Revert "pytorch implementation of MEL spectrograms (no librosa req'd)"

test/test_transforms.py

@@ -10,9 +10,8 @@ class Tester(unittest.TestCase):
 
     sr = 16000
     freq = 440
-    volume = .3
+    volume = 0.3
     sig = (torch.cos(2 * np.pi * torch.arange(0, 4 * sr) * freq / sr)).float()
-    # sig = (torch.cos((1+torch.arange(0, 4 * sr) * 2) / sr * 2 * np.pi * torch.arange(0, 4 * sr) * freq / sr)).float()
     sig.unsqueeze_(1)
     sig = (sig * volume * 2**31).long()
 
@@ -75,11 +74,11 @@ class Tester(unittest.TestCase):
 
         audio = self.sig.clone()
         audio = transforms.Scale()(audio)
-        self.assertTrue(audio.dim() == 2)
+        self.assertTrue(len(audio.size()) == 2)
         result = transforms.MEL()(audio)
-        self.assertTrue(result.dim() == 3)
+        self.assertTrue(len(result.size()) == 3)
         result = transforms.BLC2CBL()(result)
-        self.assertTrue(result.dim() == 3)
+        self.assertTrue(len(result.size()) == 3)
 
     def test_compose(self):
 
@@ -122,13 +121,6 @@ class Tester(unittest.TestCase):
         sig_exp = transforms.MuLawExpanding(quantization_channels)(sig_mu)
         self.assertTrue(sig_exp.min() >= -1. and sig_exp.max() <= 1.)
 
-    def test_mel2(self):
-        audio_orig = self.sig.clone()  # (16000, 1)
-        audio_scaled = transforms.Scale()(audio_orig)  # (16000, 1)
-        audio_scaled = transforms.LC2CL()(audio_scaled)  # (1, 16000)
-        spectrogram_torch = transforms.MEL2()(audio_scaled)  # (1, 319, 40)
-        self.assertTrue(spectrogram_torch.dim() == 3)
-        self.assertTrue(spectrogram_torch.max() <= 0.)
 
 if __name__ == '__main__':
     unittest.main()

torchaudio/transforms.py

 from __future__ import division, print_function
 import torch
-from torch.autograd import Variable
 import numpy as np
 try:
     import librosa
@@ -8,24 +7,6 @@ except ImportError:
     librosa = None
 
 
-def _check_is_variable(tensor):
-    if isinstance(tensor, torch.Tensor):
-        is_variable = False
-        tensor = Variable(tensor, requires_grad=False)
-    elif isinstance(tensor, Variable):
-        is_variable = True
-    else:
-        raise TypeError("tensor should be a Variable or Tensor, but is {}".format(type(tensor)))
-
-    return tensor, is_variable
-
-
-def _tlog10(x):
-    """Pytorch Log10
-    """
-    return torch.log(x) / torch.log(x.new([10]))
-
-
 class Compose(object):
     """Composes several transforms together.
 
@@ -139,200 +120,16 @@ class LC2CL(object):
         """
 
         Args:
-            tensor (Tensor): Tensor of audio signal with shape (LxC)
+            tensor (Tensor): Tensor of spectrogram with shape (BxLxC)
 
         Returns:
-            tensor (Tensor): Tensor of audio signal with shape (CxL)
+            tensor (Tensor): Tensor of spectrogram with shape (CxBxL)
 
         """
 
         return tensor.transpose(0, 1).contiguous()
 
