Comments (5)
I have tried default melgan vocoder (from https://github.com/seungwonpark/melgan). I have synthesized hello world 4x times using my model.
Output from melgan vocoder is like reversed https://soundcloud.com/tom-lysek-832824742/hello-world-melgan/s-liaMpH3Ijx5
Output from grifim limm vocoder is great.
Transformation from mels to wav was performed using this code
`
m = (torch.tensor(mels).unsqueeze(0))
with torch.no_grad():
if len(m.shape) == 2:
m = m.unsqueeze(0)
wav = model.inference(m).cpu().numpy()
ipd.Audio(wav, rate=hp.sample_rate)
`
and mels is m from synthesize funkction in notebook_utils/synthesize.py
Do you have any thoughts?
Tom
from forwardtacotron.
Hi, the synthesize.py from notebook_utils already outputs mels in the correct format for melgan, maybe you are expanding too much? In the colab notebook its using the default melgan model for LJSpeech. PWGAN should actually work similarly I believe (it has the same preprocessing as melgan). Also, if you call gen_forward.py use the melgan flag to produce .mel files that are in the correct format for melgan already (see README).
Thats the synthesize.py mel processing for melgan:
m = torch.tensor(m).unsqueeze(0).cuda()
from forwardtacotron.
There are same result when i use gen_forward.py to produce .mel file and then convert it to wav with inference.py from melgan as if I was using jupyter.
I have tried to use your function synthesize with this setup
And result is same.
If i change this bad vocoder for default vocoder (from your example) result is this https://soundcloud.com/tom-lysek-832824742/hello-world-default-melgan/s-zpnjVYgxrAu
So i assume that this vocoder was learned with some error.
I was using this hyperparameters (default hyperparameters):
left is hparams from ForwardTacotron right is default.yaml from melgan repository.
Did I miss something? Is there some misconfiguration? Only difference is filter_length vs. fft_bins, but I don't think that is the difference :/
I have used sox to see if there is problem with input files but they have same parameters:
1.wav is my own dataset, LJXXX.wav is LJ speech.
from forwardtacotron.
Hmm I don't see a mismatch. Does the pretrained ForwardTacotron model work for you with the standard melgan?
from forwardtacotron.
i have used vocgan and everything was fine. Maybe model was learned badly.
from forwardtacotron.
Related Issues (20)
- symbols.py for Arabic letters
- Feature request: model compatible to export into onnx
- implement hifigan vocoder?
- Adding pauses to the input text HOT 2
- confuse about duration extract HOT 10
- preprocess.py issues - RAM usage close to 100% but CPU usage is nonexistant HOT 16
- ValueError not enough values to unpack (expected 2 got 0) HOT 2
- making the system available for use with assistive technologies on windows HOT 1
- Bad Alignment HOT 1
- ValueError: need at least one array to stack train_tacotron.py line 192 HOT 1
- Facing problem at preprocessing
- Need instructions for fine tunning
- Problems with attention for dataset consisting of longer samples
- how to train a dataset using a pre-trained model?
- preprocess.py misuses Espeak backend, resulting in slow performance and memory leak HOT 2
- preprocess.py: list index out of range HOT 5
- Multispeaker and new neural voice creation HOT 12
- Non-Latin alphabets
- Bad Attention!
- Training a model twice using a different dataset
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from forwardtacotron.