Merge pull request #718 from LittleChenCc/develop

add related scripts of TIMIT

examples/dataset/timit/timit_kaldi_standard_split.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Prepare TIMIT dataset (Standard split from Kaldi)
+
+Create manifest files from splited datased.
+Manifest file is a json-format file with each line containing the
+meta data (i.e. audio filepath, transcript and audio duration)
+of each audio file in the data set.
+"""
+import argparse
+import codecs
+import json
+import os
+
+import soundfile
+
+parser = argparse.ArgumentParser(description=__doc__)
+parser.add_argument(
+    "--src_dir",
+    default="",
+    type=str,
+    help="Directory to kaldi splited data. (default: %(default)s)")
+parser.add_argument(
+    "--manifest_prefix",
+    default="manifest",
+    type=str,
+    help="Filepath prefix for output manifests. (default: %(default)s)")
+args = parser.parse_args()
+
+
+def create_manifest(data_dir, manifest_path_prefix):
+    print("Creating manifest %s ..." % manifest_path_prefix)
+    json_lines = []
+
+    data_types = ['train', 'dev', 'test']
+    for dtype in data_types:
+        del json_lines[:]
+        total_sec = 0.0
+        total_text = 0.0
+        total_num = 0
+
+        phn_path = os.path.join(data_dir, dtype+'.text')
+        phn_dict = {}
+        for line in codecs.open(phn_path, 'r', 'utf-8'):
+            line = line.strip()
+            if line == '':
+                continue
+            audio_id, text = line.split(' ', 1)
+            phn_dict[audio_id] = text        
+
+        audio_dir = os.path.join(data_dir, dtype+'_sph.scp')
+        for line in codecs.open(audio_dir, 'r', 'utf-8'):
+                audio_id, audio_path = line.strip().split()
+                # if no transcription for audio then raise error
+                assert audio_id in phn_dict
+                audio_data, samplerate = soundfile.read(audio_path)
+                duration = float(len(audio_data) / samplerate)
+                text = phn_dict[audio_id]
+                json_lines.append(
+                    json.dumps(
+                        {
+                            'utt': audio_id,
+                            'feat': audio_path,
+                            'feat_shape': (duration, ),  # second
+                            'text': text
+                        },
+                        ensure_ascii=False))
+
+                total_sec += duration
+                total_text += len(text)
+                total_num += 1
+
+        manifest_path = manifest_path_prefix + '.' + dtype + '.raw'
+        with codecs.open(manifest_path, 'w', 'utf-8') as fout:
+            for line in json_lines:
+                fout.write(line + '\n')
+
+
+def prepare_dataset(src_dir, manifest_path=None):
+    """create manifest file."""
+    if os.path.isdir(manifest_path):
+        manifest_path = os.path.join(manifest_path, 'manifest')
+    if manifest_path:
+        create_manifest(src_dir, manifest_path)
+
+
+def main():
+    if args.src_dir.startswith('~'):
+        args.src_dir = os.path.expanduser(args.src_dir)
+
+    prepare_dataset(
+        src_dir=args.src_dir,
+        manifest_path=args.manifest_prefix)
+
+    print("manifest prepare done!")
+
+
+if __name__ == '__main__':
+    main()

examples/timit/README.md

+# TIMIT
+
+Results will be organized and updated soon.
\ No newline at end of file

examples/timit/conf/augmentation.json

+[
+  {
+    "type": "shift",
+    "params": {
+      "min_shift_ms": -5,
+      "max_shift_ms": 5
+    },
+    "prob": 1.0
+  },
+  {
+    "type": "speed",
+    "params": {
+      "min_speed_rate": 0.9,
+      "max_speed_rate": 1.1,
+      "num_rates": 3
+    },
+    "prob": 0.0
+  },
+  {
+    "type": "specaug",
+    "params": {
+      "F": 10,
+      "T": 50,
+      "n_freq_masks": 2,
+      "n_time_masks": 2,
+      "p": 1.0,
+      "W": 80,
+      "adaptive_number_ratio": 0,
+      "adaptive_size_ratio": 0,
+      "max_n_time_masks": 20
+    },
+    "prob": 1.0
+  }
+]

