Junkun
3 years ago
parent
4ce080407c
commit
58d761f056
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Prepare TIMIT dataset (Standard split from Kaldi)
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Create manifest files from splited datased.
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Manifest file is a json-format file with each line containing the
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meta data (i.e. audio filepath, transcript and audio duration)
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of each audio file in the data set.
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"""
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import argparse
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import codecs
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import json
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import os
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import soundfile
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument(
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"--src_dir",
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default="",
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type=str,
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help="Directory to kaldi splited data. (default: %(default)s)")
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parser.add_argument(
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"--manifest_prefix",
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default="manifest",
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type=str,
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help="Filepath prefix for output manifests. (default: %(default)s)")
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args = parser.parse_args()
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def create_manifest(data_dir, manifest_path_prefix):
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print("Creating manifest %s ..." % manifest_path_prefix)
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json_lines = []
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data_types = ['train', 'dev', 'test']
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for dtype in data_types:
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del json_lines[:]
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total_sec = 0.0
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total_text = 0.0
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total_num = 0
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phn_path = os.path.join(data_dir, dtype+'.text')
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phn_dict = {}
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for line in codecs.open(phn_path, 'r', 'utf-8'):
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line = line.strip()
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if line == '':
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continue
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audio_id, text = line.split(' ', 1)
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phn_dict[audio_id] = text
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audio_dir = os.path.join(data_dir, dtype+'_sph.scp')
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for line in codecs.open(audio_dir, 'r', 'utf-8'):
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audio_id, audio_path = line.strip().split()
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# if no transcription for audio then raise error
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assert audio_id in phn_dict
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audio_data, samplerate = soundfile.read(audio_path)
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duration = float(len(audio_data) / samplerate)
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text = phn_dict[audio_id]
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json_lines.append(
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json.dumps(
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{
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'utt': audio_id,
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'feat': audio_path,
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'feat_shape': (duration, ), # second
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'text': text
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},
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ensure_ascii=False))
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total_sec += duration
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total_text += len(text)
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total_num += 1
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manifest_path = manifest_path_prefix + '.' + dtype + '.raw'
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with codecs.open(manifest_path, 'w', 'utf-8') as fout:
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for line in json_lines:
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fout.write(line + '\n')
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def prepare_dataset(src_dir, manifest_path=None):
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"""create manifest file."""
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if os.path.isdir(manifest_path):
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manifest_path = os.path.join(manifest_path, 'manifest')
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if manifest_path:
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create_manifest(src_dir, manifest_path)
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def main():
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if args.src_dir.startswith('~'):
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args.src_dir = os.path.expanduser(args.src_dir)
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prepare_dataset(
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src_dir=args.src_dir,
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manifest_path=args.manifest_prefix)
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print("manifest prepare done!")
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if __name__ == '__main__':
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main()
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# TIMIT
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Results will be organized and updated soon.
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[
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{
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"type": "shift",
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"params": {
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"min_shift_ms": -5,
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"max_shift_ms": 5
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},
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"prob": 1.0
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},
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{
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"type": "speed",
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"params": {
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"min_speed_rate": 0.9,
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"max_speed_rate": 1.1,
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"num_rates": 3
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},
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"prob": 0.0
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},
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{
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"type": "specaug",
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"params": {
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"F": 10,
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"T": 50,
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"n_freq_masks": 2,
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"n_time_masks": 2,
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"p": 1.0,
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"W": 80,
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"adaptive_number_ratio": 0,
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"adaptive_size_ratio": 0,
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"max_n_time_masks": 20
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},
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"prob": 1.0
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}
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]
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@ -0,0 +1,50 @@
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faks0
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fdac1
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fjem0
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mgwt0
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mjar0
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mmdb1
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mmdm2
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mpdf0
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fcmh0
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fkms0
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mbdg0
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mbwm0
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mcsh0
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fadg0
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fdms0
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fedw0
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mgjf0
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mglb0
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mrtk0
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mtaa0
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mtdt0
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mthc0
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mwjg0
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fnmr0
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frew0
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fsem0
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mbns0
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mmjr0
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mdls0
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mdlf0
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mdvc0
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mers0
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fmah0
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fdrw0
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mrcs0
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mrjm4
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fcal1
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mmwh0
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fjsj0
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majc0
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mjsw0
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mreb0
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fgjd0
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fjmg0
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mroa0
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mteb0
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mjfc0
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mrjr0
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fmml0
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mrws1
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@ -0,0 +1,61 @@
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aa aa aa
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ae ae ae
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ah ah ah
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ao ao aa
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aw aw aw
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ax ax ah
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ax-h ax ah
