Adding batch to v0.4.0

test/test_functional.py

@@ -75,22 +75,17 @@ def test_compute_deltas_randn(self):
         win_length = 2 * 7 + 1
         specgram = torch.randn(channel, n_mfcc, time)
         computed = F.compute_deltas(specgram, win_length=win_length)
+
         self.assertTrue(computed.shape == specgram.shape, (computed.shape, specgram.shape))
+
         _test_torchscript_functional(F.compute_deltas, specgram, win_length=win_length)
 
     def test_batch_pitch(self):
         waveform, sample_rate = torchaudio.load(self.test_filepath)
+        self._test_batch(F.detect_pitch_frequency, waveform, sample_rate)
 
-        # Single then transform then batch
-        expected = F.detect_pitch_frequency(waveform, sample_rate)
-        expected = expected.unsqueeze(0).repeat(3, 1, 1)
-
-        # Batch then transform
-        waveform = waveform.unsqueeze(0).repeat(3, 1, 1)
-        computed = F.detect_pitch_frequency(waveform, sample_rate)
-
-        self.assertTrue(computed.shape == expected.shape, (computed.shape, expected.shape))
-        self.assertTrue(torch.allclose(computed, expected))
+    def test_jit_pitch(self):
+        waveform, sample_rate = torchaudio.load(self.test_filepath)
         _test_torchscript_functional(F.detect_pitch_frequency, waveform, sample_rate)
 
     def _compare_estimate(self, sound, estimate, atol=1e-6, rtol=1e-8):
@@ -106,22 +101,13 @@ def _test_istft_is_inverse_of_stft(self, kwargs):
         for data_size in self.data_sizes:
             for i in range(self.number_of_trials):
 
-                # Non-batch
                 sound = common_utils.random_float_tensor(i, data_size)
 
                 stft = torch.stft(sound, **kwargs)
                 estimate = torchaudio.functional.istft(stft, length=sound.size(1), **kwargs)
 
                 self._compare_estimate(sound, estimate)
 
-                # Batch
-                stft = torch.stft(sound, **kwargs)
-                stft = stft.repeat(3, 1, 1, 1, 1)
-                sound = sound.repeat(3, 1, 1)
-
-                estimate = torchaudio.functional.istft(stft, length=sound.size(1), **kwargs)
-                self._compare_estimate(sound, estimate)
-
     def test_istft_is_inverse_of_stft1(self):
         # hann_window, centered, normalized, onesided
         kwargs1 = {
@@ -338,6 +324,16 @@ def test_linearity_of_istft4(self):
         data_size = (2, 7, 3, 2)
         self._test_linearity_of_istft(data_size, kwargs4, atol=1e-5, rtol=1e-8)
 
+    def test_batch_istft(self):
+
+        stft = torch.tensor([
+            [[4., 0.], [4., 0.], [4., 0.], [4., 0.], [4., 0.]],
+            [[0., 0.], [0., 0.], [0., 0.], [0., 0.], [0., 0.]],
+            [[0., 0.], [0., 0.], [0., 0.], [0., 0.], [0., 0.]]
+        ])
+
+        self._test_batch(F.istft, stft, n_fft=4, length=4)
+
     def _test_create_fb(self, n_mels=40, sample_rate=22050, n_fft=2048, fmin=0.0, fmax=8000.0):
         # Using a decorator here causes parametrize to fail on Python 2
         if not IMPORT_LIBROSA:
@@ -438,22 +434,63 @@ def test_pitch(self):
             self.assertFalse(s)
 
             # Convert to stereo and batch for testing purposes
-            freq = freq.repeat(3, 2, 1, 1)
-            waveform = waveform.repeat(3, 2, 1, 1)
+            self._test_batch(F.detect_pitch_frequency, waveform, sample_rate)
 
-            freq2 = torchaudio.functional.detect_pitch_frequency(waveform, sample_rate)
+    def _test_batch_shape(self, functional, tensor, *args, **kwargs):
 
