def transform_wav_data(wav_data):
"""Transforms with sox."""
if jitter_amount_sec:
wav_data = audio_io.jitter_wav_data(wav_data, hparams.sample_rate,
jitter_amount_sec)
wav_data = audio_transform.transform_wav_audio(wav_data, hparams)
return [wav_data]
def transform_wav_data(wav_data):
"""Transforms with sox."""
if jitter_amount_sec:
wav_data = audio_io.jitter_wav_data(wav_data, hparams.sample_rate,
jitter_amount_sec)
wav_data = audio_transform.transform_wav_audio(wav_data, hparams)
return [wav_data]
def transform_wav_data(wav_data):
"""Transforms wav data."""
# Only do audio transformations during training.
if is_training:
wav_data = audio_io.jitter_wav_data(wav_data, hparams.sample_rate,
jitter_amount_sec)
# Normalize.
if hparams.normalize_audio:
wav_data = audio_io.normalize_wav_data(wav_data, hparams.sample_rate)
return [wav_data]
def transform_wav_data(wav_data):
"""Transforms wav data."""
# Only do audio transformations during training.
if is_training:
wav_data = audio_io.jitter_wav_data(wav_data, hparams.sample_rate,
jitter_amount_sec)
# Normalize.
if hparams.normalize_audio:
wav_data = audio_io.normalize_wav_data(wav_data, hparams.sample_rate)
return [wav_data]
def transform_wav_data(wav_data, sequence_tensor):
"""Transforms with sox."""
sequence, cropped_beginning_seconds = preprocess_sequence(
sequence_tensor, hparams)
# Only do audio transformations during training.
if is_training:
wav_data = audio_io.jitter_wav_data(wav_data, hparams.sample_rate,
jitter_amount_sec)
wav_data = audio_transform.transform_wav_audio(wav_data, hparams)
# If requested, crop wav.
if hparams.crop_training_sequence_to_notes:
wav_data = audio_io.crop_wav_data(wav_data, hparams.sample_rate,
cropped_beginning_seconds,
sequence.total_time)
# Normalize.
if hparams.normalize_audio:
wav_data = audio_io.normalize_wav_data(wav_data, hparams.sample_rate)
return [wav_data]
def transform_wav_data(wav_data, sequence_tensor):
"""Transforms with sox."""
sequence, cropped_beginning_seconds = preprocess_sequence(
sequence_tensor, hparams)
# Only do audio transformations during training.
if is_training:
wav_data = audio_io.jitter_wav_data(wav_data, hparams.sample_rate,
jitter_amount_sec)
wav_data = audio_transform.transform_wav_audio(wav_data, hparams)
# If requested, crop wav.
if hparams.crop_training_sequence_to_notes:
wav_data = audio_io.crop_wav_data(wav_data, hparams.sample_rate,
cropped_beginning_seconds,
sequence.total_time)
# Normalize.
if hparams.normalize_audio:
wav_data = audio_io.normalize_wav_data(wav_data, hparams.sample_rate)
return [wav_data]
