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| import matplotlib | |
| matplotlib.use('Agg') | |
| from utils import audio | |
| import matplotlib.pyplot as plt | |
| from data_gen.tts.data_gen_utils import get_pitch | |
| from tasks.tts.fs2_utils import FastSpeechDataset | |
| from utils.cwt import cwt2f0 | |
| from utils.pl_utils import data_loader | |
| import os | |
| from multiprocessing.pool import Pool | |
| from tqdm import tqdm | |
| from modules.fastspeech.tts_modules import mel2ph_to_dur | |
| from utils.hparams import hparams | |
| from utils.plot import spec_to_figure, dur_to_figure, f0_to_figure | |
| from utils.pitch_utils import denorm_f0 | |
| from modules.fastspeech.fs2 import FastSpeech2 | |
| from tasks.tts.tts import TtsTask | |
| import torch | |
| import torch.optim | |
| import torch.utils.data | |
| import torch.nn.functional as F | |
| import utils | |
| import torch.distributions | |
| import numpy as np | |
| from modules.commons.ssim import ssim | |
| class FastSpeech2Task(TtsTask): | |
| def __init__(self): | |
| super(FastSpeech2Task, self).__init__() | |
| self.dataset_cls = FastSpeechDataset | |
| self.mse_loss_fn = torch.nn.MSELoss() | |
| mel_losses = hparams['mel_loss'].split("|") | |
| self.loss_and_lambda = {} | |
| for i, l in enumerate(mel_losses): | |
| if l == '': | |
| continue | |
| if ':' in l: | |
| l, lbd = l.split(":") | |
| lbd = float(lbd) | |
| else: | |
| lbd = 1.0 | |
| self.loss_and_lambda[l] = lbd | |
| print("| Mel losses:", self.loss_and_lambda) | |
| self.sil_ph = self.phone_encoder.sil_phonemes() | |
| def train_dataloader(self): | |
| train_dataset = self.dataset_cls(hparams['train_set_name'], shuffle=True) | |
| return self.build_dataloader(train_dataset, True, self.max_tokens, self.max_sentences, | |
| endless=hparams['endless_ds']) | |
| def val_dataloader(self): | |
| valid_dataset = self.dataset_cls(hparams['valid_set_name'], shuffle=False) | |
| return self.build_dataloader(valid_dataset, False, self.max_eval_tokens, self.max_eval_sentences) | |
| def test_dataloader(self): | |
| test_dataset = self.dataset_cls(hparams['test_set_name'], shuffle=False) | |
| return self.build_dataloader(test_dataset, False, self.max_eval_tokens, | |
| self.max_eval_sentences, batch_by_size=False) | |
| def build_tts_model(self): | |
| self.model = FastSpeech2(self.phone_encoder) | |
| def build_model(self): | |
| self.build_tts_model() | |
| if hparams['load_ckpt'] != '': | |
| self.load_ckpt(hparams['load_ckpt'], strict=True) | |
| utils.print_arch(self.model) | |
| return self.model | |
| def _training_step(self, sample, batch_idx, _): | |
| loss_output = self.run_model(self.model, sample) | |
| total_loss = sum([v for v in loss_output.values() if isinstance(v, torch.Tensor) and v.requires_grad]) | |
| loss_output['batch_size'] = sample['txt_tokens'].size()[0] | |
| return total_loss, loss_output | |
| def validation_step(self, sample, batch_idx): | |
| outputs = {} | |
| outputs['losses'] = {} | |
