|
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_teacher import GaussianDiffusion |
|
from usr.diff.net import DiffNet |
|
import os |
|
import numpy as np |
|
|
|
class ProDiffTeacherInfer(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'], |
|
) |
|
|
|
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__': |
|
ProDiffTeacherInfer.example_run() |
|
|
