How to use the librosa.filters.mel function in librosa

def _build_mel_basis(hparams):
  assert hparams.fmax <= hparams.sample_rate // 2
  return librosa.filters.mel(hparams.sample_rate,
                             hparams.n_fft,
                             n_mels=hparams.num_mels,
                             fmin=hparams.fmin,
                             fmax=hparams.fmax)

y, _ = librosa.effects.trim(y)

    # Preemphasis
    y = np.append(y[0], y[1:] - hp.preemphasis * y[:-1])

    # stft
    linear = librosa.stft(y=y,
                          n_fft=hp.n_fft,
                          hop_length=hp.hop_length,
                          win_length=hp.win_length)

    # magnitude spectrogram
    mag = np.abs(linear)  # (1+n_fft//2, T)

    # mel spectrogram
    mel_basis = librosa.filters.mel(hp.sr, hp.n_fft, hp.n_mels)  # (n_mels, 1+n_fft//2)
    mel = np.dot(mel_basis, mag ** 2)  # (n_mels, t)

    # Transpose
    mel = mel.T.astype(np.float32)  # (T, n_mels)
    mag = mag.T.astype(np.float32)  # (T, 1+n_fft//2)

    # Sequence length
    dones = np.ones_like(mel[:, 0])

    # Padding
    mel = np.pad(mel, ((0, hp.T_y - len(mel)), (0, 0)), mode="constant")[:hp.T_y]
    mag = np.pad(mag, ((0, hp.T_y - len(mag)), (0, 0)), mode="constant")[:hp.T_y]
    dones = np.pad(dones, ((0, hp.T_y - len(dones))), mode="constant")[:hp.T_y]

    # Log
    mel = np.log10(mel + 1e-8)

>>> librosa.display.specshow(librosa.amplitude_to_db(S, ref=np.max, top_db=None),
    ...                          y_axis='log', x_axis='time')
    >>> plt.colorbar()
    >>> plt.title('Original STFT')
    >>> plt.subplot(2,1,2)
    >>> librosa.display.specshow(librosa.amplitude_to_db(np.abs(S_inv - S),
    ...                                                  ref=S.max(), top_db=None),
    ...                          vmax=0, y_axis='log', x_axis='time', cmap='magma')
    >>> plt.title('Residual error (dB)')
    >>> plt.colorbar()
    >>> plt.tight_layout()
    >>> plt.show()
    '''

    # Construct a mel basis with dtype matching the input data
    mel_basis = filters.mel(sr, n_fft, n_mels=M.shape[0],
                            dtype=M.dtype,
                            **kwargs)

    # Find the non-negative least squares solution, and apply
    # the inverse exponent.
    # We'll do the exponentiation in-place.
    inverse = nnls(mel_basis, M)
    return np.power(inverse, 1./power, out=inverse)

def build_mel_basis():
    return librosa.filters.mel(hp.sample_rate, hp.n_fft, n_mels=hp.num_mels, fmin=hp.fmin)

def freq2mel(f_data,sr,fft_size,n_mel,fmax=8000):
    pre_matrix = librosa.filters.mel(sr, fft_size, n_mel, fmax=fmax)
    matrix = pre_matrix.T / np.sum(pre_matrix.T,axis=0)
    return np.dot(f_data,matrix)

Returns:
    mfcc - coefficients
    mag - magnitude spectrum
    mel
    '''
    # Pre-emphasis
    y_preem = preemphasis(wav, coeff=preemphasis_coeff)

    # Get spectrogram
    D = librosa.stft(y=y_preem, n_fft=n_fft,
                     hop_length=hop_length, win_length=win_length)
    mag = np.abs(D)

    # Get mel-spectrogram
    mel_basis = librosa.filters.mel(
        hp.Default.sr, hp.Default.n_fft, hp.Default.n_mels)  # (n_mels, 1+n_fft//2)
    mel = np.dot(mel_basis, mag)  # (n_mels, t) # mel spectrogram