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axr er er
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ay ay ay
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b b b
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bcl vcl sil
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ch ch ch
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d d d
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dcl vcl sil
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dh dh dh
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dx dx dx
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eh eh eh
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el el l
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em m m
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en en n
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eng ng ng
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epi epi sil
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er er er
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ey ey ey
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f f f
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g g g
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gcl vcl sil
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h# sil sil
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hh hh hh
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hv hh hh
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ih ih ih
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ix ix ih
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iy iy iy
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jh jh jh
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k k k
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kcl cl sil
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l l l
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m m m
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n n n
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ng ng ng
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nx n n
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ow ow ow
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oy oy oy
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p p p
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pau sil sil
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pcl cl sil
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q
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r r r
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s s s
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sh sh sh
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t t t
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tcl cl sil
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th th th
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uh uh uh
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uw uw uw
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ux uw uw
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v v v
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w w w
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y y y
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z z z
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zh zh sh
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mdab0
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mwbt0
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felc0
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mtas1
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mwew0
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fpas0
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mjmp0
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mlnt0
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fpkt0
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mlll0
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mtls0
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fjlm0
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mbpm0
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mklt0
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fnlp0
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mcmj0
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mjdh0
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fmgd0
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mgrt0
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mnjm0
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fdhc0
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mjln0
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mpam0
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fmld0
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# https://yaml.org/type/float.html
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data:
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train_manifest: data/manifest.train
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dev_manifest: data/manifest.dev
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test_manifest: data/manifest.test
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min_input_len: 0.5 # second
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max_input_len: 30.0 # second
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min_output_len: 0.0 # tokens
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max_output_len: 400.0 # tokens
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min_output_input_ratio: 0.05
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max_output_input_ratio: 100.0
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collator:
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vocab_filepath: data/vocab.txt
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unit_type: "word"
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mean_std_filepath: ""
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augmentation_config: ""
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batch_size: 64
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raw_wav: True # use raw_wav or kaldi feature
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specgram_type: fbank #linear, mfcc, fbank
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feat_dim: 80
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delta_delta: False
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dither: 1.0
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target_sample_rate: 16000
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max_freq: None
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n_fft: None
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stride_ms: 10.0
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window_ms: 25.0
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use_dB_normalization: True
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target_dB: -20
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random_seed: 0
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keep_transcription_text: False
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sortagrad: True
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shuffle_method: batch_shuffle
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num_workers: 2
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# network architecture
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model:
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cmvn_file: "data/mean_std.json"
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cmvn_file_type: "json"
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# encoder related
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encoder: transformer
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encoder_conf:
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output_size: 256 # dimension of attention
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attention_heads: 4
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linear_units: 2048 # the number of units of position-wise feed forward
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num_blocks: 12 # the number of encoder blocks
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dropout_rate: 0.1
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positional_dropout_rate: 0.1
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attention_dropout_rate: 0.0
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input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
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normalize_before: true
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# decoder related
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decoder: transformer
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decoder_conf:
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attention_heads: 4
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linear_units: 2048
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num_blocks: 6
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dropout_rate: 0.1
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positional_dropout_rate: 0.1
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self_attention_dropout_rate: 0.0
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src_attention_dropout_rate: 0.0
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# hybrid CTC/attention
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model_conf:
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ctc_weight: 0.3
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lsm_weight: 0.1 # label smoothing option
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length_normalized_loss: false
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training:
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n_epoch: 120
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accum_grad: 2
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global_grad_clip: 5.0
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optim: adam
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optim_conf:
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lr: 0.004
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weight_decay: 1e-06
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scheduler: warmuplr # pytorch v1.1.0+ required
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scheduler_conf:
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warmup_steps: 25000
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lr_decay: 1.0
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log_interval: 100
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checkpoint:
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kbest_n: 50
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latest_n: 5
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decoding:
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batch_size: 64
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error_rate_type: wer
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decoding_method: attention # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring'
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lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm
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alpha: 2.5
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beta: 0.3
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beam_size: 10
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cutoff_prob: 1.0
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cutoff_top_n: 0
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num_proc_bsearch: 8
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ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
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decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
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# <0: for decoding, use full chunk.
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# >0: for decoding, use fixed chunk size as set.
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# 0: used for training, it's prohibited here.
