_tdnn/lang_phone/HLG.pt \ + ./tmp/icefall_asr_yesno_tdnn/test_waves/0_0_1_0_1_0_0_1.wav + +The output is: + +.. code-block:: + + 2021-08-24 12:22:51,621 INFO [pretrained.py:119] {'feature_dim': 23, 'num_classes': 4, 'sample_rate': 8000, 'search_beam': 20, 'output_beam': 8, 'min_active_states': 30, 'max_active_states': 10000, 'use_double_scores': True, 'checkpoint': './tmp/icefall_asr_yesno_tdnn/pretrained.pt', 'words_file': './tmp/icefall_asr_yesno_tdnn/lang_phone/words.txt', 'HLG': './tmp/icefall_asr_yesno_tdnn/lang_phone/HLG.pt', 'sound_files': ['./tmp/icefall_asr_yesno_tdnn/test_waves/0_0_1_0_1_0_0_1.wav']} + 2021-08-24 12:22:51,645 INFO [pretrained.py:125] device: cpu + 2021-08-24 12:22:51,645 INFO [pretrained.py:127] Creating model + 2021-08-24 12:22:51,650 INFO [pretrained.py:139] Loading HLG from ./tmp/icefall_asr_yesno_tdnn/lang_phone/HLG.pt + 2021-08-24 12:22:51,651 INFO [pretrained.py:143] Constructing Fbank computer + 2021-08-24 12:22:51,652 INFO [pretrained.py:153] Reading sound files: ['./tmp/icefall_asr_yesno_tdnn/test_waves/0_0_1_0_1_0_0_1.wav'] + 2021-08-24 12:22:51,684 INFO [pretrained.py:159] Decoding started + 2021-08-24 12:22:51,708 INFO [pretrained.py:198] + ./tmp/icefall_asr_yesno_tdnn/test_waves/0_0_1_0_1_0_0_1.wav: + NO NO YES NO YES NO NO YES + + + 2021-08-24 12:22:51,708 INFO [pretrained.py:200] Decoding Done + +You can see that for the sound file ``0_0_1_0_1_0_0_1.wav``, the decoding result is +``NO NO YES NO YES NO NO YES``. + +To decode **multiple** files at the same time, you can use + +.. code-block:: bash + + ./tdnn/pretrained.py \ + --checkpoint ./tmp/icefall_asr_yesno_tdnn/pretrained.pt \ + --words-file ./tmp/icefall_asr_yesno_tdnn/lang_phone/words.txt \ + --HLG ./tmp/icefall_asr_yesno_tdnn/lang_phone/HLG.pt \ + ./tmp/icefall_asr_yesno_tdnn/test_waves/0_0_1_0_1_0_0_1.wav \ + ./tmp/icefall_asr_yesno_tdnn/test_waves/1_0_1_1_0_1_1_1.wav + +The decoding output is: + +.. code-block:: + + 2021-08-24 12:25:20,159 INFO [pretrained.py:119] {'feature_dim': 23, 'num_classes': 4, 'sample_rate': 8000, 'search_beam': 20, 'output_beam': 8, 'min_active_states': 30, 'max_active_states': 10000, 'use_double_scores': True, 'checkpoint': './tmp/icefall_asr_yesno_tdnn/pretrained.pt', 'words_file': './tmp/icefall_asr_yesno_tdnn/lang_phone/words.txt', 'HLG': './tmp/icefall_asr_yesno_tdnn/lang_phone/HLG.pt', 'sound_files': ['./tmp/icefall_asr_yesno_tdnn/test_waves/0_0_1_0_1_0_0_1.wav', './tmp/icefall_asr_yesno_tdnn/test_waves/1_0_1_1_0_1_1_1.wav']} + 2021-08-24 12:25:20,181 INFO [pretrained.py:125] device: cpu + 2021-08-24 12:25:20,181 INFO [pretrained.py:127] Creating model + 2021-08-24 12:25:20,185 INFO [pretrained.py:139] Loading HLG from ./tmp/icefall_asr_yesno_tdnn/lang_phone/HLG.pt + 2021-08-24 12:25:20,186 INFO [pretrained.py:143] Constructing Fbank computer + 2021-08-24 12:25:20,187 INFO [pretrained.py:153] Reading sound files: ['./tmp/icefall_asr_yesno_tdnn/test_waves/0_0_1_0_1_0_0_1.wav', + './tmp/icefall_asr_yesno_tdnn/test_waves/1_0_1_1_0_1_1_1.wav'] + 2021-08-24 12:25:20,213 INFO [pretrained.py:159] Decoding started + 2021-08-24 12:25:20,287 INFO [pretrained.py:198] + ./tmp/icefall_asr_yesno_tdnn/test_waves/0_0_1_0_1_0_0_1.wav: + NO NO YES NO YES NO NO YES + + ./tmp/icefall_asr_yesno_tdnn/test_waves/1_0_1_1_0_1_1_1.wav: + YES NO YES YES NO YES YES YES + + 2021-08-24 12:25:20,287 INFO [pretrained.py:200] Decoding Done + +You can see again that it decodes correctly. + +Colab notebook +-------------- + +We do provide a colab notebook for this recipe. + +|yesno colab notebook| + +.. |yesno colab notebook| image:: https://colab.research.google.com/assets/colab-badge.svg + :target: https://colab.research.google.com/drive/1tIjjzaJc3IvGyKiMCDWO-TSnBgkcuN3B?usp=sharing + + +**Congratulations!** You have finished the simplest speech recognition recipe in ``icefall``. diff --git a/egs/yesno/ASR/tdnn/decode.py b/egs/yesno/ASR/tdnn/decode.py index b600c182c..aa7b07b98 100755 --- a/egs/yesno/ASR/tdnn/decode.py +++ b/egs/yesno/ASR/tdnn/decode.py @@ -20,6 +20,7 @@ from icefall.utils import ( get_texts, setup_logger, store_transcripts, + str2bool, write_error_stats, ) @@ -44,6 +45,17 @@ def get_parser(): "consecutive checkpoints before the checkpoint specified by " "'--epoch'. ", ) + + parser.add_argument( + "--export", + type=str2bool, + default=False, + help="""When enabled, the averaged model is saved to + tdnn/exp/pretrained.pt. Note: only model.state_dict() is saved. + pretrained.pt contains a dict {"model": model.state_dict()}, + which can be loaded by `icefall.checkpoint.load_checkpoint()`. + """, + ) return parser @@ -279,6 +291,12 @@ def main(): logging.info(f"averaging {filenames}") model.load_state_dict(average_checkpoints(filenames)) + if params.export: + logging.info(f"Export averaged model to {params.exp_dir}/pretrained.pt") + torch.save( + {"model": model.state_dict()}, f"{params.exp_dir}/pretrained.pt" + ) + model.to(device) model.eval() diff --git a/egs/yesno/ASR/tdnn/pretrained.py b/egs/yesno/ASR/tdnn/pretrained.py new file mode 100755 index 000000000..fb92110e3 --- /dev/null +++ b/egs/yesno/ASR/tdnn/pretrained.py @@ -0,0 +1,209 @@ +#!/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. + + +import argparse +import logging +import math +from typing import List + +import k2 +import kaldifeat +import torch +import torchaudio +from model import Tdnn +from torch.nn.utils.rnn import pad_sequence + +from icefall.decode import get_lattice, one_best_decoding +from icefall.utils import AttributeDict, get_texts + + +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( + "--words-file", + type=str, + required=True, + help="Path to words.txt", + ) + + parser.add_argument( + "--HLG", type=str, required=True, help="Path to HLG.pt." + ) + + 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.", + ) + + return parser + + +def get_params() -> AttributeDict: + params = AttributeDict( + { + "feature_dim": 23, + "num_classes": 4, # [, N, SIL, Y] + "sample_rate": 8000, + "search_beam": 20, + "output_beam": 8, + "min_active_states": 30, + "max_active_states": 10000, + "use_double_scores": True, + } + ) + return params + + +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 + + +def main(): + parser = get_parser() + args = parser.parse_args() + + params = get_params() + params.update(vars(args)) + 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 = Tdnn( + num_features=params.feature_dim, + num_classes=params.num_classes, + ) + + checkpoint = torch.load(args.checkpoint, map_location="cpu") + model.load_state_dict(checkpoint["model"]) + model.to(device) + model.eval() + + logging.info(f"Loading HLG from {params.HLG}") + HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu")) + HLG = HLG.to(device) + + 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) + + features = pad_sequence( + features, batch_first=True, padding_value=math.log(1e-10) + ) + + # Note: We don't use key padding mask for attention during decoding + with torch.no_grad(): + nnet_output = model(features) + + batch_size = nnet_output.shape[0] + supervision_segments = torch.tensor( + [[i, 0, nnet_output.shape[1]] for i in range(batch_size)], + dtype=torch.int32, + ) + + lattice = get_lattice( + nnet_output=nnet_output, + HLG=HLG, + supervision_segments=supervision_segments, + search_beam=params.search_beam, + output_beam=params.output_beam, + min_active_states=params.min_active_states, + max_active_states=params.max_active_states, + ) + + best_path = one_best_decoding( + lattice=lattice, use_double_scores=params.use_double_scores + ) + + hyps = get_texts(best_path) + word_sym_table = k2.SymbolTable.from_file(params.words_file) + hyps = [[word_sym_table[i] for i in ids] for ids in hyps] + + 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/yesno/ASR/tdnn/train.py b/egs/yesno/ASR/tdnn/train.py index 04e1ab698..39c5ef3ef 100755 --- a/egs/yesno/ASR/tdnn/train.py +++ b/egs/yesno/ASR/tdnn/train.py @@ -60,6 +60,16 @@ def get_parser(): 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 + tdnn/exp/epoch-{start_epoch-1}.pt + """, + ) + return parser @@ -92,8 +102,6 @@ def get_params() -> AttributeDict: - start_epoch: If it is not zero, load checkpoint `start_epoch-1` and continue training from that checkpoint. - - num_epochs: Number of epochs to train. - - 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. @@ -420,6 +428,19 @@ def train_one_epoch( f"batch size: {batch_size}" ) + if tb_writer is not None: + tb_writer.add_scalar( + "train/current_loss", + loss_cpu / params.train_frames, + params.batch_idx_train, + ) + + tb_writer.add_scalar( + "train/tot_avg_loss", + tot_avg_loss, + params.batch_idx_train, + ) + if batch_idx > 0 and batch_idx % params.valid_interval == 0: compute_validation_loss( params=params, @@ -434,6 +455,12 @@ def train_one_epoch( f" best valid loss: {params.best_valid_loss:.4f} " f"best valid epoch: {params.best_valid_epoch}" ) + if tb_writer is not None: + tb_writer.add_scalar( + "train/valid_loss", + params.valid_loss, + params.batch_idx_train, + ) params.train_loss = tot_loss / tot_frames From 1bd5dcc8acdca41d5590351a9fedba2578773a24 Mon Sep 17 00:00:00 2001 From: Fangjun Kuang Date: Tue, 24 Aug 2021 20:28:32 +0800 Subject: [PATCH 05/10] WIP: Add doc for the LibriSpeech recipe. (#24) * WIP: Add doc for the LibriSpeech recipe. * Add more doc for LibriSpeech recipe. * Add more doc for the LibriSpeech recipe. * More doc. --- docs/source/index.rst | 2 +- docs/source/recipes/librispeech.rst | 8 + .../recipes/librispeech/conformer_ctc.rst | 627 ++++++++++++++++++ ...rispeech-conformer-ctc-tensorboard-log.png | Bin 0 -> 431729 bytes .../recipes/librispeech/tdnn_lstm_ctc.rst | 2 + docs/source/recipes/yesno.rst | 2 +- egs/librispeech/ASR/conformer_ctc/README.md | 353 +--------- egs/librispeech/ASR/conformer_ctc/decode.py | 86 ++- egs/librispeech/ASR/conformer_ctc/train.py | 24 +- egs/librispeech/ASR/tdnn_lstm_ctc/train.py | 56 +- egs/yesno/ASR/README.md | 15 +- egs/yesno/ASR/tdnn/README.md | 8 + icefall/decode.py | 10 +- 13 files changed, 777 insertions(+), 416 deletions(-) create mode 100644 docs/source/recipes/librispeech/conformer_ctc.rst create mode 100644 docs/source/recipes/librispeech/images/librispeech-conformer-ctc-tensorboard-log.png create mode 100644 docs/source/recipes/librispeech/tdnn_lstm_ctc.rst create mode 100644 egs/yesno/ASR/tdnn/README.md diff --git a/docs/source/index.rst b/docs/source/index.rst index c5cd2e832..9313f1a67 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -3,7 +3,7 @@ You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. -icefall +Icefall ======= .. image:: _static/logo.png diff --git a/docs/source/recipes/librispeech.rst b/docs/source/recipes/librispeech.rst index 5b6ca04d4..946b23407 100644 --- a/docs/source/recipes/librispeech.rst +++ b/docs/source/recipes/librispeech.rst @@ -1,2 +1,10 @@ LibriSpeech =========== + +We provide the following models for the LibriSpeech dataset: + +.. toctree:: + :maxdepth: 2 + + librispeech/tdnn_lstm_ctc + librispeech/conformer_ctc diff --git a/docs/source/recipes/librispeech/conformer_ctc.rst b/docs/source/recipes/librispeech/conformer_ctc.rst new file mode 100644 index 000000000..2cb04d1ba --- /dev/null +++ b/docs/source/recipes/librispeech/conformer_ctc.rst @@ -0,0 +1,627 @@ +Confromer CTC +============= + +This tutorial shows you how to run a conformer ctc model +with the `LibriSpeech `_ dataset. + + +.. HINT:: + + We assume you have read the page :ref:`install icefall` and have setup + the environment for ``icefall``. + +.. HINT:: + + We recommend you to use a GPU or several GPUs to run this recipe. + +In this tutorial, you will learn: + + - (1) How to prepare data for training and decoding + - (2) How to start the training, either with a single GPU or multiple GPUs + - (3) How to do decoding after training, with n-gram LM rescoring and attention decoder rescoring + - (4) How to use a pre-trained model, provided by us + +Data preparation +---------------- + +.. code-block:: bash + + $ cd egs/librispeech/ASR + $ ./prepare.sh + +The script ``./prepare.sh`` handles the data preparation for you, **automagically**. +All you need to do is to run it. + +The data preparation contains several stages, you can use the following two +options: + + - ``--stage`` + - ``--stop-stage`` + +to control which stage(s) should be run. By default, all stages are executed. + + +For example, + +.. code-block:: bash + + $ cd egs/yesno/ASR + $ ./prepare.sh --stage 0 --stop-stage 0 + +means to run only stage 0. + +To run stage 2 to stage 5, use: + +.. code-block:: bash + + $ ./prepare.sh --stage 2 --stop-stage 5 + +.. HINT:: + + If you have pre-downloaded the `LibriSpeech `_ + dataset and the `musan `_ dataset, say, + they are saved in ``/tmp/LibriSpeech`` and ``/tmp/musan``, you can modify + the ``dl_dir`` variable in ``./prepare.sh`` to point to ``/tmp`` so that + ``./prepare.sh`` won't re-download them. + +.. NOTE:: + + All generated files by ``./prepare.sh``, e.g., features, lexicon, etc, + are saved in ``./data`` directory. + + +Training +-------- + +Configurable options +~~~~~~~~~~~~~~~~~~~~ + +.. code-block:: bash + + $ cd egs/librispeech/ASR + $ ./conformer_ctc/train.py --help + +shows you the training options that can be passed from the commandline. +The following options are used quite often: + + - ``--full-libri`` + + If it's True, the training part uses all the training data, i.e., + 960 hours. Otherwise, the training part uses only the subset + ``train-clean-100``, which has 100 hours of training data. + + .. CAUTION:: + + The training set is perturbed by speed with two factors: 0.9 and 1.1. + If ``--full-libri`` is True, each epoch actually processes + ``3x960 == 2880`` hours of data. + + - ``--num-epochs`` + + It is the number of epochs to train. For instance, + ``./conformer_ctc/train.py --num-epochs 30`` trains for 30 epochs + and generates ``epoch-0.pt``, ``epoch-1.pt``, ..., ``epoch-29.pt`` + in the folder ``./conformer_ctc/exp``. + + - ``--start-epoch`` + + It's used to resume training. + ``./conformer_ctc/train.py --start-epoch 10`` loads the + checkpoint ``./conformer_ctc/exp/epoch-9.pt`` and starts + training from epoch 10, based on the state from epoch 9. + + - ``--world-size`` + + It is used for multi-GPU single-machine DDP training. + + - (a) If it is 1, then no DDP training is used. + + - (b) If it is 2, then GPU 0 and GPU 1 are used for DDP training. + + The following shows some use cases with it. + + **Use case 1**: You have 4 GPUs, but you only want to use GPU 0 and + GPU 2 for training. You can do the following: + + .. code-block:: bash + + $ cd egs/librispeech/ASR + $ export CUDA_VISIBLE_DEVICES="0,2" + $ ./conformer_ctc/train.py --world-size 2 + + **Use case 2**: You have 4 GPUs and you want to use all of them + for training. You can do the following: + + .. code-block:: bash + + $ cd egs/librispeech/ASR + $ ./conformer_ctc/train.py --world-size 4 + + **Use case 3**: You have 4 GPUs but you only want to use GPU 3 + for training. You can do the following: + + .. code-block:: bash + + $ cd egs/librispeech/ASR + $ export CUDA_VISIBLE_DEVICES="3" + $ ./conformer_ctc/train.py --world-size 1 + + .. CAUTION:: + + Only multi-GPU single-machine DDP training is implemented at present. + Multi-GPU multi-machine DDP training will be added later. + + - ``--max-duration`` + + It specifies the number of seconds over all utterances in a + batch, before **padding**. + If you encounter CUDA OOM, please reduce it. For instance, if + your are using V100 NVIDIA GPU, we recommend you to set it to ``200``. + + .. HINT:: + + Due to padding, the number of seconds of all utterances in a + batch will usually be larger than ``--max-duration``. + + A larger value for ``--max-duration`` may cause OOM during training, + while a smaller value may increase the training time. You have to + tune it. + + +Pre-configured options +~~~~~~~~~~~~~~~~~~~~~~ + +There are some training options, e.g., learning rate, +number of warmup steps, results dir, etc, +that are not passed from the commandline. +They are pre-configured by the function ``get_params()`` in +`conformer_ctc/train.py `_ + +You don't need to change these pre-configured parameters. If you really need to change +them, please modify ``./conformer_ctc/train.py`` directly. + + +Training logs +~~~~~~~~~~~~~ + +Training logs and checkpoints are saved in ``conformer_ctc/exp``. +You will find the following files in that directory: + + - ``epoch-0.pt``, ``epoch-1.pt``, ... + + These are checkpoint files, containing model ``state_dict`` and optimizer ``state_dict``. + To resume training from some checkpoint, say ``epoch-10.pt``, you can use: + + .. code-block:: bash + + $ ./conformer_ctc/train.py --start-epoch 11 + + - ``tensorboard/`` + + This folder contains TensorBoard logs. Training loss, validation loss, learning + rate, etc, are recorded in these logs. You can visualize them by: + + .. code-block:: bash + + $ cd conformer_ctc/exp/tensorboard + $ tensorboard dev upload --logdir . --description "Conformer CTC training for LibriSpeech with icefall" + + It will print something like below: + + .. code-block:: + + TensorFlow installation not found - running with reduced feature set. + Upload started and will continue reading any new data as it's added to the logdir. + + To stop uploading, press Ctrl-C. + + New experiment created. View your TensorBoard at: https://tensorboard.dev/experiment/lzGnETjwRxC3yghNMd4kPw/ + + [2021-08-24T16:42:43] Started scanning logdir. + Uploading 4540 scalars... + + Note there is a URL in the above output, click it and you will see + the following screenshot: + + .. figure:: images/librispeech-conformer-ctc-tensorboard-log.png + :width: 600 + :alt: TensorBoard screenshot + :align: center + :target: https://tensorboard.dev/experiment/lzGnETjwRxC3yghNMd4kPw/ + + TensorBoard screenshot. + + - ``log/log-train-xxxx`` + + It is the detailed training log in text format, same as the one + you saw printed to the console during training. + +Usage examples +~~~~~~~~~~~~~~ + +The following shows typical use cases: + +**Case 1** +^^^^^^^^^^ + +.. code-block:: bash + + $ cd egs/librispeech/ASR + $ ./conformer_ctc/train.py --max-duration 200 --full-libri 0 + +It uses ``--max-duration`` of 200 to avoid OOM. Also, it uses only +a subset of the LibriSpeech data for training. + + +**Case 2** +^^^^^^^^^^ + +.. code-block:: bash + + $ cd egs/librispeech/ASR + $ export CUDA_VISIBLE_DEVICES="0,3" + $ ./conformer_ctc/train.py --world-size 2 + +It uses GPU 0 and GPU 3 for DDP training. + +**Case 3** +^^^^^^^^^^ + +.. code-block:: bash + + $ cd egs/librispeech/ASR + $ ./conformer_ctc/train.py --num-epochs 10 --start-epoch 3 + +It loads checkpoint ``./conformer_ctc/exp/epoch-2.pt`` and starts +training from epoch 3. Also, it trains for 10 epochs. + +Decoding +-------- + +The decoding part uses checkpoints saved by the training part, so you have +to run the training part first. + +.. code-block:: bash + + $ cd egs/librispeech/ASR + $ ./conformer_ctc/decode.py --help + +shows the options for decoding. + +The commonly used options are: + + - ``--method`` + + This specifies the decoding method. + + The following command uses attention decoder for rescoring: + + .. code-block:: + + $ cd egs/librispeech/ASR + $ ./conformer_ctc/decode.py --method attention-decoder --max-duration 30 --lattice-score-scale 0.5 + + - ``--lattice-score-scale`` + + It is used to scaled down lattice scores so that we can more unique + paths for rescoring. + + - ``--max-duration`` + + It has the same meaning as the one during training. A larger + value may cause OOM. + +Pre-trained Model +----------------- + +We have uploaded the pre-trained model to +``_. + +We describe how to use the pre-trained model to transcribe a sound file or +multiple sound files in the following. + +Install kaldifeat +~~~~~~~~~~~~~~~~~ + +`kaldifeat `_ is used to +extract features for a single sound file or multiple soundfiles +at the same time. + +Please refer to ``_ for installation. + +Download the pre-trained model +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The following commands describe how to download the pre-trained model: + +.. code-block:: + + $ cd egs/librispeech/ASR + $ mkdir tmp + $ cd tmp + $ git lfs install + $ git clone https://huggingface.co/pkufool/icefall_asr_librispeech_conformer_ctc + +.. CAUTION:: + + You have to use ``git lfs`` to download the pre-trained model. + +After downloading, you will have the following files: + +.. code-block:: bash + + $ cd egs/librispeech/ASR + $ tree tmp + +.. code-block:: bash + + tmp + `-- icefall_asr_librispeech_conformer_ctc + |-- README.md + |-- data + | |-- lang_bpe + | | |-- HLG.pt + | | |-- bpe.model + | | |-- tokens.txt + | | `-- words.txt + | `-- lm + | `-- G_4_gram.pt + |-- exp + | `-- pretraind.pt + `-- test_wavs + |-- 1089-134686-0001.flac + |-- 1221-135766-0001.flac + |-- 1221-135766-0002.flac + `-- trans.txt + + 6 directories, 11 files + +**File descriptions**: + + - ``data/lang_bpe/HLG.pt`` + + It is the decoding graph. + + - ``data/lang_bpe/bpe.model`` + + It is a sentencepiece model. You can use it to reproduce our results. + + - ``data/lang_bpe/tokens.txt`` + + It contains tokens and their IDs, generated from ``bpe.model``. + Provided only for convenience so that you can look up the SOS/EOS ID easily. + + - ``data/lang_bpe/words.txt`` + + It contains words and their IDs. + + - ``data/lm/G_4_gram.pt`` + + It is a 4-gram LM, useful for LM rescoring. + + - ``exp/pretrained.pt`` + + It contains pre-trained model parameters, obtained by averaging + checkpoints from ``epoch-15.pt`` to ``epoch-34.pt``. + Note: We have removed optimizer ``state_dict`` to reduce file size. + + - ``test_waves/*.flac`` + + It contains some test sound files from LibriSpeech ``test-clean`` dataset. + + - `test_waves/trans.txt` + + It contains the reference transcripts for the sound files in `test_waves/`. + +The information of the test sound files is listed below: + +.. code-block:: bash + + $ soxi tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/*.flac + + Input File : 'tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1089-134686-0001.flac' + Channels : 1 + Sample Rate : 16000 + Precision : 16-bit + Duration : 00:00:06.62 = 106000 samples ~ 496.875 CDDA sectors + File Size : 116k + Bit Rate : 140k + Sample Encoding: 16-bit FLAC + + Input File : 'tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0001.flac' + Channels : 1 + Sample Rate : 16000 + Precision : 16-bit + Duration : 00:00:16.71 = 267440 samples ~ 1253.62 CDDA sectors + File Size : 343k + Bit Rate : 164k + Sample Encoding: 16-bit FLAC + + Input File : 'tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0002.flac' + Channels : 1 + Sample Rate : 16000 + Precision : 16-bit + Duration : 00:00:04.83 = 77200 samples ~ 361.875 CDDA sectors + File Size : 105k + Bit Rate : 174k + Sample Encoding: 16-bit FLAC + + Total Duration of 3 files: 00:00:28.16 + +Usage +~~~~~ + +.. code-block:: + + $ cd egs/librispeech/ASR + $ ./conformer_ctc/pretrained.py --help + +displays the help information. + +It supports three decoding methods: + + - HLG decoding + - HLG + n-gram LM rescoring + - HLG + n-gram LM rescoring + attention decoder rescoring + +HLG decoding +^^^^^^^^^^^^ + +HLG decoding uses the best path of the decoding lattice as the decoding result. + +The command to run HLG decoding is: + +.. code-block:: bash + + $ cd egs/librispeech/ASR + $ ./conformer_ctc/pretrained.py \ + --checkpoint ./tmp/icefall_asr_librispeech_conformer_ctc/exp/pretraind.pt \ + --words-file ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/words.txt \ + --HLG ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/HLG.pt \ + ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1089-134686-0001.flac \ + ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0001.flac \ + ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0002.flac + +The output is given below: + +.. code-block:: + + 2021-08-20 11:03:05,712 INFO [pretrained.py:217] device: cuda:0 + 2021-08-20 11:03:05,712 INFO [pretrained.py:219] Creating model + 2021-08-20 11:03:11,345 INFO [pretrained.py:238] Loading HLG from ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/HLG.pt + 2021-08-20 11:03:18,442 INFO [pretrained.py:255] Constructing Fbank computer + 2021-08-20 11:03:18,444 INFO [pretrained.py:265] Reading sound files: ['./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1089-134686-0001.flac', './tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0001.flac', './tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0002.flac'] + 2021-08-20 11:03:18,507 INFO [pretrained.py:271] Decoding started + 2021-08-20 11:03:18,795 INFO [pretrained.py:300] Use HLG decoding + 2021-08-20 11:03:19,149 INFO [pretrained.py:339] + ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1089-134686-0001.flac: + AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD LIGHT UP HERE AND THERE THE SQUALID QUARTER OF THE BROTHELS + + ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0001.flac: + GOD AS A DIRECT CONSEQUENCE OF THE SIN WHICH MAN THUS PUNISHED HAD GIVEN HER A LOVELY CHILD WHOSE PLACE WAS ON THAT SAME DISHONOURED + BOSOM TO CONNECT HER PARENT FOR EVER WITH THE RACE AND DESCENT OF MORTALS AND TO BE FINALLY A BLESSED SOUL IN HEAVEN + + ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0002.flac: + YET THESE THOUGHTS AFFECTED HESTER PRYNNE LESS WITH HOPE THAN APPREHENSION + + 2021-08-20 11:03:19,149 INFO [pretrained.py:341] Decoding Done + +HLG decoding + LM rescoring +^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +It uses an n-gram LM to rescore the decoding lattice and the best +path of the rescored lattice is the decoding result. + +The command to run HLG decoding + LM rescoring is: + +.. code-block:: bash + + $ cd egs/librispeech/ASR + $ ./conformer_ctc/pretrained.py \ + --checkpoint ./tmp/icefall_asr_librispeech_conformer_ctc/exp/pretraind.pt \ + --words-file ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/words.txt \ + --HLG ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/HLG.pt \ + --method whole-lattice-rescoring \ + --G ./tmp/icefall_asr_librispeech_conformer_ctc/data/lm/G_4_gram.pt \ + --ngram-lm-scale 0.8 \ + ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1089-134686-0001.flac \ + ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0001.flac \ + ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0002.flac + +Its output is: + +.. code-block:: + + 2021-08-20 11:12:17,565 INFO [pretrained.py:217] device: cuda:0 + 2021-08-20 11:12:17,565 INFO [pretrained.py:219] Creating model + 2021-08-20 11:12:23,728 INFO [pretrained.py:238] Loading HLG from ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/HLG.pt + 2021-08-20 11:12:30,035 INFO [pretrained.py:246] Loading G from ./tmp/icefall_asr_librispeech_conformer_ctc/data/lm/G_4_gram.pt + 2021-08-20 11:13:10,779 INFO [pretrained.py:255] Constructing Fbank computer + 2021-08-20 11:13:10,787 INFO [pretrained.py:265] Reading sound files: ['./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1089-134686-0001.flac', './tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0001.flac', './tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0002.flac'] + 2021-08-20 11:13:10,798 INFO [pretrained.py:271] Decoding started + 2021-08-20 11:13:11,085 INFO [pretrained.py:305] Use HLG decoding + LM rescoring + 2021-08-20 11:13:11,736 INFO [pretrained.py:339] + ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1089-134686-0001.flac: + AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD LIGHT UP HERE AND THERE THE SQUALID QUARTER OF THE BROTHELS + + ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0001.flac: + GOD AS A DIRECT CONSEQUENCE OF THE SIN WHICH MAN THUS PUNISHED HAD GIVEN HER A LOVELY CHILD WHOSE PLACE WAS ON THAT SAME DISHONOURED + BOSOM TO CONNECT HER PARENT FOR EVER WITH THE RACE AND DESCENT OF MORTALS AND TO BE FINALLY A BLESSED SOUL IN HEAVEN + + ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0002.flac: + YET THESE THOUGHTS AFFECTED HESTER PRYNNE LESS WITH HOPE THAN