[example] support hey_snips_kws_4.0 dataset (#38)

* [example] support hey_snips_kws_4.0 dataset

* format

* format

examples/hey_snips/s0/README.md

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+FRRs with FAR fixed at once per hour:
+
+| model            | params(K) | epoch      | hey_snips  |
+|------------------|-----------|------------|------------|
+| DS_TCN           | 21        | 80(avg30)  | 0.019771   |
+| MDTC_Small       | 31        | 100(avg10) | 0.008699   |

examples/hey_snips/s0/conf/ds_tcn.yaml

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+dataset_conf:
+    filter_conf:
+        max_length: 2048
+        min_length: 0
+    resample_conf:
+        resample_rate: 16000
+    speed_perturb: false
+    feature_extraction_conf:
+        feature_type: 'fbank'
+        num_mel_bins: 40
+        frame_shift: 10
+        frame_length: 25
+        dither: 1.0
+    spec_aug: true
+    spec_aug_conf:
+        num_t_mask: 1
+        num_f_mask: 1
+        max_t: 50
+        max_f: 30
+    shuffle: true
+    shuffle_conf:
+        shuffle_size: 1500
+    batch_conf:
+        batch_size: 256
+
+model:
+    hidden_dim: 64
+    preprocessing:
+        type: linear
+    backbone:
+        type: tcn
+        ds: true
+        num_layers: 4
+        kernel_size: 8
+        dropout: 0.1
+
+optim: adam
+optim_conf:
+    lr: 0.001
+
+training_config:
+    grad_clip: 5
+    max_epoch: 80
+    log_interval: 10
+

examples/hey_snips/s0/conf/mdtc_small.yaml

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+dataset_conf:
+    filter_conf:
+        max_length: 2048
+        min_length: 0
+    resample_conf:
+        resample_rate: 16000
+    speed_perturb: false
+    feature_extraction_conf:
+        feature_type: 'mfcc'
+        num_ceps: 80
+        num_mel_bins: 80
+        frame_shift: 10
+        frame_length: 25
+        dither: 1.0
+    feature_dither: 0.0
+    spec_aug: true
+    spec_aug_conf:
+        num_t_mask: 1
+        num_f_mask: 1
+        max_t: 20
+        max_f: 40
+    shuffle: true
+    shuffle_conf:
+        shuffle_size: 1500
+    batch_conf:
+        batch_size: 100
+
+model:
+    hidden_dim: 32
+    preprocessing:
+        type: none
+    backbone:
+        type: mdtc
+        num_stack: 3
+        stack_size: 4
+        kernel_size: 5
+        hidden_dim: 32
+        causal: True
+
+
+optim: adam
+optim_conf:
+    lr: 0.001
+    weight_decay: 0.00005
+
+training_config:
+    grad_clip: 5
+    max_epoch: 100
+    log_interval: 10
+    criterion: max_pooling

examples/hey_snips/s0/kws

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+../../../kws

examples/hey_snips/s0/local/prepare_data.py

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+#!/usr/bin/env python3
+
+# Copyright 2018-2020  Yiming Wang
+#           2018-2020  Daniel Povey
+#           2021       Binbin Zhang
+#                      Menglong Xu
+# Apache 2.0
+""" This script prepares the snips data into kaldi format.
+"""
+
+import argparse
+import os
+import json
+
+
+def main():
+    parser = argparse.ArgumentParser(description="""Prepare data.""")
+    parser.add_argument('wav_dir',
+                        type=str,
+                        help='dir containing all the wav files')
+    parser.add_argument('path', type=str, help='path to the json file')
+    parser.add_argument('out_dir', type=str, help='out dir')
+    args = parser.parse_args()
+
+    with open(args.path, 'r', encoding='utf-8') as f:
+        data = json.load(f)
+        utt_id, label = [], []
+        for entry in data:
+            if entry['duration'] > 0:
+                utt_id.append(entry['id'])
+                keyword_id = 0 if entry['is_hotword'] == 1 else -1
+                label.append(keyword_id)
+
+    abs_dir = os.path.abspath(args.wav_dir)
+    wav_path = os.path.join(args.out_dir, 'wav.scp')
+    text_path = os.path.join(args.out_dir, 'text')
+    with open(wav_path, 'w', encoding='utf-8') as f_wav, \
+         open(text_path, 'w', encoding='utf-8') as f_text:
+        for utt, l in zip(utt_id, label):
+            f_wav.write('{} {}\n'.format(utt,
+                                         os.path.join(abs_dir, utt + ".wav")))
+            f_text.write('{} {}\n'.format(utt, l))
+
+
+if __name__ == "__main__":
+    main()