examples/timit/conf/dev_spk.list

+faks0
+fdac1
+fjem0
+mgwt0
+mjar0
+mmdb1
+mmdm2
+mpdf0
+fcmh0
+fkms0
+mbdg0
+mbwm0
+mcsh0
+fadg0
+fdms0
+fedw0
+mgjf0
+mglb0
+mrtk0
+mtaa0
+mtdt0
+mthc0
+mwjg0
+fnmr0
+frew0
+fsem0
+mbns0
+mmjr0
+mdls0
+mdlf0
+mdvc0
+mers0
+fmah0
+fdrw0
+mrcs0
+mrjm4
+fcal1
+mmwh0
+fjsj0
+majc0
+mjsw0
+mreb0
+fgjd0
+fjmg0
+mroa0
+mteb0
+mjfc0
+mrjr0
+fmml0
+mrws1
\ No newline at end of file

examples/timit/conf/phones.60-48-39.map

+aa	aa	aa
+ae	ae	ae
+ah	ah	ah
+ao	ao	aa
+aw	aw	aw
+ax	ax	ah
+ax-h	ax	ah
+axr	er	er
+ay	ay	ay
+b	b	b
+bcl	vcl	sil
+ch	ch	ch
+d	d	d
+dcl	vcl	sil
+dh	dh	dh
+dx	dx	dx
+eh	eh	eh
+el	el	l
+em	m	m
+en	en	n
+eng	ng	ng
+epi	epi	sil
+er	er	er
+ey	ey	ey
+f	f	f
+g	g	g
+gcl	vcl	sil
+h#	sil	sil
+hh	hh	hh
+hv	hh	hh
+ih	ih	ih
+ix	ix	ih
+iy	iy	iy
+jh	jh	jh
+k	k	k
+kcl	cl	sil
+l	l	l
+m	m	m
+n	n	n
+ng	ng	ng
+nx	n	n
+ow	ow	ow
+oy	oy	oy
+p	p	p
+pau	sil	sil
+pcl	cl	sil
+q
+r	r	r
+s	s	s
+sh	sh	sh
+t	t	t
+tcl	cl	sil
+th	th	th
+uh	uh	uh
+uw	uw	uw
+ux	uw	uw
+v	v	v
+w	w	w
+y	y	y
+z	z	z
+zh	zh	sh
\ No newline at end of file

examples/timit/conf/test_spk.list

+mdab0
+mwbt0
+felc0
+mtas1
+mwew0
+fpas0
+mjmp0
+mlnt0
+fpkt0
+mlll0
+mtls0
+fjlm0
+mbpm0
+mklt0
+fnlp0
+mcmj0
+mjdh0
+fmgd0
+mgrt0
+mnjm0
+fdhc0
+mjln0
+mpam0
+fmld0
\ No newline at end of file

