diff --git a/.github/scripts/run-multi-zh_hans-zipformer.sh b/.github/scripts/run-multi-corpora-zipformer.sh similarity index 66% rename from .github/scripts/run-multi-zh_hans-zipformer.sh rename to .github/scripts/run-multi-corpora-zipformer.sh index cbd86a4d36..90f859f434 100755 --- a/.github/scripts/run-multi-zh_hans-zipformer.sh +++ b/.github/scripts/run-multi-corpora-zipformer.sh @@ -95,3 +95,41 @@ for method in modified_beam_search fast_beam_search; do $repo/test_wavs/DEV_T0000000001.wav \ $repo/test_wavs/DEV_T0000000002.wav done + +rm -rf $repo + +cd ../../../egs/multi_zh_en/ASR +log "==== Test icefall-asr-zipformer-multi-zh-en-2023-11-22 ====" +repo_url=https://huggingface.co/zrjin/icefall-asr-zipformer-multi-zh-en-2023-11-22/ + +log "Downloading pre-trained model from $repo_url" +git lfs install +git clone $repo_url +repo=$(basename $repo_url) + +log "Display test files" +tree $repo/ +ls -lh $repo/test_wavs/*.wav + +./zipformer/pretrained.py \ + --checkpoint $repo/exp/pretrained.pt \ + --bpe-model $repo/data/lang_bbpe_2000/bbpe.model \ + --method greedy_search \ +$repo/test_wavs/_1634_210_2577_1_1525157964032_3712259_29.wav \ +$repo/test_wavs/_1634_210_2577_1_1525157964032_3712259_55.wav \ +$repo/test_wavs/_1634_210_2577_1_1525157964032_3712259_75.wav + +for method in modified_beam_search fast_beam_search; do + log "$method" + + ./zipformer/pretrained.py \ + --method $method \ + --beam-size 4 \ + --checkpoint $repo/exp/pretrained.pt \ + --bpe-model $repo/data/lang_bbpe_2000/bbpe.model \ + $repo/test_wavs/_1634_210_2577_1_1525157964032_3712259_29.wav \ + $repo/test_wavs/_1634_210_2577_1_1525157964032_3712259_55.wav \ + $repo/test_wavs/_1634_210_2577_1_1525157964032_3712259_75.wav +done + +rm -rf $repo diff --git a/.github/workflows/run-multi-zh_hans-zipformer.yml b/.github/workflows/run-multi-corpora-zipformer.yml similarity index 91% rename from .github/workflows/run-multi-zh_hans-zipformer.yml rename to .github/workflows/run-multi-corpora-zipformer.yml index 72c0775a74..38f7eb9087 100644 --- a/.github/workflows/run-multi-zh_hans-zipformer.yml +++ b/.github/workflows/run-multi-corpora-zipformer.yml @@ -14,7 +14,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -name: run-multi-zh_hans-zipformer +name: run-multi-corpora-zipformer on: push: @@ -24,12 +24,12 @@ on: types: [labeled] concurrency: - group: run_multi-zh_hans_zipformer-${{ github.ref }} + group: run_multi-corpora_zipformer-${{ github.ref }} cancel-in-progress: true jobs: - run_multi-zh_hans_zipformer: - if: github.event.label.name == 'onnx' || github.event.label.name == 'ready' || github.event_name == 'push' || github.event.label.name == 'multi-zh_hans' || github.event.label.name == 'zipformer' + run_multi-corpora_zipformer: + if: github.event.label.name == 'onnx' || github.event.label.name == 'ready' || github.event_name == 'push' || github.event.label.name == 'multi-zh_hans' || github.event.label.name == 'zipformer' || github.event.label.name == 'multi-corpora' runs-on: ${{ matrix.os }} strategy: matrix: @@ -81,4 +81,4 @@ jobs: export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH - .github/scripts/run-multi-zh_hans-zipformer.sh + .github/scripts/run-multi-corpora-zipformer.sh diff --git a/egs/multi_zh_en/ASR/README.md b/egs/multi_zh_en/ASR/README.md new file mode 100644 index 0000000000..29341571de --- /dev/null +++ b/egs/multi_zh_en/ASR/README.md @@ -0,0 +1,19 @@ +# Introduction + +This recipe includes scripts for training Zipformer model using both English and Chinese datasets. + +# Included Training Sets + +1. LibriSpeech (English) +2. AiShell-2 (Chinese) +3. TAL-CSASR (Code-Switching, Chinese and English) + +|Datset| Number of hours| URL| +|---|---:|---| +|**TOTAL**|2,547|---| +|LibriSpeech|960|https://www.openslr.org/12/| +|AiShell-2|1,000|http://www.aishelltech.com/aishell_2| +|TAL-CSASR|587|https://ai.100tal.com/openData/voice| + + + diff --git a/egs/multi_zh_en/ASR/RESULTS.md b/egs/multi_zh_en/ASR/RESULTS.md new file mode 100644 index 0000000000..3562d6ac31 --- /dev/null +++ b/egs/multi_zh_en/ASR/RESULTS.md @@ -0,0 +1,44 @@ +## Results + +### Zh-En datasets bpe-based training results (Non-streaming) on Zipformer model + +This is the [pull request #1238](https://github.com/k2-fsa/icefall/pull/1265) in icefall. + +#### Non-streaming (Byte-Level BPE vocab_size=2000) + +Best results (num of params : ~69M): + +The training command: + +``` +./zipformer/train.py \ + --world-size 4 \ + --num-epochs 35 \ + --use-fp16 1 \ + --max-duration 1000 \ + --num-workers 8 +``` + +The decoding command: + +``` +for method in greedy_search modified_beam_search fast_beam_search; do + ./zipformer/decode.py \ + --epoch 34 \ + --avg 19 \ + --decoding-method $method +done +``` + +Word Error Rates (WERs) listed below are produced by the checkpoint of the 20th epoch using greedy search and BPE model (# tokens is 2000). + +| Datasets | TAL-CSASR | TAL-CSASR | AiShell-2 | AiShell-2 | LibriSpeech | LibriSpeech | +|----------------------|-----------|-----------|-----------|-----------|-------------|-------------| +| Zipformer WER (%) | dev | test | dev | test | test-clean | test-other | +| greedy_search | 6.65 | 6.69 | 6.57 | 7.03 | 2.43 | 5.70 | +| modified_beam_search | 6.46 | 6.51 | 6.18 | 6.60 | 2.41 | 5.57 | +| fast_beam_search | 6.57 | 6.68 | 6.40 | 6.74 | 2.40 | 5.56 | + +Pre-trained model can be found here : https://huggingface.co/zrjin/icefall-asr-zipformer-multi-zh-en-2023-11-22, which is trained on LibriSpeech 960-hour training set (with speed perturbation), TAL-CSASR training set (with speed perturbation) and AiShell-2 (w/o speed perturbation). + + diff --git a/egs/multi_zh_en/ASR/local/compile_lg.py b/egs/multi_zh_en/ASR/local/compile_lg.py new file mode 120000 index 0000000000..462d6d3fb9 --- /dev/null +++ b/egs/multi_zh_en/ASR/local/compile_lg.py @@ -0,0 +1 @@ +../../../librispeech/ASR/local/compile_lg.py \ No newline at end of file diff --git a/egs/multi_zh_en/ASR/local/prepare_char.py b/egs/multi_zh_en/ASR/local/prepare_char.py new file mode 120000 index 0000000000..42743b5449 --- /dev/null +++ b/egs/multi_zh_en/ASR/local/prepare_char.py @@ -0,0 +1 @@ +../../../aishell/ASR/local/prepare_char.py \ No newline at end of file diff --git a/egs/multi_zh_en/ASR/local/prepare_for_bpe_model.py b/egs/multi_zh_en/ASR/local/prepare_for_bpe_model.py new file mode 100755 index 0000000000..00514e6bb5 --- /dev/null +++ b/egs/multi_zh_en/ASR/local/prepare_for_bpe_model.py @@ -0,0 +1,65 @@ +#!/usr/bin/env python3 +# Copyright 2023 Xiaomi Corp. (authors: Zengrui Jin) +# +# 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 tokenizes the training transcript by CJK characters +# and saves the result to transcript_chars.txt, which is used +# to train the BPE model later. + +import argparse +from pathlib import Path + +from tqdm.auto import tqdm + +from icefall.utils import tokenize_by_CJK_char + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--lang-dir", + type=str, + help="""Output directory. + The generated transcript_chars.txt is saved to this directory. + """, + ) + + parser.add_argument( + "--text", + type=str, + help="Training transcript.", + ) + + return parser.parse_args() + + +def main(): + args = get_args() + lang_dir = Path(args.lang_dir) + text = Path(args.text) + + assert lang_dir.exists() and text.exists(), f"{lang_dir} or {text} does not exist!" + + transcript_path = lang_dir / "transcript_chars.txt" + + with open(text, "r", encoding="utf-8") as fin: + with open(transcript_path, "w+", encoding="utf-8") as fout: + for line in tqdm(fin): + fout.write(tokenize_by_CJK_char(line) + "\n") + + +if __name__ == "__main__": + main() diff --git a/egs/multi_zh_en/ASR/local/prepare_lang.py b/egs/multi_zh_en/ASR/local/prepare_lang.py new file mode 120000 index 0000000000..747f2ab398 --- /dev/null +++ b/egs/multi_zh_en/ASR/local/prepare_lang.py @@ -0,0 +1 @@ +../../../librispeech/ASR/local/prepare_lang.py \ No newline at end of file diff --git a/egs/multi_zh_en/ASR/local/prepare_lang_bbpe.py b/egs/multi_zh_en/ASR/local/prepare_lang_bbpe.py new file mode 120000 index 0000000000..9a0b44642d --- /dev/null +++ b/egs/multi_zh_en/ASR/local/prepare_lang_bbpe.py @@ -0,0 +1 @@ +../../../aishell/ASR/local/prepare_lang_bbpe.py \ No newline at end of file diff --git a/egs/multi_zh_en/ASR/local/prepare_lang_bpe.py b/egs/multi_zh_en/ASR/local/prepare_lang_bpe.py new file mode 120000 index 0000000000..36b40e7fc2 --- /dev/null +++ b/egs/multi_zh_en/ASR/local/prepare_lang_bpe.py @@ -0,0 +1 @@ +../../../librispeech/ASR/local/prepare_lang_bpe.py \ No newline at end of file diff --git a/egs/multi_zh_en/ASR/local/prepare_words.py b/egs/multi_zh_en/ASR/local/prepare_words.py new file mode 120000 index 0000000000..ef2b4eaf35 --- /dev/null +++ b/egs/multi_zh_en/ASR/local/prepare_words.py @@ -0,0 +1 @@ +../../../aishell2/ASR/local/prepare_words.py \ No newline at end of file diff --git a/egs/multi_zh_en/ASR/local/text2segments.py b/egs/multi_zh_en/ASR/local/text2segments.py new file mode 120000 index 0000000000..7d68a39c36 --- /dev/null +++ b/egs/multi_zh_en/ASR/local/text2segments.py @@ -0,0 +1 @@ +../../../wenetspeech/ASR/local/text2segments.py \ No newline at end of file diff --git a/egs/multi_zh_en/ASR/local/text2token.py b/egs/multi_zh_en/ASR/local/text2token.py new file mode 120000 index 0000000000..ce5cfd537b --- /dev/null +++ b/egs/multi_zh_en/ASR/local/text2token.py @@ -0,0 +1 @@ +../../../wenetspeech/ASR/local/text2token.py \ No newline at end of file diff --git a/egs/multi_zh_en/ASR/local/train_bbpe_model.py b/egs/multi_zh_en/ASR/local/train_bbpe_model.py new file mode 120000 index 0000000000..7fb4a9f9d6 --- /dev/null +++ b/egs/multi_zh_en/ASR/local/train_bbpe_model.py @@ -0,0 +1 @@ +../../../aishell/ASR/local/train_bbpe_model.py \ No newline at end of file diff --git a/egs/multi_zh_en/ASR/local/validate_bpe_lexicon.py b/egs/multi_zh_en/ASR/local/validate_bpe_lexicon.py new file mode 120000 index 0000000000..721bb48e7c --- /dev/null +++ b/egs/multi_zh_en/ASR/local/validate_bpe_lexicon.py @@ -0,0 +1 @@ +../../../librispeech/ASR/local/validate_bpe_lexicon.py \ No newline at end of file diff --git a/egs/multi_zh_en/ASR/prepare.sh b/egs/multi_zh_en/ASR/prepare.sh new file mode 100755 index 0000000000..9f2be5a5c2 --- /dev/null +++ b/egs/multi_zh_en/ASR/prepare.sh @@ -0,0 +1,149 @@ +#!/usr/bin/env bash + +# fix segmentation fault reported in https://github.com/k2-fsa/icefall/issues/674 +export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python + +set -eou pipefail + +stage=-1 +stop_stage=100 + +dl_dir=$PWD/download + +. shared/parse_options.sh || exit 1 + +vocab_sizes=( + 2000 +) + +# All files generated by this script are saved in "data". +# You can safely remove "data" and rerun this script to regenerate it. +mkdir -p data + +log() { + # This function is from espnet + local fname=${BASH_SOURCE[1]##*/} + echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*" +} + +log "dl_dir: $dl_dir" + +log "Dataset: musan" +if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then + log "Stage 1: Soft link fbank of musan" + mkdir -p data/fbank + if [ -e ../../librispeech/ASR/data/fbank/.musan.done ]; then + cd data/fbank + ln -svf $(realpath ../../../../librispeech/ASR/data/fbank/musan_feats) . + ln -svf $(realpath ../../../../librispeech/ASR/data/fbank/musan_cuts.jsonl.gz) . + cd ../.. + else + log "Abort! Please run ../../librispeech/ASR/prepare.sh --stage 4 --stop-stage 4" + exit 1 + fi +fi + +log "Dataset: LibriSpeech" +if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then + log "Stage 2: Soft link fbank of LibriSpeech" + mkdir -p data/fbank + if [ -e ../../librispeech/ASR/data/fbank/.librispeech.done ]; then + cd data/fbank + ln -svf $(realpath ../../../../librispeech/ASR/data/fbank/librispeech_cuts*) . + ln -svf $(realpath ../../../../librispeech/ASR/data/fbank/librispeech_feats*) . + cd ../.. + else + log "Abort! Please run ../../librispeech/ASR/prepare.sh --stage 3 --stop-stage 3" + exit 1 + fi +fi + +log "Dataset: AiShell-2" +if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then + log "Stage 3: Soft link fbank of AiShell-2" + mkdir -p data/fbank + if [ -e ../../aishell2/ASR/data/fbank/.aishell2.done ]; then + cd data/fbank + ln -svf $(realpath ../../../../aishell2/ASR/data/fbank/aishell2_cuts*) . + ln -svf $(realpath ../../../../aishell2/ASR/data/fbank/aishell2_feats*) . + cd ../.. + else + log "Abort! Please run ../../aishell2/ASR/prepare.sh --stage 3 --stop-stage 3" + exit 1 + fi +fi + +if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then + log "Stage 4: Prepare Byte BPE based lang" + mkdir -p data/fbank + if [ ! -d ../../aishell2/ASR/data/lang_char ] && [ ! -d ./data/lang_char ]; then + log "Abort! Please run ../../aishell2/ASR/prepare.sh --stage 3 --stop-stage 3" + exit 1 + fi + + if [ ! -d ../../librispeech/ASR/data/lang_bpe_500 ] && [ ! -d ./data/lang_bpe_500 ]; then + log "Abort! Please run ../../librispeech/ASR/prepare.sh --stage 6 --stop-stage 6" + exit 1 + fi + + cd data/ + if [ ! -d ./lang_char ]; then + ln -svf $(realpath ../../../aishell2/ASR/data/lang_char) . + fi + if [ ! -d ./lang_bpe_500 ]; then + ln -svf $(realpath ../../../librispeech/ASR/data/lang_bpe_500) . + fi + cd ../ + + for vocab_size in ${vocab_sizes[@]}; do + lang_dir=data/lang_bbpe_${vocab_size} + mkdir -p $lang_dir + + cat data/lang_char/text data/lang_bpe_500/transcript_words.txt \ + > $lang_dir/text + + if [ ! -f $lang_dir/transcript_chars.txt ]; then + ./local/prepare_for_bpe_model.py \ + --lang-dir ./$lang_dir \ + --text $lang_dir/text + fi + + if [ ! -f $lang_dir/text_words_segmentation ]; then + python3 ./local/text2segments.py \ + --input-file ./data/lang_char/text \ + --output-file $lang_dir/text_words_segmentation + + cat ./data/lang_bpe_500/transcript_words.txt \ + >> $lang_dir/text_words_segmentation + + cat ./data/lang_char/text \ + >> $lang_dir/text + fi + + cat $lang_dir/text_words_segmentation | sed 's/ /\n/g' \ + | sort -u | sed '/^$/d' | uniq > $lang_dir/words_no_ids.txt + + if [ ! -f $lang_dir/words.txt ]; then + python3 ./local/prepare_words.py \ + --input-file $lang_dir/words_no_ids.txt \ + --output-file $lang_dir/words.txt + fi + + if [ ! -f $lang_dir/bbpe.model ]; then + ./local/train_bbpe_model.py \ + --lang-dir $lang_dir \ + --vocab-size $vocab_size \ + --transcript $lang_dir/text + fi + + if [ ! -f $lang_dir/L_disambig.pt ]; then + ./local/prepare_lang_bbpe.py --lang-dir $lang_dir + + log "Validating $lang_dir/lexicon.txt" + ./local/validate_bpe_lexicon.py \ + --lexicon $lang_dir/lexicon.txt \ + --bpe-model $lang_dir/bbpe.model + fi + done +fi + diff --git a/egs/multi_zh_en/ASR/shared b/egs/multi_zh_en/ASR/shared new file mode 120000 index 0000000000..4cbd91a7e9 --- /dev/null +++ b/egs/multi_zh_en/ASR/shared @@ -0,0 +1 @@ +../../../icefall/shared \ No newline at end of file diff --git a/egs/multi_zh_en/ASR/zipformer/asr_datamodule.py b/egs/multi_zh_en/ASR/zipformer/asr_datamodule.py new file mode 100644 index 0000000000..be6e944720 --- /dev/null +++ b/egs/multi_zh_en/ASR/zipformer/asr_datamodule.py @@ -0,0 +1,385 @@ +# 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 pathlib import Path +from typing import Any, Dict, Optional + +import torch +from lhotse import CutSet, Fbank, FbankConfig, load_manifest +from lhotse.dataset import ( # noqa F401 for PrecomputedFeatures + CutConcatenate, + CutMix, + DynamicBucketingSampler, + K2SpeechRecognitionDataset, + SimpleCutSampler, + SpecAugment, +) +from lhotse.dataset.input_strategies import ( # noqa F401 For AudioSamples + OnTheFlyFeatures, +) +from lhotse.utils import fix_random_seed +from torch.utils.data import DataLoader + +from icefall.utils import str2bool + + +class _SeedWorkers: + def __init__(self, seed: int): + self.seed = seed + + def __call__(self, worker_id: int): + fix_random_seed(self.seed + worker_id) + + +class AsrDataModule: + """ + DataModule for k2 ASR experiments. + It assumes there is always one train and valid dataloader, + but there can be multiple test dataloaders (e.g. LibriSpeech test-clean + and test-other). + + It contains all the common data pipeline modules used in ASR + experiments, e.g.: + - dynamic batch size, + - bucketing samplers, + - cut concatenation, + - augmentation, + - on-the-fly feature extraction + + This class should be derived for specific corpora used in ASR tasks. + """ + + def __init__(self, args: argparse.Namespace): + self.args = args + + @classmethod + def add_arguments(cls, parser: argparse.ArgumentParser): + group = parser.add_argument_group( + title="ASR data related options", + description="These options are used for the preparation of " + "PyTorch DataLoaders from Lhotse CutSet's -- they control the " + "effective batch sizes, sampling strategies, applied data " + "augmentations, etc.", + ) + group.add_argument( + "--manifest-dir", + type=Path, + default=Path("data/fbank"), + help="Path to directory with train/valid/test cuts.", + ) + group.add_argument( + "--max-duration", + type=int, + default=300.0, + help="Maximum pooled recordings duration (seconds) in a " + "single batch. You can reduce it if it causes CUDA OOM.", + ) + group.add_argument( + "--bucketing-sampler", + type=str2bool, + default=True, + help="When enabled, the batches will come from buckets of " + "similar duration (saves padding frames).", + ) + group.add_argument( + "--num-buckets", + type=int, + default=30, + help="The number of buckets for the DynamicBucketingSampler" + "(you might want to increase it for larger datasets).", + ) + group.add_argument( + "--concatenate-cuts", + type=str2bool, + default=False, + help="When enabled, utterances (cuts) will be concatenated " + "to minimize the amount of padding.", + ) + group.add_argument( + "--duration-factor", + type=float, + default=1.0, + help="Determines the maximum duration of a concatenated cut " + "relative to the duration of the longest cut in a batch.", + ) + group.add_argument( + "--gap", + type=float, + default=1.0, + help="The amount of padding (in seconds) inserted between " + "concatenated cuts. This padding is filled with noise when " + "noise augmentation is used.", + ) + group.add_argument( + "--on-the-fly-feats", + type=str2bool, + default=False, + help="When enabled, use on-the-fly cut mixing and feature " + "extraction. Will drop existing precomputed feature manifests " + "if available.", + ) + group.add_argument( + "--shuffle", + type=str2bool, + default=True, + help="When enabled (=default), the examples will be " + "shuffled for each epoch.", + ) + group.add_argument( + "--drop-last", + type=str2bool, + default=True, + help="Whether to drop last batch. Used by sampler.", + ) + group.add_argument( + "--return-cuts", + type=str2bool, + default=True, + help="When enabled, each batch will have the " + "field: batch['supervisions']['cut'] with the cuts that " + "were used to construct it.", + ) + + group.add_argument( + "--num-workers", + type=int, + default=2, + help="The number of training dataloader workers that " + "collect the batches.", + ) + + group.add_argument( + "--enable-spec-aug", + type=str2bool, + default=True, + help="When enabled, use SpecAugment for training dataset.", + ) + + group.add_argument( + "--spec-aug-time-warp-factor", + type=int, + default=80, + help="Used only when --enable-spec-aug is True. " + "It specifies the factor for time warping in SpecAugment. " + "Larger values mean more warping. " + "A value less than 1 means to disable time warp.", + ) + + group.add_argument( + "--enable-musan", + type=str2bool, + default=True, + help="When enabled, select noise from MUSAN and mix it" + "with training dataset. ", + ) + + group.add_argument( + "--input-strategy", + type=str, + default="PrecomputedFeatures", + help="AudioSamples or PrecomputedFeatures", + ) + + 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 / "musan_cuts.jsonl.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( + input_strategy=eval(self.args.input_strategy)(), + cut_transforms=transforms, + input_transforms=input_transforms, + return_cuts=self.args.return_cuts, + ) + + if self.args.on_the_fly_feats: + # NOTE: the PerturbSpeed transform should be added only if we + # remove it from data prep stage. + # Add on-the-fly speed perturbation; since originally it would + # have increased epoch size by 3, we will apply prob 2/3 and use + # 3x more epochs. + # Speed perturbation probably should come first before + # concatenation, but in principle the transforms order doesn't have + # to be strict (e.g. could be randomized) + # transforms = [PerturbSpeed(factors=[0.9, 1.1], p=2/3)] + transforms # noqa + # Drop feats to be on the safe side. + train = K2SpeechRecognitionDataset( + cut_transforms=transforms, + input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))), + input_transforms=input_transforms, + return_cuts=self.args.return_cuts, + ) + + if self.args.bucketing_sampler: + logging.info("Using DynamicBucketingSampler.") + train_sampler = DynamicBucketingSampler( + cuts_train, + max_duration=self.args.max_duration, + shuffle=self.args.shuffle, + num_buckets=self.args.num_buckets, + drop_last=self.args.drop_last, + ) + else: + logging.info("Using SimpleCutSampler.") + train_sampler = SimpleCutSampler( + 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=True, + 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, + ) + valid_sampler = DynamicBucketingSampler( + cuts_valid, + max_duration=self.args.max_duration, + shuffle=False, + ) + logging.info("About to create dev dataloader") + valid_dl = DataLoader( + validate, + sampler=valid_sampler, + batch_size=None, + num_workers=2, + persistent_workers=False, + ) + + return valid_dl + + def test_dataloaders(self, cuts: CutSet) -> DataLoader: + logging.debug("About to create test dataset") + test = K2SpeechRecognitionDataset( + input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))) + if self.args.on_the_fly_feats + else eval(self.args.input_strategy)(), + return_cuts=self.args.return_cuts, + ) + sampler = DynamicBucketingSampler( + cuts, + max_duration=self.args.max_duration, + shuffle=False, + ) + logging.debug("About to create test dataloader") + test_dl = DataLoader( + test, + batch_size=None, + sampler=sampler, + num_workers=self.args.num_workers, + ) + return test_dl diff --git a/egs/multi_zh_en/ASR/zipformer/beam_search.py b/egs/multi_zh_en/ASR/zipformer/beam_search.py new file mode 120000 index 0000000000..8e2c0a65c5 --- /dev/null +++ b/egs/multi_zh_en/ASR/zipformer/beam_search.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/beam_search.py \ No newline at end of file diff --git a/egs/multi_zh_en/ASR/zipformer/decode.py b/egs/multi_zh_en/ASR/zipformer/decode.py new file mode 100755 index 0000000000..e21e8f052c --- /dev/null +++ b/egs/multi_zh_en/ASR/zipformer/decode.py @@ -0,0 +1,851 @@ +#!/usr/bin/env python3 +# +# Copyright 2021-2023 Xiaomi Corporation (Author: Fangjun Kuang, +# Zengwei Yao) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Usage: +(1) greedy search +./zipformer/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./zipformer/exp \ + --max-duration 600 \ + --decoding-method greedy_search + +(2) beam search (not recommended) +./zipformer/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./zipformer/exp \ + --max-duration 600 \ + --decoding-method beam_search \ + --beam-size 4 + +(3) modified beam search +./zipformer/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./zipformer/exp \ + --max-duration 600 \ + --decoding-method modified_beam_search \ + --beam-size 4 + +(4) fast beam search (one best) +./zipformer/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./zipformer/exp \ + --max-duration 600 \ + --decoding-method fast_beam_search \ + --beam 20.0 \ + --max-contexts 8 \ + --max-states 64 + +(5) fast beam search (nbest) +./zipformer/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./zipformer/exp \ + --max-duration 600 \ + --decoding-method fast_beam_search_nbest \ + --beam 20.0 \ + --max-contexts 8 \ + --max-states 64 \ + --num-paths 200 \ + --nbest-scale 0.5 + +(6) fast beam search (nbest oracle WER) +./zipformer/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./zipformer/exp \ + --max-duration 600 \ + --decoding-method fast_beam_search_nbest_oracle \ + --beam 20.0 \ + --max-contexts 8 \ + --max-states 64 \ + --num-paths 200 \ + --nbest-scale 0.5 + +(7) fast beam search (with LG) +./zipformer/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./zipformer/exp \ + --max-duration 600 \ + --decoding-method fast_beam_search_nbest_LG \ + --beam 20.0 \ + --max-contexts 8 \ + --max-states 64 +""" + + +import argparse +import logging +import math +from collections import defaultdict +from pathlib import Path +from typing import Dict, List, Optional, Tuple + +import k2 +import sentencepiece as spm +import torch +import torch.nn as nn +from asr_datamodule import AsrDataModule +from beam_search import ( + beam_search, + fast_beam_search_nbest, + fast_beam_search_nbest_LG, + fast_beam_search_nbest_oracle, + fast_beam_search_one_best, + greedy_search, + greedy_search_batch, + modified_beam_search, +) +from lhotse.cut import Cut +from multi_dataset import MultiDataset +from train import add_model_arguments, get_model, get_params + +from icefall import byte_encode, smart_byte_decode, tokenize_by_CJK_char +from icefall.checkpoint import ( + average_checkpoints, + average_checkpoints_with_averaged_model, + find_checkpoints, + load_checkpoint, +) +from icefall.lexicon import Lexicon +from icefall.utils import ( + AttributeDict, + setup_logger, + store_transcripts, + str2bool, + write_error_stats, +) + +LOG_EPS = math.log(1e-10) + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--epoch", + type=int, + default=30, + help="""It specifies the checkpoint to use for decoding. + Note: Epoch counts from 1. + You can specify --avg to use more checkpoints for model averaging.""", + ) + + parser.add_argument( + "--iter", + type=int, + default=0, + help="""If positive, --epoch is ignored and it + will use the checkpoint exp_dir/checkpoint-iter.pt. + You can specify --avg to use more checkpoints for model averaging. + """, + ) + + parser.add_argument( + "--avg", + type=int, + default=15, + help="Number of checkpoints to average. Automatically select " + "consecutive checkpoints before the checkpoint specified by " + "'--epoch' and '--iter'", + ) + + parser.add_argument( + "--use-averaged-model", + type=str2bool, + default=True, + help="Whether to load averaged model. Currently it only supports " + "using --epoch. If True, it would decode with the averaged model " + "over the epoch range from `epoch-avg` (excluded) to `epoch`." + "Actually only the models with epoch number of `epoch-avg` and " + "`epoch` are loaded for averaging. ", + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="zipformer/exp", + help="The experiment dir", + ) + + parser.add_argument( + "--bpe-model", + type=str, + default="data/lang_bbpe_2000/bbpe.model", + help="Path to the BPE model", + ) + + parser.add_argument( + "--lang-dir", + type=Path, + default="data/lang_bbpe_2000", + help="The lang dir containing word table and LG graph", + ) + + parser.add_argument( + "--decoding-method", + type=str, + default="greedy_search", + help="""Possible values are: + - greedy_search + - beam_search + - modified_beam_search + - fast_beam_search + - fast_beam_search_nbest + - fast_beam_search_nbest_oracle + - fast_beam_search_nbest_LG + If you use fast_beam_search_nbest_LG, you have to specify + `--lang-dir`, which should contain `LG.pt`. + """, + ) + + parser.add_argument( + "--beam-size", + type=int, + default=4, + help="""An integer indicating how many candidates we will keep for each + frame. Used only when --decoding-method is beam_search or + modified_beam_search.""", + ) + + parser.add_argument( + "--beam", + type=float, + default=20.0, + 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, + fast_beam_search_nbest, fast_beam_search_nbest_LG, + and fast_beam_search_nbest_oracle + """, + ) + + parser.add_argument( + "--ngram-lm-scale", + type=float, + default=0.01, + help=""" + Used only when --decoding_method is fast_beam_search_nbest_LG. + It specifies the scale for n-gram LM scores. + """, + ) + + parser.add_argument( + "--max-contexts", + type=int, + default=8, + help="""Used only when --decoding-method is + fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG, + and fast_beam_search_nbest_oracle""", + ) + + parser.add_argument( + "--max-states", + type=int, + default=64, + help="""Used only when --decoding-method is + fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG, + and fast_beam_search_nbest_oracle""", + ) + + 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( + "--num-paths", + type=int, + default=200, + help="""Number of paths for nbest decoding. + Used only when the decoding method is fast_beam_search_nbest, + fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""", + ) + + parser.add_argument( + "--nbest-scale", + type=float, + default=0.5, + help="""Scale applied to lattice scores when computing nbest paths. + Used only when the decoding method is fast_beam_search_nbest, + fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""", + ) + + parser.add_argument( + "--use-tal-csasr", + type=str2bool, + default=False, + help="Whether to use TAL-CSASR training data.", + ) + + parser.add_argument( + "--use-librispeech", + type=str2bool, + default=False, + help="Whether to use LibriSpeech training data.", + ) + + parser.add_argument( + "--use-aishell2", + type=str2bool, + default=False, + help="Whether to use Aishell-2 training data.", + ) + + add_model_arguments(parser) + + return parser + + +def decode_one_batch( + params: AttributeDict, + model: nn.Module, + sp: spm.SentencePieceProcessor, + batch: dict, + word_table: Optional[k2.SymbolTable] = None, + 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`. + word_table: + The word symbol table. