From 092f69b477c759026ab5cb9129766485f531ec37 Mon Sep 17 00:00:00 2001 From: Valentin Berkes Date: Wed, 11 May 2022 09:32:40 +0200 Subject: [PATCH] docker + ubiqus + pyonmttok --- docker/Makefile | 11 + .../Dockerfile | 30 +- .../ASR/transducer_emformer/__init__.py | 0 .../ASR/transducer_emformer/asr_datamodule.py | 394 +++++++ .../ASR/transducer_emformer/beam_search.py | 1 + egs/ubiqus/ASR/transducer_emformer/decode.py | 549 +++++++++ egs/ubiqus/ASR/transducer_emformer/decoder.py | 1 + .../ASR/transducer_emformer/emformer.py | 271 +++++ .../ASR/transducer_emformer/emformer_raw.py | 289 +++++ .../transducer_emformer/encoder_interface.py | 1 + egs/ubiqus/ASR/transducer_emformer/export.py | 184 +++ egs/ubiqus/ASR/transducer_emformer/joiner.py | 1 + egs/ubiqus/ASR/transducer_emformer/model.py | 1 + egs/ubiqus/ASR/transducer_emformer/noam.py | 104 ++ .../transducer_emformer/streaming_decode.py | 748 ++++++++++++ .../streaming_feature_extractor.py | 132 +++ .../ASR/transducer_emformer/subsampling.py | 1 + .../ASR/transducer_emformer/test_emformer.py | 107 ++ .../test_streaming_feature_extractor.py | 53 + .../ASR/transducer_emformer/tokenizer.py | 57 + egs/ubiqus/ASR/transducer_emformer/train.py | 1014 ++++++++++++++++ .../ASR/transducer_emformer/train_raw.py | 1017 +++++++++++++++++ 22 files changed, 4952 insertions(+), 14 deletions(-) create mode 100644 docker/Makefile create mode 100644 egs/ubiqus/ASR/transducer_emformer/__init__.py create mode 100644 egs/ubiqus/ASR/transducer_emformer/asr_datamodule.py create mode 120000 egs/ubiqus/ASR/transducer_emformer/beam_search.py create mode 100755 egs/ubiqus/ASR/transducer_emformer/decode.py create mode 120000 egs/ubiqus/ASR/transducer_emformer/decoder.py create mode 100644 egs/ubiqus/ASR/transducer_emformer/emformer.py create mode 100644 egs/ubiqus/ASR/transducer_emformer/emformer_raw.py create mode 120000 egs/ubiqus/ASR/transducer_emformer/encoder_interface.py create mode 100755 egs/ubiqus/ASR/transducer_emformer/export.py create mode 120000 egs/ubiqus/ASR/transducer_emformer/joiner.py create mode 120000 egs/ubiqus/ASR/transducer_emformer/model.py create mode 100644 egs/ubiqus/ASR/transducer_emformer/noam.py create mode 100755 egs/ubiqus/ASR/transducer_emformer/streaming_decode.py create mode 100644 egs/ubiqus/ASR/transducer_emformer/streaming_feature_extractor.py create mode 120000 egs/ubiqus/ASR/transducer_emformer/subsampling.py create mode 100755 egs/ubiqus/ASR/transducer_emformer/test_emformer.py create mode 100755 egs/ubiqus/ASR/transducer_emformer/test_streaming_feature_extractor.py create mode 100644 egs/ubiqus/ASR/transducer_emformer/tokenizer.py create mode 100755 egs/ubiqus/ASR/transducer_emformer/train.py create mode 100755 egs/ubiqus/ASR/transducer_emformer/train_raw.py diff --git a/docker/Makefile b/docker/Makefile new file mode 100644 index 000000000..28768d5c5 --- /dev/null +++ b/docker/Makefile @@ -0,0 +1,11 @@ +build_docker: + docker build -t icefall/pytorch1.7.1:latest -f ./Ubuntu18.04-pytorch1.7.1-cuda11.0-cudnn8/Dockerfile ./ + +run_docker: + docker run -it --rm --runtime=nvidia \ + --gpus all \ + -v /data1:/data1 \ + -v /data1/merge_all_short/raw/fr_token_list/bpe_unigram5000/bpe.pyonmttok.vocab:/data/vocab \ + -v /data1/merge_all_short/raw/fr_token_list/bpe_unigram5000/bpe.pyonmttok:/data/bpe.pyonmttok \ + -v /nas-labs/ASR/valentin_work/icefall:/workspace/icefall \ + --name val_icefall_3 icefall/pytorch1.7.1:latest bash \ No newline at end of file diff --git a/docker/Ubuntu18.04-pytorch1.7.1-cuda11.0-cudnn8/Dockerfile b/docker/Ubuntu18.04-pytorch1.7.1-cuda11.0-cudnn8/Dockerfile index a9caf07ed..805965497 100644 --- a/docker/Ubuntu18.04-pytorch1.7.1-cuda11.0-cudnn8/Dockerfile +++ b/docker/Ubuntu18.04-pytorch1.7.1-cuda11.0-cudnn8/Dockerfile @@ -1,7 +1,10 @@ -FROM pytorch/pytorch:1.7.1-cuda11.0-cudnn8-devel +FROM pytorch/pytorch:1.11.0-cuda11.3-cudnn8-devel # install normal source +RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub +RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub + RUN apt-get update && \ apt-get install -y --no-install-recommends \ g++ \ @@ -26,13 +29,6 @@ RUN apt-get update && \ rm -rf /var/lib/apt/lists/* -RUN mv /opt/conda/lib/libcufft.so.10 /opt/libcufft.so.10.bak && \ - mv /opt/conda/lib/libcurand.so.10 /opt/libcurand.so.10.bak && \ - mv /opt/conda/lib/libcublas.so.11 /opt/libcublas.so.11.bak && \ - mv /opt/conda/lib/libnvrtc.so.11.0 /opt/libnvrtc.so.11.1.bak && \ - mv /opt/conda/lib/libnvToolsExt.so.1 /opt/libnvToolsExt.so.1.bak && \ - mv /opt/conda/lib/libcudart.so.11.0 /opt/libcudart.so.11.0.bak - # cmake RUN wget -P /opt https://cmake.org/files/v3.18/cmake-3.18.0.tar.gz && \ @@ -72,20 +68,26 @@ RUN git clone https://github.com/csukuangfj/kaldifeat.git /opt/kaldifeat && \ cd - +RUN conda install pytorch torchvision torchaudio=0.11 cudatoolkit=11.3 -c pytorch + + + #install k2 from source +# RUN conda install -c k2-fsa -c pytorch -c conda-forge k2 cudatoolkit=11.3 pytorch=1.10.0 RUN git clone https://github.com/k2-fsa/k2.git /opt/k2 && \ cd /opt/k2 && \ python3 setup.py install && \ cd - +# RUN pip install k2 # install lhotse -RUN pip install git+https://github.com/lhotse-speech/lhotse -#RUN pip install lhotse +# RUN pip install git+https://github.com/lhotse-speech/lhotse +RUN pip install lhotse # install icefall -RUN git clone https://github.com/k2-fsa/icefall && \ - cd icefall && \ - pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple - +# RUN git clone https://github.com/k2-fsa/icefall && \ +# cd icefall && \ +# pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple + ENV PYTHONPATH /workspace/icefall:$PYTHONPATH diff --git a/egs/ubiqus/ASR/transducer_emformer/__init__.py b/egs/ubiqus/ASR/transducer_emformer/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/egs/ubiqus/ASR/transducer_emformer/asr_datamodule.py b/egs/ubiqus/ASR/transducer_emformer/asr_datamodule.py new file mode 100644 index 000000000..0c6c9aa78 --- /dev/null +++ b/egs/ubiqus/ASR/transducer_emformer/asr_datamodule.py @@ -0,0 +1,394 @@ +# Copyright 2021 Piotr Żelasko +# Copyright 2022 Xiaomi Corporation (Author: Mingshuang Luo) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import argparse +import inspect +import logging +from functools import lru_cache +from pathlib import Path +from typing import Any, Dict, Optional + +import torch +from lhotse import CutSet, Fbank, FbankConfig, load_manifest +from lhotse.dataset import ( + BucketingSampler, + CutConcatenate, + CutMix, + K2SpeechRecognitionDataset, + PrecomputedFeatures, + SingleCutSampler, + SpecAugment, +) +from lhotse.dataset.input_strategies import OnTheFlyFeatures, AudioSamples +from lhotse.utils import fix_random_seed +from torch.utils.data import DataLoader + +from icefall.utils import str2bool + + +class _SeedWorkers: + def __init__(self, seed: int): + self.seed = seed + + def __call__(self, worker_id: int): + fix_random_seed(self.seed + worker_id) + + +class UbiqusAsrDataModule: + """ + DataModule for k2 ASR experiments. + It assumes there is always one train and valid dataloader, + but there can be multiple test dataloaders (e.g. LibriSpeech test-clean + and test-other). + + It contains all the common data pipeline modules used in ASR + experiments, e.g.: + - dynamic batch size, + - bucketing samplers, + - cut concatenation, + - augmentation, + - on-the-fly feature extraction + + This class should be derived for specific corpora used in ASR tasks. + """ + + def __init__(self, args: argparse.Namespace): + self.args = args + + @classmethod + def add_arguments(cls, parser: argparse.ArgumentParser): + group = parser.add_argument_group( + title="ASR data related options", + description="These options are used for the preparation of " + "PyTorch DataLoaders from Lhotse CutSet's -- they control the " + "effective batch sizes, sampling strategies, applied data " + "augmentations, etc.", + ) + group.add_argument( + "--manifest-dir", + type=Path, + default=Path("/data1/merge_all_manifest/raw"), + help="Path to directory with train/valid/test cuts.", + ) + group.add_argument( + "--max-duration", + type=int, + default=200.0, + help="Maximum pooled recordings duration (seconds) in a " + "single batch. You can reduce it if it causes CUDA OOM.", + ) + group.add_argument( + "--bucketing-sampler", + type=str2bool, + default=True, + help="When enabled, the batches will come from buckets of " + "similar duration (saves padding frames).", + ) + group.add_argument( + "--num-buckets", + type=int, + default=300, + help="The number of buckets for the 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=True, + help="When enabled, use on-the-fly cut mixing and feature " + "extraction. Will drop existing precomputed feature manifests " + "if available.", + ) + group.add_argument( + "--shuffle", + type=str2bool, + default=True, + help="When enabled (=default), the examples will be " + "shuffled for each epoch.", + ) + group.add_argument( + "--return-cuts", + type=str2bool, + default=True, + help="When enabled, each batch will have the " + "field: batch['supervisions']['cut'] with the cuts that " + "were used to construct it.", + ) + + group.add_argument( + "--num-workers", + type=int, + default=2, + help="The number of training dataloader workers that " + "collect the batches.", + ) + + group.add_argument( + "--enable-spec-aug", + type=str2bool, + default=True, + help="When enabled, use SpecAugment for training dataset.", + ) + + group.add_argument( + "--spec-aug-time-warp-factor", + type=int, + default=80, + help="Used only when --enable-spec-aug is True. " + "It specifies the factor for time warping in SpecAugment. " + "Larger values mean more warping. " + "A value less than 1 means to disable time warp.", + ) + + group.add_argument( + "--enable-musan", + type=str2bool, + default=True, + help="When enabled, select noise from MUSAN and mix it" + "with training dataset. ", + ) + + def train_dataloaders( + self, + cuts_train: CutSet, + sampler_state_dict: Optional[Dict[str, Any]] = None, + ) -> DataLoader: + """ + Args: + cuts_train: + CutSet for training. + sampler_state_dict: + The state dict for the training sampler. + """ + transforms = [] + # if self.args.enable_musan: + # logging.info("Enable MUSAN") + # logging.info("About to get Musan cuts") + # cuts_musan = load_manifest( + # self.args.manifest_dir / "cuts_musan.json.gz" + # ) + # transforms.append( + # CutMix( + # cuts=cuts_musan, prob=0.5, snr=(10, 20), preserve_id=True + # ) + # ) + # else: + # logging.info("Disable MUSAN") + + if self.args.concatenate_cuts: + logging.info( + f"Using cut concatenation with duration factor " + f"{self.args.duration_factor} and gap {self.args.gap}." + ) + # Cut concatenation should be the first transform in the list, + # so that if we e.g. mix noise in, it will fill the gaps between + # different utterances. + transforms = [ + CutConcatenate( + duration_factor=self.args.duration_factor, gap=self.args.gap + ) + ] + transforms + + input_transforms = [] + if self.args.enable_spec_aug: + logging.info("Enable SpecAugment") + logging.info( + f"Time warp factor: {self.args.spec_aug_time_warp_factor}" + ) + # Set the value of num_frame_masks according to Lhotse's version. + # In different Lhotse's versions, the default of num_frame_masks is + # different. + num_frame_masks = 10 + num_frame_masks_parameter = inspect.signature( + SpecAugment.__init__ + ).parameters["num_frame_masks"] + if num_frame_masks_parameter.default == 1: + num_frame_masks = 2 + logging.info(f"Num frame mask: {num_frame_masks}") + input_transforms.append( + SpecAugment( + time_warp_factor=self.args.spec_aug_time_warp_factor, + num_frame_masks=num_frame_masks, + features_mask_size=27, + num_feature_masks=2, + frames_mask_size=100, + ) + ) + else: + logging.info("Disable SpecAugment") + + logging.info("About to create train dataset") + train = K2SpeechRecognitionDataset( + cut_transforms=transforms, + input_transforms=input_transforms, + return_cuts=self.args.return_cuts, + input_strategy=AudioSamples(), + ) + + if self.args.on_the_fly_feats: + # NOTE: the PerturbSpeed transform should be added only if we + # remove it from data prep stage. + # Add on-the-fly speed perturbation; since originally it would + # have increased epoch size by 3, we will apply prob 2/3 and use + # 3x more epochs. + # Speed perturbation probably should come first before + # concatenation, but in principle the transforms order doesn't have + # to be strict (e.g. could be randomized) + # transforms = [PerturbSpeed(factors=[0.9, 1.1], p=2/3)] + transforms # noqa + # Drop feats to be on the safe side. + train = K2SpeechRecognitionDataset( + cut_transforms=transforms, + input_strategy=OnTheFlyFeatures( + Fbank(FbankConfig(num_mel_bins=80)) + ), + input_transforms=input_transforms, + return_cuts=self.args.return_cuts, + ) + + if self.args.bucketing_sampler: + logging.info("Using 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") + + if sampler_state_dict is not None: + logging.info("Loading sampler state dict") + train_sampler.load_state_dict(sampler_state_dict) + + # 'seed' is derived from the current random state, which will have + # previously been set in the main process. + seed = torch.randint(0, 100000, ()).item() + worker_init_fn = _SeedWorkers(seed) + + train_dl = DataLoader( + train, + sampler=train_sampler, + batch_size=None, + num_workers=self.args.num_workers, + persistent_workers=False, + worker_init_fn=worker_init_fn, + ) + + return train_dl + + def valid_dataloaders(self, cuts_valid: CutSet) -> DataLoader: + transforms = [] + if self.args.concatenate_cuts: + transforms = [ + CutConcatenate( + duration_factor=self.args.duration_factor, gap=self.args.gap + ) + ] + transforms + + logging.info("About to create dev dataset") + if self.args.on_the_fly_feats: + validate = K2SpeechRecognitionDataset( + cut_transforms=transforms, + input_strategy=OnTheFlyFeatures( + Fbank(FbankConfig(num_mel_bins=80)) + ), + return_cuts=self.args.return_cuts, + ) + else: + validate = K2SpeechRecognitionDataset( + cut_transforms=transforms, + return_cuts=self.args.return_cuts, + input_strategy=AudioSamples(), + ) + valid_sampler = BucketingSampler( + cuts_valid, + max_duration=self.args.max_duration, + shuffle=False, + ) + logging.info("About to create dev dataloader") + valid_dl = DataLoader( + validate, + sampler=valid_sampler, + batch_size=None, + num_workers=2, + persistent_workers=False, + ) + + return valid_dl + + @lru_cache() + def train_cuts(self) -> CutSet: + logging.info("About to get train cuts") + rec = load_manifest( + self.args.manifest_dir / "train_sp/recordings.jsonl.gz" + ) + sup = load_manifest( + self.args.manifest_dir / "train_sp/supervisions.jsonl.gz" + ) + return CutSet.from_manifests( + recordings=rec, + supervisions=sup, + ) + return load_manifest( + self.args.manifest_dir / "train_sp/supervisions.jsonl.gz" + ) + + @lru_cache() + def dev_cuts(self) -> CutSet: + logging.info("About to get dev cuts") + rec = load_manifest(self.args.manifest_dir / "dev/recordings.jsonl.gz") + sup = load_manifest( + self.args.manifest_dir / "dev/supervisions.jsonl.gz" + ) + return CutSet.from_manifests( + recordings=rec, + supervisions=sup, + ) + return load_manifest( + self.args.manifest_dir / "dev/supervisions.jsonl.gz" + ) diff --git a/egs/ubiqus/ASR/transducer_emformer/beam_search.py b/egs/ubiqus/ASR/transducer_emformer/beam_search.py new file mode 120000 index 000000000..7f9f6263f --- /dev/null +++ b/egs/ubiqus/ASR/transducer_emformer/beam_search.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless/beam_search.py \ No newline at end of file diff --git a/egs/ubiqus/ASR/transducer_emformer/decode.py b/egs/ubiqus/ASR/transducer_emformer/decode.py new file mode 100755 index 000000000..c40b01dfa --- /dev/null +++ b/egs/ubiqus/ASR/transducer_emformer/decode.py @@ -0,0 +1,549 @@ +#!