examples/timit/conf/transformer.yaml

+# https://yaml.org/type/float.html
+data:
+  train_manifest: data/manifest.train
+  dev_manifest: data/manifest.dev
+  test_manifest: data/manifest.test
+  min_input_len: 0.5  # second
+  max_input_len: 30.0 # second
+  min_output_len: 0.0 # tokens
+  max_output_len: 400.0 # tokens
+  min_output_input_ratio: 0.05
+  max_output_input_ratio: 100.0
+
+collator:
+  vocab_filepath: data/vocab.txt
+  unit_type: "word"
+  mean_std_filepath: ""
+  augmentation_config: ""
+  batch_size: 64
+  raw_wav: True  # use raw_wav or kaldi feature
+  specgram_type: fbank #linear, mfcc, fbank
+  feat_dim: 80
+  delta_delta: False
+  dither: 1.0
+  target_sample_rate: 16000
+  max_freq: None
+  n_fft: None
+  stride_ms: 10.0
+  window_ms: 25.0
+  use_dB_normalization: True
+  target_dB: -20
+  random_seed: 0
+  keep_transcription_text: False
+  sortagrad: True 
+  shuffle_method: batch_shuffle
+  num_workers: 2
+
+
+# network architecture
+model:
+    cmvn_file: "data/mean_std.json"
+    cmvn_file_type: "json"
+    # encoder related
+    encoder: transformer
+    encoder_conf:
+        output_size: 256    # dimension of attention
+        attention_heads: 4
+        linear_units: 2048  # the number of units of position-wise feed forward
+        num_blocks: 12      # the number of encoder blocks
+        dropout_rate: 0.1
+        positional_dropout_rate: 0.1
+        attention_dropout_rate: 0.0
+        input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
+        normalize_before: true
+
+    # decoder related
+    decoder: transformer
+    decoder_conf:
+        attention_heads: 4
+        linear_units: 2048
+        num_blocks: 6
+        dropout_rate: 0.1
+        positional_dropout_rate: 0.1
+        self_attention_dropout_rate: 0.0
+        src_attention_dropout_rate: 0.0
+
+    # hybrid CTC/attention
+    model_conf:
+        ctc_weight: 0.3
+        lsm_weight: 0.1     # label smoothing option
+        length_normalized_loss: false
+
+
+training:
+  n_epoch: 120
+  accum_grad: 2
+  global_grad_clip: 5.0
+  optim: adam
+  optim_conf:
+    lr: 0.004
+    weight_decay: 1e-06
+  scheduler: warmuplr     # pytorch v1.1.0+ required
+  scheduler_conf:
+    warmup_steps: 25000
+    lr_decay: 1.0
+  log_interval: 100
+  checkpoint:
+    kbest_n: 50
+    latest_n: 5
+
+
+decoding:
+  batch_size: 64
+  error_rate_type: wer
+  decoding_method: attention  # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring'
+  lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm
+  alpha: 2.5
+  beta: 0.3
+  beam_size: 10
+  cutoff_prob: 1.0
+  cutoff_top_n: 0
+  num_proc_bsearch: 8
+  ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
+  decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
+      # <0: for decoding, use full chunk.
+      # >0: for decoding, use fixed chunk size as set.
+      # 0: used for training, it's prohibited here. 
+  num_decoding_left_chunks: -1  # number of left chunks for decoding. Defaults to -1.
+  simulate_streaming: False  # simulate streaming inference. Defaults to False.
+
+

examples/timit/local/align.sh

+#!/bin/bash
+
+if [ $# != 2 ];then
+    echo "usage: ${0} config_path ckpt_path_prefix"
+    exit -1
+fi
+
+ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+echo "using $ngpu gpus..."
+
+device=gpu
+if [ ${ngpu} == 0 ];then
+    device=cpu
+fi
+config_path=$1
+ckpt_prefix=$2
+
+batch_size=1
+output_dir=${ckpt_prefix}
+mkdir -p ${output_dir}
+
+# align dump in `result_file`
+# .tier, .TextGrid dump in `dir of result_file`
+python3 -u ${BIN_DIR}/alignment.py \
+--device ${device} \
+--nproc 1 \
+--config ${config_path} \
+--result_file ${output_dir}/${type}.align \
+--checkpoint_path ${ckpt_prefix} \
+--opts decoding.batch_size ${batch_size}
+
+if [ $? -ne 0 ]; then
+    echo "Failed in ctc alignment!"
+    exit 1
+fi
+
+exit 0

