From 5aafbb970e8b9195d5b6ed3b86da655d9ff2ccc4 Mon Sep 17 00:00:00 2001 From: Desh Raj Date: Mon, 16 May 2022 08:52:14 -0400 Subject: [PATCH] SPGISpeech recipe (#334) * initial commit for SPGISpeech recipe * add decoding * add spgispeech transducer * remove conformer ctc; minor fixes in RNN-T * add results * add tensorboard * add pretrained model to HF * remove unused scripts and soft link common scripts * remove duplicate files * pre commit hooks * remove change in librispeech * pre commit hook * add CER numbers --- egs/spgispeech/ASR/README.md | 32 + egs/spgispeech/ASR/RESULTS.md | 73 ++ egs/spgispeech/ASR/local/__init__.py | 0 egs/spgispeech/ASR/local/compile_hlg.py | 1 + .../ASR/local/compute_fbank_musan.py | 104 ++ .../ASR/local/compute_fbank_spgispeech.py | 145 +++ egs/spgispeech/ASR/local/prepare_lang.py | 1 + egs/spgispeech/ASR/local/prepare_lang_bpe.py | 1 + egs/spgispeech/ASR/local/prepare_splits.py | 79 ++ egs/spgispeech/ASR/local/train_bpe_model.py | 1 + egs/spgispeech/ASR/prepare.sh | 196 ++++ .../pruned_transducer_stateless2/__init__.py | 0 .../asr_datamodule.py | 366 ++++++ .../beam_search.py | 1 + .../pruned_transducer_stateless2/conformer.py | 1 + .../pruned_transducer_stateless2/decode.py | 594 ++++++++++ .../pruned_transducer_stateless2/decoder.py | 1 + .../encoder_interface.py | 1 + .../pruned_transducer_stateless2/export.py | 201 ++++ .../pruned_transducer_stateless2/joiner.py | 1 + .../ASR/pruned_transducer_stateless2/model.py | 1 + .../ASR/pruned_transducer_stateless2/optim.py | 1 + .../pruned_transducer_stateless2/scaling.py | 1 + .../ASR/pruned_transducer_stateless2/train.py | 1031 +++++++++++++++++ egs/spgispeech/ASR/shared | 1 + 25 files changed, 2834 insertions(+) create mode 100644 egs/spgispeech/ASR/README.md create mode 100644 egs/spgispeech/ASR/RESULTS.md create mode 100644 egs/spgispeech/ASR/local/__init__.py create mode 120000 egs/spgispeech/ASR/local/compile_hlg.py create mode 100755 egs/spgispeech/ASR/local/compute_fbank_musan.py create mode 100755 egs/spgispeech/ASR/local/compute_fbank_spgispeech.py create mode 120000 egs/spgispeech/ASR/local/prepare_lang.py create mode 120000 egs/spgispeech/ASR/local/prepare_lang_bpe.py create mode 100755 egs/spgispeech/ASR/local/prepare_splits.py create mode 120000 egs/spgispeech/ASR/local/train_bpe_model.py create mode 100755 egs/spgispeech/ASR/prepare.sh create mode 100644 egs/spgispeech/ASR/pruned_transducer_stateless2/__init__.py create mode 100644 egs/spgispeech/ASR/pruned_transducer_stateless2/asr_datamodule.py create mode 120000 egs/spgispeech/ASR/pruned_transducer_stateless2/beam_search.py create mode 120000 egs/spgispeech/ASR/pruned_transducer_stateless2/conformer.py create mode 100755 egs/spgispeech/ASR/pruned_transducer_stateless2/decode.py create mode 120000 egs/spgispeech/ASR/pruned_transducer_stateless2/decoder.py create mode 120000 egs/spgispeech/ASR/pruned_transducer_stateless2/encoder_interface.py create mode 100755 egs/spgispeech/ASR/pruned_transducer_stateless2/export.py create mode 120000 egs/spgispeech/ASR/pruned_transducer_stateless2/joiner.py create mode 120000 egs/spgispeech/ASR/pruned_transducer_stateless2/model.py create mode 120000 egs/spgispeech/ASR/pruned_transducer_stateless2/optim.py create mode 120000 egs/spgispeech/ASR/pruned_transducer_stateless2/scaling.py create mode 100755 egs/spgispeech/ASR/pruned_transducer_stateless2/train.py create mode 120000 egs/spgispeech/ASR/shared diff --git a/egs/spgispeech/ASR/README.md b/egs/spgispeech/ASR/README.md new file mode 100644 index 0000000000..f60408cc11 --- /dev/null +++ b/egs/spgispeech/ASR/README.md @@ -0,0 +1,32 @@ +# SPGISpeech + +SPGISpeech consists of 5,000 hours of recorded company earnings calls and their respective +transcriptions. The original calls were split into slices ranging from 5 to 15 seconds in +length to allow easy training for speech recognition systems. Calls represent a broad +cross-section of international business English; SPGISpeech contains approximately 50,000 +speakers, one of the largest numbers of any speech corpus, and offers a variety of L1 and +L2 English accents. The format of each WAV file is single channel, 16kHz, 16 bit audio. + +Transcription text represents the output of several stages of manual post-processing. +As such, the text contains polished English orthography following a detailed style guide, +including proper casing, punctuation, and denormalized non-standard words such as numbers +and acronyms, making SPGISpeech suited for training fully formatted end-to-end models. + +Official reference: + +O’Neill, P.K., Lavrukhin, V., Majumdar, S., Noroozi, V., Zhang, Y., Kuchaiev, O., Balam, +J., Dovzhenko, Y., Freyberg, K., Shulman, M.D., Ginsburg, B., Watanabe, S., & Kucsko, G. +(2021). SPGISpeech: 5, 000 hours of transcribed financial audio for fully formatted +end-to-end speech recognition. ArXiv, abs/2104.02014. + +ArXiv link: https://arxiv.org/abs/2104.02014 + +## Performance Record + +| Decoding method | val WER | val CER | +|---------------------------|------------|---------| +| greedy search | 2.40 | 0.99 | +| modified beam search | 2.24 | 0.91 | +| fast beam search | 2.35 | 0.97 | + +See [RESULTS](/egs/spgispeech/ASR/RESULTS.md) for details. diff --git a/egs/spgispeech/ASR/RESULTS.md b/egs/spgispeech/ASR/RESULTS.md new file mode 100644 index 0000000000..de9e35c5ab --- /dev/null +++ b/egs/spgispeech/ASR/RESULTS.md @@ -0,0 +1,73 @@ +## Results + +### SPGISpeech BPE training results (Pruned Transducer) + +#### 2022-05-11 + +#### Conformer encoder + embedding decoder + +Conformer encoder + non-current decoder. The decoder +contains only an embedding layer, a Conv1d (with kernel size 2) and a linear +layer (to transform tensor dim). + +The WERs are + +| | dev | val | comment | +|---------------------------|------------|------------|------------------------------------------| +| greedy search | 2.46 | 2.40 | --avg-last-n 10 --max-duration 500 | +| modified beam search | 2.28 | 2.24 | --avg-last-n 10 --max-duration 500 --beam-size 4 | +| fast beam search | 2.38 | 2.35 | --avg-last-n 10 --max-duration 500 --beam-size 4 --max-contexts 4 --max-states 8 | + +**NOTE:** SPGISpeech transcripts can be prepared in `ortho` or `norm` ways, which refer to whether the +transcripts are orthographic or normalized. These WERs correspond to the normalized transcription +scenario. + +The training command for reproducing is given below: + +``` +export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7" + +./pruned_transducer_stateless2/train.py \ + --world-size 8 \ + --num-epochs 20 \ + --start-epoch 0 \ + --exp-dir pruned_transducer_stateless2/exp \ + --max-duration 200 \ + --prune-range 5 \ + --lr-factor 5 \ + --lm-scale 0.25 \ + --use-fp16 True +``` + +The decoding command is: +``` +# greedy search +./pruned_transducer_stateless2/decode.py \ + --iter 696000 --avg 10 \ + --exp-dir ./pruned_transducer_stateless2/exp \ + --max-duration 100 \ + --decoding-method greedy_search + +# modified beam search +./pruned_transducer_stateless2/decode.py \ + --iter 696000 --avg 10 \ + --exp-dir ./pruned_transducer_stateless2/exp \ + --max-duration 100 \ + --decoding-method modified_beam_search \ + --beam-size 4 + +# fast beam search +./pruned_transducer_stateless2/decode.py \ + --iter 696000 --avg 10 \ + --exp-dir ./pruned_transducer_stateless2/exp \ + --max-duration 1500 \ + --decoding-method fast_beam_search \ + --beam 4 \ + --max-contexts 4 \ + --max-states 8 +``` + +Pretrained model is available at + +The tensorboard training log can be found at + diff --git a/egs/spgispeech/ASR/local/__init__.py b/egs/spgispeech/ASR/local/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/egs/spgispeech/ASR/local/compile_hlg.py b/egs/spgispeech/ASR/local/compile_hlg.py new file mode 120000 index 0000000000..471aa7fb40 --- /dev/null +++ b/egs/spgispeech/ASR/local/compile_hlg.py @@ -0,0 +1 @@ +../../../librispeech/ASR/local/compile_hlg.py \ No newline at end of file diff --git a/egs/spgispeech/ASR/local/compute_fbank_musan.py b/egs/spgispeech/ASR/local/compute_fbank_musan.py new file mode 100755 index 0000000000..b4f409ba6d --- /dev/null +++ b/egs/spgispeech/ASR/local/compute_fbank_musan.py @@ -0,0 +1,104 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This file computes fbank features of the musan dataset. +It looks for manifests in the directory data/manifests. + +The generated fbank features are saved in data/fbank. +""" + +import logging +from pathlib import Path + +import torch +from lhotse import LilcomChunkyWriter, CutSet, combine +from lhotse.features.kaldifeat import ( + KaldifeatFbank, + KaldifeatFbankConfig, + KaldifeatMelOptions, + KaldifeatFrameOptions, +) +from lhotse.recipes.utils import read_manifests_if_cached + +from icefall.utils import get_executor + +# Torch's multithreaded behavior needs to be disabled or +# it wastes a lot of CPU and slow things down. +# Do this outside of main() in case it needs to take effect +# even when we are not invoking the main (e.g. when spawning subprocesses). +torch.set_num_threads(1) +torch.set_num_interop_threads(1) + + +def compute_fbank_musan(): + src_dir = Path("data/manifests") + output_dir = Path("data/fbank") + + sampling_rate = 16000 + num_mel_bins = 80 + + extractor = KaldifeatFbank( + KaldifeatFbankConfig( + frame_opts=KaldifeatFrameOptions(sampling_rate=sampling_rate), + mel_opts=KaldifeatMelOptions(num_bins=num_mel_bins), + device="cuda", + ) + ) + + dataset_parts = ( + "music", + "speech", + "noise", + ) + manifests = read_manifests_if_cached( + dataset_parts=dataset_parts, output_dir=src_dir + ) + assert manifests is not None + + musan_cuts_path = src_dir / "cuts_musan.jsonl.gz" + + if musan_cuts_path.is_file(): + logging.info(f"{musan_cuts_path} already exists - skipping") + return + + logging.info("Extracting features for Musan") + + # create chunks of Musan with duration 5 - 10 seconds + musan_cuts = ( + CutSet.from_manifests( + recordings=combine(part["recordings"] for part in manifests.values()) + ) + .cut_into_windows(10.0) + .filter(lambda c: c.duration > 5) + .compute_and_store_features_batch( + extractor=extractor, + storage_path=output_dir / f"feats_musan", + manifest_path=src_dir / f"cuts_musan.jsonl.gz", + batch_duration=500, + num_workers=4, + storage_type=LilcomChunkyWriter, + ) + ) + + +if __name__ == "__main__": + formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + + logging.basicConfig(format=formatter, level=logging.INFO) + compute_fbank_musan() diff --git a/egs/spgispeech/ASR/local/compute_fbank_spgispeech.py b/egs/spgispeech/ASR/local/compute_fbank_spgispeech.py new file mode 100755 index 0000000000..cc8c8a670d --- /dev/null +++ b/egs/spgispeech/ASR/local/compute_fbank_spgispeech.py @@ -0,0 +1,145 @@ +#!