| outputs['losses'], model_out = self.run_model(self.model, sample, return_output=True) | |
| outputs['total_loss'] = sum(outputs['losses'].values()) | |
| outputs['nsamples'] = sample['nsamples'] | |
| mel_out = self.model.out2mel(model_out['mel_out']) | |
| outputs = utils.tensors_to_scalars(outputs) | |
| # if sample['mels'].shape[0] == 1: | |
| # self.add_laplace_var(mel_out, sample['mels'], outputs) | |
| if batch_idx < hparams['num_valid_plots']: | |
| self.plot_mel(batch_idx, sample['mels'], mel_out) | |
| self.plot_dur(batch_idx, sample, model_out) | |
| if hparams['use_pitch_embed']: | |
| self.plot_pitch(batch_idx, sample, model_out) | |
| return outputs | |
| def _validation_end(self, outputs): | |
| all_losses_meter = { | |
| 'total_loss': utils.AvgrageMeter(), | |
| } | |
| for output in outputs: | |
| n = output['nsamples'] | |
| for k, v in output['losses'].items(): | |
| if k not in all_losses_meter: | |
| all_losses_meter[k] = utils.AvgrageMeter() | |
| all_losses_meter[k].update(v, n) | |
| all_losses_meter['total_loss'].update(output['total_loss'], n) | |
| return {k: round(v.avg, 4) for k, v in all_losses_meter.items()} | |
| def run_model(self, model, sample, return_output=False): | |
| txt_tokens = sample['txt_tokens'] # [B, T_t] | |
| target = sample['mels'] # [B, T_s, 80] | |
| mel2ph = sample['mel2ph'] # [B, T_s] | |
| f0 = sample['f0'] | |
| uv = sample['uv'] | |
| energy = sample['energy'] | |
| 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=False) | |
| losses = {} | |
| self.add_mel_loss(output['mel_out'], target, losses) | |
| 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 | |
| ############ | |
| # losses | |
| ############ | |
| def add_mel_loss(self, mel_out, target, losses, postfix='', mel_mix_loss=None): | |
| if mel_mix_loss is None: | |
| for loss_name, lbd in self.loss_and_lambda.items(): | |
| if 'l1' == loss_name: | |
| l = self.l1_loss(mel_out, target) | |
| elif 'mse' == loss_name: | |
| raise NotImplementedError | |
| elif 'ssim' == loss_name: | |
| l = self.ssim_loss(mel_out, target) | |
| elif 'gdl' == loss_name: | |
| raise NotImplementedError | |
| losses[f'{loss_name}{postfix}'] = l * lbd | |
| else: | |
| raise NotImplementedError | |
| def l1_loss(self, decoder_output, target): | |
| # decoder_output : B x T x n_mel | |
| # target : B x T x n_mel | |
| l1_loss = F.l1_loss(decoder_output, target, reduction='none') | |
| weights = self.weights_nonzero_speech(target) | |
| l1_loss = (l1_loss * weights).sum() / weights.sum() | |
| return l1_loss | |
| def ssim_loss(self, decoder_output, target, bias=6.0): | |
| # decoder_output : B x T x n_mel | |
| # target : B x T x n_mel | |
| assert decoder_output.shape == target.shape | |
| weights = self.weights_nonzero_speech(target) | |
| decoder_output = decoder_output[:, None] + bias | |
| target = target[:, None] + bias | |
| ssim_loss = 1 - ssim(decoder_output, target, size_average=False) | |
| ssim_loss = (ssim_loss * weights).sum() / weights.sum() | |
