Comments (3)
I did not log voice conversion in validation.
If you want that, just modify the evaluate
function (from line 221) in train.py
.
from freevc.
a simple example (not tested yet):
def evaluate(hps, generator, eval_loader, writer_eval):
generator.eval()
with torch.no_grad():
for batch_idx, items in enumerate(eval_loader):
if hps.model.use_spk:
c, spec, y, spk = items
g = spk[:1].cuda(0)
else:
c, spec, y = items
g = None
y_source = y[:-1] # modified
spec, y = spec[:1].cuda(0), y[:1].cuda(0)
c = c[:-1].cuda(0) # modified
break
mel = spec_to_mel_torch(
spec,
hps.data.filter_length,
hps.data.n_mel_channels,
hps.data.sampling_rate,
hps.data.mel_fmin,
hps.data.mel_fmax)
y_hat = generator.module.infer(c, g=g, mel=mel)
y_hat_mel = mel_spectrogram_torch(
y_hat.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 = {
"gen/mel": utils.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy()),
"target/mel": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy()) # modified
}
audio_dict = {
"gen/audio": y_hat[0],
"source/audio": y_source[0], # modified
"target/audio": y[0] # modified
}
utils.summarize(
writer=writer_eval,
global_step=global_step,
images=image_dict,
audios=audio_dict,
audio_sampling_rate=hps.data.sampling_rate
)
generator. Train()
from freevc.
Thanks you, will try.
from freevc.
Related Issues (20)
- Inference or train with WavLM-Base or WavLM-Base+? HOT 1
- Condition decoder on desired output length to have control over speech rate in inference?
- 基于您现有的模型使用aishell3训练,大概要训练多久,作者有试过吗
- Unseen Male to Male results in Female output HOT 1
- 音色转换程度不一致
- Epoch duration
- 关于算法的类型 HOT 1
- 训练了500个epoch,按照freevc.json配置进行训练,无论wav_tgt使用何种音色,测试出来的音色都是同一个?
- Changing batch size to 16 or 32
- poor performance on seen-to-unseen task while finetuning on Hindi language HOT 2
- 2023.01.10 update: code below can deteriorate model performance HOT 3
- Vocoder version
- Fine tuning with custom (multilingual) data HOT 1
- How to start inference example? HOT 1
- 关于训练问题
- target pitch issue after training (not appearing if using the pretrained checkpoint) HOT 1
- Config file for the FreeVC-24 checkpoint HOT 1
- training a model with 44.1k data
- Why is the speaker embedding g used to condition the Posterior Encoder and the Decoder?
- Poor results with: voice_conversion_models--multilingual--vctk--freevc24.zip CoquiTTS
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from freevc.