diff --git a/egs/ami/ASR/README.md b/egs/ami/ASR/README.md new file mode 100644 index 0000000000..1c9714bd43 --- /dev/null +++ b/egs/ami/ASR/README.md @@ -0,0 +1,48 @@ +# AMI + +This is an ASR recipe for the AMI corpus. AMI provides recordings from the speaker's +headset and lapel microphones, and also 2 array microphones containing 8 channels each. +We pool data in the following 4 ways and train a single model on the pooled data: + +(i) individual headset microphone (IHM) +(ii) IHM with simulated reverb +(iii) Single distant microphone (SDM) +(iv) GSS-enhanced array microphones + +Speed perturbation and MUSAN noise augmentation are additionally performed on the pooled +data. Here are the statistics of the combined training data: + +```python +>>> cuts_train.describe() +Cuts count: 1222053 +Total duration (hh:mm:ss): 905:00:28 +Speech duration (hh:mm:ss): 905:00:28 (99.9%) +Duration statistics (seconds): +mean 2.7 +std 2.8 +min 0.0 +25% 0.6 +50% 1.6 +75% 3.8 +99% 12.3 +99.5% 13.9 +99.9% 18.4 +max 36.8 +``` + +**Note:** This recipe additionally uses [GSS](https://github.com/desh2608/gss) for enhancement +of far-field array microphones, but this is optional (see `prepare.sh` for details). + +## Performance Record + +### pruned_transducer_stateless7 + +The following are decoded using `modified_beam_search`: + +| Evaluation set | dev WER | test WER | +|--------------------------|------------|---------| +| IHM | 18.92 | 17.40 | +| SDM | 31.25 | 32.21 | +| MDM (GSS-enhanced) | 21.67 | 22.43 | + +See [RESULTS](/egs/ami/ASR/RESULTS.md) for details. diff --git a/egs/ami/ASR/RESULTS.md b/egs/ami/ASR/RESULTS.md new file mode 100644 index 0000000000..1639860214 --- /dev/null +++ b/egs/ami/ASR/RESULTS.md @@ -0,0 +1,92 @@ +## Results + +### AMI training results (Pruned Transducer) + +#### 2022-11-20 + +#### Zipformer (pruned_transducer_stateless7) + +Zipformer 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). + +All the results below are using a single model that is trained by combining the following +data: IHM, IHM+reverb, SDM, and GSS-enhanced MDM. Speed perturbation and MUSAN noise +augmentation are applied on top of the pooled data. + +**WERs for IHM:** + +| | dev | test | comment | +|---------------------------|------------|------------|------------------------------------------| +| greedy search | 19.25 | 17.83 | --epoch 14 --avg 8 --max-duration 500 | +| modified beam search | 18.92 | 17.40 | --epoch 14 --avg 8 --max-duration 500 --beam-size 4 | +| fast beam search | 19.44 | 18.04 | --epoch 14 --avg 8 --max-duration 500 --beam-size 4 --max-contexts 4 --max-states 8 | + +**WERs for SDM:** + +| | dev | test | comment | +|---------------------------|------------|------------|------------------------------------------| +| greedy search | 31.32 | 32.38 | --epoch 14 --avg 8 --max-duration 500 | +| modified beam search | 31.25 | 32.21 | --epoch 14 --avg 8 --max-duration 500 --beam-size 4 | +| fast beam search | 31.11 | 32.10 | --epoch 14 --avg 8 --max-duration 500 --beam-size 4 --max-contexts 4 --max-states 8 | + +**WERs for GSS-enhanced MDM:** + +| | dev | test | comment | +|---------------------------|------------|------------|------------------------------------------| +| greedy search | 22.05 | 22.93 | --epoch 14 --avg 8 --max-duration 500 | +| modified beam search | 21.67 | 22.43 | --epoch 14 --avg 8 --max-duration 500 --beam-size 4 | +| fast beam search | 22.21 | 22.83 | --epoch 14 --avg 8 --max-duration 500 --beam-size 4 --max-contexts 4 --max-states 8 | + +The training command for reproducing is given below: + +``` +export CUDA_VISIBLE_DEVICES="0,1,2,3" + +./pruned_transducer_stateless7/train.py \ + --world-size 4 \ + --num-epochs 15 \ + --exp-dir pruned_transducer_stateless7/exp \ + --max-duration 150 \ + --max-cuts 150 \ + --prune-range 5 \ + --lr-factor 5 \ + --lm-scale 0.25 \ + --use-fp16 True +``` + +The decoding command is: +``` +# greedy search +./pruned_transducer_stateless7/decode.py \ + --epoch 14 \ + --avg 8 \ + --exp-dir ./pruned_transducer_stateless7/exp \ + --max-duration 500 \ + --decoding-method greedy_search + +# modified beam search +./pruned_transducer_stateless7/decode.py \ + --iter 105000 \ + --avg 10 \ + --exp-dir ./pruned_transducer_stateless7/exp \ + --max-duration 500 \ + --decoding-method modified_beam_search \ + --beam-size 4 + +# fast beam search +./pruned_transducer_stateless7/decode.py \ + --iter 105000 \ + --avg 10 \ + --exp-dir ./pruned_transducer_stateless5/exp \ + --max-duration 500 \ + --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/ami/ASR/local/__init__.py b/egs/ami/ASR/local/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/egs/ami/ASR/local/compute_fbank_ami.py b/egs/ami/ASR/local/compute_fbank_ami.py new file mode 100755 index 0000000000..4892b40e31 --- /dev/null +++ b/egs/ami/ASR/local/compute_fbank_ami.py @@ -0,0 +1,194 @@ +#!/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 AMI dataset. +For the training data, we pool together IHM, reverberated IHM, and GSS-enhanced +audios. For the test data, we separately prepare IHM, SDM, and GSS-enhanced +parts (which are the 3 evaluation settings). +It looks for manifests in the directory data/manifests. + +The generated fbank features are saved in data/fbank. +""" +import logging +import math +from pathlib import Path + +import torch +import torch.multiprocessing +from lhotse import CutSet, LilcomChunkyWriter +from lhotse.features.kaldifeat import ( + KaldifeatFbank, + KaldifeatFbankConfig, + KaldifeatFrameOptions, + KaldifeatMelOptions, +) +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) +torch.multiprocessing.set_sharing_strategy("file_system") + + +def compute_fbank_ami(): + 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", + ) + ) + + logging.info("Reading manifests") + manifests_ihm = read_manifests_if_cached( + dataset_parts=["train", "dev", "test"], + output_dir=src_dir, + prefix="ami-ihm", + suffix="jsonl.gz", + ) + manifests_sdm = read_manifests_if_cached( + dataset_parts=["train", "dev", "test"], + output_dir=src_dir, + prefix="ami-sdm", + suffix="jsonl.gz", + ) + # For GSS we already have cuts so we read them directly. + manifests_gss = read_manifests_if_cached( + dataset_parts=["train", "dev", "test"], + output_dir=src_dir, + prefix="ami-gss", + suffix="jsonl.gz", + ) + + def _extract_feats(cuts: CutSet, storage_path: Path, manifest_path: Path) -> None: + cuts = cuts + cuts.perturb_speed(0.9) + cuts.perturb_speed(1.1) + _ = cuts.compute_and_store_features_batch( + extractor=extractor, + storage_path=storage_path, + manifest_path=manifest_path, + batch_duration=5000, + num_workers=8, + storage_type=LilcomChunkyWriter, + ) + + logging.info( + "Preparing training cuts: IHM + reverberated IHM + SDM + GSS (optional)" + ) + + logging.info("Processing train split IHM") + cuts_ihm = ( + CutSet.from_manifests(**manifests_ihm["train"]) + .trim_to_supervisions(keep_overlapping=False, keep_all_channels=False) + .modify_ids(lambda x: x + "-ihm") + ) + _extract_feats( + cuts_ihm, + output_dir / "feats_train_ihm", + src_dir / "cuts_train_ihm.jsonl.gz", + ) + + logging.info("Processing train split IHM + reverberated IHM") + cuts_ihm_rvb = cuts_ihm.reverb_rir() + _extract_feats( + cuts_ihm_rvb, + output_dir / "feats_train_ihm_rvb", + src_dir / "cuts_train_ihm_rvb.jsonl.gz", + ) + + logging.info("Processing train split SDM") + cuts_sdm = ( + CutSet.from_manifests(**manifests_sdm["train"]) + .trim_to_supervisions(keep_overlapping=False) + .modify_ids(lambda x: x + "-sdm") + ) + _extract_feats( + cuts_sdm, + output_dir / "feats_train_sdm", + src_dir / "cuts_train_sdm.jsonl.gz", + ) + + logging.info("Processing train split GSS") + cuts_gss = ( + CutSet.from_manifests(**manifests_gss["train"]) + .trim_to_supervisions(keep_overlapping=False) + .modify_ids(lambda x: x + "-gss") + ) + _extract_feats( + cuts_gss, + output_dir / "feats_train_gss", + src_dir / "cuts_train_gss.jsonl.gz", + ) + + logging.info("Preparing test cuts: IHM, SDM, GSS (optional)") + for split in ["dev", "test"]: + logging.info(f"Processing {split} IHM") + cuts_ihm = ( + CutSet.from_manifests(**manifests_ihm[split]) + .trim_to_supervisions(keep_overlapping=False, keep_all_channels=False) + .compute_and_store_features_batch( + extractor=extractor, + storage_path=output_dir / f"feats_{split}_ihm", + manifest_path=src_dir / f"cuts_{split}_ihm.jsonl.gz", + batch_duration=5000, + num_workers=8, + storage_type=LilcomChunkyWriter, + ) + ) + logging.info(f"Processing {split} SDM") + cuts_sdm = ( + CutSet.from_manifests(**manifests_sdm[split]) + .trim_to_supervisions(keep_overlapping=False) + .compute_and_store_features_batch( + extractor=extractor, + storage_path=output_dir / f"feats_{split}_sdm", + manifest_path=src_dir / f"cuts_{split}_sdm.jsonl.gz", + batch_duration=500, + num_workers=4, + storage_type=LilcomChunkyWriter, + ) + ) + logging.info(f"Processing {split} GSS") + cuts_gss = ( + CutSet.from_manifests(**manifests_gss[split]) + .trim_to_supervisions(keep_overlapping=False) + .compute_and_store_features_batch( + extractor=extractor, + storage_path=output_dir / f"feats_{split}_gss", + manifest_path=src_dir / f"cuts_{split}_gss.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_ami() diff --git a/egs/ami/ASR/local/compute_fbank_musan.py b/egs/ami/ASR/local/compute_fbank_musan.py new file mode 100755 index 0000000000..1fcf951f9d --- /dev/null +++ b/egs/ami/ASR/local/compute_fbank_musan.py @@ -0,0 +1,114 @@ +#!/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 CutSet, LilcomChunkyWriter, combine +from lhotse.features.kaldifeat import ( + KaldifeatFbank, + KaldifeatFbankConfig, + KaldifeatFrameOptions, + KaldifeatMelOptions, +) +from lhotse.recipes.utils import read_manifests_if_cached + +# Torch's multithreaded behavior needs to be disabled or +# it wastes a lot of CPU and slow things down. +# Do this outside of main() in case it needs to take effect +# even when we are not invoking the main (e.g. when spawning subprocesses). +torch.set_num_threads(1) +torch.set_num_interop_threads(1) + + +def compute_fbank_musan(): + src_dir = Path("data/manifests") + output_dir = Path("data/fbank") + + sampling_rate = 16000 + num_mel_bins = 80 + + dataset_parts = ( + "music", + "speech", + "noise", + ) + prefix = "musan" + suffix = "jsonl.gz" + manifests = read_manifests_if_cached( + dataset_parts=dataset_parts, + output_dir=src_dir, + prefix=prefix, + suffix=suffix, + ) + assert manifests is not None + + assert len(manifests) == len(dataset_parts), ( + len(manifests), + len(dataset_parts), + list(manifests.keys()), + dataset_parts, + ) + + musan_cuts_path = src_dir / "musan_cuts.jsonl.gz" + + if musan_cuts_path.is_file(): + logging.info(f"{musan_cuts_path} already exists - skipping") + return + + logging.info("Extracting features for Musan") + + extractor = KaldifeatFbank( + KaldifeatFbankConfig( + frame_opts=KaldifeatFrameOptions(sampling_rate=sampling_rate), + mel_opts=KaldifeatMelOptions(num_bins=num_mel_bins), + device="cuda", + ) + ) + + # create chunks of Musan with duration 5 - 10 seconds + _ = ( + 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 / "musan_feats", + manifest_path=musan_cuts_path, + 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/ami/ASR/local/prepare_ami_enhanced.py b/egs/ami/ASR/local/prepare_ami_enhanced.py new file mode 100644 index 0000000000..bed220eb33 --- /dev/null +++ b/egs/ami/ASR/local/prepare_ami_enhanced.py @@ -0,0 +1,158 @@ +#!/usr/local/bin/python +# -*- coding: utf-8 -*- +# Data preparation for AMI GSS-enhanced dataset. + +import logging +from concurrent.futures import ThreadPoolExecutor +from pathlib import Path + +from lhotse import Recording, RecordingSet, SupervisionSet +from lhotse.qa import fix_manifests +from lhotse.recipes.utils import read_manifests_if_cached +from lhotse.utils import fastcopy +from tqdm import tqdm + +logging.basicConfig( + format="%(asctime)s %(levelname)-8s %(message)s", + level=logging.INFO, + datefmt="%Y-%m-%d %H:%M:%S", +) + + +def get_args(): + import argparse + + parser = argparse.ArgumentParser(description="AMI enhanced dataset preparation.") + parser.add_argument( + "manifests_dir", + type=Path, + help="Path to directory containing AMI manifests.", + ) + parser.add_argument( + "enhanced_dir", + type=Path, + help="Path to enhanced data directory.", + ) + parser.add_argument( + "--num-jobs", + "-j", + type=int, + default=1, + help="Number of parallel jobs to