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used + only when --decoding_method is fast_beam_search, fast_beam_search_nbest, + fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG. + Returns: + Return the decoding result. See above description for the format of + the returned dict. + """ + device = next(model.parameters()).device + feature = batch["inputs"] + assert feature.ndim == 3 + + feature = feature.to(device) + # at entry, feature is (N, T, C) + + supervisions = batch["supervisions"] + feature_lens = supervisions["num_frames"].to(device) + + if params.causal: + # this seems to cause insertions at the end of the utterance if used with zipformer. + pad_len = 30 + feature_lens += pad_len + feature = torch.nn.functional.pad( + feature, + pad=(0, 0, 0, pad_len), + value=LOG_EPS, + ) + + encoder_out, encoder_out_lens = model.forward_encoder(feature, feature_lens) + + hyps = [] + + if params.decoding_method == "fast_beam_search": + hyp_tokens = fast_beam_search_one_best( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(smart_byte_decode(hyp).split()) + elif params.decoding_method == "fast_beam_search_nbest_LG": + hyp_tokens = fast_beam_search_nbest_LG( + 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, + num_paths=params.num_paths, + nbest_scale=params.nbest_scale, + ) + for hyp in hyp_tokens: + hyps.append([word_table[i] for i in hyp]) + elif params.decoding_method == "fast_beam_search_nbest": + hyp_tokens = fast_beam_search_nbest( + 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, + num_paths=params.num_paths, + nbest_scale=params.nbest_scale, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(smart_byte_decode(hyp).split()) + elif params.decoding_method == "fast_beam_search_nbest_oracle": + hyp_tokens = fast_beam_search_nbest_oracle( + 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, + num_paths=params.num_paths, + ref_texts=sp.encode( + byte_encode(tokenize_by_CJK_char(supervisions["text"])) + ), + nbest_scale=params.nbest_scale, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(smart_byte_decode(hyp).split()) + elif params.decoding_method == "greedy_search" and params.max_sym_per_frame == 1: + hyp_tokens = greedy_search_batch( + model=model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(smart_byte_decode(hyp).split()) + elif params.decoding_method == "modified_beam_search": + hyp_tokens = modified_beam_search( + model=model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam_size, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(smart_byte_decode(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(smart_byte_decode(sp.decode(hyp)).split()) + if params.decoding_method == "greedy_search": + return {"greedy_search": hyps} + elif "fast_beam_search" in params.decoding_method: + key = f"beam_{params.beam}_" + key += f"max_contexts_{params.max_contexts}_" + key += f"max_states_{params.max_states}" + if "nbest" in params.decoding_method: + key += f"_num_paths_{params.num_paths}_" + key += f"nbest_scale_{params.nbest_scale}" + if "LG" in params.decoding_method: + key += f"_ngram_lm_scale_{params.ngram_lm_scale}" + + return {key: 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, + word_table: Optional[k2.SymbolTable] = None, + decoding_graph: Optional[k2.Fsa] = None, +) -> Dict[str, List[Tuple[str, 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. + word_table: + The word symbol table. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used + only when --decoding_method is fast_beam_search, fast_beam_search_nbest, + fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG. + Returns: + Return a dict, whose key may be "greedy_search" if greedy search + is used, or it may be "beam_7" if beam size of 7 is used. + Its value is a list of tuples. Each tuple contains two elements: + The first is the reference transcript, and the second is the + predicted result. + """ + num_cuts = 0 + + try: + num_batches = len(dl) + except TypeError: + num_batches = "?" + + if params.decoding_method == "greedy_search": + log_interval = 50 + else: + log_interval = 20 + + results = defaultdict(list) + for batch_idx, batch in enumerate(dl): + texts = batch["supervisions"]["text"] + texts = [tokenize_by_CJK_char(str(text)).split() for text in texts] + # print(texts) + # exit() + cut_ids = [cut.id for cut in batch["supervisions"]["cut"]] + + hyps_dict = decode_one_batch( + params=params, + model=model, + sp=sp, + decoding_graph=decoding_graph, + word_table=word_table, + batch=batch, + ) + + for name, hyps in hyps_dict.items(): + this_batch = [] + assert len(hyps) == len(texts) + for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts): + this_batch.append((cut_id, ref_text, hyp_words)) + + results[name].extend(this_batch) + + num_cuts += len(texts) + + if batch_idx % log_interval == 0: + batch_str = f"{batch_idx}/{num_batches}" + + logging.info(f"batch {batch_str}, cuts processed until now is {num_cuts}") + return results + + +def save_results( + params: AttributeDict, + test_set_name: str, + results_dict: Dict[str, List[Tuple[str, List[str], List[str]]]], +): + 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" + ) + results = sorted(results) + 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() + AsrDataModule.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", + "fast_beam_search_nbest", + "fast_beam_search_nbest_LG", + "fast_beam_search_nbest_oracle", + "modified_beam_search", + ) + params.res_dir = params.exp_dir / params.decoding_method + + if params.iter > 0: + params.suffix = f"iter-{params.iter}-avg-{params.avg}" + else: + params.suffix = f"epoch-{params.epoch}-avg-{params.avg}" + + if params.causal: + assert ( + "," not in params.chunk_size + ), "chunk_size should be one value in decoding." + assert ( + "," not in params.left_context_frames + ), "left_context_frames should be one value in decoding." + params.suffix += f"-chunk-{params.chunk_size}" + params.suffix += f"-left-context-{params.left_context_frames}" + + 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}" + if "nbest" in params.decoding_method: + params.suffix += f"-nbest-scale-{params.nbest_scale}" + params.suffix += f"-num-paths-{params.num_paths}" + if "LG" in params.decoding_method: + params.suffix += f"-ngram-lm-scale-{params.ngram_lm_scale}" + elif "beam_search" in params.decoding_method: + params.suffix += f"-{params.decoding_method}-beam-size-{params.beam_size}" + else: + params.suffix += f"-context-{params.context_size}" + params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}" + + if params.use_averaged_model: + params.suffix += "-use-averaged-model" + + setup_logger(f"{params.res_dir}/log-decode-{params.suffix}") + logging.info("Decoding started") + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"Device: {device}") + + sp = spm.SentencePieceProcessor() + sp.load(params.bpe_model) + + # and are defined in local/train_bpe_model.py + params.blank_id = sp.piece_to_id("") + params.unk_id = sp.piece_to_id("") + params.vocab_size = sp.get_piece_size() + + logging.info(params) + + logging.info("About to create model") + model = get_model(params) + + if not params.use_averaged_model: + if params.iter > 0: + filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[ + : params.avg + ] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + elif params.avg == 1: + load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) + else: + start = params.epoch - params.avg + 1 + filenames = [] + for i in range(start, params.epoch + 1): + if i >= 1: + filenames.append(f"{params.exp_dir}/epoch-{i}.pt") + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + else: + if params.iter > 0: + filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[ + : params.avg + 1 + ] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg + 1: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + filename_start = filenames[-1] + filename_end = filenames[0] + logging.info( + "Calculating the averaged model over iteration checkpoints" + f" from {filename_start} (excluded) to {filename_end}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + else: + assert params.avg > 0, params.avg + start = params.epoch - params.avg + assert start >= 1, start + filename_start = f"{params.exp_dir}/epoch-{start}.pt" + filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt" + logging.info( + f"Calculating the averaged model over epoch range from " + f"{start} (excluded) to {params.epoch}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + + model.to(device) + model.eval() + + if "fast_beam_search" in params.decoding_method: + if params.decoding_method == "fast_beam_search_nbest_LG": + lexicon = Lexicon(params.lang_dir) + word_table = lexicon.word_table + lg_filename = params.lang_dir / "LG.pt" + logging.info(f"Loading {lg_filename}") + decoding_graph = k2.Fsa.from_dict( + torch.load(lg_filename, map_location=device) + ) + decoding_graph.scores *= params.ngram_lm_scale + else: + word_table = None + decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device) + else: + decoding_graph = None + word_table = None + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + # we need cut ids to display recognition results. + args.return_cuts = True + data_module = AsrDataModule(args) + multi_dataset = MultiDataset(args) + + def remove_short_utt(c: Cut): + T = ((c.num_frames - 7) // 2 + 1) // 2 + if T <= 0: + logging.warning( + f"Excluding cut with ID: {c.id} from decoding, num_frames: {c.num_frames}" + ) + return T > 0 + + test_sets_cuts = multi_dataset.test_cuts() + + test_sets = test_sets_cuts.keys() + test_dl = [ + data_module.test_dataloaders(test_sets_cuts[cuts_name].filter(remove_short_utt)) + for cuts_name in test_sets + ] + + for test_set, test_dl in zip(test_sets, test_dl): + logging.info(f"Start decoding test set: {test_set}") + + results_dict = decode_dataset( + dl=test_dl, + params=params, + model=model, + sp=sp, + word_table=word_table, + 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/multi_zh_en/ASR/zipformer/decoder.py b/egs/multi_zh_en/ASR/zipformer/decoder.py new file mode 120000 index 0000000000..5a8018680d --- /dev/null +++ b/egs/multi_zh_en/ASR/zipformer/decoder.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/decoder.py \ No newline at end of file diff --git a/egs/multi_zh_en/ASR/zipformer/encoder_interface.py b/egs/multi_zh_en/ASR/zipformer/encoder_interface.py new file mode 120000 index 0000000000..c2eaca6712 --- /dev/null +++ b/egs/multi_zh_en/ASR/zipformer/encoder_interface.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/encoder_interface.py \ No newline at end of file diff --git a/egs/multi_zh_en/ASR/zipformer/export-onnx-streaming.py b/egs/multi_zh_en/ASR/zipformer/export-onnx-streaming.py new file mode 120000 index 0000000000..2962eb7847 --- /dev/null +++ b/egs/multi_zh_en/ASR/zipformer/export-onnx-streaming.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/export-onnx-streaming.py \ No newline at end of file diff --git a/egs/multi_zh_en/ASR/zipformer/export-onnx.py b/egs/multi_zh_en/ASR/zipformer/export-onnx.py new file mode 120000 index 0000000000..70a15683c2 --- /dev/null +++ b/egs/multi_zh_en/ASR/zipformer/export-onnx.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/export-onnx.py \ No newline at end of file diff --git a/egs/multi_zh_en/ASR/zipformer/export.py b/egs/multi_zh_en/ASR/zipformer/export.py new file mode 100755 index 0000000000..fbd9ce0dd7 --- /dev/null +++ b/egs/multi_zh_en/ASR/zipformer/export.py @@ -0,0 +1,541 @@ +#!