/usr/bin/env python3 +# Copyright 2022 Johns Hopkins University (authors: Desh Raj) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This file computes fbank features of the SPGISpeech dataset. +It looks for manifests in the directory data/manifests. + +The generated fbank features are saved in data/fbank. +""" +import argparse +import logging +from pathlib import Path +from tqdm import tqdm + +import torch +from lhotse import load_manifest_lazy, LilcomChunkyWriter +from lhotse.features.kaldifeat import ( + KaldifeatFbank, + KaldifeatFbankConfig, + KaldifeatMelOptions, + KaldifeatFrameOptions, +) +from lhotse.manipulation import combine + +# Torch's multithreaded behavior needs to be disabled or +# it wastes a lot of CPU and slow things down. +# Do this outside of main() in case it needs to take effect +# even when we are not invoking the main (e.g. when spawning subprocesses). +torch.set_num_threads(1) +torch.set_num_interop_threads(1) + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--num-splits", + type=int, + default=20, + help="Number of splits for the train set.", + ) + parser.add_argument( + "--start", + type=int, + default=0, + help="Start index of the train set split.", + ) + parser.add_argument( + "--stop", + type=int, + default=-1, + help="Stop index of the train set split.", + ) + parser.add_argument( + "--test", + action="store_true", + help="If set, only compute features for the dev and val set.", + ) + parser.add_argument( + "--train", + action="store_true", + help="If set, only compute features for the train set.", + ) + + return parser.parse_args() + + +def compute_fbank_spgispeech(args): + assert args.train or args.test, "Either train or test must be set." + + src_dir = Path("data/manifests") + output_dir = Path("data/fbank") + + sampling_rate = 16000 + num_mel_bins = 80 + + extractor = KaldifeatFbank( + KaldifeatFbankConfig( + frame_opts=KaldifeatFrameOptions(sampling_rate=sampling_rate), + mel_opts=KaldifeatMelOptions(num_bins=num_mel_bins), + device="cuda", + ) + ) + + if args.train: + logging.info(f"Processing train") + cut_set = load_manifest_lazy(src_dir / f"cuts_train_raw.jsonl.gz") + chunk_size = len(cut_set) // args.num_splits + cut_sets = cut_set.split_lazy( + output_dir=src_dir / f"cuts_train_raw_split{args.num_splits}", + chunk_size=chunk_size, + ) + start = args.start + stop = min(args.stop, args.num_splits) if args.stop > 0 else args.num_splits + num_digits = len(str(args.num_splits)) + for i in range(start, stop): + idx = f"{i + 1}".zfill(num_digits) + logging.info(f"Processing train split {i}") + cs = cut_sets[i].compute_and_store_features_batch( + extractor=extractor, + storage_path=output_dir / f"feats_train_{idx}", + manifest_path=src_dir / f"cuts_train_{idx}.jsonl.gz", + batch_duration=500, + num_workers=4, + storage_type=LilcomChunkyWriter, + ) + + if args.test: + for partition in ["dev", "val"]: + if (output_dir / f"cuts_{partition}.jsonl.gz").is_file(): + logging.info(f"{partition} already exists - skipping.") + continue + logging.info(f"Processing {partition}") + cut_set = load_manifest_lazy(src_dir / f"cuts_{partition}_raw.jsonl.gz") + cut_set = cut_set.compute_and_store_features_batch( + extractor=extractor, + storage_path=output_dir / f"feats_{partition}", + manifest_path=src_dir / f"cuts_{partition}.jsonl.gz", + batch_duration=500, + num_workers=4, + storage_type=LilcomChunkyWriter, + ) + + +if __name__ == "__main__": + formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + + logging.basicConfig(format=formatter, level=logging.INFO) + + args = get_args() + compute_fbank_spgispeech(args) diff --git a/egs/spgispeech/ASR/local/prepare_lang.py b/egs/spgispeech/ASR/local/prepare_lang.py new file mode 120000 index 0000000000..747f2ab398 --- /dev/null +++ b/egs/spgispeech/ASR/local/prepare_lang.py @@ -0,0 +1 @@ +../../../librispeech/ASR/local/prepare_lang.py \ No newline at end of file diff --git a/egs/spgispeech/ASR/local/prepare_lang_bpe.py b/egs/spgispeech/ASR/local/prepare_lang_bpe.py new file mode 120000 index 0000000000..36b40e7fc2 --- /dev/null +++ b/egs/spgispeech/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/spgispeech/ASR/local/prepare_splits.py b/egs/spgispeech/ASR/local/prepare_splits.py new file mode 100755 index 0000000000..2d18186498 --- /dev/null +++ b/egs/spgispeech/ASR/local/prepare_splits.py @@ -0,0 +1,79 @@ +#!/usr/bin/env python3 +# Copyright 2022 Johns Hopkins University (authors: Desh Raj) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This file splits the training set into train and dev sets. +""" +import logging +from pathlib import Path + +import torch +from lhotse import CutSet + +from lhotse.recipes.utils import read_manifests_if_cached + +# Torch's multithreaded behavior needs to be disabled or +# it wastes a lot of CPU and slow things down. +# Do this outside of main() in case it needs to take effect +# even when we are not invoking the main (e.g. when spawning subprocesses). +torch.set_num_threads(1) +torch.set_num_interop_threads(1) + + +def split_spgispeech_train(): + src_dir = Path("data/manifests") + + manifests = read_manifests_if_cached( + dataset_parts=["train", "val"], + output_dir=src_dir, + prefix="spgispeech", + suffix="jsonl.gz", + lazy=True, + ) + assert manifests is not None + + train_dev_cuts = CutSet.from_manifests( + recordings=manifests["train"]["recordings"], + supervisions=manifests["train"]["supervisions"], + ) + dev_cuts = train_dev_cuts.subset(first=4000) + train_cuts = train_dev_cuts.filter(lambda c: c not in dev_cuts) + + # Add speed perturbation + train_cuts = ( + train_cuts + train_cuts.perturb_speed(0.9) + train_cuts.perturb_speed(1.1) + ) + + # Write the manifests to disk. + train_cuts.to_file(src_dir / "cuts_train_raw.jsonl.gz") + dev_cuts.to_file(src_dir / "cuts_dev_raw.jsonl.gz") + + # Also write the val set to disk. + val_cuts = CutSet.from_manifests( + recordings=manifests["val"]["recordings"], + supervisions=manifests["val"]["supervisions"], + ) + val_cuts.to_file(src_dir / "cuts_val_raw.jsonl.gz") + + +if __name__ == "__main__": + formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + + logging.basicConfig(format=formatter, level=logging.INFO) + + split_spgispeech_train() diff --git a/egs/spgispeech/ASR/local/train_bpe_model.py b/egs/spgispeech/ASR/local/train_bpe_model.py new file mode 120000 index 0000000000..6fad36421e --- /dev/null +++ b/egs/spgispeech/ASR/local/train_bpe_model.py @@ -0,0 +1 @@ +../../../librispeech/ASR/local/train_bpe_model.py \ No newline at end of file diff --git a/egs/spgispeech/ASR/prepare.sh b/egs/spgispeech/ASR/prepare.sh new file mode 100755 index 0000000000..231ebd742c --- /dev/null +++ b/egs/spgispeech/ASR/prepare.sh @@ -0,0 +1,196 @@ +#!