run.", + ) + parser.add_argument( + "--min-segment-duration", + "-d", + type=float, + default=0.0, + help="Minimum duration of a segment in seconds.", + ) + return parser.parse_args() + + +def find_recording_and_create_new_supervision(enhanced_dir, supervision): + """ + Given a supervision (corresponding to original AMI recording), this function finds the + enhanced recording correspoding to the supervision, and returns this recording and + a new supervision whose start and end times are adjusted to match the enhanced recording. + """ + file_name = Path( + f"{supervision.recording_id}-{supervision.speaker}-{int(100*supervision.start):06d}_{int(100*supervision.end):06d}.flac" + ) + save_path = enhanced_dir / f"{supervision.recording_id}" / file_name + if save_path.exists(): + recording = Recording.from_file(save_path) + if recording.duration == 0: + logging.warning(f"Skipping {save_path} which has duration 0 seconds.") + return None + + # Old supervision is wrt to the original recording, we create new supervision + # wrt to the enhanced segment + new_supervision = fastcopy( + supervision, + recording_id=recording.id, + start=0, + duration=recording.duration, + ) + return recording, new_supervision + else: + logging.warning(f"{save_path} does not exist.") + return None + + +def main(args): + # Get arguments + manifests_dir = args.manifests_dir + enhanced_dir = args.enhanced_dir + + # Load manifests from cache if they exist (saves time) + manifests = read_manifests_if_cached( + dataset_parts=["train", "dev", "test"], + output_dir=manifests_dir, + prefix="ami-sdm", + suffix="jsonl.gz", + ) + if not manifests: + raise ValueError("AMI SDM manifests not found in {}".format(manifests_dir)) + + with ThreadPoolExecutor(args.num_jobs) as ex: + for part in ["train", "dev", "test"]: + logging.info(f"Processing {part}...") + supervisions_orig = manifests[part]["supervisions"].filter( + lambda s: s.duration >= args.min_segment_duration + ) + # Remove TS3009d supervisions since they are not present in the enhanced data + supervisions_orig = supervisions_orig.filter( + lambda s: s.recording_id != "TS3009d" + ) + futures = [] + + for supervision in tqdm( + supervisions_orig, + desc="Distributing tasks", + ): + futures.append( + ex.submit( + find_recording_and_create_new_supervision, + enhanced_dir, + supervision, + ) + ) + + recordings = [] + supervisions = [] + for future in tqdm( + futures, + total=len(futures), + desc="Processing tasks", + ): + result = future.result() + if result is not None: + recording, new_supervision = result + recordings.append(recording) + supervisions.append(new_supervision) + + # Remove duplicates from the recordings + recordings_nodup = {} + for recording in recordings: + if recording.id not in recordings_nodup: + recordings_nodup[recording.id] = recording + else: + logging.warning("Recording {} is duplicated.".format(recording.id)) + recordings = RecordingSet.from_recordings(recordings_nodup.values()) + supervisions = SupervisionSet.from_segments(supervisions) + + recordings, supervisions = fix_manifests( + recordings=recordings, supervisions=supervisions + ) + + logging.info(f"Writing {part} enhanced manifests") + recordings.to_file(manifests_dir / f"ami-gss_recordings_{part}.jsonl.gz") + supervisions.to_file( + manifests_dir / f"ami-gss_supervisions_{part}.jsonl.gz" + ) + + +if __name__ == "__main__": + args = get_args() + main(args) diff --git a/egs/ami/ASR/local/prepare_ami_gss.sh b/egs/ami/ASR/local/prepare_ami_gss.sh new file mode 100755 index 0000000000..d5422458bd --- /dev/null +++ b/egs/ami/ASR/local/prepare_ami_gss.sh @@ -0,0 +1,98 @@ +#!/bin/bash +# This script is used to run GSS-based enhancement on AMI data. +set -euo pipefail +nj=4 +stage=0 + +. shared/parse_options.sh || exit 1 + +if [ $# != 2 ]; then + echo "Wrong #arguments ($#, expected 2)" + echo "Usage: local/prepare_ami_gss.sh [options] " + echo "e.g. local/prepare_ami_gss.sh data/manifests exp/ami_gss" + echo "main options (for others, see top of script file)" + echo " --nj # number of parallel jobs" + echo " --stage # stage to start running from" + exit 1; +fi + +DATA_DIR=$1 +EXP_DIR=$2 + +mkdir -p $EXP_DIR + +log() { + # This function is from espnet + local fname=${BASH_SOURCE[1]##*/} + echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*" +} + +if [ $stage -le 1 ]; then + log "Stage 1: Prepare cut sets" + for part in train dev test; do + lhotse cut simple \ + -r $DATA_DIR/ami-mdm_recordings_${part}.jsonl.gz \ + -s $DATA_DIR/ami-mdm_supervisions_${part}.jsonl.gz \ + $EXP_DIR/cuts_${part}.jsonl.gz + done +fi + +if [ $stage -le 2 ]; then + log "Stage 2: Trim cuts to supervisions (1 cut per supervision segment)" + for part in train dev test; do + lhotse cut trim-to-supervisions --discard-overlapping \ + $EXP_DIR/cuts_${part}.jsonl.gz $EXP_DIR/cuts_per_segment_${part}.jsonl.gz + done +fi + +if [ $stage -le 3 ]; then + log "Stage 3: Split manifests for multi-GPU processing (optional)" + for part in train; do + gss utils split $nj $EXP_DIR/cuts_per_segment_${part}.jsonl.gz \ + $EXP_DIR/cuts_per_segment_${part}_split$nj + done +fi + +if [ $stage -le 4 ]; then + log "Stage 4: Enhance train segments using GSS (requires GPU)" + # for train, we use smaller context and larger batches to speed-up processing + for JOB in $(seq $nj); do + gss enhance cuts $EXP_DIR/cuts_train.jsonl.gz \ + $EXP_DIR/cuts_per_segment_train_split$nj/cuts_per_segment_train.JOB.jsonl.gz $EXP_DIR/enhanced \ + --bss-iterations 10 \ + --context-duration 5.0 \ + --use-garbage-class \ + --channels 0,1,2,3,4,5,6,7 \ + --min-segment-length 0.05 \ + --max-segment-length 35.0 \ + --max-batch-duration 60.0 \ + --num-buckets 3 \ + --num-workers 2 + done +fi + +if [ $stage -le 5 ]; then + log "Stage 5: Enhance dev/test segments using GSS (using GPU)" + # for dev/test, we use larger context and smaller batches to get better quality + for part in dev test; do + for JOB in $(seq $nj); do + gss enhance cuts $EXP_DIR/cuts_${part}.jsonl.gz \ + $EXP_DIR/cuts_per_segment_${part}_split$nj/cuts_per_segment_${part}.JOB.jsonl.gz \ + $EXP_DIR/enhanced \ + --bss-iterations 10 \ + --context-duration 15.0 \ + --use-garbage-class \ + --channels 0,1,2,3,4,5,6,7 \ + --min-segment-length 0.05 \ + --max-segment-length 30.0 \ + --max-batch-duration 45.0 \ + --num-buckets 3 \ + --num-workers 2 + done + done +fi + +if [ $stage -le 6 ]; then + log "Stage 6: Prepare manifests for GSS-enhanced data" + python local/prepare_ami_enhanced.py $DATA_DIR $EXP_DIR/enhanced -j $nj --min-segment-duration 0.05 +fi diff --git a/egs/ami/ASR/local/prepare_lang_bpe.py b/egs/ami/ASR/local/prepare_lang_bpe.py new file mode 120000 index 0000000000..36b40e7fc2 --- /dev/null +++ b/egs/ami/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/ami/ASR/local/train_bpe_model.py b/egs/ami/ASR/local/train_bpe_model.py new file mode 120000 index 0000000000..6fad36421e --- /dev/null +++ b/egs/ami/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/ami/ASR/prepare.sh b/egs/ami/ASR/prepare.sh new file mode 100755 index 0000000000..fb21a8ec69 --- /dev/null +++ b/egs/ami/ASR/prepare.sh @@ -0,0 +1,144 @@ +#!