/usr/bin/env python3 +# +# Copyright 2021-2023 Xiaomi Corporation (Author: Fangjun Kuang, +# Zengwei Yao, +# Wei Kang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# This script converts several saved checkpoints +# to a single one using model averaging. +""" + +Usage: + +Note: This is a example for librispeech dataset, if you are using different +dataset, you should change the argument values according to your dataset. + +(1) Export to torchscript model using torch.jit.script() + +- For non-streaming model: + +./zipformer/export.py \ + --exp-dir ./zipformer/exp \ + --tokens data/lang_bbpe_2000/tokens.txt \ + --epoch 20 \ + --avg 1 \ + --jit 1 + +It will generate a file `jit_script.pt` in the given `exp_dir`. You can later +load it by `torch.jit.load("jit_script.pt")`. + +Check ./jit_pretrained.py for its usage. + +Check https://github.com/k2-fsa/sherpa +for how to use the exported models outside of icefall. + +- For streaming model: + +./zipformer/export.py \ + --exp-dir ./zipformer/exp \ + --causal 1 \ + --chunk-size 16 \ + --left-context-frames 128 \ + --tokens data/lang_bbpe_2000/tokens.txt \ + --epoch 20 \ + --avg 1 \ + --jit 1 + +It will generate a file `jit_script_chunk_16_left_128.pt` in the given `exp_dir`. +You can later load it by `torch.jit.load("jit_script_chunk_16_left_128.pt")`. + +Check ./jit_pretrained_streaming.py for its usage. + +Check https://github.com/k2-fsa/sherpa +for how to use the exported models outside of icefall. + +(2) Export `model.state_dict()` + +- For non-streaming model: + +./zipformer/export.py \ + --exp-dir ./zipformer/exp \ + --tokens data/lang_bbpe_2000/tokens.txt \ + --epoch 20 \ + --avg 1 + +- For streaming model: + +./zipformer/export.py \ + --exp-dir ./zipformer/exp \ + --causal 1 \ + --tokens data/lang_bbpe_2000/tokens.txt \ + --epoch 20 \ + --avg 1 + +It will generate a file `pretrained.pt` in the given `exp_dir`. You can later +load it by `icefall.checkpoint.load_checkpoint()`. + +- For non-streaming model: + +To use the generated file with `zipformer/decode.py`, +you can do: + + cd /path/to/exp_dir + ln -s pretrained.pt epoch-9999.pt + + cd /path/to/egs/librispeech/ASR + ./zipformer/decode.py \ + --exp-dir ./zipformer/exp \ + --epoch 9999 \ + --avg 1 \ + --max-duration 600 \ + --decoding-method greedy_search \ + --bpe-model data/lang_bbpe_2000/bpe.model + +- For streaming model: + +To use the generated file with `zipformer/decode.py` and `zipformer/streaming_decode.py`, you can do: + + cd /path/to/exp_dir + ln -s pretrained.pt epoch-9999.pt + + cd /path/to/egs/librispeech/ASR + + # simulated streaming decoding + ./zipformer/decode.py \ + --exp-dir ./zipformer/exp \ + --epoch 9999 \ + --avg 1 \ + --max-duration 600 \ + --causal 1 \ + --chunk-size 16 \ + --left-context-frames 128 \ + --decoding-method greedy_search \ + --bpe-model data/lang_bbpe_2000/bpe.model + + # chunk-wise streaming decoding + ./zipformer/streaming_decode.py \ + --exp-dir ./zipformer/exp \ + --epoch 9999 \ + --avg 1 \ + --max-duration 600 \ + --causal 1 \ + --chunk-size 16 \ + --left-context-frames 128 \ + --decoding-method greedy_search \ + --bpe-model data/lang_bbpe_2000/bpe.model + +Check ./pretrained.py for its usage. + +Note: If you don't want to train a model from scratch, we have +provided one for you. You can get it at + +- non-streaming model: +https://huggingface.co/zrjin/icefall-asr-multi-zh-hans-zipformer-2023-9-2/ + +with the following commands: + + sudo apt-get install git-lfs + git lfs install + git clone https://huggingface.co/zrjin/icefall-asr-multi-zh-hans-zipformer-2023-9-2/ + # You will find the pre-trained models in exp dir +""" + +import argparse +import logging +import re +from pathlib import Path +from typing import List, Tuple + +import k2 +import torch +from scaling_converter import convert_scaled_to_non_scaled +from torch import Tensor, nn +from train import add_model_arguments, get_model, get_params + +from icefall.checkpoint import ( + average_checkpoints, + average_checkpoints_with_averaged_model, + find_checkpoints, + load_checkpoint, +) +from icefall.utils import make_pad_mask, str2bool + + +def num_tokens( + token_table: k2.SymbolTable, disambig_pattern: str = re.compile(r"^#\d+$") +) -> int: + """Return the number of tokens excluding those from + disambiguation symbols. + + Caution: + 0 is not a token ID so it is excluded from the return value. + """ + symbols = token_table.symbols + ans = [] + for s in symbols: + if not disambig_pattern.match(s): + ans.append(token_table[s]) + num_tokens = len(ans) + if 0 in ans: + num_tokens -= 1 + return num_tokens + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--epoch", + type=int, + default=20, + help="""It specifies the checkpoint to use for decoding. + Note: Epoch counts from 1. + You can specify --avg to use more checkpoints for model averaging.""", + ) + + parser.add_argument( + "--iter", + type=int, + default=0, + help="""If positive, --epoch is ignored and it + will use the checkpoint exp_dir/checkpoint-iter.pt. + You can specify --avg to use more checkpoints for model averaging. + """, + ) + + parser.add_argument( + "--avg", + type=int, + default=1, + help="Number of checkpoints to average. Automatically select " + "consecutive checkpoints before the checkpoint specified by " + "'--epoch' and '--iter'", + ) + + parser.add_argument( + "--use-averaged-model", + type=str2bool, + default=True, + help="Whether to load averaged model. Currently it only supports " + "using --epoch. If True, it would decode with the averaged model " + "over the epoch range from `epoch-avg` (excluded) to `epoch`." + "Actually only the models with epoch number of `epoch-avg` and " + "`epoch` are loaded for averaging. ", + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="zipformer/exp", + help="""It specifies the directory where all training related + files, e.g., checkpoints, log, etc, are saved + """, + ) + + parser.add_argument( + "--tokens", + type=str, + default="data/lang_bbpe_2000/tokens.txt", + help="Path to the tokens.txt", + ) + + parser.add_argument( + "--jit", + type=str2bool, + default=False, + help="""True to save a model after applying torch.jit.script. + It will generate a file named jit_script.pt. + Check ./jit_pretrained.py for how to use it. + """, + ) + + 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 + + +class EncoderModel(nn.Module): + """A wrapper for encoder and encoder_embed""" + + def __init__(self, encoder: nn.Module, encoder_embed: nn.Module) -> None: + super().__init__() + self.encoder = encoder + self.encoder_embed = encoder_embed + + def forward( + self, features: Tensor, feature_lengths: Tensor + ) -> Tuple[Tensor, Tensor]: + """ + Args: + features: (N, T, C) + feature_lengths: (N,) + """ + x, x_lens = self.encoder_embed(features, feature_lengths) + + src_key_padding_mask = make_pad_mask(x_lens) + x = x.permute(1, 0, 2) # (N, T, C) -> (T, N, C) + + encoder_out, encoder_out_lens = self.encoder(x, x_lens, src_key_padding_mask) + encoder_out = encoder_out.permute(1, 0, 2) # (T, N, C) ->(N, T, C) + + return encoder_out, encoder_out_lens + + +class StreamingEncoderModel(nn.Module): + """A wrapper for encoder and encoder_embed""" + + def __init__(self, encoder: nn.Module, encoder_embed: nn.Module) -> None: + super().__init__() + assert len(encoder.chunk_size) == 1, encoder.chunk_size + assert len(encoder.left_context_frames) == 1, encoder.left_context_frames + self.chunk_size = encoder.chunk_size[0] + self.left_context_len = encoder.left_context_frames[0] + + # The encoder_embed subsample features (T - 7) // 2 + # The ConvNeXt module needs (7 - 1) // 2 = 3 frames of right padding after subsampling + self.pad_length = 7 + 2 * 3 + + self.encoder = encoder + self.encoder_embed = encoder_embed + + def forward( + self, features: Tensor, feature_lengths: Tensor, states: List[Tensor] + ) -> Tuple[Tensor, Tensor, List[Tensor]]: + """Streaming forward for encoder_embed and encoder. + + Args: + features: (N, T, C) + feature_lengths: (N,) + states: a list of Tensors + + Returns encoder outputs, output lengths, and updated states. + """ + chunk_size = self.chunk_size + left_context_len = self.left_context_len + + cached_embed_left_pad = states[-2] + x, x_lens, new_cached_embed_left_pad = self.encoder_embed.streaming_forward( + x=features, + x_lens=feature_lengths, + cached_left_pad=cached_embed_left_pad, + ) + assert x.size(1) == chunk_size, (x.size(1), chunk_size) + + src_key_padding_mask = make_pad_mask(x_lens) + + # processed_mask is used to mask out initial states + processed_mask = torch.arange(left_context_len, device=x.device).expand( + x.size(0), left_context_len + ) + processed_lens = states[-1] # (batch,) + # (batch, left_context_size) + processed_mask = (processed_lens.unsqueeze(1) <= processed_mask).flip(1) + # Update processed lengths + new_processed_lens = processed_lens + x_lens + + # (batch, left_context_size + chunk_size) + src_key_padding_mask = torch.cat([processed_mask, src_key_padding_mask], dim=1) + + x = x.permute(1, 0, 2) # (N, T, C) -> (T, N, C) + encoder_states = states[:-2] + + ( + encoder_out, + encoder_out_lens, + new_encoder_states, + ) = self.encoder.streaming_forward( + x=x, + x_lens=x_lens, + states=encoder_states, + src_key_padding_mask=src_key_padding_mask, + ) + encoder_out = encoder_out.permute(1, 0, 2) # (T, N, C) ->(N, T, C) + + new_states = new_encoder_states + [ + new_cached_embed_left_pad, + new_processed_lens, + ] + return encoder_out, encoder_out_lens, new_states + + @torch.jit.export + def get_init_states( + self, + batch_size: int = 1, + device: torch.device = torch.device("cpu"), + ) -> List[torch.Tensor]: + """ + Returns a list of cached tensors of all encoder layers. For layer-i, states[i*6:(i+1)*6] + is (cached_key, cached_nonlin_attn, cached_val1, cached_val2, cached_conv1, cached_conv2). + states[-2] is the cached left padding for ConvNeXt module, + of shape (batch_size, num_channels, left_pad, num_freqs) + states[-1] is processed_lens of shape (batch,), which records the number + of processed frames (at 50hz frame rate, after encoder_embed) for each sample in batch. + """ + states = self.encoder.get_init_states(batch_size, device) + + embed_states = self.encoder_embed.get_init_states(batch_size, device) + states.append(embed_states) + + processed_lens = torch.zeros(batch_size, dtype=torch.int32, device=device) + states.append(processed_lens) + + return states + + +@torch.no_grad() +def main(): + args = get_parser().parse_args() + args.exp_dir = Path(args.exp_dir) + + params = get_params() + params.update(vars(args)) + + device = torch.device("cpu") + # if torch.cuda.is_available(): + # device = torch.device("cuda", 0) + + logging.info(f"device: {device}") + + token_table = k2.SymbolTable.from_file(params.tokens) + params.blank_id = token_table[""] + params.vocab_size = num_tokens(token_table) + 1 + + logging.info(params) + + logging.info("About to create model") + model = get_model(params) + + if not params.use_averaged_model: + if params.iter > 0: + filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[ + : params.avg + ] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + logging.info(f"averaging {filenames}") + model.load_state_dict(average_checkpoints(filenames, device=device)) + elif params.avg == 1: + load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) + else: + start = params.epoch - params.avg + 1 + filenames = [] + for i in range(start, params.epoch + 1): + if i >= 1: + filenames.append(f"{params.exp_dir}/epoch-{i}.pt") + logging.info(f"averaging {filenames}") + model.load_state_dict(average_checkpoints(filenames, device=device)) + else: + if params.iter > 0: + filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[ + : params.avg + 1 + ] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg + 1: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + filename_start = filenames[-1] + filename_end = filenames[0] + logging.info( + "Calculating the averaged model over iteration checkpoints" + f" from {filename_start} (excluded) to {filename_end}" + ) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + else: + assert params.avg > 0, params.avg + start = params.epoch - params.avg + assert start >= 1, start + filename_start = f"{params.exp_dir}/epoch-{start}.pt" + filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt" + logging.info( + f"Calculating the averaged model over epoch range from " + f"{start} (excluded) to {params.epoch}" + ) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + + model.eval() + + if params.jit is True: + convert_scaled_to_non_scaled(model, inplace=True) + # We won't use the forward() method of the model in C++, so just ignore + # it here. + # Otherwise, one of its arguments is a ragged tensor and is not + # torch scriptabe. + model.__class__.forward = torch.jit.ignore(model.__class__.forward) + + # Wrap encoder and encoder_embed as a module + if params.causal: + model.encoder = StreamingEncoderModel(model.encoder, model.encoder_embed) + chunk_size = model.encoder.chunk_size + left_context_len = model.encoder.left_context_len + filename = f"jit_script_chunk_{chunk_size}_left_{left_context_len}.pt" + else: + model.encoder = EncoderModel(model.encoder, model.encoder_embed) + filename = "jit_script.pt" + + logging.info("Using torch.jit.script") + model = torch.jit.script(model) + model.save(str(params.exp_dir / filename)) + logging.info(f"Saved to {filename}") + else: + logging.info("Not using torchscript. Export model.state_dict()") + # 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/multi_zh_en/ASR/zipformer/generate_averaged_model.py b/egs/multi_zh_en/ASR/zipformer/generate_averaged_model.py new file mode 100755 index 0000000000..68111fad71 --- /dev/null +++ b/egs/multi_zh_en/ASR/zipformer/generate_averaged_model.py @@ -0,0 +1,193 @@ +#!/usr/bin/env python3 +# +# Copyright 2021-2022 Xiaomi Corporation (Author: Yifan Yang) +# +# 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) use the checkpoint exp_dir/epoch-xxx.pt +./zipformer/generate_averaged_model.