bshall / soft-vc Goto Github PK

What accuracy did you achieve while training the k-means model in the content encoder?

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 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!

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

• extract units for both English and Vietnamese dataset
• train kmeans on both types of units & extract discrete labels
• train soft encoder
• extract soft units
• train acoustic model
• train hifigan on Vietnamese dataset

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?

@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 @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?

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 @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, 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, 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?

Hello, do You plan to add local training and inference scripts other than jupyter notebook demo? thanks in advance!

Nice work, I found that there is only the inference code here, will the training code be also released?

Found it!