35a75a4fb59ba84e62d360dfdef2fcc2451c2343,open_seq2seq/data/speech2text/speech_utils.py,,augment_audio_signal,#Any#Any#Any#,209

Before Change

  signal_float += np.random.randn(signal_float.shape[0]) * \
                  10.0 ** (noise_level_db / 20.0)

  return (normalize_signal(signal_float) * 32767.0).astype(np.int16)


def get_speech_features(signal, sample_freq, num_features, pad_to=8,
                        features_type="spectrogram",

After Change

    )

  // noise
  if "noise_level_min" in augmentation and "noise_level_max" in augmentation:
    noise_level_db = np.random.randint(low=augmentation["noise_level_min"],
                                       high=augmentation["noise_level_max"])
    signal_float += np.random.randn(signal_float.shape[0]) * \
                    10.0 ** (noise_level_db / 20.0)

  return normalize_signal(signal_float)


def preemphasis(signal, coeff=0.97):

Instances