mirror of
https://github.com/k2-fsa/icefall.git
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Add multidataset (#1010)
* Add Common Voice for multidataset * Add prepare_multidataset.sh * Add dataset mixing * Update prepare_multidataset.sh * Update prepare_giga_speech.sh * update comments * Add split and shuffle mechanism * Add multi-dataset train * Fix for deleting * Fix for modifying * Add comments * Change type for perturb_speed * Fix for style check * Small fix * Add filter * Remove warning
This commit is contained in:
Yifan Yang
GitHub
parent
57d6482a79
commit
d67a49afe4
@ -35,7 +35,7 @@ from filter_cuts import filter_cuts
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from lhotse import CutSet, Fbank, FbankConfig, LilcomChunkyWriter
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from lhotse.recipes.utils import read_manifests_if_cached
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from icefall.utils import get_executor
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from icefall.utils import get_executor, str2bool
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# Torch's multithreaded behavior needs to be disabled or
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# it wastes a lot of CPU and slow things down.
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@ -61,12 +61,20 @@ def get_args():
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help="""Dataset parts to compute fbank. If None, we will use all""",
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)
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parser.add_argument(
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"--perturb-speed",
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type=str2bool,
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default=True,
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help="""Perturb speed with factor 0.9 and 1.1 on train subset.""",
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)
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return parser.parse_args()
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def compute_fbank_librispeech(
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bpe_model: Optional[str] = None,
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dataset: Optional[str] = None,
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perturb_speed: Optional[bool] = True,
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):
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src_dir = Path("data/manifests")
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output_dir = Path("data/fbank")
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@ -125,8 +133,12 @@ def compute_fbank_librispeech(
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if "train" in partition:
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if bpe_model:
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cut_set = filter_cuts(cut_set, sp)
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if perturb_speed:
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logging.info(f"Doing speed perturb")
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cut_set = (
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cut_set + cut_set.perturb_speed(0.9) + cut_set.perturb_speed(1.1)
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cut_set
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+ cut_set.perturb_speed(0.9)
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+ cut_set.perturb_speed(1.1)
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)
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cut_set = cut_set.compute_and_store_features(
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extractor=extractor,
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@ -145,4 +157,8 @@ if __name__ == "__main__":
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logging.basicConfig(format=formatter, level=logging.INFO)
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args = get_args()
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logging.info(vars(args))
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compute_fbank_librispeech(bpe_model=args.bpe_model, dataset=args.dataset)
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compute_fbank_librispeech(
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bpe_model=args.bpe_model,
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dataset=args.dataset,
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perturb_speed=args.perturb_speed,
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)
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117
egs/librispeech/ASR/prepare_common_voice.sh
Executable file
117
egs/librispeech/ASR/prepare_common_voice.sh
Executable file
@ -0,0 +1,117 @@
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#!/usr/bin/env bash
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set -eou pipefail
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nj=16
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stage=-1
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stop_stage=100
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# Split data/${lang}set to this number of pieces
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# This is to avoid OOM during feature extraction.
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num_splits=1000
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# We assume dl_dir (download dir) contains the following
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# directories and files. If not, they will be downloaded
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# by this script automatically.
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#
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# - $dl_dir/$release/$lang
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# This directory contains the following files downloaded from
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# https://mozilla-common-voice-datasets.s3.dualstack.us-west-2.amazonaws.com/${release}/${release}-${lang}.tar.gz
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#
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# - clips
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# - dev.tsv
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# - invalidated.tsv
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# - other.tsv
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# - reported.tsv
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# - test.tsv
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# - train.tsv
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# - validated.tsv
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dl_dir=$PWD/download
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release=cv-corpus-13.0-2023-03-09
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lang=en
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. shared/parse_options.sh || exit 1
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# All files generated by this script are saved in "data/${lang}".
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# You can safely remove "data/${lang}" and rerun this script to regenerate it.
