wehub-resource-sync
88 lines
2.6 KiB
Python
88 lines
2.6 KiB
Python
import os
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os.environ["LRU_CACHE_CAPACITY"] = "3"
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import torch
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import torch.utils.data
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import numpy as np
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from librosa.filters import mel as librosa_mel_fn
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import torch.nn.functional as F
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def dynamic_range_compression(x, C=1, clip_val=1e-5):
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return np.log(np.clip(x, a_min=clip_val, a_max=None) * C)
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def dynamic_range_decompression(x, C=1):
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return np.exp(x) / C
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def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
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return torch.log(torch.clamp(x, min=clip_val) * C)
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def dynamic_range_decompression_torch(x, C=1):
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return torch.exp(x) / C
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class STFT:
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def __init__(
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self, sr=22050,
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n_mels=80, n_fft=1024, win_size=1024, hop_length=256,
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fmin=20, fmax=11025, clip_val=1e-5,
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device=None
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):
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self.target_sr = sr
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self.n_mels = n_mels
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self.n_fft = n_fft
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self.win_size = win_size
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self.hop_length = hop_length
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self.fmin = fmin
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self.fmax = fmax
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self.clip_val = clip_val
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if device is None:
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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self.device = device
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mel_basis = librosa_mel_fn(sr=sr, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax)
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self.mel_basis = torch.from_numpy(mel_basis).float().to(device)
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def get_mel(self, y, keyshift=0, speed=1, center=False):
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factor = 2 ** (keyshift / 12)
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n_fft_new = int(np.round(self.n_fft * factor))
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win_size_new = int(np.round(self.win_size * factor))
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hop_length_new = int(np.round(self.hop_length * speed))
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if torch.min(y) < -1.:
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print('min value is ', torch.min(y))
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if torch.max(y) > 1.:
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print('max value is ', torch.max(y))
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window = torch.hann_window(win_size_new, device=self.device)
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y = torch.nn.functional.pad(y.unsqueeze(1), (
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(win_size_new - hop_length_new) // 2,
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(win_size_new - hop_length_new + 1) // 2
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), mode='reflect')
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y = y.squeeze(1)
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spec = torch.stft(
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y, n_fft_new, hop_length=hop_length_new,
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win_length=win_size_new, window=window,
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center=center, pad_mode='reflect',
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normalized=False, onesided=True, return_complex=True
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).abs()
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if keyshift != 0:
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size = self.n_fft // 2 + 1
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resize = spec.size(1)
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if resize < size:
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spec = F.pad(spec, (0, 0, 0, size - resize))
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spec = spec[:, :size, :] * self.win_size / win_size_new
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spec = torch.matmul(self.mel_basis, spec)
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spec = dynamic_range_compression_torch(spec, clip_val=self.clip_val)
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return spec
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