-            assert torch.allclose(freq, freq2, atol=1e-5)
+        kwargs_compare = {}
+        if 'atol' in kwargs:
+            atol = kwargs['atol']
+            del kwargs['atol']
+            kwargs_compare['atol'] = atol
 
-    def _test_batch(self, functional):
-        waveform, sample_rate = torchaudio.load(self.test_filepath)  # (2, 278756), 44100
+        if 'rtol' in kwargs:
+            rtol = kwargs['rtol']
+            del kwargs['rtol']
+            kwargs_compare['rtol'] = rtol
 
         # Single then transform then batch
-        expected = functional(waveform).unsqueeze(0).repeat(3, 1, 1, 1)
 
-        # Batch then transform
-        waveform = waveform.unsqueeze(0).repeat(3, 1, 1)
-        computed = functional(waveform)
+        torch.random.manual_seed(42)
+        expected = functional(tensor.clone(), *args, **kwargs)
+        expected = expected.unsqueeze(0).unsqueeze(0)
+
+        # 1-Batch then transform
+
+        tensors = tensor.unsqueeze(0).unsqueeze(0)
+
+        torch.random.manual_seed(42)
+        computed = functional(tensors.clone(), *args, **kwargs)
+
+        self._compare_estimate(computed, expected, **kwargs_compare)
+
+        return tensors, expected
+
+    def _test_batch(self, functional, tensor, *args, **kwargs):
+
+        tensors, expected = self._test_batch_shape(functional, tensor, *args, **kwargs)
+
+        kwargs_compare = {}
+        if 'atol' in kwargs:
+            atol = kwargs['atol']
+            del kwargs['atol']
+            kwargs_compare['atol'] = atol
+
+        if 'rtol' in kwargs:
+            rtol = kwargs['rtol']
+            del kwargs['rtol']
+            kwargs_compare['rtol'] = rtol
+
+        # 3-Batch then transform
+
+        ind = [3] + [1] * (int(tensors.dim()) - 1)
+        tensors = tensor.repeat(*ind)
+
+        ind = [3] + [1] * (int(expected.dim()) - 1)
+        expected = expected.repeat(*ind)
+
+        torch.random.manual_seed(42)
+        computed = functional(tensors.clone(), *args, **kwargs)
 
 
 def _num_stft_bins(signal_len, fft_len, hop_length, pad):

test/test_transforms.py

@@ -363,6 +363,19 @@ def test_compute_deltas_twochannel(self):
         computed = transform(specgram)
         self.assertTrue(computed.shape == specgram.shape, (computed.shape, specgram.shape))
 
+    def test_batch_MelScale(self):
+        specgram = torch.randn(2, 31, 2786)
+
+        # Single then transform then batch
+        expected = transforms.MelScale()(specgram).repeat(3, 1, 1, 1)
+
+        # Batch then transform
+        computed = transforms.MelScale()(specgram.repeat(3, 1, 1, 1))
+
+        # shape = (3, 2, 201, 1394)
+        self.assertTrue(computed.shape == expected.shape, (computed.shape, expected.shape))
+        self.assertTrue(torch.allclose(computed, expected))
+
     def test_batch_compute_deltas(self):
         specgram = torch.randn(2, 31, 2786)
 
@@ -422,6 +435,30 @@ def test_batch_spectrogram(self):
         self.assertTrue(computed.shape == expected.shape, (computed.shape, expected.shape))
         self.assertTrue(torch.allclose(computed, expected))
 