/usr/bin/env bash + +set -eou pipefail + +nj=20 +stage=-1 +stop_stage=100 + +# We assume dl_dir (download dir) contains the following +# directories and files. If not, they will be downloaded +# by this script automatically. +# +# - $dl_dir/spgispeech +# You can find train.csv, val.csv, train, and val in this directory, which belong +# to the SPGISpeech dataset. +# +# - $dl_dir/musan +# This directory contains the following directories downloaded from +# http://www.openslr.org/17/ +# +# - music +# - noise +# - speech +dl_dir=$PWD/download + +. shared/parse_options.sh || exit 1 + +# vocab size for sentence piece models. +# It will generate data/lang_bpe_xxx, +# data/lang_bpe_yyy if the array contains xxx, yyy +vocab_sizes=( + 500 +) + +# All files generated by this script are saved in "data". +# You can safely remove "data" and rerun this script to regenerate it. +mkdir -p data + +log() { + # This function is from espnet + local fname=${BASH_SOURCE[1]##*/} + echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*" +} + +log "dl_dir: $dl_dir" + +if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then + log "Stage 0: Download data" + + # If you have pre-downloaded it to /path/to/spgispeech, + # you can create a symlink + # + # ln -sfv /path/to/spgispeech $dl_dir/spgispeech + # + if [ ! -d $dl_dir/spgispeech/train.csv ]; then + lhotse download spgispeech $dl_dir + fi + + # If you have pre-downloaded it to /path/to/musan, + # you can create a symlink + # + # ln -sfv /path/to/musan $dl_dir/ + # + if [ ! -d $dl_dir/musan ]; then + lhotse download musan $dl_dir + fi +fi + +if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then + log "Stage 1: Prepare SPGISpeech manifest (may take ~1h)" + # We assume that you have downloaded the SPGISpeech corpus + # to $dl_dir/spgispeech. We perform text normalization for the transcripts. + mkdir -p data/manifests + lhotse prepare spgispeech -j $nj --normalize-text $dl_dir/spgispeech data/manifests +fi + +if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then + log "Stage 2: Prepare musan manifest" + # We assume that you have downloaded the musan corpus + # to data/musan + mkdir -p data/manifests + lhotse prepare musan $dl_dir/musan data/manifests + lhotse combine data/manifests/recordings_{music,speech,noise}.json data/manifests/recordings_musan.jsonl.gz + lhotse cut simple -r data/manifests/recordings_musan.jsonl.gz data/manifests/cuts_musan_raw.jsonl.gz +fi + +if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then + log "Stage 3: Split train into train and dev and create cut sets." + python local/prepare_splits.py +fi + +if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then + log "Stage 4: Compute fbank features for spgispeech dev and val" + mkdir -p data/fbank + python local/compute_fbank_spgispeech.py --test +fi + +if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then + log "Stage 5: Compute fbank features for train" + mkdir -p data/fbank + python local/compute_fbank_spgispeech.py --train --num-splits 20 + + log "Combine features from train splits (may take ~1h)" + if [ ! -f data/manifests/cuts_train.jsonl.gz ]; then + pieces=$(find data/manifests -name "cuts_train_[0-9]*.jsonl.gz") + lhotse combine $pieces data/manifests/cuts_train.jsonl.gz + fi + gunzip -c data/manifests/train_cuts.jsonl.gz | shuf | gzip -c > data/manifests/train_cuts_shuf.jsonl.gz +fi + +if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then + log "Stage 6: Compute fbank features for musan" + mkdir -p data/fbank + python local/compute_fbank_musan.py +fi + +if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then + log "Stage 7: Dump transcripts for LM training" + mkdir -p data/lm + gunzip -c data/manifests/cuts_train_raw.jsonl.gz \ + | jq '.supervisions[0].text' \ + | sed 's:"::g' \ + > data/lm/transcript_words.txt +fi + +if [ $stage -le 8 ] && [ $stop_stage -ge 8 ]; then + log "Stage 8: Prepare BPE based lang" + + for vocab_size in ${vocab_sizes[@]}; do + lang_dir=data/lang_bpe_${vocab_size} + mkdir -p $lang_dir + + # Add special words to words.txt + echo " 0" > $lang_dir/words.txt + echo "!SIL 1" >> $lang_dir/words.txt + echo "[UNK] 2" >> $lang_dir/words.txt + + # Add regular words to words.txt + gunzip -c data/manifests/cuts_train_raw.jsonl.gz \ + | jq '.supervisions[0].text' \ + | sed 's:"::g' \ + | sed 's: :\n:g' \ + | sort \ + | uniq \ + | sed '/^$/d' \ + | awk '{print $0,NR+2}' \ + >> $lang_dir/words.txt + + # Add remaining special word symbols expected by LM scripts. + num_words=$(cat $lang_dir/words.txt | wc -l) + echo " ${num_words}" >> $lang_dir/words.txt + num_words=$(cat $lang_dir/words.txt | wc -l) + echo " ${num_words}" >> $lang_dir/words.txt + num_words=$(cat $lang_dir/words.txt | wc -l) + echo "#0 ${num_words}" >> $lang_dir/words.txt + + ./local/train_bpe_model.py \ + --lang-dir $lang_dir \ + --vocab-size $vocab_size \ + --transcript data/lm/transcript_words.txt + + if [ ! -f $lang_dir/L_disambig.pt ]; then + ./local/prepare_lang_bpe.py --lang-dir $lang_dir + fi + done +fi + +if [ $stage -le 9 ] && [ $stop_stage -ge 9 ]; then + log "Stage 9: Train LM" + lm_dir=data/lm + + if [ ! -f $lm_dir/G.arpa ]; then + ./shared/make_kn_lm.py \ + -ngram-order 3 \ + -text $lm_dir/transcript_words.txt \ + -lm $lm_dir/G.arpa + fi + + if [ ! -f $lm_dir/G_3_gram.fst.txt ]; then + python3 -m kaldilm \ + --read-symbol-table="data/lang_phone/words.txt" \ + --disambig-symbol='#0' \ + --max-order=3 \ + $lm_dir/G.arpa > $lm_dir/G_3_gram.fst.txt + fi +fi + +if [ $stage -le 10 ] && [ $stop_stage -ge 10 ]; then + log "Stage 10: Compile HLG" + ./local/compile_hlg.py --lang-dir data/lang_phone + + for vocab_size in ${vocab_sizes[@]}; do + lang_dir=data/lang_bpe_${vocab_size} + ./local/compile_hlg.py --lang-dir $lang_dir + done +fi diff --git a/egs/spgispeech/ASR/pruned_transducer_stateless2/__init__.py b/egs/spgispeech/ASR/pruned_transducer_stateless2/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/egs/spgispeech/ASR/pruned_transducer_stateless2/asr_datamodule.py b/egs/spgispeech/ASR/pruned_transducer_stateless2/asr_datamodule.py new file mode 100644 index 0000000000..f165f6e602 --- /dev/null +++ b/egs/spgispeech/ASR/pruned_transducer_stateless2/asr_datamodule.py @@ -0,0 +1,366 @@ +# Copyright 2021 Piotr Żelasko +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import argparse +import logging +from functools import lru_cache +from pathlib import Path +from typing import Any, Dict, Optional + +import torch +from lhotse import CutSet, Fbank, FbankConfig, load_manifest, load_manifest_lazy +from lhotse.dataset import ( + CutConcatenate, + CutMix, + DynamicBucketingSampler, + K2SpeechRecognitionDataset, + PrecomputedFeatures, + SpecAugment, +) +from lhotse.dataset.input_strategies import OnTheFlyFeatures +from lhotse.utils import fix_random_seed +from torch.utils.data import DataLoader +from tqdm import tqdm + +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 SPGISpeechAsrDataModule: + """ + 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/manifests"), + help="Path to directory with train/valid/test cuts.", + ) + 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( + "--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( + "--max-duration", + type=int, + default=100.0, + help="Maximum pooled recordings duration (seconds) in a " + "single batch. You can reduce it if it causes CUDA OOM.", + ) + group.add_argument( + "--num-buckets", + type=int, + default=30, + help="The number of buckets for the BucketingSampler" + "(you might want to increase it for larger datasets).", + ) + 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( + "--num-workers", + type=int, + default=8, + 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.", + ) + + def train_dataloaders( + self, + cuts_train: CutSet, + sampler_state_dict: Optional[Dict[str, Any]] = None, + ) -> DataLoader: + """ + Args: + cuts_train: + CutSet for training. + sampler_state_dict: + The state dict for the training sampler. + """ + logging.info("About to get Musan cuts") + cuts_musan = load_manifest( + self.args.manifest_dir / "cuts_musan.jsonl.gz" + ) + + transforms = [] + if self.args.enable_musan: + logging.info("Enable MUSAN") + transforms.append( + CutMix( + cuts=cuts_musan, prob=0.5, snr=(10, 20), preserve_id=True + ) + ) + else: + logging.info("Disable MUSAN") + + if self.args.concatenate_cuts: + logging.info( + f"Using cut concatenation with duration factor " + f"{self.args.duration_factor} and gap {self.args.gap}." + ) + # Cut concatenation should be the first transform in the list, + # so that if we e.g. mix noise in, it will fill the gaps between + # different utterances. + transforms = [ + CutConcatenate( + duration_factor=self.args.duration_factor, gap=self.args.gap + ) + ] + transforms + + input_transforms = [] + if self.args.enable_spec_aug: + logging.info("Enable SpecAugment") + logging.info( + f"Time warp factor: {self.args.spec_aug_time_warp_factor}" + ) + input_transforms.append( + SpecAugment( + time_warp_factor=self.args.spec_aug_time_warp_factor, + num_frame_masks=2, + features_mask_size=27, + num_feature_masks=2, + frames_mask_size=100, + ) + ) + else: + logging.info("Disable SpecAugment") + + logging.info("About to create train dataset") + if self.args.on_the_fly_feats: + train = K2SpeechRecognitionDataset( + cut_transforms=transforms, + input_strategy=OnTheFlyFeatures( + Fbank(FbankConfig(num_mel_bins=80)) + ), + input_transforms=input_transforms, + ) + else: + train = K2SpeechRecognitionDataset( + cut_transforms=transforms, + input_transforms=input_transforms, + ) + + logging.info("Using DynamicBucketingSampler.") + train_sampler = DynamicBucketingSampler( + cuts_train, + max_duration=self.args.max_duration, + shuffle=False, + num_buckets=self.args.num_buckets, + drop_last=True, + ) + logging.info("About to create train dataloader") + + if sampler_state_dict is not None: + logging.info("Loading sampler state dict") + train_sampler.load_state_dict(sampler_state_dict) + + # 'seed' is derived from the current random state, which will have + # previously been set in the main process. + seed = torch.randint(0, 100000, ()).item() + worker_init_fn = _SeedWorkers(seed) + + train_dl = DataLoader( + train, + sampler=train_sampler, + batch_size=None, + num_workers=self.args.num_workers, + persistent_workers=False, + worker_init_fn=worker_init_fn, + ) + + return train_dl + + def valid_dataloaders(self, cuts_valid: CutSet) -> DataLoader: + + transforms = [] + if self.args.concatenate_cuts: + transforms = [ + CutConcatenate( + duration_factor=self.args.duration_factor, gap=self.args.gap + ) + ] + transforms + + logging.info("About to create dev dataset") + if