---
license: apache-2.0
language:
- en
- ca
datasets:
- mythicinfinity/libritts_r
- projecte-aina/festcat_trimmed_denoised
- projecte-aina/openslr-slr69-ca-trimmed-denoised
- keithito/lj_speech
base_model:
- facebook/encodec_24khz
---

# Wavenext-encodec

<!-- 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 encodec tokens as input features, it's trained using the following bandwidths from encodec (1.5, 3.0, 6.0, 12.0) . 

## 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 encodec discrete codes. 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 encodec tokens

You need to provide a bandwidth_id which corresponds to the embedding for bandwidth from the list: [1.5, 3.0, 6.0, 12.0].

```python
import torch

from vocos import Vocos

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

audio_tokens = torch.randint(low=0, high=1024, size=(8, 200))  # 8 codeboooks, 200 frames
features = vocos.codes_to_features(audio_tokens)
bandwidth_id = torch.tensor([2])  # 6 kbps

audio = vocos.decode(features, bandwidth_id=bandwidth_id)

```

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=24000)
y_hat = vocos(y, bandwidth_id=bandwidth_id)
```

## 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 99 epochs with a batch size of 16 for stability. We used a Cosine scheduler with a initial learning rate of 1e-4. 


#### 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 vocos repo, Note that this metrics are calculated using the codecs corresponding to a bandwidth of 1.5 kbps, after 99 epochs we achieve:

* val_loss: 5.52
* f1_score: 0.93
* mel_loss: 0.53
* periodicity_loss:0.14
* pesq_score: 2.12
* pitch_loss: 47.73
* utmos_score: 2.89


## 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/).