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./zipformer/exp + +It will generate a file `epoch-28-avg-15.pt` in the given `exp_dir`. +You can later load it by `torch.load("epoch-28-avg-15.pt")`. + +(2) use the checkpoint exp_dir/checkpoint-iter.pt +./zipformer/generate_averaged_model.py \ + --iter 22000 \ + --avg 5 \ + --exp-dir ./zipformer/exp + +It will generate a file `iter-22000-avg-5.pt` in the given `exp_dir`. +You can later load it by `torch.load("iter-22000-avg-5.pt")`. +""" + + +import argparse +from pathlib import Path + +import k2 +import torch +from train import add_model_arguments, get_model, get_params + +from icefall.checkpoint import average_checkpoints_with_averaged_model, find_checkpoints + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--epoch", + type=int, + default=30, + help="""It specifies the checkpoint to use for decoding. + Note: Epoch counts from 1. + You can specify --avg to use more checkpoints for model averaging.""", + ) + + parser.add_argument( + "--iter", + type=int, + default=0, + help="""If positive, --epoch is ignored and it + will use the checkpoint exp_dir/checkpoint-iter.pt. + You can specify --avg to use more checkpoints for model averaging. + """, + ) + + parser.add_argument( + "--avg", + type=int, + default=9, + help="Number of checkpoints to average. Automatically select " + "consecutive checkpoints before the checkpoint specified by " + "'--epoch' and '--iter'", + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="zipformer/exp", + help="The experiment dir", + ) + + parser.add_argument( + "--tokens", + type=str, + default="data/lang_bpe_500/tokens.txt", + help="Path to the tokens.txt", + ) + + 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 + + +@torch.no_grad() +def main(): + parser = get_parser() + args = parser.parse_args() + args.exp_dir = Path(args.exp_dir) + + params = get_params() + params.update(vars(args)) + + if params.iter > 0: + params.suffix = f"iter-{params.iter}-avg-{params.avg}" + else: + params.suffix = f"epoch-{params.epoch}-avg-{params.avg}" + + print("Script started") + + device = torch.device("cpu") + print(f"Device: {device}") + + symbol_table = k2.SymbolTable.from_file(params.tokens) + params.blank_id = symbol_table[""] + params.unk_id = symbol_table[""] + params.vocab_size = len(symbol_table) + + print("About to create model") + model = get_model(params) + + if params.iter > 0: + filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[ + : params.avg + 1 + ] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg + 1: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + filename_start = filenames[-1] + filename_end = filenames[0] + print( + "Calculating the averaged model over iteration checkpoints" + f" from {filename_start} (excluded) to {filename_end}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + filename = params.exp_dir / f"iter-{params.iter}-avg-{params.avg}.pt" + torch.save({"model": model.state_dict()}, filename) + else: + assert params.avg > 0, params.avg + start = params.epoch - params.avg + assert start >= 1, start + filename_start = f"{params.exp_dir}/epoch-{start}.pt" + filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt" + print( + f"Calculating the averaged model over epoch range from " + f"{start} (excluded) to {params.epoch}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + filename = params.exp_dir / f"epoch-{params.epoch}-avg-{params.avg}.pt" + torch.save({"model": model.state_dict()}, filename) + + num_param = sum([p.numel() for p in model.parameters()]) + print(f"Number of model parameters: {num_param}") + + print("Done!") + + +if __name__ == "__main__": + main() diff --git a/egs/multi_zh_en/ASR/zipformer/jit_pretrained.py b/egs/multi_zh_en/ASR/zipformer/jit_pretrained.py new file mode 120000 index 0000000000..25108391fa --- /dev/null +++ b/egs/multi_zh_en/ASR/zipformer/jit_pretrained.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/jit_pretrained.py \ No newline at end of file diff --git a/egs/multi_zh_en/ASR/zipformer/jit_pretrained_ctc.py b/egs/multi_zh_en/ASR/zipformer/jit_pretrained_ctc.py new file mode 120000 index 0000000000..9a8da58444 --- /dev/null +++ b/egs/multi_zh_en/ASR/zipformer/jit_pretrained_ctc.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/jit_pretrained_ctc.py \ No newline at end of file diff --git a/egs/multi_zh_en/ASR/zipformer/jit_pretrained_streaming.py b/egs/multi_zh_en/ASR/zipformer/jit_pretrained_streaming.py new file mode 120000 index 0000000000..1962351e9a --- /dev/null +++ b/egs/multi_zh_en/ASR/zipformer/jit_pretrained_streaming.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/jit_pretrained_streaming.py \ No newline at end of file diff --git a/egs/multi_zh_en/ASR/zipformer/joiner.py b/egs/multi_zh_en/ASR/zipformer/joiner.py new file mode 120000 index 0000000000..5b8a36332e --- /dev/null +++ b/egs/multi_zh_en/ASR/zipformer/joiner.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/joiner.py \ No newline at end of file diff --git a/egs/multi_zh_en/ASR/zipformer/model.py b/egs/multi_zh_en/ASR/zipformer/model.py new file mode 120000 index 0000000000..cd7e07d72b --- /dev/null +++ b/egs/multi_zh_en/ASR/zipformer/model.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/model.py \ No newline at end of file diff --git a/egs/multi_zh_en/ASR/zipformer/multi_dataset.py b/egs/multi_zh_en/ASR/zipformer/multi_dataset.py new file mode 100644 index 0000000000..1155a3dccf --- /dev/null +++ b/egs/multi_zh_en/ASR/zipformer/multi_dataset.py @@ -0,0 +1,247 @@ +# Copyright 2023 Xiaomi Corp. (authors: Zengrui Jin) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import argparse +import logging +from functools import lru_cache +from pathlib import Path +from typing import Dict + +from lhotse import CutSet, load_manifest_lazy + + +class MultiDataset: + def __init__(self, args: argparse.Namespace): + """ + Args: + manifest_dir: + It is expected to contain the following files: + - aishell2_cuts_train.jsonl.gz + """ + self.fbank_dir = Path(args.manifest_dir) + self.use_tal_csasr = args.use_tal_csasr + self.use_librispeech = args.use_librispeech + self.use_aishell2 = args.use_aishell2 + + def train_cuts(self) -> CutSet: + logging.info("About to get multidataset train cuts") + + # AISHELL-2 + if self.use_aishell2: + logging.info("Loading Aishell-2 in lazy mode") + aishell_2_cuts = load_manifest_lazy( + self.fbank_dir / "aishell2_cuts_train.jsonl.gz" + ) + + # TAL-CSASR + if self.use_tal_csasr: + logging.info("Loading TAL-CSASR in lazy mode") + tal_csasr_cuts = load_manifest_lazy( + self.fbank_dir / "tal_csasr_cuts_train_set.jsonl.gz" + ) + + # LibriSpeech + if self.use_librispeech: + logging.info("Loading LibriSpeech in lazy mode") + train_clean_100_cuts = self.train_clean_100_cuts() + train_clean_360_cuts = self.train_clean_360_cuts() + train_other_500_cuts = self.train_other_500_cuts() + + if self.use_tal_csasr and self.use_librispeech and self.use_aishell2: + return CutSet.mux( + aishell_2_cuts, + train_clean_100_cuts, + train_clean_360_cuts, + train_other_500_cuts, + tal_csasr_cuts, + weights=[ + len(aishell_2_cuts), + len(train_clean_100_cuts), + len(train_clean_360_cuts), + len(train_other_500_cuts), + len(tal_csasr_cuts), + ], + ) + elif not self.use_tal_csasr and self.use_librispeech and self.use_aishell2: + return CutSet.mux( + aishell_2_cuts, + train_clean_100_cuts, + train_clean_360_cuts, + train_other_500_cuts, + weights=[ + len(aishell_2_cuts), + len(train_clean_100_cuts), + len(train_clean_360_cuts), + len(train_other_500_cuts), + ], + ) + elif self.use_tal_csasr and not self.use_librispeech and self.use_aishell2: + return CutSet.mux( + aishell_2_cuts, + tal_csasr_cuts, + weights=[ + len(aishell_2_cuts), + len(tal_csasr_cuts), + ], + ) + elif self.use_tal_csasr and self.use_librispeech and not self.use_aishell2: + return CutSet.mux( + train_clean_100_cuts, + train_clean_360_cuts, + train_other_500_cuts, + tal_csasr_cuts, + weights=[ + len(train_clean_100_cuts), + len(train_clean_360_cuts), + len(train_other_500_cuts), + len(tal_csasr_cuts), + ], + ) + else: + raise NotImplementedError( + f"""Not implemented for + use_aishell2: {self.use_aishell2} + use_librispeech: {self.use_librispeech} + use_tal_csasr: {self.use_tal_csasr}""" + ) + + def dev_cuts(self) -> CutSet: + logging.info("About to get multidataset dev cuts") + + # AISHELL-2 + logging.info("Loading Aishell-2 DEV set in lazy mode") + aishell2_dev_cuts = load_manifest_lazy( + self.fbank_dir / "aishell2_cuts_dev.jsonl.gz" + ) + + # LibriSpeech + dev_clean_cuts = self.dev_clean_cuts() + dev_other_cuts = self.dev_other_cuts() + + logging.info("Loading TAL-CSASR set in lazy mode") + tal_csasr_dev_cuts = load_manifest_lazy( + self.fbank_dir / "tal_csasr_cuts_dev_set.jsonl.gz" + ) + + return CutSet.mux( + aishell2_dev_cuts, + dev_clean_cuts, + dev_other_cuts, + tal_csasr_dev_cuts, + weights=[ + len(aishell2_dev_cuts), + len(dev_clean_cuts), + len(dev_other_cuts), + len(tal_csasr_dev_cuts), + ], + ) + + def test_cuts(self) -> Dict[str, CutSet]: + logging.info("About to get multidataset test cuts") + + # AISHELL-2 + if self.use_aishell2: + logging.info("Loading Aishell-2 set in lazy mode") + aishell2_test_cuts = load_manifest_lazy( + self.fbank_dir / "aishell2_cuts_test.jsonl.gz" + ) + aishell2_dev_cuts = load_manifest_lazy( + self.fbank_dir / "aishell2_cuts_dev.jsonl.gz" + ) + + # LibriSpeech + if self.use_librispeech: + test_clean_cuts = self.test_clean_cuts() + test_other_cuts = self.test_other_cuts() + + logging.info("Loading TAL-CSASR set in lazy mode") + tal_csasr_test_cuts = load_manifest_lazy( + self.fbank_dir / "tal_csasr_cuts_test_set.jsonl.gz" + ) + tal_csasr_dev_cuts = load_manifest_lazy( + self.fbank_dir / "tal_csasr_cuts_dev_set.jsonl.gz" + ) + + test_cuts = { + "tal_csasr_test": tal_csasr_test_cuts, + "tal_csasr_dev": tal_csasr_dev_cuts, + } + + if self.use_aishell2: + test_cuts.update( + { + "aishell-2_test": aishell2_test_cuts, + "aishell-2_dev": aishell2_dev_cuts, + } + ) + if self.use_librispeech: + test_cuts.update( + { + "librispeech_test_clean": test_clean_cuts, + "librispeech_test_other": test_other_cuts, + } + ) + return test_cuts + + @lru_cache() + def train_clean_100_cuts(self) -> CutSet: + logging.info("About to get train-clean-100 cuts") + return load_manifest_lazy( + self.fbank_dir / "librispeech_cuts_train-clean-100.jsonl.gz" + ) + + @lru_cache() + def train_clean_360_cuts(self) -> CutSet: + logging.info("About to get train-clean-360 cuts") + return load_manifest_lazy( + self.fbank_dir / "librispeech_cuts_train-clean-360.jsonl.gz" + ) + + @lru_cache() + def train_other_500_cuts(self) -> CutSet: + logging.info("About to get train-other-500 cuts") + return load_manifest_lazy( + self.fbank_dir / "librispeech_cuts_train-other-500.jsonl.gz" + ) + + @lru_cache() + def dev_clean_cuts(self) -> CutSet: + logging.info("About to get dev-clean cuts") + return load_manifest_lazy( + self.fbank_dir / "librispeech_cuts_dev-clean.jsonl.gz" + ) + + @lru_cache() + def dev_other_cuts(self) -> CutSet: + logging.info("About to get dev-other cuts") + return load_manifest_lazy( + self.fbank_dir / "librispeech_cuts_dev-other.jsonl.gz" + ) + + @lru_cache() + def test_clean_cuts(self) -> CutSet: + logging.info("About to get test-clean cuts") + return load_manifest_lazy( + self.fbank_dir / "librispeech_cuts_test-clean.jsonl.gz" + ) + + @lru_cache() + def test_other_cuts(self) -> CutSet: + logging.info("About to get test-other cuts") + return load_manifest_lazy( + self.fbank_dir / "librispeech_cuts_test-other.jsonl.gz" + ) diff --git a/egs/multi_zh_en/ASR/zipformer/onnx_check.py b/egs/multi_zh_en/ASR/zipformer/onnx_check.py new file mode 120000 index 0000000000..f3dd420046 --- /dev/null +++ b/egs/multi_zh_en/ASR/zipformer/onnx_check.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/onnx_check.py \ No newline at end of file diff --git a/egs/multi_zh_en/ASR/zipformer/onnx_decode.py b/egs/multi_zh_en/ASR/zipformer/onnx_decode.py new file mode 120000 index 0000000000..0573b88c5b --- /dev/null +++ b/egs/multi_zh_en/ASR/zipformer/onnx_decode.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/onnx_decode.py \ No newline at end of file diff --git a/egs/multi_zh_en/ASR/zipformer/onnx_pretrained-streaming.py b/egs/multi_zh_en/ASR/zipformer/onnx_pretrained-streaming.py new file mode 120000 index 0000000000..cfea104c27 --- /dev/null +++ b/egs/multi_zh_en/ASR/zipformer/onnx_pretrained-streaming.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/onnx_pretrained-streaming.py \ No newline at end of file diff --git a/egs/multi_zh_en/ASR/zipformer/onnx_pretrained.py b/egs/multi_zh_en/ASR/zipformer/onnx_pretrained.py new file mode 120000 index 0000000000..8f32f4ee7a --- /dev/null +++ b/egs/multi_zh_en/ASR/zipformer/onnx_pretrained.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/onnx_pretrained.py \ No newline at end of file diff --git a/egs/multi_zh_en/ASR/zipformer/optim.py b/egs/multi_zh_en/ASR/zipformer/optim.py new file mode 120000 index 0000000000..5eaa3cffd4 --- /dev/null +++ b/egs/multi_zh_en/ASR/zipformer/optim.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/optim.py \ No newline at end of file diff --git a/egs/multi_zh_en/ASR/zipformer/pretrained.py b/egs/multi_zh_en/ASR/zipformer/pretrained.py new file mode 100755 index 0000000000..676272e1fc --- /dev/null +++ b/egs/multi_zh_en/ASR/zipformer/pretrained.py @@ -0,0 +1,378 @@ +#!