examples/timit/local/data.sh

+#!/bin/bash
+
+stage=-1
+stop_stage=100
+
+unit_type=word
+TIMIT_path=
+
+source ${MAIN_ROOT}/utils/parse_options.sh
+
+mkdir -p data
+TARGET_DIR=${MAIN_ROOT}/examples/dataset
+mkdir -p ${TARGET_DIR}
+
+if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
+    # download data, generate manifests
+    python3 ${TARGET_DIR}/timit/timit_kaldi_standard_split.py \
+    --manifest_prefix="data/manifest" \
+    --src="data/local" \
+
+    if [ $? -ne 0 ]; then
+        echo "Prepare TIMIT failed. Terminated."
+        exit 1
+    fi
+fi
+
+if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
+    # build vocabulary
+    python3 ${MAIN_ROOT}/utils/build_vocab.py \
+    --unit_type ${unit_type} \
+    --count_threshold=0 \
+    --vocab_path="data/vocab.txt" \
+    --manifest_paths="data/manifest.train.raw"
+
+    if [ $? -ne 0 ]; then
+        echo "Build vocabulary failed. Terminated."
+        exit 1
+    fi
+fi
+
+
+if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
+    # compute mean and stddev for normalizer
+    num_workers=$(nproc)
+    python3 ${MAIN_ROOT}/utils/compute_mean_std.py \
+    --manifest_path="data/manifest.train.raw" \
+    --num_samples=-1 \
+    --specgram_type="fbank" \
+    --feat_dim=80 \
+    --delta_delta=false \
+    --sample_rate=16000 \
+    --stride_ms=10.0 \
+    --window_ms=25.0 \
+    --use_dB_normalization=False \
+    --num_workers=${num_workers} \
+    --output_path="data/mean_std.json"
+
+    if [ $? -ne 0 ]; then
+        echo "Compute mean and stddev failed. Terminated."
+        exit 1
+    fi
+fi
+
+
+if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
+    # format manifest with tokenids, vocab size
+    for set in train dev test; do
+    {
+        python3 ${MAIN_ROOT}/utils/format_data.py \
+        --feat_type "raw" \
+        --cmvn_path "data/mean_std.json" \
+        --unit_type ${unit_type} \
+        --vocab_path="data/vocab.txt" \
+        --manifest_path="data/manifest.${set}.raw" \
+        --output_path="data/manifest.${set}"
+
+        if [ $? -ne 0 ]; then
+            echo "Formt mnaifest.${set} failed. Terminated."
+            exit 1
+        fi
+    }&
+    done
+    wait
+fi
+
+echo "TIMIT Data preparation done."
+exit 0

examples/timit/local/export.sh

+#!/bin/bash
+
+if [ $# != 3 ];then
+    echo "usage: $0 config_path ckpt_prefix jit_model_path"
+    exit -1
+fi
+
+ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+echo "using $ngpu gpus..."
+
+config_path=$1
+ckpt_path_prefix=$2
+jit_model_export_path=$3
+
+device=gpu
+if [ ${ngpu} == 0 ];then
+    device=cpu
+fi
+
+python3 -u ${BIN_DIR}/export.py \
+--device ${device} \
+--nproc ${ngpu} \
+--config ${config_path} \
+--checkpoint_path ${ckpt_path_prefix} \
+--export_path ${jit_model_export_path}
+
+
+if [ $? -ne 0 ]; then
+    echo "Failed in export!"
+    exit 1
+fi
+
+
+exit 0

examples/timit/local/test.sh

+#!/bin/bash
+
+if [ $# != 2 ];then
+    echo "usage: ${0} config_path ckpt_path_prefix"
+    exit -1
+fi
+
+ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+echo "using $ngpu gpus..."
+
+device=gpu
+if [ ${ngpu} == 0 ];then
+    device=cpu
+fi
+
+config_path=$1
+ckpt_prefix=$2
+
+chunk_mode=false
+if [[ ${config_path} =~ ^chunk_ ]];then
+    chunk_mode=true
+fi
+
+
+# download language model
+#bash local/download_lm_en.sh
+#if [ $? -ne 0 ]; then
+#    exit 1
+#fi
+
+for type in attention ctc_greedy_search; do
+    echo "decoding ${type}"
+    if [ ${chunk_mode} == true ];then
+        # stream decoding only support batchsize=1
+        batch_size=1
+    else
+        batch_size=64
+    fi
+    python3 -u ${BIN_DIR}/test.py \
+    --device ${device} \
+    --nproc 1 \
+    --config ${config_path} \
+    --result_file ${ckpt_prefix}.${type}.rsl \
+    --checkpoint_path ${ckpt_prefix} \
+    --opts decoding.decoding_method ${type} decoding.batch_size ${batch_size}
+
+    if [ $? -ne 0 ]; then
+        echo "Failed in evaluation!"
+        exit 1
+    fi
+done
+
+for type in ctc_prefix_beam_search attention_rescoring; do
+    echo "decoding ${type}"
+    batch_size=1
+    python3 -u ${BIN_DIR}/test.py \
+    --device ${device} \
+    --nproc 1 \
+    --config ${config_path} \
+    --result_file ${ckpt_prefix}.${type}.rsl \
+    --checkpoint_path ${ckpt_prefix} \
+    --opts decoding.decoding_method ${type} decoding.batch_size ${batch_size}
+
+    if [ $? -ne 0 ]; then
+        echo "Failed in evaluation!"
+        exit 1
+    fi
+done
+
+
+exit 0