/usr/bin/env bash + +set -eou pipefail + +stage=-1 +stop_stage=100 +use_gss=true # Use GSS-based enhancement with MDM setting + +# We assume dl_dir (download dir) contains the following +# directories and files. If not, they will be downloaded +# by this script automatically. +# +# - $dl_dir/amicorpus +# You can find audio and transcripts in this path. +# +# - $dl_dir/musan +# This directory contains the following directories downloaded from +# http://www.openslr.org/17/ +# +# - music +# - noise +# - speech +# +# - $dl_dir/{LDC2004S13,LDC2005S13,LDC2004T19,LDC2005T19} +# These contain the Fisher English audio and transcripts. We will +# only use the transcripts as extra LM training data (similar to Kaldi). +# +dl_dir=$PWD/download + +. shared/parse_options.sh || exit 1 + +# All files generated by this script are saved in "data". +# You can safely remove "data" and rerun this script to regenerate it. +mkdir -p data +vocab_size=500 + +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/amicorpus, + # you can create a symlink + # + # ln -sfv /path/to/amicorpus $dl_dir/amicorpus + # + if [ ! -d $dl_dir/amicorpus ]; then + lhotse download ami --mic ihm $dl_dir/amicorpus + lhotse download ami --mic mdm $dl_dir/amicorpus + 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 AMI manifests" + # We assume that you have downloaded the AMI corpus + # to $dl_dir/amicorpus. We perform text normalization for the transcripts. + mkdir -p data/manifests + for mic in ihm sdm mdm; do + lhotse prepare ami --mic $mic --partition full-corpus-asr --normalize-text kaldi \ + --max-words-per-segment 30 $dl_dir/amicorpus data/manifests/ + done +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 $dl_dir/musan + mkdir -p data/manifests + lhotse prepare musan $dl_dir/musan data/manifests +fi + +if [ $stage -le 3 ] && [ $stop_stage -ge 3 ] && [ $use_gss = true ]; then + log "Stage 3: Apply GSS enhancement on MDM data (this stage requires a GPU)" + # We assume that you have installed the GSS package: https://github.com/desh2608/gss + local/prepare_ami_gss.sh data/manifests exp/ami_gss +fi + +if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then + log "Stage 4: Compute fbank features for AMI" + mkdir -p data/fbank + python local/compute_fbank_ami.py + log "Combine features from train splits" + lhotse combine data/manifests/cuts_train_{ihm,ihm_rvb,sdm,gss}.jsonl.gz - | shuf |\ + gzip -c > data/manifests/cuts_train_all.jsonl.gz +fi + +if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then + log "Stage 5: Compute fbank features for musan" + mkdir -p data/fbank + python local/compute_fbank_musan.py +fi + +if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then + log "Stage 6: Dump transcripts for BPE model training." + mkdir -p data/lm + cat <(gunzip -c data/manifests/ami-sdm_supervisions_train.jsonl.gz | jq '.text' | sed 's:"::g')> data/lm/transcript_words.txt +fi + +if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then + log "Stage 7: Prepare BPE based lang" + + lang_dir=data/lang_bpe_${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 " 2" >> $lang_dir/words.txt + + # Add regular words to words.txt + cat data/lm/transcript_words.txt | grep -o -E '\w+' | sort -u | 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 +fi diff --git a/egs/ami/ASR/pruned_transducer_stateless7/__init__.py b/egs/ami/ASR/pruned_transducer_stateless7/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/egs/ami/ASR/pruned_transducer_stateless7/asr_datamodule.py b/egs/ami/ASR/pruned_transducer_stateless7/asr_datamodule.py new file mode 100644 index 0000000000..f7ee9c9620 --- /dev/null +++ b/egs/ami/ASR/pruned_transducer_stateless7/asr_datamodule.py @@ -0,0 +1,430 @@ +# 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 +import re +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.cut import Cut +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 AmiAsrDataModule: + """ + 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( + "--max-cuts", type=int, default=None, help="Maximum cuts in a single batch." + ) + group.add_argument( + "--num-buckets", + type=int, + default=50, + 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." + ), + ) + group.add_argument( + "--ihm-only", + type=str2bool, + default=False, + help="When enabled, only use IHM data for training.", + ) + + 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") + + transforms = [] + if self.args.enable_musan: + logging.info("Enable MUSAN") + 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( + "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, + max_cuts=self.args.max_cuts, + 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(), + return_cuts=True, + ) + 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 + + def remove_short_cuts(self, cut: Cut) -> bool: + """ + See: https://github.com/k2-fsa/icefall/issues/500 + Basically, the zipformer model subsamples the input using the following formula: + num_out_frames = (num_in_frames - 7)//2 + For num_out_frames to be at least 1, num_in_frames must be at least 9. + """ + return cut.duration >= 0.09 + + @lru_cache() + def train_cuts(self, sp: Optional[Any] = None) -> CutSet: + logging.info("About to get AMI train cuts") + + def _remove_short_and_long_utt(c: Cut): + if c.duration < 0.2 or c.duration > 25.0: + 