examples/hey_snips/s0/local/snips_data_extract.sh

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+#!/bin/bash
+
+# Copyright  2018-2020  Yiming Wang
+#            2018-2020  Daniel Povey
+#            2021       Binbin Zhang
+#                       Menglong Xu
+
+[ -f ./path.sh ] && . ./path.sh
+
+dl_dir=data/download
+
+. tools/parse_options.sh || exit 1;
+
+mkdir -p $dl_dir
+
+# Fill the following form:
+# https://forms.gle/JtmFYM7xK1SaMfZYA
+# to download the dataset
+dataset=hey_snips_kws_4.0.tar.gz
+src_path=$dl_dir
+
+if [ -d $dl_dir/$(basename "$dataset" .tar.gz) ]; then
+  echo "Not extracting $(basename "$dataset" .tar.gz) as it is already there."
+else
+  echo "Extracting $dataset..."
+  tar -xvzf $src_path/$dataset -C $dl_dir || exit 1;
+  echo "Done extracting $dataset."
+fi
+
+exit 0

examples/hey_snips/s0/path.sh

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+export PATH=$PWD:$PATH
+
+# NOTE(kan-bayashi): Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
+export PYTHONIOENCODING=UTF-8
+export PYTHONPATH=../../../:$PYTHONPATH

examples/hey_snips/s0/run.sh

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+#!/bin/bash
+# Copyright 2021  Binbin Zhang
+#                 Menglong Xu
+
+. ./path.sh
+
+export CUDA_VISIBLE_DEVICES="0"
+
+stage=0
+stop_stage=4
+num_keywords=1
+
+config=conf/ds_tcn.yaml
+norm_mean=true
+norm_var=true
+gpu_id=0
+
+checkpoint=
+dir=exp/ds_tcn
+
+num_average=30
+score_checkpoint=$dir/avg_${num_average}.pt
+
+download_dir=./data/local # your data dir
+
+. tools/parse_options.sh || exit 1;
+
+set -euo pipefail
+
+if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
+  echo "Extracte all datasets"
+  local/snips_data_extract.sh --dl_dir $download_dir
+fi
+
+
+if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
+  echo "Preparing datasets..."
+  mkdir -p dict
+  echo "<filler> -1" > dict/words.txt
+  echo "Hey_Snips 0" >> dict/words.txt
+
+  for folder in train dev test; do
+    mkdir -p data/$folder
+    json_path=$download_dir/hey_snips_research_6k_en_train_eval_clean_ter/$folder.json
+    local/prepare_data.py $download_dir/hey_snips_research_6k_en_train_eval_clean_ter/audio_files $json_path \
+      data/$folder
+  done
+fi
+
+
+if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
+  echo "Compute CMVN and Format datasets"
+  tools/compute_cmvn_stats.py --num_workers 16 --train_config $config \
+    --in_scp data/train/wav.scp \
+    --out_cmvn data/train/global_cmvn
+
+  for x in train dev test; do
+    tools/wav_to_duration.sh --nj 8 data/$x/wav.scp data/$x/wav.dur
+    tools/make_list.py data/$x/wav.scp data/$x/text \
+      data/$x/wav.dur data/$x/data.list
+  done
+fi
+
+
+if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
+  echo "Start training ..."
+  mkdir -p $dir
+  cmvn_opts=
+  $norm_mean && cmvn_opts="--cmvn_file data/train/global_cmvn"
+  $norm_var && cmvn_opts="$cmvn_opts --norm_var"
+  python kws/bin/train.py --gpu $gpu_id \
+    --config $config \
+    --train_data data/train/data.list \
+    --cv_data data/dev/data.list \
+    --model_dir $dir \
+    --num_workers 8 \
+    --num_keywords $num_keywords \
+    --min_duration 50 \
+    --seed 777 \
+    $cmvn_opts \
+    ${checkpoint:+--checkpoint $checkpoint}
+fi
+
+if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
+  # Do model average
+  python kws/bin/average_model.py \
+    --dst_model $score_checkpoint \
+    --src_path $dir  \
+    --num ${num_average} \
+    --val_best
+
+  # Compute posterior score
+  result_dir=$dir/test_$(basename $score_checkpoint)
+  mkdir -p $result_dir
+  python kws/bin/score.py --gpu $gpu_id \
+    --config $dir/config.yaml \
+    --test_data data/test/data.list \
+    --batch_size 256 \
+    --checkpoint $score_checkpoint \
+    --score_file $result_dir/score.txt \
+    --num_workers 8
+fi
+
+if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
+  # Compute detection error tradeoff
+  result_dir=$dir/test_$(basename $score_checkpoint)
+  first_keyword=0
+  last_keyword=$(($num_keywords+$first_keyword-1))
+  for keyword in $(seq $first_keyword $last_keyword); do
+    python kws/bin/compute_det.py \
+      --keyword $keyword \
+      --test_data data/test/data.list \
+      --score_file $result_dir/score.txt \
+      --stats_file $result_dir/stats.${keyword}.txt
+  done
+fi
+
+
+if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
+  python kws/bin/export_jit.py --config $dir/config.yaml \
+    --checkpoint $score_checkpoint \
+    --output_file $dir/final.zip \
+    --output_quant_file $dir/final.quant.zip
+fi
+

examples/hey_snips/s0/tools

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+../../../tools