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, 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]