141 lines
5.1 KiB
Python
141 lines
5.1 KiB
Python
import os
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import librosa
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import numpy as np
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import soundfile as sf
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import torch
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import torch.nn.functional as F
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import torch.utils.data
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from librosa.filters import mel as librosa_mel_fn
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os.environ["LRU_CACHE_CAPACITY"] = "3"
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def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
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sampling_rate = None
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try:
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data, sampling_rate = sf.read(full_path, always_2d=True) # than soundfile.
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except Exception as ex:
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print(f"'{full_path}' failed to load.\nException:")
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print(ex)
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if return_empty_on_exception:
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return [], sampling_rate or target_sr or 48000
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else:
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raise Exception(ex)
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if len(data.shape) > 1:
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data = data[:, 0]
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assert len(
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data) > 2 # check duration of audio file is > 2 samples (because otherwise the slice operation was on the wrong dimension)
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if np.issubdtype(data.dtype, np.integer): # if audio data is type int
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max_mag = -np.iinfo(data.dtype).min # maximum magnitude = min possible value of intXX
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else: # if audio data is type fp32
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max_mag = max(np.amax(data), -np.amin(data))
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max_mag = (2 ** 31) + 1 if max_mag > (2 ** 15) else ((
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2 ** 15) + 1 if max_mag > 1.01 else 1.0) # data should be either 16-bit INT, 32-bit INT or [-1 to 1] float32
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data = torch.FloatTensor(data.astype(np.float32)) / max_mag
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if (torch.isinf(data) | torch.isnan(
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data)).any() and return_empty_on_exception: # resample will crash with inf/NaN inputs. return_empty_on_exception will return empty arr instead of except
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return [], sampling_rate or target_sr or 48000
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if target_sr is not None and sampling_rate != target_sr:
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data = torch.from_numpy(librosa.core.resample(data.numpy(), orig_sr=sampling_rate, target_sr=target_sr))
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sampling_rate = target_sr
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return data, sampling_rate
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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__(self, sr=22050, n_mels=80, n_fft=1024, win_size=1024, hop_length=256, fmin=20, fmax=11025,
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clip_val=1e-5):
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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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self.mel_basis = {}
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self.hann_window = {}
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def get_mel(self, y, keyshift=0, speed=1, center=False):
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sampling_rate = self.target_sr
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n_mels = self.n_mels
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n_fft = self.n_fft
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win_size = self.win_size
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hop_length = self.hop_length
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fmin = self.fmin
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fmax = self.fmax
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clip_val = self.clip_val
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factor = 2 ** (keyshift / 12)
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n_fft_new = int(np.round(n_fft * factor))
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win_size_new = int(np.round(win_size * factor))
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hop_length_new = int(np.round(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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mel_basis_key = str(fmax) + '_' + str(y.device)
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if mel_basis_key not in self.mel_basis:
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mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax)
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self.mel_basis[mel_basis_key] = torch.from_numpy(mel).float().to(y.device)
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keyshift_key = str(keyshift) + '_' + str(y.device)
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if keyshift_key not in self.hann_window:
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self.hann_window[keyshift_key] = torch.hann_window(win_size_new).to(y.device)
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y = torch.nn.functional.pad(y.unsqueeze(1),
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((win_size_new - hop_length_new) // 2, (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(y, n_fft_new, hop_length=hop_length_new, win_length=win_size_new,
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window=self.hann_window[keyshift_key],
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center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
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# print(111,spec)
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spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9))
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if keyshift != 0:
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size = 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, :] * win_size / win_size_new
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# print(222,spec)
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spec = torch.matmul(self.mel_basis[mel_basis_key], spec)
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# print(333,spec)
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spec = dynamic_range_compression_torch(spec, clip_val=clip_val)
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# print(444,spec)
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return spec
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def __call__(self, audiopath):
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audio, sr = load_wav_to_torch(audiopath, target_sr=self.target_sr)
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spect = self.get_mel(audio.unsqueeze(0)).squeeze(0)
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return spect
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stft = STFT()
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