From 44d01195c09d092703bc8ac06a8971ea3d610603 Mon Sep 17 00:00:00 2001 From: marcoyang Date: Fri, 14 Jul 2023 23:50:27 +0800 Subject: [PATCH 01/14] initial commit for libriheavy --- egs/libriheavy/ASR/prepare.sh | 130 ++++++++++++++++++++++++++++++++++ egs/libriheavy/ASR/shared | 1 + 2 files changed, 131 insertions(+) create mode 100755 egs/libriheavy/ASR/prepare.sh create mode 120000 egs/libriheavy/ASR/shared diff --git a/egs/libriheavy/ASR/prepare.sh b/egs/libriheavy/ASR/prepare.sh new file mode 100755 index 000000000..cca0cbf67 --- /dev/null +++ b/egs/libriheavy/ASR/prepare.sh @@ -0,0 +1,130 @@ +#!/usr/bin/env bash + +# fix segmentation fault reported in https://github.com/k2-fsa/icefall/issues/674 +export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python + +set -eou pipefail + +nj=15 +stage=-1 +stop_stage=100 +start=0 +stop=-1 +num_per_split=2000 + +. shared/parse_options.sh || exit 1 + +# vocab size for sentence piece models. +# It will generate data/lang_bpe_xxx, +# data/lang_bpe_yyy if the array contains xxx, yyy +vocab_sizes=( + 500 +) + +mkdir -p data + +log() { + # This function is from espnet + local fname=${BASH_SOURCE[1]##*/} + echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*" +} + +manifest_dir=data/manifests +fbank_dir=data/fbank_new + +mkdir -p $manifest_dir + +subset="medium" + +if [ $stage -le 1 ] && [ $stop_stage -ge 2 ]; then + log "Stage 1: Split libri-heavy medium" + + split_dir=$fbank_dir/libriheavy_${subset}_split + mkdir -p $split_dir + if [ ! -e $split_dir/.split_completed ]; then + lhotse split-lazy $manifest_dir/librilight_cuts_${subset}_raw.jsonl.gz $split_dir $num_per_split + touch $split_dir/.split_completed + fi +fi + +if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then + log "Stage 2: Compute fbank for Libri-heavy ${subset}" + mkdir -p $fbank_dir + num_splits=$(find $fbank_dir/libriheavy_${subset}_split -name "librilight_cuts_${subset}_raw.*.jsonl.gz" | wc -l) + if [ ! -e $fbank_dir/.libriheavy.${subset}.done ]; then + for i in $(seq 0 1 7); do + start=${i}00 + end=$(( i+1 ))00 + ./local/compute_fbank_libriheavy.py \ + --dataset ${subset} \ + --fbank-dir $fbank_dir \ + --num-splits $num_splits \ + --num-workers $nj \ + --start $start \ + --stop $end & + done + wait + touch $fbank_dir/.libriheavy.${subset}.done + fi +fi + +if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then + log "Stage 3: Combine features for ${subset}" + if [ ! -f $fbank_dir/librilight_cuts_${subset}.jsonl.gz ]; then + pieces=$(find $fbank_dir/libriheavy_${subset}_split -name "librilight_cuts_${subset}.*.jsonl.gz") + lhotse combine $pieces $fbank_dir/librilight_cuts_${subset}.jsonl.gz + fi +fi + +if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then + log "Stage 4: Prepare BPE model" + + tmp_dir=data/tmp + mkdir -p $tmp_dir + if [ ! -f $tmp_dir/transcript_words.txt ]; then + gunzip -c $manifest_dir/librilight_cuts_${subset}_raw.jsonl.gz | + jq '.supervisions[].custom.texts[]' | sed 's/" //' | sed 's/\(.*\)"/\1/' > $tmp_dir/transcript_words.txt + fi + + if [ ! -f $tmp_dir/words.txt ]; then + cat $tmp_dir/transcript_words.txt | sed 's/ /\n/g' \ + | sort -u | sed '/^$/d' > $tmp_dir/words.txt + (echo '!SIL'; echo ''; echo ''; ) | + cat - $tmp_dir/words.txt | sort | uniq | awk ' + BEGIN { + print " 0"; + } + { + if ($1 == "") { + + print " is in the vocabulary!" | "cat 1>&2" + exit 1; + } + if ($1 == "") { + print " is in the vocabulary!" | "cat 1>&2" + exit 1; + } + printf("%s %d\n", $1, NR); + } + END { + printf("#0 %d\n", NR+1); + printf(" %d\n", NR+2); + printf(" %d\n", NR+3); + }' > $tmp_dir/words || exit 1; + mv $tmp_dir/words $tmp_dir/words.txt + fi + + for vocab_size in ${vocab_sizes[@]}; do + lang_dir=data/lang_bpe_${vocab_size}_${subset} + mkdir -p $lang_dir + cp $tmp_dir/words.txt $lang_dir/words.txt + + if [ ! -f $lang_dir/bpe.model ]; then + ./local/train_bpe_model.py \ + --lang-dir $lang_dir \ + --vocab-size $vocab_size \ + --transcript $tmp_dir/transcript_words.txt + fi + + done +fi diff --git a/egs/libriheavy/ASR/shared b/egs/libriheavy/ASR/shared new file mode 120000 index 000000000..4cbd91a7e --- /dev/null +++ b/egs/libriheavy/ASR/shared @@ -0,0 +1 @@ +../../../icefall/shared \ No newline at end of file From fef229e0247176cbfeb8ce14d80fca1b4ca622f7 Mon Sep 17 00:00:00 2001 From: marcoyang Date: Mon, 17 Jul 2023 10:36:25 +0800 Subject: [PATCH 02/14] add necessary files to compute features --- .../ASR/local/compute_fbank_libriheavy.py | 242 ++++++++++++++++++ egs/libriheavy/ASR/local/filter_cuts.py | 1 + egs/libriheavy/ASR/local/prepare_lang_bpe.py | 1 + egs/libriheavy/ASR/local/train_bpe_model.py | 1 + 4 files changed, 245 insertions(+) create mode 100755 egs/libriheavy/ASR/local/compute_fbank_libriheavy.py create mode 120000 egs/libriheavy/ASR/local/filter_cuts.py create mode 120000 egs/libriheavy/ASR/local/prepare_lang_bpe.py create mode 120000 egs/libriheavy/ASR/local/train_bpe_model.py diff --git a/egs/libriheavy/ASR/local/compute_fbank_libriheavy.py b/egs/libriheavy/ASR/local/compute_fbank_libriheavy.py new file mode 100755 index 000000000..61c645ced --- /dev/null +++ b/egs/libriheavy/ASR/local/compute_fbank_libriheavy.py @@ -0,0 +1,242 @@ +#!/usr/bin/env python3 +# Copyright 2023 Xiaomi Corp. (authors: Xiaoyu Yang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This file computes fbank features of the LibriSpeech dataset. +It looks for manifests in the directory data/manifests. + +The generated fbank features are saved in data/fbank. +""" + +import argparse +import logging +import os +from pathlib import Path +from typing import Optional + +import sentencepiece as spm +import torch +from filter_cuts import filter_cuts +from lhotse import ( + CutSet, + Fbank, + FbankConfig, + KaldifeatFbank, + KaldifeatFbankConfig, + LilcomChunkyWriter, +) +from lhotse.recipes.utils import read_manifests_if_cached + +from icefall.utils import get_executor, str2bool + +# Torch's multithreaded behavior needs to be disabled or +# it wastes a lot of CPU and slow things down. +# Do this outside of main() in case it needs to take effect +# even when we are not invoking the main (e.g. when spawning subprocesses). +torch.set_num_threads(1) +torch.set_num_interop_threads(1) + + +def get_args(): + parser = argparse.ArgumentParser() + + parser.add_argument( + "--bpe-model", + type=str, + help="""Path to the bpe.model. If not None, we will remove short and + long utterances before extracting features""", + ) + + parser.add_argument( + "--fbank-dir", + type=str, + help="""Fbank output dir + """, + default="data/fbank", + ) + + parser.add_argument( + "--dataset", + type=str, + help="""Dataset parts to compute fbank. If None, we will use all""", + ) + + parser.add_argument( + "--num-workers", + type=int, + default=20, + help="Number of dataloading workers used for reading the audio.", + ) + + parser.add_argument( + "--batch-duration", + type=float, + default=600.0, + help="The maximum number of audio seconds in a batch." + "Determines batch size dynamically.", + ) + + parser.add_argument( + "--num-splits", + type=int, + required=True, + help="The number of splits of the medium and large subset.", + ) + + parser.add_argument( + "--start", + type=int, + default=0, + help="Process pieces starting from this number (inclusive). Only used in medium and large subset", + ) + + parser.add_argument( + "--stop", + type=int, + default=-1, + help="Stop processing pieces until this number (exclusive). Only used in medium and large subset", + ) + + return parser.parse_args() + + +def compute_fbank_libriheavy( + bpe_model: Optional[str] = None, + dataset: Optional[str] = None, + perturb_speed: Optional[bool] = True, +): + src_dir = Path("data/manifests") + output_dir = Path("data/fbank") + num_jobs = min(15, os.cpu_count()) + num_mel_bins = 80 + + if bpe_model: + logging.info(f"Loading {bpe_model}") + sp = spm.SentencePieceProcessor() + sp.load(bpe_model) + + if dataset is None: + dataset_parts = ("small",) + else: + dataset_parts = dataset.split(" ", -1) + + extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins)) + + with get_executor() as ex: # Initialize the executor only once. + for part in dataset_parts: + output_cuts_path = output_dir / f"librilight_cuts_{part}.jsonl.gz" + if output_cuts_path.exists(): + logging.info(f"{output_cuts_path} exists - skipping") + continue + + input_cuts_path = src_dir / f"librilight_cuts_{part}.jsonl.gz" + assert input_cuts_path.exists(), f"{input_cuts_path} does not exist!" + logging.info(f"Loading {input_cuts_path}") + cut_set = CutSet.from_file(input_cuts_path) + + logging.info("Computing features") + + if bpe_model: + cut_set = filter_cuts(cut_set, sp) + + cut_set = cut_set.compute_and_store_features( + extractor=extractor, + storage_path=f"{output_dir}/libriheavy_feats_{part}", + # when an executor is specified, make more partitions + num_jobs=num_jobs if ex is None else 80, + executor=ex, + storage_type=LilcomChunkyWriter, + ) + + logging.info(f"Saving to {output_cuts_path}") + cut_set.to_file(output_cuts_path) + + +def compute_fbank_libriheavy_splits(args): + num_splits = args.num_splits + dataset = args.dataset + output_dir = f"{args.fbank_dir}/libriheavy_{dataset}_split" + output_dir = Path(output_dir) + assert output_dir.exists(), f"{output_dir} does not exist!" + + num_digits = len(str(num_splits)) + + start = args.start + stop = args.stop + if stop < start: + stop = num_splits + + stop = min(stop, num_splits) + + device = torch.device("cpu") + # if torch.cuda.is_available(): + # device = torch.device("cuda", 0) + extractor = KaldifeatFbank(KaldifeatFbankConfig(device=device)) + logging.info(f"device: {device}") + + prefix = "librilight" + + num_digits = 8 # num_digits is fixed by lhotse split-lazy + for i in range(start, stop): + idx = f"{i + 1}".zfill(num_digits) + logging.info(f"Processing {idx}/{num_splits}") + + cuts_path = output_dir / f"{prefix}_cuts_{dataset}.{idx}.jsonl.gz" + if cuts_path.is_file(): + logging.info(f"{cuts_path} exists - skipping") + continue + + raw_cuts_path = output_dir / f"{prefix}_cuts_{dataset}_raw.{idx}.jsonl.gz" + if not raw_cuts_path.is_file(): + logging.info(f"{raw_cuts_path} does not exist - skipping it") + continue + + logging.info(f"Loading {raw_cuts_path}") + cut_set = CutSet.from_file(raw_cuts_path) + + logging.info("Computing features") + if (output_dir / f"{prefix}_feats_{dataset}_{idx}.lca").exists(): + logging.info(f"Removing {output_dir}/{prefix}_feats_{dataset}_{idx}.lca") + os.remove(output_dir / f"{prefix}_feats_{dataset}_{idx}.lca") + + cut_set = cut_set.compute_and_store_features_batch( + extractor=extractor, + storage_path=f"{output_dir}/{prefix}_feats_{dataset}_{idx}", + num_workers=args.num_workers, + batch_duration=args.batch_duration, + overwrite=True, + ) + + logging.info("About to split cuts into smaller chunks.") + cut_set = cut_set.trim_to_supervisions( + keep_overlapping=False, min_duration=None + ) + + logging.info(f"Saving to {cuts_path}") + cut_set.to_file(cuts_path) + logging.info(f"Saved to {cuts_path}") + + +if __name__ == "__main__": + formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + + logging.basicConfig(format=formatter, level=logging.INFO) + args = get_args() + logging.info(vars(args)) + + compute_fbank_libriheavy_splits(args) diff --git a/egs/libriheavy/ASR/local/filter_cuts.py b/egs/libriheavy/ASR/local/filter_cuts.py new file mode 120000 index 000000000..27aca1729 --- /dev/null +++ b/egs/libriheavy/ASR/local/filter_cuts.py @@ -0,0 +1 @@ +../../../librispeech/ASR/local/filter_cuts.py \ No newline at end of file diff --git a/egs/libriheavy/ASR/local/prepare_lang_bpe.py b/egs/libriheavy/ASR/local/prepare_lang_bpe.py new file mode 120000 index 000000000..36b40e7fc --- /dev/null +++ b/egs/libriheavy/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/libriheavy/ASR/local/train_bpe_model.py b/egs/libriheavy/ASR/local/train_bpe_model.py new file mode 120000 index 000000000..6fad36421 --- /dev/null +++ b/egs/libriheavy/ASR/local/train_bpe_model.py @@ -0,0 +1 @@ +../../../librispeech/ASR/local/train_bpe_model.py \ No newline at end of file From 189d424b257498e0d4381ad93e66c905a3e8cd9d Mon Sep 17 00:00:00 2001 From: marcoyang Date: Tue, 18 Jul 2023 10:06:01 +0800 Subject: [PATCH 03/14] only use medium text to train the BPE as the whole corpus is tooooo large --- egs/libriheavy/ASR/local/train_bpe_model.py | 102 +++++++++++++++++++- egs/libriheavy/ASR/prepare.sh | 41 +++++--- 2 files changed, 130 insertions(+), 13 deletions(-) mode change 120000 => 100755 egs/libriheavy/ASR/local/train_bpe_model.py diff --git a/egs/libriheavy/ASR/local/train_bpe_model.py b/egs/libriheavy/ASR/local/train_bpe_model.py deleted file mode 120000 index 6fad36421..000000000 --- a/egs/libriheavy/ASR/local/train_bpe_model.py +++ /dev/null @@ -1 +0,0 @@ -../../../librispeech/ASR/local/train_bpe_model.py \ No newline at end of file diff --git a/egs/libriheavy/ASR/local/train_bpe_model.py b/egs/libriheavy/ASR/local/train_bpe_model.py new file mode 100755 index 000000000..55a7d26a6 --- /dev/null +++ b/egs/libriheavy/ASR/local/train_bpe_model.py @@ -0,0 +1,101 @@ +#!/usr/bin/env python3 +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +# You can install sentencepiece via: +# +# pip install sentencepiece +# +# Due to an issue reported in +# https://github.com/google/sentencepiece/pull/642#issuecomment-857972030 +# +# Please install a version >=0.1.96 + +import argparse +import shutil +from pathlib import Path + +import sentencepiece as spm + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--lang-dir", + type=str, + help="""Input and output directory. + The generated bpe.model is saved to this directory. + """, + ) + + parser.add_argument( + "--transcript", + type=str, + help="Training transcript.", + ) + + parser.add_argument( + "--vocab-size", + type=int, + help="Vocabulary size for BPE training", + ) + + return parser.parse_args() + + +def main(): + args = get_args() + vocab_size = args.vocab_size + lang_dir = Path(args.lang_dir) + + model_type = "unigram" + + model_prefix = f"{lang_dir}/{model_type}_{vocab_size}" + train_text = args.transcript + character_coverage = 1.0 + input_sentence_size = 100000000 + + user_defined_symbols = ["", ""] + unk_id = len(user_defined_symbols) + # Note: unk_id is fixed to 2. + # If you change it, you should also change other + # places that are using it. + + model_file = Path(model_prefix + ".model") + if not model_file.is_file(): + spm.SentencePieceTrainer.train( + input=train_text, + vocab_size=vocab_size, + model_type=model_type, + model_prefix=model_prefix, + input_sentence_size=input_sentence_size, + character_coverage=character_coverage, + user_defined_symbols=user_defined_symbols, + unk_id=unk_id, + bos_id=-1, + eos_id=-1, + train_extremely_large_corpus=False, + ) + else: + print(f"{model_file} exists - skipping") + return + + shutil.copyfile(model_file, f"{lang_dir}/bpe.model") + + +if __name__ == "__main__": + main() diff --git a/egs/libriheavy/ASR/prepare.sh b/egs/libriheavy/ASR/prepare.sh index cca0cbf67..0aa6c91ae 100755 --- a/egs/libriheavy/ASR/prepare.sh +++ b/egs/libriheavy/ASR/prepare.sh @@ -2,6 +2,7 @@ # fix segmentation fault reported in https://github.com/k2-fsa/icefall/issues/674 export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python +export PYTHONPATH=/star-data/xiaoyu/icefall_libriheavy:$PYTHONPATH set -eou pipefail @@ -18,7 +19,7 @@ num_per_split=2000 # It will generate data/lang_bpe_xxx, # data/lang_bpe_yyy if the array contains xxx, yyy vocab_sizes=( - 500 + 1000 ) mkdir -p data @@ -30,14 +31,19 @@ log() { } manifest_dir=data/manifests -fbank_dir=data/fbank_new +fbank_dir=data/fbank mkdir -p $manifest_dir -subset="medium" +subset="large" -if [ $stage -le 1 ] && [ $stop_stage -ge 2 ]; then - log "Stage 1: Split libri-heavy medium" +if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then + log "Stage 1: Split libri-heavy ${subset}" + + if [ $subset == "large" ]; then + num_per_split=8000 + log "Change num_per_split to ${num_per_split} 8000 for large" + fi split_dir=$fbank_dir/libriheavy_${subset}_split mkdir -p $split_dir @@ -53,8 +59,8 @@ if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then num_splits=$(find $fbank_dir/libriheavy_${subset}_split -name "librilight_cuts_${subset}_raw.*.jsonl.gz" | wc -l) if [ ! -e $fbank_dir/.libriheavy.${subset}.done ]; then for i in $(seq 0 1 7); do - start=${i}00 - end=$(( i+1 ))00 + start=$(( i * 200 )) + end=$(( (i+1) * 200 )) ./local/compute_fbank_libriheavy.py \ --dataset ${subset} \ --fbank-dir $fbank_dir \ @@ -76,14 +82,18 @@ if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then fi fi + if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then log "Stage 4: Prepare BPE model" tmp_dir=data/tmp mkdir -p $tmp_dir if [ ! -f $tmp_dir/transcript_words.txt ]; then - gunzip -c $manifest_dir/librilight_cuts_${subset}_raw.jsonl.gz | - jq '.supervisions[].custom.texts[]' | sed 's/" //' | sed 's/\(.