mirror of
https://github.com/k2-fsa/icefall.git
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remove multi from librispeech
This commit is contained in:
Yifan Yang
parent
db84bab890
commit
85507307b9
@ -1,340 +0,0 @@
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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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# - $dl_dir/musan
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# This directory contains the following directories downloaded from
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# http://www.openslr.org/17/
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#
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# - music
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# - noise
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# - speech
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dl_dir=$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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"peoples_speech",
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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
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(echo '!SIL SIL'; echo '<SPOKEN_NOISE> SPN'; echo '<UNK> SPN'; ) |
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cat - $dl_dir/lm/librispeech-lexicon.txt |
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sort | uniq > $lang_dir/lexicon.txt
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if [ ! -f $lang_dir/L_disambig.pt ]; then
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./local/prepare_lang.py --lang-dir $lang_dir
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fi
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if [ ! -f $lang_dir/L.fst ]; then
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log "Converting L.pt to L.fst"
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./shared/convert-k2-to-openfst.py \
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--olabels aux_labels \
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$lang_dir/L.pt \
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$lang_dir/L.fst
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fi
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if [ ! -f $lang_dir/L_disambig.fst ]; then
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log "Converting L_disambig.pt to L_disambig.fst"
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./shared/convert-k2-to-openfst.py \
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--olabels aux_labels \
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$lang_dir/L_disambig.pt \
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$lang_dir/L_disambig.fst
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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: Prepare BPE based lang"
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for vocab_size in ${vocab_sizes[@]}; do
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lang_dir=data/lang_bpe_${vocab_size}
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mkdir -p $lang_dir
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# We reuse words.txt from phone based lexicon
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# so that the two can share G.pt later.
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cp data/lang_phone/words.txt $lang_dir
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if [ ! -f $lang_dir/transcript_words.txt ]; then
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log "Generate data for BPE training"
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files=$(
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find "$dl_dir/LibriSpeech/train-clean-100" -name "*.trans.txt"
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find "$dl_dir/LibriSpeech/train-clean-360" -name "*.trans.txt"
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find "$dl_dir/LibriSpeech/train-other-500" -name "*.trans.txt"
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)
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for f in ${files[@]}; do
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cat $f | cut -d " " -f 2-
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done > $lang_dir/transcript_words.txt
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fi
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if [ ! -f $lang_dir/bpe.model ]; then
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./local/train_bpe_model.py \
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--lang-dir $lang_dir \
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--vocab-size $vocab_size \
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--transcript $lang_dir/transcript_words.txt
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fi
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if [ ! -f $lang_dir/L_disambig.pt ]; then
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./local/prepare_lang_bpe.py --lang-dir $lang_dir
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log "Validating $lang_dir/lexicon.txt"
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./local/validate_bpe_lexicon.py \
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--lexicon $lang_dir/lexicon.txt \
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--bpe-model $lang_dir/bpe.model
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fi
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if [ ! -f $lang_dir/L.fst ]; then
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log "Converting L.pt to L.fst"
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./shared/convert-k2-to-openfst.py \
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--olabels aux_labels \
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$lang_dir/L.pt \
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$lang_dir/L.fst
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fi
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if [ ! -f $lang_dir/L_disambig.fst ]; then
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log "Converting L_disambig.pt to L_disambig.fst"
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./shared/convert-k2-to-openfst.py \
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--olabels aux_labels \
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$lang_dir/L_disambig.pt \
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$lang_dir/L_disambig.fst
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fi
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done
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fi
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if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then
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log "Stage 7: Prepare G"
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# We assume you have install kaldilm, if not, please install
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# it using: pip install kaldilm
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mkdir -p data/lm
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if [ ! -f data/lm/G_3_gram.fst.txt ]; then
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# It is used in building HLG
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python3 -m kaldilm \
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--read-symbol-table="data/lang_phone/words.txt" \
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--disambig-symbol='#0' \
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--max-order=3 \
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$dl_dir/lm/3-gram.pruned.1e-7.arpa > data/lm/G_3_gram.fst.txt
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fi
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if [ ! -f data/lm/G_4_gram.fst.txt ]; then
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# It is used for LM rescoring
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python3 -m kaldilm \
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--read-symbol-table="data/lang_phone/words.txt" \
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--disambig-symbol='#0' \
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--max-order=4 \
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$dl_dir/lm/4-gram.arpa > data/lm/G_4_gram.fst.txt
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fi
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fi
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if [ $stage -le 8 ] && [ $stop_stage -ge 8 ]; then
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log "Stage 8: Compile LG"
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./local/compile_lg.py --lang-dir data/lang_phone
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for vocab_size in ${vocab_sizes[@]}; do
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lang_dir=data/lang_bpe_${vocab_size}
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./local/compile_lg.py --lang-dir $lang_dir
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done
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fi
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if [ $stage -le 9 ] && [ $stop_stage -ge 9 ]; then
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log "Stage 9: Prepare the other datasets"
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# GigaSpeech
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if [[ "${multidataset[@]}" =~ "gigaspeech" ]] && [ ! -f data/fbank/.gigaspeech.done ]; then
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log "Dataset: GigaSpeech"
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cd data/fbank
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if [ -f ../../../../gigaspeech/ASR/data/fbank/XL_split/.split_completed ]; then
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ln -svf $(realpath ../../../../gigaspeech/ASR/data/fbank/XL_split) .
