import os from glob import glob import librosa import torch import torchaudio import numpy as np from scipy.io.wavfile import read from tortoise.utils.stft import STFT BUILTIN_VOICES_DIR = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../voices') 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): extension = os.path.splitext(audiopath)[1].casefold() if extension == '.wav': audio, lsr = load_wav_to_torch(audiopath) elif extension == '.mp3': audio, lsr = librosa.load(audiopath, sr=sampling_rate) audio = torch.FloatTensor(audio) else: assert False, f"Unsupported audio format provided: {audiopath[-4:]}" # 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 def get_voices(extra_voice_dirs=[]): dirs = [BUILTIN_VOICES_DIR] + extra_voice_dirs voices = {} for d in dirs: subs = os.listdir(d) for sub in subs: subj = os.path.join(d, sub) if os.path.isdir(subj): voices[sub] = list(glob(f'{subj}/*.wav')) + list(glob(f'{subj}/*.mp3')) + list(glob(f'{subj}/*.pth')) return voices def load_voice(voice, extra_voice_dirs=[]): if voice == 'random': return None, None voices = get_voices(extra_voice_dirs) paths = voices[voice] if len(paths) == 1 and paths[0].endswith('.pth'): return None, torch.load(paths[0]) else: conds = [] for cond_path in paths: c = load_audio(cond_path, 22050) conds.append(c) return conds, None def load_voices(voices, extra_voice_dirs=[]): latents = [] clips = [] for voice in voices: if voice == 'random': if len(voices) > 1: print("Cannot combine a random voice with a non-random voice. Just using a random voice.") return None, None clip, latent = load_voice(voice, extra_voice_dirs) if latent is None: assert len(latents) == 0, "Can only combine raw audio voices or latent voices, not both. Do it yourself if you want this." clips.extend(clip) elif clip is None: assert len(clips) == 0, "Can only combine raw audio voices or latent voices, not both. Do it yourself if you want this." latents.append(latent) if len(latents) == 0: return clips, None else: latents_0 = torch.stack([l[0] for l in latents], dim=0).mean(dim=0) latents_1 = torch.stack([l[1] for l in latents], dim=0).mean(dim=0) latents = (latents_0,latents_1) return None, latents 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( sr=sampling_rate, n_fft=filter_length, n_mels=n_mel_channels, fmin=mel_fmin, fmax=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, device='cuda' if not torch.backends.mps.is_available() else 'mps'): stft = TacotronSTFT(1024, 256, 1024, 100, 24000, 0, 12000) stft = stft.to(device) mel = stft.mel_spectrogram(wav) if do_normalization: mel = normalize_tacotron_mel(mel) return mel