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long_file_recog.sh
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long_file_recog.sh
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#!/usr/bin/env bash
# fix segmentation fault reported in https://github.com/k2-fsa/icefall/issues/674
export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
export CUDA_VISIBLE_DEVICES="0,1,2,3"
set -eou pipefail
# This script is used to recogize long audios. The process is as follows:
# 1) Split long audios into chunks with overlaps.
# 2) Perform speech recognition on chunks, getting tokens and timestamps.
# 3) Merge the overlapped chunks into utterances acording to the timestamps.
# Each chunk (except the first and the last) is padded with extra left side and right side.
# The chunk length is: left_side + chunk_size + right_side.
chunk=30.0
extra=2.0
stage=1
stop_stage=4
# We assume that you have downloaded the LibriLight dataset
# with audio files in $corpus_dir and texts in $text_dir
corpus_dir=$PWD/download/libri-light
text_dir=$PWD/download/librilight_text
# Path to save the manifests
output_dir=$PWD/data/librilight
world_size=4
log() {
# This function is from espnet
local fname=${BASH_SOURCE[1]##*/}
echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
}
if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
# We will get librilight_recodings_{subset}.jsonl.gz and librilight_supervisions_{subset}.jsonl.gz
# saved in $output_dir/manifests
log "Stage 1: Prepare LibriLight manifest"
lhotse prepare librilight $corpus_dir $text_dir $output_dir/manifests -j 10
fi
if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
# Chunk manifests are saved to $output_dir/manifests_chunk/librilight_cuts_{subset}.jsonl.gz
log "Stage 2: Split long audio into chunks"
./long_file_recog/split_into_chunks.py \
--manifest-in-dir $output_dir/manifests \
--manifest-out-dir $output_dir/manifests_chunk \
--chunk $chunk \
--extra $extra # Extra duration (in seconds) at both sides
fi
if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
# Recognized tokens and timestamps are saved to $output_dir/manifests_chunk_recog/librilight_cuts_{subset}.jsonl.gz
# This script loads torchscript models, exported by `torch.jit.script()`,
# and uses it to decode waves.
# You can download the jit model from https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless7-2022-11-11
log "Stage 3: Perform speech recognition on splitted chunks"
for subset in small median large; do
./long_file_recog/recognize.py \
--world-size $world_size \
--num-workers 8 \
--subset $subset \
--manifest-in-dir $output_dir/manifests_chunk \
--manifest-out-dir $output_dir/manifests_chunk_recog \
--nn-model-filename long_file_recog/exp/jit_model.pt \
--bpe-model data/lang_bpe_500/bpe.model \
--max-duration 2400 \
--decoding-method greedy_search
--master 12345
if [ $world_size -gt 1 ]; then
# Combine manifests from different jobs
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
fi
done
fi
if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
# Final results are saved in $output_dir/manifests/librilight_cuts_{subset}.jsonl.gz
log "Stage 4: Merge splitted chunks into utterances."
./long_file_recog/merge_chunks.py \
--manifest-in-dir $output_dir/manifests_chunk_recog \
--manifest-out-dir $output_dir/manifests \
--bpe-model data/lang_bpe_500/bpe.model \
--extra $extra
fi