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import torch
import utils
from utils.hparams import hparams
from .diff.net import DiffNet
from .diff.shallow_diffusion_tts import GaussianDiffusion
from .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, inference=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)
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