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else
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log "Abort! Please run ../../gigaspeech/ASR/prepare.sh --stage 5 --stop-stage 6"
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exit 1
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fi
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touch .gigaspeech.done
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cd ../..
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fi
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# CommonVoice
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if [[ "${multidataset[@]}" =~ "commonvoice" ]] && [ ! -f data/fbank/.commonvoice.done ]; then
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log "Dataset: CommonVoice"
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cd data/fbank
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if [ -f ../../../../commonvoice/ASR/data/en/fbank/.cv-en_train.done ]; then
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ln -svf $(realpath ../../../../commonvoice/ASR/data/en/fbank/cv-en_train_split_1000) .
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ln -svf $(realpath ../../../../commonvoice/ASR/data/en/fbank/cv-en_cuts_train.jsonl.gz) .
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else
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log "Abort! Please run ../../commonvoice/ASR/prepare.sh --stage 5 --stop-stage 6"
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exit 1
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fi
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touch .commonvoice.done
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cd ../..
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fi
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# People's Speech
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if [[ "${multidataset[@]}" =~ "peoples_speech" ]] && [ ! -f data/fbank/.peoples_speech.done ]; then
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log "Dataset: People's Speech"
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cd data/fbank
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if [ -f ../../../../peoples_speech/ASR/data/fbank/.peoples_speech_train.done ]; then
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ln -svf $(realpath ../../../../peoples_speech/ASR/data/fbank/peoples_speech_train_split) .
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else
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log "Abort! Please run ../../peoples_speech/prepare.sh --stage 5 --stop-stage 6"
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exit 1
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fi
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touch .peoples_speech.done
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cd ../..
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fi
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fi
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@ -1,100 +0,0 @@
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# Copyright 2023 Xiaomi Corp. (authors: Yifan Yang)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import glob
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import logging
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import re
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from pathlib import Path
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import lhotse
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from lhotse import CutSet, load_manifest_lazy
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class MultiDataset:
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def __init__(self, manifest_dir: str):
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"""
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Args:
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manifest_dir:
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It is expected to contain the following files:
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- librispeech_cuts_train-all-shuf.jsonl.gz
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- XL_split_2000/cuts_XL.*.jsonl.gz
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- cv-en_cuts_train.jsonl.gz
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- peoples_speech_train_split/peoples_speech_cuts_dirty.*.jsonl.gz
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- peoples_speech_train_split/peoples_speech_cuts_dirty_sa.*.jsonl.gz
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- peoples_speech_train_split/peoples_speech_cuts_clean.*.jsonl.gz
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- peoples_speech_train_split/peoples_speech_cuts_clean_sa.*.jsonl.gz
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"""
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self.manifest_dir = Path(manifest_dir)
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def train_cuts(self) -> CutSet:
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logging.info("About to get multidataset train cuts")
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# LibriSpeech
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logging.info("Loading LibriSpeech in lazy mode")
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librispeech_cuts = load_manifest_lazy(
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self.manifest_dir / "librispeech_cuts_train-all-shuf.jsonl.gz"
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)
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# GigaSpeech
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filenames = glob.glob(f"{self.manifest_dir}/XL_split/cuts_XL.*.jsonl.gz")
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pattern = re.compile(r"cuts_XL.([0-9]+).jsonl.gz")
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idx_filenames = ((int(pattern.search(f).group(1)), f) for f in filenames)
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idx_filenames = sorted(idx_filenames, key=lambda x: x[0])
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sorted_filenames = [f[1] for f in idx_filenames]
