How to use the torchaudio.transforms.MelSpectrogram function in torchaudio

input_path = os.path.join(self.test_dirpath, 'assets', 'sinewave.wav')
            sound, sample_rate = torchaudio.load(input_path)
            sound_librosa = sound.cpu().numpy().squeeze()  # (64000)

            # test core spectrogram
            spect_transform = torchaudio.transforms.Spectrogram(n_fft=n_fft, hop_length=hop_length, power=2)
            out_librosa, _ = librosa.core.spectrum._spectrogram(y=sound_librosa,
                                                                n_fft=n_fft,
                                                                hop_length=hop_length,
                                                                power=2)

            out_torch = spect_transform(sound).squeeze().cpu()
            self.assertTrue(torch.allclose(out_torch, torch.from_numpy(out_librosa), atol=1e-5))

            # test mel spectrogram
            melspect_transform = torchaudio.transforms.MelSpectrogram(
                sample_rate=sample_rate, window_fn=torch.hann_window,
                hop_length=hop_length, n_mels=n_mels, n_fft=n_fft)
            librosa_mel = librosa.feature.melspectrogram(y=sound_librosa, sr=sample_rate,
                                                         n_fft=n_fft, hop_length=hop_length, n_mels=n_mels,
                                                         htk=True, norm=None)
            librosa_mel_tensor = torch.from_numpy(librosa_mel)
            torch_mel = melspect_transform(sound).squeeze().cpu()

            self.assertTrue(torch.allclose(torch_mel.type(librosa_mel_tensor.dtype), librosa_mel_tensor, atol=5e-3))

            # test s2db
            db_transform = torchaudio.transforms.AmplitudeToDB('power', 80.)
            db_torch = db_transform(spect_transform(sound)).squeeze().cpu()
            db_librosa = librosa.core.spectrum.power_to_db(out_librosa)
            self.assertTrue(torch.allclose(db_torch, torch.from_numpy(db_librosa), atol=5e-3))

def test_melspectrogram_load_save(self):
        waveform = self.waveform.float()
        mel_spectrogram_transform = transforms.MelSpectrogram()
        mel_spectrogram_transform(waveform)

        mel_spectrogram_transform_copy = transforms.MelSpectrogram()
        mel_spectrogram_transform_copy.load_state_dict(mel_spectrogram_transform.state_dict())

        window = mel_spectrogram_transform.spectrogram.window
        window_copy = mel_spectrogram_transform_copy.spectrogram.window

        fb = mel_spectrogram_transform.mel_scale.fb
        fb_copy = mel_spectrogram_transform_copy.mel_scale.fb

        self.assertTrue(torch.allclose(window, window_copy))
        # the default for n_fft = 400 and n_mels = 128
        self.assertEqual(fb_copy.size(), (201, 128))
        self.assertTrue(torch.allclose(fb, fb_copy))

def test_scriptmodule_MelSpectrogram(self):
        tensor = torch.rand((1, 1000), device="cuda")

        self._test_script_module(tensor, transforms.MelSpectrogram)

waveform = self.waveform.clone()  # (1, 16000)
        waveform_scaled = self.scale(waveform)  # (1, 16000)
        mel_transform = transforms.MelSpectrogram()
        # check defaults
        spectrogram_torch = s2db(mel_transform(waveform_scaled))  # (1, 128, 321)
        self.assertTrue(spectrogram_torch.dim() == 3)
        self.assertTrue(spectrogram_torch.ge(spectrogram_torch.max() - top_db).all())
        self.assertEqual(spectrogram_torch.size(1), mel_transform.n_mels)
        # check correctness of filterbank conversion matrix
        self.assertTrue(mel_transform.mel_scale.fb.sum(1).le(1.).all())
        self.assertTrue(mel_transform.mel_scale.fb.sum(1).ge(0.).all())
        # check options
        kwargs = {'window_fn': torch.hamming_window, 'pad': 10, 'win_length': 500,
                  'hop_length': 125, 'n_fft': 800, 'n_mels': 50}
        mel_transform2 = transforms.MelSpectrogram(**kwargs)
        spectrogram2_torch = s2db(mel_transform2(waveform_scaled))  # (1, 50, 513)
        self.assertTrue(spectrogram2_torch.dim() == 3)
        self.assertTrue(spectrogram_torch.ge(spectrogram_torch.max() - top_db).all())
        self.assertEqual(spectrogram2_torch.size(1), mel_transform2.n_mels)
        self.assertTrue(mel_transform2.mel_scale.fb.sum(1).le(1.).all())
        self.assertTrue(mel_transform2.mel_scale.fb.sum(1).ge(0.).all())
        # check on multi-channel audio
        x_stereo, sr_stereo = torchaudio.load(self.test_filepath)  # (2, 278756), 44100
        spectrogram_stereo = s2db(mel_transform(x_stereo))  # (2, 128, 1394)
        self.assertTrue(spectrogram_stereo.dim() == 3)
        self.assertTrue(spectrogram_stereo.size(0) == 2)
        self.assertTrue(spectrogram_torch.ge(spectrogram_torch.max() - top_db).all())
        self.assertEqual(spectrogram_stereo.size(1), mel_transform.n_mels)
        # check filterbank matrix creation
        fb_matrix_transform = transforms.MelScale(
            n_mels=100, sample_rate=16000, f_min=0., f_max=None, n_stft=400)

