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Running
on
Zero
File size: 3,081 Bytes
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import numpy as np
import torch
import torchaudio
from librosa.filters import mel as librosa_mel_fn
def dynamic_range_compression(x, C=1, clip_val=1e-5):
return np.log(np.clip(x, a_min=clip_val, a_max=None) * C)
def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
return torch.log(torch.clamp(x, min=clip_val) * C)
def spectral_normalize_torch(magnitudes):
output = dynamic_range_compression_torch(magnitudes)
return output
mel_basis = {}
hann_window = {}
def mel_spectrogram(
y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False
):
global mel_basis, hann_window # pylint: disable=global-statement
if f"{str(fmax)}_{str(y.device)}" not in mel_basis:
mel = librosa_mel_fn(
sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax
)
mel_basis[str(fmax) + "_" + str(y.device)] = (
torch.from_numpy(mel).float().to(y.device)
)
hann_window[str(y.device)] = torch.hann_window(win_size).to(y.device)
y = torch.nn.functional.pad(
y.unsqueeze(1),
(int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
mode="reflect",
)
y = y.squeeze(1)
spec = torch.view_as_real(
torch.stft(
y,
n_fft,
hop_length=hop_size,
win_length=win_size,
window=hann_window[str(y.device)],
center=center,
pad_mode="reflect",
normalized=False,
onesided=True,
return_complex=True,
)
)
spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9))
spec = torch.matmul(mel_basis[str(fmax) + "_" + str(y.device)], spec)
spec = spectral_normalize_torch(spec)
return spec
class MelExtractor(object):
def __init__(
self,
num_mels: int = 80,
n_fft: int = 1920,
hop_size: int = 480,
win_size: int = 1920,
sampling_rate: int = 24000,
fmin: int = 0,
fmax: int = 8000,
center: bool = False,
):
super().__init__()
self.num_mels = num_mels
self.n_fft = n_fft
self.hop_size = hop_size
self.win_size = win_size
self.sampling_rate = sampling_rate
self.fmin = fmin
self.fmax = fmax
self.center = center
def __call__(self, audio: torch.Tensor, audio_sr: int):
"""Args:
audio(torch.Tensor): shape (1, t)
Returns:
mel(torch.Tensor): shape (1, num_mels, t')
"""
if audio_sr != self.sampling_rate:
audio = torchaudio.functional.resample(
audio, orig_freq=audio_sr, new_freq=self.sampling_rate
)
audio_sr = self.sampling_rate
mel = mel_spectrogram(
audio,
self.n_fft,
self.num_mels,
self.sampling_rate,
self.hop_size,
self.win_size,
self.fmin,
self.fmax,
self.center,
) # (1, num_mels, t)
return mel
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