/usr/bin/env python3 +# +# Copyright 2021 Xiaomi Corporation (Author: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Usage: +(1) greedy search +./transducer_emformer/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./transducer_emformer/exp \ + --max-duration 100 \ + --decoding-method greedy_search + +(2) beam search +./transducer_emformer/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./transducer_emformer/exp \ + --max-duration 100 \ + --decoding-method beam_search \ + --beam-size 4 + +(3) modified beam search +./transducer_emformer/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./transducer_emformer/exp \ + --max-duration 100 \ + --decoding-method modified_beam_search \ + --beam-size 4 + +(4) fast beam search +./transducer_emformer/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./transducer_emformer/exp \ + --max-duration 1500 \ + --decoding-method fast_beam_search \ + --beam 4 \ + --max-contexts 4 \ + --max-states 8 +""" + + +import argparse +import logging +from collections import defaultdict +from pathlib import Path +from typing import Dict, List, Optional, Tuple + +import k2 +import sentencepiece as spm +import torch +import torch.nn as nn +from asr_datamodule import LibriSpeechAsrDataModule +from beam_search import ( + beam_search, + fast_beam_search, + greedy_search, + greedy_search_batch, + modified_beam_search, +) +from train import add_model_arguments, get_params, get_transducer_model + +from icefall.checkpoint import ( + average_checkpoints, + find_checkpoints, + load_checkpoint, +) +from icefall.utils import ( + AttributeDict, + setup_logger, + store_transcripts, + write_error_stats, +) + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--epoch", + type=int, + default=28, + help="It specifies the checkpoint to use for decoding." + "Note: Epoch counts from 0.", + ) + parser.add_argument( + "--avg", + type=int, + default=15, + help="Number of checkpoints to average. Automatically select " + "consecutive checkpoints before the checkpoint specified by " + "'--epoch'. ", + ) + + parser.add_argument( + "--avg-last-n", + type=int, + default=0, + help="""If positive, --epoch and --avg are ignored and it + will use the last n checkpoints exp_dir/checkpoint-xxx.pt + where xxx is the number of processed batches while + saving that checkpoint. + """, + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="transducer_emformer/exp", + help="The experiment dir", + ) + + parser.add_argument( + "--bpe-model", + type=str, + default="data/lang_bpe_500/bpe.model", + help="Path to the BPE model", + ) + + parser.add_argument( + "--decoding-method", + type=str, + default="greedy_search", + help="""Possible values are: + - greedy_search + - beam_search + - modified_beam_search + - fast_beam_search + """, + ) + + parser.add_argument( + "--beam-size", + type=int, + default=4, + help="""An interger indicating how many candidates we will keep for each + frame. Used only when --decoding-method is beam_search or + modified_beam_search.""", + ) + + parser.add_argument( + "--beam", + type=float, + default=4, + help="""A floating point value to calculate the cutoff score during beam + search (i.e., `cutoff = max-score - beam`), which is the same as the + `beam` in Kaldi. + Used only when --decoding-method is fast_beam_search""", + ) + + parser.add_argument( + "--max-contexts", + type=int, + default=4, + help="""Used only when --decoding-method is + fast_beam_search""", + ) + + parser.add_argument( + "--max-states", + type=int, + default=8, + help="""Used only when --decoding-method is + fast_beam_search""", + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; " + "2 means tri-gram", + ) + parser.add_argument( + "--max-sym-per-frame", + type=int, + default=1, + help="""Maximum number of symbols per frame. + Used only when --decoding_method is greedy_search""", + ) + + add_model_arguments(parser) + + return parser + + +def decode_one_batch( + params: AttributeDict, + model: nn.Module, + sp: spm.SentencePieceProcessor, + batch: dict, + decoding_graph: Optional[k2.Fsa] = None, +) -> Dict[str, List[List[str]]]: + """Decode one batch and return the result in a dict. The dict has the + following format: + + - key: It indicates the setting used for decoding. For example, + if greedy_search is used, it would be "greedy_search" + If beam search with a beam size of 7 is used, it would be + "beam_7" + - value: It contains the decoding result. `len(value)` equals to + batch size. `value[i]` is the decoding result for the i-th + utterance in the given batch. + Args: + params: + It's the return value of :func:`get_params`. + model: + The neural model. + sp: + The BPE model. + batch: + It is the return value from iterating + `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation + for the format of the `batch`. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used + only when --decoding_method is fast_beam_search. + Returns: + Return the decoding result. See above description for the format of + the returned dict. + """ + device = model.device + feature = batch["inputs"] + assert feature.ndim == 3 + + feature = feature.to(device) + # at entry, feature is (N, T, C) + + supervisions = batch["supervisions"] + feature_lens = supervisions["num_frames"].to(device) + + encoder_out, encoder_out_lens = model.encoder( + x=feature, x_lens=feature_lens + ) + hyps = [] + + if params.decoding_method == "fast_beam_search": + hyp_tokens = fast_beam_search( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) + elif ( + params.decoding_method == "greedy_search" + and params.max_sym_per_frame == 1 + ): + hyp_tokens = greedy_search_batch( + model=model, + encoder_out=encoder_out, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) + elif params.decoding_method == "modified_beam_search": + hyp_tokens = modified_beam_search( + model=model, + encoder_out=encoder_out, + beam=params.beam_size, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) + else: + batch_size = encoder_out.size(0) + + for i in range(batch_size): + # fmt: off + encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]] + # fmt: on + if params.decoding_method == "greedy_search": + hyp = greedy_search( + model=model, + encoder_out=encoder_out_i, + max_sym_per_frame=params.max_sym_per_frame, + ) + elif params.decoding_method == "beam_search": + hyp = beam_search( + model=model, + encoder_out=encoder_out_i, + beam=params.beam_size, + ) + else: + raise ValueError( + f"Unsupported decoding method: {params.decoding_method}" + ) + hyps.append(sp.decode(hyp).split()) + + if params.decoding_method == "greedy_search": + return {"greedy_search": hyps} + elif params.decoding_method == "fast_beam_search": + return { + ( + f"beam_{params.beam}_" + f"max_contexts_{params.max_contexts}_" + f"max_states_{params.max_states}" + ): hyps + } + else: + return {f"beam_size_{params.beam_size}": hyps} + + +def decode_dataset( + dl: torch.utils.data.DataLoader, + params: AttributeDict, + model: nn.Module, + sp: spm.SentencePieceProcessor, + decoding_graph: Optional[k2.Fsa] = None, +) -> Dict[str, List[Tuple[List[str], List[str]]]]: + """Decode dataset. + + Args: + dl: + PyTorch's dataloader containing the dataset to decode. + params: + It is returned by :func:`get_params`. + model: + The neural model. + sp: + The BPE model. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used + only when --decoding_method is fast_beam_search. + Returns: + Return a dict, whose key may be "greedy_search" if greedy search + is used, or it may be "beam_7" if beam size of 7 is used. + Its value is a list of tuples. Each tuple contains two elements: + The first is the reference transcript, and the second is the + predicted result. + """ + num_cuts = 0 + + try: + num_batches = len(dl) + except TypeError: + num_batches = "?" + + if params.decoding_method == "greedy_search": + log_interval = 100 + else: + log_interval = 2 + + results = defaultdict(list) + for batch_idx, batch in enumerate(dl): + texts = batch["supervisions"]["text"] + + hyps_dict = decode_one_batch( + params=params, + model=model, + sp=sp, + decoding_graph=decoding_graph, + batch=batch, + ) + + for name, hyps in hyps_dict.items(): + this_batch = [] + assert len(hyps) == len(texts) + for hyp_words, ref_text in zip(hyps, texts): + ref_words = ref_text.split() + this_batch.append((ref_words, hyp_words)) + + results[name].extend(this_batch) + + num_cuts += len(texts) + + if batch_idx % log_interval == 0: + batch_str = f"{batch_idx}/{num_batches}" + + logging.info( + f"batch {batch_str}, cuts processed until now is {num_cuts}" + ) + return results + + +def save_results( + params: AttributeDict, + test_set_name: str, + results_dict: Dict[str, List[Tuple[List[int], List[int]]]], +): + test_set_wers = dict() + for key, results in results_dict.items(): + recog_path = ( + params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt" + ) + store_transcripts(filename=recog_path, texts=results) + logging.info(f"The transcripts are stored in {recog_path}") + + # The following prints out WERs, per-word error statistics and aligned + # ref/hyp pairs. + errs_filename = ( + params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt" + ) + with open(errs_filename, "w") as f: + wer = write_error_stats( + f, f"{test_set_name}-{key}", results, enable_log=True + ) + test_set_wers[key] = wer + + logging.info("Wrote detailed error stats to {}".format(errs_filename)) + + test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1]) + errs_info = ( + params.res_dir + / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt" + ) + with open(errs_info, "w") as f: + print("settings\tWER", file=f) + for key, val in test_set_wers: + print("{}\t{}".format(key, val), file=f) + + s = "\nFor {}, WER of different settings are:\n".format(test_set_name) + note = "\tbest for {}".format(test_set_name) + for key, val in test_set_wers: + s += "{}\t{}{}\n".format(key, val, note) + note = "" + logging.info(s) + + +@torch.no_grad() +def main(): + parser = get_parser() + LibriSpeechAsrDataModule.add_arguments(parser) + args = parser.parse_args() + args.exp_dir = Path(args.exp_dir) + + params = get_params() + params.update(vars(args)) + + assert params.decoding_method in ( + "greedy_search", + "beam_search", + "fast_beam_search", + "modified_beam_search", + ) + params.res_dir = params.exp_dir / params.decoding_method + + params.suffix = f"epoch-{params.epoch}-avg-{params.avg}" + if "fast_beam_search" in params.decoding_method: + params.suffix += f"-beam-{params.beam}" + params.suffix += f"-max-contexts-{params.max_contexts}" + params.suffix += f"-max-states-{params.max_states}" + elif "beam_search" in params.decoding_method: + params.suffix += f"-beam-{params.beam_size}" + else: + params.suffix += f"-context-{params.context_size}" + params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}" + + setup_logger(f"{params.res_dir}/log-decode-{params.suffix}") + logging.info("Decoding started") + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"Device: {device}") + + sp = spm.SentencePieceProcessor() + sp.load(params.bpe_model) + + # is defined in local/train_bpe_model.py + params.blank_id = sp.piece_to_id("") + params.vocab_size = sp.get_piece_size() + + logging.info(params) + + logging.info("About to create model") + model = get_transducer_model(params) + + if params.avg_last_n > 0: + filenames = find_checkpoints(params.exp_dir)[: params.avg_last_n] + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + elif params.avg == 1: + load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) + else: + start = params.epoch - params.avg + 1 + filenames = [] + for i in range(start, params.epoch + 1): + if start >= 0: + filenames.append(f"{params.exp_dir}/epoch-{i}.pt") + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + + model.to(device) + model.eval() + model.device = device + + if params.decoding_method == "fast_beam_search": + decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device) + else: + decoding_graph = None + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + librispeech = LibriSpeechAsrDataModule(args) + + test_clean_cuts = librispeech.test_clean_cuts() + test_other_cuts = librispeech.test_other_cuts() + + test_clean_dl = librispeech.test_dataloaders(test_clean_cuts) + test_other_dl = librispeech.test_dataloaders(test_other_cuts) + + test_sets = ["test-clean", "test-other"] + test_dl = [test_clean_dl, test_other_dl] + + for test_set, test_dl in zip(test_sets, test_dl): + results_dict = decode_dataset( + dl=test_dl, + params=params, + model=model, + sp=sp, + decoding_graph=decoding_graph, + ) + + save_results( + params=params, + test_set_name=test_set, + results_dict=results_dict, + ) + + logging.info("Done!") + + +if __name__ == "__main__": + main() diff --git a/egs/ubiqus/ASR/transducer_emformer/decoder.py b/egs/ubiqus/ASR/transducer_emformer/decoder.py new file mode 120000 index 000000000..206384eaa --- /dev/null +++ b/egs/ubiqus/ASR/transducer_emformer/decoder.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless/decoder.py \ No newline at end of file diff --git a/egs/ubiqus/ASR/transducer_emformer/emformer.py b/egs/ubiqus/ASR/transducer_emformer/emformer.py new file mode 100644 index 000000000..f7868068b --- /dev/null +++ b/egs/ubiqus/ASR/transducer_emformer/emformer.py @@ -0,0 +1,271 @@ +# Copyright 2022 Xiaomi Corporation (Author: Mingshuang Luo) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +import warnings +from typing import List, Optional, Tuple + +import torch +import torch.nn as nn +from encoder_interface import EncoderInterface +from subsampling import Conv2dSubsampling, VggSubsampling +from torchaudio.models import Emformer as _Emformer + +LOG_EPSILON = math.log(1e-10) + + +def unstack_states( + states: List[List[torch.Tensor]], +) -> List[List[List[torch.Tensor]]]: + """Unstack the emformer state corresponding to a batch of utterances + into a list of states, were the i-th entry is the state from the i-th + utterance in the batch. + + Args: + states: + A list-of-list of tensors. ``len(states)`` equals to number of + layers in the emformer. ``states[i]]`` contains the states for + the i-th layer. ``states[i][k]`` is either a 3-D tensor of shape + ``(T, N, C)`` or a 2-D tensor of shape ``(C, N)`` + """ + batch_size = states[0][0].size(1) + num_layers = len(states) + + ans = [None] * batch_size + for i in range(batch_size): + ans[i] = [[] for _ in range(num_layers)] + + for li, layer in enumerate(states): + for s in layer: + s_list = s.unbind(dim=1) + # We will use stack(dim=1) later in stack_states() + for bi, b in enumerate(ans): + b[li].append(s_list[bi]) + return ans + + +def stack_states( + state_list: List[List[List[torch.Tensor]]], +) -> List[List[torch.Tensor]]: + """Stack list of emformer states that correspond to separate utterances + into a single emformer state so that it can be used as an input for + emformer when those utterances are formed into a batch. + + Note: + It is the inverse of :func:`unstack_states`. + + Args: + state_list: + Each element in state_list corresponding to the internal state + of the emformer model for a single utterance. + Returns: + Return a new state corresponding to a batch of utterances. + See the input argument of :func:`unstack_states` for the meaning + of the returned tensor. + """ + batch_size = len(state_list) + ans = [] + for layer in state_list[0]: + # layer is a list of tensors + if batch_size > 1: + ans.append([[s] for s in layer]) + # Note: We will stack ans[layer][s][] later to get ans[layer][s] + else: + ans.append([s.unsqueeze(1) for s in layer]) + + for b, states in enumerate(state_list[1:], 1): + for li, layer in enumerate(states): + for si, s in enumerate(layer): + ans[li][si].append(s) + if b == batch_size - 1: + ans[li][si] = torch.stack(ans[li][si], dim=1) + # We will use unbind(dim=1) later in unstack_states() + return ans + + +class Emformer(EncoderInterface): + """This is just a simple wrapper around torchaudio.models.Emformer. + We may replace it with our own implementation some time later. + """ + + def __init__( + self, + num_features: int, + output_dim: int, + d_model: int, + nhead: int, + dim_feedforward: int, + num_encoder_layers: int, + segment_length: int, + left_context_length: int, + right_context_length: int, + max_memory_size: int = 0, + dropout: float = 0.1, + subsampling_factor: int = 4, + vgg_frontend: bool = False, + ) -> None: + """ + Args: + num_features: + The input dimension of the model. + output_dim: + The output dimension of the model. + d_model: + Attention dimension. + nhead: + Number of heads in multi-head attention. + dim_feedforward: + The output dimension of the feedforward layers in encoder. + num_encoder_layers: + Number of encoder layers. + segment_length: + Number of frames per segment before subsampling. + left_context_length: + Number of frames in the left context before subsampling. + right_context_length: + Number of frames in the right context before subsampling. + max_memory_size: + TODO. + dropout: + Dropout in encoder. + subsampling_factor: + Number of output frames is num_in_frames // subsampling_factor. + Currently, subsampling_factor MUST be 4. + vgg_frontend: + True to use vgg style frontend for subsampling. + """ + super().