examples/timit/local/timit_data_prep.sh

+#!/usr/bin/env bash
+
+# Copyright 2013   (Authors: Bagher BabaAli, Daniel Povey, Arnab Ghoshal)
+#           2014   Brno University of Technology (Author: Karel Vesely)
+# Apache 2.0.
+
+if [ $# -ne 1 ]; then
+   echo "Argument should be the Timit directory, see ../run.sh for example."
+   exit 1;
+fi
+
+dir=`pwd`/data/local
+mkdir -p $dir
+local=`pwd`/local
+utils=`pwd`/utils
+conf=`pwd`/conf
+
+[ -f $conf/test_spk.list ] || error_exit "$PROG: Eval-set speaker list not found.";
+[ -f $conf/dev_spk.list ] || error_exit "$PROG: dev-set speaker list not found.";
+
+# First check if the train & test directories exist (these can either be upper-
+# or lower-cased
+if [ ! -d $*/TRAIN -o ! -d $*/TEST ] && [ ! -d $*/train -o ! -d $*/test ]; then
+  echo "timit_data_prep.sh: Spot check of command line argument failed"
+  echo "Command line argument must be absolute pathname to TIMIT directory"
+  echo "with name like /export/corpora5/LDC/LDC93S1/timit/TIMIT"
+  exit 1;
+fi
+
+# Now check what case the directory structure is
+uppercased=false
+train_dir=train
+test_dir=test
+if [ -d $*/TRAIN ]; then
+  uppercased=true
+  train_dir=TRAIN
+  test_dir=TEST
+fi
+
+tmpdir=$(mktemp -d /tmp/kaldi.XXXX);
+trap 'rm -rf "$tmpdir"' EXIT
+
+# Get the list of speakers. The list of speakers in the 24-speaker core test
+# set and the 50-speaker development set must be supplied to the script. All
+# speakers in the 'train' directory are used for training.
+if $uppercased; then
+  tr '[:lower:]' '[:upper:]' < $conf/dev_spk.list > $tmpdir/dev_spk
+  tr '[:lower:]' '[:upper:]' < $conf/test_spk.list > $tmpdir/test_spk
+  ls -d "$*"/TRAIN/DR*/* | sed -e "s:^.*/::" > $tmpdir/train_spk
+else
+  tr '[:upper:]' '[:lower:]' < $conf/dev_spk.list > $tmpdir/dev_spk
+  tr '[:upper:]' '[:lower:]' < $conf/test_spk.list > $tmpdir/test_spk
+  ls -d "$*"/train/dr*/* | sed -e "s:^.*/::" > $tmpdir/train_spk
+fi
+
+cd $dir
+for x in train dev test; do
+  # First, find the list of audio files (use only si & sx utterances).
+  # Note: train & test sets are under different directories, but doing find on
+  # both and grepping for the speakers will work correctly.
+  find $*/{$train_dir,$test_dir} -not \( -iname 'SA*' \) -iname '*.WAV' \
+    | grep -f $tmpdir/${x}_spk > ${x}_sph.flist
+
+  sed -e 's:.*/\(.*\)/\(.*\).\(WAV\|wav\)$:\1_\2:' ${x}_sph.flist \
+    > $tmpdir/${x}_sph.uttids
+  paste $tmpdir/${x}_sph.uttids ${x}_sph.flist \
+    | sort -k1,1 > ${x}_sph.scp
+
+  cat ${x}_sph.scp | awk '{print $1}' > ${x}.uttids
+
+  # Now, Convert the transcripts into our format (no normalization yet)
+  # Get the transcripts: each line of the output contains an utterance
+  # ID followed by the transcript.
+  find $*/{$train_dir,$test_dir} -not \( -iname 'SA*' \) -iname '*.PHN' \
+    | grep -f $tmpdir/${x}_spk > $tmpdir/${x}_phn.flist
+  sed -e 's:.*/\(.*\)/\(.*\).\(PHN\|phn\)$:\1_\2:' $tmpdir/${x}_phn.flist \
+    > $tmpdir/${x}_phn.uttids
+  while read line; do
+    [ -f $line ] || error_exit "Cannot find transcription file '$line'";
+    cut -f3 -d' ' "$line" | tr '\n' ' ' | perl -ape 's: *$:\n:;'
+  done < $tmpdir/${x}_phn.flist > $tmpdir/${x}_phn.trans
+  paste $tmpdir/${x}_phn.uttids $tmpdir/${x}_phn.trans \
+    | sort -k1,1 > ${x}.trans
+
+  # Do normalization steps.
+  cat ${x}.trans | $local/timit_norm_trans.pl -i - -m $conf/phones.60-48-39.map -to 39 | sort > $x.text || exit 1;
+
+done
+
+echo "Data preparation succeeded"
\ No newline at end of file