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mkdir -p data/${lang}
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log() {
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# This function is from espnet
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local fname=${BASH_SOURCE[1]##*/}
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echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
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}
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log "dl_dir: $dl_dir"
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if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
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log "Stage 0: Download data"
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# If you have pre-downloaded it to /path/to/$release,
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# you can create a symlink
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#
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# ln -sfv /path/to/$release $dl_dir/$release
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#
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if [ ! -d $dl_dir/$release/$lang/clips ]; then
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lhotse download commonvoice --languages $lang --release $release $dl_dir
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fi
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fi
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if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
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log "Stage 1: Prepare CommonVoice manifest"
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# We assume that you have downloaded the CommonVoice corpus
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# to $dl_dir/$release
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mkdir -p data/${lang}/manifests
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if [ ! -e data/${lang}/manifests/.cv-${lang}.done ]; then
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lhotse prepare commonvoice --language $lang -j $nj $dl_dir/$release data/${lang}/manifests
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touch data/${lang}/manifests/.cv-${lang}.done
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fi
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fi
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if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
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log "Stage 2: Preprocess CommonVoice manifest"
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if [ ! -e data/${lang}/fbank/.preprocess_complete ]; then
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./local/preprocess_commonvoice.py --language $lang
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touch data/${lang}/fbank/.preprocess_complete
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fi
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fi
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if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
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log "Stage 3: Compute fbank for dev and test subsets of CommonVoice"
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mkdir -p data/${lang}/fbank
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if [ ! -e data/${lang}/fbank/.cv-${lang}_dev_test.done ]; then
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./local/compute_fbank_commonvoice_dev_test.py --language $lang
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touch data/${lang}/fbank/.cv-${lang}_dev_test.done
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fi
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fi
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if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
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log "Stage 4: Split train subset into ${num_splits} pieces"
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split_dir=data/${lang}/fbank/cv-${lang}_train_split_${num_splits}
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if [ ! -e $split_dir/.cv-${lang}_train_split.done ]; then
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lhotse split $num_splits ./data/${lang}/fbank/cv-${lang}_cuts_train_raw.jsonl.gz $split_dir
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touch $split_dir/.cv-${lang}_train_split.done
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fi
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fi
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if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
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log "Stage 5: Compute features for train subset of CommonVoice"
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if [ ! -e data/${lang}/fbank/.cv-${lang}_train.done ]; then
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./local/compute_fbank_commonvoice_splits.py \
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--num-workers $nj \
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--batch-duration 600 \
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--start 0 \
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--num-splits $num_splits \
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--language $lang
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touch data/${lang}/fbank/.cv-${lang}_train.done
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fi
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fi
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if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
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log "Stage 6: Combine features for train"
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if [ ! -f data/${lang}/fbank/cv-${lang}_cuts_train.jsonl.gz ]; then
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pieces=$(find data/${lang}/fbank/cv-${lang}_train_split_${num_splits} -name "cv-${lang}_cuts_train.*.jsonl.gz")
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lhotse combine $pieces data/${lang}/fbank/cv-${lang}_cuts_train.jsonl.gz
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fi
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fi
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@ -95,6 +95,7 @@ if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
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log "Stage 1: Prepare GigaSpeech manifest (may take 30 minutes)"
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# We assume that you have downloaded the GigaSpeech corpus
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# to $dl_dir/GigaSpeech
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if [ ! -f data/manifests/.gigaspeech.done ]; then
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mkdir -p data/manifests
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lhotse prepare gigaspeech \
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--subset XL \
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@ -106,28 +107,33 @@ if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
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--subset TEST \
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-j $nj \
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$dl_dir/GigaSpeech data/manifests
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touch data/manifests/.gigaspeech.done
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fi
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fi
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if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
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log "Stage 2: Preprocess GigaSpeech manifest"
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if [ ! -f data/fbank/.preprocess_complete ]; then
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if [ ! -f data/fbank/.gigaspeech_preprocess.done ]; then
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log "It may take 2 hours for this stage"
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python3 ./local/preprocess_gigaspeech.py
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touch data/fbank/.preprocess_complete
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./local/preprocess_gigaspeech.py
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touch data/fbank/.gigaspeech_preprocess.done
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fi
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fi
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if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
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log "Stage 3: Compute features for DEV and TEST subsets of GigaSpeech (may take 2 minutes)"
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python3 ./local/compute_fbank_gigaspeech_dev_test.py
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if [ ! -f data/fbank/.gigaspeech_dev_test.done ]; then
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./local/compute_fbank_gigaspeech_dev_test.py
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touch data/fbank/.gigaspeech_dev_test.done
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fi
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fi
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if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
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log "Stage 4: Split XL subset into ${num_splits} pieces"
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split_dir=data/fbank/gigaspeech_XL_split_${num_splits}
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if [ ! -f $split_dir/.split_completed ]; then
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if [ ! -f $split_dir/.gigaspeech_XL_split.done ]; then
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lhotse split-lazy ./data/fbank/gigaspeech_cuts_XL_raw.jsonl.gz $split_dir $chunk_size
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touch $split_dir/.split_completed
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touch $split_dir/.gigaspeech_XL_split.done
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fi
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fi
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@ -135,8 +141,19 @@ if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
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log "Stage 5: Compute features for XL"
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# Note: The script supports --start and --stop options.