| return ssim_loss | |
| def add_dur_loss(self, dur_pred, mel2ph, txt_tokens, losses=None): | |
| """ | |
| :param dur_pred: [B, T], float, log scale | |
| :param mel2ph: [B, T] | |
| :param txt_tokens: [B, T] | |
| :param losses: | |
| :return: | |
| """ | |
| B, T = txt_tokens.shape | |
| nonpadding = (txt_tokens != 0).float() | |
| dur_gt = mel2ph_to_dur(mel2ph, T).float() * nonpadding | |
| is_sil = torch.zeros_like(txt_tokens).bool() | |
| for p in self.sil_ph: | |
| is_sil = is_sil | (txt_tokens == self.phone_encoder.encode(p)[0]) | |
| is_sil = is_sil.float() # [B, T_txt] | |
| # phone duration loss | |
| if hparams['dur_loss'] == 'mse': | |
| losses['pdur'] = F.mse_loss(dur_pred, (dur_gt + 1).log(), reduction='none') | |
| losses['pdur'] = (losses['pdur'] * nonpadding).sum() / nonpadding.sum() | |
| dur_pred = (dur_pred.exp() - 1).clamp(min=0) | |
| elif hparams['dur_loss'] == 'mog': | |
| return NotImplementedError | |
| elif hparams['dur_loss'] == 'crf': | |
| losses['pdur'] = -self.model.dur_predictor.crf( | |
| dur_pred, dur_gt.long().clamp(min=0, max=31), mask=nonpadding > 0, reduction='mean') | |
| losses['pdur'] = losses['pdur'] * hparams['lambda_ph_dur'] | |
| # use linear scale for sent and word duration | |
| if hparams['lambda_word_dur'] > 0: | |
| word_id = (is_sil.cumsum(-1) * (1 - is_sil)).long() | |
| word_dur_p = dur_pred.new_zeros([B, word_id.max() + 1]).scatter_add(1, word_id, dur_pred)[:, 1:] | |
| word_dur_g = dur_gt.new_zeros([B, word_id.max() + 1]).scatter_add(1, word_id, dur_gt)[:, 1:] | |
| wdur_loss = F.mse_loss((word_dur_p + 1).log(), (word_dur_g + 1).log(), reduction='none') | |
| word_nonpadding = (word_dur_g > 0).float() | |
| wdur_loss = (wdur_loss * word_nonpadding).sum() / word_nonpadding.sum() | |
| losses['wdur'] = wdur_loss * hparams['lambda_word_dur'] | |
| if hparams['lambda_sent_dur'] > 0: | |
| sent_dur_p = dur_pred.sum(-1) | |
| sent_dur_g = dur_gt.sum(-1) | |
| sdur_loss = F.mse_loss((sent_dur_p + 1).log(), (sent_dur_g + 1).log(), reduction='mean') | |
| losses['sdur'] = sdur_loss.mean() * hparams['lambda_sent_dur'] | |
| def add_pitch_loss(self, output, sample, losses): | |
| if hparams['pitch_type'] == 'ph': | |
| nonpadding = (sample['txt_tokens'] != 0).float() | |
| pitch_loss_fn = F.l1_loss if hparams['pitch_loss'] == 'l1' else F.mse_loss | |
| losses['f0'] = (pitch_loss_fn(output['pitch_pred'][:, :, 0], sample['f0'], | |
| reduction='none') * nonpadding).sum() \ | |
| / nonpadding.sum() * hparams['lambda_f0'] | |
| return | |
| mel2ph = sample['mel2ph'] # [B, T_s] | |
| f0 = sample['f0'] | |
| uv = sample['uv'] | |
| nonpadding = (mel2ph != 0).float() | |
| if hparams['pitch_type'] == 'cwt': | |
| cwt_spec = sample[f'cwt_spec'] | |
| f0_mean = sample['f0_mean'] | |
| f0_std = sample['f0_std'] | |
| cwt_pred = output['cwt'][:, :, :10] | |
| f0_mean_pred = output['f0_mean'] | |
| f0_std_pred = output['f0_std'] | |
| losses['C'] = self.cwt_loss(cwt_pred, cwt_spec) * hparams['lambda_f0'] | |
