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https://github.com/k2-fsa/icefall.git
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c1ce7ca9e3
This recipe is mostly based on egs/csj, but tweaked to the point that can be run with ReazonSpeech corpus. That being said, there are some big caveats: * Currently the model quality is not very good. Actually, it is very bad. I trained a model with 1000h corpus, and it resulted in >80% CER on JSUT. * The core issue seems that Zipformer is prone to ignore untterances as sielent segments. It often produces an empty hypothesis despite that the audio actually contains human voice. * This issue is already reported in the upstream and not fully resolved yet as of Dec 2023. Signed-off-by: Fujimoto Seiji <fujimoto@ceptord.net>
53 lines
1.9 KiB
Bash
Executable File
53 lines
1.9 KiB
Bash
Executable File
#!/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=8
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stage=-1
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stop_stage=100
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reazonspeech_dir=corpus
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reazonspeech_manifest_dir=data
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. shared/parse_options.sh || exit 1
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mkdir -p data
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log() {
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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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if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
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log "Stage 1: Prepare ReazonSpeech manifest"
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if [ ! -e $reazonspeech_manifest_dir/.reazonspeech.done ]; then
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lhotse prepare reazonspeech $reazonspeech_dir $reazonspeech_manifest_dir
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touch $reazonspeech_manifest_dir/.reazonspeech.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: Compute ReazonSpeech fbank"
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if [ ! -e $reazonspeech_manifest_dir/.reazonspeech-validated.done ]; then
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python local/compute_fbank_reazonspeech.py --manifest-dir $reazonspeech_manifest_dir
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python local/validate_manifest.py --manifest $reazonspeech_manifest_dir/reazonspeech_cuts_train.jsonl.gz
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python local/validate_manifest.py --manifest $reazonspeech_manifest_dir/reazonspeech_cuts_valid.jsonl.gz
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python local/validate_manifest.py --manifest $reazonspeech_manifest_dir/reazonspeech_cuts_test.jsonl.gz
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touch $reazonspeech_manifest_dir/.reazonspeech-validated.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: Prepare ReazonSpeech lang_char"
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python local/prepare_lang_char.py $reazonspeech_manifest_dir/reazonspeech_cuts_train.jsonl.gz
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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: Show manifest statistics"
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python local/display_manifest_statistics.py --manifest-dir $reazonspeech_manifest_dir > $reazonspeech_manifest_dir/manifest_statistics.txt
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cat $reazonspeech_manifest_dir/manifest_statistics.txt
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fi
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