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# You can use several machines to compute the features in parallel.
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python3 ./local/compute_fbank_gigaspeech_splits.py \
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if [ ! -f data/fbank/.gigaspeech_XL.done ]; then
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./local/compute_fbank_gigaspeech_splits.py \
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--num-workers $nj \
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--batch-duration 600 \
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--num-splits $num_splits
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touch data/fbank/.gigaspeech_XL.done
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fi
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fi
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if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
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log "Stage 6: Combine features for XL (may take 15 hours)"
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if [ ! -f data/fbank/gigaspeech_cuts_XL.jsonl.gz ]; then
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pieces=$(find data/fbank/gigaspeech_XL_split_${num_splits} -name "gigaspeech_cuts_XL.*.jsonl.gz")
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lhotse combine $pieces data/fbank/gigaspeech_cuts_XL.jsonl.gz
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fi
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fi
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373
egs/librispeech/ASR/prepare_multidataset.sh
Executable file
373
egs/librispeech/ASR/prepare_multidataset.sh
Executable file
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#!/usr/bin/env bash
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# fix segmentation fault reported in https://github.com/k2-fsa/icefall/issues/674
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export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
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set -eou pipefail
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nj=16
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stage=-1
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stop_stage=100
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# We assume dl_dir (download dir) contains the following
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# directories and files. If not, they will be downloaded
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# by this script automatically.
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#
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# - $dl_dir/LibriSpeech
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# You can find BOOKS.TXT, test-clean, train-clean-360, etc, inside it.
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# You can download them from https://www.openslr.org/12
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#
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# - $dl_dir/lm
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# This directory contains the following files downloaded from
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# http://www.openslr.org/resources/11
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#
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# - 3-gram.pruned.1e-7.arpa.gz
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# - 3-gram.pruned.1e-7.arpa
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# - 4-gram.arpa.gz
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# - 4-gram.arpa
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# - librispeech-vocab.txt
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# - librispeech-lexicon.txt
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# - librispeech-lm-norm.txt.gz
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#
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# - $dl_dir/musan
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# This directory contains the following directories downloaded from
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# http://www.openslr.org/17/
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#
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# - music
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# - noise
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# - speech
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# Split all dataset to this number of pieces and mix each dataset pieces
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# into multidataset pieces with shuffling.
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num_splits=1998
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dl_dir=$PWD/download
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. shared/parse_options.sh || exit 1
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# vocab size for sentence piece models.
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# It will generate data/lang_bpe_xxx,
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# data/lang_bpe_yyy if the array contains xxx, yyy
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vocab_sizes=(
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# 5000
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# 2000
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# 1000
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500
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)
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# multidataset list.
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# LibriSpeech and musan are required.
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# The others are optional.
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multidataset=(
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"gigaspeech",
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"commonvoice",
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)
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# All files generated by this script are saved in "data".
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# You can safely remove "data" and rerun this script to regenerate it.