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num_decoding_left_chunks: -1 # number of left chunks for decoding. Defaults to -1.
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simulate_streaming: False # simulate streaming inference. Defaults to False.
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#!/bin/bash
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if [ $# != 2 ];then
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echo "usage: ${0} config_path ckpt_path_prefix"
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exit -1
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fi
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ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
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echo "using $ngpu gpus..."
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device=gpu
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if [ ${ngpu} == 0 ];then
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device=cpu
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fi
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config_path=$1
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ckpt_prefix=$2
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batch_size=1
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output_dir=${ckpt_prefix}
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mkdir -p ${output_dir}
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# align dump in `result_file`
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# .tier, .TextGrid dump in `dir of result_file`
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python3 -u ${BIN_DIR}/alignment.py \
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--device ${device} \
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--nproc 1 \
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--config ${config_path} \
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--result_file ${output_dir}/${type}.align \
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--checkpoint_path ${ckpt_prefix} \
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--opts decoding.batch_size ${batch_size}
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if [ $? -ne 0 ]; then
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echo "Failed in ctc alignment!"
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exit 1
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fi
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exit 0
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#!/bin/bash
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stage=-1
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stop_stage=100
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unit_type=word
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TIMIT_path=
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source ${MAIN_ROOT}/utils/parse_options.sh
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mkdir -p data
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TARGET_DIR=${MAIN_ROOT}/examples/dataset
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mkdir -p ${TARGET_DIR}
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if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
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# download data, generate manifests
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python3 ${TARGET_DIR}/timit/timit_kaldi_standard_split.py \
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--manifest_prefix="data/manifest" \
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--src="data/local" \
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if [ $? -ne 0 ]; then
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echo "Prepare TIMIT failed. Terminated."
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exit 1
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fi
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fi
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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# build vocabulary
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python3 ${MAIN_ROOT}/utils/build_vocab.py \
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--unit_type ${unit_type} \
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--count_threshold=0 \
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--vocab_path="data/vocab.txt" \
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--manifest_paths="data/manifest.train.raw"
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if [ $? -ne 0 ]; then
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echo "Build vocabulary failed. Terminated."
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exit 1
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fi
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fi
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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# compute mean and stddev for normalizer
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num_workers=$(nproc)
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python3 ${MAIN_ROOT}/utils/compute_mean_std.py \
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--manifest_path="data/manifest.train.raw" \
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--num_samples=-1 \
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--specgram_type="fbank" \
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--feat_dim=80 \
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--delta_delta=false \
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--sample_rate=16000 \
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--stride_ms=10.0 \
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--window_ms=25.0 \
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--use_dB_normalization=False \
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--num_workers=${num_workers} \
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--output_path="data/mean_std.json"
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if [ $? -ne 0 ]; then
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echo "Compute mean and stddev failed. Terminated."
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exit 1
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fi
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fi
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if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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# format manifest with tokenids, vocab size
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for set in train dev test; do
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{
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python3 ${MAIN_ROOT}/utils/format_data.py \
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--feat_type "raw" \
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--cmvn_path "data/mean_std.json" \
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--unit_type ${unit_type} \
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--vocab_path="data/vocab.txt" \
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--manifest_path="data/manifest.${set}.raw" \
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--output_path="data/manifest.${set}"
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if [ $? -ne 0 ]; then
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echo "Formt mnaifest.${set} failed. Terminated."
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exit 1
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fi
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}&
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done
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wait
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fi
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echo "TIMIT Data preparation done."
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exit 0
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@ -0,0 +1,34 @@
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#!/bin/bash
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if [ $# != 3 ];then
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echo "usage: $0 config_path ckpt_prefix jit_model_path"
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exit -1
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fi
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ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
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echo "using $ngpu gpus..."
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config_path=$1
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ckpt_path_prefix=$2
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jit_model_export_path=$3
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||||
|
||||
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
|
||||
@ -0,0 +1,71 @@
|
||||
#!/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
|
||||
@ -0,0 +1,90 @@
|
||||
#!/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"
|
||||
@ -0,0 +1,91 @@
|
||||
#!/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";
|
||||
}
|
||||
@ -0,0 +1,33 @@
|
||||
#!/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 [ $? -ne 0 ]; then
|
||||
echo "Failed in training!"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
exit 0
|
||||
@ -0,0 +1,13 @@
|
||||
export MAIN_ROOT=${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
|
||||
@ -0,0 +1,45 @@
|
||||
#!/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
|
||||
Loading…
Reference in new issue