 
-class SPECTROGRAM(object):
-    """Create a spectrogram from a raw audio signal
-
-    Args:
-        sr (int): sample rate of audio signal
-        ws (int): window size, often called the fft size as well
-        hop (int, optional): length of hop between STFT windows. default: ws // 2
-        n_fft (int, optional): number of fft bins. default: ws // 2 + 1
-        pad (int): two sided padding of signal
-        window (torch windowing function): default: torch.hann_window
-        wkwargs (dict, optional): arguments for window function
-
-    """
-    def __init__(self, sr=16000, ws=400, hop=None, n_fft=None,
-                 pad=0, window=torch.hann_window, wkwargs=None):
-        if isinstance(window, Variable):
-            self.window = window
-        else:
-            self.window = window(ws) if wkwargs is None else window(ws, **wkwargs)
-            self.window = Variable(self.window, volatile=True)
-        self.sr = sr
-        self.ws = ws
-        self.hop = hop if hop is not None else ws // 2
-        self.n_fft = n_fft  # number of fft bins
-        self.pad = pad
-        self.wkwargs = wkwargs
-
-    def __call__(self, sig):
-        """
-        Args:
-            sig (Tensor or Variable): Tensor of audio of size (c, n)
-
-        Returns:
-            spec_f (Tensor or Variable): channels x hops x n_fft (c, l, f), where channels
-                is unchanged, hops is the number of hops, and n_fft is the
-                number of fourier bins, which should be the window size divided
-                by 2 plus 1.
-
-        """
-        sig, is_variable = _check_is_variable(sig)
-
-        assert sig.dim() == 2
-
-        spec_f = torch.stft(sig, self.ws, self.hop, self.n_fft,
-                            True, self.window, self.pad)  # (c, l, n_fft, 2)
-        spec_f /= self.window.pow(2).sum().sqrt()
-        spec_f = spec_f.pow(2).sum(-1)  # get power of "complex" tensor (c, l, n_fft)
-        return spec_f if is_variable else spec_f.data
-
-
-class F2M(object):
-    """This turns a normal STFT into a MEL Frequency STFT, using a conversion
-       matrix.  This uses triangular filter banks.
-
-    Args:
-        n_mels (int): number of MEL bins
-        sr (int): sample rate of audio signal
-        f_max (float, optional): maximum frequency. default: sr // 2
-        f_min (float): minimum frequency. default: 0
-    """
-    def __init__(self, n_mels=40, sr=16000, f_max=None, f_min=0.):
-        self.n_mels = n_mels
-        self.sr = sr
-        self.f_max = f_max if f_max is not None else sr // 2
-        self.f_min = f_min
-
-    def __call__(self, spec_f):
-
-        spec_f, is_variable = _check_is_variable(spec_f)
-        n_fft = spec_f.size(2)
-
-        m_min = 0. if self.f_min == 0 else 2595 * np.log10(1. + (self.f_min / 700))
-        m_max = 2595 * np.log10(1. + (self.f_max / 700))
-
-        m_pts = torch.linspace(m_min, m_max, self.n_mels + 2)
-        f_pts = (700 * (10**(m_pts / 2595) - 1))
-
-        bins = torch.floor(((n_fft - 1) * 2) * f_pts / self.sr).long()
-
-        fb = torch.zeros(n_fft, self.n_mels)
-        for m in range(1, self.n_mels + 1):
-            f_m_minus = bins[m - 1]
-            f_m = bins[m]
-            f_m_plus = bins[m + 1]
-
-            if f_m_minus != f_m:
-                fb[f_m_minus:f_m, m - 1] = (torch.arange(f_m_minus, f_m) - f_m_minus) / (f_m - f_m_minus)
-            if f_m != f_m_plus:
-                fb[f_m:f_m_plus, m - 1] = (f_m_plus - torch.arange(f_m, f_m_plus)) / (f_m_plus - f_m)
-
-        fb = Variable(fb)
-        spec_m = torch.matmul(spec_f, fb)  # (c, l, n_fft) dot (n_fft, n_mels) -> (c, l, n_mels)
-        return spec_m if is_variable else spec_m.data
-
-
-class SPEC2DB(object):
-    """Turns a spectrogram from the power/amplitude scale to the decibel scale.
-
-    Args:
-        stype (str): scale of input spectrogram ("power" or "magnitude").  The
-            power being the elementwise square of the magnitude. default: "power"
