def _CreateAsrFeatures():
# First pass: extract transcription files.
if False: #os.path.exists(FLAGS.transcripts_filepath):
trans = _LoadTranscriptionsFromFile()
else:
tf.logging.info('Running first pass on the fly')
trans = _ReadTranscriptionsFromCSV()
total_utts = len(trans)
tf.logging.info('Total transcripts: %d', len(trans))
tf_bytes = tf.placeholder(dtype=tf.string)
log_mel = audio_lib.ExtractLogMelFeatures(tf_bytes)
# Second pass: transcode the flac.
file_obj = tf.io.gfile.GFile(FLAGS.input_tarball, mode='rb')
tar = tarfile.open(fileobj=file_obj, mode='r:gz')
n = 0
recordio_writers = _OpenSubShards()
tfconf = tf.config_pb2.ConfigProto()
tfconf.gpu_options.allow_growth = True
with tf.Session(config=tfconf) as sess:
for tarinfo in tar:
# We can actually decode essentially any audio format, but we
# want to avoid non-audio data. Thus, this condition.
if not (tarinfo.name.endswith('.flac')
or tarinfo.name.endswith('.wav')
or tarinfo.name.endswith('.mp3')):
continue
n += 1
if n % FLAGS.num_shards != FLAGS.shard_id:
continue
f = tar.extractfile(tarinfo)
fmt = tarinfo.name.split('.')[-1]
uttid = tarinfo.name
audio_bytes = f.read()
f.close()
try:
wav_bytes = audio_lib.DecodeToWav(audio_bytes, fmt)
frames = sess.run(log_mel, feed_dict={tf_bytes: wav_bytes})
except Exception as e:
# raise
trans.pop(uttid)
tf.logging.info(f'{uttid} FAILED featurization')
continue
assert uttid in trans, uttid
num_words = len(trans[uttid])
tf.logging.info('utt[%d]: %s [%d frames, %d chars]', n, uttid,
frames.shape[1], num_words)
ex = _MakeTfExample(uttid, frames, trans[uttid])
outf = _SelectRandomShard(recordio_writers)
outf.write(ex.SerializeToString())
tar.close()
file_obj.close()
_CloseSubShards(recordio_writers)
tf.logging.info(f'Processed {len(trans)} / {total_utts}')
def testExtractLogMelFeatures(self):
with open(
test_helper.test_src_dir_path(
'tools/testdata/gan_or_vae.16k.wav'), 'rb') as f:
wav = f.read()
wav_bytes_t = tf.constant(wav, dtype=tf.string)
log_mel_t = audio_lib.ExtractLogMelFeatures(wav_bytes_t)
with self.session() as sess:
log_mel = sess.run(log_mel_t)
# Expect 314, 80 dimensional channels.
self.assertAllEqual(log_mel.shape, [1, 314, 80, 1])
def testExtractLogMelFeatures(self):
with open(
test_helper.test_src_dir_path(
'tools/testdata/gan_or_vae.16k.wav'), 'r') as f:
wav = f.read()
wav_bytes_t = tf.constant(wav, dtype=tf.string)
log_mel_t = audio_lib.ExtractLogMelFeatures(wav_bytes_t)
with self.session() as sess:
log_mel = sess.run(log_mel_t)
# We expect 105 frames, each of which consists of three 80 dimensional
# stacked frames.
self.assertAllEqual(log_mel.shape, [1, 105, 80 * 3, 1])
def _CreateAsrFeatures():
# First pass: extract transcription files.
if os.path.exists(FLAGS.transcripts_filepath):
trans = _LoadTranscriptionsFromFile()
else:
tf.logging.info('Running first pass on the fly')
trans = _ReadTranscriptions()
tf.logging.info('Total transcripts: %d', len(trans))
tf_bytes = tf.placeholder(dtype=tf.string)
# Great! It uses the frontend directly
log_mel = audio_lib.ExtractLogMelFeatures(tf_bytes)
# Second pass: transcode the flac.
file_obj = tf.io.gfile.GFile(FLAGS.input_tarball, mode='rb')
tar = tarfile.open(fileobj=file_obj, mode='r:gz')
n = 0
recordio_writers = _OpenSubShards()
tfconf = tf.config_pb2.ConfigProto()
tfconf.gpu_options.allow_growth = True
with tf.Session(config=tfconf) as sess:
for tarinfo in tar:
if not tarinfo.name.endswith('.flac'):
continue
n += 1
if n % FLAGS.num_shards != FLAGS.shard_id:
continue
uttid = re.sub('.*/(.+)\\.flac', '\\1', tarinfo.name)
f = tar.extractfile(tarinfo)
wav_bytes = audio_lib.DecodeFlacToWav(f.read())
f.close()
frames = sess.run(log_mel, feed_dict={tf_bytes: wav_bytes})
assert uttid in trans, uttid
num_words = len(trans[uttid])
tf.logging.info('utt[%d]: %s [%d frames, %d words]', n, uttid,
frames.shape[1], num_words)
ex = _MakeTfExample(uttid, frames, trans[uttid])
outf = _SelectRandomShard(recordio_writers)
outf.write(ex.SerializeToString())
tar.close()
file_obj.close()
_CloseSubShards(recordio_writers)
