import torch import utils from utils.hparams import hparams from modules.diff.net import DiffNet from modules.diff.shallow_diffusion_tts import GaussianDiffusion from tasks.svs.task import DiffFsTask from vocoders.base_vocoder import get_vocoder_cls, BaseVocoder from utils.pitch_utils import denorm_f0 from tasks.tts.fs2_utils import FastSpeechDataset DIFF_DECODERS = { 'wavenet': lambda hp: DiffNet(hp['audio_num_mel_bins']), } class DiffSpeechTask(DiffFsTask): def __init__(self): super(DiffSpeechTask, self).__init__() self.dataset_cls = FastSpeechDataset self.vocoder: BaseVocoder = get_vocoder_cls(hparams)() def build_tts_model(self): mel_bins = hparams['audio_num_mel_bins'] self.model = GaussianDiffusion( phone_encoder=self.phone_encoder, out_dims=mel_bins, denoise_fn=DIFF_DECODERS[hparams['diff_decoder_type']](hparams), timesteps=hparams['timesteps'], K_step=hparams['K_step'], loss_type=hparams['diff_loss_type'], spec_min=hparams['spec_min'], spec_max=hparams['spec_max'], ) if hparams['fs2_ckpt'] != '': utils.load_ckpt(self.model.fs2, hparams['fs2_ckpt'], 'model', strict=True) # self.model.fs2.decoder = None for k, v in self.model.fs2.named_parameters(): if not 'predictor' in k: v.requires_grad = False def build_optimizer(self, model): self.optimizer = optimizer = torch.optim.AdamW( filter(lambda p: p.requires_grad, model.parameters()), lr=hparams['lr'], betas=(hparams['optimizer_adam_beta1'], hparams['optimizer_adam_beta2']), weight_decay=hparams['weight_decay']) return optimizer def run_model(self, model, sample, return_output=False, infer=False): txt_tokens = sample['txt_tokens'] # [B, T_t] target = sample['mels'] # [B, T_s, 80] # mel2ph = sample['mel2ph'] if hparams['use_gt_dur'] else None # [B, T_s] mel2ph = sample['mel2ph'] f0 = sample['f0'] uv = sample['uv'] energy = sample['energy'] # fs2_mel = sample['fs2_mels'] spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids') if hparams['pitch_type'] == 'cwt': cwt_spec = sample[f'cwt_spec'] f0_mean = sample['f0_mean'] f0_std = sample['f0_std'] sample['f0_cwt'] = f0 = model.cwt2f0_norm(cwt_spec, f0_mean, f0_std, mel2ph) output = model(txt_tokens, mel2ph=mel2ph, spk_embed=spk_embed, ref_mels=target, f0=f0, uv=uv, energy=energy, infer=infer) losses = {} if 'diff_loss' in output: losses['mel'] = output['diff_loss'] self.add_dur_loss(output['dur'], mel2ph, txt_tokens, losses=losses) if hparams['use_pitch_embed']: self.add_pitch_loss(output, sample, losses) if hparams['use_energy_embed']: self.add_energy_loss(output['energy_pred'], energy, losses) if not return_output: return losses else: return losses, output def validation_step(self, sample, batch_idx): outputs = {} txt_tokens = sample['txt_tokens'] # [B, T_t] energy = sample['energy'] spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids') mel2ph = sample['mel2ph'] f0 = sample['f0'] uv = sample['uv'] outputs['losses'] = {} outputs['losses'], model_out = self.run_model(self.model, sample, return_output=True, infer=False) outputs['total_loss'] = sum(outputs['losses'].values()) outputs['nsamples'] = sample['nsamples'] outputs = utils.tensors_to_scalars(outputs) if batch_idx < hparams['num_valid_plots']: # model_out = self.model( # txt_tokens, spk_embed=spk_embed, mel2ph=None, f0=None, uv=None, energy=None, ref_mels=None, infer=True) # self.plot_mel(batch_idx, model_out['mel_out'], model_out['fs2_mel'], name=f'diffspeech_vs_fs2_{batch_idx}') model_out = self.model( txt_tokens, spk_embed=spk_embed, mel2ph=mel2ph, f0=f0, uv=uv, energy=energy, ref_mels=None, infer=True) gt_f0 = denorm_f0(sample['f0'], sample['uv'], hparams) self.plot_wav(batch_idx, sample['mels'], model_out['mel_out'], is_mel=True, gt_f0=gt_f0, f0=model_out.get('f0_denorm')) self.plot_mel(batch_idx, sample['mels'], model_out['mel_out']) return outputs ############ # validation plots ############ def plot_wav(self, batch_idx, gt_wav, wav_out, is_mel=False, gt_f0=None, f0=None, name=None): gt_wav = gt_wav[0].cpu().numpy() wav_out = wav_out[0].cpu().numpy() gt_f0 = gt_f0[0].cpu().numpy() f0 = f0[0].cpu().numpy() if is_mel: gt_wav = self.vocoder.spec2wav(gt_wav, f0=gt_f0) wav_out = self.vocoder.spec2wav(wav_out, f0=f0) self.logger.experiment.add_audio(f'gt_{batch_idx}', gt_wav, sample_rate=hparams['audio_sample_rate'], global_step=self.global_step) self.logger.experiment.add_audio(f'wav_{batch_idx}', wav_out, sample_rate=hparams['audio_sample_rate'], global_step=self.global_step)