__init__() + + self.subsampling_factor = subsampling_factor + if subsampling_factor != 4: + raise NotImplementedError("Support only 'subsampling_factor=4'.") + + # self.encoder_embed converts the input of shape (N, T, num_features) + # to the shape (N, T//subsampling_factor, d_model). + # That is, it does two things simultaneously: + # (1) subsampling: T -> T//subsampling_factor + # (2) embedding: num_features -> d_model + print(num_features, d_model, output_dim) + if vgg_frontend: + self.encoder_embed = VggSubsampling(num_features, d_model) + else: + self.encoder_embed = Conv2dSubsampling(num_features, d_model) + + self.segment_length = segment_length + self.right_context_length = right_context_length + + assert right_context_length % subsampling_factor == 0 + assert segment_length % subsampling_factor == 0 + assert left_context_length % subsampling_factor == 0 + + left_context_length = left_context_length // subsampling_factor + right_context_length = right_context_length // subsampling_factor + segment_length = segment_length // subsampling_factor + + self.model = _Emformer( + input_dim=d_model, + num_heads=nhead, + ffn_dim=dim_feedforward, + num_layers=num_encoder_layers, + segment_length=segment_length, + dropout=dropout, + activation="relu", + left_context_length=left_context_length, + right_context_length=right_context_length, + max_memory_size=max_memory_size, + weight_init_scale_strategy="depthwise", + tanh_on_mem=False, + negative_inf=-1e8, + ) + + self.encoder_output_layer = nn.Sequential( + nn.Dropout(p=dropout), nn.Linear(d_model, output_dim) + ) + + def forward( + self, + x: torch.Tensor, + x_lens: torch.Tensor, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Args: + x: + Input features of shape (N, T, C). + x_lens: + A int32 tensor of shape (N,) containing valid frames in `x` before + padding. We have `x.size(1) == x_lens.max()` + Returns: + Return a tuple containing two tensors: + + - encoder_out, a tensor of shape (N, T', C) + - encoder_out_lens, a int32 tensor of shape (N,) containing the + valid frames in `encoder_out` before padding + """ + x = nn.functional.pad( + x, + # (left, right, top, bottom) + # left/right are for the channel dimension, i.e., axis 2 + # top/bottom are for the time dimension, i.e., axis 1 + (0, 0, 0, self.right_context_length), + value=LOG_EPSILON, + ) # (N, T, C) -> (N, T+right_context_length, C) + + x = self.encoder_embed(x) + + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + # Caution: We assume the subsampling factor is 4! + x_lens = ((x_lens - 1) // 2 - 1) // 2 + + emformer_out, emformer_out_lens = self.model(x, x_lens) + logits = self.encoder_output_layer(emformer_out) + + return logits, emformer_out_lens + + def streaming_forward( + self, + x: torch.Tensor, + x_lens: torch.Tensor, + states: Optional[List[List[torch.Tensor]]] = None, + ): + """ + Args: + x: + A 3-D tensor of shape (N, T, C). + x_lens: + A 2-D tensor of shap containing the number of valid frames for each + element in `x` before padding. + states: + Internal states of the model. + Returns: + Return a tuple containing 3 tensors: + - encoder_out, a 3-D tensor of shape (N, T, C) + - encoder_out_lens: a 1-D tensor of shape (N,) + - next_state, internal model states for the next chunk + """ + x = self.encoder_embed(x) + + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + # Caution: We assume the subsampling factor is 4! + x_lens = ((x_lens - 1) // 2 - 1) // 2 + emformer_out, emformer_out_lens, states = self.model.infer( + x, x_lens, states + ) + + logits = self.encoder_output_layer(emformer_out) + + return logits, emformer_out_lens, states diff --git a/egs/ubiqus/ASR/transducer_emformer/emformer_raw.py b/egs/ubiqus/ASR/transducer_emformer/emformer_raw.py new file mode 100644 index 000000000..63c83c508 --- /dev/null +++ b/egs/ubiqus/ASR/transducer_emformer/emformer_raw.py @@ -0,0 +1,289 @@ +# Copyright 2022 Xiaomi Corporation (Author: Mingshuang Luo) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +import warnings +from typing import List, Optional, Tuple + +import torch +import torch.nn as nn +from encoder_interface import EncoderInterface +from subsampling import Conv2dSubsampling, VggSubsampling +from torchaudio.models import Emformer as _Emformer +from torchaudio.models.wav2vec2 import components + +LOG_EPSILON = math.log(1e-10) + + +def unstack_states( + states: List[List[torch.Tensor]], +) -> List[List[List[torch.Tensor]]]: + """Unstack the emformer state corresponding to a batch of utterances + into a list of states, were the i-th entry is the state from the i-th + utterance in the batch. + + Args: + states: + A list-of-list of tensors. ``len(states)`` equals to number of + layers in the emformer. ``states[i]]`` contains the states for + the i-th layer. ``states[i][k]`` is either a 3-D tensor of shape + ``(T, N, C)`` or a 2-D tensor of shape ``(C, N)`` + """ + batch_size = states[0][0].size(1) + num_layers = len(states) + + ans = [None] * batch_size + for i in range(batch_size): + ans[i] = [[] for _ in range(num_layers)] + + for li, layer in enumerate(states): + for s in layer: + s_list = s.unbind(dim=1) + # We will use stack(dim=1) later in stack_states() + for bi, b in enumerate(ans): + b[li].append(s_list[bi]) + return ans + + +def stack_states( + state_list: List[List[List[torch.Tensor]]], +) -> List[List[torch.Tensor]]: + """Stack list of emformer states that correspond to separate utterances + into a single emformer state so that it can be used as an input for + emformer when those utterances are formed into a batch. + + Note: + It is the inverse of :func:`unstack_states`. + + Args: + state_list: + Each element in state_list corresponding to the internal state + of the emformer model for a single utterance. + Returns: + Return a new state corresponding to a batch of utterances. + See the input argument of :func:`unstack_states` for the meaning + of the returned tensor. + """ + batch_size = len(state_list) + ans = [] + for layer in state_list[0]: + # layer is a list of tensors + if batch_size > 1: + ans.append([[s] for s in layer]) + # Note: We will stack ans[layer][s][] later to get ans[layer][s] + else: + ans.append([s.unsqueeze(1) for s in layer]) + + for b, states in enumerate(state_list[1:], 1): + for li, layer in enumerate(states): + for si, s in enumerate(layer): + ans[li][si].append(s) + if b == batch_size - 1: + ans[li][si] = torch.stack(ans[li][si], dim=1) + # We will use unbind(dim=1) later in unstack_states() + return ans + + +class EmformerRaw(EncoderInterface): + """This is just a simple wrapper around torchaudio.models.Emformer. + We may replace it with our own implementation some time later. + """ + + def __init__( + self, + num_features: int, + output_dim: int, + d_model: int, + nhead: int, + dim_feedforward: int, + num_encoder_layers: int, + segment_length: int, + left_context_length: int, + right_context_length: int, + max_memory_size: int = 0, + dropout: float = 0.1, + subsampling_factor: int = 4, + vgg_frontend: bool = False, + ) -> None: + """ + Args: + num_features: + The input dimension of the model. + output_dim: + The output dimension of the model. + d_model: + Attention dimension. + nhead: + Number of heads in multi-head attention. + dim_feedforward: + The output dimension of the feedforward layers in encoder. + num_encoder_layers: + Number of encoder layers. + segment_length: + Number of frames per segment before subsampling. + left_context_length: + Number of frames in the left context before subsampling. + right_context_length: + Number of frames in the right context before subsampling. + max_memory_size: + TODO. + dropout: + Dropout in encoder. + subsampling_factor: + Number of output frames is num_in_frames // subsampling_factor. + Currently, subsampling_factor MUST be 4. + vgg_frontend: + True to use vgg style frontend for subsampling. + """ + super().__init__() + + self.subsampling_factor = subsampling_factor + if subsampling_factor != 4: + raise NotImplementedError("Support only 'subsampling_factor=4'.") + + # self.encoder_embed converts the input of shape (N, T, num_features) + # to the shape (N, T//subsampling_factor, d_model). + # That is, it does two things simultaneously: + # (1) subsampling: T -> T//subsampling_factor + # (2) embedding: num_features -> d_model + extractor_conv_layer_config = ( + [(512, 10, 5)] + [(512, 3, 2)] * 4 + [(512, 2, 2)] * 2 + ) + extractor_mode = "layer_norm" + extractor_conv_bias = True + + self.feature_extractor = components._get_feature_extractor( + extractor_mode, extractor_conv_layer_config, extractor_conv_bias + ) + print(num_features, d_model, output_dim) + # if vgg_frontend: + # self.encoder_embed = VggSubsampling(num_features, d_model) + # else: + # self.encoder_embed = Conv2dSubsampling(num_features, d_model) + + self.segment_length = segment_length + self.right_context_length = right_context_length + + assert right_context_length % subsampling_factor == 0 + assert segment_length % subsampling_factor == 0 + assert left_context_length % subsampling_factor == 0 + + left_context_length = left_context_length // subsampling_factor + right_context_length = right_context_length // subsampling_factor + segment_length = segment_length // subsampling_factor + + print(extractor_conv_layer_config[-1][0]) + print(dim_feedforward) + self.model = _Emformer( + input_dim=extractor_conv_layer_config[-1][0], + num_heads=nhead, + ffn_dim=dim_feedforward, + num_layers=num_encoder_layers, + segment_length=segment_length, + dropout=dropout, + activation="relu", + left_context_length=left_context_length, + right_context_length=right_context_length, + max_memory_size=max_memory_size, + weight_init_scale_strategy="depthwise", + tanh_on_mem=False, + negative_inf=-1e8, + ) + + self.encoder_output_layer = nn.Sequential( + nn.Dropout(p=dropout), nn.Linear(d_model, output_dim) + ) + + def forward( + self, + x: torch.Tensor, + x_lens: torch.Tensor, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Args: + x: + Input features of shape (N, T, C). + x_lens: + A int32 tensor of shape (N,) containing valid frames in `x` before + padding. We have `x.size(1) == x_lens.max()` + Returns: + Return a tuple containing two tensors: + + - encoder_out, a tensor of shape (N, T', C) + - encoder_out_lens, a int32 tensor of shape (N,) containing the + valid frames in `encoder_out` before padding + """ + print(x.shape) + x = nn.functional.pad( + x, + # (left, right, top, bottom) + # left/right are for the channel dimension, i.e., axis 2 + # top/bottom are for the time dimension, i.e., axis 1 + (0, 0, 0, self.right_context_length), + value=LOG_EPSILON, + ) # (N, T, C) -> (N, T+right_context_length, C) + + print(x.shape, x_lens) + x, x_lens = self.feature_extractor(x.squeeze(-1), x_lens) + x_lens -= 1 + print(x.shape, x_lens) + + # with warnings.catch_warnings(): + # warnings.simplefilter("ignore") + # # Caution: We assume the subsampling factor is 4! + # x_lens = ((x_lens - 1) // 2 - 1) // 2 + + emformer_out, emformer_out_lens = self.model(x, x_lens) + logits = self.encoder_output_layer(emformer_out) + + return logits, emformer_out_lens + + def streaming_forward( + self, + x: torch.Tensor, + x_lens: torch.Tensor, + states: Optional[List[List[torch.Tensor]]] = None, + ): + """ + Args: + x: + A 3-D tensor of shape (N, T, C). + x_lens: + A 2-D tensor of shap containing the number of valid frames for each + element in `x` before padding. + states: + Internal states of the model. + Returns: + Return a tuple containing 3 tensors: + - encoder_out, a 3-D tensor of shape (N, T, C) + - encoder_out_lens: a 1-D tensor of shape (N,) + - next_state, internal model states for the next chunk + """ + x, x_lens = self.feature_extractor(x, x_lens) + x_lens -= 1 + # Sure about that ? + + # with warnings.catch_warnings(): + # warnings.simplefilter("ignore") + # # Caution: We assume the subsampling factor is 4! + # x_lens = ((x_lens - 1) // 2 - 1) // 2 + emformer_out, emformer_out_lens, states = self.model.infer( + x, x_lens, states + ) + + logits = self.encoder_output_layer(emformer_out) + + return logits, emformer_out_lens, states diff --git a/egs/ubiqus/ASR/transducer_emformer/encoder_interface.py b/egs/ubiqus/ASR/transducer_emformer/encoder_interface.py new file mode 120000 index 000000000..dbaf9d4a8 --- /dev/null +++ b/egs/ubiqus/ASR/transducer_emformer/encoder_interface.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless/encoder_interface.py \ No newline at end of file diff --git a/egs/ubiqus/ASR/transducer_emformer/export.py b/egs/ubiqus/ASR/transducer_emformer/export.py new file mode 100755 index 000000000..9c665ea1a --- /dev/null +++ b/egs/ubiqus/ASR/transducer_emformer/export.py @@ -0,0 +1,184 @@ +#!/usr/bin/env python3 +# +# Copyright 2021 Xiaomi Corporation (Author: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# This script converts several saved checkpoints +# to a single one using model averaging. +""" +Usage: +./transducer_emformer/export.py \ + --exp-dir ./transducer_emformer/exp \ + --bpe-model data/lang_bpe_500/bpe.model \ + --epoch 20 \ + --avg 10 + +It will generate a file exp_dir/pretrained.pt + +To use the generated file with `transducer_emformer/decode.py`, +you can do: + + cd /path/to/exp_dir + ln -s pretrained.pt epoch-9999.pt + + cd /path/to/egs/librispeech/ASR + ./transducer_emformer/decode.py \ + --exp-dir ./transducer_emformer/exp \ + --epoch 9999 \ + --avg 1 \ + --max-duration 1000 \ + --bpe-model data/lang_bpe_500/bpe.model +""" + +import argparse +import logging +from pathlib import Path + +import sentencepiece as spm +import torch +from train import add_model_arguments, get_params, get_transducer_model + +from icefall.checkpoint import average_checkpoints, load_checkpoint +from icefall.utils import str2bool + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--epoch", + type=int, + default=28, + help="It specifies the checkpoint to use for decoding." + "Note: Epoch counts from 0.", + ) + + parser.add_argument( + "--avg", + type=int, + default=15, + help="Number of checkpoints to average. Automatically select " + "consecutive checkpoints before the checkpoint specified by " + "'--epoch'. ", + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="pruned_transducer_stateless/exp", + help="""It specifies the directory where all training related + files, e.g., checkpoints, log, etc, are saved + """, + ) + + parser.add_argument( + "--bpe-model", + type=str, + default="data/lang_bpe_500/bpe.model", + help="Path to the BPE model", + ) + + parser.add_argument( + "--jit", + type=str2bool, + default=False, + help="""True to save a model after applying torch.jit.script. + """, + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; " + "2 means tri-gram", + ) + + add_model_arguments(parser) + + return parser + + +def main(): + args = get_parser().parse_args() + args.exp_dir = Path(args.exp_dir) + + assert args.jit is False, "Support torchscript will be added later" + + params = get_params() + params.update(vars(args)) + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"device: {device}") + + sp = spm.SentencePieceProcessor() + sp.load(params.bpe_model) + + # is defined in local/train_bpe_model.py + params.blank_id = sp.piece_to_id("") + params.vocab_size = sp.get_piece_size() + + logging.info(params) + + logging.info("About to create model") + model = get_transducer_model(params) + + model.to(device) + + if params.avg == 1: + load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) + else: + start = params.epoch - params.avg + 1 + filenames = [] + for i in range(start, params.epoch + 1): + if start >= 0: + filenames.append(f"{params.exp_dir}/epoch-{i}.pt") + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + + model.eval() + + model.to("cpu") + model.eval() + + if params.jit: + logging.info("Using torch.jit.script") + model = torch.jit.script(model) + filename = params.exp_dir / "cpu_jit.pt" + model.save(str(filename)) + logging.info(f"Saved to {filename}") + else: + logging.info("Not using torch.jit.script") + # Save it using a format so that it can be loaded + # by :func:`load_checkpoint` + filename = params.exp_dir / "pretrained.pt" + torch.save({"model": model.state_dict()}, str(filename)) + logging.info(f"Saved to {filename}") + + +if __name__ == "__main__": + formatter = ( + "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + ) + + logging.basicConfig(format=formatter, level=logging.INFO) + main() diff --git a/egs/ubiqus/ASR/transducer_emformer/joiner.py b/egs/ubiqus/ASR/transducer_emformer/joiner.py new file mode 120000 index 000000000..b3d677eb5 --- /dev/null +++ b/egs/ubiqus/ASR/transducer_emformer/joiner.