mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-08-09 01:52:41 +00:00
5df24c1685
* add whisper fbank for wenetspeech * add whisper fbank for other dataset * add str to bool * add decode for wenetspeech * add requirments.txt * add original model decode with 30s * test feature extractor speed * add aishell2 feat * change compute feature batch * fix overwrite * fix executor * regression * add kaldifeatwhisper fbank * fix io issue * parallel jobs * use multi machines * add wenetspeech fine-tune scripts * add monkey patch codes * remove useless file * fix subsampling factor * fix too long audios * add remove long short * fix whisper version to support multi batch beam * decode all wav files * remove utterance more than 30s in test_net * only test net * using soft links * add kespeech whisper feats * fix index error * add manifests for whisper * change to licomchunky writer * add missing option * decrease cpu usage * add speed perturb for kespeech * fix kespeech speed perturb * add dataset * load checkpoint from specific path * add speechio * add speechio results --------- Co-authored-by: zr_jin <peter.jin.cn@gmail.com>
68 lines
1.8 KiB
Bash
68 lines
1.8 KiB
Bash
#!/usr/bin/env bash
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set -eou pipefail
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stage=3
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stop_stage=3
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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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# - $dl_dir/SPEECHIO_ASR_ZH00000
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# This directory contains the following files downloaded from
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# https://github.com/SpeechColab/Leaderboard
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#
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# - metadata.tsv
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# - wav
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# - wav.scp
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# - trans.txt
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#
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dl_dir=$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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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 1 ] && [ $stop_stage -ge 1 ]; then
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log "Stage 1: Prepare speechio manifest"
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# We assume that you have downloaded the speechio dataset
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# to $dl_dir
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mkdir -p data/manifests
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if [ ! -e data/manifests/.speechio.done ]; then
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lhotse prepare speechio $dl_dir data/manifests
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touch data/manifests/.speechio.done
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fi
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fi
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whisper_mel_bins=80
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if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
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log "Stage 2: Compute whisper fbank for speechio"
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if [ ! -f data/fbank/.speechio.done ]; then
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mkdir -p data/fbank
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./local/compute_fbank_speechio.py --num-mel-bins ${whisper_mel_bins} --whisper-fbank true
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touch data/fbank/.speechio.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 kaldi fbank for speechio"
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if [ ! -f data/fbank/.speechio.kaldi.done ]; then
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fbank_dir=data/fbank_kaldi
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mkdir -p $fbank_dir
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./local/compute_fbank_speechio.py --fbank-dir $fbank_dir
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touch data/fbank/.speechio.kaldi.done
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fi
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fi
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