*\)"/\1/' > $tmp_dir/transcript_words.txt + for part in "small" "medium" "large"; do + gunzip -c $manifest_dir/librilight_cuts_${part}_raw.jsonl.gz | + jq '.supervisions[].custom.texts[]' | sed 's/" //' | sed 's/\(.*\)"/\1/' > $tmp_dir/transcript_words_${part}.txt + done + cat $tmp_dir/transcript_words_small.txt $tmp_dir/transcript_words_medium.txt $tmp_dir/transcript_words_large.txt > $tmp_dir/transcript_words.txt fi if [ ! -f $tmp_dir/words.txt ]; then @@ -115,15 +125,22 @@ if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then fi for vocab_size in ${vocab_sizes[@]}; do - lang_dir=data/lang_bpe_${vocab_size}_${subset} + lang_dir=data/lang_bpe_${vocab_size} mkdir -p $lang_dir cp $tmp_dir/words.txt $lang_dir/words.txt - + pushd $lang_dir + ln -s ../$tmp_dir/transcript_words.txt transcript_words.txt + popd + if [ ! -f $lang_dir/bpe.model ]; then ./local/train_bpe_model.py \ --lang-dir $lang_dir \ --vocab-size $vocab_size \ - --transcript $tmp_dir/transcript_words.txt + --transcript $tmp_dir/transcript_words_medium.txt + fi + + if [ ! -f $lang_dir/tokens.txt ]; then + ./local/bpe2tokens.py ${lang_dir}/bpe.model > ${lang_dir}/tokens.txt fi done From 0e7df7c5c430a10bae297474ec48c80a78559bce Mon Sep 17 00:00:00 2001 From: marcoyang Date: Tue, 18 Jul 2023 10:06:22 +0800 Subject: [PATCH 04/14] add necessary utility files --- egs/libriheavy/ASR/local/bpe2tokens.py | 37 ++++++++++++++++++++++++ egs/libriheavy/ASR/local/prepare_lang.py | 1 + 2 files changed, 38 insertions(+) create mode 100755 egs/libriheavy/ASR/local/bpe2tokens.py create mode 120000 egs/libriheavy/ASR/local/prepare_lang.py diff --git a/egs/libriheavy/ASR/local/bpe2tokens.py b/egs/libriheavy/ASR/local/bpe2tokens.py new file mode 100755 index 000000000..81e7b3342 --- /dev/null +++ b/egs/libriheavy/ASR/local/bpe2tokens.py @@ -0,0 +1,37 @@ +#!/usr/bin/env python3 + +""" +This script takes `bpe.model` as input and generates a file `tokens.txt` +from it. + +Usage: +./bpe_model_to_tokens.py /path/to/input/bpe.model > tokens.txt +""" +import argparse + +import sentencepiece as spm + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + "bpe_model", + type=str, + help="Path to the input bpe.model", + ) + + return parser.parse_args() + + +def main(): + args = get_args() + + sp = spm.SentencePieceProcessor() + sp.load(args.bpe_model) + + for i in range(sp.vocab_size()): + print(sp.id_to_piece(i), i) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/egs/libriheavy/ASR/local/prepare_lang.py b/egs/libriheavy/ASR/local/prepare_lang.py new file mode 120000 index 000000000..747f2ab39 --- /dev/null +++ b/egs/libriheavy/ASR/local/prepare_lang.py @@ -0,0 +1 @@ +../../../librispeech/ASR/local/prepare_lang.py \ No newline at end of file From 6939b3d6aadf49d10de98d7de08afe7aa10aea56 Mon Sep 17 00:00:00 2001 From: marcoyang1998 Date: Tue, 18 Jul 2023 11:14:06 +0800 Subject: [PATCH 05/14] minor fixes --- egs/libriheavy/ASR/local/bpe2tokens.py | 2 +- egs/libriheavy/ASR/local/compute_fbank_libriheavy.py | 9 ++++----- 2 files changed, 5 insertions(+), 6 deletions(-) diff --git a/egs/libriheavy/ASR/local/bpe2tokens.py b/egs/libriheavy/ASR/local/bpe2tokens.py index 81e7b3342..d078e5b98 100755 --- a/egs/libriheavy/ASR/local/bpe2tokens.py +++ b/egs/libriheavy/ASR/local/bpe2tokens.py @@ -34,4 +34,4 @@ def main(): if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/egs/libriheavy/ASR/local/compute_fbank_libriheavy.py b/egs/libriheavy/ASR/local/compute_fbank_libriheavy.py index 61c645ced..05ade450c 100755 --- a/egs/libriheavy/ASR/local/compute_fbank_libriheavy.py +++ b/egs/libriheavy/ASR/local/compute_fbank_libriheavy.py @@ -40,9 +40,8 @@ from lhotse import ( KaldifeatFbankConfig, LilcomChunkyWriter, ) -from lhotse.recipes.utils import read_manifests_if_cached -from icefall.utils import get_executor, str2bool +from icefall.utils import get_executor # Torch's multithreaded behavior needs to be disabled or # it wastes a lot of CPU and slow things down. @@ -61,7 +60,7 @@ def get_args(): help="""Path to the bpe.model. If not None, we will remove short and long utterances before extracting features""", ) - + parser.add_argument( "--fbank-dir", type=str, @@ -102,14 +101,14 @@ def get_args(): "--start", type=int, default=0, - help="Process pieces starting from this number (inclusive). Only used in medium and large subset", + help="Process pieces starting from this number (inclusive).", ) parser.add_argument( "--stop", type=int, default=-1, - help="Stop processing pieces until this number (exclusive). Only used in medium and large subset", + help="Stop processing pieces until this number (exclusive).", ) return parser.parse_args() From b53c0d1e5f674e7cbda3565f411cd9b751afd3e1 Mon Sep 17 00:00:00 2001 From: marcoyang1998 Date: Tue, 18 Jul 2023 11:42:19 +0800 Subject: [PATCH 06/14] initial commit for zipformer recipe --- .../ASR/zipformer/asr_datamodule.py | 450 ++++++++++++++++++ egs/libriheavy/ASR/zipformer/optim.py | 1 + egs/libriheavy/ASR/zipformer/scaling.py | 1 + .../ASR/zipformer/scaling_converter.py | 1 + egs/libriheavy/ASR/zipformer/subsampling.py | 1 + egs/libriheavy/ASR/zipformer/zipformer.py | 1 + 6 files changed, 455 insertions(+) create mode 100644 egs/libriheavy/ASR/zipformer/asr_datamodule.py create mode 120000 egs/libriheavy/ASR/zipformer/optim.py create mode 120000 egs/libriheavy/ASR/zipformer/scaling.py create mode 120000 egs/libriheavy/ASR/zipformer/scaling_converter.py create mode 120000 egs/libriheavy/ASR/zipformer/subsampling.py create mode 120000 egs/libriheavy/ASR/zipformer/zipformer.py diff --git a/egs/libriheavy/ASR/zipformer/asr_datamodule.py b/egs/libriheavy/ASR/zipformer/asr_datamodule.py new file mode 100644 index 000000000..80dc8134a --- /dev/null +++ b/egs/libriheavy/ASR/zipformer/asr_datamodule.py @@ -0,0 +1,450 @@ +# 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 inspect +import logging +from functools import lru_cache +from pathlib import Path +from typing import Any, Callable, Dict, List, Optional + +import torch +from dataset import PromptASRDataset +from lhotse import CutSet, Fbank, FbankConfig, load_manifest, load_manifest_lazy +from lhotse.dataset import ( + CutConcatenate, + CutMix, + DynamicBucketingSampler, + K2SpeechRecognitionDataset, + PrecomputedFeatures, + SingleCutSampler, + SpecAugment, +) +from lhotse.dataset.input_strategies import OnTheFlyFeatures +from lhotse.utils import fix_random_seed +from torch.utils.data import DataLoader + +from icefall.utils import str2bool + + +class _SeedWorkers: + def __init__(self, seed: int): + self.seed = seed + + def __call__(self, worker_id: int): + fix_random_seed(self.seed + worker_id) + + +class LibriHeavyAsrDataModule: + """ + DataModule for k2 ASR experiments. + It assumes there is always one train and valid dataloader, + but there can be multiple test dataloaders (e.g. LibriSpeech test-clean + and test-other). + + It contains all the common data pipeline modules used in ASR + experiments, e.g.: + - dynamic batch size, + - bucketing samplers, + - cut concatenation, + - augmentation, + - on-the-fly feature extraction + + This class should be derived for specific corpora used in ASR tasks. + """ + + def __init__(self, args: argparse.Namespace): + self.args = args + + @classmethod + def add_arguments(cls, parser: argparse.ArgumentParser): + group = parser.add_argument_group( + title="ASR data related options", + description="These options are used for the preparation of " + "PyTorch DataLoaders from Lhotse CutSet's -- they control the " + "effective batch sizes, sampling strategies, applied data " + "augmentations, etc.", + ) + group.add_argument( + "--manifest-dir", + type=Path, + default=Path("data/fbank"), + help="Path to directory with train/valid/test cuts.", + ) + group.add_argument( + "--max-duration", + type=int, + default=200.0, + help="Maximum pooled recordings duration (seconds) in a " + "single batch. You can reduce it if it causes CUDA OOM.", + ) + group.add_argument( + "--bucketing-sampler", + type=str2bool, + default=True, + help="When enabled, the batches will come from buckets of " + "similar duration (saves padding frames).", + ) + group.add_argument( + "--num-buckets", + type=int, + default=30, + help="The number of buckets for the DynamicBucketingSampler" + "(you might want to increase it for larger datasets).", + ) + group.add_argument( + "--concatenate-cuts", + type=str2bool, + default=False, + help="When enabled, utterances (cuts) will be concatenated " + "to minimize the amount of padding.", + ) + group.add_argument( + "--duration-factor", + type=float, + default=1.0, + help="Determines the maximum duration of a concatenated cut " + "relative to the duration of the longest cut in a batch.", + ) + group.add_argument( + "--gap", + type=float, + default=1.0, + help="The amount of padding (in seconds) inserted between " + "concatenated cuts. This padding is filled with noise when " + "noise augmentation is used.", + ) + group.add_argument( + "--on-the-fly-feats", + type=str2bool, + default=False, + help="When enabled, use on-the-fly cut mixing and feature " + "extraction. Will drop existing precomputed feature manifests " + "if available.", + ) + group.add_argument( + "--shuffle", + type=str2bool, + default=True, + help="When enabled (=default), the examples will be " + "shuffled for each epoch.", + ) + group.add_argument( + "--return-cuts", + type=str2bool, + default=True, + help="When enabled, each batch will have the " + "field: batch['supervisions']['cut'] with the cuts that " + "were used to construct it.", + ) + + group.add_argument( + "--num-workers", + type=int, + default=2, + help="The number of training dataloader workers that " + "collect the batches.", + ) + + group.add_argument( + "--enable-spec-aug", + type=str2bool, + default=True, + help="When enabled, use SpecAugment for training dataset.", + ) + + group.add_argument( + "--spec-aug-time-warp-factor", + type=int, + default=80, + help="Used only when --enable-spec-aug is True. " + "It specifies the factor for time warping in SpecAugment. " + "Larger values mean more warping. " + "A value less than 1 means to disable time warp.", + ) + + group.add_argument( + "--enable-musan", + type=str2bool, + default=True, + help="When enabled, select noise from MUSAN and mix it " + "with training dataset. ", + ) + + # Libriheavy specific arguments + group.add_argument( + "--subset", + type=str, + default="small", + help="Select the Libriheavy subset (small|medium|large)", + ) + + def train_dataloaders( + self, + cuts_train: CutSet, + sampler_state_dict: Optional[Dict[str, Any]] = None, + text_sampling_func: Callable[[List[str]], str] = None, + ) -> DataLoader: + """ + Args: + cuts_train: + CutSet for training. + sampler_state_dict: + The state dict for the training sampler. + """ + + transforms = [] + if self.args.enable_musan: + logging.info("Enable MUSAN") + logging.info("About to get Musan cuts") + cuts_musan = load_manifest(self.args.manifest_dir / "musan_cuts.jsonl.gz") + transforms.append( + CutMix(cuts=cuts_musan, prob=0.5, snr=(10, 20), preserve_id=True) + ) + else: + logging.info("Disable MUSAN") + + if self.args.concatenate_cuts: + logging.info( + f"Using cut concatenation with duration factor " + f"{self.args.duration_factor} and gap {self.args.gap}." + ) + # Cut concatenation should be the first transform in the list, + # so that if we e.g. mix noise in, it will fill the gaps between + # different utterances. + transforms = [ + CutConcatenate( + duration_factor=self.args.duration_factor, gap=self.args.gap + ) + ] + transforms + + input_transforms = [] + if self.args.enable_spec_aug: + logging.info("Enable SpecAugment") + logging.info(f"Time warp factor: {self.args.spec_aug_time_warp_factor}") + # Set the value of num_frame_masks according to Lhotse's version. + # In different Lhotse's versions, the default of num_frame_masks is + # different. + num_frame_masks = 10 + num_frame_masks_parameter = inspect.signature( + SpecAugment.__init__ + ).parameters["num_frame_masks"] + if num_frame_masks_parameter.default == 1: + num_frame_masks = 2 + logging.info(f"Num frame mask: {num_frame_masks}") + input_transforms.append( + SpecAugment( + time_warp_factor=self.args.spec_aug_time_warp_factor, + num_frame_masks=num_frame_masks, + features_mask_size=27, + num_feature_masks=2, + frames_mask_size=100, + ) + ) + else: + logging.info("Disable SpecAugment") + + logging.info("About to create train dataset") + train = PromptASRDataset( + cut_transforms=transforms, + input_transforms=input_transforms, + return_cuts=self.args.return_cuts, + text_sampling_func=text_sampling_func, + ) + + if self.args.on_the_fly_feats: + # NOTE: the PerturbSpeed transform should be added only if we + # remove it from data prep stage. + # Add on-the-fly speed perturbation; since originally it would + # have increased epoch size by 3, we will apply prob 2/3 and use + # 3x more epochs. + # Speed perturbation probably should come first before + # concatenation, but in principle the transforms order doesn't have + # to be strict (e.g. could be randomized) + # transforms = [PerturbSpeed(factors=[0.9, 1.1], p=2/3)] + transforms # noqa + # Drop feats to be on the safe side. + train = PromptASRDataset( + cut_transforms=transforms, + input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))), + input_transforms=input_transforms, + return_cuts=self.args.return_cuts, + text_sampling_func=text_sampling_func, + ) + + if self.args.bucketing_sampler: + logging.info("Using DynamicBucketingSampler.") + train_sampler = DynamicBucketingSampler( + cuts_train, + max_duration=self.args.max_duration, + shuffle=self.args.shuffle, + num_buckets=self.args.num_buckets, + drop_last=True, + ) + else: + logging.info("Using SingleCutSampler.") + train_sampler = SingleCutSampler( + cuts_train, + max_duration=self.args.max_duration, + shuffle=self.args.shuffle, + ) + logging.info("About to create train dataloader") + + if sampler_state_dict is not None: + logging.info("Loading sampler state dict") + train_sampler.load_state_dict(sampler_state_dict) + + # 'seed' is derived from the current random state, which will have + # previously been set in the main process. + seed = torch.randint(0, 100000, ()).item() + worker_init_fn = _SeedWorkers(seed) + + train_dl = DataLoader( + train, + sampler=train_sampler, + batch_size=None, + num_workers=self.args.num_workers, + persistent_workers=False, + worker_init_fn=worker_init_fn, + ) + + return train_dl + + def valid_dataloaders( + self, + cuts_valid: CutSet, + text_sampling_func: Callable[[List[str]], str] = None, + ) -> 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 = PromptASRDataset( + cut_transforms=transforms, + input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))), + return_cuts=self.args.return_cuts, + text_sampling_func=text_sampling_func, + ) + else: + validate = PromptASRDataset( + cut_transforms=transforms, + return_cuts=self.args.return_cuts, + text_sampling_func=text_sampling_func, + ) + 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=self.args.return_cuts, + ) + sampler = DynamicBucketingSampler( + cuts, + max_duration=self.args.max_duration, + shuffle=False, + ) + logging.debug("About to create test dataloader") + test_dl = DataLoader( + test, + batch_size=None, + sampler=sampler, + num_workers=self.args.num_workers, + ) + return test_dl + + @lru_cache() + def train_cuts(self) -> CutSet: + logging.info(f"About to get {self.args.subset} cuts") + path = self.args.manifest_dir / f"librilight_cuts_{self.args.subset}.jsonl.gz" + cuts_train = CutSet.from_jsonl_lazy(path) + return cuts_train + + @lru_cache() + def dev_cuts(self) -> CutSet: + logging.info("About to get dev cuts") + cuts_valid = load_manifest_lazy( + self.args.manifest_dir / "librilight_cuts_dev.jsonl.gz" + ) + return cuts_valid + + @lru_cache() + def test_cuts(self) -> CutSet: + logging.info("About to get test cuts") + cuts_valid = load_manifest_lazy( + self.args.manifest_dir / "librilight_cuts_test.jsonl.gz" + ) + return cuts_valid + + @lru_cache() + def test_medium_cuts(self) -> CutSet: + logging.info("About to get 2000 cuts from the medium set") + cuts_medium_2k = load_manifest_lazy( + self.args.manifest_dir / "librilight_cuts_medium_2000.jsonl.gz" + ) + return cuts_medium_2k + + @lru_cache() + def test_clean_cuts(self) -> CutSet: + logging.info("About to get test-clean cuts") + cuts = load_manifest_lazy( + self.args.manifest_dir / "librilight_finetuning_clean.jsonl.gz" + ) + return cuts + + @lru_cache() + def test_other_cuts(self) -> CutSet: + logging.info("About to get test-other cuts") + cuts = load_manifest_lazy( + self.args.manifest_dir / "librilight_finetuning_other.jsonl.gz" + ) + return cuts + + @lru_cache() + def librispeech_test_clean_cuts(self) -> CutSet: + logging.info("About to get test-clean cuts") + return load_manifest_lazy( + self.args.manifest_dir / "librispeech_cuts_test-clean.jsonl.gz" + ) + + @lru_cache() + def librispeech_test_other_cuts(self) -> CutSet: + logging.info("About to get test-other cuts") + return load_manifest_lazy( + self.args.manifest_dir / "librispeech_cuts_test-other.jsonl.gz" + ) diff --git a/egs/libriheavy/ASR/zipformer/optim.py b/egs/libriheavy/ASR/zipformer/optim.py new file mode 120000 index 000000000..5eaa3cffd --- /dev/null +++ b/egs/libriheavy/ASR/zipformer/optim.