kandi X-RAY | waveglow Summary
kandi X-RAY | waveglow Summary
A Flow-based Generative Network for Speech Synthesis
Top functions reviewed by kandi - BETA
- Train the WaveGlow model
- Unflatten a list of tensors
- Flatten a list of tensors
- Load a checkpoint
- Save model and optimizer state
- Reduce tensor into num_gpus
- Apply gradients to modules
- Recursively update the model
- Update res_skip
- Check if a model has old version
- Update the model conditional weight
- Perform the forward pass on the input
- Concatenate tanh
- Removes weights from the waveglow model
- Remove weights from conv_norm
- Return a list of files
- Compute the mel spectrogram from the input audio
- Load a WAV file into a torch torch torch torch torch torch torch torch Tensor
waveglow Key Features
waveglow Examples and Code Snippets
import torch import librosa y,sr = librosa.load(librosa.util.example_audio_file(), sr=22050, mono=True, duration=10, offset=30) y_tensor = torch.from_numpy(y).to(device='cuda', dtype=torch.float32) from waveglow_vocoder import WaveGlowVocoder WV =
cd src python3 dataset/procaudio.py Audio Name without extension|Text only for notation|True Text LJ001-0008|has never been surpassed.|has never been surpassed. LJ001-0009|Printing, then, for our purpose, may be considered as the art of making book
(without GPU) python train.py (with GPU #n) python train.py -g n python train.py -r snapshot_iter_100000
Trending Discussions on waveglow
I'm quite new to AI and I'm currently developing a model for non-parallel voice conversions. One confusing problem that I have is the use of vocoders.
So my model needs Mel spectrograms as the input and the current model that I'm working on is using the MelGAN vocoder (Github link) which can generate 22050Hz Mel spectrograms from raw wav files (which is what I need) and back. I recently tried WaveGlow Vocoder (PyPI link) which can also generate Mel spectrograms from raw wav files and back.
But, in other models such as, WaveRNN , VocGAN , WaveGrad There's no clear explanation about wav to Mel spectrograms generation. Do most of these models don't require the wav to Mel spectrograms feature because they largely cater to TTS models like Tacotron? or is it possible that all of these have that feature and I'm just not aware of it?
A clarification would be highly appreciated....
ANSWERAnswered 2022-Feb-01 at 23:05
Check e.g. this part of the MelGAN code: https://github.com/descriptinc/melgan-neurips/blob/master/mel2wav/modules.py#L26
Audio2Mel module simply uses standard methods to create log-magnitude mel spectrograms like this:
- Compute the STFT by applying the Fourier transform to windows of the input audio,
- Take the magnitude of the resulting complex spectrogram,
- Multiply the magnitude spectrogram by a mel filter matrix. Note that they actually get this matrix from
- Take the logarithm of the resulting mel spectrogram.
Your confusion might stem from the fact that, usually, authors of Deep Learning papers only mean their mel-to-audio "decoder" when they talk about "vocoders" -- the audio-to-mel part is always more or less the same. I say this might be confusing since, to my understanding, the classical meaning of the term "vocoder" includes both an encoder and a decoder.
Unfortunately, these methods will not always work exactly in the same manner as there are e.g. different methods to create the mel filter matrix, different padding conventions etc.
librosa.stft has a
center argument that will pad the audio before applying the STFT, while
tensorflow.signal.stft does not have this (it would require manual padding beforehand).
An example for the different methods to create mel filters would be the
htk argument in
librosa.filters.mel, which switches between the "HTK" method and "Slaney". Again taking Tensorflow as an example,
tf.signal.linear_to_mel_weight_matrix does not support this argument and always uses the HTK method. Unfortunately, I am not familiar with
torchaudio, so I don't know if you need to be careful there, as well.
Finally, there are of course many parameters such as the STFT window size, hop length, the frequencies covered by the mel filters etc, and changing these relative to what a reference implementation used may impact your results. Since different code repositories likely use slightly different parameters, I suppose the answer to your question "will every method do the operation(to create a mel spectrogram) in the same manner?" is "not really". At the end of the day, you will have to settle for one set of parameters either way...Bonus: Why are these all only decoders and the encoder is always the same?
The direction Mel -> Audio is hard. Not even Mel -> ("normal") spectrogram is well-defined since the conversion to mel spectrum is lossy and cannot be inverted. Finally, converting a spectrogram to audio is difficult since the phase needs to be estimated. You may be familiar with methods like Griffin-Lim (again, librosa has it so you can try it out). These produce noisy, low-quality audio. So the research focuses on improving this process using powerful models.
On the other hand, Audio -> Mel is simple, well-defined and fast. There is no need to define "custom encoders".
Now, a whole different question is whether mel spectrograms are a "good" encoding. Using methods like variational autoencoders, you could perhaps find better (e.g. more compact, less lossy) audio encodings. These would include custom encoders and decoders and you would not get away with standard librosa functions...
I am executing my test configuration and this is the error I am facing. I have a trained model of size 327mb and layers of 250mb required for the inference of my Text To Speech trained model. So the size of model and layers might be the reason?? Please help me clarify and provide a solution. I am importing the trained model from s3 bucket and then loading it for the further processing. HERE IS THE CODE AND ERROR....
ANSWERAnswered 2021-Jun-30 at 13:56
AWS Lambdas local storage in
/tmp is only
512MB. You are apparently exceeding this limit.
There are five solutions I can think of:
- Mount a EFS volume (which already contains your trained model) to the Lambda.
- Reduce the size of your model.
- Stream the model in chunks to your Lambda (might be hard).
- Not use Lambda (maybe just a plain EC2 or EKS).
- Use a Docker container that already contains your model as Lambda.
It is hard to tell what the best solution for you is, since so much information is missing. But those solutions should give you a good starting point.
No vulnerabilities reported
Clone our repo and initialize submodule git clone https://github.com/NVIDIA/waveglow.git cd waveglow git submodule init git submodule update
Install requirements pip3 install -r requirements.txt
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