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| # 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 | |
| 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(): | |
| mels = mels.to(device) | |
| if f0s != None: | |
| f0s = f0s.to(device) | |
| if f0s == None and not cfg.preprocess.extract_amplitude_phase: | |
| output = model.forward(mels) | |
| elif cfg.preprocess.extract_amplitude_phase: | |
| ( | |
| _, | |
| _, | |
| _, | |
| _, | |
| output, | |
| ) = model.forward(mels) | |
| else: | |
| output = model.forward(mels, f0s) | |
| return output.squeeze(1).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) | |
| # # Apply fade_out to make the sound more natural | |
| # fade_out = torch.linspace( | |
| # 1, 0, steps=20 * model.cfg.preprocess.hop_size | |
| # ).cpu() | |
| # calculate the audio length | |
| audio_length = frame * model.cfg.preprocess.hop_size | |
| audio = audio[:audio_length] | |
| # audio[-20 * model.cfg.preprocess.hop_size :] *= fade_out | |
| 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) | |
| # # Apply fade_out to make the sound more natural | |
| # fade_out = torch.linspace( | |
| # 1, 0, steps=20 * model.cfg.preprocess.hop_size | |
| # ).cpu() | |
| # calculate the audio length | |
| audio_length = frame * model.cfg.preprocess.hop_length | |
| audio = audio[:audio_length] | |
| # audio[-20 * model.cfg.preprocess.hop_size :] *= fade_out | |
| audios.append(audio) | |
| return audios | |