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mkdir -p data
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log() {
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# This function is from espnet
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local fname=${BASH_SOURCE[1]##*/}
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echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
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}
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log "dl_dir: $dl_dir"
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log "Dataset: LibriSpeech and musan"
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if [ $stage -le -1 ] && [ $stop_stage -ge -1 ]; then
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log "Stage -1: Download LM"
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mkdir -p $dl_dir/lm
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if [ ! -e $dl_dir/lm/.done ]; then
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./local/download_lm.py --out-dir=$dl_dir/lm
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touch $dl_dir/lm/.done
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fi
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fi
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if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
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log "Stage 0: Download data"
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# If you have pre-downloaded it to /path/to/LibriSpeech,
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# you can create a symlink
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#
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# ln -sfv /path/to/LibriSpeech $dl_dir/LibriSpeech
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#
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if [ ! -d $dl_dir/LibriSpeech/train-other-500 ]; then
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lhotse download librispeech --full $dl_dir
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fi
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# If you have pre-downloaded it to /path/to/musan,
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# you can create a symlink
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#
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# ln -sfv /path/to/musan $dl_dir/
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#
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if [ ! -d $dl_dir/musan ]; then
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lhotse download musan $dl_dir
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fi
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fi
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if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
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log "Stage 1: Prepare LibriSpeech manifest"
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# We assume that you have downloaded the LibriSpeech corpus
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# to $dl_dir/LibriSpeech
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mkdir -p data/manifests
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if [ ! -e data/manifests/.librispeech.done ]; then
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lhotse prepare librispeech -j $nj $dl_dir/LibriSpeech data/manifests
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touch data/manifests/.librispeech.done
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fi
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fi
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if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
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log "Stage 2: Prepare musan manifest"
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# We assume that you have downloaded the musan corpus
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# to data/musan
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mkdir -p data/manifests
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if [ ! -e data/manifests/.musan.done ]; then
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lhotse prepare musan $dl_dir/musan data/manifests
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touch data/manifests/.musan.done
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fi
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fi
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if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
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log "Stage 3: Compute fbank for librispeech"
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mkdir -p data/fbank
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if [ ! -e data/fbank/.librispeech.done ]; then
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./local/compute_fbank_librispeech.py --perturb-speed False
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touch data/fbank/.librispeech.done
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fi
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if [ ! -f data/fbank/librispeech_cuts_train-all-shuf.jsonl.gz ]; then
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cat <(gunzip -c data/fbank/librispeech_cuts_train-clean-100.jsonl.gz) \
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<(gunzip -c data/fbank/librispeech_cuts_train-clean-360.jsonl.gz) \
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<(gunzip -c data/fbank/librispeech_cuts_train-other-500.jsonl.gz) | \
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shuf | gzip -c > data/fbank/librispeech_cuts_train-all-shuf.jsonl.gz
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fi
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if [ ! -e data/fbank/.librispeech-validated.done ]; then
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log "Validating data/fbank for LibriSpeech"
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parts=(
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train-clean-100
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train-clean-360
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train-other-500
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test-clean
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test-other
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dev-clean
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dev-other
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)
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for part in ${parts[@]}; do
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python3 ./local/validate_manifest.py \
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data/fbank/librispeech_cuts_${part}.jsonl.gz
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done
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touch data/fbank/.librispeech-validated.done
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fi
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fi
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if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
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log "Stage 4: Compute fbank for musan"
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mkdir -p data/fbank
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if [ ! -e data/fbank/.musan.done ]; then
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./local/compute_fbank_musan.py
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touch data/fbank/.musan.done
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fi
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fi
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if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
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log "Stage 5: Prepare phone based lang"
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lang_dir=data/lang_phone
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mkdir -p $lang_dir
|
||||
|
||||
(echo '!SIL SIL'; echo '<SPOKEN_NOISE> SPN'; echo '<UNK> SPN'; ) |
|
||||
cat - $dl_dir/lm/librispeech-lexicon.txt |
|
||||
sort | uniq > $lang_dir/lexicon.txt
|
||||
|
||||
if [ ! -f $lang_dir/L_disambig.pt ]; then
|
||||
./local/prepare_lang.py --lang-dir $lang_dir
|
||||
fi
|
||||
|
||||
if [ ! -f $lang_dir/L.fst ]; then
|
||||
log "Converting L.pt to L.fst"
|
||||
./shared/convert-k2-to-openfst.py \
|
||||
--olabels aux_labels \
|
||||
$lang_dir/L.pt \
|
||||
$lang_dir/L.fst
|
||||
fi
|
||||
|
||||
if [ ! -f $lang_dir/L_disambig.fst ]; then
|
||||
log "Converting L_disambig.pt to L_disambig.fst"
|
||||
./shared/convert-k2-to-openfst.py \
|
||||
--olabels aux_labels \
|
||||
$lang_dir/L_disambig.pt \
|
||||
$lang_dir/disambig_L.fst
|
||||
fi
|
||||
fi
|
||||
|
||||
if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
|
||||
log "Stage 6: Prepare BPE based lang"