APPREHENSION + + 2021-08-20 11:13:11,737 INFO [pretrained.py:341] Decoding Done + +HLG decoding + LM rescoring + attention decoder rescoring +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +It uses an n-gram LM to rescore the decoding lattice, extracts +n paths from the rescored lattice, recores the extracted paths with +an attention decoder. The path with the highest score is the decoding result. + +The command to run HLG decoding + LM rescoring + attention decoder rescoring is: + +.. code-block:: bash + + $ cd egs/librispeech/ASR + $ ./conformer_ctc/pretrained.py \ + --checkpoint ./tmp/icefall_asr_librispeech_conformer_ctc/exp/pretraind.pt \ + --words-file ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/words.txt \ + --HLG ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/HLG.pt \ + --method attention-decoder \ + --G ./tmp/icefall_asr_librispeech_conformer_ctc/data/lm/G_4_gram.pt \ + --ngram-lm-scale 1.3 \ + --attention-decoder-scale 1.2 \ + --lattice-score-scale 0.5 \ + --num-paths 100 \ + --sos-id 1 \ + --eos-id 1 \ + ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1089-134686-0001.flac \ + ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0001.flac \ + ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0002.flac + +The output is below: + +.. code-block:: + + 2021-08-20 11:19:11,397 INFO [pretrained.py:217] device: cuda:0 + 2021-08-20 11:19:11,397 INFO [pretrained.py:219] Creating model + 2021-08-20 11:19:17,354 INFO [pretrained.py:238] Loading HLG from ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/HLG.pt + 2021-08-20 11:19:24,615 INFO [pretrained.py:246] Loading G from ./tmp/icefall_asr_librispeech_conformer_ctc/data/lm/G_4_gram.pt + 2021-08-20 11:20:04,576 INFO [pretrained.py:255] Constructing Fbank computer + 2021-08-20 11:20:04,584 INFO [pretrained.py:265] Reading sound files: ['./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1089-134686-0001.flac', './tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0001.flac', './tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0002.flac'] + 2021-08-20 11:20:04,595 INFO [pretrained.py:271] Decoding started + 2021-08-20 11:20:04,854 INFO [pretrained.py:313] Use HLG + LM rescoring + attention decoder rescoring + 2021-08-20 11:20:05,805 INFO [pretrained.py:339] + ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1089-134686-0001.flac: + AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD LIGHT UP HERE AND THERE THE SQUALID QUARTER OF THE BROTHELS + + ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0001.flac: + GOD AS A DIRECT CONSEQUENCE OF THE SIN WHICH MAN THUS PUNISHED HAD GIVEN HER A LOVELY CHILD WHOSE PLACE WAS ON THAT SAME DISHONOURED + BOSOM TO CONNECT HER PARENT FOR EVER WITH THE RACE AND DESCENT OF MORTALS AND TO BE FINALLY A BLESSED SOUL IN HEAVEN + + ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0002.flac: + YET THESE THOUGHTS AFFECTED HESTER PRYNNE LESS WITH HOPE THAN APPREHENSION + + 2021-08-20 11:20:05,805 INFO [pretrained.py:341] Decoding Done + +Colab notebook +-------------- + +We do provide a colab notebook for this recipe showing how to use a pre-trained model. + +|librispeech asr conformer ctc colab notebook| + +.. |librispeech asr conformer ctc colab notebook| image:: https://colab.research.google.com/assets/colab-badge.svg + :target: https://colab.research.google.com/drive/1huyupXAcHsUrKaWfI83iMEJ6J0Nh0213?usp=sharing + +.. HINT:: + + Due to limited memory provided by Colab, you have to upgrade to Colab Pro to + run ``HLG decoding + LM rescoring`` and + ``HLG decoding + LM rescoring + attention decoder rescoring``. + Otherwise, you can only run ``HLG decoding`` with Colab. + +**Congratulations!** You have finished the librispeech ASR recipe with +conformer CTC models in ``icefall``. diff --git a/docs/source/recipes/librispeech/images/librispeech-conformer-ctc-tensorboard-log.png b/docs/source/recipes/librispeech/images/librispeech-conformer-ctc-tensorboard-log.png new file mode 100644 index 0000000000000000000000000000000000000000..4e8c2ea7cd8cb07962c6d29c4a846be14bad5ffc GIT binary patch literal 431729 zcmb5U1yo#3(m#y56N1Cw5P~}l?iSpFI|L03Ft}TShG4;hLvVL@2oT)e-F1*}p51-r z{P#WI+2@{fr+eDFs_w6=sw-0UgDfT*2^tIx45qxClsXIy3Iz-dQVs*`Y=YJnMt1-t+T&ZRxU zUXf?hmzwET-#_#mndnWZFr3lj$$KzASCDQ>sl~CbbDvSFOEekd;eMLK2&e*f(Ww|D zD6~}Bwfe58gv7jww+-VK^o;L+Kcu~un!ft@jj@+pk-Tv!lA;>f8-Eg2yz0mY7z@;f zk=t`u0XdBlMN2oLTZ`4+6^Nh4TlMw+`h@wAAyrf^1jDFR8l|MZlv@CYNH~klPSt|; zkgXP{OckzpWuC+HSZXiNkIlKvvX3E8#iqvP5if_E^z))gT!cr&J)&mEAjZm(+*qWs ze7IdXHMw`sy8bu>!aNwXd{89C3A2q>Pt%In%r;T2(2eToyDs{Uw_X zfxq9%(@0FeR7Kf)f?NBIw0{x}6EYJhf;mXa-@jpy`E?Y0^7b5gi!W#y@Xh60*i*!t z;t9zn6zOc!!{sXXcy9RwHg^?mBD{*!CFYUXsrfAnHNOxAC&UFQweLSuzs9nJq-mT` z#wHQ;)BW1&<#K)4+MK>vmj;kHi!++Q(%I{oycXu~bZ zrH&+YetLqENxkf5j2z#HK7e|a&&8H0W5ec5=9fS7 zo(jD=F}@8M2*K)x@79uF*w}p|ANf?G+aLU4U(_Js^K3HlmiwPVLW&?{v9mv1W4+1& z=%=W28|&iEmTW~kpEk93#vb`Rv8>OfM|s@e=%6VOi=!6s`v_|+$uHT>fBltXJS$6X zH|wm`)I(!jG5(x0CNVGT>k!G(jb&0;xiwFG%sz{vJI_&yDilkuc5zPO#oZVlru~&N ziUIo(x*OqkD`hP@g7UiZm2yfR^Pj5RxvFl#p9_brhUo-0CEg;pBTBu`)MWR1GqN(X zf(bJtB>kM?)ko%|1NdFzf1vOM2F00-h=~6wnRSscAZz_9aq&5M3|3^TmIa2tP44G+ z!eY0^Lx}(tcyh2TF}$k@CYg!MX(#D@un7)Qf1qw0%4Da7t~JNq8)34-#`}`jk1hBgv8sT^r%@%1Cfd&7H0@FFdgT<8(w%;bI4sfxeX#?*#`qCk<;(q6K&)cSENLmS(m z>hA^;<$yDKN;+d|QK~x)5L5jdQHA%?a;frqG7BGU3k-gO)E zulLnKC!nTgpOvO&t>$o0#nJ5n$My2H*$vut(*DV;Q0|>(R-RKKWdYl?aZ!izYsiZv zQh%kDGcjC0U(`udLKG`}PGB4CIAHGQvPp$_i+DwLdv>8|^#(@IyB?ab45cXBfo+bf z)R7cO>Rbw?OiJ{66i&1*FvqbsAJ-{bh6Ir`GHN*LFuI(?m)(juoOqA8g58n3-7YAN zKUIl6)vFcGOiSUd z%qQtA!5#>r8~iRV0^a;=5(bjnRLGL6F3*jwjV#%^ zHc~cvHr^?Iy6W?fmEIfL8v+}38*R8oxHZ%tsZrzA;+d%{u6~2zj4&%a(#IPGwia`2 z)KJxqaw(*Qq(^eo26DFsqK$c8K14Z0aTjpB?Y%V9>06DiiA{TZcZIMS(uk^sPchKa zHy$IJ*eRFttx{-1*-UP;x_3z)OMW3@VtjB2@|6p)P`y}GlKaG`IQ>r;DFzU4U!?x{`@c|1lCneA3 z9d`ib*nuv6mQSGcdgzN{*%WcrzetzsNRU2$btv1UHx@weWG>)pTaDxF3h#-2^wGCt#`ot-5l(iB zb&5Ora~)T&j#tz^@fKZH>WMh5YTC+KYw!}VW%5sv9U#2{EZVQKVb_JyZ8XJ05o-A%JiG`EpC zUjsHH|GIa1zx;v^!um*89p5;%)-r)LR;+49chW@dkQrccsJ{F9AX<7 zoJ4QnA>lvB>4kRz^Nee6)19%Zw5xybC*Z_~lx>^l`kD zZ4LRB=}s8JaW!BE$-Ql$Frh4h4BZxDA`>Hzki>V)5mX@Bx! 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/dev/null +++ b/docs/source/recipes/librispeech/tdnn_lstm_ctc.rst @@ -0,0 +1,2 @@ +TDNN LSTM CTC +============= diff --git a/docs/source/recipes/yesno.rst b/docs/source/recipes/yesno.rst index e4bcb6f0b..cb425ad1d 100644 --- a/docs/source/recipes/yesno.rst +++ b/docs/source/recipes/yesno.rst @@ -1,7 +1,7 @@ yesno ===== -This page shows you how to run the ``yesno`` recipe. It contains: +This page shows you how to run the `yesno `_ recipe. It contains: - (1) Prepare data for training - (2) Train a TDNN model diff --git a/egs/librispeech/ASR/conformer_ctc/README.md b/egs/librispeech/ASR/conformer_ctc/README.md index 130d21351..0092fd14e 100644 --- a/egs/librispeech/ASR/conformer_ctc/README.md +++ b/egs/librispeech/ASR/conformer_ctc/README.md @@ -1,351 +1,4 @@ -# How to use a pre-trained model to transcribe a sound file or multiple sound files - -(See the bottom of this document for the link to a colab notebook.) - -You need to prepare 4 files: - - - a model checkpoint file, e.g., epoch-20.pt - - HLG.pt, the decoding graph - - words.txt, the word symbol table - - a sound file, whose sampling rate has to be 16 kHz. - Supported formats are those supported by `torchaudio.load()`, - e.g., wav and flac. - -Also, you need to install `kaldifeat`. Please refer to - for installation. - -```bash -./conformer_ctc/pretrained.py --help -``` - -displays the help information. - -## HLG decoding - -Once you have the above files ready and have `kaldifeat` installed, -you can run: - -```bash -./conformer_ctc/pretrained.py \ - --checkpoint /path/to/your/checkpoint.pt \ - --words-file /path/to/words.txt \ - --HLG /path/to/HLG.pt \ - /path/to/your/sound.wav -``` - -and you will see the transcribed result. - -If you want to transcribe multiple files at the same time, you can use: - -```bash -./conformer_ctc/pretrained.py \ - --checkpoint /path/to/your/checkpoint.pt \ - --words-file /path/to/words.txt \ - --HLG /path/to/HLG.pt \ - /path/to/your/sound1.wav \ - /path/to/your/sound2.wav \ - /path/to/your/sound3.wav -``` - -**Note**: This is the fastest decoding method. - -## HLG decoding + LM rescoring - -`./conformer_ctc/pretrained.py` also supports `whole lattice LM rescoring` -and `attention decoder rescoring`. - -To use whole lattice LM rescoring, you also need the following files: - - - G.pt, e.g., `data/lm/G_4_gram.pt` if you have run `./prepare.sh` - -The command to run decoding with LM rescoring is: - -```bash -./conformer_ctc/pretrained.py \ - --checkpoint /path/to/your/checkpoint.pt \ - --words-file /path/to/words.txt \ - --HLG /path/to/HLG.pt \ - --method whole-lattice-rescoring \ - --G data/lm/G_4_gram.pt \ - --ngram-lm-scale 0.8 \ - /path/to/your/sound1.wav \ - /path/to/your/sound2.wav \ - /path/to/your/sound3.wav -``` - -## HLG Decoding + LM rescoring + attention decoder rescoring - -To use attention decoder for rescoring, you need the following extra information: - - - sos token ID - - eos token ID - -The command to run decoding with attention decoder rescoring is: - -```bash -./conformer_ctc/pretrained.py \ - --checkpoint /path/to/your/checkpoint.pt \ - --words-file /path/to/words.txt \ - --HLG /path/to/HLG.pt \ - --method attention-decoder \ - --G data/lm/G_4_gram.pt \ - --ngram-lm-scale 1.3 \ - --attention-decoder-scale 1.2 \ - --lattice-score-scale 0.5 \ - --num-paths 100 \ - --sos-id 1 \ - --eos-id 1 \ - /path/to/your/sound1.wav \ - /path/to/your/sound2.wav \ - /path/to/your/sound3.wav -``` - -# Decoding with a pre-trained model in action - -We have uploaded a pre-trained model to - -The following shows the steps about the usage of the provided pre-trained model. - -### (1) Download the pre-trained model - -```bash -sudo apt-get install git-lfs -cd /path/to/icefall/egs/librispeech/ASR -git lfs install -mkdir tmp -cd tmp -git clone https://huggingface.co/pkufool/conformer_ctc -``` - -**CAUTION**: You have to install `git-lfst` to download the pre-trained model. - -You will find the following files: - -``` -tmp -`-- conformer_ctc - |-- README.md - |-- data - | |-- lang_bpe - | | |-- HLG.pt - | | |-- bpe.model - | | |-- tokens.txt - | | `-- words.txt - | `-- lm - | `-- G_4_gram.pt - |-- exp - | `-- pretraind.pt - `-- test_wavs - |-- 1089-134686-0001.flac - |-- 1221-135766-0001.flac - |-- 1221-135766-0002.flac - `-- trans.txt - -6 directories, 11 files -``` - -**File descriptions**: - - - `data/lang_bpe/HLG.pt` - - It is the decoding graph. - - - `data/lang_bpe/bpe.model` - - It is a sentencepiece model. You can use it to reproduce our results. - - - `data/lang_bpe/tokens.txt` - - It contains tokens and their IDs, generated from `bpe.model`. - Provided only for convienice so that you can look up the SOS/EOS ID easily. - - - `data/lang_bpe/words.txt` - - It contains words and their IDs. - - - `data/lm/G_4_gram.pt` - - It is a 4-gram LM, useful for LM rescoring. - - - `exp/pretrained.pt` - - It contains pre-trained model parameters, obtained by averaging - checkpoints from `epoch-15.pt` to `epoch-34.pt`. - Note: We have removed optimizer `state_dict` to reduce file size. - - - `test_waves/*.flac` - - It contains some test sound files from LibriSpeech `test-clean` dataset. - - - `test_waves/trans.txt` - - It contains the reference transcripts for the sound files in `test_waves/`. - -The information of the test sound files is listed below: - -``` -$ soxi tmp/conformer_ctc/test_wavs/*.flac - -Input File : 'tmp/conformer_ctc/test_wavs/1089-134686-0001.flac' -Channels : 1 -Sample Rate : 16000 -Precision : 16-bit -Duration : 00:00:06.62 = 106000 samples ~ 496.875 CDDA sectors -File Size : 116k -Bit Rate : 140k -Sample Encoding: 16-bit FLAC - -Input File : 'tmp/conformer_ctc/test_wavs/1221-135766-0001.flac' -Channels : 1 -Sample Rate : 16000 -Precision : 16-bit -Duration : 00:00:16.71 = 267440 samples ~ 1253.62 CDDA sectors -File Size : 343k -Bit Rate : 164k -Sample Encoding: 16-bit FLAC - -Input File : 'tmp/conformer_ctc/test_wavs/1221-135766-0002.flac' -Channels : 1 -Sample Rate : 16000 -Precision : 16-bit -Duration : 00:00:04.83 = 77200 samples ~ 361.875 CDDA sectors -File Size : 105k -Bit Rate : 174k -Sample Encoding: 16-bit FLAC - -Total Duration of 3 files: 00:00:28.16 -``` - -### (2) Use HLG decoding - -```bash -cd /path/to/icefall/egs/librispeech/ASR - -./conformer_ctc/pretrained.py \ - --checkpoint ./tmp/conformer_ctc/exp/pretraind.pt \ - --words-file ./tmp/conformer_ctc/data/lang_bpe/words.txt \ - --HLG ./tmp/conformer_ctc/data/lang_bpe/HLG.pt \ - ./tmp/conformer_ctc/test_wavs/1089-134686-0001.flac \ - ./tmp/conformer_ctc/test_wavs/1221-135766-0001.flac \ - ./tmp/conformer_ctc/test_wavs/1221-135766-0002.flac -``` - -The output is given below: - -``` -2021-08-20 11:03:05,712 INFO [pretrained.py:217] device: cuda:0 -2021-08-20 11:03:05,712 INFO [pretrained.py:219] Creating model -2021-08-20 11:03:11,345 INFO [pretrained.py:238] Loading HLG from ./tmp/conformer_ctc/data/lang_bpe/HLG.pt -2021-08-20 11:03:18,442 INFO [pretrained.py:255] Constructing Fbank computer -2021-08-20 11:03:18,444 INFO [pretrained.py:265] Reading sound files: ['./tmp/conformer_ctc/test_wavs/1089-134686-0001.flac', './tmp/conformer_ctc/test_wavs/1221-135766-0001.flac', './tmp/conformer_ctc/test_wavs/1221-135766-0002.flac'] -2021-08-20 11:03:18,507 INFO [pretrained.py:271] Decoding started -2021-08-20 11:03:18,795 INFO [pretrained.py:300] Use HLG decoding -2021-08-20 11:03:19,149 INFO [pretrained.py:339] -./tmp/conformer_ctc/test_wavs/1089-134686-0001.flac: -AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD LIGHT UP HERE AND THERE THE SQUALID QUARTER OF THE BROTHELS - -./tmp/conformer_ctc/test_wavs/1221-135766-0001.flac: -GOD AS A DIRECT CONSEQUENCE OF THE SIN WHICH MAN THUS PUNISHED HAD GIVEN HER A LOVELY CHILD WHOSE PLACE WAS ON THAT SAME DISHONOURED -BOSOM TO CONNECT HER PARENT FOR EVER WITH THE RACE AND DESCENT OF MORTALS AND TO BE FINALLY A BLESSED SOUL IN HEAVEN - -./tmp/conformer_ctc/test_wavs/1221-135766-0002.flac: -YET THESE THOUGHTS AFFECTED HESTER PRYNNE LESS WITH HOPE THAN APPREHENSION - - -2021-08-20 11:03:19,149 INFO [pretrained.py:341] Decoding Done -``` - -### (3) Use HLG decoding + LM rescoring - -```bash -./conformer_ctc/pretrained.py \ - --checkpoint ./tmp/conformer_ctc/exp/pretraind.pt \ - --words-file ./tmp/conformer_ctc/data/lang_bpe/words.txt \ - --HLG ./tmp/conformer_ctc/data/lang_bpe/HLG.pt \ - --method whole-lattice-rescoring \ - --G ./tmp/conformer_ctc/data/lm/G_4_gram.pt \ - --ngram-lm-scale 0.8 \ - ./tmp/conformer_ctc/test_wavs/1089-134686-0001.flac \ - ./tmp/conformer_ctc/test_wavs/1221-135766-0001.flac \ - ./tmp/conformer_ctc/test_wavs/1221-135766-0002.flac -``` - -The output is: - -``` -2021-08-20 11:12:17,565 INFO [pretrained.py:217] device: cuda:0 -2021-08-20 11:12:17,565 INFO [pretrained.py:219] Creating model -2021-08-20 11:12:23,728 INFO [pretrained.py:238] Loading HLG from ./tmp/conformer_ctc/data/lang_bpe/HLG.pt -2021-08-20 11:12:30,035 INFO [pretrained.py:246] Loading G from ./tmp/conformer_ctc/data/lm/G_4_gram.pt -2021-08-20 11:13:10,779 INFO [pretrained.py:255] Constructing Fbank computer -2021-08-20 11:13:10,787 INFO [pretrained.py:265] Reading sound files: ['./tmp/conformer_ctc/test_wavs/1089-134686-0001.flac', './tmp/conformer_ctc/test_wavs/1221-135766-0001.flac', './tmp/conformer_ctc/test_wavs/1221-135766-0002.flac'] -2021-08-20 11:13:10,798 INFO [pretrained.py:271] Decoding started -2021-08-20 11:13:11,085 INFO [pretrained.py:305] Use HLG decoding + LM rescoring -2021-08-20 11:13:11,736 INFO [pretrained.py:339] -./tmp/conformer_ctc/test_wavs/1089-134686-0001.flac: -AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD LIGHT UP HERE AND THERE THE SQUALID QUARTER OF THE BROTHELS - -./tmp/conformer_ctc/test_wavs/1221-135766-0001.flac: -GOD AS A DIRECT CONSEQUENCE OF THE SIN WHICH MAN THUS PUNISHED HAD GIVEN HER A LOVELY CHILD WHOSE PLACE WAS ON THAT SAME DISHONOURED -BOSOM TO CONNECT HER PARENT FOR EVER WITH THE RACE AND DESCENT OF MORTALS AND TO BE FINALLY A BLESSED SOUL IN HEAVEN - -./tmp/conformer_ctc/test_wavs/1221-135766-0002.flac: -YET THESE THOUGHTS AFFECTED HESTER PRYNNE LESS WITH HOPE THAN APPREHENSION - - -2021-08-20 11:13:11,737 INFO [pretrained.py:341] Decoding Done -``` - -### (4) Use HLG decoding + LM rescoring + attention decoder rescoring - -```bash -./conformer_ctc/pretrained.py \ - --checkpoint ./tmp/conformer_ctc/exp/pretraind.pt \ - --words-file ./tmp/conformer_ctc/data/lang_bpe/words.txt \ - --HLG ./tmp/conformer_ctc/data/lang_bpe/HLG.pt \ - --method attention-decoder \ - --G ./tmp/conformer_ctc/data/lm/G_4_gram.pt \ - --ngram-lm-scale 1.3 \ - --attention-decoder-scale 1.2 \ - --lattice-score-scale 0.5 \ - --num-paths 100 \ - --sos-id 1 \ - --eos-id 1 \ - ./tmp/conformer_ctc/test_wavs/1089-134686-0001.flac \ - ./tmp/conformer_ctc/test_wavs/1221-135766-0001.flac \ - ./tmp/conformer_ctc/test_wavs/1221-135766-0002.flac -``` - -The output is: - -``` -2021-08-20 11:19:11,397 INFO [pretrained.py:217] device: cuda:0 -2021-08-20 11:19:11,397 INFO [pretrained.py:219] Creating model -2021-08-20 11:19:17,354 INFO [pretrained.py:238] Loading HLG from ./tmp/conformer_ctc/data/lang_bpe/HLG.pt -2021-08-20 11:19:24,615 INFO [pretrained.py:246] Loading G from ./tmp/conformer_ctc/data/lm/G_4_gram.pt -2021-08-20 11:20:04,576 INFO [pretrained.py:255] Constructing Fbank computer -2021-08-20 11:20:04,584 INFO [pretrained.py:265] Reading sound files: ['./tmp/conformer_ctc/test_wavs/1089-134686-0001.flac', './tmp/conformer_ctc/test_wavs/1221-135766-0001.flac', './tmp/conformer_ctc/test_wavs/1221-135766-0002.flac'] -2021-08-20 11:20:04,595 INFO [pretrained.py:271] Decoding started -2021-08-20 11:20:04,854 INFO [pretrained.py:313] Use HLG + LM rescoring + attention decoder rescoring -2021-08-20 11:20:05,805 INFO [pretrained.py:339] -./tmp/conformer_ctc/test_wavs/1089-134686-0001.flac: -AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD LIGHT UP HERE AND THERE THE SQUALID QUARTER OF THE BROTHELS - -./tmp/conformer_ctc/test_wavs/1221-135766-0001.flac: -GOD AS A DIRECT CONSEQUENCE OF THE SIN WHICH MAN THUS PUNISHED HAD GIVEN HER A LOVELY CHILD WHOSE PLACE WAS ON THAT SAME DISHONOURED -BOSOM TO CONNECT HER PARENT FOR EVER WITH THE RACE AND DESCENT OF MORTALS AND TO BE FINALLY A BLESSED SOUL IN HEAVEN - -./tmp/conformer_ctc/test_wavs/1221-135766-0002.flac: -YET THESE THOUGHTS AFFECTED HESTER PRYNNE LESS WITH HOPE THAN APPREHENSION - - -2021-08-20 11:20:05,805 INFO [pretrained.py:341] Decoding Done -``` - -**NOTE**: We provide a colab notebook for demonstration. -[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1huyupXAcHsUrKaWfI83iMEJ6J0Nh0213?usp=sharing) - -Due to limited memory provided by Colab, you have to upgrade to Colab Pro to -run `HLG decoding + LM rescoring` and `HLG decoding + LM rescoring + attention decoder rescoring`. -Otherwise, you can only run `HLG decoding` with Colab. +Please visit + +for how to run this recipe. diff --git a/egs/librispeech/ASR/conformer_ctc/decode.py b/egs/librispeech/ASR/conformer_ctc/decode.py index 6abcf3385..321ce970e 100755 --- a/egs/librispeech/ASR/conformer_ctc/decode.py +++ b/egs/librispeech/ASR/conformer_ctc/decode.py @@ -57,28 +57,63 @@ def get_parser(): parser.add_argument( "--epoch", type=int, - default=9, + default=34, help="It specifies the checkpoint to use for decoding." "Note: Epoch counts from 0.", ) parser.add_argument( "--avg", type=int, - default=1, + default=20, help="Number of checkpoints to average. Automatically select " "consecutive checkpoints before the checkpoint specified by " "'--epoch'. ", ) + parser.add_argument( + "--method", + type=str, + default="attention-decoder", + help="""Decoding method. + Supported values are: + - (1) 1best. Extract the best path from the decoding lattice as the + decoding result. + - (2) nbest. Extract n paths from the decoding lattice; the path with + the highest score is the decoding result. + - (3) nbest-rescoring. Extract n paths from the decoding lattice, + rescore them with an n-gram LM (e.g., a 4-gram LM), the path with + the highest score is the decoding result. + - (4) whole-lattice. Rescore the decoding lattice with an n-gram LM + (e.g., a 4-gram LM), the best path of rescored lattice is the + decoding result. + - (5) attention-decoder. Extract n paths from the LM rescored lattice, + the path with the highest score is the decoding result. + - (6) nbest-oracle. Its WER is the lower bound of any n-best + rescoring method can achieve. Useful for debugging n-best + rescoring method. + """, + ) + + parser.add_argument( + "--num-paths", + type=int, + default=100, + help="""Number of paths for n-best based decoding method. + Used only when "method" is one of the following values: + nbest, nbest-rescoring, attention-decoder, and nbest-oracle + """, + ) + parser.add_argument( "--lattice-score-scale", type=float, default=1.0, - help="The scale to be applied to `lattice.scores`." - "It's needed if you use any kinds of n-best based rescoring. " - "Currently, it is used when the decoding method is: nbest, " - "nbest-rescoring, attention-decoder, and nbest-oracle. " - "A smaller value results in more unique paths.", + help="""The scale to be applied to `lattice.scores`. + It's needed if you use any kinds of n-best based rescoring. + Used only when "method" is one of the following values: + nbest, nbest-rescoring, attention-decoder, and nbest-oracle + A smaller value results in more unique paths. + """, ) return parser @@ -104,21 +139,6 @@ def get_params() -> AttributeDict: "min_active_states": 30, "max_active_states": 10000, "use_double_scores": True, - # Possible values for method: - # - 1best - # - nbest - # - nbest-rescoring - # - whole-lattice-rescoring - # - attention-decoder - # - nbest-oracle - # "method": "nbest", - # "method": "nbest-rescoring", - # "method": "whole-lattice-rescoring", - "method": "attention-decoder", - # "method": "nbest-oracle", - # num_paths is used when method is "nbest", "nbest-rescoring", - # attention-decoder, and nbest-oracle - "num_paths": 100, } ) return params @@ -129,7 +149,7 @@ def decode_one_batch( model: nn.Module, HLG: k2.Fsa, batch: dict, - lexicon: Lexicon, + word_table: k2.SymbolTable, sos_id: int, eos_id: int, G: Optional[k2.Fsa] = None, @@ -163,8 +183,8 @@ def decode_one_batch( It is the return value from iterating `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation for the format of the `batch`. - lexicon: - It contains word symbol table. + word_table: + The word symbol table. sos_id: The token ID of the SOS. eos_id: @@ -217,7 +237,7 @@ def decode_one_batch( lattice=lattice, num_paths=params.num_paths, ref_texts=supervisions["text"], - lexicon=lexicon, + word_table=word_table, scale=params.lattice_score_scale, ) @@ -237,7 +257,7 @@ def decode_one_batch( key = f"no_rescore-scale-{params.lattice_score_scale}-{params.num_paths}" # noqa hyps = get_texts(best_path) - hyps = [[lexicon.word_table[i] for i in ids] for ids in hyps] + hyps = [[word_table[i] for i in ids] for ids in hyps] return {key: hyps} assert params.method in [ @@ -283,7 +303,7 @@ def decode_one_batch( ans = dict() for lm_scale_str, best_path in best_path_dict.items(): hyps = get_texts(best_path) - hyps = [[lexicon.word_table[i] for i in ids] for ids in hyps] + hyps = [[word_table[i] for i in ids] for ids in hyps] ans[lm_scale_str] = hyps return ans @@ -293,7 +313,7 @@ def decode_dataset( params: AttributeDict, model: nn.Module, HLG: k2.Fsa, - lexicon: Lexicon, + word_table: k2.SymbolTable, sos_id: int, eos_id: int, G: Optional[k2.Fsa] = None, @@ -309,8 +329,8 @@ def decode_dataset( The neural model. HLG: The decoding graph. - lexicon: - It contains word symbol table. + word_table: + It is the word symbol table. sos_id: The token ID for SOS. eos_id: @@ -344,7 +364,7 @@ def decode_dataset( model=model, HLG=HLG, batch=batch, - lexicon=lexicon, + word_table=word_table, G=G, sos_id=sos_id, eos_id=eos_id, @@ -540,7 +560,7 @@ def main(): params=params, model=model, HLG=HLG, - lexicon=lexicon, + word_table=lexicon.word_table, G=G, sos_id=sos_id, eos_id=eos_id, diff --git a/egs/librispeech/ASR/conformer_ctc/train.py b/egs/librispeech/ASR/conformer_ctc/train.py index df9637c34..b0dbe72ad 100755 --- a/egs/librispeech/ASR/conformer_ctc/train.py +++ b/egs/librispeech/ASR/conformer_ctc/train.py @@ -74,6 +74,23 @@ def get_parser(): help="Should various information be logged in tensorboard.", ) + parser.add_argument( + "--num-epochs", + type=int, + default=35, + 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 + conformer_ctc/exp/epoch-{start_epoch-1}.pt + """, + ) + return parser @@ -103,11 +120,6 @@ def get_params() -> AttributeDict: - subsampling_factor: The subsampling factor for the model. - - start_epoch: If it is not zero, load checkpoint `start_epoch-1` - and continue training from that checkpoint. - - - num_epochs: Number of epochs to train. - - 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. @@ -143,8 +155,6 @@ def get_params() -> AttributeDict: "feature_dim": 80, "weight_decay": 1e-6, "subsampling_factor": 4, - "start_epoch": 0, - "num_epochs": 20, "best_train_loss": float("inf"), "best_valid_loss": float("inf"), "best_train_epoch": -1, diff --git a/egs/librispeech/ASR/tdnn_lstm_ctc/train.py b/egs/librispeech/ASR/tdnn_lstm_ctc/train.py index 23e224f76..4d45d197b 100755 --- a/egs/librispeech/ASR/tdnn_lstm_ctc/train.py +++ b/egs/librispeech/ASR/tdnn_lstm_ctc/train.py @@ -75,6 +75,23 @@ def get_parser(): help="Should various information be logged in tensorboard.", ) + parser.add_argument( + "--num-epochs", + type=int, + default=20, + 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 + tdnn_lstm_ctc/exp/epoch-{start_epoch-1}.pt + """, + ) + return parser @@ -104,11 +121,6 @@ def get_params() -> AttributeDict: - subsampling_factor: The subsampling factor for the model. - - start_epoch: If it is not zero, load checkpoint `start_epoch-1` - and continue training from that checkpoint. - - - num_epochs: Number of epochs to train. - - 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. @@ -127,6 +139,8 @@ def get_params() -> AttributeDict: - 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 - beam_size: It is used in k2.ctc_loss @@ -143,14 +157,13 @@ def get_params() -> AttributeDict: "feature_dim": 80, "weight_decay": 5e-4, "subsampling_factor": 3, - "start_epoch": 0, - "num_epochs": 10, "best_train_loss": float("inf"), "best_valid_loss": float("inf"), "best_train_epoch": -1, "best_valid_epoch": -1, "batch_idx_train": 0, "log_interval": 10, + "reset_interval": 200, "valid_interval": 1000, "beam_size": 10, "reduction": "sum", @@ -398,8 +411,12 @@ def train_one_epoch( """ model.train() - tot_loss = 0.0 # sum of losses over all batches - tot_frames = 0.0 # sum of frames over all batches + tot_loss = 0.0 # reset after params.reset_interval of batches + tot_frames = 0.0 # reset after params.reset_interval of batches + + params.tot_loss = 0.0 + params.tot_frames = 0.0 + for batch_idx, batch in enumerate(train_dl): params.batch_idx_train += 1 batch_size = len(batch["supervisions"]["text"]) @@ -426,6 +443,9 @@ def train_one_epoch( tot_loss += loss_cpu tot_avg_loss = tot_loss / tot_frames + params.tot_frames += params.train_frames + params.tot_loss += loss_cpu + if batch_idx % params.log_interval == 0: logging.info( f"Epoch {params.cur_epoch}, batch {batch_idx}, " @@ -433,6 +453,22 @@ def train_one_epoch( f"total avg loss: {tot_avg_loss:.4f}, " f"batch size: {batch_size}" ) + if tb_writer is not None: + tb_writer.add_scalar( + "train/current_loss", + loss_cpu / params.train_frames, + params.batch_idx_train, + ) + + tb_writer.add_scalar( + "train/tot_avg_loss", + tot_avg_loss, + params.batch_idx_train, + ) + + if batch_idx > 0 and batch_idx % params.reset_interval == 0: + tot_loss = 0 + tot_frames = 0 if batch_idx > 0 and batch_idx % params.valid_interval == 0: compute_validation_loss( @@ -449,7 +485,7 @@ def train_one_epoch( f"best valid epoch: {params.best_valid_epoch}" ) - params.train_loss = tot_loss / tot_frames + params.train_loss = params.tot_loss / params.tot_frames if params.train_loss < params.best_train_loss: params.best_train_epoch = params.cur_epoch diff --git a/egs/yesno/ASR/README.md b/egs/yesno/ASR/README.md index 653c576fa..6db2f782f 100644 --- a/egs/yesno/ASR/README.md +++ b/egs/yesno/ASR/README.md @@ -1,15 +1,14 @@ ## Yesno recipe -You can run the recipe with **CPU**. +This is the simplest ASR recipe in `icefall`. - -[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1tIjjzaJc3IvGyKiMCDWO-TSnBgkcuN3B?usp=sharing) - -The above Colab notebook finishes the training using **CPU** -within two minutes (50 epochs in total). - -The WER is +It can be run on CPU and takes less than 30 seconds to +get the following WER: ``` [test_set] %WER 0.42% [1 / 240, 0 ins, 1 del, 0 sub ] ``` + +Please refer to + +for detailed instructions. diff --git a/egs/yesno/ASR/tdnn/README.md b/egs/yesno/ASR/tdnn/README.md new file mode 100644 index 000000000..49722a779 --- /dev/null +++ b/egs/yesno/ASR/tdnn/README.md @@ -0,0 +1,8 @@ + +## How to run this recipe + +You can find detailed instructions by visiting + + +It describes how to run this recipe and how to use +a pre-trained model with `./pretrained.py`. diff --git a/icefall/decode.py b/icefall/decode.py index bcc869e99..de3219401 100644 --- a/icefall/decode.py +++ b/icefall/decode.py @@ -22,8 +22,6 @@ import kaldialign import torch import torch.nn as nn -from icefall.lexicon import Lexicon - def _get_random_paths( lattice: k2.Fsa, @@ -623,7 +621,7 @@ def nbest_oracle( lattice: k2.Fsa, num_paths: int, ref_texts: List[str], - lexicon: Lexicon, + word_table: k2.SymbolTable, scale: float = 1.0, ) -> Dict[str, List[List[int]]]: """Select the best hypothesis given a lattice and a reference transcript. @@ -644,8 +642,8 @@ def nbest_oracle( ref_texts: A list of reference transcript. Each entry contains space(s) separated words - lexicon: - It is used to convert word IDs to word symbols. + word_table: + It is the word symbol table. scale: It's the scale applied to the lattice.scores. A smaller value yields more unique paths. @@ -680,7 +678,7 @@ def nbest_oracle( best_hyp_words = None min_error = float("inf") for hyp_words in hyps: - hyp_words = [lexicon.word_table[i] for i in hyp_words] + hyp_words = [word_table[i] for i in hyp_words] this_error = kaldialign.edit_distance(ref_words, hyp_words)["total"] if this_error < min_error: min_error = this_error From f4223ee1109db2dbdd944a15fe9046579b587249 Mon Sep 17 00:00:00 2001 From: pkufool Date: Tue, 24 Aug 2021 21:09:27 +0800 Subject: [PATCH 06/10] Add TDNN-LSTM-CTC Results (#25) * Add tdnn-lstm pretrained model and results * Add docs for TDNN-LSTM-CTC * Minor fix * Fix typo * Fix style checking --- .flake8 | 1 + .../recipes/librispeech/tdnn_lstm_ctc.rst | 322 +++++++++++++++++- egs/librispeech/ASR/RESULTS.md | 25 +- egs/librispeech/ASR/conformer_ctc/README.md | 1 - egs/librispeech/ASR/conformer_ctc/decode.py | 2 +- .../ASR/tdnn_lstm_ctc/Pre-trained.md | 270 +++++++++++++++ egs/librispeech/ASR/tdnn_lstm_ctc/README.md | 4 +- egs/librispeech/ASR/tdnn_lstm_ctc/decode.py | 19 +- .../ASR/tdnn_lstm_ctc/pretrained.py | 277 +++++++++++++++ 9 files changed, 915 insertions(+), 6 deletions(-) create mode 100644 egs/librispeech/ASR/tdnn_lstm_ctc/Pre-trained.md create mode 100644 egs/librispeech/ASR/tdnn_lstm_ctc/pretrained.py diff --git a/.flake8 b/.flake8 index 3f1227b9b..b2bbb0a44 100644 --- a/.flake8 +++ b/.flake8 @@ -5,6 +5,7 @@ max-line-length = 80 per-file-ignores = # line too long egs/librispeech/ASR/conformer_ctc/conformer.py: E501, + egs/librispeech/ASR/conformer_ctc/decode.py: E501, exclude = .git, diff --git a/docs/source/recipes/librispeech/tdnn_lstm_ctc.rst b/docs/source/recipes/librispeech/tdnn_lstm_ctc.rst index 373bb5905..a59f34db7 100644 --- a/docs/source/recipes/librispeech/tdnn_lstm_ctc.rst +++ b/docs/source/recipes/librispeech/tdnn_lstm_ctc.rst @@ -1,2 +1,322 @@ -TDNN LSTM CTC +TDNN-LSTM-CTC ============= + +This tutorial shows you how to run a TDNN-LSTM-CTC model with the `LibriSpeech `_ dataset. + + +.. HINT:: + + We assume you have read the page :ref:`install icefall` and have setup + the environment for ``icefall``. + + +Data preparation +---------------- + +.. code-block:: bash + + $ cd egs/librispeech/ASR + $ ./prepare.sh + +The script ``./prepare.sh`` handles the data preparation for you, **automagically**. +All you need to do is to run it. + +The data preparation contains several stages, you can use the following two +options: + + - ``--stage`` + - ``--stop-stage`` + +to control which stage(s) should be run. By default, all stages are executed. + + +For example, + +.. code-block:: bash + + $ cd egs/librispeech/ASR + $ ./prepare.sh --stage 0 --stop-stage 0 + +means to run only stage 0. + +To run stage 2 to stage 5, use: + +.. code-block:: bash + + $ ./prepare.sh --stage 2 --stop-stage 5 + + +Training +-------- + +Now describing the training of TDNN-LSTM-CTC model, contained in +the `tdnn_lstm_ctc `_ +folder. + +The command to run the training part is: + +.. code-block:: bash + + $ cd egs/librispeech/ASR + $ export CUDA_VISIBLE_DEVICES="0,1,2,3" + $ ./tdnn_lstm_ctc/train.py --world-size 4 + +By default, it will run ``20`` epochs. Training logs and checkpoints are saved +in ``tdnn_lstm_ctc/exp``. + +In ``tdnn_lstm_ctc/exp``, you will find the following files: + + - ``epoch-0.pt``, ``epoch-1.pt``, ..., ``epoch-19.pt`` + + These are checkpoint files, containing model ``state_dict`` and optimizer ``state_dict``. + To resume training from some checkpoint, say ``epoch-10.pt``, you can use: + + .. code-block:: bash + + $ ./tdnn_lstm_ctc/train.py --start-epoch 11 + + - ``tensorboard/`` + + This folder contains TensorBoard logs. Training loss, validation loss, learning + rate, etc, are recorded in these logs. You can visualize them by: + + .. code-block:: bash + + $ cd tdnn_lstm_ctc/exp/tensorboard + $ tensorboard dev upload --logdir . --description "TDNN LSTM training for librispeech with icefall" + + - ``log/log-train-xxxx`` + + It is the detailed training log in text format, same as the one + you saw printed to the console during training. + + +To see available training options, you can use: + +.. code-block:: bash + + $ ./tdnn_lstm_ctc/train.py --help + +Other training options, e.g., learning rate, results dir, etc., are +pre-configured in the function ``get_params()`` +in `tdnn_lstm_ctc/train.py `_. +Normally, you don't need to change them. You can change them by modifying the code, if +you want. + +Decoding +-------- + +The decoding part uses checkpoints saved by the training part, so you have +to run the training part first. + +The command for decoding is: + +.. code-block:: bash + + $ export CUDA_VISIBLE_DEVICES="0" + $ ./tdnn_lstm_ctc/decode.py + +You will see the WER in the output log. + +Decoded results are saved in ``tdnn_lstm_ctc/exp``. + +.. code-block:: bash + + $ ./tdnn_lstm_ctc/decode.py --help + +shows you the available decoding options. + +Some commonly used options are: + + - ``--epoch`` + + You can select which checkpoint to be used for decoding. + For instance, ``./tdnn_lstm_ctc/decode.py --epoch 10`` means to use + ``./tdnn_lstm_ctc/exp/epoch-10.pt`` for decoding. + + - ``--avg`` + + It's related to model averaging. It specifies number of checkpoints + to be averaged. The averaged model is used for decoding. + For example, the following command: + + .. code-block:: bash + + $ ./tdnn_lstm_ctc/decode.py --epoch 10 --avg 3 + + uses the average of ``epoch-8.pt``, ``epoch-9.pt`` and ``epoch-10.pt`` + for decoding. + + - ``--export`` + + If it is ``True``, i.e., ``./tdnn_lstm_ctc/decode.py --export 1``, the code + will save the averaged model to ``tdnn_lstm_ctc/exp/pretrained.pt``. + See :ref:`tdnn_lstm_ctc use a pre-trained model` for how to use it. + +.. HINT:: + + There are several decoding methods provided in `tdnn_lstm_ctc/decode.py `_, you can change the decoding method by modifying ``method`` parameter in function ``get_params()``. + + +.. _tdnn_lstm_ctc use a pre-trained model: + +Pre-trained Model +----------------- + +We have uploaded the pre-trained model to +``_. + +The following shows you how to use the pre-trained model. + +Download the pre-trained model +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code-block:: bash + + $ cd egs/librispeech/ASR + $ mkdir tmp + $ cd tmp + $ git lfs install + $ git clone https://huggingface.co/pkufool/icefall_asr_librispeech_tdnn-lstm_ctc + +.. CAUTION:: + + You have to use ``git lfs`` to download the pre-trained model. + +After downloading, you will have the following files: + +.. code-block:: bash + + $ cd egs/librispeech/ASR + $ tree tmp + +.. code-block:: bash + + tmp/ + `-- icefall_asr_librispeech_tdnn-lstm_ctc + |-- README.md + |-- data + | |-- lang_phone + | | |-- HLG.pt + | | |-- tokens.txt + | | `-- words.txt + | `-- lm + | `-- G_4_gram.pt + |-- exp + | `-- pretrained.pt + `-- test_wavs + |-- 1089-134686-0001.flac + |-- 1221-135766-0001.flac + |-- 1221-135766-0002.flac + `-- trans.txt + + 6 directories, 10 files + + +Download kaldifeat +~~~~~~~~~~~~~~~~~~ + +`kaldifeat `_ is used for extracting +features from a single or multiple sound files. Please refer to +``_ to install ``kaldifeat`` first. + +Inference with a pre-trained model +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code-block:: bash + + $ cd egs/librispeech/ASR + $ ./tdnn_lstm_ctc/pretrained.py --help + +shows the usage information of ``./tdnn_lstm_ctc/pretrained.py``. + +To decode with ``1best`` method, we can use: + +.. code-block:: bash + + ./tdnn_lstm_ctc/pretrained.py \ + --checkpoint ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/exp/pretraind.pt \ + --words-file ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lang_phone/words.txt \ + --HLG ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lang_phone/HLG.pt \ + ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1089-134686-0001.flac \ + ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0001.flac \ + ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0002.flac + +The output is: + +.. code-block:: + + 2021-08-24 16:57:13,315 INFO [pretrained.py:168] device: cuda:0 + 2021-08-24 16:57:13,315 INFO [pretrained.py:170] Creating model + 2021-08-24 16:57:18,331 INFO [pretrained.py:182] Loading HLG from ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lang_phone/HLG.pt + 2021-08-24 16:57:27,581 INFO [pretrained.py:199] Constructing Fbank computer + 2021-08-24 16:57:27,584 INFO [pretrained.py:209] Reading sound files: ['./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1089-134686-0001.flac', './tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0001.flac', './tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0002.flac'] + 2021-08-24 16:57:27,599 INFO [pretrained.py:215] Decoding started + 2021-08-24 16:57:27,791 INFO [pretrained.py:245] Use HLG decoding + 2021-08-24 16:57:28,098 INFO [pretrained.py:266] + ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1089-134686-0001.flac: + AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD LIGHT UP HERE AND THERE THE SQUALID QUARTER OF THE BROTHELS + + ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0001.flac: + GOD AS A DIRECT CONSEQUENCE OF THE SIN WHICH MAN THUS PUNISHED HAD GIVEN HER A LOVELY CHILD WHOSE PLACE WAS ON THAT SAME DISHONORED BOSOM TO CONNECT HER PARENT FOREVER WITH THE RACE AND DESCENT OF MORTALS AND TO BE FINALLY A BLESSED SOUL IN HEAVEN + + ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0002.flac: + YET THESE THOUGHTS AFFECTED HESTER PRYNNE LESS WITH HOPE THAN APPREHENSION + + + 2021-08-24 16:57:28,099 INFO [pretrained.py:268] Decoding Done + + +To decode with ``whole-lattice-rescoring`` methond, you can use + +.. code-block:: bash + + ./tdnn_lstm_ctc/pretrained.py \ + --checkpoint ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/exp/pretraind.pt \ + --words-file ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lang_phone/words.txt \ + --HLG ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lang_phone/HLG.pt \ + --method whole-lattice-rescoring \ + --G ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lm/G_4_gram.pt \ + --ngram-lm-scale 0.8 \ + ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1089-134686-0001.flac \ + ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0001.flac \ + ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0002.flac + +The decoding output is: + +.. code-block:: + + 2021-08-24 16:39:24,725 INFO [pretrained.py:168] device: cuda:0 + 2021-08-24 16:39:24,725 INFO [pretrained.py:170] Creating model + 2021-08-24 16:39:29,403 INFO [pretrained.py:182] Loading HLG from ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lang_phone/HLG.pt + 2021-08-24 16:39:40,631 INFO [pretrained.py:190] Loading G from ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lm/G_4_gram.pt + 2021-08-24 16:39:53,098 INFO [pretrained.py:199] Constructing Fbank computer + 2021-08-24 16:39:53,107 INFO [pretrained.py:209] Reading sound files: ['./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1089-134686-0001.flac', './tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0001.flac', './tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0002.flac'] + 2021-08-24 16:39:53,121 INFO [pretrained.py:215] Decoding started + 2021-08-24 16:39:53,443 INFO [pretrained.py:250] Use HLG decoding + LM rescoring + 2021-08-24 16:39:54,010 INFO [pretrained.py:266] + ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1089-134686-0001.flac: + AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD LIGHT UP HERE AND THERE THE SQUALID QUARTER OF THE BROTHELS + + ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0001.flac: + GOD AS A DIRECT CONSEQUENCE OF THE SIN WHICH MAN THUS PUNISHED HAD GIVEN HER A LOVELY CHILD WHOSE PLACE WAS ON THAT SAME DISHONORED BOSOM TO CONNECT HER PARENT FOREVER WITH THE RACE AND DESCENT OF MORTALS AND TO BE FINALLY A BLESSED SOUL IN HEAVEN + + ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0002.flac: + YET THESE THOUGHTS AFFECTED HESTER PRYNNE LESS WITH HOPE THAN APPREHENSION + + + 2021-08-24 16:39:54,010 INFO [pretrained.py:268] Decoding Done + + +Colab notebook +-------------- + +We provide a colab notebook for decoding with pre-trained model. + +|librispeech tdnn_lstm_ctc colab notebook| + +.. |librispeech tdnn_lstm_ctc colab notebook| image:: https://colab.research.google.com/assets/colab-badge.svg + :target: https://colab.research.google.com/drive/1kNmDXNMwREi0rZGAOIAOJo93REBuOTcd + + +**Congratulations!