import torch import torchaudio import numpy as np from scipy.io.wavfile import read from utils.stft import STFT def load_wav_to_torch(full_path): sampling_rate, data = read(full_path) if data.dtype == np.int32: norm_fix = 2 ** 31 elif data.dtype == np.int16: norm_fix = 2 ** 15 elif data.dtype == np.float16 or data.dtype == np.float32: norm_fix = 1. else: raise NotImplemented(f"Provided data dtype not supported: {data.dtype}") return (torch.FloatTensor(data.astype(np.float32)) / norm_fix, sampling_rate) def load_audio(audiopath, sampling_rate): if audiopath[-4:] == '.wav': audio, lsr = load_wav_to_torch(audiopath) elif audiopath[-4:] == '.mp3': # https://github.com/neonbjb/pyfastmp3decoder - Definitely worth it. from pyfastmp3decoder.mp3decoder import load_mp3 audio, lsr = load_mp3(audiopath, sampling_rate) audio = torch.FloatTensor(audio) # Remove any channel data. if len(audio.shape) > 1: if audio.shape[0] < 5: audio = audio[0] else: assert audio.shape[1] < 5 audio = audio[:, 0] if lsr != sampling_rate: audio = torchaudio.functional.resample(audio, lsr, sampling_rate) # Check some assumptions about audio range. This should be automatically fixed in load_wav_to_torch, but might not be in some edge cases, where we should squawk. # '2' is arbitrarily chosen since it seems like audio will often "overdrive" the [-1,1] bounds. if torch.any(audio > 2) or not torch.any(audio < 0): print(f"Error with {audiopath}. Max={audio.max()} min={audio.min()}") audio.clip_(-1, 1) return audio.unsqueeze(0) TACOTRON_MEL_MAX = 2.3143386840820312 TACOTRON_MEL_MIN = -11.512925148010254 def denormalize_tacotron_mel(norm_mel): return ((norm_mel+1)/2)*(TACOTRON_MEL_MAX-TACOTRON_MEL_MIN)+TACOTRON_MEL_MIN def normalize_tacotron_mel(mel): return 2 * ((mel - TACOTRON_MEL_MIN) / (TACOTRON_MEL_MAX - TACOTRON_MEL_MIN)) - 1 def dynamic_range_compression(x, C=1, clip_val=1e-5): """ PARAMS ------ C: compression factor """ return torch.log(torch.clamp(x, min=clip_val) * C) def dynamic_range_decompression(x, C=1): """ PARAMS ------ C: compression factor used to compress """ return torch.exp(x) / C class TacotronSTFT(torch.nn.Module): def __init__(self, filter_length=1024, hop_length=256, win_length=1024, n_mel_channels=80, sampling_rate=22050, mel_fmin=0.0, mel_fmax=8000.0): super(TacotronSTFT, self).__init__() self.n_mel_channels = n_mel_channels self.sampling_rate = sampling_rate self.stft_fn = STFT(filter_length, hop_length, win_length) from librosa.filters import mel as librosa_mel_fn mel_basis = librosa_mel_fn( sampling_rate, filter_length, n_mel_channels, mel_fmin, mel_fmax) mel_basis = torch.from_numpy(mel_basis).float() self.register_buffer('mel_basis', mel_basis) def spectral_normalize(self, magnitudes): output = dynamic_range_compression(magnitudes) return output def spectral_de_normalize(self, magnitudes): output = dynamic_range_decompression(magnitudes) return output def mel_spectrogram(self, y): """Computes mel-spectrograms from a batch of waves PARAMS ------ y: Variable(torch.FloatTensor) with shape (B, T) in range [-1, 1] RETURNS ------- mel_output: torch.FloatTensor of shape (B, n_mel_channels, T) """ assert(torch.min(y.data) >= -10) assert(torch.max(y.data) <= 10) y = torch.clip(y, min=-1, max=1) magnitudes, phases = self.stft_fn.transform(y) magnitudes = magnitudes.data mel_output = torch.matmul(self.mel_basis, magnitudes) mel_output = self.spectral_normalize(mel_output) return mel_output def wav_to_univnet_mel(wav, do_normalization=False): stft = TacotronSTFT(1024, 256, 1024, 100, 24000, 0, 12000) stft = stft.cuda() mel = stft.mel_spectrogram(wav) if do_normalization: mel = normalize_tacotron_mel(mel) return mel