extend batch support (#391)

* extend batch support

closes #383

* function for batch test.

* set seed.

* adjust tolerance for griffinlim.

test/test_functional.py

@@ -103,6 +103,25 @@ class TestFunctional(unittest.TestCase):
 
         self.assertTrue(torch.allclose(ta_out, lr_out, atol=5e-5))
 
+    def test_batch_griffinlim(self):
+
+        torch.random.manual_seed(42)
+        tensor = torch.rand((1, 201, 6))
+
+        n_fft = 400
+        ws = 400
+        hop = 200
+        window = torch.hann_window(ws)
+        power = 2
+        normalize = False
+        momentum = 0.99
+        n_iter = 32
+        length = 1000
+
+        self._test_batch(
+            F.griffinlim, tensor, window, n_fft, hop, ws, power, normalize, n_iter, momentum, length, 0, atol=5e-5
+        )
+
     def _test_compute_deltas(self, specgram, expected, win_length=3, atol=1e-6, rtol=1e-8):
         computed = F.compute_deltas(specgram, win_length=win_length)
         self.assertTrue(computed.shape == expected.shape, (computed.shape, expected.shape))
@@ -126,22 +145,17 @@ class TestFunctional(unittest.TestCase):
         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):
@@ -157,7 +171,6 @@ class TestFunctional(unittest.TestCase):
         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)
@@ -165,14 +178,6 @@ class TestFunctional(unittest.TestCase):
 
                 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 = {
@@ -389,6 +394,16 @@ class TestFunctional(unittest.TestCase):
         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:
@@ -489,22 +504,63 @@ class TestFunctional(unittest.TestCase):
             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 test_torchscript_create_fb_matrix(self):
 

test/test_transforms.py

@@ -381,6 +381,19 @@ class Tester(unittest.TestCase):
         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)
 
@@ -440,6 +453,30 @@ class Tester(unittest.TestCase):
         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

@@ -99,7 +99,7 @@ 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``)
@@ -230,7 +230,7 @@ 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
@@ -242,8 +242,8 @@ 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).
     """
 
@@ -292,7 +292,7 @@ def griffinlim(
         IEEE Trans. ASSP, vol.32, no.2, pp.236–243, Apr. 1984.
 
     Args:
-        specgram (torch.Tensor): A magnitude-only STFT spectrogram of dimension (channel, freq, frames)
+        specgram (torch.Tensor): A magnitude-only STFT spectrogram of dimension (..., freq, frames)
             where freq is ``n_fft // 2 + 1``.
         window (torch.Tensor): Window tensor that is applied/multiplied to each frame/window
         n_fft (int): Size of FFT, creates ``n_fft // 2 + 1`` bins
@@ -310,11 +310,15 @@ def griffinlim(
         rand_init (bool): Initializes phase randomly if True, to zero otherwise. (Default: ``True``)
 
     Returns:
-        torch.Tensor: waveform of (channel, time), where time equals the ``length`` parameter if given.
+        torch.Tensor: waveform of (..., time), where time equals the ``length`` parameter if given.
     """
     assert momentum < 1, 'momentum=%s > 1 can be unstable' % momentum
     assert momentum > 0, 'momentum=%s < 0' % momentum
 
+    # pack batch
+    shape = specgram.size()
+    specgram = specgram.reshape([-1] + list(shape[-2:]))
+
     specgram = specgram.pow(1 / power)
 
     # randomly initialize the phase
@@ -351,13 +355,18 @@ def griffinlim(
         angles = angles.div_(complex_norm(angles).add_(1e-16).unsqueeze(-1).expand_as(angles))
 
     # Return the final phase estimates
-    return istft(specgram * angles,
+    waveform = istft(specgram * angles,
                      n_fft=n_fft,
                      hop_length=hop_length,
                      win_length=win_length,
                      window=window,
                      length=length)
 
+    # unpack batch
+    waveform = waveform.reshape(shape[:-2] + waveform.shape[-1:])
+
+    return waveform
+
 
 def amplitude_to_DB(x, multiplier, amin, db_multiplier, top_db=None):
     # type: (Tensor, float, float, float, Optional[float]) -> Tensor
@@ -699,7 +708,7 @@ 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]
@@ -708,7 +717,7 @@ 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
@@ -732,13 +741,13 @@ 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.
@@ -761,13 +770,13 @@ 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.
@@ -790,14 +799,14 @@ 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))
@@ -886,7 +895,7 @@ 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"):
@@ -946,7 +955,7 @@ 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:
@@ -999,7 +1008,7 @@ 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`
@@ -1008,6 +1017,8 @@ 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])
 
@@ -1047,6 +1058,8 @@ 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:])
 
 
@@ -1056,7 +1069,7 @@ 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`

torchaudio/transforms.py

@@ -62,11 +62,11 @@ class Spectrogram(torch.nn.Module):
     def forward(self, waveform):
         r"""
         Args:
-            waveform (torch.Tensor): Tensor of audio of dimension (channel, time)
+            waveform (torch.Tensor): Tensor of audio of dimension (..., time)
 
