mirror of
https://github.com/k2-fsa/icefall.git
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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
99 lines
3.1 KiB
Bash
Executable File
99 lines
3.1 KiB
Bash
Executable File
#!/bin/bash
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# This script is used to run GSS-based enhancement on AMI data.
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set -euo pipefail
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nj=4
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stage=0
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. shared/parse_options.sh || exit 1
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if [ $# != 2 ]; then
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echo "Wrong #arguments ($#, expected 2)"
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echo "Usage: local/prepare_ami_gss.sh [options] <data-dir> <exp-dir>"
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echo "e.g. local/prepare_ami_gss.sh data/manifests exp/ami_gss"
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echo "main options (for others, see top of script file)"
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echo " --nj <nj> # number of parallel jobs"
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echo " --stage <stage> # stage to start running from"
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exit 1;
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fi
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DATA_DIR=$1
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EXP_DIR=$2
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mkdir -p $EXP_DIR
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log() {
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# This function is from espnet
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local fname=${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 ]; then
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log "Stage 1: Prepare cut sets"
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for part in train dev test; do
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lhotse cut simple \
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-r $DATA_DIR/ami-mdm_recordings_${part}.jsonl.gz \
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-s $DATA_DIR/ami-mdm_supervisions_${part}.jsonl.gz \
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$EXP_DIR/cuts_${part}.jsonl.gz
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done
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fi
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if [ $stage -le 2 ]; then
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log "Stage 2: Trim cuts to supervisions (1 cut per supervision segment)"
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for part in train dev test; do
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lhotse cut trim-to-supervisions --discard-overlapping \
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$EXP_DIR/cuts_${part}.jsonl.gz $EXP_DIR/cuts_per_segment_${part}.jsonl.gz
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done
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fi
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if [ $stage -le 3 ]; then
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log "Stage 3: Split manifests for multi-GPU processing (optional)"
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for part in train; do
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gss utils split $nj $EXP_DIR/cuts_per_segment_${part}.jsonl.gz \
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$EXP_DIR/cuts_per_segment_${part}_split$nj
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done
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fi
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if [ $stage -le 4 ]; then
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log "Stage 4: Enhance train segments using GSS (requires GPU)"
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# for train, we use smaller context and larger batches to speed-up processing
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for JOB in $(seq $nj); do
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gss enhance cuts $EXP_DIR/cuts_train.jsonl.gz \
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$EXP_DIR/cuts_per_segment_train_split$nj/cuts_per_segment_train.JOB.jsonl.gz $EXP_DIR/enhanced \
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--bss-iterations 10 \
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--context-duration 5.0 \
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--use-garbage-class \
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--channels 0,1,2,3,4,5,6,7 \
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--min-segment-length 0.05 \
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--max-segment-length 35.0 \
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--max-batch-duration 60.0 \
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--num-buckets 3 \
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--num-workers 2
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done
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fi
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if [ $stage -le 5 ]; then
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log "Stage 5: Enhance dev/test segments using GSS (using GPU)"
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# for dev/test, we use larger context and smaller batches to get better quality
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for part in dev test; do
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for JOB in $(seq $nj); do
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gss enhance cuts $EXP_DIR/cuts_${part}.jsonl.gz \
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$EXP_DIR/cuts_per_segment_${part}_split$nj/cuts_per_segment_${part}.JOB.jsonl.gz \
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$EXP_DIR/enhanced \
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--bss-iterations 10 \
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--context-duration 15.0 \
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--use-garbage-class \
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--channels 0,1,2,3,4,5,6,7 \
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--min-segment-length 0.05 \
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--max-segment-length 30.0 \
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--max-batch-duration 45.0 \
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--num-buckets 3 \
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--num-workers 2
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done
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done
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fi
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if [ $stage -le 6 ]; then
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log "Stage 6: Prepare manifests for GSS-enhanced data"
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python local/prepare_ami_enhanced.py $DATA_DIR $EXP_DIR/enhanced -j $nj --min-segment-duration 0.05
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fi
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