examples/timit/local/timit_norm_trans.pl

+#!/usr/bin/env perl
+use warnings; #sed replacement for -w perl parameter
+
+# Copyright 2012  Arnab Ghoshal
+
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#  http://www.apache.org/licenses/LICENSE-2.0
+#
+# THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
+# WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
+# MERCHANTABLITY OR NON-INFRINGEMENT.
+# See the Apache 2 License for the specific language governing permissions and
+# limitations under the License.
+
+
+# This script normalizes the TIMIT phonetic transcripts that have been 
+# extracted in a format where each line contains an utterance ID followed by 
+# the transcript, e.g.:
+# fcke0_si1111 h# hh ah dx ux w iy dcl d ix f ay n ih q h#
+
+my $usage = "Usage: timit_norm_trans.pl -i transcript -m phone_map -from [60|48] -to [48|39] > normalized\n
+Normalizes phonetic transcriptions for TIMIT, by mapping the phones to a 
+smaller set defined by the -m option. This script assumes that the mapping is 
+done in the \"standard\" fashion, i.e. to 48 or 39 phones.  The input is 
+assumed to have 60 phones (+1 for glottal stop, which is deleted), but that can
+be changed using the -from option. The input format is assumed to be utterance 
+ID followed by transcript on the same line.\n";
+
+use strict;
+use Getopt::Long;
+die "$usage" unless(@ARGV >= 1);
+my ($in_trans, $phone_map, $num_phones_out);
+my $num_phones_in = 60;
+GetOptions ("i=s" => \$in_trans,          # Input transcription
+	    "m=s" => \$phone_map,         # File containing phone mappings
+	    "from=i" => \$num_phones_in,  # Input #phones: must be 60 or 48
+	    "to=i" => \$num_phones_out ); # Output #phones: must be 48 or 39
+
+die $usage unless(defined($in_trans) && defined($phone_map) && 
+		  defined($num_phones_out));
+if ($num_phones_in != 60 && $num_phones_in != 48) {
+  die "Can only used 60 or 48 for -from (used $num_phones_in)."
+}
+if ($num_phones_out != 48 && $num_phones_out != 39) {
+  die "Can only used 48 or 39 for -to (used $num_phones_out)."
+}
+unless ($num_phones_out < $num_phones_in) {
+  die "Argument to -from ($num_phones_in) must be greater than that to -to ($num_phones_out)."
+}
+
+
+open(M, "<$phone_map") or die "Cannot open mappings file '$phone_map': $!";
+my (%phonemap, %seen_phones);
+my $num_seen_phones = 0;
+while (<M>) {
+  chomp;
+  next if ($_ =~ /^q\s*.*$/); # Ignore glottal stops.
+  m:^(\S+)\s+(\S+)\s+(\S+)$: or die "Bad line: $_";
+  my $mapped_from = ($num_phones_in == 60)? $1 : $2;
+  my $mapped_to = ($num_phones_out == 48)? $2 : $3;
+  if (!defined($seen_phones{$mapped_to})) {
+    $seen_phones{$mapped_to} = 1;
+    $num_seen_phones += 1;
+  }
+  $phonemap{$mapped_from} = $mapped_to;
+}
+if ($num_seen_phones != $num_phones_out) {
+  die "Trying to map to $num_phones_out phones, but seen only $num_seen_phones";
+}
+
+open(T, "<$in_trans") or die "Cannot open transcription file '$in_trans': $!";
+while (<T>) {
+  chomp;
+  $_ =~ m:^(\S+)\s+(.+): or die "Bad line: $_";
+  my $utt_id = $1;
+  my $trans = $2;
+
+  $trans =~ s/q//g;  # Remove glottal stops.
+  $trans =~ s/^\s*//; $trans =~ s/\s*$//;  # Normalize spaces
+
+  print $utt_id;
+  for my $phone (split(/\s+/, $trans)) {
+    if(exists $phonemap{$phone}) { print " $phonemap{$phone}"; }
+    if(not exists $phonemap{$phone}) { print " $phone"; }
+  }
+  print "\n";
+}
\ No newline at end of file