+    def test_batch_melspectrogram(self):
+        waveform, sample_rate = torchaudio.load(self.test_filepath)
+
+        # Single then transform then batch
+        expected = transforms.MelSpectrogram()(waveform).repeat(3, 1, 1, 1)
+
+        # Batch then transform
+        computed = transforms.MelSpectrogram()(waveform.repeat(3, 1, 1))
+
+        self.assertTrue(computed.shape == expected.shape, (computed.shape, expected.shape))
+        self.assertTrue(torch.allclose(computed, expected))
+
+    def test_batch_mfcc(self):
+        waveform, sample_rate = torchaudio.load(self.test_filepath)
+
+        # Single then transform then batch
+        expected = transforms.MFCC()(waveform).repeat(3, 1, 1, 1)
+
+        # Batch then transform
+        computed = transforms.MFCC()(waveform.repeat(3, 1, 1))
+
+        self.assertTrue(computed.shape == expected.shape, (computed.shape, expected.shape))
+        self.assertTrue(torch.allclose(computed, expected, atol=1e-5))
+
     def test_scriptmodule_TimeStretch(self):
         n_freq = 400
         hop_length = 512

torchaudio/functional.py

@@ -96,7 +96,7 @@ def istft(
 
     Args:
         stft_matrix (torch.Tensor): Output of stft where each row of a channel is a frequency and each
-            column is a window. it has a size of either (..., fft_size, n_frame, 2)
+            column is a window. It has a size of either (..., fft_size, n_frame, 2)
         n_fft (int): Size of Fourier transform
         hop_length (Optional[int]): The distance between neighboring sliding window frames.
             (Default: ``win_length // 4``)
@@ -229,7 +229,7 @@ def spectrogram(
     The spectrogram can be either magnitude-only or complex.
 
     Args:
-        waveform (torch.Tensor): Tensor of audio of dimension (..., channel, time)
+        waveform (torch.Tensor): Tensor of audio of dimension (..., time)
         pad (int): Two sided padding of signal
         window (torch.Tensor): Window tensor that is applied/multiplied to each frame/window
         n_fft (int): Size of FFT
@@ -241,8 +241,8 @@ def spectrogram(
         normalized (bool): Whether to normalize by magnitude after stft
 
     Returns:
-        torch.Tensor: Dimension (..., channel, freq, time), where channel
-        is unchanged, freq is ``n_fft // 2 + 1`` and ``n_fft`` is the number of
+        torch.Tensor: Dimension (..., freq, time), freq is
+        ``n_fft // 2 + 1`` and ``n_fft`` is the number of
         Fourier bins, and time is the number of window hops (n_frame).
     """
 
@@ -613,7 +613,7 @@ def biquad(waveform, b0, b1, b2, a0, a1, a2):
     https://en.wikipedia.org/wiki/Digital_biquad_filter
 
     Args:
-        waveform (torch.Tensor): audio waveform of dimension of `(channel, time)`
+        waveform (torch.Tensor): audio waveform of dimension of `(..., time)`
         b0 (float): numerator coefficient of current input, x[n]
         b1 (float): numerator coefficient of input one time step ago x[n-1]
         b2 (float): numerator coefficient of input two time steps ago x[n-2]
@@ -622,7 +622,7 @@ def biquad(waveform, b0, b1, b2, a0, a1, a2):
         a2 (float): denominator coefficient of current output y[n-2]
 
     Returns:
-        output_waveform (torch.Tensor): Dimension of `(channel, time)`
+        output_waveform (torch.Tensor): Dimension of `(..., time)`
     """
 
     device = waveform.device
@@ -646,13 +646,13 @@ def highpass_biquad(waveform, sample_rate, cutoff_freq, Q=0.707):
     r"""Design biquad highpass filter and perform filtering.  Similar to SoX implementation.
 
     Args:
-        waveform (torch.Tensor): audio waveform of dimension of `(channel, time)`
+        waveform (torch.Tensor): audio waveform of dimension of `(..., time)`
         sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz)
         cutoff_freq (float): filter cutoff frequency
         Q (float): https://en.wikipedia.org/wiki/Q_factor
 
     Returns:
-        output_waveform (torch.Tensor): Dimension of `(channel, time)`
+        output_waveform (torch.Tensor): Dimension of `(..., time)`
     """
 
     GAIN = 1.
@@ -675,13 +675,13 @@ def lowpass_biquad(waveform, sample_rate, cutoff_freq, Q=0.707):
     r"""Design biquad lowpass filter and perform filtering.  Similar to SoX implementation.
 
