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Make some buffers non-persistent (e.g. window, fbank...) #3849

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Make some buffers non-persistent (e.g. window, fbank...) #3849

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6 changes: 3 additions & 3 deletions src/torchaudio/prototype/transforms/_transforms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -55,7 +55,7 @@ def __init__(
raise ValueError("Require f_min: {} <= f_max: {}".format(f_min, self.f_max))

fb = barkscale_fbanks(n_stft, self.f_min, self.f_max, self.n_barks, self.sample_rate, self.bark_scale)
self.register_buffer("fb", fb)
self.register_buffer("fb", fb, persistent=False)

def forward(self, specgram: torch.Tensor) -> torch.Tensor:
r"""
Expand Down Expand Up @@ -138,7 +138,7 @@ def __init__(
raise ValueError("Require f_min: {} <= f_max: {}".format(f_min, self.f_max))

fb = barkscale_fbanks(n_stft, self.f_min, self.f_max, self.n_barks, self.sample_rate, bark_scale)
self.register_buffer("fb", fb)
self.register_buffer("fb", fb, persistent=False)

def forward(self, barkspec: torch.Tensor) -> torch.Tensor:
r"""
Expand Down Expand Up @@ -343,7 +343,7 @@ def __init__(
fb = chroma_filterbank(
sample_rate, n_freqs, n_chroma, tuning=tuning, ctroct=ctroct, octwidth=octwidth, norm=norm, base_c=base_c
)
self.register_buffer("fb", fb)
self.register_buffer("fb", fb, persistent=False)

def forward(self, x: torch.Tensor) -> torch.Tensor:
r"""
Expand Down
25 changes: 13 additions & 12 deletions src/torchaudio/transforms/_transforms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -83,7 +83,7 @@ def __init__(
self.win_length = win_length if win_length is not None else n_fft
self.hop_length = hop_length if hop_length is not None else self.win_length // 2
window = window_fn(self.win_length) if wkwargs is None else window_fn(self.win_length, **wkwargs)
self.register_buffer("window", window)
self.register_buffer("window", window, persistent=False)
self.pad = pad
self.power = power
self.normalized = normalized
Expand Down Expand Up @@ -177,7 +177,7 @@ def __init__(
self.win_length = win_length if win_length is not None else n_fft
self.hop_length = hop_length if hop_length is not None else self.win_length // 2
window = window_fn(self.win_length) if wkwargs is None else window_fn(self.win_length, **wkwargs)
self.register_buffer("window", window)
self.register_buffer("window", window, persistent=False)
self.pad = pad
self.normalized = normalized
self.center = center
Expand Down Expand Up @@ -266,7 +266,7 @@ def __init__(
self.win_length = win_length if win_length is not None else n_fft
self.hop_length = hop_length if hop_length is not None else self.win_length // 2
window = window_fn(self.win_length) if wkwargs is None else window_fn(self.win_length, **wkwargs)
self.register_buffer("window", window)
self.register_buffer("window", window, persistent=False)
self.length = length
self.power = power
self.momentum = momentum
Expand Down Expand Up @@ -397,7 +397,7 @@ def __init__(
raise ValueError("Require f_min: {} <= f_max: {}".format(f_min, self.f_max))

fb = F.melscale_fbanks(n_stft, self.f_min, self.f_max, self.n_mels, self.sample_rate, self.norm, self.mel_scale)
self.register_buffer("fb", fb)
self.register_buffer("fb", fb, persistent=False)

def forward(self, specgram: Tensor) -> Tensor:
r"""
Expand Down Expand Up @@ -476,7 +476,7 @@ def __init__(
raise ValueError(f'driver must be one of ["gels", "gelsy", "gelsd", "gelss"]. Found {driver}.')

fb = F.melscale_fbanks(n_stft, self.f_min, self.f_max, self.n_mels, self.sample_rate, norm, mel_scale)
self.register_buffer("fb", fb)
self.register_buffer("fb", fb, persistent=False)

def forward(self, melspec: Tensor) -> Tensor:
r"""
Expand Down Expand Up @@ -685,7 +685,7 @@ def __init__(
if self.n_mfcc > self.MelSpectrogram.n_mels:
raise ValueError("Cannot select more MFCC coefficients than # mel bins")
dct_mat = F.create_dct(self.n_mfcc, self.MelSpectrogram.n_mels, self.norm)
self.register_buffer("dct_mat", dct_mat)
self.register_buffer("dct_mat", dct_mat, persistent=False)
self.log_mels = log_mels

def forward(self, waveform: Tensor) -> Tensor:
Expand Down Expand Up @@ -788,10 +788,10 @@ def __init__(
n_filter=self.n_filter,
sample_rate=self.sample_rate,
)
self.register_buffer("filter_mat", filter_mat)
self.register_buffer("filter_mat", filter_mat, persistent=False)

dct_mat = F.create_dct(self.n_lfcc, self.n_filter, self.norm)
self.register_buffer("dct_mat", dct_mat)
self.register_buffer("dct_mat", dct_mat, persistent=False)
self.log_lf = log_lf

def forward(self, waveform: Tensor) -> Tensor:
Expand Down Expand Up @@ -964,7 +964,7 @@ def __init__(
beta,
dtype=dtype,
)
self.register_buffer("kernel", kernel)
self.register_buffer("kernel", kernel, persistent=False)

def forward(self, waveform: Tensor) -> Tensor:
r"""
Expand Down Expand Up @@ -1051,7 +1051,8 @@ def __init__(self, hop_length: Optional[int] = None, n_freq: int = 201, fixed_ra

n_fft = (n_freq - 1) * 2
hop_length = hop_length if hop_length is not None else n_fft // 2
self.register_buffer("phase_advance", torch.linspace(0, math.pi * hop_length, n_freq)[..., None])
phase_advance = torch.linspace(0, math.pi * hop_length, n_freq)[..., None]
self.register_buffer("phase_advance", phase_advance, persistent=False)

def forward(self, complex_specgrams: Tensor, overriding_rate: Optional[float] = None) -> Tensor:
r"""
Expand Down Expand Up @@ -1652,7 +1653,7 @@ def __init__(
self.win_length = win_length if win_length is not None else n_fft
self.hop_length = hop_length if hop_length is not None else self.win_length // 2
window = window_fn(self.win_length) if wkwargs is None else window_fn(self.win_length, **wkwargs)
self.register_buffer("window", window)
self.register_buffer("window", window, persistent=False)
self.pad = pad

def forward(self, waveform: Tensor) -> Tensor:
Expand Down Expand Up @@ -1717,7 +1718,7 @@ def __init__(
self.win_length = win_length if win_length is not None else n_fft
self.hop_length = hop_length if hop_length is not None else self.win_length // 4
window = window_fn(self.win_length) if wkwargs is None else window_fn(self.win_length, **wkwargs)
self.register_buffer("window", window)
self.register_buffer("window", window, persistent=False)
rate = 2.0 ** (-float(n_steps) / bins_per_octave)
self.orig_freq = int(sample_rate / rate)
self.gcd = math.gcd(int(self.orig_freq), int(sample_rate))
Expand Down