|
||||
|
||||
for vocab_size in ${vocab_sizes[@]}; do
|
||||
lang_dir=data/lang_bpe_${vocab_size}
|
||||
mkdir -p $lang_dir
|
||||
# We reuse words.txt from phone based lexicon
|
||||
# so that the two can share G.pt later.
|
||||
cp data/lang_phone/words.txt $lang_dir
|
||||
|
||||
if [ ! -f $lang_dir/transcript_words.txt ]; then
|
||||
log "Generate data for BPE training"
|
||||
files=$(
|
||||
find "$dl_dir/LibriSpeech/train-clean-100" -name "*.trans.txt"
|
||||
find "$dl_dir/LibriSpeech/train-clean-360" -name "*.trans.txt"
|
||||
find "$dl_dir/LibriSpeech/train-other-500" -name "*.trans.txt"
|
||||
)
|
||||
for f in ${files[@]}; do
|
||||
cat $f | cut -d " " -f 2-
|
||||
done > $lang_dir/transcript_words.txt
|
||||
fi
|
||||
|
||||
if [ ! -f $lang_dir/bpe.model ]; then
|
||||
./local/train_bpe_model.py \
|
||||
--lang-dir $lang_dir \
|
||||
--vocab-size $vocab_size \
|
||||
--transcript $lang_dir/transcript_words.txt
|
||||
fi
|
||||
|
||||
if [ ! -f $lang_dir/L_disambig.pt ]; then
|
||||
./local/prepare_lang_bpe.py --lang-dir $lang_dir
|
||||
|
||||
log "Validating $lang_dir/lexicon.txt"
|
||||
./local/validate_bpe_lexicon.py \
|
||||
--lexicon $lang_dir/lexicon.txt \
|
||||
--bpe-model $lang_dir/bpe.model
|
||||
fi
|
||||
|
||||
if [ ! -f $lang_dir/L.fst ]; then
|
||||
log "Converting L.pt to L.fst"
|
||||
./shared/convert-k2-to-openfst.py \
|
||||
--olabels aux_labels \
|
||||
$lang_dir/L.pt \
|
||||
$lang_dir/L.fst
|
||||
fi
|
||||
|
||||
if [ ! -f $lang_dir/L_disambig.fst ]; then
|
||||
log "Converting L_disambig.pt to L_disambig.fst"
|
||||
./shared/convert-k2-to-openfst.py \
|
||||
--olabels aux_labels \
|
||||
$lang_dir/L_disambig.pt \
|
||||
$lang_dir/L_disambig.fst
|
||||
fi
|
||||
done
|
||||
fi
|
||||
|
||||
if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then
|
||||
log "Stage 7: Prepare G"
|
||||
# We assume you have install kaldilm, if not, please install
|
||||
# it using: pip install kaldilm
|
||||
|
||||
mkdir -p data/lm
|
||||
if [ ! -f data/lm/G_3_gram.fst.txt ]; then
|
||||
# It is used in building HLG
|
||||
python3 -m kaldilm \
|
||||
--read-symbol-table="data/lang_phone/words.txt" \
|
||||
--disambig-symbol='#0' \
|
||||
--max-order=3 \
|
||||
$dl_dir/lm/3-gram.pruned.1e-7.arpa > data/lm/G_3_gram.fst.txt
|
||||
fi
|
||||
|
||||
if [ ! -f data/lm/G_4_gram.fst.txt ]; then
|
||||
# It is used for LM rescoring
|
||||
python3 -m kaldilm \
|
||||
--read-symbol-table="data/lang_phone/words.txt" \
|
||||
--disambig-symbol='#0' \
|
||||
--max-order=4 \
|
||||
$dl_dir/lm/4-gram.arpa > data/lm/G_4_gram.fst.txt
|
||||
fi
|
||||
fi
|
||||
|
||||
if [ $stage -le 8 ] && [ $stop_stage -ge 8 ]; then
|
||||
log "Stage 8: Compile HLG"
|
||||
./local/compile_hlg.py --lang-dir data/lang_phone
|
||||
|
||||
# Note If ./local/compile_hlg.py throws OOM,
|
||||
# please switch to the following command
|
||||
#
|
||||
# ./local/compile_hlg_using_openfst.py --lang-dir data/lang_phone
|
||||
|
||||
for vocab_size in ${vocab_sizes[@]}; do
|
||||
lang_dir=data/lang_bpe_${vocab_size}
|
||||
./local/compile_hlg.py --lang-dir $lang_dir
|
||||
|
||||
# Note If ./local/compile_hlg.py throws OOM,
|
||||
# please switch to the following command
|
