import os import librosa import numpy as np import soundfile as sf import torch import torch.nn.functional as F import torch.utils.data from librosa.filters import mel as librosa_mel_fn os.environ["LRU_CACHE_CAPACITY"] = "3" 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, train=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 not train: mel_basis = self.mel_basis hann_window = self.hann_window else: mel_basis = {} hann_window = {} 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 mel_basis: mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax) 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 hann_window: 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=hann_window[keyshift_key], center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=True) spec = torch.sqrt(spec.real.pow(2) + spec.imag.pow(2) + (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 spec = torch.matmul(mel_basis[mel_basis_key], spec) spec = dynamic_range_compression_torch(spec, clip_val=clip_val) 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()