test/torchaudio_unittest/compliance_kaldi_test.py

import torch
import torchaudio.compliance.kaldi as kaldi

from torchaudio_unittest import common_utils


def extract_window(window, wave, f, frame_length, frame_shift, snip_edges):
    # just a copy of ExtractWindow from feature-window.cc in python
    def first_sample_of_frame(frame, window_size, window_shift, snip_edges):
        if snip_edges:
            return frame * window_shift
        else:
            midpoint_of_frame = frame * window_shift + window_shift // 2
            beginning_of_frame = midpoint_of_frame - window_size // 2
            return beginning_of_frame

    sample_offset = 0
    num_samples = sample_offset + wave.size(0)
    start_sample = first_sample_of_frame(f, frame_length, frame_shift, snip_edges)
    end_sample = start_sample + frame_length

    if snip_edges:
        assert(start_sample >= sample_offset and end_sample <= num_samples)
    else:
        assert(sample_offset == 0 or start_sample >= sample_offset)

    wave_start = start_sample - sample_offset
    wave_end = wave_start + frame_length
    if wave_start >= 0 and wave_end <= wave.size(0):
        window[f, :] = wave[wave_start:(wave_start + frame_length)]
    else:
        wave_dim = wave.size(0)
        for s in range(frame_length):
            s_in_wave = s + wave_start
            while s_in_wave < 0 or s_in_wave >= wave_dim:
                if s_in_wave < 0:
                    s_in_wave = - s_in_wave - 1
                else:
                    s_in_wave = 2 * wave_dim - 1 - s_in_wave
            window[f, s] = wave[s_in_wave]


class Test_Kaldi(common_utils.TempDirMixin, common_utils.TorchaudioTestCase):

    def _test_get_strided_helper(self, num_samples, window_size, window_shift, snip_edges):
        waveform = torch.arange(num_samples).float()
        output = kaldi._get_strided(waveform, window_size, window_shift, snip_edges)

        # from NumFrames in feature-window.cc
        n = window_size
        if snip_edges:
            m = 0 if num_samples < window_size else 1 + (num_samples - window_size) // window_shift
        else:
            m = (num_samples + (window_shift // 2)) // window_shift

        self.assertTrue(output.dim() == 2)
        self.assertTrue(output.shape[0] == m and output.shape[1] == n)

        window = torch.empty((m, window_size))

        for r in range(m):
            extract_window(window, waveform, r, window_size, window_shift, snip_edges)
        self.assertEqual(window, output)

    def test_get_strided(self):
        # generate any combination where 0 < window_size <= num_samples and
        # 0 < window_shift.
        for num_samples in range(1, 20):
            for window_size in range(1, num_samples + 1):
                for window_shift in range(1, 2 * num_samples + 1):
                    for snip_edges in range(0, 2):
                        self._test_get_strided_helper(num_samples, window_size, window_shift, snip_edges)

    def test_mfcc_empty(self):
        # Passing in an empty tensor should result in an error
        self.assertRaises(AssertionError, kaldi.mfcc, torch.empty(0))