||||
#
|
||||
# ./local/compile_hlg_using_openfst.py --lang-dir $lang_dir
|
||||
done
|
||||
fi
|
||||
|
||||
# Compile LG for RNN-T fast_beam_search decoding
|
||||
if [ $stage -le 9 ] && [ $stop_stage -ge 9 ]; then
|
||||
log "Stage 9: Compile LG"
|
||||
./local/compile_lg.py --lang-dir data/lang_phone
|
||||
|
||||
for vocab_size in ${vocab_sizes[@]}; do
|
||||
lang_dir=data/lang_bpe_${vocab_size}
|
||||
./local/compile_lg.py --lang-dir $lang_dir
|
||||
done
|
||||
fi
|
||||
|
||||
if [ $stage -le 10 ] && [ $stop_stage -ge 10 ]; then
|
||||
log "Stage 10: Prepare the other datasets"
|
||||
# GigaSpeech
|
||||
if [[ "${multidataset[@]}" =~ "gigaspeech" ]]; then
|
||||
log "Dataset: GigaSpeech"
|
||||
./prepare_giga_speech.sh --stop_stage 5
|
||||
fi
|
||||
|
||||
# CommonVoice
|
||||
if [[ "${multidataset[@]}" =~ "commonvoice" ]]; then
|
||||
log "Dataset: CommonVoice"
|
||||
./prepare_common_voice.sh
|
||||
fi
|
||||
fi
|
||||
|
||||
if [ $stage -le 11 ] && [ $stop_stage -ge 11 ]; then
|
||||
log "Stage 11: Create multidataset"
|
||||
split_dir=data/fbank/multidataset_split_${num_splits}
|
||||
if [ ! -f data/fbank/multidataset_split/.multidataset.done ]; then
|
||||
mkdir -p $split_dir/multidataset
|
||||
log "Split LibriSpeech"
|
||||
if [ ! -f $split_dir/.librispeech_split.done ]; then
|
||||
lhotse split $num_splits ./data/fbank/librispeech_cuts_train-all-shuf.jsonl.gz $split_dir
|
||||
touch $split_dir/.librispeech_split.done
|
||||
fi
|
||||
|
||||
if [[ "${multidataset[@]}" =~ "gigaspeech" ]]; then
|
||||
log "Split GigaSpeech XL"
|
||||
if [ ! -f $split_dir/.gigaspeech_XL_split.done ]; then
|
||||
cd $split_dir
|
||||
ln -sv ../gigaspeech_XL_split_2000/gigaspeech_cuts_XL.*.jsonl.gz .
|
||||
cd ../../..
|
||||
touch $split_dir/.gigaspeech_XL_split.done
|
||||
fi
|
||||
fi
|
||||
|
||||
if [[ "${multidataset[@]}" =~ "commonvoice" ]]; then
|
||||
log "Split CommonVoice"
|
||||
if [ ! -f $split_dir/.cv-en_train_split.done ]; then
|
||||
lhotse split $num_splits ./data/en/fbank/cv-en_cuts_train.jsonl.gz $split_dir
|
||||
touch $split_dir/.cv-en_train_split.done
|
||||
fi
|
||||
fi
|
||||
|
||||
if [ ! -f $split_dir/.multidataset_mix.done ]; then
|
||||
log "Mix multidataset"
|
||||
for ((seq=1; seq<=$num_splits; seq++)); do
|
||||
fseq=$(printf "%04d" $seq)
|
||||
gunzip -c $split_dir/*.*${fseq}.jsonl.gz | \
|
||||
shuf | gzip -c > $split_dir/multidataset/multidataset_cuts_train.${fseq}.jsonl.gz
|
||||
done
|
||||
touch $split_dir/.multidataset_mix.done
|
||||
fi
|
||||
|
||||
touch data/fbank/multidataset_split/.multidataset.done
|
||||
fi
|
||||
fi
|
||||
@ -30,7 +30,7 @@ class GigaSpeech:
|
||||
"""
|
||||
Args:
|
||||
manifest_dir:
|
||||
It is expected to contain the following files::
|
||||
It is expected to contain the following files:
|
||||
|
||||
- gigaspeech_XL_split_2000/gigaspeech_cuts_XL.*.jsonl.gz
|
||||
- gigaspeech_cuts_L_raw.jsonl.gz
|
||||
|
||||
@ -0,0 +1,53 @@
|
||||
# Copyright 2023 Xiaomi Corp. (authors: Yifan Yang)
|
||||
#
|
||||
# See ../../../../LICENSE for clarification regarding multiple authors
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
|
||||
import glob
|
||||
