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| import math | |
| import os | |
| os.environ["LRU_CACHE_CAPACITY"] = "3" | |
| import random | |
| import torch | |
| import torch.utils.data | |
| import numpy as np | |
| import librosa | |
| from librosa.util import normalize | |
| from librosa.filters import mel as librosa_mel_fn | |
| from scipy.io.wavfile import read | |
| import soundfile as sf | |
| import torch.nn.functional as F | |
| def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False): | |
| sampling_rate = None | |
| try: | |
| data, sampling_rate = sf.read(full_path, always_2d=True)# than soundfile. | |
| except Exception as ex: | |
| print(f"'{full_path}' failed to load.\nException:") | |
| print(ex) | |
| if return_empty_on_exception: | |
| return [], sampling_rate or target_sr or 48000 | |
| else: | |
| raise Exception(ex) | |
| if len(data.shape) > 1: | |
| data = data[:, 0] | |
| assert len(data) > 2# check duration of audio file is > 2 samples (because otherwise the slice operation was on the wrong dimension) | |
| if np.issubdtype(data.dtype, np.integer): # if audio data is type int | |
| max_mag = -np.iinfo(data.dtype).min # maximum magnitude = min possible value of intXX | |
| else: # if audio data is type fp32 | |
| max_mag = max(np.amax(data), -np.amin(data)) | |
| max_mag = (2**31)+1 if max_mag > (2**15) else ((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 | |
| data = torch.FloatTensor(data.astype(np.float32))/max_mag | |
| if (torch.isinf(data) | torch.isnan(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 | |
| return [], sampling_rate or target_sr or 48000 | |
| if target_sr is not None and sampling_rate != target_sr: | |
| data = torch.from_numpy(librosa.core.resample(data.numpy(), orig_sr=sampling_rate, target_sr=target_sr)) | |
| sampling_rate = target_sr | |
| return data, sampling_rate | |
| 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_decompression(x, C=1): | |
| return np.exp(x) / C | |
| def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): | |
| return torch.log(torch.clamp(x, min=clip_val) * C) | |
| def dynamic_range_decompression_torch(x, C=1): | |
| return torch.exp(x) / C | |
| class STFT(): | |
| def __init__(self, sr=22050, n_mels=80, n_fft=1024, win_size=1024, hop_length=256, fmin=20, fmax=11025, clip_val=1e-5): | |
| self.target_sr = sr | |
| self.n_mels = n_mels | |
| self.n_fft = n_fft | |
| self.win_size = win_size | |
| self.hop_length = hop_length | |
| self.fmin = fmin | |
| self.fmax = fmax | |
| self.clip_val = clip_val | |
| self.mel_basis = {} | |
| self.hann_window = {} | |
| def get_mel(self, y, keyshift=0, speed=1, center=False): | |
| sampling_rate = self.target_sr | |
| n_mels = self.n_mels | |
| n_fft = self.n_fft | |
| win_size = self.win_size | |
| hop_length = self.hop_length | |
| fmin = self.fmin | |
| fmax = self.fmax | |
| clip_val = self.clip_val | |
| factor = 2 ** (keyshift / 12) | |
| n_fft_new = int(np.round(n_fft * factor)) | |
| win_size_new = int(np.round(win_size * factor)) | |
| hop_length_new = int(np.round(hop_length * speed)) | |
| if torch.min(y) < -1.: | |
| print('min value is ', torch.min(y)) | |
| if torch.max(y) > 1.: | |
| print('max value is ', torch.max(y)) | |
| mel_basis_key = str(fmax)+'_'+str(y.device) | |
| if mel_basis_key not in self.mel_basis: | |
| mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax) | |
| self.mel_basis[mel_basis_key] = torch.from_numpy(mel).float().to(y.device) | |
| keyshift_key = str(keyshift)+'_'+str(y.device) | |
| if keyshift_key not in self.hann_window: | |
| self.hann_window[keyshift_key] = torch.hann_window(win_size_new).to(y.device) | |
| pad_left = (win_size_new - hop_length_new) //2 | |
| pad_right = max((win_size_new- hop_length_new + 1) //2, win_size_new - y.size(-1) - pad_left) | |
| if pad_right < y.size(-1): | |
| mode = 'reflect' | |
| else: | |
| mode = 'constant' | |
| y = torch.nn.functional.pad(y.unsqueeze(1), (pad_left, pad_right), mode = mode) | |
| y = y.squeeze(1) | |
| spec = torch.stft(y, n_fft_new, hop_length=hop_length_new, win_length=win_size_new, window=self.hann_window[keyshift_key], | |
| center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False) | |
| # print(111,spec) | |
| spec = torch.sqrt(spec.pow(2).sum(-1)+(1e-9)) | |
| if keyshift != 0: | |
| size = n_fft // 2 + 1 | |
| resize = spec.size(1) | |
| if resize < size: | |
| spec = F.pad(spec, (0, 0, 0, size-resize)) | |
| spec = spec[:, :size, :] * win_size / win_size_new | |
| # print(222,spec) | |
| spec = torch.matmul(self.mel_basis[mel_basis_key], spec) | |
| # print(333,spec) | |
| spec = dynamic_range_compression_torch(spec, clip_val=clip_val) | |
| # print(444,spec) | |
| return spec | |
| def __call__(self, audiopath): | |
| audio, sr = load_wav_to_torch(audiopath, target_sr=self.target_sr) | |
| spect = self.get_mel(audio.unsqueeze(0)).squeeze(0) | |
| return spect | |
| stft = STFT() | |