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https://github.com/k2-fsa/icefall.git
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a7e142b7ff
* support long file transcription * rename recipe as long_file_recog * add docs * support multi-gpu decoding * style fix
95 lines
3.4 KiB
Bash
Executable File
95 lines
3.4 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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export CUDA_VISIBLE_DEVICES="0,1,2,3"
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set -eou pipefail
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# This script is used to recogize long audios. The process is as follows:
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# 1) Split long audios into chunks with overlaps.
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# 2) Perform speech recognition on chunks, getting tokens and timestamps.
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# 3) Merge the overlapped chunks into utterances acording to the timestamps.
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# Each chunk (except the first and the last) is padded with extra left side and right side.
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# The chunk length is: left_side + chunk_size + right_side.
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chunk=30.0
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extra=2.0
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stage=1
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stop_stage=4
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# We assume that you have downloaded the LibriLight dataset
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# with audio files in $corpus_dir and texts in $text_dir
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corpus_dir=$PWD/download/libri-light
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text_dir=$PWD/download/librilight_text
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# Path to save the manifests
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output_dir=$PWD/data/librilight
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world_size=4
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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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if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
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# We will get librilight_recodings_{subset}.jsonl.gz and librilight_supervisions_{subset}.jsonl.gz
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# saved in $output_dir/manifests
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log "Stage 1: Prepare LibriLight manifest"
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lhotse prepare librilight $corpus_dir $text_dir $output_dir/manifests -j 10
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fi
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if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
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# Chunk manifests are saved to $output_dir/manifests_chunk/librilight_cuts_{subset}.jsonl.gz
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log "Stage 2: Split long audio into chunks"
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./long_file_recog/split_into_chunks.py \
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--manifest-in-dir $output_dir/manifests \
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--manifest-out-dir $output_dir/manifests_chunk \
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--chunk $chunk \
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--extra $extra # Extra duration (in seconds) at both sides
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fi
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if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
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# Recognized tokens and timestamps are saved to $output_dir/manifests_chunk_recog/librilight_cuts_{subset}.jsonl.gz
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# This script loads torchscript models, exported by `torch.jit.script()`,
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# and uses it to decode waves.
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# You can download the jit model from https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless7-2022-11-11
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log "Stage 3: Perform speech recognition on splitted chunks"
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for subset in small median large; do
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./long_file_recog/recognize.py \
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--world-size $world_size \
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--num-workers 8 \
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--subset $subset \
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--manifest-in-dir $output_dir/manifests_chunk \
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--manifest-out-dir $output_dir/manifests_chunk_recog \
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--nn-model-filename long_file_recog/exp/jit_model.pt \
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--bpe-model data/lang_bpe_500/bpe.model \
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--max-duration 2400 \
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--decoding-method greedy_search
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--master 12345
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if [ $world_size -gt 1 ]; then
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# Combine manifests from different jobs
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lhotse combine $(find $output_dir/manifests_chunk_recog -name librilight_cuts_${subset}_job_*.jsonl.gz | tr "\n" " ") $output_dir/manifests_chunk_recog/librilight_cuts_${subset}.jsonl.gz
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fi
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done
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fi
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if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
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# Final results are saved in $output_dir/manifests/librilight_cuts_{subset}.jsonl.gz
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log "Stage 4: Merge splitted chunks into utterances."
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./long_file_recog/merge_chunks.py \
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--manifest-in-dir $output_dir/manifests_chunk_recog \
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--manifest-out-dir $output_dir/manifests \
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--bpe-model data/lang_bpe_500/bpe.model \
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--extra $extra
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fi
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