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/optim.py \ No newline at end of file diff --git a/egs/libriheavy/ASR/zipformer/scaling.py b/egs/libriheavy/ASR/zipformer/scaling.py new file mode 120000 index 000000000..6f398f431 --- /dev/null +++ b/egs/libriheavy/ASR/zipformer/scaling.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/scaling.py \ No newline at end of file diff --git a/egs/libriheavy/ASR/zipformer/scaling_converter.py b/egs/libriheavy/ASR/zipformer/scaling_converter.py new file mode 120000 index 000000000..b0ecee05e --- /dev/null +++ b/egs/libriheavy/ASR/zipformer/scaling_converter.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/scaling_converter.py \ No newline at end of file diff --git a/egs/libriheavy/ASR/zipformer/subsampling.py b/egs/libriheavy/ASR/zipformer/subsampling.py new file mode 120000 index 000000000..01ae9002c --- /dev/null +++ b/egs/libriheavy/ASR/zipformer/subsampling.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/subsampling.py \ No newline at end of file diff --git a/egs/libriheavy/ASR/zipformer/zipformer.py b/egs/libriheavy/ASR/zipformer/zipformer.py new file mode 120000 index 000000000..23011dda7 --- /dev/null +++ b/egs/libriheavy/ASR/zipformer/zipformer.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/zipformer.py \ No newline at end of file From 0d1cd4f5950d59a45a6d310510c8bf72eec1476c Mon Sep 17 00:00:00 2001 From: marcoyang1998 Date: Wed, 19 Jul 2023 10:55:57 +0800 Subject: [PATCH 07/14] add char coverage option to avoid having a lot of rarely used tokens in the BPE; add the option to use byte-fallback in training BPE --- egs/libriheavy/ASR/local/train_bpe_model.py | 19 ++++++++++++++++++- 1 file changed, 18 insertions(+), 1 deletion(-) diff --git a/egs/libriheavy/ASR/local/train_bpe_model.py b/egs/libriheavy/ASR/local/train_bpe_model.py index 55a7d26a6..31eda7401 100755 --- a/egs/libriheavy/ASR/local/train_bpe_model.py +++ b/egs/libriheavy/ASR/local/train_bpe_model.py @@ -31,6 +31,8 @@ from pathlib import Path import sentencepiece as spm +from icefall.utils import str2bool + def get_args(): parser = argparse.ArgumentParser() @@ -54,11 +56,25 @@ def get_args(): help="Vocabulary size for BPE training", ) + parser.add_argument( + "--byte-fallback", + type=str2bool, + default=False, + ) + + parser.add_argument( + "--character-coverage", + type=float, + default=0.99, + help="Character coverage when training BPE", + ) + return parser.parse_args() def main(): args = get_args() + print(args) vocab_size = args.vocab_size lang_dir = Path(args.lang_dir) @@ -83,12 +99,13 @@ def main(): model_type=model_type, model_prefix=model_prefix, input_sentence_size=input_sentence_size, - character_coverage=character_coverage, + character_coverage=args.character_coverage, user_defined_symbols=user_defined_symbols, unk_id=unk_id, bos_id=-1, eos_id=-1, train_extremely_large_corpus=False, + byte_fallback=args.byte_fallback, ) else: print(f"{model_file} exists - skipping") From 0aee07fb4ce3167dff8c5325bb25d247ce5a7318 Mon Sep 17 00:00:00 2001 From: marcoyang1998 Date: Wed, 19 Jul 2023 11:00:07 +0800 Subject: [PATCH 08/14] change the valid/test sets; only do simple normalization in the dataloader, i.e only replace full-width symbol, replace double hyphen with space --- .../ASR/zipformer/asr_datamodule.py | 221 ++++++++++++++---- 1 file changed, 180 insertions(+), 41 deletions(-) diff --git a/egs/libriheavy/ASR/zipformer/asr_datamodule.py b/egs/libriheavy/ASR/zipformer/asr_datamodule.py index 80dc8134a..9d9ecc63c 100644 --- a/egs/libriheavy/ASR/zipformer/asr_datamodule.py +++ b/egs/libriheavy/ASR/zipformer/asr_datamodule.py @@ -20,27 +20,150 @@ import inspect import logging from functools import lru_cache from pathlib import Path -from typing import Any, Callable, Dict, List, Optional +from typing import Any, Callable, Dict, List, Optional, Union import torch -from dataset import PromptASRDataset -from lhotse import CutSet, Fbank, FbankConfig, load_manifest, load_manifest_lazy +from lhotse import ( + CutSet, + Fbank, + FbankConfig, + load_manifest, + load_manifest_lazy, + validate, +) from lhotse.dataset import ( CutConcatenate, CutMix, DynamicBucketingSampler, K2SpeechRecognitionDataset, - PrecomputedFeatures, SingleCutSampler, SpecAugment, ) -from lhotse.dataset.input_strategies import OnTheFlyFeatures -from lhotse.utils import fix_random_seed -from torch.utils.data import DataLoader +from lhotse.dataset.input_strategies import ( + BatchIO, + OnTheFlyFeatures, + PrecomputedFeatures, +) +from lhotse.utils import fix_random_seed, ifnone +from text_normalization import ( + ref_text_normalization, + replace_full_width_symbol, + simple_normalization, +) +from torch.utils.data.dataloader import DataLoader, default_collate from icefall.utils import str2bool +class LibriHeavyASRDataset(torch.utils.data.Dataset): + """This is a dataset for LibriHeavy dataset""" + + def __init__( + self, + return_cuts: bool = False, + cut_transforms: List[Callable[[CutSet], CutSet]] = None, + input_transforms: List[Callable[[torch.Tensor], torch.Tensor]] = None, + input_strategy: BatchIO = PrecomputedFeatures(), + text_sampling_func: Optional[Callable[[List[str]], str]] = None, + ): + """ + Icefall ASR IterableDataset constructor. See https://github.com/lhotse-speech/lhotse/blob/master/lhotse/dataset/speech_recognition.py + for more details. + + :param return_cuts: When ``True``, will additionally return a "cut" field in each batch with the Cut + objects used to create that batch. + :param cut_transforms: A list of transforms to be applied on each sampled batch, + before converting cuts to an input representation (audio/features). + Examples: cut concatenation, noise cuts mixing, etc. + :param input_transforms: A list of transforms to be applied on each sampled batch, + after the cuts are converted to audio/features. + Examples: normalization, SpecAugment, etc. + :param input_strategy: Converts cuts into a collated batch of audio/features. + By default, reads pre-computed features from disk. + :param text_sampling_func: Sampling a text as transcription from a list of texts. + """ + super().__init__() + # Initialize the fields + self.return_cuts = return_cuts + self.cut_transforms = ifnone(cut_transforms, []) + self.input_transforms = ifnone(input_transforms, []) + self.input_strategy = input_strategy + + # a text selection function + self.text_sampling_func = text_sampling_func + + def __getitem__(self, cuts: CutSet) -> Dict[str, Union[torch.Tensor, List[str]]]: + """ + Return a new batch, with the batch size automatically determined using the constraints + of max_frames and max_cuts. + """ + validate_for_asr(cuts) + + # Sort the cuts by duration so that the first one determines the batch time dimensions. + cuts = cuts.sort_by_duration(ascending=False) + + # Optional CutSet transforms - e.g. padding, or speed perturbation that adjusts + # the supervision boundaries. + for tnfm in self.cut_transforms: + cuts = tnfm(cuts) + + # Sort the cuts again after transforms + cuts = cuts.sort_by_duration(ascending=False) + + # Get a tensor with batched feature matrices, shape (B, T, F) + # Collation performs auto-padding, if necessary. + input_tpl = self.input_strategy(cuts) + if len(input_tpl) == 3: + # An input strategy with fault tolerant audio reading mode. + # "cuts" may be a subset of the original "cuts" variable, + # that only has cuts for which we succesfully read the audio. + inputs, _, cuts = input_tpl + else: + inputs, _ = input_tpl + + # Get a dict of tensors that encode the positional information about supervisions + # in the batch of feature matrices. The tensors are named "sequence_idx", + # "start_frame/sample" and "num_frames/samples". + supervision_intervals = self.input_strategy.supervision_intervals(cuts) + + # Apply all available transforms on the inputs, i.e. either audio or features. + # This could be feature extraction, global MVN, SpecAugment, etc. + segments = torch.stack(list(supervision_intervals.values()), dim=1) + for tnfm in self.input_transforms: + inputs = tnfm(inputs, supervision_segments=segments) + + batch = { + "inputs": inputs, + "supervisions": default_collate( + [ + simple_normalization( + self.text_sampling_func(texts=supervision.texts) + ) + if self.text_sampling_func is not None + else { + "text": simple_normalization(supervision.texts[0]), + } + for sequence_idx, cut in enumerate(cuts) + for supervision in cut.supervisions + ] + ), + } + # Update the 'supervisions' field with sequence_idx and start/num frames/samples + batch["supervisions"].update(supervision_intervals) + if self.return_cuts: + batch["supervisions"]["cut"] = [ + cut for cut in cuts for sup in cut.supervisions + ] + + has_word_alignments = all( + s.alignment is not None and "word" in s.alignment + for c in cuts + for s in c.supervisions + ) + + return batch + + class _SeedWorkers: def __init__(self, seed: int): self.seed = seed @@ -197,7 +320,7 @@ class LibriHeavyAsrDataModule: self, cuts_train: CutSet, sampler_state_dict: Optional[Dict[str, Any]] = None, - text_sampling_func: Callable[[List[str]], str] = None, + text_sampling_func: Optional[Callable[[List[str]], str]] = None, ) -> DataLoader: """ Args: @@ -259,7 +382,7 @@ class LibriHeavyAsrDataModule: logging.info("Disable SpecAugment") logging.info("About to create train dataset") - train = PromptASRDataset( + train = LibriHeavyASRDataset( cut_transforms=transforms, input_transforms=input_transforms, return_cuts=self.args.return_cuts, @@ -277,7 +400,7 @@ class LibriHeavyAsrDataModule: # to be strict (e.g. could be randomized) # transforms = [PerturbSpeed(factors=[0.9, 1.1], p=2/3)] + transforms # noqa # Drop feats to be on the safe side. - train = PromptASRDataset( + train = LibriHeavyASRDataset( cut_transforms=transforms, input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))), input_transforms=input_transforms, @@ -326,7 +449,7 @@ class LibriHeavyAsrDataModule: def valid_dataloaders( self, cuts_valid: CutSet, - text_sampling_func: Callable[[List[str]], str] = None, + text_sampling_func: Optional[Callable[[List[str]], str]] = None, ) -> DataLoader: transforms = [] if self.args.concatenate_cuts: @@ -338,14 +461,14 @@ class LibriHeavyAsrDataModule: logging.info("About to create dev dataset") if self.args.on_the_fly_feats: - validate = PromptASRDataset( + validate = LibriHeavyASRDataset( cut_transforms=transforms, input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))), return_cuts=self.args.return_cuts, text_sampling_func=text_sampling_func, ) else: - validate = PromptASRDataset( + validate = LibriHeavyASRDataset( cut_transforms=transforms, return_cuts=self.args.return_cuts, text_sampling_func=text_sampling_func, @@ -368,7 +491,7 @@ class LibriHeavyAsrDataModule: def test_dataloaders(self, cuts: CutSet) -> DataLoader: logging.debug("About to create test dataset") - test = K2SpeechRecognitionDataset( + test = LibriHeavyASRDataset( input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))) if self.args.on_the_fly_feats else PrecomputedFeatures(), @@ -391,49 +514,44 @@ class LibriHeavyAsrDataModule: @lru_cache() def train_cuts(self) -> CutSet: logging.info(f"About to get {self.args.subset} cuts") - path = self.args.manifest_dir / f"librilight_cuts_{self.args.subset}.jsonl.gz" + + path = self.args.manifest_dir / "libriheavy_cuts_small.jsonl.gz" cuts_train = CutSet.from_jsonl_lazy(path) + if self.args.subset == "medium": + logging.info("Getting medium subset") + path = self.args.manifest_dir / "libriheavy_cuts_medium.jsonl.gz" + cuts_train += CutSet.from_jsonl_lazy(path) + elif self.args.subset == "large": + logging.info("Getting large subset") + path = self.args.manifest_dir / "libriheavy_cuts_medium.jsonl.gz" + cuts_train += CutSet.from_jsonl_lazy(path) + path = self.args.manifest_dir / "libriheavy_cuts_large.jsonl.gz" + cuts_train += CutSet.from_jsonl_lazy(path) + return cuts_train - @lru_cache() def dev_cuts(self) -> CutSet: logging.info("About to get dev cuts") - cuts_valid = load_manifest_lazy( - self.args.manifest_dir / "librilight_cuts_dev.jsonl.gz" + cuts = load_manifest_lazy( + self.args.manifest_dir / "libriheavy_cuts_dev.jsonl.gz" ) - return cuts_valid - - @lru_cache() - def test_cuts(self) -> CutSet: - logging.info("About to get test cuts") - cuts_valid = load_manifest_lazy( - self.args.manifest_dir / "librilight_cuts_test.jsonl.gz" - ) - return cuts_valid - - @lru_cache() - def test_medium_cuts(self) -> CutSet: - logging.info("About to get 2000 cuts from the medium set") - cuts_medium_2k = load_manifest_lazy( - self.args.manifest_dir / "librilight_cuts_medium_2000.jsonl.gz" - ) - return cuts_medium_2k + return cuts @lru_cache() def test_clean_cuts(self) -> CutSet: logging.info("About to get test-clean cuts") - cuts = load_manifest_lazy( - self.args.manifest_dir / "librilight_finetuning_clean.jsonl.gz" + cuts_valid = load_manifest_lazy( + self.args.manifest_dir / "libriheavy_cuts_test-clean.jsonl.gz" ) - return cuts + return cuts_valid @lru_cache() def test_other_cuts(self) -> CutSet: logging.info("About to get test-other cuts") - cuts = load_manifest_lazy( - self.args.manifest_dir / "librilight_finetuning_other.jsonl.gz" + cuts_valid = load_manifest_lazy( + self.args.manifest_dir / "libriheavy_cuts_test-other.jsonl.gz" ) - return cuts + return cuts_valid @lru_cache() def librispeech_test_clean_cuts(self) -> CutSet: @@ -448,3 +566,24 @@ class LibriHeavyAsrDataModule: return load_manifest_lazy( self.args.manifest_dir / "librispeech_cuts_test-other.jsonl.gz" ) + + +def validate_for_asr(cuts: CutSet) -> None: + validate(cuts) + tol = 2e-3 # 1ms + for cut in cuts: + for supervision in cut.supervisions: + assert supervision.start >= -tol, ( + f"Supervisions starting before the cut are not supported for ASR" + f" (sup id: {supervision.id}, cut id: {cut.id})" + ) + + # Supervision start time is relative to Cut ... + # https://lhotse.readthedocs.io/en/v0.10_e/cuts.html + # + # 'supervision.end' is end of supervision inside the Cut + assert supervision.end <= cut.duration + tol, ( + f"Supervisions ending after the cut " + f"are not supported for ASR" + f" (sup id: {supervision.id}, cut id: {cut.id})" + ) From 88a311734db3f00586a1e3c9d8c270b0a294686e Mon Sep 17 00:00:00 2001 From: marcoyang1998 Date: Wed, 19 Jul 2023 11:01:07 +0800 Subject: [PATCH 09/14] add script to prepare validation and test sets --- .../ASR/local/prepare_validation_sets.py | 77 +++++++++++++++++++ 1 file changed, 77 insertions(+) create mode 100755 egs/libriheavy/ASR/local/prepare_validation_sets.py diff --git a/egs/libriheavy/ASR/local/prepare_validation_sets.py b/egs/libriheavy/ASR/local/prepare_validation_sets.py new file mode 100755 index 000000000..23dd4bbff --- /dev/null +++ b/egs/libriheavy/ASR/local/prepare_validation_sets.py @@ -0,0 +1,77 @@ +#!