| if hparams['use_uv']: | |
| assert output['cwt'].shape[-1] == 11 | |
| uv_pred = output['cwt'][:, :, -1] | |
| losses['uv'] = (F.binary_cross_entropy_with_logits(uv_pred, uv, reduction='none') * nonpadding) \ | |
| .sum() / nonpadding.sum() * hparams['lambda_uv'] | |
| losses['f0_mean'] = F.l1_loss(f0_mean_pred, f0_mean) * hparams['lambda_f0'] | |
| losses['f0_std'] = F.l1_loss(f0_std_pred, f0_std) * hparams['lambda_f0'] | |
| if hparams['cwt_add_f0_loss']: | |
| f0_cwt_ = self.model.cwt2f0_norm(cwt_pred, f0_mean_pred, f0_std_pred, mel2ph) | |
| self.add_f0_loss(f0_cwt_[:, :, None], f0, uv, losses, nonpadding=nonpadding) | |
| elif hparams['pitch_type'] == 'frame': | |
| self.add_f0_loss(output['pitch_pred'], f0, uv, losses, nonpadding=nonpadding) | |
| def add_f0_loss(self, p_pred, f0, uv, losses, nonpadding): | |
| assert p_pred[..., 0].shape == f0.shape | |
| if hparams['use_uv']: | |
| assert p_pred[..., 1].shape == uv.shape | |
| losses['uv'] = (F.binary_cross_entropy_with_logits( | |
| p_pred[:, :, 1], uv, reduction='none') * nonpadding).sum() \ | |
| / nonpadding.sum() * hparams['lambda_uv'] | |
| nonpadding = nonpadding * (uv == 0).float() | |
| f0_pred = p_pred[:, :, 0] | |
| if hparams['pitch_loss'] in ['l1', 'l2']: | |
| pitch_loss_fn = F.l1_loss if hparams['pitch_loss'] == 'l1' else F.mse_loss | |
| losses['f0'] = (pitch_loss_fn(f0_pred, f0, reduction='none') * nonpadding).sum() \ | |
| / nonpadding.sum() * hparams['lambda_f0'] | |
| elif hparams['pitch_loss'] == 'ssim': | |
| return NotImplementedError | |
| def cwt_loss(self, cwt_p, cwt_g): | |
| if hparams['cwt_loss'] == 'l1': | |
| return F.l1_loss(cwt_p, cwt_g) | |
| if hparams['cwt_loss'] == 'l2': | |
| return F.mse_loss(cwt_p, cwt_g) | |
| if hparams['cwt_loss'] == 'ssim': | |
| return self.ssim_loss(cwt_p, cwt_g, 20) | |
| def add_energy_loss(self, energy_pred, energy, losses): | |
| nonpadding = (energy != 0).float() | |
| loss = (F.mse_loss(energy_pred, energy, reduction='none') * nonpadding).sum() / nonpadding.sum() | |
| loss = loss * hparams['lambda_energy'] | |
| losses['e'] = loss | |
| ############ | |
| # validation plots | |
| ############ | |
| def plot_mel(self, batch_idx, spec, spec_out, name=None): | |
| spec_cat = torch.cat([spec, spec_out], -1) | |
| name = f'mel_{batch_idx}' if name is None else name | |
| vmin = hparams['mel_vmin'] | |
| vmax = hparams['mel_vmax'] | |
| self.logger.experiment.add_figure(name, spec_to_figure(spec_cat[0], vmin, vmax), self.global_step) | |
| def plot_dur(self, batch_idx, sample, model_out): | |
| T_txt = sample['txt_tokens'].shape[1] | |
| dur_gt = mel2ph_to_dur(sample['mel2ph'], T_txt)[0] | |
| dur_pred = self.model.dur_predictor.out2dur(model_out['dur']).float() | |
| txt = self.phone_encoder.decode(sample['txt_tokens'][0].cpu().numpy()) | |
| txt = txt.split(" ") | |
| self.logger.experiment.add_figure( | |
| f'dur_{batch_idx}', dur_to_figure(dur_gt, dur_pred, txt), self.global_step) | |