    # Get mfccs
    db = librosa.amplitude_to_db(mel)
    mfccs = np.dot(librosa.filters.dct(hp.Default.n_mfcc, db.shape[0]), db)

    # Log
    mag = np.log(mag + sys.float_info.epsilon)
    mel = np.log(mel + sys.float_info.epsilon)

    # Normalization
    # self.y_log_spec = (y_log_spec - hp.mean_log_spec) / hp.std_log_spec
    # self.y_log_spec = (y_log_spec - hp.min_log_spec) / (hp.max_log_spec - hp.min_log_spec)

    return mfccs.T, mag.T, mel.T  # (t, n_mfccs), (t, 1+n_fft/2), (t, n_mels)

y, _ = librosa.effects.trim(y)

    # Preemphasis
    y = np.append(y[0], y[1:] - hp.preemphasis * y[:-1])

    # stft
    linear = librosa.stft(y=y,
                          n_fft=hp.n_fft,
                          hop_length=hp.hop_length,
                          win_length=hp.win_length)

    # magnitude spectrogram
    mag = np.abs(linear)  # (1+n_fft//2, T)

    # mel spectrogram
    mel_basis = librosa.filters.mel(hp.sr, hp.n_fft, hp.n_mels)  # (n_mels, 1+n_fft//2)
    mel = np.dot(mel_basis, mag)  # (n_mels, t)

    # to decibel
    mel = 20 * np.log10(np.maximum(1e-5, mel))
    mag = 20 * np.log10(np.maximum(1e-5, mag))

    # normalize
    mel = np.clip((mel - hp.ref_db + hp.max_db) / hp.max_db, 1e-8, 1)
    mag = np.clip((mag - hp.ref_db + hp.max_db) / hp.max_db, 1e-8, 1)

    # Transpose
    mel = mel.T.astype(np.float32)  # (T, n_mels)
    mag = mag.T.astype(np.float32)  # (T, 1+n_fft//2)

    return mel, mag

>>> S_dB = librosa.power_to_db(S, ref=np.max)
    >>> librosa.display.specshow(S_dB, x_axis='time',
    ...                          y_axis='mel', sr=sr,
    ...                          fmax=8000)
    >>> plt.colorbar(format='%+2.0f dB')
    >>> plt.title('Mel-frequency spectrogram')
    >>> plt.tight_layout()
    >>> plt.show()
    """

    S, n_fft = _spectrogram(y=y, S=S, n_fft=n_fft, hop_length=hop_length, power=power,
                            win_length=win_length, window=window, center=center,
                            pad_mode=pad_mode)

    # Build a Mel filter
    mel_basis = filters.mel(sr, n_fft, **kwargs)

    return np.dot(mel_basis, S)

Returns:
    mfcc - coefficients
    mag - magnitude spectrum
    mel
    '''
    # Pre-emphasis
    y_preem = preemphasis(wav, coeff=preemphasis_coeff)

    # Get spectrogram
    D = librosa.stft(y=y_preem, n_fft=n_fft,
                     hop_length=hop_length, win_length=win_length)
    mag = np.abs(D)

    # Get mel-spectrogram
    mel_basis = librosa.filters.mel(
        hp.Default.sr, hp.Default.n_fft, hp.Default.n_mels)  # (n_mels, 1+n_fft//2)
    mel = np.dot(mel_basis, mag)  # (n_mels, t) # mel spectrogram

    # Get mfccs
    db = librosa.amplitude_to_db(mel)
    mfccs = np.dot(librosa.filters.dct(hp.Default.n_mfcc, db.shape[0]), db)

    # Log
    mag = np.log(mag + sys.float_info.epsilon)
    mel = np.log(mel + sys.float_info.epsilon)

    # Normalization
    # self.y_log_spec = (y_log_spec - hp.mean_log_spec) / hp.std_log_spec
    # self.y_log_spec = (y_log_spec - hp.min_log_spec) / (hp.max_log_spec - hp.min_log_spec)

    return mfccs.T, mag.T, mel.T  # (t, n_mfccs), (t, 1+n_fft/2), (t, n_mels)