self.args.on_the_fly_feats: + validate = K2SpeechRecognitionDataset( + cut_transforms=transforms, + input_strategy=OnTheFlyFeatures( + Fbank(FbankConfig(num_mel_bins=80)) + ), + ) + else: + validate = K2SpeechRecognitionDataset( + cut_transforms=transforms, + ) + 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 PrecomputedFeatures(), + ) + 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 + + @lru_cache() + def train_cuts(self) -> CutSet: + logging.info("About to get SPGISpeech train cuts") + return load_manifest_lazy( + self.args.manifest_dir / "cuts_train_shuf.jsonl.gz" + ) + + @lru_cache() + def dev_cuts(self) -> CutSet: + logging.info("About to get SPGISpeech dev cuts") + return load_manifest_lazy(self.args.manifest_dir / "cuts_dev.jsonl.gz") + + @lru_cache() + def val_cuts(self) -> CutSet: + logging.info("About to get SPGISpeech val cuts") + return load_manifest_lazy(self.args.manifest_dir / "cuts_val.jsonl.gz") + + +def test(): + parser = argparse.ArgumentParser() + SPGISpeechAsrDataModule.add_arguments(parser) + args = parser.parse_args() + adm = SPGISpeechAsrDataModule(args) + + cuts = adm.train_cuts() + dl = adm.train_dataloaders(cuts) + for i, batch in tqdm(enumerate(dl)): + if i == 100: + break + + cuts = adm.dev_cuts() + dl = adm.valid_dataloaders(cuts) + for i, batch in tqdm(enumerate(dl)): + if i == 100: + break + + +if __name__ == "__main__": + test() diff --git a/egs/spgispeech/ASR/pruned_transducer_stateless2/beam_search.py b/egs/spgispeech/ASR/pruned_transducer_stateless2/beam_search.py new file mode 120000 index 0000000000..e24eca39f2 --- /dev/null +++ b/egs/spgispeech/ASR/pruned_transducer_stateless2/beam_search.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless2/beam_search.py \ No newline at end of file diff --git a/egs/spgispeech/ASR/pruned_transducer_stateless2/conformer.py b/egs/spgispeech/ASR/pruned_transducer_stateless2/conformer.py new file mode 120000 index 0000000000..a659571806 --- /dev/null +++ b/egs/spgispeech/ASR/pruned_transducer_stateless2/conformer.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless2/conformer.py \ No newline at end of file diff --git a/egs/spgispeech/ASR/pruned_transducer_stateless2/decode.py b/egs/spgispeech/ASR/pruned_transducer_stateless2/decode.py new file mode 100755 index 0000000000..ae49d166bc --- /dev/null +++ b/egs/spgispeech/ASR/pruned_transducer_stateless2/decode.py @@ -0,0 +1,594 @@ +#!/usr/bin/env python3 +# +# Copyright 2021 Xiaomi Corporation (Author: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Usage: +(1) greedy search +./pruned_transducer_stateless2/decode.py \ + --iter 696000 \ + --avg 10 \ + --exp-dir ./pruned_transducer_stateless2/exp \ + --max-duration 100 \ + --decoding-method greedy_search + +(2) beam search +./pruned_transducer_stateless2/decode.py \ + --iter 696000 \ + --avg 10 \ + --exp-dir ./pruned_transducer_stateless2/exp \ + --max-duration 100 \ + --decoding-method beam_search \ + --beam-size 4 + +(3) modified beam search +./pruned_transducer_stateless2/decode.py \ + --iter 696000 \ + --avg 10 \ + --exp-dir ./pruned_transducer_stateless2/exp \ + --max-duration 100 \ + --decoding-method modified_beam_search \ + --beam-size 4 + +(4) fast beam search +./pruned_transducer_stateless2/decode.py \ + --iter 696000 \ + --avg 10 \ + --exp-dir ./pruned_transducer_stateless2/exp \ + --max-duration 1500 \ + --decoding-method fast_beam_search \ + --beam 4 \ + --max-contexts 4 \ + --max-states 8 +""" + + +import argparse +import logging +from collections import defaultdict +from pathlib import Path +from typing import Dict, List, Optional, Tuple + +import k2 +import sentencepiece as spm +import torch +import torch.nn as nn +from asr_datamodule import SPGISpeechAsrDataModule +from beam_search import ( + beam_search, + fast_beam_search_one_best, + greedy_search, + greedy_search_batch, + modified_beam_search, +) +from train import get_params, get_transducer_model + +from icefall.checkpoint import ( + average_checkpoints, + find_checkpoints, + load_checkpoint, +) +from icefall.utils import ( + AttributeDict, + setup_logger, + store_transcripts, + write_error_stats, +) + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--epoch", + type=int, + default=20, + help="""It specifies the checkpoint to use for decoding. + Note: Epoch counts from 0. + You can specify --avg to use more checkpoints for model averaging.""", + ) + + parser.add_argument( + "--iter", + type=int, + default=0, + help="""If positive, --epoch is ignored and it + will use the checkpoint exp_dir/checkpoint-iter.pt. + You can specify --avg to use more checkpoints for model averaging. + """, + ) + + parser.add_argument( + "--avg", + type=int, + default=10, + help="Number of checkpoints to average. Automatically select " + "consecutive checkpoints before the checkpoint specified by " + "'--epoch' and '--iter'", + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="pruned_transducer_stateless2/exp", + help="The experiment dir", + ) + + parser.add_argument( + "--bpe-model", + type=str, + default="data/lang_bpe_500/bpe.model", + help="Path to the BPE model", + ) + + parser.add_argument( + "--decoding-method", + type=str, + default="greedy_search", + help="""Possible values are: + - greedy_search + - beam_search + - modified_beam_search + - fast_beam_search + """, + ) + + parser.add_argument( + "--beam-size", + type=int, + default=4, + help="""An interger indicating how many candidates we will keep for each + frame. Used only when --decoding-method is beam_search or + modified_beam_search.""", + ) + + parser.add_argument( + "--beam", + type=float, + default=4, + help="""A floating point value to calculate the cutoff score during beam + search (i.e., `cutoff = max-score - beam`), which is the same as the + `beam` in Kaldi. + Used only when --decoding-method is fast_beam_search""", + ) + + parser.add_argument( + "--max-contexts", + type=int, + default=4, + help="""Used only when --decoding-method is + fast_beam_search""", + ) + + parser.add_argument( + "--max-states", + type=int, + default=8, + help="""Used only when --decoding-method is + fast_beam_search""", + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; " + "2 means tri-gram", + ) + parser.add_argument( + "--max-sym-per-frame", + type=int, + default=1, + help="""Maximum number of symbols per frame. + Used only when --decoding_method is greedy_search""", + ) + + return parser + + +def decode_one_batch( + params: AttributeDict, + model: nn.Module, + sp: spm.SentencePieceProcessor, + batch: dict, + decoding_graph: Optional[k2.Fsa] = None, +) -> Dict[str, List[List[str]]]: + """Decode one batch and return the result in a dict. The dict has the + following format: + + - key: It indicates the setting used for decoding. For example, + if greedy_search is used, it would be "greedy_search" + If beam search with a beam size of 7 is used, it would be + "beam_7" + - value: It contains the decoding result. `len(value)` equals to + batch size. `value[i]` is the decoding result for the i-th + utterance in the given batch. + Args: + params: + It's the return value of :func:`get_params`. + model: + The neural model. + sp: + The BPE model. + batch: + It is the return value from iterating + `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation + for the format of the `batch`. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used + only when --decoding_method is fast_beam_search. + Returns: + Return the decoding result. See above description for the format of + the returned dict. + """ + device = model.device + feature = batch["inputs"] + assert feature.ndim == 3 + + feature = feature.to(device) + # at entry, feature is (N, T, C) + + supervisions = batch["supervisions"] + feature_lens = supervisions["num_frames"].to(device) + + encoder_out, encoder_out_lens = model.encoder( + x=feature, x_lens=feature_lens + ) + hyps = [] + + if params.decoding_method == "fast_beam_search": + hyp_tokens = fast_beam_search_one_best( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) + elif ( + params.decoding_method == "greedy_search" + and params.max_sym_per_frame == 1 + ): + hyp_tokens = greedy_search_batch( + model=model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) + elif params.decoding_method == "modified_beam_search": + hyp_tokens = modified_beam_search( + model=model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam_size, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) + else: + batch_size = encoder_out.size(0) + + for i in range(batch_size): + # fmt: off + encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]] + # fmt: on + if params.decoding_method == "greedy_search": + hyp = greedy_search( + model=model, + encoder_out=encoder_out_i, + max_sym_per_frame=params.max_sym_per_frame, + ) + elif params.decoding_method == "beam_search": + hyp = beam_search( + model=model, + encoder_out=encoder_out_i, + beam=params.beam_size, + ) + else: + raise ValueError( + f"Unsupported decoding method: {params.decoding_method}" + ) + hyps.append(sp.decode(hyp).split()) + + if params.decoding_method == "greedy_search": + return {"greedy_search": hyps} + elif params.decoding_method == "fast_beam_search": + return { + ( + f"beam_{params.beam}_" + f"max_contexts_{params.max_contexts}_" + f"max_states_{params.max_states}" + ): hyps + } + else: + return {f"beam_size_{params.beam_size}": hyps} + + +def decode_dataset( + dl: torch.utils.data.DataLoader, + params: AttributeDict, + model: nn.Module, + sp: spm.SentencePieceProcessor, + decoding_graph: Optional[k2.Fsa] = None, +) -> Dict[str, List[Tuple[List[str], List[str]]]]: + """Decode dataset. + + Args: + dl: + PyTorch's dataloader containing the dataset to decode. + params: + It is returned by :func:`get_params`. + model: + The neural model. + sp: + The BPE model. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used + only when --decoding_method is fast_beam_search. + Returns: + Return a dict, whose key may be "greedy_search" if greedy search + is used, or it may be "beam_7" if beam size of 7 is used. + Its value is a list of tuples. Each tuple contains two elements: + The first is the reference transcript, and the second is the + predicted result. + """ + num_cuts = 0 + + try: + num_batches = len(dl) + except TypeError: + num_batches = "?" + + if params.decoding_method == "greedy_search": + log_interval = 100 + else: + log_interval = 2 + + results = defaultdict(list) + for batch_idx, batch in enumerate(dl): + texts = batch["supervisions"]["text"] + + hyps_dict = decode_one_batch( + params=params, + model=model, + sp=sp, + decoding_graph=decoding_graph, + batch=batch, + ) + + for name, hyps in hyps_dict.items(): + this_batch = [] + assert len(hyps) == len(texts) + for hyp_words, ref_text in zip(hyps, texts): + ref_words = ref_text.split() + this_batch.append((ref_words, hyp_words)) + + results[name].extend(this_batch) + + num_cuts += len(texts) + + if batch_idx % log_interval == 0: + batch_str = f"{batch_idx}/{num_batches}" + + logging.info( + f"batch {batch_str}, cuts processed until now is {num_cuts}" + ) + return results + + +def save_results( + params: AttributeDict, + test_set_name: str, + results_dict: Dict[str, List[Tuple[List[int], List[int]]]], +): + test_set_wers = dict() + test_set_cers = dict() + for key, results in results_dict.items(): + recog_path = ( + params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt" + ) + store_transcripts(filename=recog_path, texts=results) + logging.info(f"The transcripts are stored in {recog_path}") + + # The following prints out WERs, per-word error statistics and aligned + # ref/hyp pairs. + wers_filename = ( + params.res_dir / f"wers-{test_set_name}-{key}-{params.suffix}.txt" + ) + with open(wers_filename, "w") as f: + wer = write_error_stats( + f, f"{test_set_name}-{key}", results, enable_log=True + ) + test_set_wers[key] = wer + + # we also compute CER for spgispeech dataset. + results_char = [] + for res in results: + results_char.append((list("".join(res[0])), list("".join(res[1])))) + cers_filename = ( + params.res_dir / f"cers-{test_set_name}-{key}-{params.suffix}.txt" + ) + with open(cers_filename, "w") as f: + cer = write_error_stats( + f, f"{test_set_name}-{key}", results_char, enable_log=True + ) + test_set_cers[key] = cer + + logging.info("Wrote detailed error stats to {}".format(wers_filename)) + + test_set_wers = { + k: v for k, v in sorted(test_set_wers.items(), key=lambda x: x[1]) + } + test_set_cers = { + k: v for k, v in sorted(test_set_cers.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\tCER", file=f) + for key in test_set_wers: + print( + "{}\t{}\t{}".format( + key, test_set_wers[key], test_set_cers[key] + ), + file=f, + ) + + s = "\nFor {}, WER/CER of different settings are:\n".format(test_set_name) + note = "\tbest for {}".format(test_set_name) + for key in test_set_wers: + s += "{}\t{}\t{}{}\n".format( + key, test_set_wers[key], test_set_cers[key], note + ) + note = "" + logging.info(s) + + +@torch.no_grad() +def main(): + parser = get_parser() + SPGISpeechAsrDataModule.add_arguments(parser) + args = parser.parse_args() + args.exp_dir = Path(args.exp_dir) + + params = get_params() + params.update(vars(args)) + + assert params.decoding_method in ( + "greedy_search", + "beam_search", + "fast_beam_search", + "modified_beam_search", + ) + params.res_dir = params.exp_dir / params.decoding_method + + if params.iter > 0: + params.suffix = f"iter-{params.iter}-avg-{params.avg}" + else: + params.suffix = f"epoch-{params.epoch}-avg-{params.avg}" + + if "fast_beam_search" in params.decoding_method: + params.suffix += f"-beam-{params.beam}" + params.suffix += f"-max-contexts-{params.max_contexts}" + params.suffix += f"-max-states-{params.max_states}" + elif "beam_search" in params.decoding_method: + params.suffix += ( + f"-{params.decoding_method}-beam-size-{params.beam_size}" + ) + else: + params.suffix += f"-context-{params.context_size}" + params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}" + + setup_logger(f"{params.res_dir}/log-decode-{params.suffix}") + logging.info("Decoding started") + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"Device: {device}") + + sp = spm.SentencePieceProcessor() + sp.load(params.bpe_model) + + # is defined in local/train_bpe_model.py + params.blank_id = sp.piece_to_id("") + params.vocab_size = sp.get_piece_size() + + logging.info(params) + + logging.info("About to create model") + model = get_transducer_model(params) + + if params.iter > 0: + filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[ + : params.avg + ] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + elif params.avg == 1: + load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) + else: + start = params.epoch - params.avg + 1 + filenames = [] + for i in range(start, params.epoch + 1): + if start >= 0: + filenames.append(f"{params.exp_dir}/epoch-{i}.pt") + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + + model.to(device) + model.eval() + model.device = device + + if params.decoding_method == "fast_beam_search": + decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device) + else: + decoding_graph = None + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + spgispeech = SPGISpeechAsrDataModule(args) + + dev_cuts = spgispeech.dev_cuts() + val_cuts = spgispeech.val_cuts() + + dev_dl = spgispeech.test_dataloaders(dev_cuts) + val_dl = spgispeech.test_dataloaders(val_cuts) + + test_sets = ["dev", "val"] + test_dl = [dev_dl, val_dl] + + for test_set, test_dl in zip(test_sets, test_dl): + results_dict = decode_dataset( + dl=test_dl, + params=params, + model=model, + sp=sp, + decoding_graph=decoding_graph, + ) + + save_results( + params=params, + test_set_name=test_set, + results_dict=results_dict, + ) + + logging.info("Done!") + + +if __name__ == "__main__": + main() diff --git a/egs/spgispeech/ASR/pruned_transducer_stateless2/decoder.py b/egs/spgispeech/ASR/pruned_transducer_stateless2/decoder.py new file mode 120000 index 0000000000..722e1c8941 --- /dev/null +++ b/egs/spgispeech/ASR/pruned_transducer_stateless2/decoder.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless2/decoder.py \ No newline at end of file diff --git a/egs/spgispeech/ASR/pruned_transducer_stateless2/encoder_interface.py b/egs/spgispeech/ASR/pruned_transducer_stateless2/encoder_interface.py new file mode 120000 index 0000000000..f582531273 --- /dev/null +++ b/egs/spgispeech/ASR/pruned_transducer_stateless2/encoder_interface.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless2/encoder_interface.py \ No newline at end of file diff --git a/egs/spgispeech/ASR/pruned_transducer_stateless2/export.py b/egs/spgispeech/ASR/pruned_transducer_stateless2/export.py new file mode 100755 index 0000000000..6119ecf2c8 --- /dev/null +++ b/egs/spgispeech/ASR/pruned_transducer_stateless2/export.py @@ -0,0 +1,201 @@ +#!/usr/bin/env python3 +# +# Copyright 2021 Xiaomi Corporation (Author: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# This script converts several saved checkpoints +# to a single one using model averaging. +""" +Usage: +./pruned_transducer_stateless2/export.py \ + --exp-dir ./pruned_transducer_stateless2/exp \ + --bpe-model data/lang_bpe_500/bpe.model \ + --avg-last-n 10 + +It will generate a file exp_dir/pretrained.pt + +To use the generated file with `pruned_transducer_stateless2/decode.py`, +you can do: + + cd /path/to/exp_dir + ln -s pretrained.pt epoch-9999.pt + + cd /path/to/egs/spgispeech/ASR + ./pruned_transducer_stateless2/decode.py \ + --exp-dir ./pruned_transducer_stateless2/exp \ + --epoch 9999 \ + --avg 1 \ + --max-duration 100 \ + --bpe-model data/lang_bpe_500/bpe.model +""" + +import argparse +import logging +from pathlib import Path + +import sentencepiece as spm +import torch +from train import get_params, get_transducer_model + +from icefall.checkpoint import ( + average_checkpoints, + find_checkpoints, + load_checkpoint, +) +from icefall.utils import str2bool + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--epoch", + type=int, + default=28, + help="It specifies the checkpoint to use for decoding." + "Note: Epoch counts from 0.", + ) + + parser.add_argument( + "--avg", + type=int, + default=15, + help="Number of checkpoints to average. Automatically select " + "consecutive checkpoints before the checkpoint specified by " + "'--epoch'. ", + ) + + parser.add_argument( + "--avg-last-n", + type=int, + default=0, + help="""If positive, --epoch and --avg are ignored and it + will use the last n checkpoints exp_dir/checkpoint-xxx.pt + where xxx is the number of processed batches while + saving that checkpoint. + """, + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="pruned_transducer_stateless2/exp", + help="""It specifies the directory where all training related + files, e.g., checkpoints, log, etc, are saved + """, + ) + + parser.add_argument( + "--bpe-model", + type=str, + default="data/lang_bpe_500/bpe.model", + help="Path to the BPE model", + ) + + parser.add_argument( + "--jit", + type=str2bool, + default=False, + help="""True to save a model after applying torch.jit.script. + """, + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; " + "2 means tri-gram", + ) + + return parser + + +def main(): + args = get_parser().parse_args() + args.exp_dir = Path(args.exp_dir) + + assert args.jit is False, "Support torchscript will be added later" + + params = get_params() + params.update(vars(args)) + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"device: {device}") + + sp = spm.SentencePieceProcessor() + sp.load(params.bpe_model) + + # is defined in local/train_bpe_model.py + params.blank_id = sp.piece_to_id("") + params.vocab_size = sp.get_piece_size() + + logging.info(params) + + logging.info("About to create model") + model = get_transducer_model(params) + + model.to(device) + + if params.avg_last_n > 0: + filenames = find_checkpoints(params.exp_dir)[: params.avg_last_n] + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + elif params.avg == 1: + load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) + else: + start = params.epoch - params.avg + 1 + filenames = [] + for i in range(start, params.epoch + 1): + if start >= 0: + filenames.append(f"{params.exp_dir}/epoch-{i}.pt") + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + + model.eval() + + model.to("cpu") + model.eval() + + if params.jit: + logging.info("Using torch.jit.script") + model = torch.jit.script(model) + filename = params.exp_dir / "cpu_jit.pt" + model.save(str(filename)) + logging.info(f"Saved to {filename}") + else: + logging.info("Not using torch.jit.script") + # Save it using a format so that it can be loaded + # by :func:`load_checkpoint` + filename = params.exp_dir / "pretrained.pt" + torch.save({"model": model.state_dict()}, str(filename)) + logging.info(f"Saved to {filename}") + + +if __name__ == "__main__": + formatter = ( + "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + ) + + logging.basicConfig(format=formatter, level=logging.INFO) + main() diff --git a/egs/spgispeech/ASR/pruned_transducer_stateless2/joiner.py b/egs/spgispeech/ASR/pruned_transducer_stateless2/joiner.py new file mode 120000 index 0000000000..9052f3cbb9 --- /dev/null +++ b/egs/spgispeech/ASR/pruned_transducer_stateless2/joiner.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless2/joiner.py \ No newline at end of file diff --git a/egs/spgispeech/ASR/pruned_transducer_stateless2/model.py b/egs/spgispeech/ASR/pruned_transducer_stateless2/model.py new file mode 120000 index 0000000000..a99e743342 --- /dev/null +++ b/egs/spgispeech/ASR/pruned_transducer_stateless2/model.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless2/model.py \ No newline at end of file diff --git a/egs/spgispeech/ASR/pruned_transducer_stateless2/optim.py b/egs/spgispeech/ASR/pruned_transducer_stateless2/optim.py new file mode 120000 index 0000000000..0a2f285aa4 --- /dev/null +++ b/egs/spgispeech/ASR/pruned_transducer_stateless2/optim.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless2/optim.py \ No newline at end of file diff --git a/egs/spgispeech/ASR/pruned_transducer_stateless2/scaling.py b/egs/spgispeech/ASR/pruned_transducer_stateless2/scaling.py new file mode 120000 index 0000000000..c10cdfe12e --- /dev/null +++ b/egs/spgispeech/ASR/pruned_transducer_stateless2/scaling.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless2/scaling.py \ No newline at end of file diff --git a/egs/spgispeech/ASR/pruned_transducer_stateless2/train.py b/egs/spgispeech/ASR/pruned_transducer_stateless2/train.py new file mode 100755 index 0000000000..6c66bfb620 --- /dev/null +++ b/egs/spgispeech/ASR/pruned_transducer_stateless2/train.py @@ -0,0 +1,1031 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang, +# Wei Kang +# Mingshuang Luo) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Usage: + +export CUDA_VISIBLE_DEVICES="0,1,2,3" + +./pruned_transducer_stateless2/train.py \ + --world-size 4 \ + --num-epochs 30 \ + --start-epoch 0 \ + --exp-dir pruned_transducer_stateless2/exp \ + --full-libri 1 \ + --max-duration 300 + +# For mix precision training: + +./pruned_transducer_stateless2/train.py \ + --world-size 4 \ + --num-epochs 30 \ + --start-epoch 0 \ + --use_fp16 1 \ + --exp-dir pruned_transducer_stateless2/exp \ + --full-libri 1 \ + --max-duration 550 + +""" + + +import argparse +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 SPGISpeechAsrDataModule +from conformer import Conformer +from decoder import Decoder +from joiner import Joiner +from lhotse.dataset.sampling.base import CutSampler +from lhotse.utils import fix_random_seed +from model import Transducer +from optim import Eden, Eve +from torch import Tensor +from torch.cuda.amp import GradScaler +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.utils.tensorboard import SummaryWriter + +from icefall import diagnostics +from icefall.checkpoint import load_checkpoint, remove_checkpoints +from icefall.checkpoint import save_checkpoint as save_checkpoint_impl +from icefall.checkpoint import save_checkpoint_with_global_batch_idx +from icefall.dist import cleanup_dist, setup_dist +from icefall.env import get_env_info +from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool + +LRSchedulerType = Union[ + torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler +] + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--world-size", + type=int, + default=1, + help="Number of GPUs for DDP training.", + ) + + parser.add_argument( + "--master-port", + type=int, + default=12354, + help="Master port to use for DDP training.", + ) + + parser.add_argument( + "--tensorboard", + type=str2bool, + default=True, + help="Should various information be logged in tensorboard.", + ) + + parser.add_argument( + "--num-epochs", + type=int, + default=20, + help="Number of epochs to train.", + ) + + parser.add_argument( + "--start-epoch", + type=int, + default=0, + help="""Resume training from from this epoch. + If it is positive, it will load checkpoint from + transducer_stateless2/exp/epoch-{start_epoch-1}.pt + """, + ) + + parser.add_argument( + "--start-batch", + type=int, + default=0, + help="""If positive, --start-epoch is ignored and + it loads the checkpoint from exp-dir/checkpoint-{start_batch}.pt + """, + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="pruned_transducer_stateless2/exp", + help="""The experiment dir. + It specifies the directory where all training related + files, e.g., checkpoints, log, etc, are saved + """, + ) + + parser.add_argument( + "--bpe-model", + type=str, + default="data/lang_bpe_500/bpe.model", + help="Path to the BPE model", + ) + + parser.add_argument( + "--initial-lr", + type=float, + default=0.003, + help="The initial learning rate. This value should not need to be " + "changed.", + ) + + parser.add_argument( + "--lr-batches", + type=float, + default=5000, + help="""Number of steps that affects how rapidly the learning rate decreases. + We suggest not to change this.""", + ) + + parser.add_argument( + "--lr-epochs", + type=float, + default=4, + help="""Number of epochs that affects how rapidly the learning rate decreases. + """, + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; " + "2 means tri-gram", + ) + + parser.add_argument( + "--prune-range", + type=int, + default=5, + help="The prune range for rnnt loss, it means how many symbols(context)" + "we are using to compute the loss", + ) + + parser.add_argument( + "--lm-scale", + type=float, + default=0.25, + help="The scale to smooth the loss with lm " + "(output of prediction network) part.", + ) + + parser.add_argument( + "--am-scale", + type=float, + default=0.0, + help="The scale to smooth the loss with am (output of encoder network)" + "part.", + ) + + parser.add_argument( + "--simple-loss-scale", + type=float, + default=0.5, + help="To get pruning ranges, we will calculate a simple version" + "loss(joiner is just addition), this simple loss also uses for" + "training (as a regularization item). We will scale the simple loss" + "with this parameter before adding to the final loss.", + ) + + parser.add_argument( + "--seed", + type=int, + default=42, + help="The seed for random generators intended for reproducibility", + ) + + parser.add_argument( + "--print-diagnostics", + type=str2bool, + default=False, + help="Accumulate stats on activations, print them and exit.", + ) + + parser.add_argument( + "--save-every-n", + type=int, + default=8000, + help="""Save checkpoint after processing this number of batches" + periodically. We save checkpoint to exp-dir/ whenever + params.batch_idx_train % save_every_n == 0. The checkpoint filename + has the form: f'exp-dir/checkpoint-{params.batch_idx_train}.pt' + Note: It also saves checkpoint to `exp-dir/epoch-xxx.pt` at the + end of each epoch where `xxx` is the epoch number counting from 0. + """, + ) + + parser.add_argument( + "--keep-last-k", + type=int, + default=10, + help="""Only keep this number of checkpoints on disk. + For instance, if it is 3, there are only 3 checkpoints + in the exp-dir with filenames `checkpoint-xxx.pt`. + It does not affect checkpoints with name `epoch-xxx.pt`. + """, + ) + + parser.add_argument( + "--use-fp16", + type=str2bool, + default=False, + help="Whether