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) + return T >= len(tokens) + + if self.args.ihm_only: + cuts_train = load_manifest_lazy( + self.args.manifest_dir / "cuts_train_ihm.jsonl.gz" + ) + else: + cuts_train = load_manifest_lazy( + self.args.manifest_dir / "cuts_train_all.jsonl.gz" + ) + + return cuts_train.filter(_remove_short_and_long_utt) + + @lru_cache() + def dev_ihm_cuts(self) -> CutSet: + logging.info("About to get AMI IHM dev cuts") + cs = load_manifest_lazy(self.args.manifest_dir / "cuts_dev_ihm.jsonl.gz") + return cs.filter(self.remove_short_cuts) + + @lru_cache() + def dev_sdm_cuts(self) -> CutSet: + logging.info("About to get AMI SDM dev cuts") + cs = load_manifest_lazy(self.args.manifest_dir / "cuts_dev_sdm.jsonl.gz") + return cs.filter(self.remove_short_cuts) + + @lru_cache() + def dev_gss_cuts(self) -> CutSet: + if not (self.args.manifest_dir / "cuts_dev_gss.jsonl.gz").exists(): + logging.info("No GSS dev cuts found") + return None + logging.info("About to get AMI GSS-enhanced dev cuts") + cs = load_manifest_lazy(self.args.manifest_dir / "cuts_dev_gss.jsonl.gz") + return cs.filter(self.remove_short_cuts) + + @lru_cache() + def test_ihm_cuts(self) -> CutSet: + logging.info("About to get AMI IHM test cuts") + cs = load_manifest_lazy(self.args.manifest_dir / "cuts_test_ihm.jsonl.gz") + return cs.filter(self.remove_short_cuts) + + @lru_cache() + def test_sdm_cuts(self) -> CutSet: + logging.info("About to get AMI SDM test cuts") + cs = load_manifest_lazy(self.args.manifest_dir / "cuts_test_sdm.jsonl.gz") + return cs.filter(self.remove_short_cuts) + + @lru_cache() + def test_gss_cuts(self) -> CutSet: + if not (self.args.manifest_dir / "cuts_test_gss.jsonl.gz").exists(): + logging.info("No GSS test cuts found") + return None + logging.info("About to get AMI GSS-enhanced test cuts") + cs = load_manifest_lazy(self.args.manifest_dir / "cuts_test_gss.jsonl.gz") + return cs.filter(self.remove_short_cuts) diff --git a/egs/ami/ASR/pruned_transducer_stateless7/beam_search.py b/egs/ami/ASR/pruned_transducer_stateless7/beam_search.py new file mode 120000 index 0000000000..37516affc8 --- /dev/null +++ b/egs/ami/ASR/pruned_transducer_stateless7/beam_search.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless7/beam_search.py \ No newline at end of file diff --git a/egs/ami/ASR/pruned_transducer_stateless7/decode.py b/egs/ami/ASR/pruned_transducer_stateless7/decode.py new file mode 100755 index 0000000000..f47228fbe4 --- /dev/null +++ b/egs/ami/ASR/pruned_transducer_stateless7/decode.py @@ -0,0 +1,747 @@ +#!/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_stateless7/decode.py \ + --iter 105000 \ + --avg 10 \ + --exp-dir ./pruned_transducer_stateless7/exp \ + --max-duration 100 \ + --decoding-method greedy_search + +(2) beam search +./pruned_transducer_stateless7/decode.py \ + --iter 105000 \ + --avg 10 \ + --exp-dir ./pruned_transducer_stateless7/exp \ + --max-duration 500 \ + --decoding-method beam_search \ + --beam-size 4 + +(3) modified beam search +./pruned_transducer_stateless7/decode.py \ + --iter 105000 \ + --avg 10 \ + --exp-dir ./pruned_transducer_stateless7/exp \ + --max-duration 500 \ + --decoding-method modified_beam_search \ + --beam-size 4 + +(4) fast beam search +./pruned_transducer_stateless7/decode.py \ + --iter 105000 \ + --avg 10 \ + --exp-dir ./pruned_transducer_stateless5/exp \ + --max-duration 500 \ + --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 AmiAsrDataModule +from beam_search import ( + beam_search, + fast_beam_search_nbest_LG, + fast_beam_search_one_best, + greedy_search, + greedy_search_batch, + modified_beam_search, +) +from train import add_model_arguments, get_params, get_transducer_model + +from icefall import NgramLm +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, +) + + +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 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( + "--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="pruned_transducer_stateless2/exp", + help="The experiment dir", + ) + + parser.add_argument( + "--lang-dir", + type=Path, + default="data/lang_bpe_500", + 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 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( + "--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""", + ) + + add_model_arguments(parser) + + return parser + + +def decode_one_batch( + params: AttributeDict, + model: nn.Module, + sp: spm.SentencePieceProcessor, + batch: dict, + decoding_graph: Optional[k2.Fsa] = None, + word_table: Optional[k2.SymbolTable] = 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. + word_table: + The word symbol table. + 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 == "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 == "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 + } + 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, + decoding_graph: Optional[k2.Fsa] = None, + word_table: Optional[k2.SymbolTable] = 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"] + 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): + ref_words = ref_text.split() + this_batch.append((cut_id, 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 AMI dataset. + results_char = [] + for res in results: + results_char.append((res[0], list("".join(res[1])), list("".join(res[2])))) + 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() + AmiAsrDataModule.