** You have finished the TDNN-LSTM-CTC recipe on librispeech in ``icefall``. diff --git a/egs/librispeech/ASR/RESULTS.md b/egs/librispeech/ASR/RESULTS.md index 159147a3e..dfc412672 100644 --- a/egs/librispeech/ASR/RESULTS.md +++ b/egs/librispeech/ASR/RESULTS.md @@ -6,7 +6,7 @@ TensorBoard log is available at https://tensorboard.dev/experiment/GnRzq8WWQW62dK4bklXBTg/#scalars -Pretrained model is available at https://huggingface.co/pkufool/conformer_ctc +Pretrained model is available at https://huggingface.co/pkufool/icefall_asr_librispeech_conformer_ctc The best decoding results (WER) are listed below, we got this results by averaging models from epoch 15 to 34, and using `attention-decoder` decoder with num_paths equals to 100. @@ -21,3 +21,26 @@ To get more unique paths, we scaled the lattice.scores with 0.5 (see https://git |test-clean|1.3|1.2| |test-other|1.2|1.1| + +### LibriSpeech training results (Tdnn-Lstm) +#### 2021-08-24 + +(Wei Kang): Result of phone based Tdnn-Lstm model. + +Icefall version: https://github.com/k2-fsa/icefall/commit/caa0b9e9425af27e0c6211048acb55a76ed5d315 + +Pretrained model is available at https://huggingface.co/pkufool/icefall_asr_librispeech_tdnn-lstm_ctc + +The best decoding results (WER) are listed below, we got this results by averaging models from epoch 19 to 14, and using `whole-lattice-rescoring` decoding method. + +||test-clean|test-other| +|--|--|--| +|WER| 6.59% | 17.69% | + +We searched the lm_score_scale for best results, the scales that produced the WER above are also listed below. + +||lm_scale| +|--|--| +|test-clean|0.8| +|test-other|0.9| + diff --git a/egs/librispeech/ASR/conformer_ctc/README.md b/egs/librispeech/ASR/conformer_ctc/README.md index 0092fd14e..23b51167b 100644 --- a/egs/librispeech/ASR/conformer_ctc/README.md +++ b/egs/librispeech/ASR/conformer_ctc/README.md @@ -1,4 +1,3 @@ - Please visit for how to run this recipe. diff --git a/egs/librispeech/ASR/conformer_ctc/decode.py b/egs/librispeech/ASR/conformer_ctc/decode.py index 321ce970e..fc1d2bf2f 100755 --- a/egs/librispeech/ASR/conformer_ctc/decode.py +++ b/egs/librispeech/ASR/conformer_ctc/decode.py @@ -83,7 +83,7 @@ def get_parser(): - (3) nbest-rescoring. Extract n paths from the decoding lattice, rescore them with an n-gram LM (e.g., a 4-gram LM), the path with the highest score is the decoding result. - - (4) whole-lattice. Rescore the decoding lattice with an n-gram LM + - (4) whole-lattice-rescoring. Rescore the decoding lattice with an n-gram LM (e.g., a 4-gram LM), the best path of rescored lattice is the decoding result. - (5) attention-decoder. Extract n paths from the LM rescored lattice, diff --git a/egs/librispeech/ASR/tdnn_lstm_ctc/Pre-trained.md b/egs/librispeech/ASR/tdnn_lstm_ctc/Pre-trained.md new file mode 100644 index 000000000..83e98b37c --- /dev/null +++ b/egs/librispeech/ASR/tdnn_lstm_ctc/Pre-trained.md @@ -0,0 +1,270 @@ + +# How to use a pre-trained model to transcribe a sound file or multiple sound files + +(See the bottom of this document for the link to a colab notebook.) + +You need to prepare 4 files: + + - a model checkpoint file, e.g., epoch-20.pt + - HLG.pt, the decoding graph + - words.txt, the word symbol table + - a sound file, whose sampling rate has to be 16 kHz. + Supported formats are those supported by `torchaudio.load()`, + e.g., wav and flac. + +Also, you need to install `kaldifeat`. Please refer to + for installation. + +```bash +./tdnn_lstm_ctc/pretrained.py --help +``` + +displays the help information. + +## HLG decoding + +Once you have the above files ready and have `kaldifeat` installed, +you can run: + +```bash +./tdnn_lstm_ctc/pretrained.py \ + --checkpoint /path/to/your/checkpoint.pt \ + --words-file /path/to/words.txt \ + --HLG /path/to/HLG.pt \ + /path/to/your/sound.wav +``` + +and you will see the transcribed result. + +If you want to transcribe multiple files at the same time, you can use: + +```bash +./tdnn_lstm_ctc/pretrained.py \ + --checkpoint /path/to/your/checkpoint.pt \ + --words-file /path/to/words.txt \ + --HLG /path/to/HLG.pt \ + /path/to/your/sound1.wav \ + /path/to/your/sound2.wav \ + /path/to/your/sound3.wav +``` + +**Note**: This is the fastest decoding method. + +## HLG decoding + LM rescoring + +`./tdnn_lstm_ctc/pretrained.py` also supports `whole lattice LM rescoring`. + +To use whole lattice LM rescoring, you also need the following files: + + - G.pt, e.g., `data/lm/G_4_gram.pt` if you have run `./prepare.sh` + +The command to run decoding with LM rescoring is: + +```bash +./tdnn_lstm_ctc/pretrained.py \ + --checkpoint /path/to/your/checkpoint.pt \ + --words-file /path/to/words.txt \ + --HLG /path/to/HLG.pt \ + --method whole-lattice-rescoring \ + --G data/lm/G_4_gram.pt \ + --ngram-lm-scale 0.8 \ + /path/to/your/sound1.wav \ + /path/to/your/sound2.wav \ + /path/to/your/sound3.wav +``` + +# Decoding with a pre-trained model in action + +We have uploaded a pre-trained model to + +The following shows the steps about the usage of the provided pre-trained model. + +### (1) Download the pre-trained model + +```bash +sudo apt-get install git-lfs +cd /path/to/icefall/egs/librispeech/ASR +git lfs install +mkdir tmp +cd tmp +git clone https://huggingface.co/pkufool/icefall_asr_librispeech_tdnn-lstm_ctc +``` + +**CAUTION**: You have to install `git-lfs` to download the pre-trained model. + +You will find the following files: + +``` +tmp/ +`-- icefall_asr_librispeech_tdnn-lstm_ctc + |-- README.md + |-- data + | |-- lang_phone + | | |-- HLG.pt + | | |-- tokens.txt + | | `-- words.txt + | `-- lm + | `-- G_4_gram.pt + |-- exp + | `-- pretrained.pt + `-- test_wavs + |-- 1089-134686-0001.flac + |-- 1221-135766-0001.flac + |-- 1221-135766-0002.flac + `-- trans.txt + +6 directories, 10 files +``` + +**File descriptions**: + + - `data/lang_phone/HLG.pt` + + It is the decoding graph. + + - `data/lang_phone/tokens.txt` + + It contains tokens and their IDs. + + - `data/lang_phone/words.txt` + + It contains words and their IDs. + + - `data/lm/G_4_gram.pt` + + It is a 4-gram LM, useful for LM rescoring. + + - `exp/pretrained.pt` + + It contains pre-trained model parameters, obtained by averaging + checkpoints from `epoch-14.pt` to `epoch-19.pt`. + Note: We have removed optimizer `state_dict` to reduce file size. + + - `test_waves/*.flac` + + It contains some test sound files from LibriSpeech `test-clean` dataset. + + - `test_waves/trans.txt` + + It contains the reference transcripts for the sound files in `test_waves/`. + +The information of the test sound files is listed below: + +``` +$ soxi tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/*.flac + +Input File : 'tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1089-134686-0001.flac' +Channels : 1 +Sample Rate : 16000 +Precision : 16-bit +Duration : 00:00:06.62 = 106000 samples ~ 496.875 CDDA sectors +File Size : 116k +Bit Rate : 140k +Sample Encoding: 16-bit FLAC + + +Input File : 'tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0001.flac' +Channels : 1 +Sample Rate : 16000 +Precision : 16-bit +Duration : 00:00:16.71 = 267440 samples ~ 1253.62 CDDA sectors +File Size : 343k +Bit Rate : 164k +Sample Encoding: 16-bit FLAC + + +Input File : 'tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0002.flac' +Channels : 1 +Sample Rate : 16000 +Precision : 16-bit +Duration : 00:00:04.83 = 77200 samples ~ 361.875 CDDA sectors +File Size : 105k +Bit Rate : 174k +Sample Encoding: 16-bit FLAC + +Total Duration of 3 files: 00:00:28.16 +``` + +### (2) Use HLG decoding + +```bash +cd /path/to/icefall/egs/librispeech/ASR + +./tdnn_lstm_ctc/pretrained.py \ + --checkpoint ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/exp/pretraind.pt \ + --words-file ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lang_phone/words.txt \ + --HLG ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lang_phone/HLG.pt \ + ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1089-134686-0001.flac \ + ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0001.flac \ + ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0002.flac +``` + +The output is given below: + +``` +2021-08-24 16:57:13,315 INFO [pretrained.py:168] device: cuda:0 +2021-08-24 16:57:13,315 INFO [pretrained.py:170] Creating model +2021-08-24 16:57:18,331 INFO [pretrained.py:182] Loading HLG from ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lang_phone/HLG.pt +2021-08-24 16:57:27,581 INFO [pretrained.py:199] Constructing Fbank computer +2021-08-24 16:57:27,584 INFO [pretrained.py:209] Reading sound files: ['./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1089-134686-0001.flac', './tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0001.flac', './tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0002.flac'] +2021-08-24 16:57:27,599 INFO [pretrained.py:215] Decoding started +2021-08-24 16:57:27,791 INFO [pretrained.py:245] Use HLG decoding +2021-08-24 16:57:28,098 INFO [pretrained.py:266] +./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1089-134686-0001.flac: +AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD LIGHT UP HERE AND THERE THE SQUALID QUARTER OF THE BROTHELS + +./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0001.flac: +GOD AS A DIRECT CONSEQUENCE OF THE SIN WHICH MAN THUS PUNISHED HAD GIVEN HER A LOVELY CHILD WHOSE PLACE WAS ON THAT SAME DISHONORED BOSOM TO CONNECT HER PARENT FOREVER WITH THE RACE AND DESCENT OF MORTALS AND TO BE FINALLY A BLESSED SOUL IN HEAVEN + +./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0002.flac: +YET THESE THOUGHTS AFFECTED HESTER PRYNNE LESS WITH HOPE THAN APPREHENSION + + +2021-08-24 16:57:28,099 INFO [pretrained.py:268] Decoding Done +``` + +### (3) Use HLG decoding + LM rescoring + +```bash +./tdnn_lstm_ctc/pretrained.py \ + --checkpoint ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/exp/pretraind.pt \ + --words-file ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lang_phone/words.txt \ + --HLG ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lang_phone/HLG.pt \ + --method whole-lattice-rescoring \ + --G ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lm/G_4_gram.pt \ + --ngram-lm-scale 0.8 \ + ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1089-134686-0001.flac \ + ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0001.flac \ + ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0002.flac +``` + +The output is: + +``` +2021-08-24 16:39:24,725 INFO [pretrained.py:168] device: cuda:0 +2021-08-24 16:39:24,725 INFO [pretrained.py:170] Creating model +2021-08-24 16:39:29,403 INFO [pretrained.py:182] Loading HLG from ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lang_phone/HLG.pt +2021-08-24 16:39:40,631 INFO [pretrained.py:190] Loading G from ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lm/G_4_gram.pt +2021-08-24 16:39:53,098 INFO [pretrained.py:199] Constructing Fbank computer +2021-08-24 16:39:53,107 INFO [pretrained.py:209] Reading sound files: ['./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1089-134686-0001.flac', './tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0001.flac', './tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0002.flac'] +2021-08-24 16:39:53,121 INFO [pretrained.py:215] Decoding started +2021-08-24 16:39:53,443 INFO [pretrained.py:250] Use HLG decoding + LM rescoring +2021-08-24 16:39:54,010 INFO [pretrained.py:266] +./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1089-134686-0001.flac: +AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD LIGHT UP HERE AND THERE THE SQUALID QUARTER OF THE BROTHELS + +./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0001.flac: +GOD AS A DIRECT CONSEQUENCE OF THE SIN WHICH MAN THUS PUNISHED HAD GIVEN HER A LOVELY CHILD WHOSE PLACE WAS ON THAT SAME DISHONORED BOSOM TO CONNECT HER PARENT FOREVER WITH THE RACE AND DESCENT OF MORTALS AND TO BE FINALLY A BLESSED SOUL IN HEAVEN + +./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0002.flac: +YET THESE THOUGHTS AFFECTED HESTER PRYNNE LESS WITH HOPE THAN APPREHENSION + + +2021-08-24 16:39:54,010 INFO [pretrained.py:268] Decoding Done +``` + +**NOTE**: We provide a colab notebook for demonstration. +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1kNmDXNMwREi0rZGAOIAOJo93REBuOTcd?usp=sharing) + +Due to limited memory provided by Colab, you have to upgrade to Colab Pro to run `HLG decoding + LM rescoring`. +Otherwise, you can only run `HLG decoding` with Colab. diff --git a/egs/librispeech/ASR/tdnn_lstm_ctc/README.md b/egs/librispeech/ASR/tdnn_lstm_ctc/README.md index df98a0e11..94d4ed6a3 100644 --- a/egs/librispeech/ASR/tdnn_lstm_ctc/README.md +++ b/egs/librispeech/ASR/tdnn_lstm_ctc/README.md @@ -1,2 +1,4 @@ -Will add results later. +Please visit + +for how to run this recipe. diff --git a/egs/librispeech/ASR/tdnn_lstm_ctc/decode.py b/egs/librispeech/ASR/tdnn_lstm_ctc/decode.py index 27e0b9643..26946e0d0 100755 --- a/egs/librispeech/ASR/tdnn_lstm_ctc/decode.py +++ b/egs/librispeech/ASR/tdnn_lstm_ctc/decode.py @@ -43,6 +43,7 @@ from icefall.utils import ( setup_logger, store_transcripts, write_error_stats, + str2bool, ) @@ -54,7 +55,7 @@ def get_parser(): parser.add_argument( "--epoch", type=int, - default=9, + default=19, help="It specifies the checkpoint to use for decoding." "Note: Epoch counts from 0.", ) @@ -66,6 +67,16 @@ def get_parser(): "consecutive checkpoints before the checkpoint specified by " "'--epoch'. ", ) + parser.add_argument( + "--export", + type=str2bool, + default=False, + help="""When enabled, the averaged model is saved to + tdnn/exp/pretrained.pt. Note: only model.state_dict() is saved. + pretrained.pt contains a dict {"model": model.state_dict()}, + which can be loaded by `icefall.checkpoint.load_checkpoint()`. + """, + ) return parser @@ -408,6 +419,12 @@ def main(): logging.info(f"averaging {filenames}") model.load_state_dict(average_checkpoints(filenames)) + if params.export: + logging.info(f"Export averaged model to {params.exp_dir}/pretrained.pt") + torch.save( + {"model": model.state_dict()}, f"{params.exp_dir}/pretrained.pt" + ) + model.to(device) model.eval() diff --git a/egs/librispeech/ASR/tdnn_lstm_ctc/pretrained.py b/egs/librispeech/ASR/tdnn_lstm_ctc/pretrained.py new file mode 100644 index 000000000..4f82a989c --- /dev/null +++ b/egs/librispeech/ASR/tdnn_lstm_ctc/pretrained.py @@ -0,0 +1,277 @@ +#!