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Unverified Commit b8203182 authored by Vincent QB's avatar Vincent QB Committed by GitHub
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standardizing n_* (#298)

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Showing with 46 additions and 47 deletions
README.md
View file @ b8203182
......@@ -85,8 +85,8 @@ Quick Usage
```python
import torchaudio
sound, sample_rate = torchaudio.load('foo.mp3')
torchaudio.save('foo_save.mp3', sound, sample_rate) # saves tensor to file
waveform, sample_rate = torchaudio.load('foo.mp3') # load tensor from file
torchaudio.save('foo_save.mp3', waveform, sample_rate) # save tensor to file
```
API Reference
......@@ -118,7 +118,7 @@ dimension (channel, time)")
* `win_length`: the length of the STFT window
* `window_fn`: for functions that creates windows e.g. `torch.hann_window`
Transforms expect the following dimensions.
Transforms expect and return the following dimensions.
* `Spectrogram`: (channel, time) -> (channel, freq, time)
* `AmplitudeToDB`: (channel, freq, time) -> (channel, freq, time)
......
torchaudio/functional.py
View file @ b8203182
......@@ -83,7 +83,7 @@ def istft(
Example: Suppose the last window is:
[17, 18, 0, 0, 0] vs [18, 0, 0, 0, 0]
The n_frames, hop_length, win_length are all the same which prevents the calculation of right padding.
The n_frame, hop_length, win_length are all the same which prevents the calculation of right padding.
These additional values could be zeros or a reflection of the signal so providing ``length``
could be useful. If ``length`` is ``None`` then padding will be aggressively removed
(some loss of signal).
......@@ -93,8 +93,8 @@ 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 (channel, fft_size, n_frames, 2) or (
fft_size, n_frames, 2)
column is a window. it has a size of either (channel, fft_size, n_frame, 2) or (
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``)
......@@ -156,17 +156,17 @@ def istft(
assert window.size(0) == n_fft
# win_length and n_fft are synonymous from here on
stft_matrix = stft_matrix.transpose(1, 2) # size (channel, n_frames, fft_size, 2)
stft_matrix = stft_matrix.transpose(1, 2) # size (channel, n_frame, fft_size, 2)
stft_matrix = torch.irfft(
stft_matrix, 1, normalized, onesided, signal_sizes=(n_fft,)
) # size (channel, n_frames, n_fft)
) # size (channel, n_frame, n_fft)
assert stft_matrix.size(2) == n_fft
n_frames = stft_matrix.size(1)
n_frame = stft_matrix.size(1)
ytmp = stft_matrix * window.view(1, 1, n_fft) # size (channel, n_frames, n_fft)
ytmp = stft_matrix * window.view(1, 1, n_fft) # size (channel, n_frame, n_fft)
# each column of a channel is a frame which needs to be overlap added at the right place
ytmp = ytmp.transpose(1, 2) # size (channel, n_fft, n_frames)
ytmp = ytmp.transpose(1, 2) # size (channel, n_fft, n_frame)
eye = torch.eye(n_fft, requires_grad=False, device=device, dtype=dtype).unsqueeze(
1
......@@ -180,13 +180,13 @@ def istft(
# do the same for the window function
window_sq = (
window.pow(2).view(n_fft, 1).repeat((1, n_frames)).unsqueeze(0)
) # size (1, n_fft, n_frames)
window.pow(2).view(n_fft, 1).repeat((1, n_frame)).unsqueeze(0)
) # size (1, n_fft, n_frame)
window_envelop = torch.nn.functional.conv_transpose1d(
window_sq, eye, stride=hop_length, padding=0
) # size (1, 1, expected_signal_len)
expected_signal_len = n_fft + hop_length * (n_frames - 1)
expected_signal_len = n_fft + hop_length * (n_frame - 1)
assert y.size(2) == expected_signal_len
assert window_envelop.size(2) == expected_signal_len
......@@ -233,9 +233,9 @@ def spectrogram(
normalized (bool): Whether to normalize by magnitude after stft
Returns:
torch.Tensor: Dimension (channel, n_freq, time), where channel
is unchanged, n_freq is ``n_fft // 2 + 1`` where ``n_fft`` is the number of
Fourier bins, and time is the number of window hops (n_frames).
torch.Tensor: Dimension (channel, freq, time), where channel
is unchanged, 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).