     Args:
-        waveform (torch.Tensor): audio waveform of dimension of `(channel, time)`
+        waveform (torch.Tensor): audio waveform of dimension of `(..., time)`
         sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz)
         cutoff_freq (float): filter cutoff frequency
         Q (float): https://en.wikipedia.org/wiki/Q_factor
 
     Returns:
-        output_waveform (torch.Tensor): Dimension of `(channel, time)`
+        output_waveform (torch.Tensor): Dimension of `(..., time)`
     """
 
     GAIN = 1.
@@ -704,14 +704,14 @@ def equalizer_biquad(waveform, sample_rate, center_freq, gain, Q=0.707):
     r"""Design biquad peaking equalizer filter and perform filtering.  Similar to SoX implementation.
 
     Args:
-        waveform (torch.Tensor): audio waveform of dimension of `(channel, time)`
+        waveform (torch.Tensor): audio waveform of dimension of `(..., time)`
         sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz)
         center_freq (float): filter's central frequency
         gain (float): desired gain at the boost (or attenuation) in dB
         q_factor (float): https://en.wikipedia.org/wiki/Q_factor
 
     Returns:
-        output_waveform (torch.Tensor): Dimension of `(channel, time)`
+        output_waveform (torch.Tensor): Dimension of `(..., time)`
     """
     w0 = 2 * math.pi * center_freq / sample_rate
     A = math.exp(gain / 40.0 * math.log(10))
@@ -800,7 +800,7 @@ def mask_along_axis(specgram, mask_param, mask_value, axis):
     # unpack batch
     specgram = specgram.reshape(shape[:-2] + specgram.shape[-2:])
 
-    return specgram.reshape(shape[:-2] + specgram.shape[-2:])
+    return specgram
 
 
 def compute_deltas(specgram, win_length=5, mode="replicate"):
@@ -860,7 +860,7 @@ def gain(waveform, gain_db=1.0):
     r"""Apply amplification or attenuation to the whole waveform.
 
     Args:
-       waveform (torch.Tensor): Tensor of audio of dimension (channel, time).
+       waveform (torch.Tensor): Tensor of audio of dimension (..., time).
        gain_db (float) Gain adjustment in decibels (dB) (Default: `1.0`).
 
     Returns:
@@ -913,7 +913,7 @@ def _apply_probability_distribution(waveform, density_function="TPDF"):
     The relationship of probabilities of results follows a bell-shaped,
     or Gaussian curve, typical of dither generated by analog sources.
     Args:
-        waveform (torch.Tensor): Tensor of audio of dimension (channel, time)
+        waveform (torch.Tensor): Tensor of audio of dimension (..., time)
         probability_density_function (string): The density function of a
            continuous random variable (Default: `TPDF`)
            Options: Triangular Probability Density Function - `TPDF`
@@ -922,6 +922,8 @@ def _apply_probability_distribution(waveform, density_function="TPDF"):
     Returns:
         torch.Tensor: waveform dithered with TPDF
     """
+
+    # pack batch
     shape = waveform.size()
     waveform = waveform.reshape(-1, shape[-1])
 
@@ -961,6 +963,8 @@ def _apply_probability_distribution(waveform, density_function="TPDF"):
 
     quantised_signal_scaled = torch.round(signal_scaled_dis)
     quantised_signal = quantised_signal_scaled / down_scaling
+
+    # unpack batch
     return quantised_signal.reshape(shape[:-1] + quantised_signal.shape[-1:])
 
 
@@ -970,7 +974,7 @@ def dither(waveform, density_function="TPDF", noise_shaping=False):
     particular bit-depth by eliminating nonlinear truncation distortion
     (i.e. adding minimally perceived noise to mask distortion caused by quantization).
     Args:
-       waveform (torch.Tensor): Tensor of audio of dimension (channel, time)
+       waveform (torch.Tensor): Tensor of audio of dimension (..., time)
        density_function (string): The density function of a
            continuous random variable (Default: `TPDF`)
            Options: Triangular Probability Density Function - `TPDF`