mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-08-08 09:32:20 +00:00
db75627e92
* remove unnecessary changes * add AMI prepare scripts * add zipformer scripts for AMI * added logs and pretrained model * minor fix * remove unwanted changes * fix missing link * make suggested changes * update results
145 lines
4.4 KiB
Bash
Executable File
145 lines
4.4 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/amicorpus
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# You can find audio and transcripts 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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# - $dl_dir/{LDC2004S13,LDC2005S13,LDC2004T19,LDC2005T19}
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# These contain the Fisher English audio and transcripts. We will
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# only use the transcripts as extra LM training data (similar to Kaldi).
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#
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dl_dir=$PWD/download
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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/amicorpus,
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# you can create a symlink
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#
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# ln -sfv /path/to/amicorpus $dl_dir/amicorpus
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#
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if [ ! -d $dl_dir/amicorpus ]; then
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lhotse download ami --mic ihm $dl_dir/amicorpus
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lhotse download ami --mic mdm $dl_dir/amicorpus
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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 AMI manifests"
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# We assume that you have downloaded the AMI corpus
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# to $dl_dir/amicorpus. We perform text normalization for the transcripts.
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mkdir -p data/manifests
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for mic in ihm sdm mdm; do
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lhotse prepare ami --mic $mic --partition full-corpus-asr --normalize-text kaldi \
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--max-words-per-segment 30 $dl_dir/amicorpus data/manifests/
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done
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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 $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 3 ] && [ $stop_stage -ge 3 ] && [ $use_gss = true ]; then
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log "Stage 3: Apply GSS enhancement on MDM data (this stage requires a GPU)"
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# We assume that you have installed the GSS package: https://github.com/desh2608/gss
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local/prepare_ami_gss.sh data/manifests exp/ami_gss
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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 features for AMI"
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mkdir -p data/fbank
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python local/compute_fbank_ami.py
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log "Combine features from train splits"
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lhotse combine data/manifests/cuts_train_{ihm,ihm_rvb,sdm,gss}.jsonl.gz - | shuf |\
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gzip -c > data/manifests/cuts_train_all.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: Compute fbank features for musan"
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mkdir -p data/fbank
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python local/compute_fbank_musan.py
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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: Dump transcripts for BPE model training."
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mkdir -p data/lm
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cat <(gunzip -c data/manifests/ami-sdm_supervisions_train.jsonl.gz | jq '.text' | sed 's:"::g')> data/lm/transcript_words.txt
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fi
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if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then
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log "Stage 7: Prepare BPE based lang"
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lang_dir=data/lang_bpe_${vocab_size}
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mkdir -p $lang_dir
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# Add special words to words.txt
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echo "<eps> 0" > $lang_dir/words.txt
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echo "!SIL 1" >> $lang_dir/words.txt
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echo "<UNK> 2" >> $lang_dir/words.txt
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# Add regular words to words.txt
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cat data/lm/transcript_words.txt | grep -o -E '\w+' | sort -u | awk '{print $0,NR+2}' >> $lang_dir/words.txt
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# Add remaining special word symbols expected by LM scripts.
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num_words=$(cat $lang_dir/words.txt | wc -l)
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echo "<s> ${num_words}" >> $lang_dir/words.txt
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num_words=$(cat $lang_dir/words.txt | wc -l)
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echo "</s> ${num_words}" >> $lang_dir/words.txt
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num_words=$(cat $lang_dir/words.txt | wc -l)
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echo "#0 ${num_words}" >> $lang_dir/words.txt
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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 data/lm/transcript_words.txt
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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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fi
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fi
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