"""
assert waveform.dim() == 2
......@@ -537,7 +537,7 @@ def lfilter(waveform, a_coeffs, b_coeffs):
Performs an IIR filter by evaluating difference equation.
Args:
waveform (torch.Tensor): audio waveform of dimension of `(n_channel, n_frames)`. Must be normalized to -1 to 1.
waveform (torch.Tensor): audio waveform of dimension of `(channel, time)`. Must be normalized to -1 to 1.
a_coeffs (torch.Tensor): denominator coefficients of difference equation of dimension of `(n_order + 1)`.
Lower delays coefficients are first, e.g. `[a0, a1, a2, ...]`.
Must be same size as b_coeffs (pad with 0's as necessary).
......@@ -546,8 +546,7 @@ def lfilter(waveform, a_coeffs, b_coeffs):
Must be same size as a_coeffs (pad with 0's as necessary).
Returns:
output_waveform (torch.Tensor): Dimension of `(n_channel, n_frames)`. Output will be clipped to -1 to 1.
Will be on the same device as the inputs.
output_waveform (torch.Tensor): Dimension of `(channel, time)`. Output will be clipped to -1 to 1.
"""
......@@ -558,37 +557,37 @@ def lfilter(waveform, a_coeffs, b_coeffs):
device = waveform.device
dtype = waveform.dtype
n_channels, n_frames = waveform.size()
n_channel, n_sample = waveform.size()
n_order = a_coeffs.size(0)
assert(n_order > 0)
# Pad the input and create output
padded_waveform = torch.zeros(n_channels, n_frames + n_order - 1, dtype=dtype, device=device)
padded_waveform = torch.zeros(n_channel, n_sample + n_order - 1, dtype=dtype, device=device)
padded_waveform[:, (n_order - 1):] = waveform
padded_output_waveform = torch.zeros(n_channels, n_frames + n_order - 1, dtype=dtype, device=device)
padded_output_waveform = torch.zeros(n_channel, n_sample + n_order - 1, dtype=dtype, device=device)
# Set up the coefficients matrix
# Flip order, repeat, and transpose
a_coeffs_filled = a_coeffs.flip(0).repeat(n_channels, 1).t()
b_coeffs_filled = b_coeffs.flip(0).repeat(n_channels, 1).t()
a_coeffs_filled = a_coeffs.flip(0).repeat(n_channel, 1).t()
b_coeffs_filled = b_coeffs.flip(0).repeat(n_channel, 1).t()
# Set up a few other utilities
a0_repeated = torch.ones(n_channels, dtype=dtype, device=device) * a_coeffs[0]
ones = torch.ones(n_channels, n_frames, dtype=dtype, device=device)
a0_repeated = torch.ones(n_channel, dtype=dtype, device=device) * a_coeffs[0]
ones = torch.ones(n_channel, n_sample, dtype=dtype, device=device)
for i_frame in range(n_frames):
for i_sample in range(n_sample):
o0 = torch.zeros(n_channels, dtype=dtype, device=device)
o0 = torch.zeros(n_channel, dtype=dtype, device=device)
windowed_input_signal = padded_waveform[:, i_frame:(i_frame + n_order)]
windowed_output_signal = padded_output_waveform[:, i_frame:(i_frame + n_order)]
windowed_input_signal = padded_waveform[:, i_sample:(i_sample + n_order)]
windowed_output_signal = padded_output_waveform[:, i_sample:(i_sample + n_order)]
o0.add_(torch.diag(torch.mm(windowed_input_signal, b_coeffs_filled)))
o0.sub_(torch.diag(torch.mm(windowed_output_signal, a_coeffs_filled)))
o0.div_(a0_repeated)
padded_output_waveform[:, i_frame + n_order - 1] = o0
padded_output_waveform[:, i_sample + n_order - 1] = o0
return torch.min(ones, torch.max(ones * -1, padded_output_waveform[:, (n_order - 1):]))
......@@ -599,7 +598,7 @@ 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 `(n_channel, n_frames)`
waveform (torch.Tensor): audio waveform of dimension of `(channel, 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]
......@@ -608,7 +607,7 @@ 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 `(n_channel, n_frames)`
output_waveform (torch.Tensor): Dimension of `(channel, time)`
"""
device = waveform.device
......@@ -631,13 +630,13 @@ def highpass_biquad(waveform, sample_rate, cutoff_freq, Q=0.707):
r"""Designs biquad highpass filter and performs filtering. Similar to SoX implementation.