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_LG", + "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}" + 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}" + + 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(f"{params.lang_dir}/bpe.model") + + # is defined in local/train_bpe_model.py + params.blank_id = sp.piece_to_id("") + params.unk_id = sp.piece_to_id("") + params.vocab_size = sp.get_piece_size() + + logging.info(params) + + logging.info("About to create model") + model = get_transducer_model(params) + + 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() + model.device = device + + 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}") + + ami = AmiAsrDataModule(args) + + dev_ihm_cuts = ami.dev_ihm_cuts() + test_ihm_cuts = ami.test_ihm_cuts() + dev_sdm_cuts = ami.dev_sdm_cuts() + test_sdm_cuts = ami.test_sdm_cuts() + dev_gss_cuts = ami.dev_gss_cuts() + test_gss_cuts = ami.test_gss_cuts() + + dev_ihm_dl = ami.test_dataloaders(dev_ihm_cuts) + test_ihm_dl = ami.test_dataloaders(test_ihm_cuts) + dev_sdm_dl = ami.test_dataloaders(dev_sdm_cuts) + test_sdm_dl = ami.test_dataloaders(test_sdm_cuts) + if dev_gss_cuts is not None: + dev_gss_dl = ami.test_dataloaders(dev_gss_cuts) + if test_gss_cuts is not None: + test_gss_dl = ami.test_dataloaders(test_gss_cuts) + + test_sets = { + "dev_ihm": (dev_ihm_dl, dev_ihm_cuts), + "test_ihm": (test_ihm_dl, test_ihm_cuts), + "dev_sdm": (dev_sdm_dl, dev_sdm_cuts), + "test_sdm": (test_sdm_dl, test_sdm_cuts), + } + if dev_gss_cuts is not None: + test_sets["dev_gss"] = (dev_gss_dl, dev_gss_cuts) + if test_gss_cuts is not None: + test_sets["test_gss"] = (test_gss_dl, test_gss_cuts) + + for test_set in test_sets: + logging.info(f"Decoding {test_set}") + dl, cuts = test_sets[test_set] + results_dict = decode_dataset( + dl=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/ami/ASR/pruned_transducer_stateless7/decoder.py b/egs/ami/ASR/pruned_transducer_stateless7/decoder.py new file mode 120000 index 0000000000..8283d8c5a0 --- /dev/null +++ b/egs/ami/ASR/pruned_transducer_stateless7/decoder.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless7/decoder.py \ No newline at end of file diff --git a/egs/ami/ASR/pruned_transducer_stateless7/encoder_interface.py b/egs/ami/ASR/pruned_transducer_stateless7/encoder_interface.py new file mode 120000 index 0000000000..0c2673d464 --- /dev/null +++ b/egs/ami/ASR/pruned_transducer_stateless7/encoder_interface.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless7/encoder_interface.py \ No newline at end of file diff --git a/egs/ami/ASR/pruned_transducer_stateless7/export.py b/egs/ami/ASR/pruned_transducer_stateless7/export.py new file mode 120000 index 0000000000..2713792e68 --- /dev/null +++ b/egs/ami/ASR/pruned_transducer_stateless7/export.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless7/export.py \ No newline at end of file diff --git a/egs/ami/ASR/pruned_transducer_stateless7/joiner.py b/egs/ami/ASR/pruned_transducer_stateless7/joiner.py new file mode 120000 index 0000000000..0f0c3c90a3 --- /dev/null +++ b/egs/ami/ASR/pruned_transducer_stateless7/joiner.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless7/joiner.py \ No newline at end of file diff --git a/egs/ami/ASR/pruned_transducer_stateless7/model.py b/egs/ami/ASR/pruned_transducer_stateless7/model.py new file mode 120000 index 0000000000..0d8bc665be --- /dev/null +++ b/egs/ami/ASR/pruned_transducer_stateless7/model.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless7/model.py \ No newline at end of file diff --git a/egs/ami/ASR/pruned_transducer_stateless7/optim.py b/egs/ami/ASR/pruned_transducer_stateless7/optim.py new file mode 120000 index 0000000000..8a05abb5f2 --- /dev/null +++ b/egs/ami/ASR/pruned_transducer_stateless7/optim.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless7/optim.py \ No newline at end of file diff --git a/egs/ami/ASR/pruned_transducer_stateless7/scaling.py b/egs/ami/ASR/pruned_transducer_stateless7/scaling.py new file mode 120000 index 0000000000..5f9be9fe04 --- /dev/null +++ b/egs/ami/ASR/pruned_transducer_stateless7/scaling.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless7/scaling.py \ No newline at end of file diff --git a/egs/ami/ASR/pruned_transducer_stateless7/scaling_converter.py b/egs/ami/ASR/pruned_transducer_stateless7/scaling_converter.py new file mode 120000 index 0000000000..f9960e5c6b --- /dev/null +++ b/egs/ami/ASR/pruned_transducer_stateless7/scaling_converter.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless7/scaling_converter.py \ No newline at end of file diff --git a/egs/ami/ASR/pruned_transducer_stateless7/train.py b/egs/ami/ASR/pruned_transducer_stateless7/train.py new file mode 100755 index 0000000000..b5efb3405d --- /dev/null +++ b/egs/ami/ASR/pruned_transducer_stateless7/train.py @@ -0,0 +1,1184 @@ +#!/usr/bin/env python3 +# Copyright 2021-2022 Xiaomi Corp. (authors: Fangjun Kuang, +# Wei Kang, +# Mingshuang Luo,) +# Zengwei Yao) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Usage: + +export CUDA_VISIBLE_DEVICES="0,1,2,3" + +./pruned_transducer_stateless7/train.py \ + --world-size 4 \ + --num-epochs 15 \ + --start-epoch 1 \ + --exp-dir pruned_transducer_stateless7/exp \ + --max-duration 150 \ + --use-fp16 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 AmiAsrDataModule +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, ScaledAdam +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 Zipformer + +from icefall import diagnostics +from icefall.checkpoint import load_checkpoint, remove_checkpoints +from icefall.checkpoint import save_checkpoint as save_checkpoint_impl +from icefall.checkpoint import ( + save_checkpoint_with_global_batch_idx, + update_averaged_model, +) +from icefall.dist import cleanup_dist, setup_dist +from icefall.env import get_env_info +from icefall.hooks import register_inf_check_hooks +from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool + +LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler] + + +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 module in model.modules(): + if hasattr(module, "batch_count"): + module.batch_count = batch_count + + +def add_model_arguments(parser: argparse.ArgumentParser): + parser.add_argument( + "--num-encoder-layers", + type=str, + default="2,4,3,2,4", + help="Number of zipformer encoder layers, comma separated.", + ) + + parser.add_argument( + "--feedforward-dims", + type=str, + default="1024,1024,2048,2048,1024", + help="Feedforward dimension of the zipformer encoder layers, comma separated.", + ) + + parser.add_argument( + "--nhead", + type=str, + default="8,8,8,8,8", + help="Number of attention heads in the zipformer encoder layers.", + ) + + parser.add_argument( + "--encoder-dims", + type=str, + default="384,384,384,384,384", + help="Embedding dimension in the 2 blocks of zipformer encoder layers, comma separated", + ) + + parser.add_argument( + "--attention-dims", + type=str, + default="192,192,192,192,192", + help="""Attention dimension in the 2 blocks of zipformer encoder