-        top_db (float, optional): minimum negative cut-off in decibels.  A reasonable number
-            is -80.
-    """
-    def __init__(self, stype="power", top_db=None):
-        self.stype = stype
-        self.top_db = -top_db if top_db > 0 else top_db
-        self.multiplier = 10. if stype == "power" else 20.
-
-    def __call__(self, spec):
-
-        spec, is_variable = _check_is_variable(spec)
-        spec_db = self.multiplier * _tlog10(spec / spec.max())  # power -> dB
-        if self.top_db is not None:
-            spec_db = torch.max(spec_db, spec_db.new([self.top_db]))
-        return spec_db if is_variable else spec_db.data
-
-
-class MEL2(object):
-    """Create MEL Spectrograms from a raw audio signal using the stft
-       function in PyTorch.  Hopefully this solves the speed issue of using
-       librosa.
-
-    Sources:
-        * https://gist.github.com/kastnerkyle/179d6e9a88202ab0a2fe
-        * https://timsainb.github.io/spectrograms-mfccs-and-inversion-in-python.html
-        * http://haythamfayek.com/2016/04/21/speech-processing-for-machine-learning.html
-
-    Args:
-        sr (int): sample rate of audio signal
-        ws (int): window size, often called the fft size as well
-        hop (int, optional): length of hop between STFT windows. default: ws // 2
-        n_fft (int, optional): number of fft bins. default: ws // 2 + 1
-        pad (int): two sided padding of signal
-        n_mels (int): number of MEL bins
-        window (torch windowing function): default: torch.hann_window
-        wkwargs (dict, optional): arguments for window function
-
-    Example:
-        >>> sig, sr = torchaudio.load("test.wav", normalization=True)
-        >>> sig = transforms.LC2CL()(sig)  # (n, c) -> (c, n)
-        >>> spec_mel = transforms.MEL2(sr)(sig)  # (c, l, m)
-    """
-    def __init__(self, sr=16000, ws=400, hop=None, n_fft=None,
-                 pad=0, n_mels=40, window=torch.hann_window, wkwargs=None):
-        self.window = window(ws) if wkwargs is None else window(ws, **wkwargs)
-        self.window = Variable(self.window, requires_grad=False)
-        self.sr = sr
-        self.ws = ws
-        self.hop = hop if hop is not None else ws // 2
-        self.n_fft = n_fft  # number of fourier bins (ws // 2 + 1 by default)
-        self.pad = pad
-        self.n_mels = n_mels  # number of mel frequency bins
-        self.wkwargs = wkwargs
-        self.top_db = -80.
-        self.f_max = None
-        self.f_min = 0.
-
-    def __call__(self, sig):
-        """
-        Args:
-            sig (Tensor): Tensor of audio of size (channels [c], samples [n])
-
-        Returns:
-            spec_mel_db (Tensor): channels x hops x n_mels (c, l, m), where channels
-                is unchanged, hops is the number of hops, and n_mels is the
-                number of mel bins.
-
-        """
-
-        sig, is_variable = _check_is_variable(sig)
-
-        transforms = Compose([
-            SPECTROGRAM(self.sr, self.ws, self.hop, self.n_fft,
-                        self.pad, self.window),
-            F2M(self.n_mels, self.sr, self.f_max, self.f_min),
-            SPEC2DB("power", self.top_db),
-        ])
-
-        spec_mel_db = transforms(sig)
-
-        return spec_mel_db if is_variable else spec_mel_db.data
-
-
 class MEL(object):
     """Create MEL Spectrograms from a raw audio signal. Relatively pretty slow.
 
@@ -347,7 +144,7 @@ class MEL(object):
         """
 
         Args:
-            tensor (Tensor): Tensor of audio of size (samples [n] x channels [c])
+            tensor (Tensor): Tensor of audio of size (samples x channels)
 
         Returns:
             tensor (Tensor): n_mels x hops x channels (BxLxC), where n_mels is
@@ -355,7 +152,6 @@ class MEL(object):
                 is unchanged.
 
         """
-
         if librosa is None:
             print("librosa not installed, cannot create spectrograms")
             return tensor