/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. + + +import argparse +import logging +import math +from typing import List + +import k2 +import kaldifeat +import torch +import torchaudio +from model import TdnnLstm +from torch.nn.utils.rnn import pad_sequence + +from icefall.decode import ( + get_lattice, + one_best_decoding, + rescore_with_whole_lattice, +) +from icefall.utils import AttributeDict, get_texts + + +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( + "--words-file", + type=str, + required=True, + help="Path to words.txt", + ) + + parser.add_argument( + "--HLG", type=str, required=True, help="Path to HLG.pt." + ) + + parser.add_argument( + "--method", + type=str, + default="1best", + help="""Decoding method. + Possible values are: + (1) 1best - Use the best path as decoding output. Only + the transformer encoder output is used for decoding. + We call it HLG decoding. + (2) whole-lattice-rescoring - Use an LM to rescore the + decoding lattice and then use 1best to decode the + rescored lattice. + We call it HLG decoding + n-gram LM rescoring. + """, + ) + + parser.add_argument( + "--G", + type=str, + help="""An LM for rescoring. + Used only when method is + whole-lattice-rescoring. + It's usually a 4-gram LM. + """, + ) + + parser.add_argument( + "--ngram-lm-scale", + type=float, + default=0.8, + help=""" + Used only when method is whole-lattice-rescoring. + It specifies the scale for n-gram LM scores. + (Note: You need to tune it on a dataset.) + """, + ) + + 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.", + ) + + return parser + + +def get_params() -> AttributeDict: + params = AttributeDict( + { + "feature_dim": 80, + "subsampling_factor": 3, + "num_classes": 72, + "sample_rate": 16000, + "search_beam": 20, + "output_beam": 5, + "min_active_states": 30, + "max_active_states": 10000, + "use_double_scores": True, + } + ) + return params + + +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 + + +def main(): + parser = get_parser() + args = parser.parse_args() + + params = get_params() + params.update(vars(args)) + 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 = TdnnLstm( + num_features=params.feature_dim, + num_classes=params.num_classes, + subsampling_factor=params.subsampling_factor, + ) + + checkpoint = torch.load(args.checkpoint, map_location="cpu") + model.load_state_dict(checkpoint["model"]) + model.to(device) + model.eval() + + logging.info(f"Loading HLG from {params.HLG}") + HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu")) + HLG = HLG.to(device) + if not hasattr(HLG, "lm_scores"): + # For whole-lattice-rescoring and attention-decoder + HLG.lm_scores = HLG.scores.clone() + + if params.method == "whole-lattice-rescoring": + logging.info(f"Loading G from {params.G}") + G = k2.Fsa.from_dict(torch.load(params.G, map_location="cpu")) + # Add epsilon self-loops to G as we will compose + # it with the whole lattice later + G = G.to(device) + G = k2.add_epsilon_self_loops(G) + G = k2.arc_sort(G) + G.lm_scores = G.scores.clone() + + 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) + + features = pad_sequence( + features, batch_first=True, padding_value=math.log(1e-10) + ) + features = features.permute(0, 2, 1) # now features is [N, C, T] + + with torch.no_grad(): + nnet_output = model(features) + # nnet_output is [N, T, C] + + batch_size = nnet_output.shape[0] + supervision_segments = torch.tensor( + [[i, 0, nnet_output.shape[1]] for i in range(batch_size)], + dtype=torch.int32, + ) + + lattice = get_lattice( + nnet_output=nnet_output, + HLG=HLG, + supervision_segments=supervision_segments, + search_beam=params.search_beam, + output_beam=params.output_beam, + min_active_states=params.min_active_states, + max_active_states=params.max_active_states, + subsampling_factor=params.subsampling_factor, + ) + + if params.method == "1best": + logging.info("Use HLG decoding") + best_path = one_best_decoding( + lattice=lattice, use_double_scores=params.use_double_scores + ) + elif params.method == "whole-lattice-rescoring": + logging.info("Use HLG decoding + LM rescoring") + best_path_dict = rescore_with_whole_lattice( + lattice=lattice, + G_with_epsilon_loops=G, + lm_scale_list=[params.ngram_lm_scale], + ) + best_path = next(iter(best_path_dict.values())) + + hyps = get_texts(best_path) + word_sym_table = k2.SymbolTable.from_file(params.words_file) + hyps = [[word_sym_table[i] for i in ids] for ids in hyps] + + 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() From 96e7f5c7ea543d40b73ff773cb776b6f1178c503 Mon Sep 17 00:00:00 2001 From: Fangjun Kuang Date: Tue, 24 Aug 2021 21:30:30 +0800 Subject: [PATCH 07/10] Release v0.1 (#26) --- README.md | 107 ++++++++++++++-------------------- egs/librispeech/ASR/README.md | 65 +-------------------- 2 files changed, 46 insertions(+), 126 deletions(-) diff --git a/README.md b/README.md index 0a9b657b3..dc03c5883 100644 --- a/README.md +++ b/README.md @@ -1,80 +1,61 @@ - -# Table of Contents - -- [Installation](#installation) - * [Install k2](#install-k2) - * [Install lhotse](#install-lhotse) - * [Install icefall](#install-icefall) -- [Run recipes](#run-recipes) +

    ## Installation -`icefall` depends on [k2][k2] for FSA operations and [lhotse][lhotse] for -data preparations. To use `icefall`, you have to install its dependencies first. -The following subsections describe how to setup the environment. - -CAUTION: There are various ways to setup the environment. What we describe -here is just one alternative. - -### Install k2 - -Please refer to [k2's installation documentation][k2-install] to install k2. -If you have any issues about installing k2, please open an issue at -. - -### Install lhotse - -Please refer to [lhotse's installation documentation][lhotse-install] to install -lhotse. - -### Install icefall - -`icefall` is a set of Python scripts. What you need to do is just to set -the environment variable `PYTHONPATH`: - -```bash -cd $HOME/open-source -git clone https://github.com/k2-fsa/icefall -cd icefall -pip install -r requirements.txt -export PYTHONPATH=$HOME/open-source/icefall:$PYTHONPATHON -``` - -To verify `icefall` was installed successfully, you can run: - -```bash -python3 -c "import icefall; print(icefall.__file__)" -``` - -It should print the path to `icefall`. +Please refer to +for installation. ## Recipes -At present, two recipes are provided: +Please refer to +for more information. - - [LibriSpeech][LibriSpeech] - - [yesno][yesno] [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1tIjjzaJc3IvGyKiMCDWO-TSnBgkcuN3B?usp=sharing) +We provide two recipes at present: -### Yesno + - [yesno][yesno] + - [LibriSpeech][librispeech] -For the yesno recipe, training with 50 epochs takes less than 2 minutes using **CPU**. +### yesno -The WER is +This is the simplest ASR recipe in `icefall` and can be run on CPU. +Training takes less than 30 seconds and gives you the following WER: ``` [test_set] %WER 0.42% [1 / 240, 0 ins, 1 del, 0 sub ] ``` +We do provide a Colab notebook for this recipe. -## Use Pre-trained models - -See [egs/librispeech/ASR/conformer_ctc/README.md](egs/librispeech/ASR/conformer_ctc/README.md) -for how to use pre-trained models. -[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1huyupXAcHsUrKaWfI83iMEJ6J0Nh0213?usp=sharing) +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1tIjjzaJc3IvGyKiMCDWO-TSnBgkcuN3B?usp=sharing) -[yesno]: egs/yesno/ASR/README.md -[LibriSpeech]: egs/librispeech/ASR/README.md -[k2-install]: https://k2.readthedocs.io/en/latest/installation/index.html# -[k2]: https://github.com/k2-fsa/k2 -[lhotse]: https://github.com/lhotse-speech/lhotse -[lhotse-install]: https://lhotse.readthedocs.io/en/latest/getting-started.html#installation +### LibriSpeech + +We provide two models for this recipe: [conformer CTC model][LibriSpeech_conformer_ctc] +and [TDNN LSTM CTC model][LibriSpeech_tdnn_lstm_ctc]. + +#### Conformer CTC Model + +The best WER we currently have is: + +||test-clean|test-other| +|--|--|--| +|WER| 2.57% | 5.94% | + +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/1huyupXAcHsUrKaWfI83iMEJ6J0Nh0213?usp=sharing) + +#### TDNN LSTM CTC Model + +The WER for this model is: + +||test-clean|test-other| +|--|--|--| +|WER| 6.59% | 17.69% | + +We provide a Colab notebook to run a pre-trained TDNN LSTM CTC model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1kNmDXNMwREi0rZGAOIAOJo93REBuOTcd?usp=sharing) + +[LibriSpeech_tdnn_lstm_ctc]: egs/librispeech/ASR/tdnn_lstm_ctc +[LibriSpeech_conformer_ctc]: egs/librispeech/ASR/conformer_ctc +[yesno]: egs/yesno/ASR +[librispeech]: egs/librispeech/ASR diff --git a/egs/librispeech/ASR/README.md b/egs/librispeech/ASR/README.md index 30778ed05..ae0c2684d 100644 --- a/egs/librispeech/ASR/README.md +++ b/egs/librispeech/ASR/README.md @@ -1,64 +1,3 @@ -## Data preparation - -If you want to use `./prepare.sh` to download everything for you, -you can just run - -``` -./prepare.sh -``` - -If you have pre-downloaded the LibriSpeech dataset, please -read `./prepare.sh` and modify it to point to the location -of your dataset so that it won't re-download it. After modification, -please run - -``` -./prepare.sh -``` - -The script `./prepare.sh` prepares features, lexicon, LMs, etc. -All generated files are saved in the folder `./data`. - -**HINT:** `./prepare.sh` supports options `--stage` and `--stop-stage`. - -## TDNN-LSTM CTC training - -The folder `tdnn_lstm_ctc` contains scripts for CTC training -with TDNN-LSTM models. - -Pre-configured parameters for training and decoding are set in the function -`get_params()` within `tdnn_lstm_ctc/train.py` -and `tdnn_lstm_ctc/decode.py`. - -Parameters that can be passed from the command-line can be found by - -``` -./tdnn_lstm_ctc/train.py --help -./tdnn_lstm_ctc/decode.py --help -``` - -If you have 4 GPUs on a machine and want to use GPU 0, 2, 3 for -mutli-GPU training, you can run - -``` -export CUDA_VISIBLE_DEVICES="0,2,3" -./tdnn_lstm_ctc/train.py \ - --master-port 12345 \ - --world-size 3 -``` - -If you want to decode by averaging checkpoints `epoch-8.pt`, -`epoch-9.pt` and `epoch-10.pt`, you can run - -``` -./tdnn_lstm_ctc/decode.py \ - --epoch 10 \ - --avg 3 -``` - -## Conformer CTC training - -The folder `conformer-ctc` contains scripts for CTC training -with conformer models. The steps of running the training and -decoding are similar to `tdnn_lstm_ctc`. +Please refer to +for how to run models in this recipe. From 184dbb3ea5620d10ffd74bd7b43c919defc6754a Mon Sep 17 00:00:00 2001 From: Fangjun Kuang Date: Wed, 25 Aug 2021 14:48:41 +0800 Subject: [PATCH 08/10] Add documentation about code style and creating new recipes. (#27) --- .flake8 | 1 - .github/workflows/style_check.yml | 2 +- docs/requirements.txt | 1 + docs/source/contributing/code-style.rst | 67 ++++++++ docs/source/contributing/doc.rst | 45 +++++ .../contributing/how-to-create-a-recipe.rst | 156 ++++++++++++++++++ .../contributing/images/doc-contrib.png | Bin 0 -> 203222 bytes .../images/pre-commit-check-success.png | Bin 0 -> 156261 bytes .../contributing/images/pre-commit-check.png | Bin 0 -> 218729 bytes docs/source/contributing/index.rst | 22 +++ docs/source/index.rst | 1 + docs/source/installation/index.rst | 16 -- egs/librispeech/ASR/conformer_ctc/decode.py | 14 +- egs/librispeech/ASR/tdnn_lstm_ctc/decode.py | 4 +- egs/yesno/ASR/README.md | 2 +- egs/yesno/ASR/tdnn/README.md | 2 +- 16 files changed, 304 insertions(+), 29 deletions(-) create mode 100644 docs/source/contributing/code-style.rst create mode 100644 docs/source/contributing/doc.rst create mode 100644 docs/source/contributing/how-to-create-a-recipe.rst create mode 100644 docs/source/contributing/images/doc-contrib.png create mode 100644 docs/source/contributing/images/pre-commit-check-success.png create mode 100644 docs/source/contributing/images/pre-commit-check.png create mode 100644 docs/source/contributing/index.rst diff --git a/.flake8 b/.flake8 index b2bbb0a44..3f1227b9b 100644 --- a/.flake8 +++ b/.flake8 @@ -5,7 +5,6 @@ max-line-length = 80 per-file-ignores = # line too long egs/librispeech/ASR/conformer_ctc/conformer.py: E501, - egs/librispeech/ASR/conformer_ctc/decode.py: E501, exclude = .git, diff --git a/.github/workflows/style_check.yml b/.github/workflows/style_check.yml index 20c3363b4..2a743705a 100644 --- a/.github/workflows/style_check.yml +++ b/.github/workflows/style_check.yml @@ -45,7 +45,7 @@ jobs: - name: Install Python dependencies run: | - python3 -m pip install --upgrade pip black flake8 + python3 -m pip install --upgrade pip black==21.6b0 flake8==3.9.2 - name: Run flake8 shell: bash diff --git a/docs/requirements.txt b/docs/requirements.txt index 483a4e960..74640391e 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1 +1,2 @@ sphinx_rtd_theme +sphinx diff --git a/docs/source/contributing/code-style.rst b/docs/source/contributing/code-style.rst new file mode 100644 index 000000000..7d61a3ba1 --- /dev/null +++ b/docs/source/contributing/code-style.rst @@ -0,0 +1,67 @@ +.. _follow the code style: + +Follow the code style +===================== + +We use the following tools to make the code style to be as consistent as possible: + + - `black `_, to format the code + - `flake8 `_, to check the style and quality of the code + - `isort `_, to sort ``imports`` + +The following versions of the above