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View Code? Open in Web Editor NEWSoft speech units for voice conversion
Home Page: https://bshall.github.io/soft-vc/
License: MIT License
Soft speech units for voice conversion
Home Page: https://bshall.github.io/soft-vc/
License: MIT License
Hello, do You plan to add local training and inference scripts other than jupyter notebook demo? thanks in advance!
What accuracy did you achieve while training the k-means model in the content encoder?
@bshall since this repo gives a simple inference example based on a soft content encoder. Would you give a discrete encoder based inference example? I found some issues when I try to use a discrete encoder. Here is my code:
when it run to mel = acoustic.generate(units).transpose(1, 2)
a dimension mismatch occurred. Looking forward to your suggestions, thanks a lot.
File "D:\Software-location\Anaconda\lib\site-packages\torch\autograd\grad_mode.py", line 27, in decorate_context
return func(*args, **kwargs)
File "C:\Users\YoungTown/.cache\torch\hub\bshall_acoustic-model_main\acoustic\model.py", line 23, in generate
x = self.encoder(x)
File "D:\Software-location\Anaconda\lib\site-packages\torch\nn\modules\module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "C:\Users\YoungTown/.cache\torch\hub\bshall_acoustic-model_main\acoustic\model.py", line 49, in forward
x = self.convs(x.transpose(1, 2))
IndexError: Dimension out of range (expected to be in range of [-2, 1], but got 2)
import torch, torchaudio
hubert = torch.hub.load("bshall/hubert:main", "hubert_discrete").cuda()
acoustic = torch.hub.load("bshall/acoustic-model:main", "hubert_discrete").cuda()
hifigan = torch.hub.load("bshall/hifigan:main", "hifigan_hubert_discrete").cuda()
source, sr = torchaudio.load("./huohuo.wav")
source = torchaudio.transforms.Resample(orig_freq=sr, new_freq=16000)(source)
source = source[0].unsqueeze(0).unsqueeze(0).cuda()
with torch.inference_mode():
units = hubert.units(source)
mel = acoustic.generate(units).transpose(1, 2)
target = hifigan(mel)
target = target.squeeze().cpu()
target = target.unsqueeze(0).cpu()
torchaudio.save('./huohuo_new.wav', target, 16000)
hi, thank you for sharing your code.
i am trying to do voice conversion from English speech to Vietnamese speaker. to do that, i did the following steps
the output for Vietnamese speech (input audio is Vietnamese, of a different speaker) is okay. but output for English is not that good. phonemes are often skipped or mispronouced. do you have any suggestions on how i can improve the results?
Hi @bshall . I got impressive results trying to convert singing voice samples. So, I was trying to understand how to fine-tune a specific singer's voice. Do I need to train each individual component for this?
Found it!
I would like to ask if anyone has encountered this problem while doing experiments? How was it solved?
Using cache found in /data0/home/Liqy/.cache/torch/hub/bshall_hubert_main
Traceback (most recent call last):
File "/tmp/pycharm_project_363/test.py", line 4, in
hubert = torch.hub.load("bshall/hubert:main", "hubert_soft", trust_repo=True).cuda()
File "/Nas/Liqy/hubert-main/lib/python3.10/site-packages/torch/hub.py", line 404, in load
model = _load_local(repo_or_dir, model, *args, **kwargs)
File "/Nas/Liqy/hubert-main/lib/python3.10/site-packages/torch/hub.py", line 433, in _load_local
model = entry(*args, **kwargs)
TypeError: hubert_soft() got an unexpected keyword argument 'trust_repo'
Is it my pytorch version? Can you give me your pytorch version for reference? Thank you very much
Hi- very impressed by the VC framework. It's very fast and accurate.
I'm wondering is real-time possible? I have a simple WS server that receives audio, but when i push the data through soft-vc, the end result is just noise. In the code below, I save the input stream just to confirm the audio is being received correctly (which it is).
Here is a snippet of my code:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
hubert = torch.hub.load("bshall/hubert:main", "hubert_soft").cuda()
acoustic_load_path = "./pretrained_models/acoustic.pt"
checkpoint = torch.load(acoustic_load_path, map_location=device)["acoustic-model"]
acoustic = AcousticModel().to(device)
acoustic.load_state_dict(checkpoint)
acoustic.eval()
# load custom vocoder
hifigan_load_path = "./pretrained_models/hifigan.pt"
checkpoint = torch.load(hifigan_load_path, map_location=device)[
"generator"]["model"]
hifigan = HifiganGenerator().to(device)
consume_prefix_in_state_dict_if_present(checkpoint, "module.")
hifigan.load_state_dict(checkpoint)
hifigan.eval()
hifigan.remove_weight_norm()
inputs = []
outputs = []
while True:
data = None
try:
data = await websocket.recv()
except:
break
if isinstance(data, str):
print(f"string -> {data}")
continue
source = torch.from_numpy(numpy.frombuffer(
data, dtype=numpy.int16).astype('float32') / 32767)
source = source.reshape((1, -1))
source = source.unsqueeze(0).cuda()
# # Convert to the target speaker
with torch.inference_mode():
# Extract speech units
units = hubert.units(source)
# Generate target spectrogram
mel = acoustic.generate(units).transpose(1, 2)
# Generate audio waveform
target = hifigan(mel)
inputs.append(source.squeeze(0).cpu())
outputs.append(target.squeeze(0).cpu())
await ws.send(data)
print(f"saving files...")
input_result = torch.cat(inputs, dim=1)
torchaudio.save("inputs.wav", input_result, sample_rate=16_000)
output_result = torch.cat(outputs, dim=1)
torchaudio.save("outputs.wav", output_result, sample_rate=16_000)
Hi, I really appreciate your work; the demo sounds great.
I also read papers about PPG-based VC, which uses ASR for PPG extraction. I just wonder about the difference between SSL and PPG-based methods. It seems they both extract some information about linguistics. Have you ever compared them?
Thank you!
Hi @bshall , can the pre-trained hubert-soft or discrete model be used for encoding mandarin Chinese language data ? I want to train a hubert soft model for Chinese language VC.
Hi, thanks for the sample codes! very easy to use with impressive results! I am wondering if it is possible to resynthesize the speaker's voice, instead of speech conversion, using your model?
Hi Benjamin,
Looked at your demo page, it looks nice!
Will the paper be on arxiv?
Looking forward to it.
I have tried the inference example and the result is exciting.
Can you share the training scripts to me or open source?
This will help me a lot.
Thanks!
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