| def plot_pitch(self, batch_idx, sample, model_out): | |
| f0 = sample['f0'] | |
| if hparams['pitch_type'] == 'ph': | |
| mel2ph = sample['mel2ph'] | |
| f0 = self.expand_f0_ph(f0, mel2ph) | |
| f0_pred = self.expand_f0_ph(model_out['pitch_pred'][:, :, 0], mel2ph) | |
| self.logger.experiment.add_figure( | |
| f'f0_{batch_idx}', f0_to_figure(f0[0], None, f0_pred[0]), self.global_step) | |
| return | |
| f0 = denorm_f0(f0, sample['uv'], hparams) | |
| if hparams['pitch_type'] == 'cwt': | |
| # cwt | |
| cwt_out = model_out['cwt'] | |
| cwt_spec = cwt_out[:, :, :10] | |
| cwt = torch.cat([cwt_spec, sample['cwt_spec']], -1) | |
| self.logger.experiment.add_figure(f'cwt_{batch_idx}', spec_to_figure(cwt[0]), self.global_step) | |
| # f0 | |
| f0_pred = cwt2f0(cwt_spec, model_out['f0_mean'], model_out['f0_std'], hparams['cwt_scales']) | |
| if hparams['use_uv']: | |
| assert cwt_out.shape[-1] == 11 | |
| uv_pred = cwt_out[:, :, -1] > 0 | |
| f0_pred[uv_pred > 0] = 0 | |
| f0_cwt = denorm_f0(sample['f0_cwt'], sample['uv'], hparams) | |
| self.logger.experiment.add_figure( | |
| f'f0_{batch_idx}', f0_to_figure(f0[0], f0_cwt[0], f0_pred[0]), self.global_step) | |
| elif hparams['pitch_type'] == 'frame': | |
| # f0 | |
| uv_pred = model_out['pitch_pred'][:, :, 1] > 0 | |
| pitch_pred = denorm_f0(model_out['pitch_pred'][:, :, 0], uv_pred, hparams) | |
| self.logger.experiment.add_figure( | |
| f'f0_{batch_idx}', f0_to_figure(f0[0], None, pitch_pred[0]), self.global_step) | |
| ############ | |
| # infer | |
| ############ | |
| def test_step(self, sample, batch_idx): | |
| spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids') | |
| txt_tokens = sample['txt_tokens'] | |
| mel2ph, uv, f0 = None, None, None | |
| ref_mels = None | |
| if hparams['profile_infer']: | |
| pass | |
| else: | |
| if hparams['use_gt_dur']: | |
| mel2ph = sample['mel2ph'] | |
| if hparams['use_gt_f0']: | |
| f0 = sample['f0'] | |
| uv = sample['uv'] | |
| print('Here using gt f0!!') | |
| if hparams.get('use_midi') is not None and hparams['use_midi']: | |
| outputs = self.model( | |
| txt_tokens, spk_embed=spk_embed, mel2ph=mel2ph, f0=f0, uv=uv, ref_mels=ref_mels, infer=True, | |
| pitch_midi=sample['pitch_midi'], midi_dur=sample.get('midi_dur'), is_slur=sample.get('is_slur')) | |
| else: | |
| outputs = self.model( | |
| txt_tokens, spk_embed=spk_embed, mel2ph=mel2ph, f0=f0, uv=uv, ref_mels=ref_mels, infer=True) | |
| sample['outputs'] = self.model.out2mel(outputs['mel_out']) | |
| sample['mel2ph_pred'] = outputs['mel2ph'] | |
| if hparams.get('pe_enable') is not None and hparams['pe_enable']: | |
| sample['f0'] = self.pe(sample['mels'])['f0_denorm_pred'] # pe predict from GT mel | |
| sample['f0_pred'] = self.pe(sample['outputs'])['f0_denorm_pred'] # pe predict from Pred mel | |
| else: | |
| sample['f0'] = denorm_f0(sample['f0'], sample['uv'], hparams) | |
| sample['f0_pred'] = outputs.get('f0_denorm') | |
| return self.after_infer(sample) | |
| def after_infer(self, predictions): | |