Args:
waveform (torch.Tensor): audio waveform of dimension of `(n_channel, n_frames)`
waveform (torch.Tensor): audio waveform of dimension of `(channel, 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 `(n_channel, n_frames)`
output_waveform (torch.Tensor): Dimension of `(channel, time)`
"""
GAIN = 1 # TBD - add as a parameter
......@@ -660,13 +659,13 @@ def lowpass_biquad(waveform, sample_rate, cutoff_freq, Q=0.707):
r"""Designs biquad lowpass filter and performs filtering. Similar to SoX implementation.
Args:
waveform (torch.Tensor): audio waveform of dimension of `(n_channel, n_frames)`
waveform (torch.Tensor): audio waveform of dimension of `(channel, 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 `(n_channel, n_frames)`
output_waveform (torch.Tensor): Dimension of `(channel, time)`
"""
GAIN = 1
......@@ -693,13 +692,13 @@ def mask_along_axis_iid(specgrams, mask_param, mask_value, axis):
All examples will have the same mask interval.
Args:
specgrams (Tensor): Real spectrograms (batch, channel, n_freq, time)
specgrams (Tensor): Real spectrograms (batch, channel, freq, time)
mask_param (int): Number of columns to be masked will be uniformly sampled from [0, mask_param]
mask_value (float): Value to assign to the masked columns
axis (int): Axis to apply masking on (2 -> frequency, 3 -> time)
Returns:
torch.Tensor: Masked spectrograms of dimensions (batch, channel, n_freq, time)
torch.Tensor: Masked spectrograms of dimensions (batch, channel, freq, time)
"""
if axis != 2 and axis != 3:
......@@ -730,13 +729,13 @@ def mask_along_axis(specgram, mask_param, mask_value, axis):
All examples will have the same mask interval.
Args:
specgram (Tensor): Real spectrogram (channel, n_freq, time)
specgram (Tensor): Real spectrogram (channel, freq, time)
mask_param (int): Number of columns to be masked will be uniformly sampled from [0, mask_param]
mask_value (float): Value to assign to the masked columns
axis (int): Axis to apply masking on (1 -> frequency, 2 -> time)
Returns:
torch.Tensor: Masked spectrogram of dimensions (channel, n_freq, time)
torch.Tensor: Masked spectrogram of dimensions (channel, freq, time)
"""
value = torch.rand(1) * mask_param
......@@ -769,12 +768,12 @@ def compute_deltas(specgram, win_length=5, mode="replicate"):
:math:`N` is (`win_length`-1)//2.
Args:
specgram (torch.Tensor): Tensor of audio of dimension (channel, n_mfcc, time)
specgram (torch.Tensor): Tensor of audio of dimension (channel, freq, time)
win_length (int): The window length used for computing delta
mode (str): Mode parameter passed to padding
Returns:
deltas (torch.Tensor): Tensor of audio of dimension (channel, n_mfcc, time)
deltas (torch.Tensor): Tensor of audio of dimension (channel, freq, time)
Example
>>> specgram = torch.randn(1, 40, 1000)
......
torchaudio/transforms.py
View file @ b8203182
......@@ -62,7 +62,7 @@ class Spectrogram(torch.jit.ScriptModule):
Returns:
torch.Tensor: Dimension (channel, freq, time), where channel
is unchanged, freq is ``n_fft // 2 + 1`` where ``n_fft`` is the number of
Fourier bins, and time is the number of window hops (n_frames).
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,
self.win_length, self.power, self.normalized)
......@@ -409,9 +409,9 @@ class ComputeDeltas(torch.jit.ScriptModule):
def forward(self, specgram):
r"""
Args:
specgram (torch.Tensor): Tensor of audio of dimension (channel, n_mfcc, time)
specgram (torch.Tensor): Tensor of audio of dimension (channel, freq, time)
Returns:
deltas (torch.Tensor): Tensor of audio of dimension (channel, n_mfcc, time)
deltas (torch.Tensor): Tensor of audio of dimension (channel, freq, time)
"""
return F.compute_deltas(specgram, win_length=self.win_length, mode=self.mode)
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