tools are used: + + - ``black == 12.6b0`` + - ``flake8 == 3.9.2`` + - ``isort == 5.9.2`` + +After running the following commands: + + .. code-block:: + + $ git clone https://github.com/k2-fsa/icefall + $ cd icefall + $ pip install pre-commit + $ pre-commit install + +it will run the following checks whenever you run ``git commit``, **automatically**: + + .. figure:: images/pre-commit-check.png + :width: 600 + :align: center + + pre-commit hooks invoked by ``git commit`` (Failed). + +If any of the above checks failed, your ``git commit`` was not successful. +Please fix any issues reported by the check tools. + +.. HINT:: + + Some of the check tools, i.e., ``black`` and ``isort`` will modify + the files to be commited **in-place**. So please run ``git status`` + after failure to see which file has been modified by the tools + before you make any further changes. + +After fixing all the failures, run ``git commit`` again and +it should succeed this time: + + .. figure:: images/pre-commit-check-success.png + :width: 600 + :align: center + + pre-commit hooks invoked by ``git commit`` (Succeeded). + +If you want to check the style of your code before ``git commit``, you +can do the following: + + .. code-block:: bash + + $ cd icefall + $ pip install black==21.6b0 flake8==3.9.2 isort==5.9.2 + $ black --check your_changed_file.py + $ black your_changed_file.py # modify it in-place + $ + $ flake8 your_changed_file.py + $ + $ isort --check your_changed_file.py # modify it in-place + $ isort your_changed_file.py diff --git a/docs/source/contributing/doc.rst b/docs/source/contributing/doc.rst new file mode 100644 index 000000000..893d8a15e --- /dev/null +++ b/docs/source/contributing/doc.rst @@ -0,0 +1,45 @@ +Contributing to Documentation +============================= + +We use `sphinx `_ +for documentation. + +Before writing documentation, you have to prepare the environment: + + .. code-block:: bash + + $ cd docs + $ pip install -r requirements.txt + +After setting up the environment, you are ready to write documentation. +Please refer to `reStructuredText Primer `_ +if you are not familiar with ``reStructuredText``. + +After writing some documentation, you can build the documentation **locally** +to preview what it looks like if it is published: + + .. code-block:: bash + + $ cd docs + $ make html + +The generated documentation is in ``docs/build/html`` and can be viewed +with the following commands: + + .. code-block:: bash + + $ cd docs/build/html + $ python3 -m http.server + +It will print:: + + Serving HTTP on 0.0.0.0 port 8000 (http://0.0.0.0:8000/) ... + +Open your browser, go to ``_, and you will see +the following: + + .. figure:: images/doc-contrib.png + :width: 600 + :align: center + + View generated documentation locally with ``python3 -m http.server``. diff --git a/docs/source/contributing/how-to-create-a-recipe.rst b/docs/source/contributing/how-to-create-a-recipe.rst new file mode 100644 index 000000000..2d53fd89f --- /dev/null +++ b/docs/source/contributing/how-to-create-a-recipe.rst @@ -0,0 +1,156 @@ +How to create a recipe +====================== + +.. HINT:: + + Please read :ref:`follow the code style` to adjust your code sytle. + +.. CAUTION:: + + ``icefall`` is designed to be as Pythonic as possible. Please use + Python in your recipe if possible. + +Data Preparation +---------------- + +We recommend you to prepare your training/test/validate dataset +with `lhotse `_. + +Please refer to ``_ +for how to create a recipe in ``lhotse``. + +.. HINT:: + + The ``yesno`` recipe in ``lhotse`` is a very good example. + + Please refer to ``_, + which shows how to add a new recipe to ``lhotse``. + +Suppose you would like to add a recipe for a dataset named ``foo``. +You can do the following: + +.. code-block:: + + $ cd egs + $ mkdir -p foo/ASR + $ cd foo/ASR + $ touch prepare.sh + $ chmod +x prepare.sh + +If your dataset is very simple, please follow +`egs/yesno/ASR/prepare.sh `_ +to write your own ``prepare.sh``. +Otherwise, please refer to +`egs/librispeech/ASR/prepare.sh `_ +to prepare your data. + + +Training +-------- + +Assume you have a fancy model, called ``bar`` for the ``foo`` recipe, you can +organize your files in the following way: + +.. code-block:: + + $ cd egs/foo/ASR + $ mkdir bar + $ cd bar + $ tourch README.md model.py train.py decode.py asr_datamodule.py pretrained.py + +For instance , the ``yesno`` recipe has a ``tdnn`` model and its directory structure +looks like the following: + +.. code-block:: bash + + egs/yesno/ASR/tdnn/ + |-- README.md + |-- asr_datamodule.py + |-- decode.py + |-- model.py + |-- pretrained.py + `-- train.py + +**File description**: + + - ``README.md`` + + It contains information of this recipe, e.g., how to run it, what the WER is, etc. + + - ``asr_datamodule.py`` + + It provides code to create PyTorch dataloaders with train/test/validation dataset. + + - ``decode.py`` + + It takes as inputs the checkpoints saved during the training stage to decode the test + dataset(s). + + - ``model.py`` + + It contains the definition of your fancy neural network model. + + - ``pretrained.py`` + + We can use this script to do inference with a pre-trained model. + + - ``train.py`` + + It contains training code. + + +.. HINT:: + + Please take a look at + + - `egs/yesno/tdnn `_ + - `egs/librispeech/tdnn_lstm_ctc `_ + - `egs/librispeech/conformer_ctc `_ + + to get a feel what the resulting files look like. + +.. NOTE:: + + Every model in a recipe is kept to be as self-contained as possible. + We tolerate duplicate code among different recipes. + + +The training stage should be invocable by: + + .. code-block:: + + $ cd egs/foo/ASR + $ ./bar/train.py + $ ./bar/train.py --help + + +Decoding +-------- + +Please refer to + + - ``_ + + If your model is transformer/conformer based. + + - ``_ + + If your model is TDNN/LSTM based, i.e., there is no attention decoder. + + - ``_ + + If there is no LM rescoring. + +The decoding stage should be invocable by: + + .. code-block:: + + $ cd egs/foo/ASR + $ ./bar/decode.py + $ ./bar/decode.py --help + +Pre-trained model +----------------- + +Please demonstrate how to use your model for inference in ``egs/foo/ASR/bar/pretrained.py``. +If possible, please consider creating a Colab notebook to show that. diff --git a/docs/source/contributing/images/doc-contrib.png b/docs/source/contributing/images/doc-contrib.png new file mode 100644 index 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installation/index recipes/index + contributing/index diff --git a/docs/source/installation/index.rst b/docs/source/installation/index.rst index 6edea12d8..e8dc9b461 100644 --- a/docs/source/installation/index.rst +++ b/docs/source/installation/index.rst @@ -202,22 +202,6 @@ The following shows an example about setting up the environment. valtree-3.1.0 lhotse-0.8.0.dev-2a1410b-clean lilcom-1.1.1 numpy-1.21.2 packaging-21.0 pycparser-2.20 pyparsing-2.4.7 pyyaml-5.4.1 sor tedcontainers-2.4.0 toolz-0.11.1 torchaudio-0.9.0 tqdm-4.62.1 -**NOTE**: After installing ``lhotse``, you will encounter the following error: - -.. code-block:: - - $ lhotse download --help - -bash: /ceph-fj/fangjun/test-icefall/bin/lhotse: python: bad interpreter: No such file or directory - -The correct fix is: - -.. code-block:: - - echo '#!/usr/bin/env python3' | cat - $(which lhotse) > /tmp/lhotse-bin - chmod +x /tmp/lhotse-bin - mv /tmp/lhotse-bin $(which lhotse) - - (5) Download icefall ~~~~~~~~~~~~~~~~~~~~ diff --git a/egs/librispeech/ASR/conformer_ctc/decode.py b/egs/librispeech/ASR/conformer_ctc/decode.py index fc1d2bf2f..ff6374d73 100755 --- a/egs/librispeech/ASR/conformer_ctc/decode.py +++ b/egs/librispeech/ASR/conformer_ctc/decode.py @@ -78,16 +78,16 @@ def get_parser(): Supported values are: - (1) 1best. Extract the best path from the decoding lattice as the decoding result. - - (2) nbest. Extract n paths from the decoding lattice; the path with - the highest score is the decoding result. + - (2) nbest. Extract n paths from the decoding lattice; the path + with the highest score is the decoding result. - (3) nbest-rescoring. Extract n paths from the decoding lattice, rescore them with an n-gram LM (e.g., a 4-gram LM), the path with the highest score is the decoding result. - - (4) whole-lattice-rescoring. Rescore the decoding lattice with an n-gram LM - (e.g., a 4-gram LM), the best path of rescored lattice is the - decoding result. - - (5) attention-decoder. Extract n paths from the LM rescored lattice, - the path with the highest score is the decoding result. + - (4) whole-lattice-rescoring. Rescore the decoding lattice with an + n-gram LM (e.g., a 4-gram LM), the best path of rescored lattice + is the decoding result. + - (5) attention-decoder. Extract n paths from the LM rescored + lattice, the path with the highest score is the decoding result. - (6) nbest-oracle. Its WER is the lower bound of any n-best rescoring method can achieve. Useful for debugging n-best rescoring method. diff --git a/egs/librispeech/ASR/tdnn_lstm_ctc/decode.py b/egs/librispeech/ASR/tdnn_lstm_ctc/decode.py index 26946e0d0..afdebd12b 100755 --- a/egs/librispeech/ASR/tdnn_lstm_ctc/decode.py +++ b/egs/librispeech/ASR/tdnn_lstm_ctc/decode.py @@ -42,8 +42,8 @@ from icefall.utils import ( get_texts, setup_logger, store_transcripts, - write_error_stats, str2bool, + write_error_stats, ) @@ -98,7 +98,7 @@ def get_params() -> AttributeDict: # - nbest # - nbest-rescoring # - whole-lattice-rescoring - "method": "1best", + "method": "whole-lattice-rescoring", # num_paths is used when method is "nbest" and "nbest-rescoring" "num_paths": 30, } diff --git a/egs/yesno/ASR/README.md b/egs/yesno/ASR/README.md index 6db2f782f..6f57412c0 100644 --- a/egs/yesno/ASR/README.md +++ b/egs/yesno/ASR/README.md @@ -10,5 +10,5 @@ get the following WER: ``` Please refer to - + for detailed instructions. diff --git a/egs/yesno/ASR/tdnn/README.md b/egs/yesno/ASR/tdnn/README.md index 49722a779..2b6116f0a 100644 --- a/egs/yesno/ASR/tdnn/README.md +++ b/egs/yesno/ASR/tdnn/README.md @@ -2,7 +2,7 @@ ## How to run this recipe You can find detailed instructions by visiting - + It describes how to run this recipe and how to use a pre-trained model with `./pretrained.py`. From eed3fc561024ab6754134032e0286c9da02d4c1b Mon Sep 17 00:00:00 2001 From: Mingshuang Luo <37799481+luomingshuang@users.noreply.github.com> Date: Wed, 25 Aug 2021 17:48:34 +0800 Subject: [PATCH 09/10] Correct some spelling mistakes (#28) * Update index.rst (AS->ASR) * Update conformer_ctc.rst (pretraind->pretrained) --- docs/source/installation/index.rst | 2 +- docs/source/recipes/librispeech/conformer_ctc.rst | 8 ++++---- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/source/installation/index.rst b/docs/source/installation/index.rst index e8dc9b461..bcef669c8 100644 --- a/docs/source/installation/index.rst +++ b/docs/source/installation/index.rst @@ -308,7 +308,7 @@ Data preparation $ export PYTHONPATH=/tmp/icefall:$PYTHONPATH $ cd /tmp/icefall - $ cd egs/yesno/AS + $ cd egs/yesno/ASR $ ./prepare.sh The log of running ``./prepare.sh`` is: diff --git a/docs/source/recipes/librispeech/conformer_ctc.rst b/docs/source/recipes/librispeech/conformer_ctc.rst index 2cb04d1ba..50f262a54 100644 --- a/docs/source/recipes/librispeech/conformer_ctc.rst +++ b/docs/source/recipes/librispeech/conformer_ctc.rst @@ -367,7 +367,7 @@ After downloading, you will have the following files: | `-- lm | `-- G_4_gram.pt |-- exp - | `-- pretraind.pt + | `-- pretrained.pt `-- test_wavs |-- 1089-134686-0001.flac |-- 1221-135766-0001.flac @@ -475,7 +475,7 @@ The command to run HLG decoding is: $ cd egs/librispeech/ASR $ ./conformer_ctc/pretrained.py \ - --checkpoint ./tmp/icefall_asr_librispeech_conformer_ctc/exp/pretraind.pt \ + --checkpoint ./tmp/icefall_asr_librispeech_conformer_ctc/exp/pretrained.pt \ --words-file ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/words.txt \ --HLG ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/HLG.pt \ ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1089-134686-0001.flac \ @@ -518,7 +518,7 @@ The command to run HLG decoding + LM rescoring is: $ cd egs/librispeech/ASR $ ./conformer_ctc/pretrained.py \ - --checkpoint ./tmp/icefall_asr_librispeech_conformer_ctc/exp/pretraind.pt \ + --checkpoint ./tmp/icefall_asr_librispeech_conformer_ctc/exp/pretrained.pt \ --words-file ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/words.txt \ --HLG ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/HLG.pt \ --method whole-lattice-rescoring \ @@ -566,7 +566,7 @@ The command to run HLG decoding + LM rescoring + attention decoder rescoring is: $ cd egs/librispeech/ASR $ ./conformer_ctc/pretrained.py \ - --checkpoint ./tmp/icefall_asr_librispeech_conformer_ctc/exp/pretraind.pt \ + --checkpoint ./tmp/icefall_asr_librispeech_conformer_ctc/exp/pretrained.pt \ --words-file ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/words.txt \ --HLG ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/HLG.pt \ --method attention-decoder \ From 5baa6a9f1c4ef977b10c0e3adace5d8c455c935a Mon Sep 17 00:00:00 2001 From: Mingshuang Luo <37799481+luomingshuang@users.noreply.github.com> Date: Wed, 25 Aug 2021 21:41:46 +0800 Subject: [PATCH 10/10] fix a spelling mistake (tourch->touch) (#29) --- docs/source/contributing/how-to-create-a-recipe.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/contributing/how-to-create-a-recipe.rst b/docs/source/contributing/how-to-create-a-recipe.rst index 2d53fd89f..a30fb9056 100644 --- a/docs/source/contributing/how-to-create-a-recipe.rst +++ b/docs/source/contributing/how-to-create-a-recipe.rst @@ -56,7 +56,7 @@ organize your files in the following way: $ cd egs/foo/ASR $ mkdir bar $ cd bar - $ tourch README.md model.py train.py decode.py asr_datamodule.py pretrained.py + $ touch README.md model.py train.py decode.py asr_datamodule.py pretrained.py For instance , the ``yesno`` recipe has a ``tdnn`` model and its directory structure looks like the following:

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