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless/joiner.py \ No newline at end of file diff --git a/egs/ubiqus/ASR/transducer_emformer/model.py b/egs/ubiqus/ASR/transducer_emformer/model.py new file mode 120000 index 000000000..6b78aed54 --- /dev/null +++ b/egs/ubiqus/ASR/transducer_emformer/model.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless/model.py \ No newline at end of file diff --git a/egs/ubiqus/ASR/transducer_emformer/noam.py b/egs/ubiqus/ASR/transducer_emformer/noam.py new file mode 100644 index 000000000..e46bf35fb --- /dev/null +++ b/egs/ubiqus/ASR/transducer_emformer/noam.py @@ -0,0 +1,104 @@ +# Copyright 2021 University of Chinese Academy of Sciences (author: Han Zhu) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch + + +class Noam(object): + """ + Implements Noam optimizer. + + Proposed in + "Attention Is All You Need", https://arxiv.org/pdf/1706.03762.pdf + + Modified from + https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/transformer/optimizer.py # noqa + + Args: + params: + iterable of parameters to optimize or dicts defining parameter groups + model_size: + attention dimension of the transformer model + factor: + learning rate factor + warm_step: + warmup steps + """ + + def __init__( + self, + params, + model_size: int = 256, + factor: float = 10.0, + warm_step: int = 25000, + weight_decay=0, + ) -> None: + """Construct an Noam object.""" + self.optimizer = torch.optim.Adam( + params, lr=0, betas=(0.9, 0.98), eps=1e-9, weight_decay=weight_decay + ) + self._step = 0 + self.warmup = warm_step + self.factor = factor + self.model_size = model_size + self._rate = 0 + + @property + def param_groups(self): + """Return param_groups.""" + return self.optimizer.param_groups + + def step(self): + """Update parameters and rate.""" + self._step += 1 + rate = self.rate() + for p in self.optimizer.param_groups: + p["lr"] = rate + self._rate = rate + self.optimizer.step() + + def rate(self, step=None): + """Implement `lrate` above.""" + if step is None: + step = self._step + return ( + self.factor + * self.model_size ** (-0.5) + * min(step ** (-0.5), step * self.warmup ** (-1.5)) + ) + + def zero_grad(self): + """Reset gradient.""" + self.optimizer.zero_grad() + + def state_dict(self): + """Return state_dict.""" + return { + "_step": self._step, + "warmup": self.warmup, + "factor": self.factor, + "model_size": self.model_size, + "_rate": self._rate, + "optimizer": self.optimizer.state_dict(), + } + + def load_state_dict(self, state_dict): + """Load state_dict.""" + for key, value in state_dict.items(): + if key == "optimizer": + self.optimizer.load_state_dict(state_dict["optimizer"]) + else: + setattr(self, key, value) diff --git a/egs/ubiqus/ASR/transducer_emformer/streaming_decode.py b/egs/ubiqus/ASR/transducer_emformer/streaming_decode.py new file mode 100755 index 000000000..8ebfbb210 --- /dev/null +++ b/egs/ubiqus/ASR/transducer_emformer/streaming_decode.py @@ -0,0 +1,748 @@ +#!/usr/bin/env python3 +# +# Copyright 2021 Xiaomi Corporation (Author: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import logging +import warnings +from pathlib import Path +from typing import List, Optional, Tuple + +import k2 +import numpy as np +import sentencepiece as spm +import torch +import torch.nn as nn +from asr_datamodule import LibriSpeechAsrDataModule +from beam_search import Hypothesis, HypothesisList, get_hyps_shape +from emformer import LOG_EPSILON, stack_states, unstack_states +from streaming_feature_extractor import FeatureExtractionStream +from train import add_model_arguments, get_params, get_transducer_model + +from icefall.checkpoint import ( + average_checkpoints, + find_checkpoints, + load_checkpoint, +) +from icefall.utils import AttributeDict, setup_logger + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--epoch", + type=int, + default=28, + help="It specifies the checkpoint to use for decoding." + "Note: Epoch counts from 0.", + ) + + parser.add_argument( + "--avg", + type=int, + default=15, + help="Number of checkpoints to average. Automatically select " + "consecutive checkpoints before the checkpoint specified by " + "'--epoch'. ", + ) + + parser.add_argument( + "--avg-last-n", + type=int, + default=0, + help="""If positive, --epoch and --avg are ignored and it + will use the last n checkpoints exp_dir/checkpoint-xxx.pt + where xxx is the number of processed batches while + saving that checkpoint. + """, + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="transducer_emformer/exp", + help="The experiment dir", + ) + + parser.add_argument( + "--bpe-model", + type=str, + default="data/lang_bpe_500/bpe.model", + help="Path to the BPE model", + ) + + parser.add_argument( + "--decoding-method", + type=str, + default="greedy_search", + help="""Possible values are: + - greedy_search + - beam_search + - modified_beam_search + - fast_beam_search + """, + ) + + parser.add_argument( + "--beam-size", + type=int, + default=4, + help="""An interger indicating how many candidates we will keep for each + frame. Used only when --decoding-method is beam_search or + modified_beam_search.""", + ) + + parser.add_argument( + "--beam", + type=float, + default=4, + help="""A floating point value to calculate the cutoff score during beam + search (i.e., `cutoff = max-score - beam`), which is the same as the + `beam` in Kaldi. + Used only when --decoding-method is fast_beam_search""", + ) + + parser.add_argument( + "--max-contexts", + type=int, + default=4, + help="""Used only when --decoding-method is + fast_beam_search""", + ) + + parser.add_argument( + "--max-states", + type=int, + default=8, + help="""Used only when --decoding-method is + fast_beam_search""", + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; " + "2 means tri-gram", + ) + parser.add_argument( + "--max-sym-per-frame", + type=int, + default=1, + help="""Maximum number of symbols per frame. + Used only when --decoding_method is greedy_search""", + ) + + parser.add_argument( + "--sampling-rate", + type=float, + default=16000, + help="Sample rate of the audio", + ) + + add_model_arguments(parser) + + return parser + + +class StreamingAudioSamples(object): + """This class takes as input a list of audio samples and returns + them in a streaming fashion. + """ + + def __init__(self, samples: List[torch.Tensor]) -> None: + """ + Args: + samples: + A list of audio samples. Each entry is a 1-D tensor of dtype + torch.float32, containing the audio samples of an utterance. + """ + self.samples = samples + self.cur_indexes = [0] * len(self.samples) + + @property + def done(self) -> bool: + """Return True if all samples have been processed. + Return False otherwise. + """ + for i, samples in zip(self.cur_indexes, self.samples): + if i < samples.numel(): + return False + return True + + def get_next(self) -> List[torch.Tensor]: + """Return a list of audio samples. Each entry may have different + lengths. It is OK if an entry contains no samples at all, which + means it reaches the end of the utterance. + """ + ans = [] + + num = [1024] * len(self.samples) + + for i in range(len(self.samples)): + start = self.cur_indexes[i] + end = start + num[i] + self.cur_indexes[i] = end + + s = self.samples[i][start:end] + ans.append(s) + + return ans + + +class StreamList(object): + def __init__( + self, + batch_size: int, + context_size: int, + decoding_method: str, + ): + """ + Args: + batch_size: + Size of this batch. + context_size: + Context size of the RNN-T decoder model. + decoding_method: + Decoding method. The possible values are: + - greedy_search + - modified_beam_search + """ + + self.streams = [ + FeatureExtractionStream( + context_size=context_size, decoding_method=decoding_method + ) + for _ in range(batch_size) + ] + + @property + def done(self) -> bool: + """Return True if all streams have reached end of utterance. + That is, no more audio samples are available for all utterances. + """ + return all(stream.done for stream in self.streams) + + def accept_waveform( + self, + audio_samples: List[torch.Tensor], + sampling_rate: float, + ): + """Feed audio samples to each stream. + Args: + audio_samples: + A list of 1-D tensors containing the audio samples for each + utterance in the batch. If an entry is empty, it means + end-of-utterance has been reached. + sampling_rate: + Sampling rate of the given audio samples. + """ + assert len(audio_samples) == len(self.streams) + for stream, samples in zip(self.streams, audio_samples): + + if stream.done: + assert samples.numel() == 0 + continue + + stream.accept_waveform( + sampling_rate=sampling_rate, + waveform=samples, + ) + + if samples.numel() == 0: + stream.input_finished() + + def build_batch( + self, + chunk_length: int, + segment_length: int, + ) -> Tuple[Optional[torch.Tensor], Optional[List[FeatureExtractionStream]]]: + """ + Args: + chunk_length: + Number of frames for each chunk. It equals to + ``segment_length + right_context_length``. + segment_length + Number of frames for each segment. + Returns: + Return a tuple containing: + - features, a 3-D tensor of shape ``(num_active_streams, T, C)`` + - active_streams, a list of active streams. We say a stream is + active when it has enough feature frames to be fed into the + encoder model. + """ + feature_list = [] + stream_list = [] + for stream in self.streams: + if len(stream.feature_frames) >= chunk_length: + # this_chunk is a list of tensors, each of which + # has a shape (1, feature_dim) + chunk = stream.feature_frames[:chunk_length] + stream.feature_frames = stream.feature_frames[segment_length:] + features = torch.cat(chunk, dim=0) + feature_list.append(features) + stream_list.append(stream) + elif stream.done and len(stream.feature_frames) > 0: + chunk = stream.feature_frames[:chunk_length] + stream.feature_frames = [] + features = torch.cat(chunk, dim=0) + features = torch.nn.functional.pad( + features, + (0, 0, 0, chunk_length - features.size(0)), + mode="constant", + value=LOG_EPSILON, + ) + feature_list.append(features) + stream_list.append(stream) + + if len(feature_list) == 0: + return None, None + + features = torch.stack(feature_list, dim=0) + return features, stream_list + + +def greedy_search( + model: nn.Module, + streams: List[FeatureExtractionStream], + encoder_out: torch.Tensor, + sp: spm.SentencePieceProcessor, +): + """ + Args: + model: + The RNN-T model. + streams: + A list of stream objects. + encoder_out: + A 3-D tensor of shape (N, T, encoder_out_dim) containing the output of + the encoder model. + sp: + The BPE model. + """ + assert len(streams) == encoder_out.size(0) + assert encoder_out.ndim == 3 + + blank_id = model.decoder.blank_id + context_size = model.decoder.context_size + device = model.device + T = encoder_out.size(1) + + if streams[0].decoder_out is None: + for stream in streams: + stream.hyp = [blank_id] * context_size + decoder_input = torch.tensor( + [stream.hyp[-context_size:] for stream in streams], + device=device, + dtype=torch.int64, + ) + decoder_out = model.decoder(decoder_input, need_pad=False).squeeze(1) + # decoder_out is of shape (N, decoder_out_dim) + else: + decoder_out = torch.stack( + [stream.decoder_out for stream in streams], + dim=0, + ) + + for t in range(T): + current_encoder_out = encoder_out[:, t] + # current_encoder_out's shape: (batch_size, encoder_out_dim) + + logits = model.joiner(current_encoder_out, decoder_out) + # logits'shape (batch_size, vocab_size) + + assert logits.ndim == 2, logits.shape + y = logits.argmax(dim=1).tolist() + emitted = False + for i, v in enumerate(y): + if v != blank_id: + streams[i].hyp.append(v) + emitted = True + if emitted: + # update decoder output + decoder_input = torch.tensor( + [stream.hyp[-context_size:] for stream in streams], + device=device, + dtype=torch.int64, + ) + decoder_out = model.decoder( + decoder_input, + need_pad=False, + ).squeeze(1) + + for k, stream in enumerate(streams): + result = sp.decode(stream.decoding_result()) + logging.info(f"Partial result {k}:\n{result}") + + decoder_out_list = decoder_out.unbind(dim=0) + for i, d in enumerate(decoder_out_list): + streams[i].decoder_out = d + + +def modified_beam_search( + model: nn.Module, + streams: List[FeatureExtractionStream], + encoder_out: torch.Tensor, + sp: spm.SentencePieceProcessor, + beam: int = 4, +): + """ + Args: + model: + The RNN-T model. + streams: + A list of stream objects. + encoder_out: + A 3-D tensor of shape (N, T, encoder_out_dim) containing the output of + the encoder model. + sp: + The BPE model. + beam: + Number of active paths during the beam search. + """ + assert encoder_out.ndim == 3, encoder_out.shape + assert len(streams) == encoder_out.size(0) + + blank_id = model.decoder.blank_id + context_size = model.decoder.context_size + device = model.device + batch_size = len(streams) + T = encoder_out.size(1) + + for stream in streams: + if len(stream.hyps) == 0: + stream.hyps.add( + Hypothesis( + ys=[blank_id] * context_size, + log_prob=torch.zeros(1, dtype=torch.float32, device=device), + ) + ) + B = [stream.hyps for stream in streams] + for t in range(T): + current_encoder_out = encoder_out[:, t] + # current_encoder_out's shape: (batch_size, encoder_out_dim) + + hyps_shape = get_hyps_shape(B).to(device) + + A = [list(b) for b in B] + B = [HypothesisList() for _ in range(batch_size)] + + ys_log_probs = torch.stack( + [hyp.log_prob.reshape(1) for hyps in A for hyp in hyps], dim=0 + ) # (num_hyps, 1) + + decoder_input = torch.tensor( + [hyp.ys[-context_size:] for hyps in A for hyp in hyps], + device=device, + dtype=torch.int64, + ) # (num_hyps, context_size) + + decoder_out = model.decoder(decoder_input, need_pad=False).squeeze(1) + # decoder_out is of shape (num_hyps, decoder_output_dim) + + # Note: For torch 1.7.1 and below, it requires a torch.int64 tensor + # as index, so we use `to(torch.int64)` below. + current_encoder_out = torch.index_select( + current_encoder_out, + dim=0, + index=hyps_shape.row_ids(1).to(torch.int64), + ) # (num_hyps, encoder_out_dim) + + logits = model.joiner(current_encoder_out, decoder_out) + # logits is of shape (num_hyps, vocab_size) + + log_probs = logits.log_softmax(dim=-1) # (num_hyps, vocab_size) + + log_probs.add_(ys_log_probs) + + vocab_size = log_probs.size(-1) + + log_probs = log_probs.reshape(-1) + + row_splits = hyps_shape.row_splits(1) * vocab_size + log_probs_shape = k2.ragged.create_ragged_shape2( + row_splits=row_splits, cached_tot_size=log_probs.numel() + ) + ragged_log_probs = k2.RaggedTensor( + shape=log_probs_shape, value=log_probs + ) + + for i in range(batch_size): + topk_log_probs, topk_indexes = ragged_log_probs[i].topk(beam) + + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + topk_hyp_indexes = (topk_indexes // vocab_size).tolist() + topk_token_indexes = (topk_indexes % vocab_size).tolist() + + for k in range(len(topk_hyp_indexes)): + hyp_idx = topk_hyp_indexes[k] + hyp = A[i][hyp_idx] + + new_ys = hyp.ys[:] + new_token = topk_token_indexes[k] + if new_token != blank_id: + new_ys.append(new_token) + + new_log_prob = topk_log_probs[k] + new_hyp = Hypothesis(ys=new_ys, log_prob=new_log_prob) + B[i].add(new_hyp) + + streams[i].hyps = B[i] + result = sp.decode(streams[i].decoding_result()) + logging.info(f"Partial result {i}:\n{result}") + + +def process_features( + model: nn.Module, + features: torch.Tensor, + streams: List[FeatureExtractionStream], + params: AttributeDict, + sp: spm.SentencePieceProcessor, +) -> None: + """Process features for each stream in parallel. + + Args: + model: + The RNN-T model. + features: + A 3-D tensor of shape (N, T, C). + streams: + A list of streams of size (N,). + params: + It is the return value of :func:`get_params`. + sp: + The BPE model. + """ + assert features.ndim == 3 + assert features.size(0) == len(streams) + batch_size = features.size(0) + + device = model.device + features = features.to(device) + feature_lens = torch.full( + (batch_size,), + fill_value=features.size(1), + device=device, + ) + + # Caution: It has a limitation as it assumes that + # if one of the stream has an empty state, then all other + # streams also have empty states. + if streams[0].states is None: + states = None + else: + state_list = [stream.states for stream in streams] + states = stack_states(state_list) + + (encoder_out, encoder_out_lens, states,) = model.encoder.streaming_forward( + features, + feature_lens, + states, + ) + state_list = unstack_states(states) + for i, s in enumerate(state_list): + streams[i].states = s + + if params.decoding_method == "greedy_search": + greedy_search( + model=model, + streams=streams, + encoder_out=encoder_out, + sp=sp, + ) + elif params.decoding_method == "modified_beam_search": + modified_beam_search( + model=model, + streams=streams, + encoder_out=encoder_out, + sp=sp, + beam=params.beam_size, + ) + else: + raise ValueError( + f"Unsupported decoding method: {params.decoding_method}" + ) + + +def decode_batch( + batched_samples: List[torch.Tensor], + model: nn.Module, + params: AttributeDict, + sp: spm.SentencePieceProcessor, +) -> List[str]: + """ + Args: + batched_samples: + A list of 1-D tensors containing the audio samples of each utterance. + model: + The RNN-T model. + params: + It is the return value of :func:`get_params`. + sp: + The BPE model. + """ + # number of frames before subsampling + segment_length = model.encoder.segment_length + + right_context_length = model.encoder.right_context_length + + # We add 3 here since the subsampling method is using + # ((len - 1) // 2 - 1) // 2) + chunk_length = (segment_length + 3) + right_context_length + + batch_size = len(batched_samples) + streaming_audio_samples = StreamingAudioSamples(batched_samples) + + stream_list = StreamList( + batch_size=batch_size, + context_size=params.context_size, + decoding_method=params.decoding_method, + ) + + while not streaming_audio_samples.done: + samples = streaming_audio_samples.get_next() + stream_list.accept_waveform( + audio_samples=samples, + sampling_rate=params.sampling_rate, + ) + features, active_streams = stream_list.build_batch( + chunk_length=chunk_length, + segment_length=segment_length, + ) + if features is not None: + process_features( + model=model, + features=features, + streams=active_streams, + params=params, + sp=sp, + ) + results = [] + for stream in stream_list.streams: + text = sp.decode(stream.decoding_result()) + results.append(text) + return results + + +@torch.no_grad() +def main(): + parser = get_parser() + LibriSpeechAsrDataModule.add_arguments(parser) + args = parser.parse_args() + args.exp_dir = Path(args.exp_dir) + + params = get_params() + params.update(vars(args)) + + # Note: params.decoding_method is currently not used. + params.res_dir = params.exp_dir / "streaming" / params.decoding_method + + setup_logger(f"{params.res_dir}/log-streaming-decode") + logging.info("Decoding started") + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"Device: {device}") + + sp = spm.SentencePieceProcessor() + sp.load(params.bpe_model) + + # is defined in local/train_bpe_model.py + params.blank_id = sp.piece_to_id("") + params.vocab_size = sp.get_piece_size() + + params.device = device + + logging.info(params) + + logging.info("About to create model") + model = get_transducer_model(params) + + if params.avg_last_n > 0: + filenames = find_checkpoints(params.exp_dir)[: params.avg_last_n] + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + elif params.avg == 1: + load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) + else: + start = params.epoch - params.avg + 1 + filenames = [] + for i in range(start, params.epoch + 1): + if start >= 0: + filenames.append(f"{params.exp_dir}/epoch-{i}.pt") + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + + model.to(device) + model.eval() + model.device = device + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + librispeech = LibriSpeechAsrDataModule(args) + + test_clean_cuts = librispeech.test_clean_cuts() + + batch_size = 3 + + ground_truth = [] + batched_samples = [] + for num, cut in enumerate(test_clean_cuts): + audio: np.ndarray = cut.load_audio() + # audio.shape: (1, num_samples) + assert len(audio.shape) == 2 + assert audio.shape[0] == 1, "Should be single channel" + assert audio.dtype == np.float32, audio.dtype + + # The trained model is using normalized samples + assert audio.max() <= 1, "Should be normalized to [-1, 1])" + + samples = torch.from_numpy(audio).squeeze(0) + + batched_samples.append(samples) + ground_truth.append(cut.supervisions[0].text) + + if len(batched_samples) >= batch_size: + decoded_results = decode_batch( + batched_samples=batched_samples, + model=model, + params=params, + sp=sp, + ) + s = "\n" + for i, (hyp, ref) in enumerate(zip(decoded_results, ground_truth)): + s += f"hyp {i}:\n{hyp}\n" + s += f"ref {i}:\n{ref}\n\n" + logging.info(s) + batched_samples = [] + ground_truth = [] + # break after processing the first batch for test purposes + break + + +if __name__ == "__main__": + torch.manual_seed(20220410) + main() diff --git a/egs/ubiqus/ASR/transducer_emformer/streaming_feature_extractor.py b/egs/ubiqus/ASR/transducer_emformer/streaming_feature_extractor.py new file mode 100644 index 000000000..ea323103b --- /dev/null +++ b/egs/ubiqus/ASR/transducer_emformer/streaming_feature_extractor.py @@ -0,0 +1,132 @@ +# Copyright 2022 Xiaomi Corp. (authors: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import List, Optional + +import torch +from beam_search import HypothesisList +from kaldifeat import FbankOptions, OnlineFbank, OnlineFeature + + +def _create_streaming_feature_extractor() -> OnlineFeature: + """Create a CPU streaming feature extractor. + + At present, we assume it returns a fbank feature extractor with + fixed options. In the future, we will support passing in the options + from outside. + + Returns: + Return a CPU streaming feature extractor. + """ + opts = FbankOptions() + opts.device = "cpu" + opts.frame_opts.dither = 0 + opts.frame_opts.snip_edges = False + opts.frame_opts.samp_freq = 16000 + opts.mel_opts.num_bins = 80 + return OnlineFbank(opts) + + +class FeatureExtractionStream(object): + def __init__(self, context_size: int, decoding_method: str) -> None: + """ + Args: + context_size: + Context size of the RNN-T decoder model. + decoding_method: + Decoding method. The possible values are: + - greedy_search + - modified_beam_search + """ + self.feature_extractor = _create_streaming_feature_extractor() + # It contains a list of 1-D tensors representing the feature frames. + self.feature_frames: List[torch.Tensor] = [] + self.num_fetched_frames = 0 + # After calling `self.input_finished()`, we set this flag to True + self._done = False + + # For the emformer model, it contains the states of each + # encoder layer. + self.states: Optional[List[List[torch.Tensor]]] = None + + # It use different attributes for different decoding methods. + self.context_size = context_size + self.decoding_method = decoding_method + if decoding_method == "greedy_search": + self.hyp: Optional[List[int]] = None + self.decoder_out: Optional[torch.Tensor] = None + elif decoding_method == "modified_beam_search": + self.hyps = HypothesisList() + else: + raise ValueError(f"Unsupported decoding method: {decoding_method}") + + def accept_waveform( + self, + sampling_rate: float, + waveform: torch.Tensor, + ) -> None: + """Feed audio samples to the feature extractor and compute features + if there are enough samples available. + + Caution: + The range of the audio samples should match the one used in the + training. That is, if you use the range [-1, 1] in the training, then + the input audio samples should also be normalized to [-1, 1]. + + Args + sampling_rate: + The sampling rate of the input audio samples. It is used for sanity + check to ensure that the input sampling rate equals to the one + used in the extractor. If they are not equal, then no resampling + will be performed; instead an error will be thrown. + waveform: + A 1-D torch tensor of dtype torch.float32 containing audio samples. + It should be on CPU. + """ + self.feature_extractor.accept_waveform( + sampling_rate=sampling_rate, + waveform=waveform, + ) + self._fetch_frames() + + def input_finished(self) -> None: + """Signal that no more audio samples available and the feature + extractor should flush the buffered samples to compute frames. + """ + self.feature_extractor.input_finished() + self._fetch_frames() + self._done = True + + @property + def done(self) -> bool: + """Return True if `self.input_finished()` has been invoked""" + return self._done + + def _fetch_frames(self) -> None: + """Fetch frames from the feature extractor""" + while self.num_fetched_frames < self.feature_extractor.num_frames_ready: + frame = self.feature_extractor.get_frame(self.num_fetched_frames) + self.feature_frames.append(frame) + self.num_fetched_frames += 1 + + def decoding_result(self) -> List[int]: + """Obtain current decoding result.""" + if self.decoding_method == "greedy_search": + return self.hyp[self.context_size :] + else: + assert self.decoding_method == "modified_beam_search" + best_hyp = self.hyps.get_most_probable(length_norm=True) + return best_hyp.ys[self.context_size :] diff --git a/egs/ubiqus/ASR/transducer_emformer/subsampling.py b/egs/ubiqus/ASR/transducer_emformer/subsampling.py new file mode 120000 index 000000000..16354dc73 --- /dev/null +++ b/egs/ubiqus/ASR/transducer_emformer/subsampling.py @@ -0,0 +1 @@ +../../../librispeech/ASR/conformer_ctc/subsampling.py \ No newline at end of file diff --git a/egs/ubiqus/ASR/transducer_emformer/test_emformer.py b/egs/ubiqus/ASR/transducer_emformer/test_emformer.py new file mode 100755 index 000000000..239ed24ac --- /dev/null +++ b/egs/ubiqus/ASR/transducer_emformer/test_emformer.py @@ -0,0 +1,107 @@ +#!/usr/bin/env python3 +# Copyright 2022 Xiaomi Corp. (authors: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +To run this file, do: + + cd icefall/egs/librispeech/ASR + python ./transducer_emformer/test_emformer.py +""" + +import warnings + +import torch +from emformer import Emformer, stack_states, unstack_states + + +def test_emformer(): + N = 3 + T = 300 + C = 80 + + output_dim = 500 + + encoder = Emformer( + num_features=C, + output_dim=output_dim, + d_model=512, + nhead=8, + dim_feedforward=2048, + num_encoder_layers=20, + segment_length=16, + left_context_length=120, + right_context_length=4, + vgg_frontend=False, + ) + + x = torch.rand(N, T, C) + x_lens = torch.randint(100, T, (N,)) + x_lens[0] = T + + y, y_lens = encoder(x, x_lens) + + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + assert (y_lens == ((x_lens - 1) // 2 - 1) // 2).all() + assert x.size(0) == x.size(0) + assert y.size(1) == max(y_lens) + assert y.size(2) == output_dim + + num_param = sum([p.numel() for p in encoder.parameters()]) + print(f"Number of encoder parameters: {num_param}") + + +def test_emformer_streaming_forward(): + N = 3 + C = 80 + + output_dim = 500 + + encoder = Emformer( + num_features=C, + output_dim=output_dim, + d_model=512, + nhead=8, + dim_feedforward=2048, + num_encoder_layers=20, + segment_length=16, + left_context_length=120, + right_context_length=4, + vgg_frontend=False, + ) + + x = torch.rand(N, 23, C) + x_lens = torch.full((N,), 23) + y, y_lens, states = encoder.streaming_forward(x=x, x_lens=x_lens) + + state_list = unstack_states(states) + states2 = stack_states(state_list) + + for ss, ss2 in zip(states, states2): + for s, s2 in zip(ss, ss2): + assert torch.allclose(s, s2), f"{s.sum()}, {s2.sum()}" + + +@torch.no_grad() +def main(): + # test_emformer() + test_emformer_streaming_forward() + + +if __name__ == "__main__": + torch.manual_seed(20220329) + main() diff --git a/egs/ubiqus/ASR/transducer_emformer/test_streaming_feature_extractor.py b/egs/ubiqus/ASR/transducer_emformer/test_streaming_feature_extractor.py new file mode 100755 index 000000000..4ce9c3284 --- /dev/null +++ b/egs/ubiqus/ASR/transducer_emformer/test_streaming_feature_extractor.py @@ -0,0 +1,53 @@ +#!/usr/bin/env python3 +# Copyright 2022 Xiaomi Corp. (authors: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +To run this file, do: + + cd icefall/egs/librispeech/ASR + python ./transducer_emformer/test_streaming_feature_extractor.py +""" + +import torch +from streaming_feature_extractor import FeatureExtractionStream + + +def test_streaming_feature_extractor(): + stream = FeatureExtractionStream(context_size=2, blank_id=0) + samples = torch.rand(16000) + start = 0 + while True: + n = torch.randint(50, 500, (1,)).item() + end = start + n + this_chunk = samples[start:end] + start = end + + if len(this_chunk) == 0: + break + stream.accept_waveform(sampling_rate=16000, waveform=this_chunk) + print(len(stream.feature_frames)) + stream.input_finished() + print(len(stream.feature_frames)) + + +def main(): + test_streaming_feature_extractor() + + +if __name__ == "__main__": + main() diff --git a/egs/ubiqus/ASR/transducer_emformer/tokenizer.py b/egs/ubiqus/ASR/transducer_emformer/tokenizer.py new file mode 100644 index 000000000..d312071eb --- /dev/null +++ b/egs/ubiqus/ASR/transducer_emformer/tokenizer.py @@ -0,0 +1,57 @@ +# sp = spm.SentencePieceProcessor() +# sp.load(params.bpe_model) + +# # is defined in local/train_bpe_model.py +# params.blank_id = sp.piece_to_id("") +# params.vocab_size = sp.get_piece_size() +# sp.encode(texts, out_type=int) +from typing import List + + +import pyonmttok + + +class PyonmttokProcessor: + def __init__(self): + self.tok = None + + def load(self, path: str) -> None: + args = { + "mode": "aggressive", + "joiner_annotate": True, + "preserve_placeholders": True, + "case_markup": True, + "soft_case_regions": True, + "preserve_segmented_tokens": True, + } + self.tok = pyonmttok.Tokenizer( + **args, + bpe_model_path="/data/bpe.pyonmttok", + vocabulary_path="/data/vocab" + ) + self.vocab = [] + self.reverse_vocab = dict() + with open("/data/vocab", "r") as f: + for i, l in enumerate(f): + word = l.rstrip("\n") + self.vocab.append(word) + self.reverse_vocab[word] = i + + def piece_to_id(self, token: str) -> int: + return self.reverse_vocab.get(token, self.reverse_vocab[""]) + + def encode(self, texts: List[str], out_type: type = int) -> List[int]: + batch_tokens = [self.tok.tokenize(text)[0] for text in texts] + # print(texts) + # print(batch_tokens) + if out_type == str: + return batch_tokens + elif out_type == int: + return [ + [self.piece_to_id(token) for token in tokens] + for tokens in batch_tokens + ] + raise ValueError + + def get_piece_size(self) -> int: + return len(self.vocab) diff --git a/egs/ubiqus/ASR/transducer_emformer/train.py b/egs/ubiqus/ASR/transducer_emformer/train.py new file mode 100755 index 000000000..72ae8f319 --- /dev/null +++ b/egs/ubiqus/ASR/transducer_emformer/train.py @@ -0,0 +1,1014 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang, +# Wei Kang +# Mingshuang Luo) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Usage: + +cd icefall + +pip install -r requirements.txt +pip install pyonmttok + +cd egs/ubiqus/ASR + +export CUDA_VISIBLE_DEVICES="0" + +git config --global --add safe.directory /workspace/icefall + +./transducer_emformer/train.py \ + --world-size 1 \ + --num-epochs 30 \ + --start-epoch 0 \ + --exp-dir transducer_emformer_lr/exp \ + --lr-factor 2 \ + --max-duration 50 + +tolerance +add to vocab +squeeze + ontheflyfeature + no specaug +""" + + +import argparse +import logging +import warnings +from pathlib import Path +from shutil import copyfile +from typing import Any, Dict, Optional, Tuple + +import k2 +import sentencepiece as spm +from tokenizer import PyonmttokProcessor +import torch +import torch.multiprocessing as mp +import torch.nn as nn +from asr_datamodule import UbiqusAsrDataModule +from decoder import Decoder +from emformer import Emformer +from joiner import Joiner +from lhotse.cut import Cut +from lhotse.dataset.sampling.base import CutSampler +from lhotse.utils import fix_random_seed +from model import Transducer +from noam import Noam +from torch import Tensor +from torch.