/usr/bin/env python3 +# Copyright 2023 Xiaomi Corp. (authors: Xiaoyu Yang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +""" +This file computes fbank features of the LibriSpeech dataset. +It looks for manifests in the directory data/manifests. + +The generated fbank features are saved in data/fbank. +""" + +import argparse +import logging +import os +from pathlib import Path +from typing import Optional + +from lhotse import load_manifest_lazy + + +def get_args(): + parser = argparse.ArgumentParser() + + parser.add_argument( + "--manifest", type=str, help="The original manifest coming from" + ) + + return parser.parse_args() + + +def main(args): + + logging.info(f"Loading manifest {args.manifest}") + cuts = load_manifest_lazy(args.manifest) + + all_test_sets = [ + "dev", + "test-clean", + "test-other", + ] + + for test_set in all_test_sets: + logging.info(f"Processing test set: {test_set}") + with open(f"data/manifests/{test_set}.txt", "r") as f: + books = f.read().split("\n") + + # find the cuts belonging to the given books + selected_cuts = cuts.filter(lambda c: c.text_path.split("/")[-2] in books) + selected_cuts.describe() + + out_name = f"data/manifests/libriheavy_cuts_{test_set}.jsonl.gz" + logging.info(f"Saving the cuts contained in the book list to {out_name}") + selected_cuts.to_file(out_name) + + +if __name__ == "__main__": + formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + + logging.basicConfig(format=formatter, level=logging.INFO) + args = get_args() + logging.info(vars(args)) + + main(args) From 4f3a6606ad14a355c957e877d01139ffb7aafec8 Mon Sep 17 00:00:00 2001 From: marcoyang1998 Date: Wed, 19 Jul 2023 22:04:11 +0800 Subject: [PATCH 10/14] add necessary files for training --- egs/libriheavy/ASR/zipformer/decoder.py | 123 ++ .../ASR/zipformer/encoder_interface.py | 1 + egs/libriheavy/ASR/zipformer/joiner.py | 66 + egs/libriheavy/ASR/zipformer/model.py | 358 +++++ .../ASR/zipformer/text_normalization.py | 27 + egs/libriheavy/ASR/zipformer/train.py | 1395 +++++++++++++++++ 6 files changed, 1970 insertions(+) create mode 100644 egs/libriheavy/ASR/zipformer/decoder.py create mode 120000 egs/libriheavy/ASR/zipformer/encoder_interface.py create mode 100644 egs/libriheavy/ASR/zipformer/joiner.py create mode 100644 egs/libriheavy/ASR/zipformer/model.py create mode 100644 egs/libriheavy/ASR/zipformer/text_normalization.py create mode 100644 egs/libriheavy/ASR/zipformer/train.py diff --git a/egs/libriheavy/ASR/zipformer/decoder.py b/egs/libriheavy/ASR/zipformer/decoder.py new file mode 100644 index 000000000..33e38b199 --- /dev/null +++ b/egs/libriheavy/ASR/zipformer/decoder.py @@ -0,0 +1,123 @@ +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang, +# Xiaoyu Yang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from scaling import Balancer + + +class Decoder(nn.Module): + """This class modifies the stateless decoder from the following paper: + + RNN-transducer with stateless prediction network + https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9054419 + + It removes the recurrent connection from the decoder, i.e., the prediction + network. Different from the above paper, it adds an extra Conv1d + right after the embedding layer. + + TODO: Implement https://arxiv.org/pdf/2109.07513.pdf + """ + + def __init__( + self, + vocab_size: int, + decoder_dim: int, + blank_id: int, + context_size: int, + ): + """ + Args: + vocab_size: + Number of tokens of the modeling unit including blank. + decoder_dim: + Dimension of the input embedding, and of the decoder output. + blank_id: + The ID of the blank symbol. + context_size: + Number of previous words to use to predict the next word. + 1 means bigram; 2 means trigram. n means (n+1)-gram. + """ + super().__init__() + + self.embedding = nn.Embedding( + num_embeddings=vocab_size, + embedding_dim=decoder_dim, + ) + # the balancers are to avoid any drift in the magnitude of the + # embeddings, which would interact badly with parameter averaging. + self.balancer = Balancer(decoder_dim, channel_dim=-1, + min_positive=0.0, max_positive=1.0, + min_abs=0.5, max_abs=1.0, + prob=0.05) + + self.blank_id = blank_id + + assert context_size >= 1, context_size + self.context_size = context_size + self.vocab_size = vocab_size + + if context_size > 1: + self.conv = nn.Conv1d( + in_channels=decoder_dim, + out_channels=decoder_dim, + kernel_size=context_size, + padding=0, + groups=decoder_dim // 4, # group size == 4 + bias=False, + ) + self.balancer2 = Balancer(decoder_dim, channel_dim=-1, + min_positive=0.0, max_positive=1.0, + min_abs=0.5, max_abs=1.0, + prob=0.05) + + def forward(self, y: torch.Tensor, need_pad: bool = True) -> torch.Tensor: + """ + Args: + y: + A 2-D tensor of shape (N, U). + need_pad: + True to left pad the input. Should be True during training. + False to not pad the input. Should be False during inference. + Returns: + Return a tensor of shape (N, U, decoder_dim). + """ + y = y.to(torch.int64) + # this stuff about clamp() is a temporary fix for a mismatch + # at utterance start, we use negative ids in beam_search.py + embedding_out = self.embedding(y.clamp(min=0)) * (y >= 0).unsqueeze(-1) + + embedding_out = self.balancer(embedding_out) + + if self.context_size > 1: + embedding_out = embedding_out.permute(0, 2, 1) + if need_pad is True: + embedding_out = F.pad( + embedding_out, pad=(self.context_size - 1, 0) + ) + else: + # During inference time, there is no need to do extra padding + # as we only need one output + assert embedding_out.size(-1) == self.context_size + embedding_out = self.conv(embedding_out) + embedding_out = embedding_out.permute(0, 2, 1) + embedding_out = F.relu(embedding_out) + embedding_out = self.balancer2(embedding_out) + + return embedding_out diff --git a/egs/libriheavy/ASR/zipformer/encoder_interface.py b/egs/libriheavy/ASR/zipformer/encoder_interface.py new file mode 120000 index 000000000..c2eaca671 --- /dev/null +++ b/egs/libriheavy/ASR/zipformer/encoder_interface.py @@ -0,0 +1 @@ +../../../librispeech/ASR/zipformer/encoder_interface.py \ No newline at end of file diff --git a/egs/libriheavy/ASR/zipformer/joiner.py b/egs/libriheavy/ASR/zipformer/joiner.py new file mode 100644 index 000000000..f03cc930e --- /dev/null +++ b/egs/libriheavy/ASR/zipformer/joiner.py @@ -0,0 +1,66 @@ +# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +import torch.nn as nn +from scaling import ScaledLinear + + +class Joiner(nn.Module): + def __init__( + self, + encoder_dim: int, + decoder_dim: int, + joiner_dim: int, + vocab_size: int, + ): + super().__init__() + + self.encoder_proj = ScaledLinear(encoder_dim, joiner_dim, initial_scale=0.25) + self.decoder_proj = ScaledLinear(decoder_dim, joiner_dim, initial_scale=0.25) + self.output_linear = nn.Linear(joiner_dim, vocab_size) + + def forward( + self, + encoder_out: torch.Tensor, + decoder_out: torch.Tensor, + project_input: bool = True, + ) -> torch.Tensor: + """ + Args: + encoder_out: + Output from the encoder. Its shape is (N, T, s_range, C). + decoder_out: + Output from the decoder. Its shape is (N, T, s_range, C). + project_input: + If true, apply input projections encoder_proj and decoder_proj. + If this is false, it is the user's responsibility to do this + manually. + Returns: + Return a tensor of shape (N, T, s_range, C). + """ + assert encoder_out.ndim == decoder_out.ndim, (encoder_out.shape, decoder_out.shape) + + if project_input: + logit = self.encoder_proj(encoder_out) + self.decoder_proj( + decoder_out + ) + else: + logit = encoder_out + decoder_out + + logit = self.output_linear(torch.tanh(logit)) + + return logit diff --git a/egs/libriheavy/ASR/zipformer/model.py b/egs/libriheavy/ASR/zipformer/model.py new file mode 100644 index 000000000..b541ee697 --- /dev/null +++ b/egs/libriheavy/ASR/zipformer/model.py @@ -0,0 +1,358 @@ +# Copyright 2021-2023 Xiaomi Corp. (authors: Fangjun Kuang, +# Wei Kang, +# 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. + +from typing import Optional, Tuple + +import k2 +import torch +import torch.nn as nn +from encoder_interface import EncoderInterface + +from icefall.utils import add_sos, make_pad_mask +from scaling import ScaledLinear + + +class AsrModel(nn.Module): + def __init__( + self, + encoder_embed: nn.Module, + encoder: EncoderInterface, + decoder: Optional[nn.Module] = None, + joiner: Optional[nn.Module] = None, + encoder_dim: int = 384, + decoder_dim: int = 512, + vocab_size: int = 500, + use_transducer: bool = True, + use_ctc: bool = False, + ): + """A joint CTC & Transducer ASR model. + + - Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks (http://imagine.enpc.fr/~obozinsg/teaching/mva_gm/papers/ctc.pdf) + - Sequence Transduction with Recurrent Neural Networks (https://arxiv.org/pdf/1211.3711.pdf) + - Pruned RNN-T for fast, memory-efficient ASR training (https://arxiv.org/pdf/2206.13236.pdf) + + Args: + encoder_embed: + It is a Convolutional 2D subsampling module. It converts + an input of shape (N, T, idim) to an output of of shape + (N, T', odim), where T' = (T-3)//2-2 = (T-7)//2. + encoder: + It is the transcription network in the paper. Its accepts + two inputs: `x` of (N, T, encoder_dim) and `x_lens` of shape (N,). + It returns two tensors: `logits` of shape (N, T, encoder_dim) and + `logit_lens` of shape (N,). + decoder: + It is the prediction network in the paper. Its input shape + is (N, U) and its output shape is (N, U, decoder_dim). + It should contain one attribute: `blank_id`. + It is used when use_transducer is True. + joiner: + It has two inputs with shapes: (N, T, encoder_dim) and (N, U, decoder_dim). + Its output shape is (N, T, U, vocab_size). Note that its output contains + unnormalized probs, i.e., not processed by log-softmax. + It is used when use_transducer is True. + use_transducer: + Whether use transducer head. Default: True. + use_ctc: + Whether use CTC head. Default: False. + """ + super().__init__() + + assert ( + use_transducer or use_ctc + ), f"At least one of them should be True, but got use_transducer={use_transducer}, use_ctc={use_ctc}" + + assert isinstance(encoder, EncoderInterface), type(encoder) + + self.encoder_embed = encoder_embed + self.encoder = encoder + + self.use_transducer = use_transducer + if use_transducer: + # Modules for Transducer head + assert decoder is not None + assert hasattr(decoder, "blank_id") + assert joiner is not None + + self.decoder = decoder + self.joiner = joiner + + self.simple_am_proj = ScaledLinear( + encoder_dim, vocab_size, initial_scale=0.25 + ) + self.simple_lm_proj = ScaledLinear( + decoder_dim, vocab_size, initial_scale=0.25 + ) + else: + assert decoder is None + assert joiner is None + + self.use_ctc = use_ctc + if use_ctc: + # Modules for CTC head + self.ctc_output = nn.Sequential( + nn.Dropout(p=0.1), + nn.Linear(encoder_dim, vocab_size), + nn.LogSoftmax(dim=-1), + ) + + def forward_encoder( + self, x: torch.Tensor, x_lens: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Compute encoder outputs. + Args: + x: + A 3-D tensor of shape (N, T, C). + x_lens: + A 1-D tensor of shape (N,). It contains the number of frames in `x` + before padding. + + Returns: + encoder_out: + Encoder output, of shape (N, T, C). + encoder_out_lens: + Encoder output lengths, of shape (N,). + """ + # logging.info(f"Memory allocated at entry: {torch.cuda.memory_allocated() // 1000000}M") + x, x_lens = self.encoder_embed(x, x_lens) + # logging.info(f"Memory allocated after encoder_embed: {torch.cuda.memory_allocated() // 1000000}M") + + src_key_padding_mask = make_pad_mask(x_lens) + x = x.permute(1, 0, 2) # (N, T, C) -> (T, N, C) + + encoder_out, encoder_out_lens = self.encoder(x, x_lens, src_key_padding_mask) + + encoder_out = encoder_out.permute(1, 0, 2) # (T, N, C) ->(N, T, C) + assert torch.all(encoder_out_lens > 0), (x_lens, encoder_out_lens) + + return encoder_out, encoder_out_lens + + def forward_ctc( + self, + encoder_out: torch.Tensor, + encoder_out_lens: torch.Tensor, + targets: torch.Tensor, + target_lengths: torch.Tensor, + ) -> torch.Tensor: + """Compute CTC loss. + Args: + encoder_out: + Encoder output, of shape (N, T, C). + encoder_out_lens: + Encoder output lengths, of shape (N,). + targets: + Target Tensor of shape (sum(target_lengths)). The targets are assumed + to be un-padded and concatenated within 1 dimension. + """ + # Compute CTC log-prob + ctc_output = self.ctc_output(encoder_out) # (N, T, C) + + ctc_loss = torch.nn.functional.ctc_loss( + log_probs=ctc_output.permute(1, 0, 2), # (T, N, C) + targets=targets, + input_lengths=encoder_out_lens, + target_lengths=target_lengths, + reduction="sum", + ) + return ctc_loss + + def forward_transducer( + self, + encoder_out: torch.Tensor, + encoder_out_lens: torch.Tensor, + y: k2.RaggedTensor, + y_lens: torch.Tensor, + prune_range: int = 5, + am_scale: float = 0.0, + lm_scale: float = 0.0, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Compute Transducer loss. + Args: + encoder_out: + Encoder output, of shape (N, T, C). + encoder_out_lens: + Encoder output lengths, of shape (N,). + y: + A ragged tensor with 2 axes [utt][label]. It contains labels of each + utterance. + prune_range: + The prune range for rnnt loss, it means how many symbols(context) + we are considering for each frame to compute the loss. + am_scale: + The scale to smooth the loss with am (output of encoder network) + part + lm_scale: + The scale to smooth the loss with lm (output of predictor network) + part + """ + # Now for the decoder, i.e., the prediction network + blank_id = self.decoder.blank_id + sos_y = add_sos(y, sos_id=blank_id) + + # sos_y_padded: [B, S + 1], start with SOS. + sos_y_padded = sos_y.pad(mode="constant", padding_value=blank_id) + + # decoder_out: [B, S + 1, decoder_dim] + decoder_out = self.decoder(sos_y_padded) + + # Note: y does not start with SOS + # y_padded : [B, S] + y_padded = y.pad(mode="constant", padding_value=0) + + y_padded = y_padded.to(torch.int64) + boundary = torch.zeros( + (encoder_out.size(0), 4), + dtype=torch.int64, + device=encoder_out.device, + ) + boundary[:, 2] = y_lens + boundary[:, 3] = encoder_out_lens + + lm = self.simple_lm_proj(decoder_out) + am = self.simple_am_proj(encoder_out) + + # if self.training and random.random() < 0.25: + # lm = penalize_abs_values_gt(lm, 100.0, 1.0e-04) + # if self.training and random.random() < 0.25: + # am = penalize_abs_values_gt(am, 30.0, 1.0e-04) + + with torch.cuda.amp.autocast(enabled=False): + simple_loss, (px_grad, py_grad) = k2.rnnt_loss_smoothed( + lm=lm.float(), + am=am.float(), + symbols=y_padded, + termination_symbol=blank_id, + lm_only_scale=lm_scale, + am_only_scale=am_scale, + boundary=boundary, + reduction="sum", + return_grad=True, + ) + + # ranges : [B, T, prune_range] + ranges = k2.get_rnnt_prune_ranges( + px_grad=px_grad, + py_grad=py_grad, + boundary=boundary, + s_range=prune_range, + ) + + # am_pruned : [B, T, prune_range, encoder_dim] + # lm_pruned : [B, T, prune_range, decoder_dim] + am_pruned, lm_pruned = k2.do_rnnt_pruning( + am=self.joiner.encoder_proj(encoder_out), + lm=self.joiner.decoder_proj(decoder_out), + ranges=ranges, + ) + + # logits : [B, T, prune_range, vocab_size] + + # project_input=False since we applied the decoder's input projections + # prior to do_rnnt_pruning (this is an optimization for speed). + logits = self.joiner(am_pruned, lm_pruned, project_input=False) + + with torch.cuda.amp.autocast(enabled=False): + pruned_loss = k2.rnnt_loss_pruned( + logits=logits.float(), + symbols=y_padded, + ranges=ranges, + termination_symbol=blank_id, + boundary=boundary, + reduction="sum", + ) + + return simple_loss, pruned_loss + + def forward( + self, + x: torch.Tensor, + x_lens: torch.Tensor, + y: k2.RaggedTensor, + prune_range: int = 5, + am_scale: float = 0.0, + lm_scale: float = 0.0, + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Args: + x: + A 3-D tensor of shape (N, T, C). + x_lens: + A 1-D tensor of shape (N,). It contains the number of frames in `x` + before padding. + y: + A ragged tensor with 2 axes [utt][label]. It contains labels of each + utterance. + prune_range: + The prune range for rnnt loss, it means how many symbols(context) + we are considering for each frame to compute the loss. + am_scale: + The scale to smooth the loss with am (output of encoder network) + part + lm_scale: + The scale to smooth the loss with lm (output of predictor network) + part + Returns: + Return the transducer losses and CTC loss, + in form of (simple_loss, pruned_loss, ctc_loss) + + Note: + Regarding am_scale & lm_scale, it will make the loss-function one of + the form: + lm_scale * lm_probs + am_scale * am_probs + + (1-lm_scale-am_scale) * combined_probs + """ + assert x.ndim == 3, x.shape + assert x_lens.ndim == 1, x_lens.shape + assert y.num_axes == 2, y.num_axes + + assert x.size(0) == x_lens.size(0) == y.dim0 + + # Compute encoder outputs + encoder_out, encoder_out_lens = self.forward_encoder(x, x_lens) + + row_splits = y.shape.row_splits(1) + y_lens = row_splits[1:] - row_splits[:-1] + + if self.use_transducer: + # Compute transducer loss + simple_loss, pruned_loss = self.forward_transducer( + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + y=y.to(x.device), + y_lens=y_lens, + prune_range=prune_range, + am_scale=am_scale, + lm_scale=lm_scale, + ) + else: + simple_loss = torch.empty(0) + pruned_loss = torch.empty(0) + + if self.use_ctc: + # Compute CTC loss + targets = y.values + ctc_loss = self.forward_ctc( + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + targets=targets, + target_lengths=y_lens, + ) + else: + ctc_loss = torch.empty(0) + + return simple_loss, pruned_loss, ctc_loss diff --git a/egs/libriheavy/ASR/zipformer/text_normalization.py b/egs/libriheavy/ASR/zipformer/text_normalization.py new file mode 100644 index 000000000..bbde95be0 --- /dev/null +++ b/egs/libriheavy/ASR/zipformer/text_normalization.py @@ -0,0 +1,27 @@ +import re + +def replace_full_width_symbol(s: str) -> str: + # replace full-width symbol with theri half width counterpart + s = s.replace("“", '"') + s = s.replace("”", '"') + s = s.replace("‘", "'") + s = s.replace("’", "'") + + return s + + +def upper_ref_text(text: str) -> str: + text = replace_full_width_symbol(text) + text = text.upper() # upper case all characters + + # Only keep all alpha-numeric characters, hypen and apostrophe + text = text.replace("--", " ") + text = re.sub("[^a-zA-Z0-9\s\'-]+", "", text) + return text + +def simple_normalization(text: str) -> str: + text = replace_full_width_symbol(text) + text = text.replace("--", " ") + + return text + \ No newline at end of file diff --git a/egs/libriheavy/ASR/zipformer/train.py b/egs/libriheavy/ASR/zipformer/train.py new file mode 100644 index 000000000..7d489a0a6 --- /dev/null +++ b/egs/libriheavy/ASR/zipformer/train.py @@ -0,0 +1,1395 @@ +#!