/usr/bin/env python3 +# Copyright 2021-2023 Xiaomi Corp. (authors: Fangjun Kuang, Zengwei Yao) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +This script loads a checkpoint and uses it to decode waves. +You can generate the checkpoint with the following command: + +Note: This is a example for librispeech dataset, if you are using different +dataset, you should change the argument values according to your dataset. + +- For non-streaming model: + +./zipformer/export.py \ + --exp-dir ./zipformer/exp \ + --tokens data/lang_bbpe_2000/tokens.txt \ + --epoch 23 \ + --avg 1 + +- For streaming model: + +./zipformer/export.py \ + --exp-dir ./zipformer/exp \ + --causal 1 \ + --tokens data/lang_bbpe_2000/tokens.txt \ + --epoch 23 \ + --avg 1 + +Usage of this script: + +- For non-streaming model: + +(1) greedy search +./zipformer/pretrained.py \ + --checkpoint ./zipformer/exp/pretrained.pt \ + --tokens data/lang_bbpe_2000/tokens.txt \ + --method greedy_search \ + /path/to/foo.wav \ + /path/to/bar.wav + +(2) modified beam search +./zipformer/pretrained.py \ + --checkpoint ./zipformer/exp/pretrained.pt \ + --tokens ./data/lang_bbpe_2000/tokens.txt \ + --method modified_beam_search \ + /path/to/foo.wav \ + /path/to/bar.wav + +(3) fast beam search +./zipformer/pretrained.py \ + --checkpoint ./zipformer/exp/pretrained.pt \ + --tokens ./data/lang_bbpe_2000/tokens.txt \ + --method fast_beam_search \ + /path/to/foo.wav \ + /path/to/bar.wav + +- For streaming model: + +(1) greedy search +./zipformer/pretrained.py \ + --checkpoint ./zipformer/exp/pretrained.pt \ + --causal 1 \ + --chunk-size 16 \ + --left-context-frames 128 \ + --tokens ./data/lang_bbpe_2000/tokens.txt \ + --method greedy_search \ + /path/to/foo.wav \ + /path/to/bar.wav + +(2) modified beam search +./zipformer/pretrained.py \ + --checkpoint ./zipformer/exp/pretrained.pt \ + --causal 1 \ + --chunk-size 16 \ + --left-context-frames 128 \ + --tokens ./data/lang_bbpe_2000/tokens.txt \ + --method modified_beam_search \ + /path/to/foo.wav \ + /path/to/bar.wav + +(3) fast beam search +./zipformer/pretrained.py \ + --checkpoint ./zipformer/exp/pretrained.pt \ + --causal 1 \ + --chunk-size 16 \ + --left-context-frames 128 \ + --tokens ./data/lang_bbpe_2000/tokens.txt \ + --method fast_beam_search \ + /path/to/foo.wav \ + /path/to/bar.wav + + +You can also use `./zipformer/exp/epoch-xx.pt`. + +Note: ./zipformer/exp/pretrained.pt is generated by ./zipformer/export.py +""" + + +import argparse +import logging +import math +from typing import List + +import k2 +import kaldifeat +import sentencepiece as spm +import torch +import torchaudio +from beam_search import ( + fast_beam_search_one_best, + greedy_search_batch, + modified_beam_search, +) +from export import num_tokens +from torch.nn.utils.rnn import pad_sequence +from train import add_model_arguments, get_model, get_params + +from icefall import smart_byte_decode + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--checkpoint", + type=str, + required=True, + help="Path to the checkpoint. " + "The checkpoint is assumed to be saved by " + "icefall.checkpoint.save_checkpoint().", + ) + + parser.add_argument( + "--bpe-model", + type=str, + help="""Path to byte-level bpe model.""", + ) + + parser.add_argument( + "--method", + type=str, + default="greedy_search", + help="""Possible values are: + - greedy_search + - modified_beam_search + - fast_beam_search + """, + ) + + parser.add_argument( + "sound_files", + type=str, + nargs="+", + help="The input sound file(s) to transcribe. " + "Supported formats are those supported by torchaudio.load(). " + "For example, wav and flac are supported. " + "The sample rate has to be 16kHz.", + ) + + parser.add_argument( + "--sample-rate", + type=int, + default=16000, + help="The sample rate of the input sound file", + ) + + parser.add_argument( + "--beam-size", + type=int, + default=4, + help="""An integer indicating how many candidates we will keep for each + frame. Used only when --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 --method is fast_beam_search""", + ) + + parser.add_argument( + "--max-contexts", + type=int, + default=4, + help="""Used only when --method is fast_beam_search""", + ) + + parser.add_argument( + "--max-states", + type=int, + default=8, + help="""Used only when --method is fast_beam_search""", + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; 2 means tri-gram", + ) + + parser.add_argument( + "--max-sym-per-frame", + type=int, + default=1, + help="""Maximum number of symbols per frame. Used only when + --method is greedy_search. + """, + ) + + add_model_arguments(parser) + + return parser + + +def read_sound_files( + filenames: List[str], expected_sample_rate: float +) -> List[torch.Tensor]: + """Read a list of sound files into a list 1-D float32 torch tensors. + Args: + filenames: + A list of sound filenames. + expected_sample_rate: + The expected sample rate of the sound files. + Returns: + Return a list of 1-D float32 torch tensors. + """ + ans = [] + for f in filenames: + wave, sample_rate = torchaudio.load(f) + assert ( + sample_rate == expected_sample_rate + ), f"expected sample rate: {expected_sample_rate}. Given: {sample_rate}" + # We use only the first channel + ans.append(wave[0].contiguous()) + return ans + + +@torch.no_grad() +def main(): + parser = get_parser() + args = parser.parse_args() + + params = get_params() + + params.update(vars(args)) + + sp = spm.SentencePieceProcessor() + sp.load(params.bpe_model) + + # and are defined in local/train_bpe_model.py + params.blank_id = sp.piece_to_id("") + params.unk_id = sp.piece_to_id("") + params.vocab_size = sp.get_piece_size() + + logging.info(f"{params}") + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"device: {device}") + + if params.causal: + assert ( + "," not in params.chunk_size + ), "chunk_size should be one value in decoding." + assert ( + "," not in params.left_context_frames + ), "left_context_frames should be one value in decoding." + + logging.info("Creating model") + model = get_model(params) + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + checkpoint = torch.load(args.checkpoint, map_location="cpu") + model.load_state_dict(checkpoint["model"], strict=False) + model.to(device) + model.eval() + + logging.info("Constructing Fbank computer") + opts = kaldifeat.FbankOptions() + opts.device = device + opts.frame_opts.dither = 0 + opts.frame_opts.snip_edges = False + opts.frame_opts.samp_freq = params.sample_rate + opts.mel_opts.num_bins = params.feature_dim + + fbank = kaldifeat.Fbank(opts) + + logging.info(f"Reading sound files: {params.sound_files}") + waves = read_sound_files( + filenames=params.sound_files, expected_sample_rate=params.sample_rate + ) + waves = [w.to(device) for w in waves] + + logging.info("Decoding started") + features = fbank(waves) + feature_lengths = [f.size(0) for f in features] + + features = pad_sequence(features, batch_first=True, padding_value=math.log(1e-10)) + feature_lengths = torch.tensor(feature_lengths, device=device) + + # model forward + encoder_out, encoder_out_lens = model.forward_encoder(features, feature_lengths) + + hyps = [] + msg = f"Using {params.method}" + logging.info(msg) + + if params.method == "fast_beam_search": + decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device) + hyp_tokens = fast_beam_search_one_best( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(smart_byte_decode(hyp).split()) + elif params.method == "modified_beam_search": + hyp_tokens = modified_beam_search( + model=model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam_size, + ) + + for hyp in sp.decode(hyp_tokens): + hyps.append(smart_byte_decode(hyp).split()) + elif params.method == "greedy_search" and params.max_sym_per_frame == 1: + hyp_tokens = greedy_search_batch( + model=model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(smart_byte_decode(hyp).split()) + else: + raise ValueError(f"Unsupported method: {params.method}") + + s = "\n" + for filename, hyp in zip(params.sound_files, hyps): + s += f"{filename}:\n{hyp}\n\n" + logging.info(s) + + logging.info("Decoding Done") + + +if __name__ == "__main__": + formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + + logging.basicConfig(format=formatter, level=logging.INFO) + main() diff --git a/egs/multi_zh_en/ASR/zipformer/scaling.py b/egs/multi_zh_en/ASR/zipformer/scaling.py new file mode 120000 index 0000000000..6f398f431d --- /dev/null +++ b/egs/multi_zh_en/ASR/zipformer/scaling.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/scaling.py \ No newline at end of file diff --git a/egs/multi_zh_en/ASR/zipformer/scaling_converter.py b/egs/multi_zh_en/ASR/zipformer/scaling_converter.py new file mode 120000 index 0000000000..b0ecee05e1 --- /dev/null +++ b/egs/multi_zh_en/ASR/zipformer/scaling_converter.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/scaling_converter.py \ No newline at end of file diff --git a/egs/multi_zh_en/ASR/zipformer/streaming_beam_search.py b/egs/multi_zh_en/ASR/zipformer/streaming_beam_search.py new file mode 120000 index 0000000000..b1ed545579 --- /dev/null +++ b/egs/multi_zh_en/ASR/zipformer/streaming_beam_search.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/streaming_beam_search.py \ No newline at end of file diff --git a/egs/multi_zh_en/ASR/zipformer/streaming_decode.py b/egs/multi_zh_en/ASR/zipformer/streaming_decode.py new file mode 120000 index 0000000000..13fd02a782 --- /dev/null +++ b/egs/multi_zh_en/ASR/zipformer/streaming_decode.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/streaming_decode.py \ No newline at end of file diff --git a/egs/multi_zh_en/ASR/zipformer/subsampling.py b/egs/multi_zh_en/ASR/zipformer/subsampling.py new file mode 120000 index 0000000000..01ae9002c6 --- /dev/null +++ b/egs/multi_zh_en/ASR/zipformer/subsampling.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/subsampling.py \ No newline at end of file diff --git a/egs/multi_zh_en/ASR/zipformer/train.py b/egs/multi_zh_en/ASR/zipformer/train.py new file mode 100755 index 0000000000..310c8fe59f --- /dev/null +++ b/egs/multi_zh_en/ASR/zipformer/train.py @@ -0,0 +1,1416 @@ +#!/usr/bin/env python3 +# Copyright 2021-2023 Xiaomi Corp. (authors: Fangjun Kuang, +# Wei Kang, +# Mingshuang Luo, +# Zengwei Yao, +# Daniel Povey) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Usage: + +export CUDA_VISIBLE_DEVICES="0,1,2,3" + +# For non-streaming model training: +./zipformer/train.py \ + --world-size 4 \ + --num-epochs 30 \ + --start-epoch 1 \ + --use-fp16 1 \ + --exp-dir zipformer/exp \ + --max-duration 1000 + +# For streaming model training: +./zipformer/train.py \ + --world-size 4 \ + --num-epochs 30 \ + --start-epoch 1 \ + --use-fp16 1 \ + --exp-dir zipformer/exp \ + --causal 1 \ + --max-duration 1000 + +It supports training with: + - transducer loss (default), with `--use-transducer True --use-ctc False` + - ctc loss (not recommended), with `--use-transducer False --use-ctc True` + - transducer loss & ctc loss, with `--use-transducer True --use-ctc True` +""" + + +import argparse +import copy +import logging +import warnings +from pathlib import Path +from shutil import copyfile +from typing import Any, Dict, Optional, Tuple, Union + +import k2 +import optim +import sentencepiece as spm +import torch +import torch.multiprocessing as mp +import torch.nn as nn +from asr_datamodule import AsrDataModule +from decoder import Decoder +from joiner import Joiner +from lhotse.cut import Cut +from lhotse.dataset.sampling.base import CutSampler +from lhotse.utils import fix_random_seed +from model import AsrModel +from multi_dataset import MultiDataset +from optim import Eden, ScaledAdam +from scaling import ScheduledFloat +from subsampling import Conv2dSubsampling +from torch import Tensor +from torch.cuda.amp import GradScaler +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.utils.tensorboard import SummaryWriter +from zipformer import Zipformer2 + +from icefall import byte_encode, diagnostics +from icefall.checkpoint import load_checkpoint, remove_checkpoints +from icefall.checkpoint import save_checkpoint as save_checkpoint_impl +from icefall.checkpoint import ( + save_checkpoint_with_global_batch_idx, + update_averaged_model, +) +from icefall.dist import cleanup_dist, setup_dist +from icefall.env import get_env_info +from icefall.hooks import register_inf_check_hooks +from icefall.utils import ( + AttributeDict, + MetricsTracker, + get_parameter_groups_with_lrs, + setup_logger, + str2bool, + tokenize_by_CJK_char, +) + +LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler] + + +def get_adjusted_batch_count(params: AttributeDict) -> float: + # returns the number of batches we would have used so far if we had used the reference + # duration. This is for purposes of set_batch_count(). + return ( + params.batch_idx_train + * (params.max_duration * params.world_size) + / params.ref_duration + ) + + +def set_batch_count(model: Union[nn.Module, DDP], batch_count: float) -> None: + if isinstance(model, DDP): + # get underlying nn.Module + model = model.module + for name, module in model.named_modules(): + if hasattr(module, "batch_count"): + module.batch_count = batch_count + if hasattr(module, "name"): + module.name = name + + +def add_model_arguments(parser: argparse.ArgumentParser): + parser.add_argument( + "--num-encoder-layers", + type=str, + default="2,2,3,4,3,2", + help="Number of zipformer encoder layers per stack, comma separated.", + ) + + parser.add_argument( + "--downsampling-factor", + type=str, + default="1,2,4,8,4,2", + help="Downsampling factor for each stack of encoder layers.", + ) + + parser.add_argument( + "--feedforward-dim", + type=str, + default="512,768,1024,1536,1024,768", + help="Feedforward dimension of the zipformer encoder layers, per stack, comma separated.", + ) + + parser.add_argument( + "--num-heads", + type=str, + default="4,4,4,8,4,4", + help="Number of attention heads in the zipformer encoder layers: a single int or comma-separated list.", + ) + + parser.add_argument( + "--encoder-dim", + type=str, + default="192,256,384,512,384,256", + help="Embedding dimension in encoder stacks: a single int or comma-separated list.", + ) + + parser.add_argument( + "--query-head-dim", + type=str, + default="32", + help="Query/key dimension per head in encoder stacks: a single int or comma-separated list.", + ) + + parser.add_argument( + "--value-head-dim", + type=str, + default="12", + help="Value dimension per head in encoder stacks: a single int or comma-separated list.", + ) + + parser.add_argument( + "--pos-head-dim", + type=str, + default="4", + help="Positional-encoding dimension per head in encoder stacks: a single int or comma-separated list.", + ) + + parser.add_argument( + "--pos-dim", + type=int, + default="48", + help="Positional-encoding embedding dimension", + ) + + parser.add_argument( + "--encoder-unmasked-dim", + type=str, + default="192,192,256,256,256,192", + help="Unmasked dimensions in the encoders, relates to augmentation during training. " + "A single int or comma-separated list. Must be <= each corresponding encoder_dim.", + ) + + parser.add_argument( + "--cnn-module-kernel", + type=str, + default="31,31,15,15,15,31", + help="Sizes of convolutional kernels in convolution modules in each encoder stack: " + "a single int or