3bd4db86630b75bbbfb6c5c0a1a85603097bf9b2,torchaudio/transforms.py,F2M,__call__,#F2M#Any#,248

Before Change

    def __call__(self, spec_f):

        spec_f, is_variable = _check_is_variable(spec_f)
        n_fft = spec_f.size(2)

        m_min = 0. if self.f_min == 0 else 2595 * np.log10(1. + (self.f_min / 700))
        m_max = 2595 * np.log10(1. + (self.f_max / 700))

        m_pts = torch.linspace(m_min, m_max, self.n_mels + 2)
        f_pts = (700 * (10**(m_pts / 2595) - 1))

        bins = torch.floor(((n_fft - 1) * 2) * f_pts / self.sr).long()

        fb = torch.zeros(n_fft, self.n_mels)
        for m in range(1, self.n_mels + 1):
            f_m_minus = bins[m - 1].item()
            f_m = bins[m].item()
            f_m_plus = bins[m + 1].item()

            if f_m_minus != f_m:
                fb[f_m_minus:f_m, m - 1] = (torch.arange(f_m_minus, f_m) - f_m_minus) / (f_m - f_m_minus)
            if f_m != f_m_plus:
                fb[f_m:f_m_plus, m - 1] = (f_m_plus - torch.arange(f_m, f_m_plus)) / (f_m_plus - f_m)

        fb = Variable(fb)
        spec_m = torch.matmul(spec_f, fb)  // (c, l, n_fft) dot (n_fft, n_mels) -> (c, l, n_mels)
        return spec_m if is_variable else spec_m.data


class SPEC2DB(object):
    Turns a spectrogram from the power/amplitude scale to the decibel scale.

After Change

                fb[f_m:f_m_plus, m - 1] = (f_m_plus - torch.arange(f_m, f_m_plus)) / (f_m_plus - f_m)

        spec_m = torch.matmul(spec_f, fb)  // (c, l, n_fft) dot (n_fft, n_mels) -> (c, l, n_mels)
        return spec_m


class SPEC2DB(object):
    Turns a spectrogram from the power/amplitude scale to the decibel scale.

Instances