import logging
|
||||
import re
|
||||
from pathlib import Path
|
||||
|
||||
import lhotse
|
||||
from lhotse import CutSet, load_manifest_lazy
|
||||
|
||||
|
||||
class MultiDataset:
|
||||
def __init__(self, manifest_dir: str):
|
||||
"""
|
||||
Args:
|
||||
manifest_dir:
|
||||
It is expected to contain the following files:
|
||||
|
||||
- multidataset_split_1998/multidataset/multidataset_cuts_train.*.jsonl.gz
|
||||
"""
|
||||
self.manifest_dir = Path(manifest_dir)
|
||||
|
||||
def train_cuts(self) -> CutSet:
|
||||
logging.info("About to get multidataset train cuts")
|
||||
|
||||
filenames = glob.glob(
|
||||
f"{self.manifest_dir}/multidataset_split_1998/multidataset/multidataset_cuts_train.*.jsonl.gz"
|
||||
)
|
||||
|
||||
pattern = re.compile(r"multidataset_cuts_train.([0-9]+).jsonl.gz")
|
||||
idx_filenames = ((int(pattern.search(f).group(1)), f) for f in filenames)
|
||||
idx_filenames = sorted(idx_filenames, key=lambda x: x[0])
|
||||
|
||||
sorted_filenames = [f[1] for f in idx_filenames]
|
||||
|
||||
logging.info(f"Loading {len(sorted_filenames)} splits")
|
||||
|
||||
return lhotse.combine(lhotse.load_manifest_lazy(p) for p in sorted_filenames)
|
||||
@ -1,8 +1,9 @@
|
||||
#!/usr/bin/env python3
|
||||
# Copyright 2021-2022 Xiaomi Corp. (authors: Fangjun Kuang,
|
||||
# Copyright 2021-2023 Xiaomi Corp. (authors: Fangjun Kuang,
|
||||
# Wei Kang,
|
||||
# Mingshuang Luo,)
|
||||
# Zengwei Yao)
|
||||
# Mingshuang Luo,
|
||||
# Zengwei Yao,
|
||||
# Yifan Yang)
|
||||
#
|
||||
# See ../../../../LICENSE for clarification regarding multiple authors
|
||||
#
|
||||
@ -59,6 +60,7 @@ import torch
|
||||
import torch.multiprocessing as mp
|
||||
import torch.nn as nn
|
||||
from asr_datamodule import LibriSpeechAsrDataModule
|
||||
from multidataset import MultiDataset
|
||||
from decoder import Decoder
|
||||
from joiner import Joiner
|
||||
from lhotse.cut import Cut
|
||||
@ -374,6 +376,13 @@ def get_parser():
|
||||
help="Whether to use half precision training.",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--use-multidataset",
|
||||
type=str2bool,
|
||||
default=False,
|
||||
help="Whether to use multidataset to train.",
|
||||
)
|
||||
|
||||
add_model_arguments(parser)
|
||||
|
||||
return parser
|
||||
@ -1043,6 +1052,10 @@ def run(rank, world_size, args):
|
||||
|
||||
librispeech = LibriSpeechAsrDataModule(args)
|
||||
|
||||
if params.use_multidataset:
|
||||
multidataset = MultiDataset(params.manifest_dir)
|
||||
train_cuts = multidataset.train_cuts()
|
||||
else:
|
||||
if params.full_libri:
|
||||
train_cuts = librispeech.train_all_shuf_cuts()
|
||||
else:
|
||||
@ -1058,9 +1071,6 @@ def run(rank, world_size, args):
|
||||
# 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
|
||||
@ -1102,7 +1112,7 @@ def run(rank, world_size, args):
|
||||
valid_cuts += librispeech.dev_other_cuts()
|
||||
valid_dl = librispeech.valid_dataloaders(valid_cuts)
|
||||
|
||||
if not params.print_diagnostics:
|
||||
if not params.use_multidataset and not params.print_diagnostics:
|
||||
scan_pessimistic_batches_for_oom(
|
||||
model=model,
|
||||
train_dl=train_dl,
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user