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, remove_checkpoints +from icefall.checkpoint import save_checkpoint as save_checkpoint_impl +from icefall.checkpoint import save_checkpoint_with_global_batch_idx +from icefall.dist import cleanup_dist, setup_dist +from icefall.env import get_env_info +from icefall.utils import ( + AttributeDict, + MetricsTracker, + measure_gradient_norms, + measure_weight_norms, + optim_step_and_measure_param_change, + setup_logger, + str2bool, +) + + +def add_model_arguments(parser: argparse.ArgumentParser): + parser.add_argument( + "--attention-dim", + type=int, + default=512, + help="Attention dim for the Emformer", + ) + + parser.add_argument( + "--nhead", + type=int, + default=8, + help="Number of attention heads for the Emformer", + ) + + parser.add_argument( + "--dim-feedforward", + type=int, + default=2048, + help="Feed-forward dimension for the Emformer", + ) + + parser.add_argument( + "--num-encoder-layers", + type=int, + default=12, + help="Number of encoder layers for the Emformer", + ) + + parser.add_argument( + "--left-context-length", + type=int, + default=120, + help="Number of frames for the left context in the Emformer", + ) + + parser.add_argument( + "--segment-length", + type=int, + default=16, + help="Number of frames for each segment in the Emformer", + ) + + parser.add_argument( + "--right-context-length", + type=int, + default=4, + help="Number of frames for right context in the Emformer", + ) + + parser.add_argument( + "--memory-size", + type=int, + default=0, + help="Number of entries in the memory for the Emformer", + ) + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--world-size", + type=int, + default=1, + help="Number of GPUs for DDP training.", + ) + + parser.add_argument( + "--master-port", + type=int, + default=12354, + help="Master port to use for DDP training.", + ) + + parser.add_argument( + "--tensorboard", + type=str2bool, + default=True, + help="Should various information be logged in tensorboard.", + ) + + parser.add_argument( + "--num-epochs", + type=int, + default=30, + help="Number of epochs to train.", + ) + + parser.add_argument( + "--start-epoch", + type=int, + default=0, + help="""Resume training from from this epoch. + If it is positive, it will load checkpoint from + transducer_emformer/exp/epoch-{start_epoch-1}.pt + """, + ) + + parser.add_argument( + "--start-batch", + type=int, + default=0, + help="""If positive, --start-epoch is ignored and + it loads the checkpoint from exp-dir/checkpoint-{start_batch}.pt + """, + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="transducer_emformer/exp", + help="""The experiment dir. + It specifies the directory where all training related + files, e.g., checkpoints, log, etc, are saved + """, + ) + + parser.add_argument( + "--bpe-model", + type=str, + default="data/lang_bpe_500/bpe.model", + help="Path to the BPE model", + ) + + parser.add_argument( + "--lr-factor", + type=float, + default=5.0, + help="The lr_factor for Noam optimizer", + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; " + "2 means tri-gram", + ) + + parser.add_argument( + "--prune-range", + type=int, + default=5, + help="The prune range for rnnt loss, it means how many symbols(context)" + "we are using to compute the loss", + ) + + parser.add_argument( + "--lm-scale", + type=float, + default=0.25, + help="The scale to smooth the loss with lm " + "(output of prediction network) part.", + ) + + parser.add_argument( + "--am-scale", + type=float, + default=0.0, + help="The scale to smooth the loss with am (output of encoder network)" + "part.", + ) + + parser.add_argument( + "--simple-loss-scale", + type=float, + default=0.5, + help="To get pruning ranges, we will calculate a simple version" + "loss(joiner is just addition), this simple loss also uses for" + "training (as a regularization item). We will scale the simple loss" + "with this parameter before adding to the final loss.", + ) + + parser.add_argument( + "--seed", + type=int, + default=42, + help="The seed for random generators intended for reproducibility", + ) + + parser.add_argument( + "--save-every-n", + type=int, + default=8000, + help="""Save checkpoint after processing this number of batches" + periodically. We save checkpoint to exp-dir/ whenever + params.batch_idx_train % save_every_n == 0. The checkpoint filename + has the form: f'exp-dir/checkpoint-{params.batch_idx_train}.pt' + Note: It also saves checkpoint to `exp-dir/epoch-xxx.pt` at the + end of each epoch where `xxx` is the epoch number counting from 0. + """, + ) + + parser.add_argument( + "--keep-last-k", + type=int, + default=1, + help="""Only keep this number of checkpoints on disk. + For instance, if it is 3, there are only 3 checkpoints + in the exp-dir with filenames `checkpoint-xxx.pt`. + It does not affect checkpoints with name `epoch-xxx.pt`. + """, + ) + + add_model_arguments(parser) + + return parser + + +def get_params() -> AttributeDict: + """Return a dict containing training parameters. + + All training related parameters that are not passed from the commandline + are saved in the variable `params`. + + Commandline options are merged into `params` after they are parsed, so + you can also access them via `params`. + + Explanation of options saved in `params`: + + - best_train_loss: Best training loss so far. It is used to select + the model that has the lowest training loss. It is + updated during the training. + + - best_valid_loss: Best validation loss so far. It is used to select + the model that has the lowest validation loss. It is + updated during the training. + + - best_train_epoch: It is the epoch that has the best training loss. + + - best_valid_epoch: It is the epoch that has the best validation loss. + + - batch_idx_train: Used to writing statistics to tensorboard. It + contains number of batches trained so far across + epochs. + + - log_interval: Print training loss if batch_idx % log_interval` is 0 + + - reset_interval: Reset statistics if batch_idx % reset_interval is 0 + + - valid_interval: Run validation if batch_idx % valid_interval is 0 + + - feature_dim: The model input dim. It has to match the one used + in computing features. + + - subsampling_factor: The subsampling factor for the model. + + - attention_dim: Hidden dim for multi-head attention model. + + - num_decoder_layers: Number of decoder layer of transformer decoder. + + - warm_step: The warm_step for Noam optimizer. + """ + params = AttributeDict( + { + "best_train_loss": float("inf"), + "best_valid_loss": float("inf"), + "best_train_epoch": -1, + "best_valid_epoch": -1, + "batch_idx_train": 0, + "log_interval": 50, + "reset_interval": 200, + "valid_interval": 3000, # For the 100h subset, use 800 + "log_diagnostics": False, + # parameters for Emformer + "feature_dim": 80, + "subsampling_factor": 4, + "vgg_frontend": False, + # parameters for decoder + "embedding_dim": 512, + # parameters for Noam + "warm_step": 80000, # For the 100h subset, use 20000 + "env_info": get_env_info(), + } + ) + + return params + + +def get_encoder_model(params: AttributeDict) -> nn.Module: + encoder = Emformer( + num_features=params.feature_dim, + output_dim=params.vocab_size, + subsampling_factor=params.subsampling_factor, + d_model=params.attention_dim, + nhead=params.nhead, + dim_feedforward=params.dim_feedforward, + num_encoder_layers=params.num_encoder_layers, + vgg_frontend=params.vgg_frontend, + left_context_length=params.left_context_length, + segment_length=params.segment_length, + right_context_length=params.right_context_length, + max_memory_size=params.memory_size, + ) + return encoder + + +def get_decoder_model(params: AttributeDict) -> nn.Module: + decoder = Decoder( + vocab_size=params.vocab_size, + embedding_dim=params.embedding_dim, + blank_id=params.blank_id, + unk_id=params.unk_id, + context_size=params.context_size, + ) + return decoder + + +def get_joiner_model(params: AttributeDict) -> nn.Module: + joiner = Joiner( + input_dim=params.vocab_size, + inner_dim=params.embedding_dim, + output_dim=params.vocab_size, + ) + return joiner + + +def get_transducer_model(params: AttributeDict) -> nn.Module: + encoder = get_encoder_model(params) + decoder = get_decoder_model(params) + joiner = get_joiner_model(params) + + model = Transducer( + encoder=encoder, + decoder=decoder, + joiner=joiner, + ) + return model + + +def load_checkpoint_if_available( + params: AttributeDict, + model: nn.Module, + optimizer: Optional[torch.optim.Optimizer] = None, +) -> Optional[Dict[str, Any]]: + """Load checkpoint from file. + + If params.start_batch is positive, it will load the checkpoint from + `params.exp_dir/checkpoint-{params.start_batch}.pt`. Otherwise, if + params.start_epoch is positive, it will load the checkpoint from + `params.start_epoch - 1`. + + Apart from loading state dict for `model` and `optimizer` it also updates + `best_train_epoch`, `best_train_loss`, `best_valid_epoch`, + and `best_valid_loss` in `params`. + + Args: + params: + The return value of :func:`get_params`. + model: + The training model. + optimizer: + The optimizer that we are using. + Returns: + Return a dict containing previously saved training info. + """ + if params.start_batch > 0: + filename = params.exp_dir / f"checkpoint-{params.start_batch}.pt" + elif params.start_epoch > 0: + filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt" + else: + return None + + assert filename.is_file(), f"{filename} does not exist!" + + saved_params = load_checkpoint( + filename, + model=model, + optimizer=optimizer, + ) + + keys = [ + "best_train_epoch", + "best_valid_epoch", + "batch_idx_train", + "best_train_loss", + "best_valid_loss", + ] + for k in keys: + params[k] = saved_params[k] + + if params.start_batch > 0: + if "cur_epoch" in saved_params: + params["start_epoch"] = saved_params["cur_epoch"] + + if "cur_batch_idx" in saved_params: + params["cur_batch_idx"] = saved_params["cur_batch_idx"] + + return saved_params + + +def save_checkpoint( + params: AttributeDict, + model: nn.Module, + optimizer: Optional[torch.optim.Optimizer] = None, + sampler: Optional[CutSampler] = None, + rank: int = 0, +) -> None: + """Save model, optimizer, scheduler and training stats to file. + + Args: + params: + It is returned by :func:`get_params`. + model: + The training model. + optimizer: + The optimizer used in the training. + sampler: + The sampler for the training dataset. + """ + 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, + sampler=sampler, + 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, + sp: spm.SentencePieceProcessor, + batch: dict, + is_training: bool, +) -> Tuple[Tensor, MetricsTracker]: + """ + Compute CTC loss given the model and its inputs. + + Args: + params: + Parameters for training. See :func:`get_params`. + model: + The model for training. It is an instance of Emformer in our case. + batch: + A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()` + for the content in it. + is_training: + True for training. False for validation. When it is True, this + function enables autograd during computation; when it is False, it + disables autograd. + """ + device = model.device + # feature = torch.unsqueeze(batch["inputs"], dim=-1) + feature = batch["inputs"] + # at entry, Emformerfeature is (N, T, C) + assert feature.ndim == 3 + feature = feature.to(device) + + supervisions = batch["supervisions"] + # print(batch["supervisions"].keys()) + # feature_lens = supervisions["num_samples"].to(device) + feature_lens = supervisions["num_frames"].to(device) + + texts = batch["supervisions"]["text"] + y = sp.encode(texts, out_type=int) + y = k2.RaggedTensor(y).to(device) + + with torch.set_grad_enabled(is_training): + simple_loss, pruned_loss = model( + x=feature, + x_lens=feature_lens, + y=y, + prune_range=params.prune_range, + am_scale=params.am_scale, + lm_scale=params.lm_scale, + ) + loss = params.simple_loss_scale * simple_loss + pruned_loss + + assert loss.requires_grad == is_training + + info = MetricsTracker() + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + info["frames"] = ( + (feature_lens // params.subsampling_factor).sum().item() + ) + + # Note: We use reduction=sum while computing the loss. + info["loss"] = loss.detach().cpu().item() + info["simple_loss"] = simple_loss.detach().cpu().item() + info["pruned_loss"] = pruned_loss.detach().cpu().item() + + return loss, info + + +def compute_validation_loss( + params: AttributeDict, + model: nn.Module, + sp: spm.SentencePieceProcessor, + valid_dl: torch.utils.data.DataLoader, + world_size: int = 1, +) -> MetricsTracker: + """Run the validation process.""" + model.eval() + + tot_loss = MetricsTracker() + + for batch_idx, batch in enumerate(valid_dl): + loss, loss_info = compute_loss( + params=params, + model=model, + sp=sp, + batch=batch, + is_training=False, + ) + assert loss.requires_grad is False + tot_loss = tot_loss + loss_info + + if world_size > 1: + tot_loss.reduce(loss.device) + + loss_value = tot_loss["loss"] / tot_loss["frames"] + if loss_value < params.best_valid_loss: + params.best_valid_epoch = params.cur_epoch + params.best_valid_loss = loss_value + + return tot_loss + + +def train_one_epoch( + params: AttributeDict, + model: nn.Module, + optimizer: torch.optim.Optimizer, + sp: spm.SentencePieceProcessor, + train_dl: torch.utils.data.DataLoader, + valid_dl: torch.utils.data.DataLoader, + tb_writer: Optional[SummaryWriter] = None, + world_size: int = 1, + rank: int = 0, +) -> None: + """Train the model for one epoch. + + The training loss from the mean of all frames is saved in + `params.train_loss`. It runs the validation process every + `params.valid_interval` batches. + + Args: + params: + It is returned by :func:`get_params`. + model: + The model for training. + optimizer: + The optimizer we are using. + train_dl: + Dataloader for the training dataset. + valid_dl: + Dataloader for the validation dataset. + tb_writer: + Writer to write log messages to tensorboard. + world_size: + Number of nodes in DDP training. If it is 1, DDP is disabled. + rank: + The rank of the node in DDP training. If no DDP is used, it should + be set to 0. + """ + model.train() + + tot_loss = MetricsTracker() + + def maybe_log_gradients(tag: str): + if ( + params.log_diagnostics + and tb_writer is not None + and params.batch_idx_train % (params.log_interval * 5) == 0 + ): + tb_writer.add_scalars( + tag, + measure_gradient_norms(model, norm="l2"), + global_step=params.batch_idx_train, + ) + + def maybe_log_weights(tag: str): + if ( + params.log_diagnostics + and tb_writer is not None + and params.batch_idx_train % (params.log_interval * 5) == 0 + ): + tb_writer.add_scalars( + tag, + measure_weight_norms(model, norm="l2"), + global_step=params.batch_idx_train, + ) + + def maybe_log_param_relative_changes(): + if ( + params.log_diagnostics + and tb_writer is not None + and params.batch_idx_train % (params.log_interval * 5) == 0 + ): + deltas = optim_step_and_measure_param_change(model, optimizer) + tb_writer.add_scalars( + "train/relative_param_change_per_minibatch", + deltas, + global_step=params.batch_idx_train, + ) + else: + optimizer.step() + + cur_batch_idx = params.get("cur_batch_idx", 0) + + for batch_idx, batch in enumerate(train_dl): + if batch_idx < cur_batch_idx: + continue + cur_batch_idx = batch_idx + + params.batch_idx_train += 1 + batch_size = len(batch["supervisions"]["text"]) + + loss, loss_info = compute_loss( + params=params, + model=model, + sp=sp, + batch=batch, + is_training=True, + ) + # summary stats + tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info + + # NOTE: We use reduction==sum and loss is computed over utterances + # in the batch and there is no normalization to it so far. + + loss.backward() + + maybe_log_weights("train/param_norms") + maybe_log_gradients("train/grad_norms") + maybe_log_param_relative_changes() + + optimizer.zero_grad() + + if ( + params.batch_idx_train > 0 + and params.batch_idx_train % params.save_every_n == 0 + ): + params.cur_batch_idx = batch_idx + save_checkpoint_with_global_batch_idx( + out_dir=params.exp_dir, + global_batch_idx=params.batch_idx_train, + model=model, + params=params, + optimizer=optimizer, + sampler=train_dl.sampler, + rank=rank, + ) + del params.cur_batch_idx + remove_checkpoints( + out_dir=params.exp_dir, + topk=params.keep_last_k, + rank=rank, + ) + + if batch_idx % params.log_interval == 0: + logging.info( + f"Epoch {params.cur_epoch}, " + f"batch {batch_idx}, loss[{loss_info}], " + f"tot_loss[{tot_loss}], batch size: {batch_size}" + ) + + if tb_writer is not None: + loss_info.write_summary( + tb_writer, "train/current_", params.batch_idx_train + ) + tot_loss.write_summary( + tb_writer, "train/tot_", params.batch_idx_train + ) + + if batch_idx > 0 and batch_idx % params.valid_interval == 0: + logging.info("Computing validation loss") + valid_info = compute_validation_loss( + params=params, + model=model, + sp=sp, + valid_dl=valid_dl, + world_size=world_size, + ) + model.train() + logging.info(f"Epoch {params.cur_epoch}, validation: {valid_info}") + if tb_writer is not None: + valid_info.write_summary( + tb_writer, "train/valid_", params.batch_idx_train + ) + + loss_value = tot_loss["loss"] / tot_loss["frames"] + params.train_loss = loss_value + if params.train_loss < params.best_train_loss: + params.best_train_epoch = params.cur_epoch + params.best_train_loss = params.train_loss + + +def run(rank, world_size, args): + """ + Args: + rank: + It is a value between 0 and `world_size-1`, which is + passed automatically by `mp.spawn()` in :func:`main`. + The node with rank 0 is responsible for saving checkpoint. + world_size: + Number of GPUs for DDP training. + args: + The return value of get_parser().parse_args() + """ + params = get_params() + params.update(vars(args)) + # if params.full_libri is False: + # params.valid_interval = 800 + # params.warm_step = 20000 + + fix_random_seed(params.seed) + if world_size > 1: + setup_dist(rank, world_size, params.master_port) + + setup_logger(f"{params.exp_dir}/log/log-train") + logging.info("Training started") + + if args.tensorboard and rank == 0: + tb_writer = SummaryWriter(log_dir=f"{params.exp_dir}/tensorboard") + else: + tb_writer = None + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", rank) + logging.info(f"Device: {device}") + + # sp = spm.SentencePieceProcessor() + sp = PyonmttokProcessor() + sp.load(params.bpe_model) + + # is defined in local/train_bpe_model.py + params.blank_id = sp.piece_to_id("") + params.unk_id = sp.piece_to_id("") + params.vocab_size = sp.get_piece_size() + + logging.info(params) + + logging.info("About to create model") + model = get_transducer_model(params) + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + checkpoints = load_checkpoint_if_available(params=params, model=model) + + model.to(device) + if world_size > 1: + logging.info("Using DDP") + model = DDP(model, device_ids=[rank]) + model.device = device + + optimizer = Noam( + model.parameters(), + model_size=params.attention_dim, + factor=params.lr_factor, + warm_step=params.warm_step, + ) + + if checkpoints and "optimizer" in checkpoints: + logging.info("Loading optimizer state dict") + optimizer.load_state_dict(checkpoints["optimizer"]) + + ubiqus = UbiqusAsrDataModule(args) + + train_cuts = ubiqus.train_cuts() + + def remove_short_and_long_utt(c: Cut): + # Keep only utterances with duration between 1 second and 20 seconds + # + # Caution: There is a reason to select 20.0 here. Please see + # ../local/display_manifest_statistics.py + # + # You should use ../local/display_manifest_statistics.py to get + # an utterance duration distribution for your dataset to select + # the threshold + return 1.0 <= c.duration <= 20.0 + + num_in_total = len(train_cuts) + + train_cuts = train_cuts.filter(remove_short_and_long_utt) + + num_left = len(train_cuts) + num_removed = num_in_total - num_left + removed_percent = num_removed / num_in_total * 100 + + logging.info(f"Before removing short and long utterances: {num_in_total}") + logging.info(f"After removing short and long utterances: {num_left}") + logging.info(f"Removed {num_removed} utterances ({removed_percent:.5f}%)") + + if params.start_batch > 0 and checkpoints and "sampler" in checkpoints: + # We only load the sampler's state dict when it loads a checkpoint + # saved in the middle of an epoch + sampler_state_dict = checkpoints["sampler"] + else: + sampler_state_dict = None + + train_dl = ubiqus.train_dataloaders( + train_cuts, sampler_state_dict=sampler_state_dict + ) + + valid_cuts = ubiqus.dev_cuts() + valid_dl = ubiqus.valid_dataloaders(valid_cuts) + + scan_pessimistic_batches_for_oom( + model=model, + train_dl=train_dl, + optimizer=optimizer, + sp=sp, + params=params, + ) + + for epoch in range(params.start_epoch, params.num_epochs): + fix_random_seed(params.seed + epoch) + train_dl.sampler.set_epoch(epoch) + + cur_lr = optimizer._rate + if tb_writer is not None: + tb_writer.add_scalar( + "train/learning_rate", cur_lr, params.batch_idx_train + ) + tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train) + + if rank == 0: + logging.info("epoch {}, learning rate {}".format(epoch, cur_lr)) + + params.cur_epoch = epoch + + train_one_epoch( + params=params, + model=model, + optimizer=optimizer, + sp=sp, + train_dl=train_dl, + valid_dl=valid_dl, + tb_writer=tb_writer, + world_size=world_size, + rank=rank, + ) + + save_checkpoint( + params=params, + model=model, + optimizer=optimizer, + sampler=train_dl.sampler, + rank=rank, + ) + + logging.info("Done!") + + if world_size > 1: + torch.distributed.barrier() + cleanup_dist() + + +def scan_pessimistic_batches_for_oom( + model: nn.Module, + train_dl: torch.utils.data.DataLoader, + optimizer: torch.optim.Optimizer, + sp: spm.SentencePieceProcessor, + params: AttributeDict, +): + from lhotse.dataset import find_pessimistic_batches + + logging.info( + "Sanity check -- see if any of the batches in epoch 0 would cause OOM." + ) + batches, crit_values = find_pessimistic_batches(train_dl.sampler) + for criterion, cuts in batches.items(): + batch = train_dl.dataset[cuts] + try: + optimizer.zero_grad() + loss, _ = compute_loss( + params=params, + model=model, + sp=sp, + batch=batch, + is_training=True, + ) + loss.backward() + clip_grad_norm_(model.parameters(), 5.0, 2.0) + optimizer.step() + except RuntimeError as e: + if "CUDA out of memory" in str(e): + logging.error( + "Your GPU ran out of memory with the current " + "max_duration setting. We recommend decreasing " + "max_duration and trying again.\n" + f"Failing criterion: {criterion} " + f"(={crit_values[criterion]}) ..." + ) + raise + + +def main(): + parser = get_parser() + UbiqusAsrDataModule.add_arguments(parser) + args = parser.parse_args() + args.exp_dir = Path(args.exp_dir) + + world_size = args.world_size + assert world_size >= 1 + if world_size > 1: + mp.spawn(run, args=(world_size, args), nprocs=world_size, join=True) + else: + run(rank=0, world_size=1, args=args) + + +torch.set_num_threads(1) +torch.set_num_interop_threads(1) + +if __name__ == "__main__": + main() diff --git a/egs/ubiqus/ASR/transducer_emformer/train_raw.py b/egs/ubiqus/ASR/transducer_emformer/train_raw.py new file mode 100755 index 000000000..45f98877d --- /dev/null +++ b/egs/ubiqus/ASR/transducer_emformer/train_raw.py @@ -0,0 +1,1017 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang, +# Wei Kang +# Mingshuang Luo) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Usage: + +cd icefall + +pip install -r requirements.txt +pip install pyonmttok + +cd egs/ubiqus/ASR + +export CUDA_VISIBLE_DEVICES="1" + +git config --global --add safe.directory /workspace/icefall + +./transducer_emformer/train_raw.py \ + --world-size 1 \ + --num-epochs 30 \ + --start-epoch 0 \ + --exp-dir transducer_emformer/exp_raw \ + --on-the-fly-feats False \ + --enable-spec-aug False \ + --max-duration 50 + +tolerance +add to vocab +squeeze + ontheflyfeature + no specaug +""" + + +import argparse +import logging +import warnings +from pathlib import Path +from shutil import copyfile +from typing import Any, Dict, Optional, Tuple + +import k2 +import sentencepiece as spm +from tokenizer import PyonmttokProcessor +import torch +import torch.multiprocessing as mp +import torch.nn as nn +from asr_datamodule import UbiqusAsrDataModule +from decoder import Decoder +from emformer_raw import EmformerRaw +from joiner import Joiner +from lhotse.cut import Cut +from lhotse.dataset.sampling.base import CutSampler +from lhotse.utils import fix_random_seed +from model import Transducer +from noam import Noam +from torch import Tensor +from torch.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, remove_checkpoints +from icefall.checkpoint import save_checkpoint as save_checkpoint_impl +from icefall.checkpoint import save_checkpoint_with_global_batch_idx +from icefall.dist import cleanup_dist, setup_dist +from icefall.env import get_env_info +from icefall.utils import ( + AttributeDict, + MetricsTracker, + measure_gradient_norms, + measure_weight_norms, + optim_step_and_measure_param_change, + setup_logger, + str2bool, +) + + +def add_model_arguments(parser: argparse.ArgumentParser): + parser.add_argument( + "--attention-dim", + type=int, + default=512, + help="Attention dim for the Emformer", + ) + + parser.add_argument( + "--nhead", + type=int, + default=8, + help="Number of attention heads for the Emformer", + ) + + parser.add_argument( + "--dim-feedforward", + type=int, + default=2048, + help="Feed-forward dimension for the Emformer", + ) + + parser.add_argument( + "--num-encoder-layers", + type=int, + default=12, + help="Number of encoder layers for the Emformer", + ) + + parser.add_argument( + "--left-context-length", + type=int, + default=120, + help="Number of frames for the left context in the Emformer", + ) + + parser.add_argument( + "--segment-length", + type=int, + default=16, + help="Number of frames for each segment in the Emformer", + ) + + parser.add_argument( + "--right-context-length", + type=int, + default=4, + help="Number of frames for right context in the Emformer", + ) + + parser.add_argument( + "--memory-size", + type=int, + default=0, + help="Number of entries in the memory for the Emformer", + ) + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--world-size", + type=int, + default=1, + help="Number of GPUs for DDP training.", + ) + + parser.add_argument( + "--master-port", + type=int, + default=12354, + help="Master port to use for DDP training.", + ) + + parser.add_argument( + "--tensorboard", + type=str2bool, + default=True, + help="Should various information be logged in tensorboard.", + ) + + parser.add_argument( + "--num-epochs", + type=int, + default=30, + help="Number of epochs to train.", + ) + + parser.add_argument( + "--start-epoch", + type=int, + default=0, + help="""Resume training from from this epoch. + If it is positive, it will load checkpoint from + transducer_emformer/exp/epoch-{start_epoch-1}.pt + """, + ) + + parser.add_argument( + "--start-batch", + type=int, + default=0, + help="""If positive, --start-epoch is ignored and + it loads the checkpoint from exp-dir/checkpoint-{start_batch}.pt + """, + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="transducer_emformer/exp", + help="""The experiment dir. + It specifies the directory where all training related + files, e.g., checkpoints, log, etc, are saved + """, + ) + + parser.add_argument( + "--bpe-model", + type=str, + default="data/lang_bpe_500/bpe.model", + help="Path to the BPE model", + ) + + parser.add_argument( + "--lr-factor", + type=float, + default=5.0, + help="The lr_factor for Noam optimizer", + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; " + "2 means tri-gram", + ) + + parser.add_argument( + "--prune-range", + type=int, + default=5, + help="The prune range for rnnt loss, it means how many symbols(context)" + "we are using to compute the loss", + ) + + parser.add_argument( + "--lm-scale", + type=float, + default=0.25, + help="The scale to smooth the loss with lm " + "(output of prediction network) part.", + ) + + parser.add_argument( + "--am-scale", + type=float, + default=0.0, + help="The scale to smooth the loss with am (output of encoder network)" + "part.", + ) + + parser.add_argument( + "--simple-loss-scale", + type=float, + default=0.5, + help="To get pruning ranges, we will calculate a simple version" + "loss(joiner is just addition), this simple loss also uses for" + "training (as a regularization item). We will scale the simple loss" + "with this parameter before adding to the final loss.", + ) + + parser.add_argument( + "--seed", + type=int, + default=42, + help="The seed for random generators intended for reproducibility", + ) + + parser.add_argument( + "--save-every-n", + type=int, + default=8000, + help="""Save checkpoint after processing this number of batches" + periodically. We save checkpoint to exp-dir/ whenever + params.batch_idx_train % save_every_n == 0. The checkpoint filename + has the form: f'exp-dir/checkpoint-{params.batch_idx_train}.pt' + Note: It also saves checkpoint to `exp-dir/epoch-xxx.pt` at the + end of each epoch where `xxx` is the epoch number counting from 0. + """, + ) + + parser.add_argument( + "--keep-last-k", + type=int, + default=1, + help="""Only keep this number of checkpoints on disk. + For instance, if it is 3, there are only 3 checkpoints + in the exp-dir with filenames `checkpoint-xxx.pt`. + It does not affect checkpoints with name `epoch-xxx.pt`. + """, + ) + + add_model_arguments(parser) + + return parser + + +def get_params() -> AttributeDict: + """Return a dict containing training parameters. + + All training related parameters that are not passed from the commandline + are saved in the variable `params`. + + Commandline options are merged into `params` after they are parsed, so + you can also access them via `params`. + + Explanation of options saved in `params`: + + - best_train_loss: Best training loss so far. It is used to select + the model that has the lowest training loss. It is + updated during the training. + + - best_valid_loss: Best validation loss so far. It is used to select + the model that has the lowest validation loss. It is + updated during the training. + + - best_train_epoch: It is the epoch that has the best training loss. + + - best_valid_epoch: It is the epoch that has the best validation loss. + + - batch_idx_train: Used to writing statistics to tensorboard. It + contains number of batches trained so far across + epochs. + + - log_interval: Print training loss if batch_idx % log_interval` is 0 + + - reset_interval: Reset statistics if batch_idx % reset_interval is 0 + + - valid_interval: Run validation if batch_idx % valid_interval is 0 + + - feature_dim: The model input dim. It has to match the one used + in computing features. + + - subsampling_factor: The subsampling factor for the model. + + - attention_dim: Hidden dim for multi-head attention model. + + - num_decoder_layers: Number of decoder layer of transformer decoder. + + - warm_step: The warm_step for Noam optimizer. + """ + params = AttributeDict( + { + "best_train_loss": float("inf"), + "best_valid_loss": float("inf"), + "best_train_epoch": -1, + "best_valid_epoch": -1, + "batch_idx_train": 0, + "log_interval": 50, + "reset_interval": 200, + "valid_interval": 3000, # For the 100h subset, use 800 + "log_diagnostics": False, + # parameters for Emformer + "feature_dim": 80, + "subsampling_factor": 4, + "vgg_frontend": False, + # parameters for decoder + "embedding_dim": 512, + # parameters for Noam + "warm_step": 80000, # For