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Zero
| # Copyright (c) 2023 Amphion. | |
| # | |
| # This source code is licensed under the MIT license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| import torch | |
| import numpy as np | |
| from tqdm import tqdm | |
| from utils.util import pad_mels_to_tensors, pad_f0_to_tensors | |
| def vocoder_inference(cfg, model, mels, f0s=None, device=None, fast_inference=False): | |
| """Inference the vocoder | |
| Args: | |
| mels: A tensor of mel-specs with the shape (batch_size, num_mels, frames) | |
| Returns: | |
| audios: A tensor of audios with the shape (batch_size, seq_len) | |
| """ | |
| model.eval() | |
| with torch.no_grad(): | |
| training_noise_schedule = np.array(cfg.model.diffwave.noise_schedule) | |
| inference_noise_schedule = ( | |
| np.array(cfg.model.diffwave.inference_noise_schedule) | |
| if fast_inference | |
| else np.array(cfg.model.diffwave.noise_schedule) | |
| ) | |
| talpha = 1 - training_noise_schedule | |
| talpha_cum = np.cumprod(talpha) | |
| beta = inference_noise_schedule | |
| alpha = 1 - beta | |
| alpha_cum = np.cumprod(alpha) | |
| T = [] | |
| for s in range(len(inference_noise_schedule)): | |
| for t in range(len(training_noise_schedule) - 1): | |
| if talpha_cum[t + 1] <= alpha_cum[s] <= talpha_cum[t]: | |
| twiddle = (talpha_cum[t] ** 0.5 - alpha_cum[s] ** 0.5) / ( | |
| talpha_cum[t] ** 0.5 - talpha_cum[t + 1] ** 0.5 | |
| ) | |
| T.append(t + twiddle) | |
| break | |
| T = np.array(T, dtype=np.float32) | |
| mels = mels.to(device) | |
| audio = torch.randn( | |
| mels.shape[0], | |
| cfg.preprocess.hop_size * mels.shape[-1], | |
| device=device, | |
| ) | |
| for n in tqdm(range(len(alpha) - 1, -1, -1)): | |
| c1 = 1 / alpha[n] ** 0.5 | |
| c2 = beta[n] / (1 - alpha_cum[n]) ** 0.5 | |
| audio = c1 * ( | |
| audio | |
| - c2 | |
| * model(audio, torch.tensor([T[n]], device=audio.device), mels).squeeze( | |
| 1 | |
| ) | |
| ) | |
| if n > 0: | |
| noise = torch.randn_like(audio) | |
| sigma = ( | |
| (1.0 - alpha_cum[n - 1]) / (1.0 - alpha_cum[n]) * beta[n] | |
| ) ** 0.5 | |
| audio += sigma * noise | |
| audio = torch.clamp(audio, -1.0, 1.0) | |
| return audio.detach().cpu() | |
| def synthesis_audios(cfg, model, mels, f0s=None, batch_size=None, fast_inference=False): | |
| """Inference the vocoder | |
| Args: | |
| mels: A list of mel-specs | |
| Returns: | |
| audios: A list of audios | |
| """ | |
| # Get the device | |
| device = next(model.parameters()).device | |
| audios = [] | |
| # Pad the given list into tensors | |
| mel_batches, mel_frames = pad_mels_to_tensors(mels, batch_size) | |
| if f0s != None: | |
| f0_batches = pad_f0_to_tensors(f0s, batch_size) | |
| if f0s == None: | |
| for mel_batch, mel_frame in zip(mel_batches, mel_frames): | |
| for i in range(mel_batch.shape[0]): | |
| mel = mel_batch[i] | |
| frame = mel_frame[i] | |
| audio = vocoder_inference( | |
| cfg, | |
| model, | |
| mel.unsqueeze(0), | |
| device=device, | |
| fast_inference=fast_inference, | |
| ).squeeze(0) | |
| # calculate the audio length | |
| audio_length = frame * cfg.preprocess.hop_size | |
| audio = audio[:audio_length] | |
| audios.append(audio) | |
| else: | |
| for mel_batch, f0_batch, mel_frame in zip(mel_batches, f0_batches, mel_frames): | |
| for i in range(mel_batch.shape[0]): | |
| mel = mel_batch[i] | |
| f0 = f0_batch[i] | |
| frame = mel_frame[i] | |
| audio = vocoder_inference( | |
| cfg, | |
| model, | |
| mel.unsqueeze(0), | |
| f0s=f0.unsqueeze(0), | |
| device=device, | |
| fast_inference=fast_inference, | |
| ).squeeze(0) | |
| # calculate the audio length | |
| audio_length = frame * cfg.preprocess.hop_size | |
| audio = audio[:audio_length] | |
| audios.append(audio) | |
| return audios | |