test_transforms.py

from __future__ import print_function
import math
import os

import torch
import torchaudio
from torchaudio.common_utils import IMPORT_LIBROSA, IMPORT_SCIPY
import torchaudio.transforms as transforms
import unittest
import test.common_utils

if IMPORT_LIBROSA:
    import librosa

if IMPORT_SCIPY:
    import scipy


class Tester(unittest.TestCase):

    # create a sinewave signal for testing
    sample_rate = 16000
    freq = 440
    volume = .3
    waveform = (torch.cos(2 * math.pi * torch.arange(0, 4 * sample_rate).float() * freq / sample_rate))
    waveform.unsqueeze_(0)  # (1, 64000)
    waveform = (waveform * volume * 2**31).long()
    # file for stereo stft test
    test_dirpath, test_dir = test.common_utils.create_temp_assets_dir()
    test_filepath = os.path.join(test_dirpath, 'assets',
                                 'steam-train-whistle-daniel_simon.mp3')

    def scale(self, waveform, factor=float(2**31)):
        # scales a waveform by a factor
        if not waveform.is_floating_point():
            waveform = waveform.to(torch.get_default_dtype())
        return waveform / factor

    def test_pad_trim(self):

        waveform = self.waveform.clone()
        length_orig = waveform.size(1)
        length_new = int(length_orig * 1.2)

        result = transforms.PadTrim(max_len=length_new)(waveform)
        self.assertEqual(result.size(1), length_new)

        length_new = int(length_orig * 0.8)

        result = transforms.PadTrim(max_len=length_new)(waveform)
        self.assertEqual(result.size(1), length_new)

    def test_mu_law_companding(self):

        quantization_channels = 256

        waveform = self.waveform.clone()
        waveform /= torch.abs(waveform).max()
        self.assertTrue(waveform.min() >= -1. and waveform.max() <= 1.)

        waveform_mu = transforms.MuLawEncoding(quantization_channels)(waveform)
        self.assertTrue(waveform_mu.min() >= 0. and waveform_mu.max() <= quantization_channels)

        waveform_exp = transforms.MuLawExpanding(quantization_channels)(waveform_mu)
        self.assertTrue(waveform_exp.min() >= -1. and waveform_exp.max() <= 1.)

    def test_mel2(self):
        top_db = 80.
        s2db = transforms.SpectrogramToDB('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)
        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)
        self.assertTrue(fb_matrix_transform.fb.sum(1).le(1.).all())
        self.assertTrue(fb_matrix_transform.fb.sum(1).ge(0.).all())
        self.assertEqual(fb_matrix_transform.fb.size(), (400, 100))

    def test_mfcc(self):
        audio_orig = self.waveform.clone()
        audio_scaled = self.scale(audio_orig)  # (1, 16000)

        sample_rate = 16000
        n_mfcc = 40
        n_mels = 128
        mfcc_transform = torchaudio.transforms.MFCC(sample_rate=sample_rate,
                                                    n_mfcc=n_mfcc,
                                                    norm='ortho')
        # check defaults
        torch_mfcc = mfcc_transform(audio_scaled)  # (1, 40, 321)
        self.assertTrue(torch_mfcc.dim() == 3)
        self.assertTrue(torch_mfcc.shape[1] == n_mfcc)
        self.assertTrue(torch_mfcc.shape[2] == 321)
        # check melkwargs are passed through
        melkwargs = {'win_length': 200}
        mfcc_transform2 = torchaudio.transforms.MFCC(sample_rate=sample_rate,
                                                     n_mfcc=n_mfcc,
                                                     norm='ortho',
                                                     melkwargs=melkwargs)
        torch_mfcc2 = mfcc_transform2(audio_scaled)  # (1, 40, 641)
        self.assertTrue(torch_mfcc2.shape[2] == 641)