         Returns:
-            torch.Tensor: Dimension (channel, freq, time), where channel
-            is unchanged, freq is ``n_fft // 2 + 1`` where ``n_fft`` is the number of
+            torch.Tensor: Dimension (..., freq, time), where freq is
+            ``n_fft // 2 + 1`` where ``n_fft`` is the number of
             Fourier bins, and time is the number of window hops (n_frame).
         """
         return F.spectrogram(waveform, self.pad, self.window, self.n_fft, self.hop_length,
@@ -207,11 +207,16 @@ class MelScale(torch.nn.Module):
     def forward(self, specgram):
         r"""
         Args:
-            specgram (torch.Tensor): A spectrogram STFT of dimension (channel, freq, time)
+            specgram (torch.Tensor): A spectrogram STFT of dimension (..., freq, time)
 
         Returns:
-            torch.Tensor: Mel frequency spectrogram of size (channel, ``n_mels``, time)
+            torch.Tensor: Mel frequency spectrogram of size (..., ``n_mels``, time)
         """
+
+        # pack batch
+        shape = specgram.size()
+        specgram = specgram.reshape(-1, shape[-2], shape[-1])
+
         if self.fb.numel() == 0:
             tmp_fb = F.create_fb_matrix(specgram.size(1), self.f_min, self.f_max, self.n_mels, self.sample_rate)
             # Attributes cannot be reassigned outside __init__ so workaround
@@ -221,6 +226,10 @@ class MelScale(torch.nn.Module):
         # (channel, frequency, time).transpose(...) dot (frequency, n_mels)
         # -> (channel, time, n_mels).transpose(...)
         mel_specgram = torch.matmul(specgram.transpose(1, 2), self.fb).transpose(1, 2)
+
+        # unpack batch
+        mel_specgram = mel_specgram.reshape(shape[:-2] + mel_specgram.shape[-2:])
+
         return mel_specgram
 
 
@@ -273,10 +282,10 @@ class MelSpectrogram(torch.nn.Module):
     def forward(self, waveform):
         r"""
         Args:
-            waveform (torch.Tensor): Tensor of audio of dimension (channel, time)
+            waveform (torch.Tensor): Tensor of audio of dimension (..., time)
 
         Returns:
-            torch.Tensor: Mel frequency spectrogram of size (channel, ``n_mels``, time)
+            torch.Tensor: Mel frequency spectrogram of size (..., ``n_mels``, time)
         """
         specgram = self.spectrogram(waveform)
         mel_specgram = self.mel_scale(specgram)
@@ -332,11 +341,16 @@ class MFCC(torch.nn.Module):
     def forward(self, waveform):
         r"""
         Args:
-            waveform (torch.Tensor): Tensor of audio of dimension (channel, time)
+            waveform (torch.Tensor): Tensor of audio of dimension (..., time)
 
         Returns:
-            torch.Tensor: specgram_mel_db of size (channel, ``n_mfcc``, time)
+            torch.Tensor: specgram_mel_db of size (..., ``n_mfcc``, time)
         """
+
+        # pack batch
+        shape = waveform.size()
+        waveform = waveform.reshape(-1, shape[-1])
+
         mel_specgram = self.MelSpectrogram(waveform)
         if self.log_mels:
             log_offset = 1e-6
@@ -346,6 +360,10 @@ class MFCC(torch.nn.Module):
         # (channel, n_mels, time).tranpose(...) dot (n_mels, n_mfcc)
         # -> (channel, time, n_mfcc).tranpose(...)
         mfcc = torch.matmul(mel_specgram.transpose(1, 2), self.dct_mat).transpose(1, 2)
+
+        # unpack batch
+        mfcc = mfcc.reshape(shape[:-1] + mfcc.shape[-2:])
+
         return mfcc
 
 
@@ -421,10 +439,10 @@ class Resample(torch.nn.Module):
     def forward(self, waveform):
         r"""
         Args:
-            waveform (torch.Tensor): The input signal of dimension (channel, time)
+            waveform (torch.Tensor): The input signal of dimension (..., time)
 
         Returns:
-            torch.Tensor: Output signal of dimension (channel, time)
+            torch.Tensor: Output signal of dimension (..., time)
         """
         if self.resampling_method == 'sinc_interpolation':
             return kaldi.resample_waveform(waveform, self.orig_freq, self.new_freq)
@@ -471,10 +489,10 @@ class ComputeDeltas(torch.nn.Module):
     def forward(self, specgram):
         r"""
         Args:
-            specgram (torch.Tensor): Tensor of audio of dimension (channel, freq, time)
+            specgram (torch.Tensor): Tensor of audio of dimension (..., freq, time)
 
         Returns:
-            deltas (torch.Tensor): Tensor of audio of dimension (channel, freq, time)
+            deltas (torch.Tensor): Tensor of audio of dimension (..., freq, time)
         """
         return F.compute_deltas(specgram, win_length=self.win_length, mode=self.mode)