comma-separated list.", + ) + + parser.add_argument( + "--decoder-dim", + type=int, + default=512, + help="Embedding dimension in the decoder model.", + ) + + parser.add_argument( + "--joiner-dim", + type=int, + default=512, + help="""Dimension used in the joiner model. + Outputs from the encoder and decoder model are projected + to this dimension before adding. + """, + ) + + parser.add_argument( + "--causal", + type=str2bool, + default=False, + help="If True, use causal version of model.", + ) + + parser.add_argument( + "--chunk-size", + type=str, + default="16,32,64,-1", + help="Chunk sizes (at 50Hz frame rate) will be chosen randomly from this list during training. " + " Must be just -1 if --causal=False", + ) + + parser.add_argument( + "--left-context-frames", + type=str, + default="64,128,256,-1", + help="Maximum left-contexts for causal training, measured in frames which will " + "be converted to a number of chunks. If splitting into chunks, " + "chunk left-context frames will be chosen randomly from this list; else not relevant.", + ) + + parser.add_argument( + "--use-transducer", + type=str2bool, + default=True, + help="If True, use Transducer head.", + ) + + parser.add_argument( + "--use-ctc", + type=str2bool, + default=False, + help="If True, use CTC head.", + ) + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--world-size", + type=int, + default=1, + help="Number of GPUs for DDP training.", + ) + + parser.add_argument( + "--master-port", + type=int, + default=12354, + help="Master port to use for DDP training.", + ) + + parser.add_argument( + "--tensorboard", + type=str2bool, + default=True, + help="Should various information be logged in tensorboard.", + ) + + parser.add_argument( + "--num-epochs", + type=int, + default=30, + help="Number of epochs to train.", + ) + + parser.add_argument( + "--start-epoch", + type=int, + default=1, + help="""Resume training from this epoch. It should be positive. + If larger than 1, it will load checkpoint from + exp-dir/epoch-{start_epoch-1}.pt + """, + ) + + parser.add_argument( + "--start-batch", + type=int, + default=0, + help="""If positive, --start-epoch is ignored and + it loads the checkpoint from exp-dir/checkpoint-{start_batch}.pt + """, + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="zipformer/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_bbpe_2000/bbpe.model", + help="Path to the BPE model", + ) + + parser.add_argument( + "--base-lr", type=float, default=0.045, help="The base learning rate." + ) + + parser.add_argument( + "--lr-batches", + type=float, + default=7500, + help="""Number of steps that affects how rapidly the learning rate + decreases. We suggest not to change this.""", + ) + + parser.add_argument( + "--lr-epochs", + type=float, + default=3.5, + help="""Number of epochs that affects how rapidly the learning rate decreases. + """, + ) + + parser.add_argument( + "--ref-duration", + type=float, + default=600, + help="Reference batch duration for purposes of adjusting batch counts for setting various " + "schedules inside the model", + ) + + 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( + "--ctc-loss-scale", + type=float, + default=0.2, + help="Scale for CTC loss.", + ) + + parser.add_argument( + "--seed", + type=int, + default=42, + help="The seed for random generators intended for reproducibility", + ) + + parser.add_argument( + "--print-diagnostics", + type=str2bool, + default=False, + help="Accumulate stats on activations, print them and exit.", + ) + + parser.add_argument( + "--inf-check", + type=str2bool, + default=False, + help="Add hooks to check for infinite module outputs and gradients.", + ) + + parser.add_argument( + "--save-every-n", + type=int, + default=4000, + 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 1. + """, + ) + + parser.add_argument( + "--keep-last-k", + type=int, + default=30, + help="""Only keep this number of checkpoints on disk. + For instance, if it is 3, there are only 3 checkpoints + in the exp-dir with filenames `checkpoint-xxx.pt`. + It does not affect checkpoints with name `epoch-xxx.pt`. + """, + ) + + parser.add_argument( + "--average-period", + type=int, + default=200, + help="""Update the averaged model, namely `model_avg`, after processing + this number of batches. `model_avg` is a separate version of model, + in which each floating-point parameter is the average of all the + parameters from the start of training. Each time we take the average, + we do: `model_avg = model * (average_period / batch_idx_train) + + model_avg * ((batch_idx_train - average_period) / batch_idx_train)`. + """, + ) + + parser.add_argument( + "--use-fp16", + type=str2bool, + default=False, + help="Whether to use half precision training.", + ) + + parser.add_argument( + "--use-tal-csasr", + type=str2bool, + default=False, + help="Whether to use TAL-CSASR training data.", + ) + + parser.add_argument( + "--use-librispeech", + type=str2bool, + default=False, + help="Whether to use LibriSpeech training data.", + ) + + parser.add_argument( + "--use-aishell2", + type=str2bool, + default=False, + help="Whether to use Aishell-2 training data.", + ) + + 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. + + - encoder_dim: Hidden dim for multi-head attention model. + + - num_decoder_layers: Number of decoder layer of transformer decoder. + + - warm_step: The warmup period that dictates the decay of the + scale on "simple" (un-pruned) loss. + """ + params = AttributeDict( + { + "best_train_loss": float("inf"), + "best_valid_loss": float("inf"), + "best_train_epoch": -1, + "best_valid_epoch": -1, + "batch_idx_train": 0, + "log_interval": 50, + "reset_interval": 200, + "valid_interval": 3000, # For the 100h subset, use 800 + # parameters for zipformer + "feature_dim": 80, + "subsampling_factor": 4, # not passed in, this is fixed. + "warm_step": 2000, + "env_info": get_env_info(), + } + ) + + return params + + +def _to_int_tuple(s: str): + return tuple(map(int, s.split(","))) + + +def get_encoder_embed(params: AttributeDict) -> nn.Module: + # encoder_embed converts the input of shape (N, T, num_features) + # to the shape (N, (T - 7) // 2, encoder_dims). + # That is, it does two things simultaneously: + # (1) subsampling: T -> (T - 7) // 2 + # (2) embedding: num_features -> encoder_dims + # In the normal configuration, we will downsample once more at the end + # by a factor of 2, and most of the encoder stacks will run at a lower + # sampling rate. + encoder_embed = Conv2dSubsampling( + in_channels=params.feature_dim, + out_channels=_to_int_tuple(params.encoder_dim)[0], + dropout=ScheduledFloat((0.0, 0.3), (20000.0, 0.1)), + ) + return encoder_embed + + +def get_encoder_model(params: AttributeDict) -> nn.Module: + encoder = Zipformer2( + output_downsampling_factor=2, + downsampling_factor=_to_int_tuple(params.downsampling_factor), + num_encoder_layers=_to_int_tuple(params.num_encoder_layers), + encoder_dim=_to_int_tuple(params.encoder_dim), + encoder_unmasked_dim=_to_int_tuple(params.encoder_unmasked_dim), + query_head_dim=_to_int_tuple(params.query_head_dim), + pos_head_dim=_to_int_tuple(params.pos_head_dim), + value_head_dim=_to_int_tuple(params.value_head_dim), + pos_dim=params.pos_dim, + num_heads=_to_int_tuple(params.num_heads), + feedforward_dim=_to_int_tuple(params.feedforward_dim), + cnn_module_kernel=_to_int_tuple(params.cnn_module_kernel), + dropout=ScheduledFloat((0.0, 0.3), (20000.0, 0.1)), + warmup_batches=4000.0, + causal=params.causal, + chunk_size=_to_int_tuple(params.chunk_size), + left_context_frames=_to_int_tuple(params.left_context_frames), + ) + return encoder + + +def get_decoder_model(params: AttributeDict) -> nn.Module: + decoder = Decoder( + vocab_size=params.vocab_size, + decoder_dim=params.decoder_dim, + blank_id=params.blank_id, + context_size=params.context_size, + ) + return decoder + + +def get_joiner_model(params: AttributeDict) -> nn.Module: + joiner = Joiner( + encoder_dim=max(_to_int_tuple(params.encoder_dim)), + decoder_dim=params.decoder_dim, + joiner_dim=params.joiner_dim, + vocab_size=params.vocab_size, + ) + return joiner + + +def get_model(params: AttributeDict) -> nn.Module: + assert params.use_transducer or params.use_ctc, ( + f"At least one of them should be True, " + f"but got params.use_transducer={params.use_transducer}, " + f"params.use_ctc={params.use_ctc}" + ) + + encoder_embed = get_encoder_embed(params) + encoder = get_encoder_model(params) + + if params.use_transducer: + decoder = get_decoder_model(params) + joiner = get_joiner_model(params) + else: + decoder = None + joiner = None + + model = AsrModel( + encoder_embed=encoder_embed, + encoder=encoder, + decoder=decoder, + joiner=joiner, + encoder_dim=max(_to_int_tuple(params.encoder_dim)), + decoder_dim=params.decoder_dim, + vocab_size=params.vocab_size, + use_transducer=params.use_transducer, + use_ctc=params.use_ctc, + ) + return model + + +def load_checkpoint_if_available( + params: AttributeDict, + model: nn.Module, + model_avg: nn.Module = None, + optimizer: Optional[torch.optim.Optimizer] = None, + scheduler: Optional[LRSchedulerType] = None, +) -> Optional[Dict[str, Any]]: + """Load checkpoint from file. + + If params.start_batch is positive, it will load the checkpoint from + `params.exp_dir/checkpoint-{params.start_batch}.pt`. Otherwise, if + params.start_epoch is larger than 1, it will load the checkpoint from + `params.start_epoch - 1`. + + Apart from loading state dict for `model` and `optimizer` it also updates + `best_train_epoch`, `best_train_loss`, `best_valid_epoch`, + and `best_valid_loss` in `params`. + + Args: + params: + The return value of :func:`get_params`. + model: + The training model. + model_avg: + The stored model averaged from the start of training. + optimizer: + The optimizer that we are using. + scheduler: + The scheduler that we are using. + Returns: + Return a dict containing previously saved training info. + """ + if params.start_batch > 0: + filename = params.exp_dir / f"checkpoint-{params.start_batch}.pt" + elif params.start_epoch > 1: + filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt" + else: + return None + + assert filename.is_file(), f"{filename} does not exist!" + + saved_params = load_checkpoint( + filename, + model=model, + model_avg=model_avg, + optimizer=optimizer, + scheduler=scheduler, + ) + + keys = [ + "best_train_epoch", + "best_valid_epoch", + "batch_idx_train", + "best_train_loss", + "best_valid_loss", + ] + for k in keys: + params[k] = saved_params[k] + + if params.start_batch > 0: + if "cur_epoch" in saved_params: + params["start_epoch"] = saved_params["cur_epoch"] + + return saved_params + + +def save_checkpoint( + params: AttributeDict, + model: Union[nn.Module, DDP], + model_avg: Optional[nn.Module] = None, + optimizer: Optional[torch.optim.Optimizer] = None, + scheduler: Optional[LRSchedulerType] = None, + sampler: Optional[CutSampler] = None, + scaler: Optional[GradScaler] = None, + rank: int = 0, +) -> None: + """Save model, optimizer, scheduler and training stats to file. + + Args: + params: + It is returned by :func:`get_params`. + model: + The training model. + model_avg: + The stored model averaged from the start of training. + optimizer: + The optimizer used in the training. + sampler: + The sampler for the training dataset. + scaler: + The scaler used for mix precision training. + """ + if rank != 0: + return + filename = params.exp_dir / f"epoch-{params.cur_epoch}.pt" + save_checkpoint_impl( + filename=filename, + model=model, + model_avg=model_avg, + params=params, + optimizer=optimizer, + scheduler=scheduler, + sampler=sampler, + scaler=scaler, + rank=rank, + ) + + if params.best_train_epoch == params.cur_epoch: + best_train_filename = params.exp_dir / "best-train-loss.pt" + copyfile(src=filename, dst=best_train_filename) + + if params.best_valid_epoch == params.cur_epoch: + best_valid_filename = params.exp_dir / "best-valid-loss.pt" + copyfile(src=filename, dst=best_valid_filename) + + +def compute_loss( + params: AttributeDict, + model: Union[nn.Module, DDP], + sp: spm.SentencePieceProcessor, + batch: dict, + is_training: bool, +) -> Tuple[Tensor, MetricsTracker]: + """ + Compute 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 Zipformer in our case. + batch: + A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()` + for the content in it. + is_training: + True for training. False for validation. When it is True, this + function enables autograd during computation; when it is False, it + disables autograd. + warmup: a floating point value which increases throughout training; + values >= 1.0 are fully warmed up and have all modules present. + """ + device = model.device if isinstance(model, DDP) else next(model.parameters()).device + feature = batch["inputs"] + # at entry, feature is (N, T, C) + assert feature.ndim == 3 + feature = feature.to(device) + + supervisions = batch["supervisions"] + feature_lens = supervisions["num_frames"].to(device) + + batch_idx_train = params.batch_idx_train + warm_step = params.warm_step + + texts = batch["supervisions"]["text"] + y = sp.encode(texts, out_type=int) + y = k2.RaggedTensor(y) + + with torch.set_grad_enabled(is_training): + simple_loss, pruned_loss, ctc_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 = 0.0 + + if params.use_transducer: + s = params.simple_loss_scale + # take down the scale on the simple loss from 1.0 at the start + # to params.simple_loss scale by warm_step. + simple_loss_scale = ( + s + if batch_idx_train >= warm_step + else 1.0 - (batch_idx_train / warm_step) * (1.0 - s) + ) + pruned_loss_scale = ( + 1.0 + if batch_idx_train >= warm_step + else 0.1 + 0.9 * (batch_idx_train / warm_step) + ) + loss += simple_loss_scale * simple_loss + pruned_loss_scale * pruned_loss + + if params.use_ctc: + loss += params.ctc_loss_scale * ctc_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() + if params.use_transducer: + info["simple_loss"] = simple_loss.detach().cpu().item() + info["pruned_loss"] = pruned_loss.detach().cpu().item() + if