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import torch
# Adapted from https://github.com/huggingface/transformers/blob/main/src/transformers/models/whisper/feature_extraction_whisper.py # noqa: E501
class FeatureExtractor:
def __init__(
self,
device: str = "auto",
feature_size=80,
sampling_rate=16000,
hop_length=160,
chunk_length=30,
n_fft=400,
):
if device == "auto":
self.device = "cuda" if torch.cuda.is_available() else "cpu"
else:
self.device = device
self.n_fft = n_fft
self.hop_length = hop_length
self.chunk_length = chunk_length
self.n_samples = chunk_length * sampling_rate
self.nb_max_frames = self.n_samples // hop_length
self.time_per_frame = hop_length / sampling_rate
self.sampling_rate = sampling_rate
self.mel_filters = self.get_mel_filters(
sampling_rate, n_fft, n_mels=feature_size
)
@staticmethod
def get_mel_filters(sr, n_fft, n_mels=128):
"""
Implementation of librosa.filters.mel in Pytorch
"""
# Initialize the weights
n_mels = int(n_mels)
# Center freqs of each FFT bin
fftfreqs = torch.fft.rfftfreq(n=n_fft, d=1.0 / sr)
# 'Center freqs' of mel bands - uniformly spaced between limits
min_mel = 0.0
max_mel = 45.245640471924965
mels = torch.linspace(min_mel, max_mel, n_mels + 2)
# Fill in the linear scale
f_min = 0.0
f_sp = 200.0 / 3
freqs = f_min + f_sp * mels
# And now the nonlinear scale
min_log_hz = 1000.0 # beginning of log region (Hz)
min_log_mel = (min_log_hz - f_min) / f_sp # same (Mels)
logstep = torch.log(torch.tensor(6.4)) / 27.0 # step size for log region
# If we have vector data, vectorize
log_t = mels >= min_log_mel
freqs[log_t] = min_log_hz * torch.exp(logstep * (mels[log_t] - min_log_mel))
mel_f = freqs
fdiff = torch.diff(mel_f)
ramps = mel_f.view(-1, 1) - fftfreqs.view(1, -1)
lower = -ramps[:-2] / fdiff[:-1].unsqueeze(1)
upper = ramps[2:] / fdiff[1:].unsqueeze(1)
# Intersect them with each other and zero, vectorized across all i
weights = torch.maximum(torch.zeros_like(lower), torch.minimum(lower, upper))
# Slaney-style mel is scaled to be approx constant energy per channel
enorm = 2.0 / (mel_f[2 : n_mels + 2] - mel_f[:n_mels])
weights *= enorm.unsqueeze(1)
return weights
def __call__(self, waveform, padding=True, chunk_length=None, to_cpu=False):
"""
Compute the log-Mel spectrogram of the provided audio.
"""
if chunk_length is not None:
self.n_samples = chunk_length * self.sampling_rate
self.nb_max_frames = self.n_samples // self.hop_length
if waveform.dtype is not torch.float32:
waveform = waveform.to(torch.float32)
waveform = (
waveform.to(self.device)
if self.device == "cuda" and not waveform.is_cuda
else waveform
)
if padding:
waveform = torch.nn.functional.pad(waveform, (0, self.n_samples))
window = torch.hann_window(self.n_fft).to(waveform.device)
stft = torch.stft(
waveform, self.n_fft, self.hop_length, window=window, return_complex=True
)
magnitudes = stft[..., :-1].abs() ** 2
mel_spec = self.mel_filters.to(waveform.device) @ magnitudes
log_spec = torch.clamp(mel_spec, min=1e-10).log10()
log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)
log_spec = (log_spec + 4.0) / 4.0
# When the model is running on multiple GPUs, the output should be moved
# to the CPU since we don't know which GPU will handle the next job.
return log_spec.cpu() if to_cpu else log_spec
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