storytold/vits-finetuning
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Make trainer generate sample sentences

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ZDisket
2023-05-25 23:48:49 -03:00
parent e0848f7715
commit 2a263444a1
4 changed files with 155 additions and 7 deletions
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train.py
+35 -7
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@@ -355,7 +355,9 @@ def evaluate(hps, generator, eval_loader, writer_eval):
bert = bert[:1]
bert_lens = bert_lens[:1]
break
image_dict = {}
audio_dict = {}
y_hat, attn, mask, *_ = generator.module.infer(x, x_lengths, tm_hidden, bert, bert_lens, max_len=1000)
y_hat_lengths = mask.sum([1,2]).long() * hps.data.hop_length
@@ -376,12 +378,38 @@ def evaluate(hps, generator, eval_loader, writer_eval):
hps.data.mel_fmin,
hps.data.mel_fmax
)
image_dict = {
"gen/mel": utils.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy())
}
audio_dict = {
"gen/audio": y_hat[0,:,:y_hat_lengths[0]]
}
image_dict["gen/mel"] = utils.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy())
audio_dict["gen/audio"] = y_hat[0,:,:y_hat_lengths[0]]
print("Inferring test sentences...")
for t_idx, test_file in enumerate(sorted(os.listdir(hps.test_dp))):
if not ".pt" in test_file:
continue
t_text_norm, t_moji, t_bert = torch.load(os.path.join(hps.test_dp, test_file))
t_text_norm = t_text_norm.unsqueeze(0).cuda(0)
t_text_lengths = torch.LongTensor([t_text_norm.size(1)]).cuda(0)
t_moji = t_moji.squeeze().unsqueeze(0).cuda(0)
t_bert_lens = torch.LongTensor([t_bert.size(1)]).cuda(0)
t_bert = t_bert.cuda(0)
audio, attn, t_mask, _ = generator.module.infer(t_text_norm, t_text_lengths, t_moji, t_bert, t_bert_lens, noise_scale=.667, noise_scale_w=0.8, length_scale=1.0)
test_audio_lengths = t_mask.sum([1,2]).long() * hps.data.hop_length
y_test_mel = mel_spectrogram_torch(
audio.squeeze(1).float(),
hps.data.filter_length,
hps.data.n_mel_channels,
hps.data.sampling_rate,
hps.data.hop_length,
hps.data.win_length,
hps.data.mel_fmin,
hps.data.mel_fmax
)
image_dict[f"gen/mel_test{t_idx}"] = utils.plot_spectrogram_to_numpy(y_test_mel[0].cpu().numpy())
audio_dict[f"gen/audio_test{t_idx}"] = audio[0,:,:test_audio_lengths[0]]
if global_step == 0:
image_dict.update({"gt/mel": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy())})
audio_dict.update({"gt/audio": y[0,:,:y_lengths[0]]})