examples/timit/local/train.sh

+#!/bin/bash
+
+if [ $# != 2 ];then
+    echo "usage: CUDA_VISIBLE_DEVICES=0 ${0} config_path ckpt_name"
+    exit -1
+fi
+
+ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+echo "using $ngpu gpus..."
+
+config_path=$1
+ckpt_name=$2
+
+device=gpu
+if [ ${ngpu} == 0 ];then
+    device=cpu
+fi
+echo "using ${device}..."
+
+mkdir -p exp
+
+python3 -u ${BIN_DIR}/train.py \
+--device ${device} \
+--nproc ${ngpu} \
+--config ${config_path} \
+--output exp/${ckpt_name}
+
+if [ $? -ne 0 ]; then
+    echo "Failed in training!"
+    exit 1
+fi
+
+exit 0

examples/timit/path.sh

+export MAIN_ROOT=${PWD}/../../
+export PATH=${MAIN_ROOT}:${MAIN_ROOT}/utils:${PATH}
+export LC_ALL=C
+
+# Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
+export PYTHONIOENCODING=UTF-8 
+export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
+
+export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/lib/
+
+
+MODEL=u2
+export BIN_DIR=${MAIN_ROOT}/deepspeech/exps/${MODEL}/bin

examples/timit/run.sh

+#!/bin/bash
+set -e
+source path.sh
+
+stage=0
+stop_stage=50
+conf_path=conf/transformer.yaml
+avg_num=10
+TIMIT_path=
+source ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
+
+avg_ckpt=avg_${avg_num}
+ckpt=$(basename ${conf_path} | awk -F'.' '{print $1}')
+echo "checkpoint name ${ckpt}"
+
+if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
+    # prepare data
+    bash ./local/timit_data_prep.sh ${TIMIT_path}
+    bash ./local/data.sh || exit -1
+fi
+
+if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
+    # train model, all `ckpt` under `exp` dir
+    CUDA_VISIBLE_DEVICES=0,1,2,3 ./local/train.sh ${conf_path}  ${ckpt}
+fi
+
+if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
+    # avg n best model
+    avg.sh exp/${ckpt}/checkpoints ${avg_num}
+fi
+
+if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
+    # test ckpt avg_n
+    CUDA_VISIBLE_DEVICES=7 ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
+fi
+
+if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
+    # ctc alignment of test data
+    CUDA_VISIBLE_DEVICES=0 ./local/align.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
+fi
+
+if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
+    # export ckpt avg_n
+    CUDA_VISIBLE_DEVICES= ./local/export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit
+fi