| if self.saving_result_pool is None and not hparams['profile_infer']: | |
| self.saving_result_pool = Pool(min(int(os.getenv('N_PROC', os.cpu_count())), 16)) | |
| self.saving_results_futures = [] | |
| predictions = utils.unpack_dict_to_list(predictions) | |
| t = tqdm(predictions) | |
| for num_predictions, prediction in enumerate(t): | |
| for k, v in prediction.items(): | |
| if type(v) is torch.Tensor: | |
| prediction[k] = v.cpu().numpy() | |
| item_name = prediction.get('item_name') | |
| text = prediction.get('text').replace(":", "%3A")[:80] | |
| # remove paddings | |
| mel_gt = prediction["mels"] | |
| mel_gt_mask = np.abs(mel_gt).sum(-1) > 0 | |
| mel_gt = mel_gt[mel_gt_mask] | |
| mel2ph_gt = prediction.get("mel2ph") | |
| mel2ph_gt = mel2ph_gt[mel_gt_mask] if mel2ph_gt is not None else None | |
| mel_pred = prediction["outputs"] | |
| mel_pred_mask = np.abs(mel_pred).sum(-1) > 0 | |
| mel_pred = mel_pred[mel_pred_mask] | |
| mel_gt = np.clip(mel_gt, hparams['mel_vmin'], hparams['mel_vmax']) | |
| mel_pred = np.clip(mel_pred, hparams['mel_vmin'], hparams['mel_vmax']) | |
| mel2ph_pred = prediction.get("mel2ph_pred") | |
| if mel2ph_pred is not None: | |
| if len(mel2ph_pred) > len(mel_pred_mask): | |
| mel2ph_pred = mel2ph_pred[:len(mel_pred_mask)] | |
| mel2ph_pred = mel2ph_pred[mel_pred_mask] | |
| f0_gt = prediction.get("f0") | |
| f0_pred = prediction.get("f0_pred") | |
| if f0_pred is not None: | |
| f0_gt = f0_gt[mel_gt_mask] | |
| if len(f0_pred) > len(mel_pred_mask): | |
| f0_pred = f0_pred[:len(mel_pred_mask)] | |
| f0_pred = f0_pred[mel_pred_mask] | |
| str_phs = None | |
| if self.phone_encoder is not None and 'txt_tokens' in prediction: | |
| str_phs = self.phone_encoder.decode(prediction['txt_tokens'], strip_padding=True) | |
| gen_dir = os.path.join(hparams['work_dir'], | |
| f'generated_{self.trainer.global_step}_{hparams["gen_dir_name"]}') | |
| wav_pred = self.vocoder.spec2wav(mel_pred, f0=f0_pred) | |
| if not hparams['profile_infer']: | |
| os.makedirs(gen_dir, exist_ok=True) | |
| os.makedirs(f'{gen_dir}/wavs', exist_ok=True) | |
| os.makedirs(f'{gen_dir}/plot', exist_ok=True) | |
| os.makedirs(os.path.join(hparams['work_dir'], 'P_mels_npy'), exist_ok=True) | |
| os.makedirs(os.path.join(hparams['work_dir'], 'G_mels_npy'), exist_ok=True) | |
| self.saving_results_futures.append( | |
| self.saving_result_pool.apply_async(self.save_result, args=[ | |
| wav_pred, mel_pred, 'P', item_name, text, gen_dir, str_phs, mel2ph_pred, f0_gt, f0_pred])) | |
| if mel_gt is not None and hparams['save_gt']: | |
| wav_gt = self.vocoder.spec2wav(mel_gt, f0=f0_gt) | |
| self.saving_results_futures.append( | |
| self.saving_result_pool.apply_async(self.save_result, args=[ | |
| wav_gt, mel_gt, 'G', item_name, text, gen_dir, str_phs, mel2ph_gt, f0_gt, f0_pred])) | |
| if hparams['save_f0']: | |
| import matplotlib.pyplot as plt | |
| # f0_pred_, _ = get_pitch(wav_pred, mel_pred, hparams) | |
| f0_pred_ = f0_pred | |