layers, comma separated; + not the same as embedding dimension.""", + ) + + parser.add_argument( + "--encoder-unmasked-dims", + type=str, + default="256,256,256,256,256", + help="Unmasked dimensions in the encoders, relates to augmentation during training. " + "Must be <= each of encoder_dims. Empirically, less than 256 seems to make performance " + " worse.", + ) + + parser.add_argument( + "--zipformer-downsampling-factors", + type=str, + default="1,2,4,8,2", + help="Downsampling factor for each stack of encoder layers.", + ) + + parser.add_argument( + "--cnn-module-kernels", + type=str, + default="31,31,31,31,31", + help="Sizes of kernels in convolution modules", + ) + + 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. + """, + ) + + +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=11, + help="Number of epochs to train.", + ) + + parser.add_argument( + "--start-epoch", + type=int, + default=1, + help="""Resume training from this epoch. It should be positive. + If larger than 1, it will load checkpoint from + exp-dir/epoch-{start_epoch-1}.pt + """, + ) + + parser.add_argument( + "--start-batch", + type=int, + default=0, + help="""If positive, --start-epoch is ignored and + it loads the checkpoint from exp-dir/checkpoint-{start_batch}.pt + """, + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="pruned_transducer_stateless7/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( + "--base-lr", type=float, default=0.05, help="The base learning rate." + ) + + 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=3.5, + 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( + "--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=5000, + 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( + "--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.", + ) + + 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": 100, + "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 get_encoder_model(params: AttributeDict) -> nn.Module: + # TODO: We can add an option to switch between Zipformer and Transformer + def to_int_tuple(s: str): + return tuple(map(int, s.split(","))) + + encoder = Zipformer( + num_features=params.feature_dim, + output_downsampling_factor=2, + zipformer_downsampling_factors=to_int_tuple( + params.zipformer_downsampling_factors + ), + encoder_dims=to_int_tuple(params.encoder_dims), + attention_dim=to_int_tuple(params.attention_dims), + encoder_unmasked_dims=to_int_tuple(params.encoder_unmasked_dims), + nhead=to_int_tuple(params.nhead), + feedforward_dim=to_int_tuple(params.feedforward_dims), + cnn_module_kernels=to_int_tuple(params.cnn_module_kernels), + num_encoder_layers=to_int_tuple(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=int(params.encoder_dims.split(",")[-1]), + 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=int(params.encoder_dims.split(",")[-1]), + 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, + model_avg: nn.Module = None, + optimizer: Optional[torch.optim.Optimizer] = None, + scheduler: Optional[LRSchedulerType] = None, +) -> Optional[Dict[str, Any]]: + """Load checkpoint from file. + + If params.start_batch is positive, it will load the checkpoint from + `params.exp_dir/checkpoint-{params.start_batch}.pt`. Otherwise, if + params.start_epoch is larger than 1, it will load the checkpoint from + `params.start_epoch - 1`. + + Apart from loading state dict for `model` and `optimizer` it also updates + `best_train_epoch`, `best_train_loss`, `best_valid_epoch`, + and `best_valid_loss` in `params`. + + Args: + params: + The return value of :func:`get_params`. + model: + The training model. + model_avg: + The stored model averaged from the start of training. + optimizer: + The optimizer that we are using. + scheduler: + The scheduler that we are using. + Returns: + Return a dict containing previously saved training info. + """ + if params.start_batch > 0: + filename = params.exp_dir / f"checkpoint-{params.start_batch}.pt" + elif params.start_epoch > 1: + filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt" + else: + return None + + assert filename.is_file(), f"{filename} does not exist!" + + saved_params = load_checkpoint( + filename, + model=model, + model_avg=model_avg, + optimizer=optimizer, + scheduler=scheduler, + ) + + keys = [ + "best_train_epoch", + "best_valid_epoch", + "batch_idx_train", + "best_train_loss", + "best_valid_loss", + ] + for k in keys: + params[k] = saved_params[k] + + if params.start_batch > 0: + if "cur_epoch" in saved_params: + params["start_epoch"] = saved_params["cur_epoch"] + + if "cur_batch_idx" in saved_params: + params["cur_batch_idx"] = saved_params["cur_batch_idx"] + + return saved_params + + +def save_checkpoint( + params: AttributeDict, + model: Union[nn.Module, DDP], + model_avg: Optional[nn.Module] = None, + optimizer: Optional[torch.optim.Optimizer] = None, + scheduler: Optional[LRSchedulerType] = None, + sampler: Optional[CutSampler] = None, + scaler: Optional[GradScaler] = None, + rank: int = 0, +) -> None: + """Save model, optimizer, scheduler and training stats to file. + + Args: + params: + It is returned by :func:`get_params`. + model: + The training model. + model_avg: + The stored model averaged from the start of training. + optimizer: + The optimizer used in the training. + sampler: + The sampler for the training dataset. + scaler: + The scaler used for mix precision training. + """ + if rank != 0: + return + filename = params.exp_dir / f"epoch-{params.cur_epoch}.pt" + save_checkpoint_impl( + filename=filename, + model=model, + model_avg=model_avg, + params=params, + optimizer=optimizer, + scheduler=scheduler, + sampler=sampler, + scaler=scaler, + rank=rank, + ) + + if params.best_train_epoch == params.cur_epoch: + best_train_filename = params.exp_dir / "best-train-loss.pt" + copyfile(src=filename, dst=best_train_filename) + + if params.best_valid_epoch == params.cur_epoch: + best_valid_filename = params.exp_dir / "best-valid-loss.pt" + copyfile(src=filename, dst=best_valid_filename) + + +def compute_loss( + params: AttributeDict, + model: Union[nn.Module, DDP], + sp: spm.SentencePieceProcessor, + batch: dict, + is_training: bool, +) -> Tuple[Tensor, MetricsTracker]: + """ + Compute transducer 