the 100h subset, use 20000 + "env_info": get_env_info(), + } + ) + + return params + + +def get_encoder_model(params: AttributeDict) -> nn.Module: + encoder = EmformerRaw( + num_features=params.feature_dim, + output_dim=params.vocab_size, + subsampling_factor=params.subsampling_factor, + d_model=params.attention_dim, + nhead=params.nhead, + dim_feedforward=params.dim_feedforward, + num_encoder_layers=params.num_encoder_layers, + vgg_frontend=params.vgg_frontend, + left_context_length=params.left_context_length, + segment_length=params.segment_length, + right_context_length=params.right_context_length, + max_memory_size=params.memory_size, + ) + return encoder + + +def get_decoder_model(params: AttributeDict) -> nn.Module: + decoder = Decoder( + vocab_size=params.vocab_size, + embedding_dim=params.embedding_dim, + blank_id=params.blank_id, + unk_id=params.unk_id, + context_size=params.context_size, + ) + return decoder + + +def get_joiner_model(params: AttributeDict) -> nn.Module: + joiner = Joiner( + input_dim=params.vocab_size, + inner_dim=params.embedding_dim, + output_dim=params.vocab_size, + ) + return joiner + + +def get_transducer_model(params: AttributeDict) -> nn.Module: + encoder = get_encoder_model(params) + decoder = get_decoder_model(params) + joiner = get_joiner_model(params) + + model = Transducer( + encoder=encoder, + decoder=decoder, + joiner=joiner, + ) + return model + + +def load_checkpoint_if_available( + params: AttributeDict, + model: nn.Module, + optimizer: Optional[torch.optim.Optimizer] = None, +) -> Optional[Dict[str, Any]]: + """Load checkpoint from file. + + If params.start_batch is positive, it will load the checkpoint from + `params.exp_dir/checkpoint-{params.start_batch}.pt`. Otherwise, if + params.start_epoch is positive, it will load the checkpoint from + `params.start_epoch - 1`. + + Apart from loading state dict for `model` and `optimizer` it also updates + `best_train_epoch`, `best_train_loss`, `best_valid_epoch`, + and `best_valid_loss` in `params`. + + Args: + params: + The return value of :func:`get_params`. + model: + The training model. + optimizer: + The optimizer that we are using. + Returns: + Return a dict containing previously saved training info. + """ + if params.start_batch > 0: + filename = params.exp_dir / f"checkpoint-{params.start_batch}.pt" + elif params.start_epoch > 0: + filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt" + else: + return None + + assert filename.is_file(), f"{filename} does not exist!" + + saved_params = load_checkpoint( + filename, + model=model, + optimizer=optimizer, + ) + + keys = [ + "best_train_epoch", + "best_valid_epoch", + "batch_idx_train", + "best_train_loss", + "best_valid_loss", + ] + for k in keys: + params[k] = saved_params[k] + + if params.start_batch > 0: + if "cur_epoch" in saved_params: + params["start_epoch"] = saved_params["cur_epoch"] + + if "cur_batch_idx" in saved_params: + params["cur_batch_idx"] = saved_params["cur_batch_idx"] + + return saved_params + + +def save_checkpoint( + params: AttributeDict, + model: nn.Module, + optimizer: Optional[torch.optim.Optimizer] = None, + sampler: Optional[CutSampler] = None, + rank: int = 0, +) -> None: + """Save model, optimizer, scheduler and training stats to file. + + Args: + params: + It is returned by :func:`get_params`. + model: + The training model. + optimizer: + The optimizer used in the training. + sampler: + The sampler for the training dataset. + """ + 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, + sampler=sampler, + 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, + sp: spm.SentencePieceProcessor, + batch: dict, + is_training: bool, +) -> Tuple[Tensor, MetricsTracker]: + """ + Compute CTC loss given the model and its inputs. + + Args: + params: + Parameters for training. See :func:`get_params`. + model: + The model for training. It is an instance of Emformer in our case. + batch: + A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()` + for the content in it. + is_training: + True for training. False for validation. When it is True, this + function enables autograd during computation; when it is False, it + disables autograd. + """ + device = model.device + feature = torch.unsqueeze(batch["inputs"], dim=-1) + # feature = batch["inputs"] + print(feature.shape) + # feature = batch["inputs"] + # at entry, Emformerfeature is (N, T, C) + assert feature.ndim == 3 + feature = feature.to(device) + + supervisions = batch["supervisions"] + # print(batch["supervisions"].keys()) + feature_lens = supervisions["num_samples"].to(device) + # feature_lens = supervisions["num_frames"].to(device) + + texts = batch["supervisions"]["text"] + y = sp.encode(texts, out_type=int) + y = k2.RaggedTensor(y).to(device) + + with torch.set_grad_enabled(is_training): + simple_loss, pruned_loss = model( + x=feature, + x_lens=feature_lens, + y=y, + prune_range=params.prune_range, + am_scale=params.am_scale, + lm_scale=params.lm_scale, + ) + loss = params.simple_loss_scale * simple_loss + pruned_loss + + assert loss.requires_grad == is_training + + info = MetricsTracker() + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + info["frames"] = ( + (feature_lens // params.subsampling_factor).sum().item() + ) + + # Note: We use reduction=sum while computing the loss. + info["loss"] = loss.detach().cpu().item() + info["simple_loss"] = simple_loss.detach().cpu().item() + info["pruned_loss"] = pruned_loss.detach().cpu().item() + + return loss, info + + +def compute_validation_loss( + params: AttributeDict, + model: nn.Module, + sp: spm.SentencePieceProcessor, + valid_dl: torch.utils.data.DataLoader, + world_size: int = 1, +) -> MetricsTracker: + """Run the validation process.""" + model.eval() + + tot_loss = MetricsTracker() + + for batch_idx, batch in enumerate(valid_dl): + loss, loss_info = compute_loss( + params=params, + model=model, + sp=sp, + batch=batch, + is_training=False, + ) + assert loss.requires_grad is False + tot_loss = tot_loss + loss_info + + if world_size > 1: + tot_loss.reduce(loss.device) + + loss_value = tot_loss["loss"] / tot_loss["frames"] + if loss_value < params.best_valid_loss: + params.best_valid_epoch = params.cur_epoch + params.best_valid_loss = loss_value + + return tot_loss + + +def train_one_epoch( + params: AttributeDict, + model: nn.Module, + optimizer: torch.optim.Optimizer, + sp: spm.SentencePieceProcessor, + train_dl: torch.utils.data.DataLoader, + valid_dl: torch.utils.data.DataLoader, + tb_writer: Optional[SummaryWriter] = None, + world_size: int = 1, + rank: int = 0, +) -> None: + """Train the model for one epoch. + + The training loss from the mean of all frames is saved in + `params.train_loss`. It runs the validation process every + `params.valid_interval` batches. + + Args: + params: + It is returned by :func:`get_params`. + model: + The model for training. + optimizer: + The optimizer we are using. + train_dl: + Dataloader for the training dataset. + valid_dl: + Dataloader for the validation dataset. + tb_writer: + Writer to write log messages to tensorboard. + world_size: + Number of nodes in DDP training. If it is 1, DDP is disabled. + rank: + The rank of the node in DDP training. If no DDP is used, it should + be set to 0. + """ + model.train() + + tot_loss = MetricsTracker() + + def maybe_log_gradients(tag: str): + if ( + params.log_diagnostics + and tb_writer is not None + and params.batch_idx_train % (params.log_interval * 5) == 0 + ): + tb_writer.add_scalars( + tag, + measure_gradient_norms(model, norm="l2"), + global_step=params.batch_idx_train, + ) + + def maybe_log_weights(tag: str): + if ( + params.log_diagnostics + and tb_writer is not None + and params.batch_idx_train % (params.log_interval * 5) == 0 + ): + tb_writer.add_scalars( + tag, + measure_weight_norms(model, norm="l2"), + global_step=params.batch_idx_train, + ) + + def maybe_log_param_relative_changes(): + if ( + params.log_diagnostics + and tb_writer is not None + and params.batch_idx_train % (params.log_interval * 5) == 0 + ): + deltas = optim_step_and_measure_param_change(model, optimizer) + tb_writer.add_scalars( + "train/relative_param_change_per_minibatch", + deltas, + global_step=params.batch_idx_train, + ) + else: + optimizer.step() + + cur_batch_idx = params.get("cur_batch_idx", 0) + + for batch_idx, batch in enumerate(train_dl): + if batch_idx < cur_batch_idx: + continue + cur_batch_idx = batch_idx + + params.batch_idx_train += 1 + batch_size = len(batch["supervisions"]["text"]) + + loss, loss_info = compute_loss( + params=params, + model=model, + sp=sp, + batch=batch, + is_training=True, + ) + # summary stats + tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info + + # NOTE: We use reduction==sum and loss is computed over utterances + # in the batch and there is no normalization to it so far. + + loss.backward() + + maybe_log_weights("train/param_norms") + maybe_log_gradients("train/grad_norms") + maybe_log_param_relative_changes() + + optimizer.zero_grad() + + if ( + params.batch_idx_train > 0 + and params.batch_idx_train % params.save_every_n == 0 + ): + params.cur_batch_idx = batch_idx + save_checkpoint_with_global_batch_idx( + out_dir=params.exp_dir, + global_batch_idx=params.batch_idx_train, + model=model, + params=params, + optimizer=optimizer, + sampler=train_dl.sampler, + rank=rank, + ) + del params.cur_batch_idx + remove_checkpoints( + out_dir=params.exp_dir, + topk=params.keep_last_k, + rank=rank, + ) + + if batch_idx % params.log_interval == 0: + logging.info( + f"Epoch {params.cur_epoch}, " + f"batch {batch_idx}, loss[{loss_info}], " + f"tot_loss[{tot_loss}], batch size: {batch_size}" + ) + + if tb_writer is not None: + loss_info.write_summary( + tb_writer, "train/current_", params.batch_idx_train + ) + tot_loss.write_summary( + tb_writer, "train/tot_", params.batch_idx_train + ) + + if batch_idx > 0 and batch_idx % params.valid_interval == 0: + logging.info("Computing validation loss") + valid_info = compute_validation_loss( + params=params, + model=model, + sp=sp, + valid_dl=valid_dl, + world_size=world_size, + ) + model.train() + logging.info(f"Epoch {params.cur_epoch}, validation: {valid_info}") + if tb_writer is not None: + valid_info.write_summary( + tb_writer, "train/valid_", params.batch_idx_train + ) + + loss_value = tot_loss["loss"] / tot_loss["frames"] + params.train_loss = loss_value + if params.train_loss < params.best_train_loss: + params.best_train_epoch = params.cur_epoch + params.best_train_loss = params.train_loss + + +def run(rank, world_size, args): + """ + Args: + rank: + It is a value between 0 and `world_size-1`, which is + passed automatically by `mp.spawn()` in :func:`main`. + The node with rank 0 is responsible for saving checkpoint. + world_size: + Number of GPUs for DDP training. + args: + The return value of get_parser().parse_args() + """ + params = get_params() + params.update(vars(args)) + # if params.full_libri is False: + # params.valid_interval = 800 + # params.warm_step = 20000 + + fix_random_seed(params.seed) + if world_size > 1: + setup_dist(rank, world_size, params.master_port) + + setup_logger(f"{params.exp_dir}/log/log-train") + logging.info("Training started") + + if args.tensorboard and rank == 0: + tb_writer = SummaryWriter(log_dir=f"{params.exp_dir}/tensorboard") + else: + tb_writer = None + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", rank) + logging.info(f"Device: {device}") + + # sp = spm.SentencePieceProcessor() + sp = PyonmttokProcessor() + sp.load(params.bpe_model) + + # is defined in local/train_bpe_model.py + params.blank_id = sp.piece_to_id("") + params.unk_id = sp.piece_to_id("") + params.vocab_size = sp.get_piece_size() + + logging.info(params) + + logging.info("About to create model") + model = get_transducer_model(params) + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + checkpoints = load_checkpoint_if_available(params=params, model=model) + + model.to(device) + if world_size > 1: + logging.info("Using DDP") + model = DDP(model, device_ids=[rank]) + model.device = device + + optimizer = Noam( + model.parameters(), + model_size=params.attention_dim, + factor=params.lr_factor, + warm_step=params.warm_step, + ) + + if checkpoints and "optimizer" in checkpoints: + logging.info("Loading optimizer state dict") + optimizer.load_state_dict(checkpoints["optimizer"]) + + ubiqus = UbiqusAsrDataModule(args) + + train_cuts = ubiqus.train_cuts() + + def remove_short_and_long_utt(c: Cut): + # Keep only utterances with duration between 1 second and 20 seconds + # + # Caution: There is a reason to select 20.0 here. Please see + # ../local/display_manifest_statistics.py + # + # You should use ../local/display_manifest_statistics.py to get + # an utterance duration distribution for your dataset to select + # the threshold + return 1.0 <= c.duration <= 20.0 + + num_in_total = len(train_cuts) + + train_cuts = train_cuts.filter(remove_short_and_long_utt) + + num_left = len(train_cuts) + num_removed = num_in_total - num_left + removed_percent = num_removed / num_in_total * 100 + + logging.info(f"Before removing short and long utterances: {num_in_total}") + logging.info(f"After removing short and long utterances: {num_left}") + logging.info(f"Removed {num_removed} utterances ({removed_percent:.5f}%)") + + if params.start_batch > 0 and checkpoints and "sampler" in checkpoints: + # We only load the sampler's state dict when it loads a checkpoint + # saved in the middle of an epoch + sampler_state_dict = checkpoints["sampler"] + else: + sampler_state_dict = None + + train_dl = ubiqus.train_dataloaders( + train_cuts, sampler_state_dict=sampler_state_dict + ) + + valid_cuts = ubiqus.dev_cuts() + valid_dl = ubiqus.valid_dataloaders(valid_cuts) + + scan_pessimistic_batches_for_oom( + model=model, + train_dl=train_dl, + optimizer=optimizer, + sp=sp, + params=params, + ) + + for epoch in range(params.start_epoch, params.num_epochs): + fix_random_seed(params.seed + epoch) + train_dl.sampler.set_epoch(epoch) + + cur_lr = optimizer._rate + if tb_writer is not None: + tb_writer.add_scalar( + "train/learning_rate", cur_lr, params.batch_idx_train + ) + tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train) + + if rank == 0: + logging.info("epoch {}, learning rate {}".format(epoch, cur_lr)) + + params.cur_epoch = epoch + + train_one_epoch( + params=params, + model=model, + optimizer=optimizer, + sp=sp, + train_dl=train_dl, + valid_dl=valid_dl, + tb_writer=tb_writer, + world_size=world_size, + rank=rank, + ) + + save_checkpoint( + params=params, + model=model, + optimizer=optimizer, + sampler=train_dl.sampler, + rank=rank, + ) + + logging.info("Done!") + + if world_size > 1: + torch.distributed.barrier() + cleanup_dist() + + +def scan_pessimistic_batches_for_oom( + model: nn.Module, + train_dl: torch.utils.data.DataLoader, + optimizer: torch.optim.Optimizer, + sp: spm.SentencePieceProcessor, + params: AttributeDict, +): + from lhotse.dataset import find_pessimistic_batches + + logging.info( + "Sanity check -- see if any of the batches in epoch 0 would cause OOM." + ) + batches, crit_values = find_pessimistic_batches(train_dl.sampler) + for criterion, cuts in batches.items(): + batch = train_dl.dataset[cuts] + try: + optimizer.zero_grad() + loss, _ = compute_loss( + params=params, + model=model, + sp=sp, + batch=batch, + is_training=True, + ) + loss.backward() + clip_grad_norm_(model.parameters(), 5.0, 2.0) + optimizer.step() + except RuntimeError as e: + if "CUDA out of memory" in str(e): + logging.error( + "Your GPU ran out of memory with the current " + "max_duration setting. We recommend decreasing " + "max_duration and trying again.\n" + f"Failing criterion: {criterion} " + f"(={crit_values[criterion]}) ..." + ) + raise + + +def main(): + parser = get_parser() + UbiqusAsrDataModule.add_arguments(parser) + args = parser.parse_args() + args.exp_dir = Path(args.exp_dir) + + world_size = args.world_size + assert world_size >= 1 + if world_size > 1: + mp.spawn(run, args=(world_size, args), nprocs=world_size, join=True) + else: + run(rank=0, world_size=1, args=args) + + +torch.set_num_threads(1) +torch.set_num_interop_threads(1) + +if __name__ == "__main__": + main()