/usr/bin/env python3 +# Copyright 2021-2023 Xiaomi Corp. (authors: Fangjun Kuang, +# Wei Kang, +# Mingshuang Luo, +# Zengwei Yao, +# Daniel Povey, +# Xiaoyu Yang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Usage: + +export CUDA_VISIBLE_DEVICES="0,1,2,3" + +# For non-streaming model training: +./zipformer/train.py \ + --world-size 4 \ + --num-epochs 30 \ + --start-epoch 1 \ + --use-fp16 1 \ + --exp-dir zipformer/exp \ + --full-libri 1 \ + --max-duration 1000 + +# For streaming model training: +./zipformer/train.py \ + --world-size 4 \ + --num-epochs 30 \ + --start-epoch 1 \ + --use-fp16 1 \ + --exp-dir zipformer/exp \ + --causal 1 \ + --full-libri 1 \ + --max-duration 1000 + +It supports training with: + - transducer loss (default), with `--use-transducer True --use-ctc False` + - ctc loss (not recommended), with `--use-transducer False --use-ctc True` + - transducer loss & ctc loss, with `--use-transducer True --use-ctc True` +""" + + +import argparse +import copy +import logging +import random +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 LibriHeavyAsrDataModule +from decoder import Decoder +from joiner import Joiner +from lhotse.cut import Cut +from lhotse.dataset.sampling.base import CutSampler +from lhotse.utils import fix_random_seed +from model import AsrModel +from optim import Eden, ScaledAdam +from scaling import ScheduledFloat +from subsampling import Conv2dSubsampling +from torch import Tensor +from torch.cuda.amp import GradScaler +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.utils.tensorboard import SummaryWriter +from zipformer import Zipformer2 + +from icefall import diagnostics +from icefall.checkpoint import load_checkpoint, remove_checkpoints +from icefall.checkpoint import save_checkpoint as save_checkpoint_impl +from icefall.checkpoint import ( + save_checkpoint_with_global_batch_idx, + update_averaged_model, +) +from icefall.dist import cleanup_dist, setup_dist +from icefall.env import get_env_info +from icefall.hooks import register_inf_check_hooks +from icefall.utils import ( + AttributeDict, + MetricsTracker, + get_parameter_groups_with_lrs, + setup_logger, + str2bool, +) + +LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler] + + +def get_adjusted_batch_count(params: AttributeDict) -> float: + # returns the number of batches we would have used so far if we had used the reference + # duration. This is for purposes of set_batch_count(). + return ( + params.batch_idx_train + * (params.max_duration * params.world_size) + / params.ref_duration + ) + + +def set_batch_count(model: Union[nn.Module, DDP], batch_count: float) -> None: + if isinstance(model, DDP): + # get underlying nn.Module + model = model.module + for name, module in model.named_modules(): + if hasattr(module, "batch_count"): + module.batch_count = batch_count + if hasattr(module, "name"): + module.name = name + + +def add_model_arguments(parser: argparse.ArgumentParser): + parser.add_argument( + "--num-encoder-layers", + type=str, + default="2,2,3,4,3,2", + help="Number of zipformer encoder layers per stack, comma separated.", + ) + + parser.add_argument( + "--downsampling-factor", + type=str, + default="1,2,4,8,4,2", + help="Downsampling factor for each stack of encoder layers.", + ) + + parser.add_argument( + "--feedforward-dim", + type=str, + default="512,768,1024,1536,1024,768", + help="Feedforward dimension of the zipformer encoder layers, per stack, comma separated.", + ) + + parser.add_argument( + "--num-heads", + type=str, + default="4,4,4,8,4,4", + help="Number of attention heads in the zipformer encoder layers: a single int or comma-separated list.", + ) + + parser.add_argument( + "--encoder-dim", + type=str, + default="192,256,384,512,384,256", + help="Embedding dimension in encoder stacks: a single int or comma-separated list.", + ) + + parser.add_argument( + "--query-head-dim", + type=str, + default="32", + help="Query/key dimension per head in encoder stacks: a single int or comma-separated list.", + ) + + parser.add_argument( + "--value-head-dim", + type=str, + default="12", + help="Value dimension per head in encoder stacks: a single int or comma-separated list.", + ) + + parser.add_argument( + "--pos-head-dim", + type=str, + default="4", + help="Positional-encoding dimension per head in encoder stacks: a single int or comma-separated list.", + ) + + parser.add_argument( + "--pos-dim", + type=int, + default="48", + help="Positional-encoding embedding dimension", + ) + + parser.add_argument( + "--encoder-unmasked-dim", + type=str, + default="192,192,256,256,256,192", + help="Unmasked dimensions in the encoders, relates to augmentation during training. " + "A single int or comma-separated list. Must be <= each corresponding encoder_dim.", + ) + + parser.add_argument( + "--cnn-module-kernel", + type=str, + default="31,31,15,15,15,31", + help="Sizes of convolutional kernels in convolution modules in each encoder stack: " + "a single int or comma-separated list.", + ) + + parser.add_argument( + "--decoder-dim", + type=int, + default=512, + help="Embedding dimension in the decoder model.", + ) + + parser.add_argument( + "--joiner-dim", + type=int, + default=512, + help="""Dimension used in the joiner model. + Outputs from the encoder and decoder model are projected + to this dimension before adding. + """, + ) + + parser.add_argument( + "--causal", + type=str2bool, + default=False, + help="If True, use causal version of model.", + ) + + parser.add_argument( + "--chunk-size", + type=str, + default="16,32,64,-1", + help="Chunk sizes (at 50Hz frame rate) will be chosen randomly from this list during training. " + " Must be just -1 if --causal=False", + ) + + parser.add_argument( + "--left-context-frames", + type=str, + default="64,128,256,-1", + help="Maximum left-contexts for causal training, measured in frames which will " + "be converted to a number of chunks. If splitting into chunks, " + "chunk left-context frames will be chosen randomly from this list; else not relevant.", + ) + + parser.add_argument( + "--use-transducer", + type=str2bool, + default=True, + help="If True, use Transducer head.", + ) + + parser.add_argument( + "--use-ctc", + type=str2bool, + default=False, + help="If True, use CTC head.", + ) + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--world-size", + type=int, + default=1, + help="Number of GPUs for DDP training.", + ) + + parser.add_argument( + "--master-port", + type=int, + default=12354, + help="Master port to use for DDP training.", + ) + + parser.add_argument( + "--tensorboard", + type=str2bool, + default=True, + help="Should various information be logged in tensorboard.", + ) + + parser.add_argument( + "--num-epochs", + type=int, + default=30, + help="Number of epochs to train.", + ) + + parser.add_argument( + "--start-epoch", + type=int, + default=1, + help="""Resume training from this epoch. It should be positive. + If larger than 1, it will load checkpoint from + exp-dir/epoch-{start_epoch-1}.pt + """, + ) + + parser.add_argument( + "--start-batch", + type=int, + default=0, + help="""If positive, --start-epoch is ignored and + it loads the checkpoint from exp-dir/checkpoint-{start_batch}.pt + """, + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="zipformer/exp", + help="""The experiment dir. + It specifies the directory where all training related + files, e.g., checkpoints, log, etc, are saved + """, + ) + + parser.add_argument( + "--bpe-model", + type=str, + default="data/lang_bpe_500/bpe.model", + help="Path to the BPE model", + ) + + parser.add_argument( + "--base-lr", type=float, default=0.045, help="The base learning rate." + ) + + parser.add_argument( + "--lr-batches", + type=float, + default=7500, + help="""Number of steps that affects how rapidly the learning rate + decreases. We suggest not to change this.""", + ) + + parser.add_argument( + "--lr-hours", + type=float, + default=30000, + help="""Number of hours that affects how rapidly the learning rate decreases. + """, + ) + + parser.add_argument( + "--ref-duration", + type=float, + default=600, + help="Reference batch duration for purposes of adjusting batch counts for setting various " + "schedules inside the model", + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; " "2 means tri-gram", + ) + + parser.add_argument( + "--prune-range", + type=int, + default=5, + help="The prune range for rnnt loss, it means how many symbols(context)" + "we are using to compute the loss", + ) + + parser.add_argument( + "--lm-scale", + type=float, + default=0.25, + help="The scale to smooth the loss with lm " + "(output of prediction network) part.", + ) + + parser.add_argument( + "--am-scale", + type=float, + default=0.0, + help="The scale to smooth the loss with am (output of encoder network)" "part.", + ) + + parser.add_argument( + "--simple-loss-scale", + type=float, + default=0.5, + help="To get pruning ranges, we will calculate a simple version" + "loss(joiner is just addition), this simple loss also uses for" + "training (as a regularization item). We will scale the simple loss" + "with this parameter before adding to the final loss.", + ) + + parser.add_argument( + "--ctc-loss-scale", + type=float, + default=0.2, + help="Scale for CTC loss.", + ) + + parser.add_argument( + "--seed", + type=int, + default=42, + help="The seed for random generators intended for reproducibility", + ) + + parser.add_argument( + "--print-diagnostics", + type=str2bool, + default=False, + help="Accumulate stats on activations, print them and exit.", + ) + + parser.add_argument( + "--inf-check", + type=str2bool, + default=False, + help="Add hooks to check for infinite module outputs and gradients.", + ) + + parser.add_argument( + "--save-every-n", + type=int, + default=4000, + help="""Save checkpoint after processing this number of batches" + periodically. We save checkpoint to exp-dir/ whenever + params.batch_idx_train % save_every_n == 0. The checkpoint filename + has the form: f'exp-dir/checkpoint-{params.batch_idx_train}.pt' + Note: It also saves checkpoint to `exp-dir/epoch-xxx.pt` at the + end of each epoch where `xxx` is the epoch number counting from 1. + """, + ) + + parser.add_argument( + "--keep-last-k", + type=int, + default=30, + help="""Only keep this number of checkpoints on disk. + For instance, if it is 3, there are only 3 checkpoints + in the exp-dir with filenames `checkpoint-xxx.pt`. + It does not affect checkpoints with name `epoch-xxx.pt`. + """, + ) + + parser.add_argument( + "--average-period", + type=int, + default=200, + help="""Update the averaged model, namely `model_avg`, after processing + this number of batches. `model_avg` is a separate version of model, + in which each floating-point parameter is the average of all the + parameters from the start of training. Each time we take the average, + we do: `model_avg = model * (average_period / batch_idx_train) + + model_avg * ((batch_idx_train - average_period) / batch_idx_train)`. + """, + ) + + parser.add_argument( + "--use-fp16", + type=str2bool, + default=False, + help="Whether to use half precision training.", + ) + + add_model_arguments(parser) + + return parser + + +def get_params() -> AttributeDict: + """Return a dict containing training parameters. + + All training related parameters that are not passed from the commandline + are saved in the variable `params`. + + Commandline options are merged into `params` after they are parsed, so + you can also access them via `params`. + + Explanation of options saved in `params`: + + - best_train_loss: Best training loss so far. It is used to select + the model that has the lowest training loss. It is + updated during the training. + + - best_valid_loss: Best validation loss so far. It is used to select + the model that has the lowest validation loss. It is + updated during the training. + + - best_train_epoch: It is the epoch that has the best training loss. + + - best_valid_epoch: It is the epoch that has the best validation loss. + + - batch_idx_train: Used to writing statistics to tensorboard. It + contains number of batches trained so far across + epochs. + + - log_interval: Print training loss if batch_idx % log_interval` is 0 + + - reset_interval: Reset statistics if batch_idx % reset_interval is 0 + + - valid_interval: Run validation if batch_idx % valid_interval is 0 + + - feature_dim: The model input dim. It has to match the one used + in computing features. + + - subsampling_factor: The subsampling factor for the model. + + - encoder_dim: Hidden dim for multi-head attention model. + + - num_decoder_layers: Number of decoder layer of transformer decoder. + + - warm_step: The warmup period that dictates the decay of the + scale on "simple" (un-pruned) loss. + """ + params = AttributeDict( + { + "best_train_loss": float("inf"), + "best_valid_loss": float("inf"), + "best_train_epoch": -1, + "best_valid_epoch": -1, + "batch_idx_train": 0, + "log_interval": 50, + "reset_interval": 200, + "valid_interval": 3000, # For the 100h subset, use 800 + # parameters for zipformer + "feature_dim": 80, + "subsampling_factor": 4, # not passed in, this is fixed. + "warm_step": 2000, + "env_info": get_env_info(), + } + ) + + return params + + +def _to_int_tuple(s: str): + return tuple(map(int, s.split(","))) + + +def get_encoder_embed(params: AttributeDict) -> nn.Module: + # encoder_embed converts the input of shape (N, T, num_features) + # to the shape (N, (T - 7) // 2, encoder_dims). + # That is, it does two things simultaneously: + # (1) subsampling: T -> (T - 7) // 2 + # (2) embedding: num_features -> encoder_dims + # In the normal configuration, we will downsample once more at the end + # by a factor of 2, and most of the encoder stacks will run at a lower + # sampling rate. + encoder_embed = Conv2dSubsampling( + in_channels=params.feature_dim, + out_channels=_to_int_tuple(params.encoder_dim)[0], + dropout=ScheduledFloat((0.0, 0.3), (20000.0, 0.1)), + ) + return encoder_embed + + +def get_encoder_model(params: AttributeDict) -> nn.Module: + encoder = Zipformer2( + output_downsampling_factor=2, + downsampling_factor=_to_int_tuple(params.downsampling_factor), + num_encoder_layers=_to_int_tuple(params.num_encoder_layers), + encoder_dim=_to_int_tuple(params.encoder_dim), + encoder_unmasked_dim=_to_int_tuple(params.encoder_unmasked_dim), + query_head_dim=_to_int_tuple(params.query_head_dim), + pos_head_dim=_to_int_tuple(params.pos_head_dim), + value_head_dim=_to_int_tuple(params.value_head_dim), + pos_dim=params.pos_dim, + num_heads=_to_int_tuple(params.num_heads), + feedforward_dim=_to_int_tuple(params.feedforward_dim), + cnn_module_kernel=_to_int_tuple(params.cnn_module_kernel), + dropout=ScheduledFloat((0.0, 0.3), (20000.0, 0.1)), + warmup_batches=4000.0, + causal=params.causal, + chunk_size=_to_int_tuple(params.chunk_size), + left_context_frames=_to_int_tuple(params.left_context_frames), + ) + return encoder + + +def get_decoder_model(params: AttributeDict) -> nn.Module: + decoder = Decoder( + vocab_size=params.vocab_size, + decoder_dim=params.decoder_dim, + blank_id=params.blank_id, + context_size=params.context_size, + ) + return decoder + + +def get_joiner_model(params: AttributeDict) -> nn.Module: + joiner = Joiner( + encoder_dim=max(_to_int_tuple(params.encoder_dim)), + decoder_dim=params.decoder_dim, + joiner_dim=params.joiner_dim, + vocab_size=params.vocab_size, + ) + return joiner + + +def get_model(params: AttributeDict) -> nn.Module: + assert ( + params.use_transducer or params.use_ctc + ), (f"At least one of them should be True, " + f"but got params.use_transducer={params.use_transducer}, " + f"params.use_ctc={params.use_ctc}") + + encoder_embed = get_encoder_embed(params) + encoder = get_encoder_model(params) + + if params.use_transducer: + decoder = get_decoder_model(params) + joiner = get_joiner_model(params) + else: + decoder = None + joiner = None + + model = AsrModel( + encoder_embed=encoder_embed, + encoder=encoder, + decoder=decoder, + joiner=joiner, + encoder_dim=max(_to_int_tuple(params.encoder_dim)), + decoder_dim=params.decoder_dim, + vocab_size=params.vocab_size, + use_transducer=params.use_transducer, + use_ctc=params.use_ctc, + ) + return model + + +def load_checkpoint_if_available( + params: AttributeDict, + model: nn.Module, + model_avg: nn.Module = None, + optimizer: Optional[torch.optim.Optimizer] = None, + scheduler: Optional[LRSchedulerType] = None, +) -> Optional[Dict[str, Any]]: + """Load checkpoint from file. + + If params.start_batch is positive, it will load the checkpoint from + `params.exp_dir/checkpoint-{params.start_batch}.pt`. Otherwise, if + params.start_epoch is larger than 1, it will load the checkpoint from + `params.start_epoch - 1`. + + Apart from loading state dict for `model` and `optimizer` it also updates + `best_train_epoch`, `best_train_loss`, `best_valid_epoch`, + and `best_valid_loss` in `params`. + + Args: + params: + The return value of :func:`get_params`. + model: + The training model. + model_avg: + The stored model averaged from the start of training. + optimizer: + The optimizer that we are using. + scheduler: + The scheduler that we are using. + Returns: + Return a dict containing previously saved training info. + """ + if params.start_batch > 0: + filename = params.exp_dir / f"checkpoint-{params.start_batch}.pt" + elif params.start_epoch > 1: + filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt" + else: + return None + + assert filename.is_file(), f"{filename} does not exist!" + + saved_params = load_checkpoint( + filename, + model=model, + model_avg=model_avg, + optimizer=optimizer, + scheduler=scheduler, + ) + + keys = [ + "best_train_epoch", + "best_valid_epoch", + "batch_idx_train", + "best_train_loss", + "best_valid_loss", + ] + for k in keys: + params[k] = saved_params[k] + + if params.start_batch > 0: + if "cur_epoch" in saved_params: + params["start_epoch"] = saved_params["cur_epoch"] + + return saved_params + + +def save_checkpoint( + params: AttributeDict, + model: Union[nn.Module, DDP], + model_avg: Optional[nn.Module] = None, + optimizer: Optional[torch.optim.Optimizer] = None, + scheduler: Optional[LRSchedulerType] = None, + sampler: Optional[CutSampler] = None, + scaler: Optional[GradScaler] = None, + rank: int = 0, +) -> None: + """Save model, optimizer, scheduler and training stats to file. + + Args: + params: + It is returned by :func:`get_params`. + model: + The training model. + model_avg: + The stored model averaged from the start of training. + optimizer: + The optimizer used in the training. + sampler: + The sampler for the training dataset. + scaler: + The scaler used for mix precision training. + """ + if rank != 0: + return + filename = params.exp_dir / f"epoch-{params.cur_epoch}.pt" + save_checkpoint_impl( + filename=filename, + model=model, + model_avg=model_avg, + params=params, + optimizer=optimizer, + scheduler=scheduler, + sampler=sampler, + scaler=scaler, + rank=rank, + ) + + if params.best_train_epoch == params.cur_epoch: + best_train_filename = params.exp_dir / "best-train-loss.pt" + copyfile(src=filename, dst=best_train_filename) + + if params.best_valid_epoch == params.cur_epoch: + best_valid_filename = params.exp_dir / "best-valid-loss.pt" + copyfile(src=filename, dst=best_valid_filename) + + +def compute_loss( + params: AttributeDict, + model: Union[nn.Module, DDP], + sp: spm.SentencePieceProcessor, + batch: dict, + is_training: bool, +) -> Tuple[Tensor, MetricsTracker]: + """ + Compute loss given the model and its inputs. + + Args: + params: + Parameters for training. See :func:`get_params`. + model: + The model for training. It is an instance of Zipformer in our case. + batch: + A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()` + for the content in it. + is_training: + True for training. False for validation. When it is True, this + function enables autograd during computation; when it is False, it + disables autograd. + warmup: a floating point value which increases throughout training; + values >= 1.0 are fully warmed up and have all modules present. + """ + device = model.device if isinstance(model, DDP) else next(model.parameters()).device + feature = batch["inputs"] + # at entry, feature is (N, T, C) + assert feature.ndim == 3 + feature = feature.to(device) + + supervisions = batch["supervisions"] + feature_lens = supervisions["num_frames"].to(device) + + batch_idx_train = params.batch_idx_train + warm_step = params.warm_step + + texts = batch["supervisions"]["text"] + + y = sp.encode(texts, out_type=int) + y = k2.RaggedTensor(y) + + if random.random() < 0.03: + logging.info(f"Training text: {texts[0]}") + logging.info(f"Training tokens: {y[0]}") + logging.info(f"Max token usage: {y.max()}") + + with torch.set_grad_enabled(is_training): + simple_loss, pruned_loss, ctc_loss = model( + x=feature, + x_lens=feature_lens, + y=y, + prune_range=params.prune_range, + am_scale=params.am_scale, + lm_scale=params.lm_scale, + ) + + loss = 0.0 + + if params.use_transducer: + s = params.simple_loss_scale + # take down the scale on the simple loss from 1.0 at the start + # to params.simple_loss scale by warm_step. + simple_loss_scale = ( + s if batch_idx_train >= warm_step + else 1.0 - (batch_idx_train / warm_step) * (1.0 - s) + ) + pruned_loss_scale = ( + 1.0 if batch_idx_train >= warm_step + else 0.1 + 0.9 * (batch_idx_train / warm_step) + ) + loss += ( + simple_loss_scale * simple_loss + + pruned_loss_scale * pruned_loss + ) + + if params.use_ctc: + loss += params.ctc_loss_scale * ctc_loss + + assert loss.requires_grad == is_training + + info = MetricsTracker() + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + info["frames"] = (feature_lens // params.subsampling_factor).sum().item() + + # Note: We use reduction=sum while computing the loss. + info["loss"] = loss.detach().cpu().item() + if params.use_transducer: + info["simple_loss"] = simple_loss.detach().cpu().item() + info["pruned_loss"] = pruned_loss.detach().cpu().item() + if params.use_ctc: + info["ctc_loss"] = ctc_loss.detach().cpu().item() + + return loss, info + + +def compute_validation_loss( + params: AttributeDict, + model: Union[nn.Module, DDP], + sp: spm.SentencePieceProcessor, + valid_dl: torch.utils.data.DataLoader, + world_size: int = 1, +) -> MetricsTracker: + """Run the validation process.""" + model.eval() + + tot_loss = MetricsTracker() + + for batch_idx, batch in enumerate(valid_dl): + loss, loss_info = compute_loss( + params=params, + model=model, + sp=sp, + batch=batch, + is_training=False, + ) + assert loss.requires_grad is False + tot_loss = tot_loss + loss_info + + if world_size > 1: + tot_loss.reduce(loss.device) + + loss_value = tot_loss["loss"] / tot_loss["frames"] + if loss_value < params.best_valid_loss: + params.best_valid_epoch = params.cur_epoch + params.best_valid_loss = loss_value + + return tot_loss + + +def train_one_epoch( + params: AttributeDict, + model: Union[nn.Module, DDP], + optimizer: torch.optim.Optimizer, + scheduler: LRSchedulerType, + sp: spm.SentencePieceProcessor, + train_dl: torch.utils.data.DataLoader, + valid_dl: torch.utils.data.DataLoader, + scaler: GradScaler, + model_avg: Optional[nn.Module] = None, + tb_writer: Optional[SummaryWriter] = None, + world_size: int = 1, + rank: int = 0, +) -> None: + """Train the model for one epoch. + + The training loss from the mean of all frames is saved in + `params.train_loss`. It runs the validation process every + `params.valid_interval` batches. + + Args: + params: + It is returned by :func:`get_params`. + model: + The model for training. + optimizer: + The optimizer we are using. + scheduler: + The learning rate scheduler, we call step() every step. + train_dl: + Dataloader for the training dataset. + valid_dl: + Dataloader for the validation dataset. + scaler: + The scaler used for mix precision training. + model_avg: + The stored model averaged from the start of training. + tb_writer: + Writer to write log messages to tensorboard. + world_size: + Number of nodes in DDP training. If it is 1, DDP is disabled. + rank: + The rank of the node in DDP training. If no DDP is used, it should + be set to 0. + """ + model.train() + + tot_loss = MetricsTracker() + + saved_bad_model = False + + def save_bad_model(suffix: str = ""): + save_checkpoint_impl( + filename=params.exp_dir / f"bad-model{suffix}-{rank}.pt", + model=model, + model_avg=model_avg, + params=params, + optimizer=optimizer, + scheduler=scheduler, + sampler=train_dl.sampler, + scaler=scaler, + rank=0, + ) + + for batch_idx, batch in enumerate(train_dl): + if batch_idx % 10 == 0: + set_batch_count(model, get_adjusted_batch_count(params)) + + params.batch_idx_train += 1 + batch_size = len(batch["supervisions"]["text"]) + + try: + with torch.cuda.amp.autocast(enabled=params.use_fp16): + loss, loss_info = compute_loss( + params=params, + model=model, + sp=sp, + batch=batch, + is_training=True, + ) + # summary stats + tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info + + # NOTE: We use reduction==sum and loss is computed over utterances + # in the batch and there is no normalization to it so far. + scaler.scale(loss).backward() + scheduler.step_batch(params.batch_idx_train) + # Use the number of hours of speech to adjust the learning rate + scheduler.step_epoch(params.batch_idx_train * params.max_duration * params.world_size / 3600) + + scaler.step(optimizer) + scaler.update() + optimizer.zero_grad() + except: # noqa + save_bad_model() + display_and_save_batch(batch, params=params, sp=sp) + raise + + if params.print_diagnostics and batch_idx == 5: + return + + if ( + rank == 0 + and params.batch_idx_train > 0 + and params.batch_idx_train % params.average_period == 0 + ): + update_averaged_model( + params=params, + model_cur=model, + model_avg=model_avg, + ) + + if ( + params.batch_idx_train > 0 + and params.batch_idx_train % params.save_every_n == 0 + ): + save_checkpoint_with_global_batch_idx( + out_dir=params.exp_dir, + global_batch_idx=params.batch_idx_train, + model=model, + model_avg=model_avg, + params=params, + optimizer=optimizer, + scheduler=scheduler, + sampler=train_dl.sampler, + scaler=scaler, + rank=rank, + ) + remove_checkpoints( + out_dir=params.exp_dir, + topk=params.keep_last_k, + rank=rank, + ) + + if batch_idx % 100 == 0 and params.use_fp16: + # If the grad scale was less than 1, try increasing it. The _growth_interval + # of the grad scaler is configurable, but we can't configure it to have different + # behavior depending on the current grad scale. + cur_grad_scale = scaler._scale.item() + + if cur_grad_scale < 8.0 or (cur_grad_scale < 32.0 and batch_idx % 400 == 0): + scaler.update(cur_grad_scale * 2.0) + if cur_grad_scale < 0.01: + if not saved_bad_model: + save_bad_model(suffix="-first-warning") + saved_bad_model = True + logging.warning(f"Grad scale is small: {cur_grad_scale}") + if cur_grad_scale < 1.0e-05: + save_bad_model() + raise RuntimeError( + f"grad_scale is too small, exiting: {cur_grad_scale}" + ) + + if batch_idx % params.log_interval == 0: + cur_lr = max(scheduler.get_last_lr()) + cur_grad_scale = scaler._scale.item() if params.use_fp16 else 1.0 + + logging.info( + f"Epoch {params.cur_epoch}, " + f"batch {batch_idx}, loss[{loss_info}], " + f"tot_loss[{tot_loss}], batch size: {batch_size}, " + f"lr: {cur_lr:.2e}, " + + (f"grad_scale: {scaler._scale.item()}" if params.use_fp16 else "") + ) + + if tb_writer is not None: + tb_writer.add_scalar( + "train/learning_rate", cur_lr, params.batch_idx_train + ) + + loss_info.write_summary( + tb_writer, "train/current_", params.batch_idx_train + ) + tot_loss.write_summary(tb_writer, "train/tot_", params.batch_idx_train) + if params.use_fp16: + tb_writer.add_scalar( + "train/grad_scale", cur_grad_scale, params.batch_idx_train + ) + + if batch_idx % params.valid_interval == 0 and not params.print_diagnostics: + logging.info("Computing validation loss") + valid_info = compute_validation_loss( + params=params, + model=model, + sp=sp, + valid_dl=valid_dl, + world_size=world_size, + ) + model.train() + logging.info(f"Epoch {params.cur_epoch}, validation: {valid_info}") + logging.info( + f"Maximum memory allocated so far is {torch.cuda.max_memory_allocated()//1000000}MB" + ) + if tb_writer is not None: + valid_info.write_summary( + tb_writer, "train/valid_", params.batch_idx_train + ) + + loss_value = tot_loss["loss"] / tot_loss["frames"] + params.train_loss = loss_value + if params.train_loss < params.best_train_loss: + params.best_train_epoch = params.cur_epoch + params.best_train_loss = params.train_loss + + +def run(rank, world_size, args): + """ + Args: + rank: + It is a value between 0 and `world_size-1`, which is + passed automatically by `mp.spawn()` in :func:`main`. + The node with rank 0 is responsible for saving checkpoint. + world_size: + Number of GPUs for DDP training. + args: + The return value of get_parser().parse_args() + """ + params = get_params() + params.update(vars(args)) + + fix_random_seed(params.seed) + if world_size > 1: + setup_dist(rank, world_size, params.master_port) + + setup_logger(f"{params.exp_dir}/log/log-train") + logging.info("Training started") + + if args.tensorboard and rank == 0: + tb_writer = SummaryWriter(log_dir=f"{params.exp_dir}/tensorboard") + else: + tb_writer = None + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", rank) + logging.info(f"Device: {device}") + + sp = spm.SentencePieceProcessor() + sp.load(params.bpe_model) + + # is defined in local/train_bpe_model.py + params.blank_id = sp.piece_to_id("") + params.vocab_size = sp.get_piece_size() + + if not params.use_transducer: + params.ctc_loss_scale = 1.0 + + logging.info(params) + + logging.info("About to create model") + model = get_model(params) + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + assert params.save_every_n >= params.average_period + model_avg: Optional[nn.Module] = None + if rank == 0: + # model_avg is only used with rank 0 + model_avg = copy.deepcopy(model).to(torch.float64) + + assert params.start_epoch > 0, params.start_epoch + checkpoints = load_checkpoint_if_available( + params=params, model=model, model_avg=model_avg + ) + + model.to(device) + if world_size > 1: + logging.info("Using DDP") + model = DDP(model, device_ids=[rank], find_unused_parameters=True) + + optimizer = ScaledAdam( + get_parameter_groups_with_lrs(model, lr=params.base_lr, include_names=True), + lr=params.base_lr, # should have no effect + clipping_scale=2.0, + ) + + scheduler = Eden(optimizer, params.lr_batches, params.lr_hours) + + 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.inf_check: + register_inf_check_hooks(model) + + libriheavy = LibriHeavyAsrDataModule(args) + + train_cuts = libriheavy.train_cuts() + + def remove_short_and_long_utt(c: Cut): + # Keep only utterances with duration between 1 second and 20 seconds + # + # Caution: There is a reason to select 20.0 here. Please see + # ../local/display_manifest_statistics.py + # + # You should use ../local/display_manifest_statistics.py to get + # an utterance duration distribution for your dataset to select + # the threshold + if c.duration < 1.0 or c.duration > 20.0: + # logging.warning( + # f"Exclude cut with ID {c.id} from training. Duration: {c.duration}" + # ) + return False + + # In pruned RNN-T, we require that T >= S + # where T is the number of feature frames after subsampling + # and S is the number of tokens in the utterance + + # In ./zipformer.py, the conv module uses the following expression + # for subsampling + T = ((c.num_frames - 7) // 2 + 1) // 2 + tokens = sp.encode(c.supervisions[0].texts[0], out_type=str) + + if T < len(tokens): + logging.warning( + f"Exclude cut with ID {c.id} from training. " + f"Number of frames (before subsampling): {c.num_frames}. " + f"Number of frames (after subsampling): {T}. " + f"Text: {c.supervisions[0].text}. " + f"Tokens: {tokens}. " + f"Number of tokens: {len(tokens)}" + ) + return False + + return True + + train_cuts = train_cuts.filter(remove_short_and_long_utt) + + if params.start_batch > 0 and checkpoints and "sampler" in checkpoints: + # We only load the sampler's state dict when it loads a checkpoint + # saved in the middle of an epoch + sampler_state_dict = checkpoints["sampler"] + else: + sampler_state_dict = None + + train_dl = libriheavy.train_dataloaders( + train_cuts, sampler_state_dict=sampler_state_dict + ) + + valid_cuts = libriheavy.dev_cuts() + valid_dl = libriheavy.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): + 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() + LibriHeavyAsrDataModule.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() From 5532bb1683784a2788cbeade577f9d658b1b6991 Mon Sep 17 00:00:00 2001 From: marcoyang1998 Date: Wed, 19 Jul 2023 22:05:53 +0800 Subject: [PATCH 11/14] add files for decoding --- egs/libriheavy/ASR/zipformer/beam_search.py | 1 + egs/libriheavy/ASR/zipformer/decode.py | 855 ++++++++++++++++++++ 2 files changed, 856 insertions(+) create mode 120000 egs/libriheavy/ASR/zipformer/beam_search.py create mode 100644 egs/libriheavy/ASR/zipformer/decode.py diff --git a/egs/libriheavy/ASR/zipformer/beam_search.py b/egs/libriheavy/ASR/zipformer/beam_search.py new file mode 120000 index 000000000..e24eca39f --- /dev/null +++ b/egs/libriheavy/ASR/zipformer/beam_search.