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"] + + batch_idx_train = params.batch_idx_train + warm_step = params.warm_step + + texts = 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, + ) + + 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 + + assert loss.requires_grad == is_training + + info = MetricsTracker() + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + info["frames"] = ((feature_lens - 7) // 2).sum().item() + + # Note: We use reduction=sum while computing the loss. + info["loss"] = loss.detach().cpu().item() + info["simple_loss"] = simple_loss.detach().cpu().item() + info["pruned_loss"] = pruned_loss.detach().cpu().item() + + return loss, info + + +def compute_validation_loss( + params: AttributeDict, + model: Union[nn.Module, DDP], + sp: spm.SentencePieceProcessor, + valid_dl: torch.utils.data.DataLoader, + world_size: int = 1, +) -> MetricsTracker: + """Run the validation process.""" + model.eval() + + tot_loss = MetricsTracker() + + for batch_idx, batch in enumerate(valid_dl): + loss, loss_info = compute_loss( + params=params, + model=model, + sp=sp, + batch=batch, + is_training=False, + ) + assert loss.requires_grad is False + tot_loss = tot_loss + loss_info + + if world_size > 1: + tot_loss.reduce(loss.device) + + loss_value = tot_loss["loss"] / tot_loss["frames"] + if loss_value < params.best_valid_loss: + params.best_valid_epoch = params.cur_epoch + params.best_valid_loss = loss_value + + return tot_loss + + +def train_one_epoch( + params: AttributeDict, + model: Union[nn.Module, DDP], + optimizer: torch.optim.Optimizer, + scheduler: LRSchedulerType, + sp: spm.SentencePieceProcessor, + train_dl: torch.utils.data.DataLoader, + valid_dl: torch.utils.data.DataLoader, + scaler: GradScaler, + model_avg: Optional[nn.Module] = None, + tb_writer: Optional[SummaryWriter] = None, + world_size: int = 1, + rank: int = 0, +) -> None: + """Train the model for one epoch. + + The training loss from the mean of all frames is saved in + `params.train_loss`. It runs the validation process every + `params.valid_interval` batches. + + Args: + params: + It is returned by :func:`get_params`. + model: + The model for training. + optimizer: + The optimizer we are using. + scheduler: + The learning rate scheduler, we call step() every step. + train_dl: + Dataloader for the training dataset. + valid_dl: + Dataloader for the validation dataset. + scaler: + The scaler used for mix precision training. + model_avg: + The stored model averaged from the start of training. + tb_writer: + Writer to write log messages to tensorboard. + world_size: + Number of nodes in DDP training. If it is 1, DDP is disabled. + rank: + The rank of the node in DDP training. If no DDP is used, it should + be set to 0. + """ + model.train() + + tot_loss = MetricsTracker() + + cur_batch_idx = params.get("cur_batch_idx", 0) + + for batch_idx, batch in enumerate(train_dl): + if batch_idx < cur_batch_idx: + continue + cur_batch_idx = batch_idx + + params.batch_idx_train += 1 + batch_size = len(batch["supervisions"]["text"]) + + 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() + set_batch_count(model, params.batch_idx_train) + scheduler.step_batch(params.batch_idx_train) + + scaler.step(optimizer) + scaler.update() + optimizer.zero_grad() + except: # noqa + 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 + ): + params.cur_batch_idx = batch_idx + save_checkpoint_with_global_batch_idx( + out_dir=params.exp_dir, + global_batch_idx=params.batch_idx_train, + model=model, + model_avg=model_avg, + params=params, + optimizer=optimizer, + scheduler=scheduler, + sampler=train_dl.sampler, + scaler=scaler, + rank=rank, + ) + del params.cur_batch_idx + remove_checkpoints( + out_dir=params.exp_dir, + topk=params.keep_last_k, + rank=rank, + ) + + if batch_idx % 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 < 1.0 or (cur_grad_scale < 8.0 and batch_idx % 400 == 0): + scaler.update(cur_grad_scale * 2.0) + if cur_grad_scale < 0.01: + logging.warning(f"Grad scale is small: {cur_grad_scale}") + if cur_grad_scale < 1.0e-05: + raise RuntimeError( + f"grad_scale is too small, exiting: {cur_grad_scale}" + ) + + if batch_idx % params.log_interval == 0: + cur_lr = scheduler.get_last_lr()[0] + 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() + + logging.info(params) + + logging.info("About to create model") + model = get_transducer_model(params) + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + assert params.save_every_n >= params.average_period + model_avg: Optional[nn.Module] = None + if rank == 0: + # model_avg is only used with rank 0 + model_avg = copy.deepcopy(model).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(model.parameters(), lr=params.base_lr, 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( + 2**22 + ) # allow 4 megabytes per sub-module + diagnostic = diagnostics.attach_diagnostics(model, opts) + + 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 + + if params.inf_check: + register_inf_check_hooks(model) + + ami = AmiAsrDataModule(args) + + # Here is the duration statistics of the training set. + # Cuts count: 1230033 + # Total duration (hh:mm:ss): 904:25:34 + # Speech duration (hh:mm:ss): 904:25:34 (100.0%) + # Duration statistics (seconds): + # mean 2.6 + # std 2.8 + # min 0.0 + # 25% 0.6 + # 50% 1.6 + # 75% 3.8 + # 99% 12.3 + # 99.5% 13.9 + # 99.9% 18.3 + # max 36.8 + + train_cuts = ami.train_cuts(sp=sp) + train_dl = ami.train_dataloaders(train_cuts, sampler_state_dict=sampler_state_dict) + + valid_cuts = ami.dev_ihm_cuts() + valid_dl = ami.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() + AmiAsrDataModule.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/ami/ASR/pruned_transducer_stateless7/zipformer.py b/egs/ami/ASR/pruned_transducer_stateless7/zipformer.py new file mode 120000 index 0000000000..f2f66041e6 --- /dev/null +++ b/egs/ami/ASR/pruned_transducer_stateless7/zipformer.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless7/zipformer.py \ No newline at end of file diff --git a/egs/ami/ASR/shared b/egs/ami/ASR/shared new file mode 120000 index 0000000000..4cbd91a7e9 --- /dev/null +++ b/egs/ami/ASR/shared @@ -0,0 +1 @@ +../../../icefall/shared \ No newline at end of file