audio_conv_utils.py 文件源码

def preprocess_input(audio_path, dim_ordering='default'):
    """Reads an audio file and outputs a Mel-spectrogram.

    # Arguments
        audio_path: path to the target audio file.
        dim_ordering: data format for the output spectrogram image.

    # Returns
        3D Numpy tensor encoding the Mel-spectrogram.

    # Raises
        ImportError: if librosa is not available.
    """
    if dim_ordering == 'default':
        dim_ordering = K.image_dim_ordering()
    assert dim_ordering in {'tf', 'th'}

    if librosa is None:
        raise ImportError('Librosa is required to process audio files. '
                          'Install it via `pip install librosa` or visit '
                          'http://librosa.github.io/librosa/ for details.')

    # mel-spectrogram parameters
    sr = 12000
    n_fft = 512
    n_mels = 96
    hop_length = 256
    duration = 29.12

    src, sr = librosa.load(audio_path, sr=sr)
    n_sample = src.shape[0]
    n_sample_wanted = int(duration * sr)

    # trim the signal at the center
    if n_sample < n_sample_wanted:  # if too short
        src = np.hstack((src, np.zeros((int(duration * sr) - n_sample,))))
    elif n_sample > n_sample_wanted:  # if too long
        src = src[(n_sample - n_sample_wanted) // 2:
                  (n_sample + n_sample_wanted) // 2]

    logam = librosa.logamplitude
    melgram = librosa.feature.melspectrogram
    x = logam(melgram(y=src, sr=sr, hop_length=hop_length,
                      n_fft=n_fft, n_mels=n_mels) ** 2,
              ref_power=1.0)

    if dim_ordering == 'th':
        x = np.expand_dims(x, axis=0)
    elif dim_ordering == 'tf':
        x = np.expand_dims(x, axis=3)
    return x