to use half precision training.", + ) + + return parser + + +def get_params() -> AttributeDict: + """Return a dict containing training parameters. + + All training related parameters that are not passed from the commandline + are saved in the variable `params`. + + Commandline options are merged into `params` after they are parsed, so + you can also access them via `params`. + + Explanation of options saved in `params`: + + - best_train_loss: Best training loss so far. It is used to select + the model that has the lowest training loss. It is + updated during the training. + + - best_valid_loss: Best validation loss so far. It is used to select + the model that has the lowest validation loss. It is + updated during the training. + + - best_train_epoch: It is the epoch that has the best training loss. + + - best_valid_epoch: It is the epoch that has the best validation loss. + + - batch_idx_train: Used to writing statistics to tensorboard. It + contains number of batches trained so far across + epochs. + + - log_interval: Print training loss if batch_idx % log_interval` is 0 + + - reset_interval: Reset statistics if batch_idx % reset_interval is 0 + + - valid_interval: Run validation if batch_idx % valid_interval is 0 + + - feature_dim: The model input dim. It has to match the one used + in computing features. + + - subsampling_factor: The subsampling factor for the model. + + - encoder_dim: Hidden dim for multi-head attention model. + + - num_decoder_layers: Number of decoder layer of transformer decoder. + + - warm_step: The warm_step for Noam optimizer. + """ + params = AttributeDict( + { + "best_train_loss": float("inf"), + "best_valid_loss": float("inf"), + "best_train_epoch": -1, + "best_valid_epoch": -1, + "batch_idx_train": 0, + "log_interval": 50, + "reset_interval": 200, + "valid_interval": 3000, # For the 100h subset, use 800 + # parameters for conformer + "feature_dim": 80, + "subsampling_factor": 4, + "encoder_dim": 512, + "nhead": 8, + "dim_feedforward": 2048, + "num_encoder_layers": 12, + # parameters for decoder + "decoder_dim": 512, + # parameters for joiner + "joiner_dim": 512, + # parameters for Noam + "model_warm_step": 3000, # arg given to model, not for lrate + "env_info": get_env_info(), + } + ) + + return params + + +def get_encoder_model(params: AttributeDict) -> nn.Module: + # TODO: We can add an option to switch between Conformer and Transformer + encoder = Conformer( + num_features=params.feature_dim, + subsampling_factor=params.subsampling_factor, + d_model=params.encoder_dim, + nhead=params.nhead, + dim_feedforward=params.dim_feedforward, + num_encoder_layers=params.num_encoder_layers, + ) + return encoder + + +def get_decoder_model(params: AttributeDict) -> nn.Module: + decoder = Decoder( + vocab_size=params.vocab_size, + decoder_dim=params.decoder_dim, + blank_id=params.blank_id, + context_size=params.context_size, + ) + return decoder + + +def get_joiner_model(params: AttributeDict) -> nn.Module: + joiner = Joiner( + encoder_dim=params.encoder_dim, + decoder_dim=params.decoder_dim, + joiner_dim=params.joiner_dim, + vocab_size=params.vocab_size, + ) + return joiner + + +def get_transducer_model(params: AttributeDict) -> nn.Module: + encoder = get_encoder_model(params) + decoder = get_decoder_model(params) + joiner = get_joiner_model(params) + + model = Transducer( + encoder=encoder, + decoder=decoder, + joiner=joiner, + encoder_dim=params.encoder_dim, + decoder_dim=params.decoder_dim, + joiner_dim=params.joiner_dim, + vocab_size=params.vocab_size, + ) + return model + + +def load_checkpoint_if_available( + params: AttributeDict, + model: nn.Module, + optimizer: Optional[torch.optim.Optimizer] = None, + scheduler: Optional[LRSchedulerType] = None, +) -> Optional[Dict[str, Any]]: + """Load checkpoint from file. + + If params.start_batch is positive, it will load the checkpoint from + `params.exp_dir/checkpoint-{params.start_batch}.pt`. Otherwise, if + params.start_epoch is positive, it will load the checkpoint from + `params.start_epoch - 1`. + + Apart from loading state dict for `model` and `optimizer` it also updates + `best_train_epoch`, `best_train_loss`, `best_valid_epoch`, + and `best_valid_loss` in `params`. + + Args: + params: + The return value of :func:`get_params`. + model: + The training model. + optimizer: + The optimizer that we are using. + scheduler: + The scheduler that we are using. + Returns: + Return a dict containing previously saved training info. + """ + if params.start_batch > 0: + filename = params.exp_dir / f"checkpoint-{params.start_batch}.pt" + elif params.start_epoch > 0: + filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt" + else: + return None + + assert filename.is_file(), f"{filename} does not exist!" + + saved_params = load_checkpoint( + filename, + model=model, + optimizer=optimizer, + scheduler=scheduler, + ) + + keys = [ + "best_train_epoch", + "best_valid_epoch", + "batch_idx_train", + "best_train_loss", + "best_valid_loss", + ] + for k in keys: + params[k] = saved_params[k] + + if params.start_batch > 0: + if "cur_epoch" in saved_params: + params["start_epoch"] = saved_params["cur_epoch"] + + if "cur_batch_idx" in saved_params: + params["cur_batch_idx"] = saved_params["cur_batch_idx"] + + return saved_params + + +def save_checkpoint( + params: AttributeDict, + model: nn.Module, + optimizer: Optional[torch.optim.Optimizer] = None, + scheduler: Optional[LRSchedulerType] = None, + sampler: Optional[CutSampler] = None, + scaler: Optional[GradScaler] = None, + rank: int = 0, +) -> None: + """Save model, optimizer, scheduler and training stats to file. + + Args: + params: + It is returned by :func:`get_params`. + model: + The training model. + optimizer: + The optimizer used in the training. + sampler: + The sampler for the training dataset. + scaler: + The scaler used for mix precision training. + """ + if rank != 0: + return + filename = params.exp_dir / f"epoch-{params.cur_epoch}.pt" + save_checkpoint_impl( + filename=filename, + model=model, + params=params, + optimizer=optimizer, + scheduler=scheduler, + sampler=sampler, + scaler=scaler, + rank=rank, + ) + + if params.best_train_epoch == params.cur_epoch: + best_train_filename = params.exp_dir / "best-train-loss.pt" + copyfile(src=filename, dst=best_train_filename) + + if params.best_valid_epoch == params.cur_epoch: + best_valid_filename = params.exp_dir / "best-valid-loss.pt" + copyfile(src=filename, dst=best_valid_filename) + + +def compute_loss( + params: AttributeDict, + model: nn.Module, + sp: spm.SentencePieceProcessor, + batch: dict, + is_training: bool, + warmup: float = 1.0, +) -> Tuple[Tensor, MetricsTracker]: + """ + Compute CTC loss given the model and its inputs. + + Args: + params: + Parameters for training. See :func:`get_params`. + model: + The model for training. It is an instance of Conformer in our case. + batch: + A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()` + for the content in it. + is_training: + True for training. False for validation. When it is True, this + function enables autograd during computation; when it is False, it + disables autograd. + warmup: a floating point value which increases throughout training; + values >= 1.0 are fully warmed up and have all modules present. + """ + device = model.device + feature = batch["inputs"] + # at entry, feature is (N, T, C) + assert feature.ndim == 3 + feature = feature.to(device) + + supervisions = batch["supervisions"] + feature_lens = supervisions["num_frames"].to(device) + + texts = batch["supervisions"]["text"] + y = sp.encode(texts, out_type=int) + y = k2.RaggedTensor(y).to(device) + + with torch.set_grad_enabled(is_training): + simple_loss, pruned_loss = model( + x=feature, + x_lens=feature_lens, + y=y, + prune_range=params.prune_range, + am_scale=params.am_scale, + lm_scale=params.lm_scale, + warmup=warmup, + ) + # after the main warmup step, we keep pruned_loss_scale small + # for the same amount of time (model_warm_step), to avoid + # overwhelming the simple_loss and causing it to diverge, + # in case it had not fully learned the alignment yet. + pruned_loss_scale = ( + 0.0 + if warmup < 1.0 + else (0.1 if warmup > 1.0 and warmup < 2.0 else 1.0) + ) + loss = ( + params.simple_loss_scale * simple_loss + + pruned_loss_scale * pruned_loss + ) + + assert loss.requires_grad == is_training + + info = MetricsTracker() + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + info["frames"] = ( + (feature_lens // params.subsampling_factor).sum().item() + ) + + # Note: We use reduction=sum while computing the loss. + info["loss"] = loss.detach().cpu().item() + info["simple_loss"] = simple_loss.detach().cpu().item() + info["pruned_loss"] = pruned_loss.detach().cpu().item() + + return loss, info + + +def compute_validation_loss( + params: AttributeDict, + model: nn.Module, + sp: spm.SentencePieceProcessor, + valid_dl: torch.utils.data.DataLoader, + world_size: int = 1, +) -> MetricsTracker: + """Run the validation process.""" + model.eval() + + tot_loss = MetricsTracker() + + for batch_idx, batch in enumerate(valid_dl): + loss, loss_info = compute_loss( + params=params, + model=model, + sp=sp, + batch=batch, + is_training=False, + ) + assert loss.requires_grad is False + tot_loss = tot_loss + loss_info + + if world_size > 1: + tot_loss.reduce(loss.device) + + loss_value = tot_loss["loss"] / tot_loss["frames"] + if loss_value < params.best_valid_loss: + params.best_valid_epoch = params.cur_epoch + params.best_valid_loss = loss_value + + return tot_loss + + +def train_one_epoch( + params: AttributeDict, + model: nn.Module, + optimizer: torch.optim.Optimizer, + scheduler: LRSchedulerType, + sp: spm.SentencePieceProcessor, + train_dl: torch.utils.data.DataLoader, + valid_dl: torch.utils.data.DataLoader, + scaler: GradScaler, + tb_writer: Optional[SummaryWriter] = None, + world_size: int = 1, + rank: int = 0, +) -> None: + """Train the model for one epoch. + + The training loss from the mean of all frames is saved in + `params.train_loss`. It runs the validation process every + `params.valid_interval` batches. + + Args: + params: + It is returned by :func:`get_params`. + model: + The model for training. + optimizer: + The optimizer we are using. + scheduler: + The learning rate scheduler, we call step() every step. + train_dl: + Dataloader for the training dataset. + valid_dl: + Dataloader for the validation dataset. + scaler: + The scaler used for mix precision training. + tb_writer: + Writer to write log messages to tensorboard. + world_size: + Number of nodes in DDP training. If it is 1, DDP is disabled. + rank: + The rank of the node in DDP training. If no DDP is used, it should + be set to 0. + """ + model.train() + + tot_loss = MetricsTracker() + + cur_batch_idx = params.get("cur_batch_idx", 0) + + for batch_idx, batch in enumerate(train_dl): + if batch_idx < cur_batch_idx: + continue + cur_batch_idx = batch_idx + + params.batch_idx_train += 1 + batch_size = len(batch["supervisions"]["text"]) + + with torch.cuda.amp.autocast(enabled=params.use_fp16): + loss, loss_info = compute_loss( + params=params, + model=model, + sp=sp, + batch=batch, + is_training=True, + warmup=(params.batch_idx_train / params.model_warm_step), + ) + # summary stats + tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info + + # NOTE: We use reduction==sum and loss is computed over utterances + # in the batch and there is no normalization to it so far. + scaler.scale(loss).backward() + scheduler.step_batch(params.batch_idx_train) + scaler.step(optimizer) + scaler.update() + optimizer.zero_grad() + + if params.print_diagnostics and batch_idx == 5: + return + + if ( + params.batch_idx_train > 0 + and params.batch_idx_train % params.save_every_n == 0 + ): + params.cur_batch_idx = batch_idx + save_checkpoint_with_global_batch_idx( + out_dir=params.exp_dir, + global_batch_idx=params.batch_idx_train, + model=model, + params=params, + optimizer=optimizer, + scheduler=scheduler, + sampler=train_dl.sampler, + scaler=scaler, + rank=rank, + ) + del params.cur_batch_idx + remove_checkpoints( + out_dir=params.exp_dir, + topk=params.keep_last_k, + rank=rank, + ) + + if batch_idx % params.log_interval == 0: + cur_lr = scheduler.get_last_lr()[0] + logging.info( + f"Epoch {params.cur_epoch}, " + f"batch {batch_idx}, loss[{loss_info}], " + f"tot_loss[{tot_loss}], batch size: {batch_size}, " + f"lr: {cur_lr:.2e}" + ) + + if tb_writer is not None: + tb_writer.add_scalar( + "train/learning_rate", cur_lr, params.batch_idx_train + ) + + loss_info.write_summary( + tb_writer, "train/current_", params.batch_idx_train + ) + tot_loss.write_summary( + tb_writer, "train/tot_", params.batch_idx_train + ) + + if batch_idx > 0 and batch_idx % params.valid_interval == 0: + logging.info("Computing validation loss") + valid_info = compute_validation_loss( + params=params, + model=model, + sp=sp, + valid_dl=valid_dl, + world_size=world_size, + ) + model.train() + logging.info(f"Epoch {params.cur_epoch}, validation: {valid_info}") + if tb_writer is not None: + valid_info.write_summary( + tb_writer, "train/valid_", params.batch_idx_train + ) + + loss_value = tot_loss["loss"] / tot_loss["frames"] + params.train_loss = loss_value + if params.train_loss < params.best_train_loss: + params.best_train_epoch = params.cur_epoch + params.best_train_loss = params.train_loss + + +def run(rank, world_size, args): + """ + Args: + rank: + It is a value between 0 and `world_size-1`, which is + passed automatically by `mp.spawn()` in :func:`main`. + The node with rank 0 is responsible for saving checkpoint. + world_size: + Number of GPUs for DDP training. + args: + The return value of get_parser().parse_args() + """ + params = get_params() + params.update(vars(args)) + + fix_random_seed(params.seed) + if world_size > 1: + setup_dist(rank, world_size, params.master_port) + + setup_logger(f"{params.exp_dir}/log/log-train") + logging.info("Training started") + + if args.tensorboard and rank == 0: + tb_writer = SummaryWriter(log_dir=f"{params.exp_dir}/tensorboard") + else: + tb_writer = None + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", rank) + logging.info(f"Device: {device}") + + sp = spm.SentencePieceProcessor() + sp.load(params.bpe_model) + + # is defined in local/train_bpe_model.py + params.blank_id = sp.piece_to_id("") + params.vocab_size = sp.get_piece_size() + + logging.info(params) + + logging.info("About to create model") + model = get_transducer_model(params) + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + checkpoints = load_checkpoint_if_available(params=params, model=model) + + model.to(device) + if world_size > 1: + logging.info("Using DDP") + model = DDP(model, device_ids=[rank]) + model.device = device + + optimizer = Eve(model.parameters(), lr=params.initial_lr) + + scheduler = Eden(optimizer, params.lr_batches, params.lr_epochs) + + if checkpoints and "optimizer" in checkpoints: + logging.info("Loading optimizer state dict") + optimizer.load_state_dict(checkpoints["optimizer"]) + + if ( + checkpoints + and "scheduler" in checkpoints + and checkpoints["scheduler"] is not None + ): + logging.info("Loading scheduler state dict") + scheduler.load_state_dict(checkpoints["scheduler"]) + + if params.print_diagnostics: + opts = diagnostics.TensorDiagnosticOptions( + 2 ** 22 + ) # allow 4 megabytes per sub-module + diagnostic = diagnostics.attach_diagnostics(model, opts) + + spgispeech = SPGISpeechAsrDataModule(args) + + train_cuts = spgispeech.train_cuts() + + # Ideally we should filter utterances that are too long or too short, + # but SPGISpeech contains regular length utterances so we don't need to + # do that. Here are the statistics of the training data (obtained by + # `train_cuts.describe()`): + + # Cuts count: 5886320 + # Total duration (hours): 15070.1 + # Speech duration (hours): 15070.1 (100.0%) + # *** + # Duration statistics (seconds): + # mean 9.2 + # std 2.8 + # min 4.6 + # 25% 6.9 + # 50% 8.9 + # 75% 11.2 + # 99% 16.0 + # 99.5% 16.3 + # 99.9% 16.6 + # max 16.7 + + 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 = spgispeech.train_dataloaders( + train_cuts, sampler_state_dict=sampler_state_dict + ) + + valid_cuts = spgispeech.dev_cuts() + valid_dl = spgispeech.valid_dataloaders(valid_cuts) + + if not params.print_diagnostics: + scan_pessimistic_batches_for_oom( + model=model, + train_dl=train_dl, + optimizer=optimizer, + sp=sp, + params=params, + ) + + scaler = GradScaler(enabled=params.use_fp16) + if checkpoints and "grad_scaler" in checkpoints: + logging.info("Loading grad scaler state dict") + scaler.load_state_dict(checkpoints["grad_scaler"]) + + for epoch in range(params.start_epoch, params.num_epochs): + scheduler.step_epoch(epoch) + fix_random_seed(params.seed + epoch) + train_dl.sampler.set_epoch(epoch) + + if tb_writer is not None: + tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train) + + params.cur_epoch = epoch + + train_one_epoch( + params=params, + model=model, + optimizer=optimizer, + scheduler=scheduler, + 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, + 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: nn.Module, + train_dl: torch.utils.data.DataLoader, + optimizer: torch.optim.Optimizer, + sp: spm.SentencePieceProcessor, + params: AttributeDict, +): + from lhotse.dataset import find_pessimistic_batches + + logging.info( + "Sanity check -- see if any of the batches in epoch 0 would cause OOM." + ) + batches, crit_values = find_pessimistic_batches(train_dl.sampler) + for criterion, cuts in batches.items(): + batch = train_dl.dataset[cuts] + try: + # warmup = 0.0 is so that the derivs for the pruned loss stay zero + # (i.e. are not remembered by the decaying-average in adam), because + # we want to avoid these params being subject to shrinkage in adam. + with torch.cuda.amp.autocast(enabled=params.use_fp16): + loss, _ = compute_loss( + params=params, + model=model, + sp=sp, + batch=batch, + is_training=True, + warmup=0.0, + ) + loss.backward() + optimizer.step() + optimizer.zero_grad() + except RuntimeError as e: + if "CUDA out of memory" in str(e): + logging.error( + "Your GPU ran out of memory with the current " + "max_duration setting. We recommend decreasing " + "max_duration and trying again.\n" + f"Failing criterion: {criterion} " + f"(={crit_values[criterion]}) ..." + ) + display_and_save_batch(batch, params=params, sp=sp) + raise + + +def main(): + parser = get_parser() + SPGISpeechAsrDataModule.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/spgispeech/ASR/shared b/egs/spgispeech/ASR/shared new file mode 120000 index 0000000000..4c5e91438c --- /dev/null +++ b/egs/spgispeech/ASR/shared @@ -0,0 +1 @@ +../../../icefall/shared/ \ No newline at end of file