| f0_gt_, _ = get_pitch(wav_gt, mel_gt, hparams) | |
| fig = plt.figure() | |
| plt.plot(f0_pred_, label=r'$f0_P$') | |
| plt.plot(f0_gt_, label=r'$f0_G$') | |
| if hparams.get('pe_enable') is not None and hparams['pe_enable']: | |
| # f0_midi = prediction.get("f0_midi") | |
| # f0_midi = f0_midi[mel_gt_mask] | |
| # plt.plot(f0_midi, label=r'$f0_M$') | |
| pass | |
| plt.legend() | |
| plt.tight_layout() | |
| plt.savefig(f'{gen_dir}/plot/[F0][{item_name}]{text}.png', format='png') | |
| plt.close(fig) | |
| t.set_description( | |
| f"Pred_shape: {mel_pred.shape}, gt_shape: {mel_gt.shape}") | |
| else: | |
| if 'gen_wav_time' not in self.stats: | |
| self.stats['gen_wav_time'] = 0 | |
| self.stats['gen_wav_time'] += len(wav_pred) / hparams['audio_sample_rate'] | |
| print('gen_wav_time: ', self.stats['gen_wav_time']) | |
| return {} | |
| def save_result(wav_out, mel, prefix, item_name, text, gen_dir, str_phs=None, mel2ph=None, gt_f0=None, pred_f0=None): | |
| item_name = item_name.replace('/', '-') | |
| base_fn = f'[{item_name}][{prefix}]' | |
| if text is not None: | |
| base_fn += text | |
| base_fn += ('-' + hparams['exp_name']) | |
| np.save(os.path.join(hparams['work_dir'], f'{prefix}_mels_npy', item_name), mel) | |
| audio.save_wav(wav_out, f'{gen_dir}/wavs/{base_fn}.wav', hparams['audio_sample_rate'], | |
| norm=hparams['out_wav_norm']) | |
| fig = plt.figure(figsize=(14, 10)) | |
| spec_vmin = hparams['mel_vmin'] | |
| spec_vmax = hparams['mel_vmax'] | |
| heatmap = plt.pcolor(mel.T, vmin=spec_vmin, vmax=spec_vmax) | |
| fig.colorbar(heatmap) | |
| if hparams.get('pe_enable') is not None and hparams['pe_enable']: | |
| gt_f0 = (gt_f0 - 100) / (800 - 100) * 80 * (gt_f0 > 0) | |
| pred_f0 = (pred_f0 - 100) / (800 - 100) * 80 * (pred_f0 > 0) | |
| plt.plot(pred_f0, c='white', linewidth=1, alpha=0.6) | |
| plt.plot(gt_f0, c='red', linewidth=1, alpha=0.6) | |
| else: | |
| f0, _ = get_pitch(wav_out, mel, hparams) | |
| f0 = (f0 - 100) / (800 - 100) * 80 * (f0 > 0) | |
| plt.plot(f0, c='white', linewidth=1, alpha=0.6) | |
| if mel2ph is not None and str_phs is not None: | |
| decoded_txt = str_phs.split(" ") | |
| dur = mel2ph_to_dur(torch.LongTensor(mel2ph)[None, :], len(decoded_txt))[0].numpy() | |
| dur = [0] + list(np.cumsum(dur)) | |
| for i in range(len(dur) - 1): | |
| shift = (i % 20) + 1 | |
| plt.text(dur[i], shift, decoded_txt[i]) | |
| plt.hlines(shift, dur[i], dur[i + 1], colors='b' if decoded_txt[i] != '|' else 'black') | |
| plt.vlines(dur[i], 0, 5, colors='b' if decoded_txt[i] != '|' else 'black', | |
| alpha=1, linewidth=1) | |
| plt.tight_layout() | |
| plt.savefig(f'{gen_dir}/plot/{base_fn}.png', format='png', dpi=1000) | |
| plt.close(fig) | |
| ############## | |
| # utils | |
| ############## | |
| def expand_f0_ph(f0, mel2ph): | |
| f0 = denorm_f0(f0, None, hparams) | |
| f0 = F.pad(f0, [1, 0]) | |
| f0 = torch.gather(f0, 1, mel2ph) # [B, T_mel] | |
| return f0 | |
| if __name__ == '__main__': | |
| FastSpeech2Task.start() | |