def test_scriptmodule_MelSpectrogram(self):
        tensor = torch.rand((1, 1000))
        _test_script_module(transforms.MelSpectrogram, tensor)

def test_melspectrogram_load_save(self):
        waveform = self.waveform.float()
        mel_spectrogram_transform = transforms.MelSpectrogram()
        mel_spectrogram_transform(waveform)

        mel_spectrogram_transform_copy = transforms.MelSpectrogram()
        mel_spectrogram_transform_copy.load_state_dict(mel_spectrogram_transform.state_dict())

        window = mel_spectrogram_transform.spectrogram.window
        window_copy = mel_spectrogram_transform_copy.spectrogram.window

        fb = mel_spectrogram_transform.mel_scale.fb
        fb_copy = mel_spectrogram_transform_copy.mel_scale.fb

        self.assertTrue(torch.allclose(window, window_copy))
        # the default for n_fft = 400 and n_mels = 128
        self.assertEqual(fb_copy.size(), (201, 128))
        self.assertTrue(torch.allclose(fb, fb_copy))

def test_mel2(self):
        top_db = 80.
        s2db = transforms.AmplitudeToDB('power', top_db)

        waveform = self.waveform.clone()  # (1, 16000)
        waveform_scaled = self.scale(waveform)  # (1, 16000)
        mel_transform = transforms.MelSpectrogram()
        # check defaults
        spectrogram_torch = s2db(mel_transform(waveform_scaled))  # (1, 128, 321)
        self.assertTrue(spectrogram_torch.dim() == 3)
        self.assertTrue(spectrogram_torch.ge(spectrogram_torch.max() - top_db).all())
        self.assertEqual(spectrogram_torch.size(1), mel_transform.n_mels)
        # check correctness of filterbank conversion matrix
        self.assertTrue(mel_transform.mel_scale.fb.sum(1).le(1.).all())
        self.assertTrue(mel_transform.mel_scale.fb.sum(1).ge(0.).all())
        # check options
        kwargs = {'window_fn': torch.hamming_window, 'pad': 10, 'win_length': 500,
                  'hop_length': 125, 'n_fft': 800, 'n_mels': 50}
        mel_transform2 = transforms.MelSpectrogram(**kwargs)
        spectrogram2_torch = s2db(mel_transform2(waveform_scaled))  # (1, 50, 513)
        self.assertTrue(spectrogram2_torch.dim() == 3)
        self.assertTrue(spectrogram_torch.ge(spectrogram_torch.max() - top_db).all())
        self.assertEqual(spectrogram2_torch.size(1), mel_transform2.n_mels)

# TODO See https://github.com/pytorch/audio/issues/165
class Spectrogram:
    forward = torchaudio.transforms.Spectrogram().forward


class AmplitudeToDB:
    forward = torchaudio.transforms.AmplitudeToDB().forward


class MelScale:
    forward = torchaudio.transforms.MelScale().forward


class MelSpectrogram:
    forward = torchaudio.transforms.MelSpectrogram().forward


class MFCC:
    forward = torchaudio.transforms.MFCC().forward


class MuLawEncoding:
    forward = torchaudio.transforms.MuLawEncoding().forward


class MuLawDecoding:
    forward = torchaudio.transforms.MuLawDecoding().forward


class Resample:
    # Resample isn't a script_method

def __init__(self, sample_rate: int, mel_size: int, n_fft: int, win_length: int,
                 hop_length: int, min_db: float, max_db: float,
                 mel_min: float = 0., mel_max: float = None):
        super().__init__()
        self.mel_size = mel_size
        # db to log
        self.min_db = np.log(np.power(10, min_db / 10))
        self.max_db = np.log(np.power(10, max_db / 10))

        self.melfunc = MelSpectrogram(sample_rate=sample_rate, n_fft=n_fft, win_length=win_length,
                                      hop_length=hop_length, f_min=mel_min, f_max=mel_max, n_mels=mel_size,
                                      window_fn=torch.hann_window)