params.use_ctc: + info["ctc_loss"] = ctc_loss.detach().cpu().item() + + return loss, info + + +def compute_validation_loss( + params: AttributeDict, + model: Union[nn.Module, DDP], + sp: spm.SentencePieceProcessor, + valid_dl: torch.utils.data.DataLoader, + world_size: int = 1, +) -> MetricsTracker: + """Run the validation process.""" + model.eval() + + tot_loss = MetricsTracker() + + for batch_idx, batch in enumerate(valid_dl): + loss, loss_info = compute_loss( + params=params, + model=model, + sp=sp, + batch=batch, + is_training=False, + ) + assert loss.requires_grad is False + tot_loss = tot_loss + loss_info + + if world_size > 1: + tot_loss.reduce(loss.device) + + loss_value = tot_loss["loss"] / tot_loss["frames"] + if loss_value < params.best_valid_loss: + params.best_valid_epoch = params.cur_epoch + params.best_valid_loss = loss_value + + return tot_loss + + +def train_one_epoch( + params: AttributeDict, + model: Union[nn.Module, DDP], + optimizer: torch.optim.Optimizer, + scheduler: LRSchedulerType, + sp: spm.SentencePieceProcessor, + train_dl: torch.utils.data.DataLoader, + valid_dl: torch.utils.data.DataLoader, + scaler: GradScaler, + model_avg: Optional[nn.Module] = None, + tb_writer: Optional[SummaryWriter] = None, + world_size: int = 1, + rank: int = 0, +) -> None: + """Train the model for one epoch. + + The training loss from the mean of all frames is saved in + `params.train_loss`. It runs the validation process every + `params.valid_interval` batches. + + Args: + params: + It is returned by :func:`get_params`. + model: + The model for training. + optimizer: + The optimizer we are using. + scheduler: + The learning rate scheduler, we call step() every step. + train_dl: + Dataloader for the training dataset. + valid_dl: + Dataloader for the validation dataset. + scaler: + The scaler used for mix precision training. + model_avg: + The stored model averaged from the start of training. + tb_writer: + Writer to write log messages to tensorboard. + world_size: + Number of nodes in DDP training. If it is 1, DDP is disabled. + rank: + The rank of the node in DDP training. If no DDP is used, it should + be set to 0. + """ + model.train() + + tot_loss = MetricsTracker() + + saved_bad_model = False + + def save_bad_model(suffix: str = ""): + save_checkpoint_impl( + filename=params.exp_dir / f"bad-model{suffix}-{rank}.pt", + model=model, + model_avg=model_avg, + params=params, + optimizer=optimizer, + scheduler=scheduler, + sampler=train_dl.sampler, + scaler=scaler, + rank=0, + ) + + for batch_idx, batch in enumerate(train_dl): + if batch_idx % 10 == 0: + set_batch_count(model, get_adjusted_batch_count(params)) + + params.batch_idx_train += 1 + batch_size = len(batch["supervisions"]["text"]) + + try: + with torch.cuda.amp.autocast(enabled=params.use_fp16): + loss, loss_info = compute_loss( + params=params, + model=model, + sp=sp, + batch=batch, + is_training=True, + ) + # summary stats + tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info + + # NOTE: We use reduction==sum and loss is computed over utterances + # in the batch and there is no normalization to it so far. + scaler.scale(loss).backward() + scheduler.step_batch(params.batch_idx_train) + + scaler.step(optimizer) + scaler.update() + optimizer.zero_grad() + except: # noqa + save_bad_model() + display_and_save_batch(batch, params=params, sp=sp) + raise + + if params.print_diagnostics and batch_idx == 5: + return + + if ( + rank == 0 + and params.batch_idx_train > 0 + and params.batch_idx_train % params.average_period == 0 + ): + update_averaged_model( + params=params, + model_cur=model, + model_avg=model_avg, + ) + + if ( + params.batch_idx_train > 0 + and params.batch_idx_train % params.save_every_n == 0 + ): + save_checkpoint_with_global_batch_idx( + out_dir=params.exp_dir, + global_batch_idx=params.batch_idx_train, + model=model, + model_avg=model_avg, + params=params, + optimizer=optimizer, + scheduler=scheduler, + sampler=train_dl.sampler, + scaler=scaler, + rank=rank, + ) + remove_checkpoints( + out_dir=params.exp_dir, + topk=params.keep_last_k, + rank=rank, + ) + + if batch_idx % 100 == 0 and params.use_fp16: + # If the grad scale was less than 1, try increasing it. The _growth_interval + # of the grad scaler is configurable, but we can't configure it to have different + # behavior depending on the current grad scale. + cur_grad_scale = scaler._scale.item() + + if cur_grad_scale < 8.0 or (cur_grad_scale < 32.0 and batch_idx % 400 == 0): + scaler.update(cur_grad_scale * 2.0) + if cur_grad_scale < 0.01: + if not saved_bad_model: + save_bad_model(suffix="-first-warning") + saved_bad_model = True + logging.warning(f"Grad scale is small: {cur_grad_scale}") + if cur_grad_scale < 1.0e-05: + save_bad_model() + raise RuntimeError( + f"grad_scale is too small, exiting: {cur_grad_scale}" + ) + + if batch_idx % params.log_interval == 0: + cur_lr = max(scheduler.get_last_lr()) + cur_grad_scale = scaler._scale.item() if params.use_fp16 else 1.0 + + logging.info( + f"Epoch {params.cur_epoch}, " + f"batch {batch_idx}, loss[{loss_info}], " + f"tot_loss[{tot_loss}], batch size: {batch_size}, " + f"lr: {cur_lr:.2e}, " + + (f"grad_scale: {scaler._scale.item()}" if params.use_fp16 else "") + ) + + if tb_writer is not None: + tb_writer.add_scalar( + "train/learning_rate", cur_lr, params.batch_idx_train + ) + + loss_info.write_summary( + tb_writer, "train/current_", params.batch_idx_train + ) + tot_loss.write_summary(tb_writer, "train/tot_", params.batch_idx_train) + if params.use_fp16: + tb_writer.add_scalar( + "train/grad_scale", cur_grad_scale, params.batch_idx_train + ) + + if batch_idx % params.valid_interval == 0 and not params.print_diagnostics: + 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}") + logging.info( + f"Maximum memory allocated so far is {torch.cuda.max_memory_allocated()//1000000}MB" + ) + if tb_writer is not None: + valid_info.write_summary( + tb_writer, "train/valid_", params.batch_idx_train + ) + + loss_value = tot_loss["loss"] / tot_loss["frames"] + params.train_loss = loss_value + if params.train_loss < params.best_train_loss: + params.best_train_epoch = params.cur_epoch + params.best_train_loss = params.train_loss + + +def run(rank, world_size, args): + """ + Args: + rank: + It is a value between 0 and `world_size-1`, which is + passed automatically by `mp.spawn()` in :func:`main`. + The node with rank 0 is responsible for saving checkpoint. + world_size: + Number of GPUs for DDP training. + args: + The return value of get_parser().parse_args() + """ + params = get_params() + params.update(vars(args)) + + fix_random_seed(params.seed) + if world_size > 1: + setup_dist(rank, world_size, params.master_port) + + setup_logger(f"{params.exp_dir}/log/log-train") + logging.info("Training started") + + if args.tensorboard and rank == 0: + tb_writer = SummaryWriter(log_dir=f"{params.exp_dir}/tensorboard") + else: + tb_writer = None + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", rank) + logging.info(f"Device: {device}") + + 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() + + if not params.use_transducer: + params.ctc_loss_scale = 1.0 + + logging.info(params) + + logging.info("About to create model") + model = get_model(params) + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + assert params.save_every_n >= params.average_period + model_avg: Optional[nn.Module] = None + if rank == 0: + # model_avg is only used with rank 0 + model_avg = copy.deepcopy(model).to(torch.float64) + + assert params.start_epoch > 0, params.start_epoch + checkpoints = load_checkpoint_if_available( + params=params, model=model, model_avg=model_avg + ) + + model.to(device) + if world_size > 1: + logging.info("Using DDP") + model = DDP(model, device_ids=[rank], find_unused_parameters=True) + + optimizer = ScaledAdam( + get_parameter_groups_with_lrs(model, lr=params.base_lr, include_names=True), + lr=params.base_lr, # should have no effect + clipping_scale=2.0, + ) + + scheduler = Eden(optimizer, params.lr_batches, params.lr_epochs) + + if checkpoints and "optimizer" in checkpoints: + logging.info("Loading optimizer state dict") + optimizer.load_state_dict(checkpoints["optimizer"]) + + if ( + checkpoints + and "scheduler" in checkpoints + and checkpoints["scheduler"] is not None + ): + logging.info("Loading scheduler state dict") + scheduler.load_state_dict(checkpoints["scheduler"]) + + if params.print_diagnostics: + opts = diagnostics.TensorDiagnosticOptions( + 512 + ) # allow 4 megabytes per sub-module + diagnostic = diagnostics.attach_diagnostics(model, opts) + + if params.inf_check: + register_inf_check_hooks(model) + + data_module = AsrDataModule(args) + multi_dataset = MultiDataset(args) + + train_cuts = multi_dataset.train_cuts() + + def remove_short_and_long_utt(c: Cut): + # Keep only utterances with duration between 1 second and 12 seconds + # + # Caution: There is a reason to select 12.0 here. Please see + # ../local/display_manifest_statistics.py + # + # You should use ../local/display_manifest_statistics.py to get + # an utterance duration distribution for your dataset to select + # the threshold + if c.duration < 1.0: + logging.warning( + f"Exclude cut with ID {c.id} from training. Duration: {c.duration}" + ) + return False + + # In pruned RNN-T, we require that T >= S + # where T is the number of feature frames after subsampling + # and S is the number of tokens in the utterance + + # In ./zipformer.py, the conv module uses the following expression + # for subsampling + T = ((c.num_frames - 7) // 2 + 1) // 2 + tokens = sp.encode(c.supervisions[0].text, out_type=str) + + if T < len(tokens): + logging.warning( + f"Exclude cut with ID {c.id} from training. " + f"Number of frames (before subsampling): {c.num_frames}. " + f"Number of frames (after subsampling): {T}. " + f"Text: {c.supervisions[0].text}. " + f"Tokens: {tokens}. " + f"Number of tokens: {len(tokens)}" + ) + return False + + return True + + def tokenize_and_encode_text(c: Cut): + # Text normalize for each sample + text = c.supervisions[0].text + text = byte_encode(tokenize_by_CJK_char(text)) + c.supervisions[0].text = text + return c + + train_cuts = train_cuts.filter(remove_short_and_long_utt) + + train_cuts = train_cuts.map(tokenize_and_encode_text) + + 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 = data_module.train_dataloaders( + train_cuts, sampler_state_dict=sampler_state_dict + ) + + valid_cuts = multi_dataset.dev_cuts() + valid_dl = data_module.valid_dataloaders(valid_cuts) + + if not params.print_diagnostics: + scan_pessimistic_batches_for_oom( + model=model, + train_dl=train_dl, + optimizer=optimizer, + sp=sp, + params=params, + ) + + scaler = GradScaler(enabled=params.use_fp16, init_scale=1.0) + if checkpoints and "grad_scaler" in checkpoints: + logging.info("Loading grad scaler state dict") + scaler.load_state_dict(checkpoints["grad_scaler"]) + + for epoch in range(params.start_epoch, params.num_epochs + 1): + scheduler.step_epoch(epoch - 1) + fix_random_seed(params.seed + epoch - 1) + train_dl.sampler.set_epoch(epoch - 1) + + if tb_writer is not None: + tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train) + + params.cur_epoch = epoch + + train_one_epoch( + params=params, + model=model, + model_avg=model_avg, + optimizer=optimizer, + scheduler=scheduler, + sp=sp, + train_dl=train_dl, + valid_dl=valid_dl, + scaler=scaler, + tb_writer=tb_writer, + world_size=world_size, + rank=rank, + ) + + if params.print_diagnostics: + diagnostic.print_diagnostics() + break + + save_checkpoint( + params=params, + model=model, + model_avg=model_avg, + optimizer=optimizer, + scheduler=scheduler, + sampler=train_dl.sampler, + scaler=scaler, + rank=rank, + ) + + logging.info("Done!") + + if world_size > 1: + torch.distributed.barrier() + cleanup_dist() + + +def display_and_save_batch( + batch: dict, + params: AttributeDict, + sp: spm.SentencePieceProcessor, +) -> None: + """Display the batch statistics and save the batch into disk. + + Args: + batch: + A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()` + for the content in it. + params: + Parameters for training. See :func:`get_params`. + sp: + The BPE model. + """ + from lhotse.utils import uuid4 + + filename = f"{params.exp_dir}/batch-{uuid4()}.pt" + logging.info(f"Saving batch to {filename}") + torch.save(batch, filename) + + supervisions = batch["supervisions"] + features = batch["inputs"] + + logging.info(f"features shape: {features.shape}") + + y = sp.encode(supervisions["text"], out_type=int) + num_tokens = sum(len(i) for i in y) + logging.info(f"num tokens: {num_tokens}") + + +def scan_pessimistic_batches_for_oom( + model: Union[nn.Module, DDP], + train_dl: torch.utils.data.DataLoader, + optimizer: torch.optim.Optimizer, + sp: spm.SentencePieceProcessor, + params: AttributeDict, +): + from lhotse.dataset import find_pessimistic_batches + + logging.info( + "Sanity check -- see if any of the batches in epoch 1 would cause OOM." + ) + batches, crit_values = find_pessimistic_batches(train_dl.sampler) + for criterion, cuts in batches.items(): + batch = train_dl.dataset[cuts] + try: + with torch.cuda.amp.autocast(enabled=params.use_fp16): + loss, _ = compute_loss( + params=params, + model=model, + sp=sp, + batch=batch, + is_training=True, + ) + loss.backward() + optimizer.zero_grad() + except Exception 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]}) ..." + ) + display_and_save_batch(batch, params=params, sp=sp) + raise + logging.info( + f"Maximum memory allocated so far is {torch.cuda.max_memory_allocated()//1000000}MB" + ) + + +def main(): + parser = get_parser() + AsrDataModule.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/multi_zh_en/ASR/zipformer/zipformer.py b/egs/multi_zh_en/ASR/zipformer/zipformer.py new file mode 120000 index 0000000000..23011dda71 --- /dev/null +++ b/egs/multi_zh_en/ASR/zipformer/zipformer.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/zipformer.py \ No newline at end of file