        # check norms work correctly
        mfcc_transform_norm_none = torchaudio.transforms.MFCC(sample_rate=sample_rate,
                                                              n_mfcc=n_mfcc,
                                                              norm=None)
        torch_mfcc_norm_none = mfcc_transform_norm_none(audio_scaled)  # (1, 40, 321)

        norm_check = torch_mfcc.clone()
        norm_check[:, 0, :] *= math.sqrt(n_mels) * 2
        norm_check[:, 1:, :] *= math.sqrt(n_mels / 2) * 2

        self.assertTrue(torch_mfcc_norm_none.allclose(norm_check))

    @unittest.skipIf(not IMPORT_LIBROSA or not IMPORT_SCIPY, 'Librosa and scipy are not available')
    def test_librosa_consistency(self):
        def _test_librosa_consistency_helper(n_fft, hop_length, power, n_mels, n_mfcc, sample_rate):
            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.SpectrogramToDB('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))

            db_torch = db_transform(melspect_transform(sound)).squeeze().cpu()
            db_librosa = librosa.core.spectrum.power_to_db(librosa_mel)
            db_librosa_tensor = torch.from_numpy(db_librosa)

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

            # test MFCC
            melkwargs = {'hop_length': hop_length, 'n_fft': n_fft}
            mfcc_transform = torchaudio.transforms.MFCC(sample_rate=sample_rate,
                                                        n_mfcc=n_mfcc,
                                                        norm='ortho',
                                                        melkwargs=melkwargs)

            # librosa.feature.mfcc doesn't pass kwargs properly since some of the
            # kwargs for melspectrogram and mfcc are the same. We just follow the
            # function body in https://librosa.github.io/librosa/_modules/librosa/feature/spectral.html#melspectrogram
            # to mirror this function call with correct args:

    #         librosa_mfcc = librosa.feature.mfcc(y=sound_librosa,
    #                                             sr=sample_rate,
    #                                             n_mfcc = n_mfcc,
    #                                             hop_length=hop_length,
    #                                             n_fft=n_fft,
    #                                             htk=True,
    #                                             norm=None,
    #                                             n_mels=n_mels)

            librosa_mfcc = scipy.fftpack.dct(db_librosa, axis=0, type=2, norm='ortho')[:n_mfcc]
            librosa_mfcc_tensor = torch.from_numpy(librosa_mfcc)
            torch_mfcc = mfcc_transform(sound).squeeze().cpu()

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

        kwargs1 = {
            'n_fft': 400,
            'hop_length': 200,
            'power': 2.0,
            'n_mels': 128,
            'n_mfcc': 40,
            'sample_rate': 16000
        }

        kwargs2 = {
            'n_fft': 600,
            'hop_length': 100,
            'power': 2.0,
            'n_mels': 128,
            'n_mfcc': 20,
            'sample_rate': 16000
        }

        kwargs3 = {
            'n_fft': 200,
            'hop_length': 50,
            'power': 2.0,
            'n_mels': 128,
            'n_mfcc': 50,
            'sample_rate': 24000
        }

        _test_librosa_consistency_helper(**kwargs1)
        _test_librosa_consistency_helper(**kwargs2)
        _test_librosa_consistency_helper(**kwargs3)

    def test_resample_size(self):
        input_path = os.path.join(self.test_dirpath, 'assets', 'sinewave.wav')
        waveform, sample_rate = torchaudio.load(input_path)

        upsample_rate = sample_rate * 2
        downsample_rate = sample_rate // 2
        invalid_resample = torchaudio.transforms.Resample(sample_rate, upsample_rate, resampling_method='foo')

        self.assertRaises(ValueError, invalid_resample, waveform)

        upsample_resample = torchaudio.transforms.Resample(
            sample_rate, upsample_rate, resampling_method='sinc_interpolation')
        up_sampled = upsample_resample(waveform)

        # we expect the upsampled signal to have twice as many samples
        self.assertTrue(up_sampled.size(-1) == waveform.size(-1) * 2)

        downsample_resample = torchaudio.transforms.Resample(
            sample_rate, downsample_rate, resampling_method='sinc_interpolation')
        down_sampled = downsample_resample(waveform)

        # we expect the downsampled signal to have half as many samples
        self.assertTrue(down_sampled.size(-1) == waveform.size(-1) // 2)

if __name__ == '__main__':
    unittest.main()