mirror of
https://github.com/k2-fsa/icefall.git
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220 lines
7.5 KiB
Bash
Executable File
220 lines
7.5 KiB
Bash
Executable File
#!/usr/bin/env bash
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set -eou pipefail
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stage=-1
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stop_stage=100
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use_gss=true # Use GSS-based enhancement with MDM setting
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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 audio and transcripts for LibriSpeech in this path.
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#
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# - $dl_dir/libricss
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# You can find audio and transcripts for LibriCSS in this path.
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#
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# - $dl_dir/musan
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# This directory contains the following directories downloaded from
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# http://www.openslr.org/17/
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#
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# - music
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# - noise
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# - speech
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#
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dl_dir=$PWD/download
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cmd="queue-freegpu.pl --config conf/gpu.conf --gpu 1 --mem 4G"
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. shared/parse_options.sh || exit 1
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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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vocab_size=500
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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/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 ]; then
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lhotse download librispeech $dl_dir/librispeech
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fi
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# If you have pre-downloaded it to /path/to/libricss,
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# you can create a symlink
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#
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# ln -sfv /path/to/libricss $dl_dir/libricss
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#
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if [ ! -d $dl_dir/libricss ]; then
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lhotse download libricss $dl_dir/libricss
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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 manifests"
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# We assume that you have downloaded the LibriSpeech corpus
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# to $dl_dir/librispeech. We perform text normalization for the transcripts.
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# NOTE: Alignments are required for this recipe.
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mkdir -p data/manifests
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lhotse prepare librispeech -p train-clean-100 -p train-clean-360 -p train-other-500 -p dev-clean \
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-j 4 --alignments-dir $dl_dir/libri_alignments/LibriSpeech $dl_dir/librispeech data/manifests/
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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 LibriCSS manifests"
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# We assume that you have downloaded the LibriCSS corpus
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# to $dl_dir/libricss. We perform text normalization for the transcripts.
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mkdir -p data/manifests
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for mic in sdm ihm-mix; do
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lhotse prepare libricss --type $mic --segmented $dl_dir/libricss data/manifests/
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done
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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: Prepare musan manifest"
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# We assume that you have downloaded the musan corpus
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# to $dl_dir/musan
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mkdir -p data/manifests
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lhotse prepare musan $dl_dir/musan data/manifests
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fi
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if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
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log "Stage 4: Extract features for LibriSpeech, trim to alignments, and shuffle the cuts"
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$cmd exp/extract_libri_fbank.log python local/compute_fbank_librispeech.py
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lhotse combine data/manifests/librispeech_cuts_train* - |\
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lhotse cut trim-to-alignments --type word --max-pause 0.2 - - |\
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shuf | gzip -c > data/manifests/librispeech_cuts_train_trimmed.jsonl.gz
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lhotse cut trim-to-alignments --type word --max-pause 0.2 data/manifests/librispeech_cuts_dev-clean.jsonl.gz - |\
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shuf | gzip -c > data/manifests/librispeech_cuts_dev_trimmed.jsonl.gz
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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: Create simulated mixtures from LibriSpeech (train and dev). This may take a while."
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# We create a 2-speaker set which will be used during the model warmup phase, and a
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# full training set (2,3,4 speakers) that will be used for the subsequent training.
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# We create anechoic and reverberant versions of both sets. For the full set, we compute
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# silence and overlap distributions based on LibriCSS sessions (no 0L).
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sim_cmd="queue.pl --mem 16G -l 'num_proc=4,h_rt=600:00:00'"
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gunzip -c data/manifests/libricss-sdm_supervisions_all.jsonl.gz |\
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grep -v "0L" | grep -v "OV10" | grep -v "OV20" |\
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gzip -c > data/manifests/libricss-sdm_supervisions_all_v2.jsonl.gz
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# 2-speaker anechoic
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# log "Generating 2-speaker anechoic training set"
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# $sim_cmd exp/sim_train_2spk.log lhotse workflows simulate-meetings \
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# --method conversational \
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# --prob-diff-spk-overlap 1.0 \
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# --num-meetings 50000 \
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# --num-speakers-per-meeting 2 \
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# --max-duration-per-speaker 20.0 \
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# --max-utterances-per-speaker 1 \
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# --seed 1234 \
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# --num-jobs 4 \
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# data/manifests/librispeech_cuts_train_trimmed.jsonl.gz \
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# data/manifests/libri-mix_cuts_train_2spk_norvb.jsonl.gz
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# 2-speaker reverberant
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# log "Generating 2-speaker reverberant training set"
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# lhotse workflows simulate-meetings \
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# --method conversational \
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# --prob-diff-spk-overlap 1.0 \
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# --num-meetings 50000 \
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# --num-speakers-per-meeting 2 \
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# --max-duration-per-speaker 20.0 \
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# --max-utterances-per-speaker 1 \
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# --seed 1234 \
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# --reverberate \
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# --num-jobs 4 \
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# data/manifests/librispeech_cuts_train_trimmed.jsonl.gz \
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# data/manifests/libri-mix_cuts_train_2spk_rvb.jsonl.gz
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# Full training set (2,3 speakers) anechoic
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for part in train; do
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if [ $part == "dev" ]; then
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num_jobs=1
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else
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num_jobs=4
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fi
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log "Generating anechoic ${part} set (full)"
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$sim_cmd exp/sim_${part}.log lhotse workflows simulate-meetings \
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--method conversational \
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--num-repeats 1 \
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--same-spk-pause 0.5 \
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--diff-spk-pause 0.5 \
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--diff-spk-overlap 2 \
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--prob-diff-spk-overlap 0.75 \
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--num-speakers-per-meeting 2,3 \
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--max-duration-per-speaker 15.0 \
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--max-utterances-per-speaker 3 \
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--seed 1234 \
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--num-jobs ${num_jobs} \
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data/manifests/librispeech_cuts_${part}_trimmed.jsonl.gz \
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data/manifests/libri-mix_cuts_${part}_norvb.jsonl.gz
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done
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# Full training set (2,3,4 speakers) reverberant
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# for part in dev train; do
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# log "Generating reverberant ${part} set (full)" ``
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# lhotse workflows simulate-meetings \
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# --method conversational \
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# --num-repeats 1 \
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# --num-speakers-per-meeting 2,3,4 \
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# --max-duration-per-speaker 20.0 \
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# --max-utterances-per-speaker 5 \
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# --seed 1234 \
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# --reverberate \
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# data/manifests/librispeech_cuts_${part}_trimmed.jsonl.gz \
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# data/manifests/libri-mix_cuts_${part}_rvb.jsonl.gz
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# done
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fi
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if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
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log "Stage 6: Compute fbank features for musan"
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mkdir -p data/fbank
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$cmd exp/feats_musan.log python local/compute_fbank_musan.py
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fi
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if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then
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log "Stage 7: Compute fbank features for simulated Libri-mix"
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mkdir -p data/fbank
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$cmd exp/feats_librimix_norvb.log python local/compute_fbank_librimix.py
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fi
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if [ $stage -le 8 ] && [ $stop_stage -ge 8 ]; then
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log "Stage 8: Compute fbank features for LibriCSS"
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mkdir -p data/fbank
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$cmd exp/feats_libricss.log python local/compute_fbank_libricss.py
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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: Download LibriSpeech BPE model from HuggingFace."
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mkdir -p data/lang_bpe_500 && pushd data/lang_bpe_500
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wget https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/resolve/main/data/lang_bpe_500/bpe.model
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popd
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fi
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