| import torch |
| from inference.base_tts_infer import BaseTTSInfer |
| from utils.ckpt_utils import load_ckpt, get_last_checkpoint |
| from utils.hparams import hparams |
| from modules.ProDiff.model.ProDiff import GaussianDiffusion |
| from usr.diff.net import DiffNet |
| import os |
| import numpy as np |
| from functools import partial |
|
|
| class ProDiffInfer(BaseTTSInfer): |
| def build_model(self): |
| f0_stats_fn = f'{hparams["binary_data_dir"]}/train_f0s_mean_std.npy' |
| if os.path.exists(f0_stats_fn): |
| hparams['f0_mean'], hparams['f0_std'] = np.load(f0_stats_fn) |
| hparams['f0_mean'] = float(hparams['f0_mean']) |
| hparams['f0_std'] = float(hparams['f0_std']) |
| model = GaussianDiffusion( |
| phone_encoder=self.ph_encoder, |
| out_dims=80, denoise_fn=DiffNet(hparams['audio_num_mel_bins']), |
| timesteps=hparams['timesteps'], |
| loss_type=hparams['diff_loss_type'], |
| spec_min=hparams['spec_min'], spec_max=hparams['spec_max'], |
| ) |
| checkpoint = torch.load(hparams['teacher_ckpt'], map_location='cpu')["state_dict"]['model'] |
| teacher_timesteps = int(checkpoint['timesteps'].item()) |
| teacher_timescales = int(checkpoint['timescale'].item()) |
| student_timesteps = teacher_timesteps // 2 |
| student_timescales = teacher_timescales * 2 |
| to_torch = partial(torch.tensor, dtype=torch.float32) |
| model.register_buffer('timesteps', to_torch(student_timesteps)) |
| model.register_buffer('timescale', to_torch(student_timescales)) |
| model.eval() |
| load_ckpt(model, hparams['work_dir'], 'model') |
| return model |
|
|
| def forward_model(self, inp): |
| sample = self.input_to_batch(inp) |
| txt_tokens = sample['txt_tokens'] |
| with torch.no_grad(): |
| output = self.model(txt_tokens, infer=True) |
| mel_out = output['mel_out'] |
| wav_out = self.run_vocoder(mel_out) |
| wav_out = wav_out.squeeze().cpu().numpy() |
| return wav_out |
|
|
|
|
| if __name__ == '__main__': |
| ProDiffInfer.example_run() |
|
|