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logging.info(f"Loading GigaSpeech {len(sorted_filenames)} splits in lazy mode")
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gigaspeech_cuts = lhotse.combine(
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lhotse.load_manifest_lazy(p) for p in sorted_filenames
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)
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# CommonVoice
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logging.info("Loading CommonVoice in lazy mode")
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commonvoice_cuts = load_manifest_lazy(
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self.manifest_dir / f"cv-en_cuts_train.jsonl.gz"
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)
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# People's Speech
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sorted_filenames = sorted(
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glob.glob(
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f"{self.manifest_dir}/peoples_speech_train_split/peoples_speech_cuts_*[yna].*.jsonl.gz"
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)
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)
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logging.info(
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f"Loading People's Speech {len(sorted_filenames)} splits in lazy mode"
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)
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peoples_speech_cuts = lhotse.combine(
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lhotse.load_manifest_lazy(p) for p in sorted_filenames
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)
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return CutSet.mux(
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librispeech_cuts,
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gigaspeech_cuts,
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commonvoice_cuts,
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peoples_speech_cuts,
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weights=[
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len(librispeech_cuts),
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len(gigaspeech_cuts),
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len(commonvoice_cuts),
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len(peoples_speech_cuts),
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],
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)
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@ -66,7 +66,6 @@ from lhotse.cut import Cut
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from lhotse.dataset.sampling.base import CutSampler
|
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from lhotse.utils import fix_random_seed
|
||||
from model import Transducer
|
||||
from multidataset import MultiDataset
|
||||
from optim import Eden, ScaledAdam
|
||||
from torch import Tensor
|
||||
from torch.cuda.amp import GradScaler
|
||||
@ -376,13 +375,6 @@ 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
|
||||
@ -1042,16 +1034,12 @@ def run(rank, world_size, args):
|
||||
|
||||
librispeech = LibriSpeechAsrDataModule(args)
|
||||
|
||||
if params.use_multidataset:
|
||||
multidataset = MultiDataset(params.manifest_dir)
|
||||
train_cuts = multidataset.train_cuts()
|
||||
if params.mini_libri:
|
||||
train_cuts = librispeech.train_clean_5_cuts()
|
||||
elif params.full_libri:
|
||||
train_cuts = librispeech.train_all_shuf_cuts()
|
||||
else:
|
||||
if params.mini_libri:
|
||||
train_cuts = librispeech.train_clean_5_cuts()
|
||||
elif params.full_libri:
|
||||
train_cuts = librispeech.train_all_shuf_cuts()
|
||||
else:
|
||||
train_cuts = librispeech.train_clean_100_cuts()
|
||||
train_cuts = librispeech.train_clean_100_cuts()
|
||||
|
||||
def remove_short_and_long_utt(c: Cut):
|
||||
# Keep only utterances with duration between 1 second and 20 seconds
|
||||
@ -1107,7 +1095,7 @@ def run(rank, world_size, args):
|
||||
valid_cuts += librispeech.dev_other_cuts()
|
||||
valid_dl = librispeech.valid_dataloaders(valid_cuts)
|
||||
|
||||
if not params.use_multidataset and not params.print_diagnostics:
|
||||
if not params.print_diagnostics:
|
||||
scan_pessimistic_batches_for_oom(
|
||||
model=model,
|
||||
train_dl=train_dl,
|
||||
|
||||
@ -1 +0,0 @@
|
||||
../pruned_transducer_stateless7/multidataset.py
|
||||
@ -68,7 +68,6 @@ from lhotse.cut import Cut
|
||||
from lhotse.dataset.sampling.base import CutSampler
|
||||
from lhotse.utils import fix_random_seed
|
||||
from model import Transducer
|
||||
from multidataset import MultiDataset
|
||||
from optim import Eden, ScaledAdam
|
||||
from scaling import ScheduledFloat
|
||||
from subsampling import Conv2dSubsampling
|
||||
@ -444,13 +443,6 @@ 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
|
||||
@ -1134,14 +1126,10 @@ def run(rank, world_size, args):
|
||||
|
||||
librispeech = LibriSpeechAsrDataModule(args)
|
||||
|
||||
if params.use_multidataset:
|
||||
multidataset = MultiDataset(params.manifest_dir)
|
||||
train_cuts = multidataset.train_cuts()
|
||||
else:
|
||||
train_cuts = librispeech.train_clean_100_cuts()
|
||||
if params.full_libri:
|
||||
train_cuts += librispeech.train_clean_360_cuts()
|
||||
train_cuts += librispeech.train_other_500_cuts()
|
||||
train_cuts = librispeech.train_clean_100_cuts()
|
||||
if params.full_libri:
|
||||
train_cuts += librispeech.train_clean_360_cuts()
|
||||
train_cuts += librispeech.train_other_500_cuts()
|
||||
|
||||
def remove_short_and_long_utt(c: Cut):
|
||||
# Keep only utterances with duration between 1 second and 20 seconds
|
||||
@ -1197,7 +1185,7 @@ def run(rank, world_size, args):
|
||||
valid_cuts += librispeech.dev_other_cuts()
|
||||
valid_dl = librispeech.valid_dataloaders(valid_cuts)
|
||||
|
||||
if not params.use_multidataset and not params.print_diagnostics:
|
||||
if not params.print_diagnostics:
|
||||
scan_pessimistic_batches_for_oom(
|
||||
model=model,
|
||||
train_dl=train_dl,
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user