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless2/beam_search.py \ No newline at end of file diff --git a/egs/libriheavy/ASR/zipformer/decode.py b/egs/libriheavy/ASR/zipformer/decode.py new file mode 100644 index 000000000..089a2f823 --- /dev/null +++ b/egs/libriheavy/ASR/zipformer/decode.py @@ -0,0 +1,855 @@ +#!/usr/bin/env python3 +# +# Copyright 2021-2023 Xiaomi Corporation (Author: Fangjun Kuang, +# Zengwei Yao, +# Xiaoyu Yang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Usage: +(1) greedy search +./zipformer/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./zipformer/exp \ + --max-duration 600 \ + --decoding-method greedy_search + +(2) beam search (not recommended) +./zipformer/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./zipformer/exp \ + --max-duration 600 \ + --decoding-method beam_search \ + --beam-size 4 + +(3) modified beam search +./zipformer/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./zipformer/exp \ + --max-duration 600 \ + --decoding-method modified_beam_search \ + --beam-size 4 + +(4) fast beam search (one best) +./zipformer/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./zipformer/exp \ + --max-duration 600 \ + --decoding-method fast_beam_search \ + --beam 20.0 \ + --max-contexts 8 \ + --max-states 64 + +(5) fast beam search (nbest) +./zipformer/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./zipformer/exp \ + --max-duration 600 \ + --decoding-method fast_beam_search_nbest \ + --beam 20.0 \ + --max-contexts 8 \ + --max-states 64 \ + --num-paths 200 \ + --nbest-scale 0.5 + +(6) fast beam search (nbest oracle WER) +./zipformer/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./zipformer/exp \ + --max-duration 600 \ + --decoding-method fast_beam_search_nbest_oracle \ + --beam 20.0 \ + --max-contexts 8 \ + --max-states 64 \ + --num-paths 200 \ + --nbest-scale 0.5 + +(7) fast beam search (with LG) +./zipformer/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./zipformer/exp \ + --max-duration 600 \ + --decoding-method fast_beam_search_nbest_LG \ + --beam 20.0 \ + --max-contexts 8 \ + --max-states 64 +""" + + +import argparse +import logging +import math +import warnings +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 LibriHeavyAsrDataModule +from beam_search import ( + beam_search, + fast_beam_search_nbest, + fast_beam_search_nbest_LG, + fast_beam_search_nbest_oracle, + fast_beam_search_one_best, + greedy_search, + greedy_search_batch, + modified_beam_search, +) +from text_normalization import simple_normalization, upper_normalization +from train import add_model_arguments, get_model, get_params + +from icefall.checkpoint import ( + average_checkpoints, + average_checkpoints_with_averaged_model, + find_checkpoints, + load_checkpoint, +) +from icefall.lexicon import Lexicon +from icefall.utils import ( + AttributeDict, + make_pad_mask, + setup_logger, + store_transcripts, + str2bool, + write_error_stats, +) + +LOG_EPS = math.log(1e-10) + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--epoch", + type=int, + default=30, + help="""It specifies the checkpoint to use for decoding. + Note: Epoch counts from 1. + You can specify --avg to use more checkpoints for model averaging.""", + ) + + parser.add_argument( + "--iter", + type=int, + default=0, + help="""If positive, --epoch is ignored and it + will use the checkpoint exp_dir/checkpoint-iter.pt. + You can specify --avg to use more checkpoints for model averaging. + """, + ) + + parser.add_argument( + "--avg", + type=int, + default=15, + help="Number of checkpoints to average. Automatically select " + "consecutive checkpoints before the checkpoint specified by " + "'--epoch' and '--iter'", + ) + + parser.add_argument( + "--use-averaged-model", + type=str2bool, + default=True, + help="Whether to load averaged model. Currently it only supports " + "using --epoch. If True, it would decode with the averaged model " + "over the epoch range from `epoch-avg` (excluded) to `epoch`." + "Actually only the models with epoch number of `epoch-avg` and " + "`epoch` are loaded for averaging. ", + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="zipformer/exp", + help="The experiment dir", + ) + + parser.add_argument( + "--bpe-model", + type=str, + default="data/lang_bpe_500/bpe.model", + help="Path to the BPE model", + ) + + 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 integer indicating how many candidates we will keep for each + frame. Used only when --decoding-method is beam_search or + modified_beam_search.""", + ) + + parser.add_argument( + "--beam", + type=float, + default=20.0, + help="""A floating point value to calculate the cutoff score during beam + search (i.e., `cutoff = max-score - beam`), which is the same as the + `beam` in Kaldi. + Used only when --decoding-method is fast_beam_search, + fast_beam_search_nbest, fast_beam_search_nbest_LG, + and fast_beam_search_nbest_oracle + """, + ) + + parser.add_argument( + "--ngram-lm-scale", + type=float, + default=0.01, + help=""" + Used only when --decoding_method is fast_beam_search_nbest_LG. + It specifies the scale for n-gram LM scores. + """, + ) + + parser.add_argument( + "--max-contexts", + type=int, + default=8, + help="""Used only when --decoding-method is + fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG, + and fast_beam_search_nbest_oracle""", + ) + + parser.add_argument( + "--max-states", + type=int, + default=64, + help="""Used only when --decoding-method is + fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG, + and fast_beam_search_nbest_oracle""", + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; " "2 means tri-gram", + ) + parser.add_argument( + "--max-sym-per-frame", + type=int, + default=1, + help="""Maximum number of symbols per frame. + Used only when --decoding_method is greedy_search""", + ) + + parser.add_argument( + "--num-paths", + type=int, + default=200, + help="""Number of paths for nbest decoding. + Used only when the decoding method is fast_beam_search_nbest, + fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""", + ) + + parser.add_argument( + "--nbest-scale", + type=float, + default=0.5, + help="""Scale applied to lattice scores when computing nbest paths. + Used only when the decoding method is fast_beam_search_nbest, + fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""", + ) + + parser.add_argument( + "--post-normalization", + type=str2bool, + default=False, + help="""Upper case and remove all chars except ' and - + """, + ) + + add_model_arguments(parser) + + return parser + + +def decode_one_batch( + params: AttributeDict, + model: nn.Module, + sp: spm.SentencePieceProcessor, + batch: dict, + word_table: Optional[k2.SymbolTable] = None, + decoding_graph: Optional[k2.Fsa] = None, +) -> Dict[str, List[List[str]]]: + """Decode one batch and return the result in a dict. The dict has the + following format: + + - key: It indicates the setting used for decoding. For example, + if greedy_search is used, it would be "greedy_search" + If beam search with a beam size of 7 is used, it would be + "beam_7" + - value: It contains the decoding result. `len(value)` equals to + batch size. `value[i]` is the decoding result for the i-th + utterance in the given batch. + Args: + params: + It's the return value of :func:`get_params`. + model: + The neural model. + sp: + The BPE model. + batch: + It is the return value from iterating + `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation + for the format of the `batch`. + word_table: + The word symbol table. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used + only when --decoding_method is fast_beam_search, fast_beam_search_nbest, + fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG. + Returns: + Return the decoding result. See above description for the format of + the returned dict. + """ + device = next(model.parameters()).device + feature = batch["inputs"] + assert feature.ndim == 3 + + feature = feature.to(device) + # at entry, feature is (N, T, C) + + supervisions = batch["supervisions"] + feature_lens = supervisions["num_frames"].to(device) + + if params.causal: + # this seems to cause insertions at the end of the utterance if used with zipformer. + pad_len = 30 + feature_lens += pad_len + feature = torch.nn.functional.pad( + feature, + pad=(0, 0, 0, pad_len), + value=LOG_EPS, + ) + + encoder_out, encoder_out_lens = model.forward_encoder(feature, feature_lens) + + hyps = [] + + if params.decoding_method == "fast_beam_search": + hyp_tokens = fast_beam_search_one_best( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) + elif params.decoding_method == "fast_beam_search_nbest_LG": + hyp_tokens = fast_beam_search_nbest_LG( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + num_paths=params.num_paths, + nbest_scale=params.nbest_scale, + ) + for hyp in hyp_tokens: + hyps.append([word_table[i] for i in hyp]) + elif params.decoding_method == "fast_beam_search_nbest": + hyp_tokens = fast_beam_search_nbest( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + num_paths=params.num_paths, + nbest_scale=params.nbest_scale, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) + elif params.decoding_method == "fast_beam_search_nbest_oracle": + hyp_tokens = fast_beam_search_nbest_oracle( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + num_paths=params.num_paths, + ref_texts=sp.encode(supervisions["text"]), + nbest_scale=params.nbest_scale, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) + elif params.decoding_method == "greedy_search" and params.max_sym_per_frame == 1: + hyp_tokens = greedy_search_batch( + model=model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) + elif params.decoding_method == "modified_beam_search": + hyp_tokens = modified_beam_search( + model=model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam_size, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) + else: + batch_size = encoder_out.size(0) + + for i in range(batch_size): + # fmt: off + encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]] + # fmt: on + if params.decoding_method == "greedy_search": + hyp = greedy_search( + model=model, + encoder_out=encoder_out_i, + max_sym_per_frame=params.max_sym_per_frame, + ) + elif params.decoding_method == "beam_search": + hyp = beam_search( + model=model, + encoder_out=encoder_out_i, + beam=params.beam_size, + ) + else: + raise ValueError( + f"Unsupported decoding method: {params.decoding_method}" + ) + hyps.append(sp.decode(hyp).split()) + + if params.decoding_method == "greedy_search": + return {"greedy_search": hyps} + elif "fast_beam_search" in params.decoding_method: + key = f"beam_{params.beam}_" + key += f"max_contexts_{params.max_contexts}_" + key += f"max_states_{params.max_states}" + if "nbest" in params.decoding_method: + key += f"_num_paths_{params.num_paths}_" + key += f"nbest_scale_{params.nbest_scale}" + if "LG" in params.decoding_method: + key += f"_ngram_lm_scale_{params.ngram_lm_scale}" + + return {key: hyps} + else: + return {f"beam_size_{params.beam_size}": hyps} + + +def decode_dataset( + dl: torch.utils.data.DataLoader, + params: AttributeDict, + model: nn.Module, + sp: spm.SentencePieceProcessor, + word_table: Optional[k2.SymbolTable] = None, + decoding_graph: Optional[k2.Fsa] = None, +) -> Dict[str, List[Tuple[str, List[str], List[str]]]]: + """Decode dataset. + + Args: + dl: + PyTorch's dataloader containing the dataset to decode. + params: + It is returned by :func:`get_params`. + model: + The neural model. + sp: + The BPE model. + word_table: + The word symbol table. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used + only when --decoding_method is fast_beam_search, fast_beam_search_nbest, + fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG. + Returns: + Return a dict, whose key may be "greedy_search" if greedy search + is used, or it may be "beam_7" if beam size of 7 is used. + Its value is a list of tuples. Each tuple contains two elements: + The first is the reference transcript, and the second is the + predicted result. + """ + num_cuts = 0 + + try: + num_batches = len(dl) + except TypeError: + num_batches = "?" + + if params.decoding_method == "greedy_search": + log_interval = 50 + else: + log_interval = 20 + + results = defaultdict(list) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + for batch_idx, batch in enumerate(dl): + texts = batch["supervisions"]["text"] + texts = [ + simple_normalization(t) for t in texts + ] # Do a simple normalization, as this is done during training + + 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[str, List[str], List[str]]]], +): + test_set_wers = dict() + for key, results in results_dict.items(): + recog_path = ( + params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt" + ) + results = sorted(results) + store_transcripts(filename=recog_path, texts=results) + logging.info(f"The transcripts are stored in {recog_path}") + + # The following prints out WERs, per-word error statistics and aligned + # ref/hyp pairs. + errs_filename = ( + params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt" + ) + with open(errs_filename, "w") as f: + wer = write_error_stats( + f, f"{test_set_name}-{key}", results, enable_log=True + ) + test_set_wers[key] = wer + + logging.info("Wrote detailed error stats to {}".format(errs_filename)) + + test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1]) + errs_info = ( + params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt" + ) + with open(errs_info, "w") as f: + print("settings\tWER", file=f) + for key, val in test_set_wers: + print("{}\t{}".format(key, val), file=f) + + s = "\nFor {}, WER of different settings are:\n".format(test_set_name) + note = "\tbest for {}".format(test_set_name) + for key, val in test_set_wers: + s += "{}\t{}{}\n".format(key, val, note) + note = "" + logging.info(s) + + +@torch.no_grad() +def main(): + parser = get_parser() + LibriHeavyAsrDataModule.add_arguments(parser) + args = parser.parse_args() + args.exp_dir = Path(args.exp_dir) + + params = get_params() + params.update(vars(args)) + + assert params.decoding_method in ( + "greedy_search", + "beam_search", + "fast_beam_search", + "fast_beam_search_nbest", + "fast_beam_search_nbest_LG", + "fast_beam_search_nbest_oracle", + "modified_beam_search", + ) + params.res_dir = params.exp_dir / params.decoding_method + + if params.iter > 0: + params.suffix = f"iter-{params.iter}-avg-{params.avg}" + else: + params.suffix = f"epoch-{params.epoch}-avg-{params.avg}" + + if params.causal: + assert ( + "," not in params.chunk_size + ), "chunk_size should be one value in decoding." + assert ( + "," not in params.left_context_frames + ), "left_context_frames should be one value in decoding." + params.suffix += f"-chunk-{params.chunk_size}" + params.suffix += f"-left-context-{params.left_context_frames}" + + if "fast_beam_search" in params.decoding_method: + params.suffix += f"-beam-{params.beam}" + params.suffix += f"-max-contexts-{params.max_contexts}" + params.suffix += f"-max-states-{params.max_states}" + if "nbest" in params.decoding_method: + params.suffix += f"-nbest-scale-{params.nbest_scale}" + params.suffix += f"-num-paths-{params.num_paths}" + if "LG" in params.decoding_method: + params.suffix += f"-ngram-lm-scale-{params.ngram_lm_scale}" + elif "beam_search" in params.decoding_method: + params.suffix += f"-{params.decoding_method}-beam-size-{params.beam_size}" + else: + params.suffix += f"-context-{params.context_size}" + params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}" + + if params.use_averaged_model: + params.suffix += "-use-averaged-model" + + setup_logger(f"{params.res_dir}/log-decode-{params.suffix}") + logging.info("Decoding started") + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"Device: {device}") + + sp = spm.SentencePieceProcessor() + sp.load(params.bpe_model) + + # and are defined in local/train_bpe_model.py + params.blank_id = sp.piece_to_id("") + params.unk_id = sp.piece_to_id("") + params.vocab_size = sp.get_piece_size() + + logging.info(params) + + logging.info("About to create model") + model = get_model(params) + + if not params.use_averaged_model: + if params.iter > 0: + filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[ + : params.avg + ] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + elif params.avg == 1: + load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) + else: + start = params.epoch - params.avg + 1 + filenames = [] + for i in range(start, params.epoch + 1): + if i >= 1: + filenames.append(f"{params.exp_dir}/epoch-{i}.pt") + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + else: + if params.iter > 0: + filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[ + : params.avg + 1 + ] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg + 1: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + filename_start = filenames[-1] + filename_end = filenames[0] + logging.info( + "Calculating the averaged model over iteration checkpoints" + f" from {filename_start} (excluded) to {filename_end}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + else: + assert params.avg > 0, params.avg + start = params.epoch - params.avg + assert start >= 1, start + filename_start = f"{params.exp_dir}/epoch-{start}.pt" + filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt" + logging.info( + f"Calculating the averaged model over epoch range from " + f"{start} (excluded) to {params.epoch}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + + model.to(device) + model.eval() + + if "fast_beam_search" in params.decoding_method: + if params.decoding_method == "fast_beam_search_nbest_LG": + lexicon = Lexicon(params.lang_dir) + word_table = lexicon.word_table + lg_filename = params.lang_dir / "LG.pt" + logging.info(f"Loading {lg_filename}") + decoding_graph = k2.Fsa.from_dict( + torch.load(lg_filename, map_location=device) + ) + decoding_graph.scores *= params.ngram_lm_scale + else: + word_table = None + decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device) + else: + decoding_graph = None + word_table = None + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + # we need cut ids to display recognition results. + args.return_cuts = True + libriheavy = LibriHeavyAsrDataModule(args) + + test_clean_cuts = libriheavy.test_clean_cuts() + test_other_cuts = libriheavy.test_other_cuts() + + test_clean_dl = libriheavy.test_dataloaders(test_clean_cuts) + test_other_dl = libriheavy.test_dataloaders(test_other_cuts) + + test_sets = ["libriheavy-test-clean", "libriheavy-test-other"] + test_dl = [test_clean_dl, test_other_dl] + + for test_set, test_dl in zip(test_sets, test_dl): + results_dict = decode_dataset( + dl=test_dl, + params=params, + model=model, + sp=sp, + word_table=word_table, + decoding_graph=decoding_graph, + ) + + save_results( + params=params, + test_set_name=test_set, + results_dict=results_dict, + ) + + if params.post_normalization: + params.suffix += "-post-normalization" + + new_res = {} + for k in results_dict: + new_ans = [] + for item in results_dict[k]: + id, hyp, ref = item + hyp = [upper_normalization(w.upper()) for w in hyp] + hyp = [w for w in hyp if w != ""] + ref = [upper_normalization(w.upper()) for w in ref] + ref = [w for w in ref if w != ""] + new_ans.append((id, hyp, ref)) + new_res[k] = new_ans + + save_results( + params=params, + test_set_name=test_set, + results_dict=new_res, + ) + + logging.info("Done!") + + +if __name__ == "__main__": + main() From 754ac00509cdbac4a9a5d5615571c7d8dd132a4f Mon Sep 17 00:00:00 2001 From: marcoyang1998 Date: Thu, 20 Jul 2023 15:50:50 +0800 Subject: [PATCH 12/14] add more normalizations such as number/year to words; fix a few bugs when feeding input to WER computation --- egs/libriheavy/ASR/zipformer/decode.py | 36 +++-- .../ASR/zipformer/text_normalization.py | 135 +++++++++++++++++- 2 files changed, 156 insertions(+), 15 deletions(-) diff --git a/egs/libriheavy/ASR/zipformer/decode.py b/egs/libriheavy/ASR/zipformer/decode.py index 089a2f823..a4e28cf5d 100644 --- a/egs/libriheavy/ASR/zipformer/decode.py +++ b/egs/libriheavy/ASR/zipformer/decode.py @@ -118,7 +118,12 @@ from beam_search import ( greedy_search_batch, modified_beam_search, ) -from text_normalization import simple_normalization, upper_normalization +from lhotse.cut import Cut +from text_normalization import ( + simple_normalization, + upper_normalization, + word_normalization, +) from train import add_model_arguments, get_model, get_params from icefall.checkpoint import ( @@ -802,14 +807,29 @@ def main(): args.return_cuts = True libriheavy = LibriHeavyAsrDataModule(args) + def add_texts(c: Cut): + text = c.supervisions[0].text + c.supervisions[0].texts = [text] + return c + test_clean_cuts = libriheavy.test_clean_cuts() test_other_cuts = libriheavy.test_other_cuts() + ls_test_clean_cuts = libriheavy.librispeech_test_clean_cuts() + ls_test_other_cuts = libriheavy.librispeech_test_other_cuts() + + ls_test_clean_cuts = ls_test_clean_cuts.map(add_texts) + ls_test_other_cuts = ls_test_other_cuts.map(add_texts) test_clean_dl = libriheavy.test_dataloaders(test_clean_cuts) test_other_dl = libriheavy.test_dataloaders(test_other_cuts) + ls_test_clean_dl = libriheavy.test_dataloaders(ls_test_clean_cuts) + ls_test_other_dl = libriheavy.test_dataloaders(ls_test_other_cuts) - test_sets = ["libriheavy-test-clean", "libriheavy-test-other"] - test_dl = [test_clean_dl, test_other_dl] + # test_sets = ["libriheavy-test-clean", "libriheavy-test-other", "librispeech-test-clean", "librispeech-test-other"] + # test_dl = [test_clean_dl, test_other_dl, ls_test_clean_dl, ls_test_other_dl] + + test_sets = ["librispeech-test-clean", "librispeech-test-other"] + test_dl = [ls_test_clean_dl, ls_test_other_dl] for test_set, test_dl in zip(test_sets, test_dl): results_dict = decode_dataset( @@ -834,12 +854,12 @@ def main(): for k in results_dict: new_ans = [] for item in results_dict[k]: - id, hyp, ref = item - hyp = [upper_normalization(w.upper()) for w in hyp] + id, ref, hyp = item + hyp = upper_normalization(" ".join(hyp)).split() + hyp = [word_normalization(w) for w in hyp] + hyp = " ".join(hyp).split() hyp = [w for w in hyp if w != ""] - ref = [upper_normalization(w.upper()) for w in ref] - ref = [w for w in ref if w != ""] - new_ans.append((id, hyp, ref)) + new_ans.append((id, ref, hyp)) new_res[k] = new_ans save_results( diff --git a/egs/libriheavy/ASR/zipformer/text_normalization.py b/egs/libriheavy/ASR/zipformer/text_normalization.py index bbde95be0..b27f0ecfc 100644 --- a/egs/libriheavy/ASR/zipformer/text_normalization.py +++ b/egs/libriheavy/ASR/zipformer/text_normalization.py @@ -1,5 +1,101 @@ import re +words = { + 0: "zero", + 1: "one", + 2: "two", + 3: "three", + 4: "four", + 5: "five", + 6: "six", + 7: "seven", + 8: "eight", + 9: "nine", + 10: "ten", + 11: "eleven", + 12: "twelve", + 13: "thirteen", + 14: "fourteen", + 15: "fifteen", + 16: "sixteen", + 17: "seventeen", + 18: "eighteen", + 19: "nineteen", + 20: "twenty", + 30: "thirty", + 40: "forty", + 50: "fifty", + 60: "sixty", + 70: "seventy", + 80: "eighty", + 90: "ninety", +} +ordinal_nums = [ + "zeroth", + "first", + "second", + "third", + "fourth", + "fifth", + "sixth", + "seventh", + "eighth", + "ninth", + "tenth", + "eleventh", + "twelfth", + "thirteenth", + "fourteenth", + "fifteenth", + "sixteenth", + "seventeenth", + "eighteenth", + "nineteenth", + "twentieth", +] + +num_ordinal_dict = {num: ordinal_nums[num] for num in range(21)} + + +def year_to_words(num: int): + assert isinstance(num, int), num + # check if a num is representing a year + if num > 1500 and num < 2000: + return words[num // 100] + " " + num_to_words(num % 100) + elif num == 2000: + return "TWO THOUSAND" + elif num > 2000: + return "TWO THOUSAND AND " + num_to_words(num % 100) + else: + return num_to_words(num) + + +def num_to_words(num: int): + # Return the English words of a integer number + + # If this is a year number + if num > 1500 and num < 2030: + return year_to_words(num) + + if num < 20: + return words[num] + if num < 100: + if num % 10 == 0: + return words[num // 10 * 10] + else: + return words[num // 10 * 10] + " " + words[num % 10] + if num < 1000: + return words[num // 100] + " hundred and " + num_to_words(num % 100) + if num < 1000000: + return num_to_words(num // 1000) + " thousand " + num_to_words(num % 1000) + return num + + +def num_to_ordinal_word(num: int): + + return num_ordinal_dict.get(num, num_to_words(num)).upper() + + def replace_full_width_symbol(s: str) -> str: # replace full-width symbol with theri half width counterpart s = s.replace("“", '"') @@ -10,18 +106,43 @@ def replace_full_width_symbol(s: str) -> str: return s -def upper_ref_text(text: str) -> str: +def upper_normalization(text: str) -> str: text = replace_full_width_symbol(text) - text = text.upper() # upper case all characters - + text = text.upper() # upper case all characters + # Only keep all alpha-numeric characters, hypen and apostrophe - text = text.replace("--", " ") - text = re.sub("[^a-zA-Z0-9\s\'-]+", "", text) + text = text.replace("-", " ") + text = re.sub("[^a-zA-Z0-9\s']+", "", text) return text + +def word_normalization(word: str) -> str: + if word == "MRS": + return "MISSUS" + if word == "MR": + return "MISTER" + if word == "ST": + return "SAINT" + if word == "ECT": + return "ET CETERA" + if word.isnumeric(): + word = num_to_words(int(word)) + return word.upper() + if word[-2:] == "TH" and word[0].isnumeric(): # e.g 9TH, 6TH + return num_to_ordinal_word(int(word[:-2])).upper() + + return word + + def simple_normalization(text: str) -> str: text = replace_full_width_symbol(text) text = text.replace("--", " ") - + return text - \ No newline at end of file + + +if __name__ == "__main__": + + s = str(1830) + out = word_normalization(s) + print(s, out) From b8540ac3c0daa0799e744ed565995953447bde0f Mon Sep 17 00:00:00 2001 From: marcoyang1998 Date: Thu, 20 Jul 2023 15:51:34 +0800 Subject: [PATCH 13/14] minor fix --- egs/libriheavy/ASR/zipformer/asr_datamodule.py | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/egs/libriheavy/ASR/zipformer/asr_datamodule.py b/egs/libriheavy/ASR/zipformer/asr_datamodule.py index 9d9ecc63c..0f029fb3a 100644 --- a/egs/libriheavy/ASR/zipformer/asr_datamodule.py +++ b/egs/libriheavy/ASR/zipformer/asr_datamodule.py @@ -45,11 +45,7 @@ from lhotse.dataset.input_strategies import ( PrecomputedFeatures, ) from lhotse.utils import fix_random_seed, ifnone -from text_normalization import ( - ref_text_normalization, - replace_full_width_symbol, - simple_normalization, -) +from text_normalization import replace_full_width_symbol, simple_normalization from torch.utils.data.dataloader import DataLoader, default_collate from icefall.utils import str2bool From cc168d104128348e9e24835c856c1bd946638e71 Mon Sep 17 00:00:00 2001 From: marcoyang1998 Date: Wed, 9 Aug 2023 12:11:43 +0800 Subject: [PATCH 14/14] update the pipeline --- .../ASR/local/compute_fbank_libriheavy.py | 2 +- .../ASR/local/prepare_validation_sets.py | 23 +++++++-- egs/libriheavy/ASR/prepare.sh | 47 +++++++++++++------ 3 files changed, 53 insertions(+), 19 deletions(-) diff --git a/egs/libriheavy/ASR/local/compute_fbank_libriheavy.py b/egs/libriheavy/ASR/local/compute_fbank_libriheavy.py index 05ade450c..73c978b98 100755 --- a/egs/libriheavy/ASR/local/compute_fbank_libriheavy.py +++ b/egs/libriheavy/ASR/local/compute_fbank_libriheavy.py @@ -188,7 +188,7 @@ def compute_fbank_libriheavy_splits(args): extractor = KaldifeatFbank(KaldifeatFbankConfig(device=device)) logging.info(f"device: {device}") - prefix = "librilight" + prefix = "libriheavy" num_digits = 8 # num_digits is fixed by lhotse split-lazy for i in range(start, stop): diff --git a/egs/libriheavy/ASR/local/prepare_validation_sets.py b/egs/libriheavy/ASR/local/prepare_validation_sets.py index 23dd4bbff..7ad4e6d81 100755 --- a/egs/libriheavy/ASR/local/prepare_validation_sets.py +++ b/egs/libriheavy/ASR/local/prepare_validation_sets.py @@ -36,7 +36,13 @@ def get_args(): parser = argparse.ArgumentParser() parser.add_argument( - "--manifest", type=str, help="The original manifest coming from" + "--in-manifest", type=str, help="The original manifest coming from" + ) + + parser.add_argument( + "--out-manifest", + type=str, + help="Where to store the manifest after filtering out the test/dev sets", ) return parser.parse_args() @@ -44,8 +50,8 @@ def get_args(): def main(args): - logging.info(f"Loading manifest {args.manifest}") - cuts = load_manifest_lazy(args.manifest) + logging.info(f"Loading manifest {args.in_manifest}") + cuts = load_manifest_lazy(args.in_manifest) all_test_sets = [ "dev", @@ -53,19 +59,28 @@ def main(args): "test-other", ] + all_books = [] for test_set in all_test_sets: logging.info(f"Processing test set: {test_set}") with open(f"data/manifests/{test_set}.txt", "r") as f: books = f.read().split("\n") + all_books += books + out_name = f"data/manifests/libriheavy_cuts_{test_set}.jsonl.gz" + if os.path.exists(out_name): + continue # find the cuts belonging to the given books selected_cuts = cuts.filter(lambda c: c.text_path.split("/")[-2] in books) selected_cuts.describe() - out_name = f"data/manifests/libriheavy_cuts_{test_set}.jsonl.gz" logging.info(f"Saving the cuts contained in the book list to {out_name}") selected_cuts.to_file(out_name) + filtered_cuts = cuts.filter(lambda c: c.text_path.split("/")[-2] not in all_books) + logging.info(f"Saving the filtered manifest to {args.out_manifest}.") + filtered_cuts.to_file(args.out_manifest) + logging.info("Done") + if __name__ == "__main__": formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" diff --git a/egs/libriheavy/ASR/prepare.sh b/egs/libriheavy/ASR/prepare.sh index 0aa6c91ae..dc00395cb 100755 --- a/egs/libriheavy/ASR/prepare.sh +++ b/egs/libriheavy/ASR/prepare.sh @@ -12,6 +12,8 @@ stop_stage=100 start=0 stop=-1 num_per_split=2000 +split_per_job=20 +char_coverage=0.99 . shared/parse_options.sh || exit 1 @@ -19,7 +21,7 @@ num_per_split=2000 # It will generate data/lang_bpe_xxx, # data/lang_bpe_yyy if the array contains xxx, yyy vocab_sizes=( - 1000 + 750 ) mkdir -p data @@ -35,20 +37,20 @@ fbank_dir=data/fbank mkdir -p $manifest_dir -subset="large" +subset="medium" if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then log "Stage 1: Split libri-heavy ${subset}" if [ $subset == "large" ]; then num_per_split=8000 - log "Change num_per_split to ${num_per_split} 8000 for large" + log "Change num_per_split to ${num_per_split} for large" fi split_dir=$fbank_dir/libriheavy_${subset}_split mkdir -p $split_dir if [ ! -e $split_dir/.split_completed ]; then - lhotse split-lazy $manifest_dir/librilight_cuts_${subset}_raw.jsonl.gz $split_dir $num_per_split + lhotse split-lazy $manifest_dir/libriheavy_cuts_${subset}_raw.jsonl.gz $split_dir $num_per_split touch $split_dir/.split_completed fi fi @@ -56,11 +58,18 @@ fi if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then log "Stage 2: Compute fbank for Libri-heavy ${subset}" mkdir -p $fbank_dir - num_splits=$(find $fbank_dir/libriheavy_${subset}_split -name "librilight_cuts_${subset}_raw.*.jsonl.gz" | wc -l) + num_splits=$(find $fbank_dir/libriheavy_${subset}_split -name "libriheavy_cuts_${subset}_raw.*.jsonl.gz" | wc -l) + if [ $subset == "large" ]; then + split_per_job=210 + log "Change split_per_job to ${split_per_job} for large" + elif [ $subset == "medium" ]; then + split_per_job=100 + log "Change split_per_job to ${split_per_job} for medium" + fi if [ ! -e $fbank_dir/.libriheavy.${subset}.done ]; then for i in $(seq 0 1 7); do - start=$(( i * 200 )) - end=$(( (i+1) * 200 )) + start=$(( i * $split_per_job )) + end=$(( (i+1) * $split_per_job )) ./local/compute_fbank_libriheavy.py \ --dataset ${subset} \ --fbank-dir $fbank_dir \ @@ -76,21 +85,29 @@ fi if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then log "Stage 3: Combine features for ${subset}" - if [ ! -f $fbank_dir/librilight_cuts_${subset}.jsonl.gz ]; then - pieces=$(find $fbank_dir/libriheavy_${subset}_split -name "librilight_cuts_${subset}.*.jsonl.gz") - lhotse combine $pieces $fbank_dir/librilight_cuts_${subset}.jsonl.gz + if [ ! -f $fbank_dir/libriheavy_cuts_${subset}.jsonl.gz ]; then + pieces=$(find $fbank_dir/libriheavy_${subset}_split -name "libriheavy_cuts_${subset}.*.jsonl.gz") + lhotse combine $pieces $fbank_dir/libriheavy_cuts_${subset}.jsonl.gz fi fi if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then - log "Stage 4: Prepare BPE model" + log "Stage 4: Prepare the validation&test sets" + + ./local/prepare_validation_sets.py \ + --in-manifest $fbank_dir/libriheavy_cuts_medium.jsonl.gz \ + --out-manifest $fbank_dir/libriheavy_cuts_medium_filtered.jsonl.gz +fi + +if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then + log "Stage 5: Prepare BPE model" tmp_dir=data/tmp mkdir -p $tmp_dir if [ ! -f $tmp_dir/transcript_words.txt ]; then for part in "small" "medium" "large"; do - gunzip -c $manifest_dir/librilight_cuts_${part}_raw.jsonl.gz | + gunzip -c $manifest_dir/libriheavy_cuts_${part}_raw.jsonl.gz | jq '.supervisions[].custom.texts[]' | sed 's/" //' | sed 's/\(.*\)"/\1/' > $tmp_dir/transcript_words_${part}.txt done cat $tmp_dir/transcript_words_small.txt $tmp_dir/transcript_words_medium.txt $tmp_dir/transcript_words_large.txt > $tmp_dir/transcript_words.txt @@ -125,17 +142,19 @@ if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then fi for vocab_size in ${vocab_sizes[@]}; do - lang_dir=data/lang_bpe_${vocab_size} + lang_dir=data/lang_bpe_${vocab_size}_fallback_coverage_${char_coverage} mkdir -p $lang_dir cp $tmp_dir/words.txt $lang_dir/words.txt pushd $lang_dir ln -s ../$tmp_dir/transcript_words.txt transcript_words.txt popd - + if [ ! -f $lang_dir/bpe.model ]; then ./local/train_bpe_model.py \ --lang-dir $lang_dir \ --vocab-size $vocab_size \ + --byte-fallback True \ + --character-coverage $char_coverage \ --transcript $tmp_dir/transcript_words_medium.txt fi