---
datasets:
- projecte-aina/festcat_trimmed_denoised
- projecte-aina/openslr-slr69-ca-trimmed-denoised
- lj_speech
- blabble-io/libritts_r
license: apache-2.0
tags:
  - vocoder
  - vocos
  - hifigan
  - tts
  - mel
---

# Wavenext-mel-22khz

<!-- Provide a quick summary of what the model is/does. -->


## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->
Wavenext is a modification of Vocos, where the last ISTFT layer is replaced with a a trainable linear layer that can directly predict speech waveform samples.

This version of Wavenext uses 80-bin mel spectrograms as acoustic features which are widespread
in the TTS domain since the introduction of [hifi-gan](https://github.com/jik876/hifi-gan/blob/master/meldataset.py)
The goal of this model is to provide an alternative to hifi-gan that is faster and compatible with the 
acoustic output of several TTS models. 

## Intended Uses and limitations

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
The model is aimed to serve as a vocoder to synthesize audio waveforms from mel spectrograms. Is trained to generate speech and if is used in other audio
domain is possible that the model won't produce high quality samples. 

## Usage
### Installation

To use Wavenext only in inference mode, install it using:

```bash
pip install git+https://github.com/langtech-bsc/wavenext_pytorch
```

### Reconstruct audio from mel-spectrogram

```python
import torch

from vocos import Vocos

vocos = Vocos.from_pretrained("BSC-LT/wavenext-mel")

mel = torch.randn(1, 80, 256)  # B, C, T
audio = vocos.decode(mel)
```

Copy-synthesis from a file:

```python
import torchaudio

y, sr = torchaudio.load(YOUR_AUDIO_FILE)
if y.size(0) > 1:  # mix to mono
    y = y.mean(dim=0, keepdim=True)
y = torchaudio.functional.resample(y, orig_freq=sr, new_freq=22050)
y_hat = vocos(y)

```

### ONNX versions

You can check in colab:

<a target="_blank" href="https://colab.research.google.com/drive/1wJiXcBypdV4sxZMiD270JJWJX51c6wez">
  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>


## Training Details

### Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

The model was trained on 4 speech datasets

| Dataset             | Language | Hours   |
|---------------------|----------|---------|
| LibriTTS-r          | en       | 585     | 
| LJSpeech            | en       | 24      |
| Festcat             | ca       | 22      |
| OpenSLR69           | ca       | 5       | 


### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
The model was trained for 1M steps and 96 epochs with a batch size of 16 for stability. We used a Cosine scheduler with a initial learning rate of 1e-4. 
We also modified the mel spectrogram loss to use 128 bins and fmax of 11025 instead of the same input mel spectrogram.


#### Training Hyperparameters


* initial_learning_rate: 1e-4
* scheduler: cosine without warmup or restarts
* mel_loss_coeff: 45
* mrd_loss_coeff: 0.1
* batch_size: 16
* num_samples: 16384

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

Evaluation was done using the metrics on the original repo, after 96 epochs we achieve:

* val_loss: 3.79
* f1_score: 0.94
* mel_loss: 0.27
* periodicity_loss:0.128
* pesq_score: 3.27
* pitch_loss: 31.33
* utmos_score: 3.20


## Citation

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

If this code contributes to your research, please cite the work:

```
@INPROCEEDINGS{10389765,
  author={Okamoto, Takuma and Yamashita, Haruki and Ohtani, Yamato and Toda, Tomoki and Kawai, Hisashi},
  booktitle={2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)}, 
  title={WaveNeXt: ConvNeXt-Based Fast Neural Vocoder Without ISTFT layer}, 
  year={2023},
  volume={},
  number={},
  pages={1-8},
  keywords={Fourier transforms;Vocoders;Conferences;Automatic speech recognition;ConvNext;end-to-end text-to-speech;linear layer-based upsampling;neural vocoder;Vocos},
  doi={10.1109/ASRU57964.2023.10389765}}

@article{siuzdak2023vocos,
  title={Vocos: Closing the gap between time-domain and Fourier-based neural vocoders for high-quality audio synthesis},
  author={Siuzdak, Hubert},
  journal={arXiv preprint arXiv:2306.00814},
  year={2023}
}
```

## Additional information

### Author
The Language Technologies Unit from Barcelona Supercomputing Center.

### Contact
For further information, please send an email to <langtech@bsc.es>.

### Copyright
Copyright(c) 2024 by Language Technologies Unit, Barcelona Supercomputing Center.

### License
[Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)

### Funding

This work has been promoted and financed by the Generalitat de Catalunya through the [Aina project](https://projecteaina.cat/).