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--- |
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license: mit |
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pipeline_tag: text-to-speech |
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tags: |
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- vocos |
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- hifigan |
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- tts |
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- melspectrogram |
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- vocoder |
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- mel |
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--- |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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**Vocos** is a fast neural vocoder designed to synthesize audio waveforms from acoustic features. |
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Unlike other typical GAN-based vocoders, Vocos does not model audio samples in the time domain. |
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Instead, it generates spectral coefficients, facilitating rapid audio reconstruction through |
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inverse Fourier transform. |
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This version of vocos uses 80-bin mel spectrograms as acoustic features which are widespread |
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in the TTS domain since the introduction of [hifi-gan](https://github.com/jik876/hifi-gan/blob/master/meldataset.py) |
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The goal of this model is to provide an alternative to hifi-gan that is faster and compatible with the |
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acoustic output of several TTS models. |
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## Intended Uses and limitations |
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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 |
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domain is possible that the model won't produce high quality samples. |
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### Installation |
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To use Vocos only in inference mode, install it using: |
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```bash |
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pip install git+https://github.com/langtech-bsc/vocos.git@matcha |
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``` |
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### Reconstruct audio from mel-spectrogram |
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```python |
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import torch |
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from vocos import Vocos |
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vocos = Vocos.from_pretrained("patriotyk/vocos-mel-hifigan-compat-44100khz") |
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mel = torch.randn(1, 80, 256) # B, C, T |
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audio = vocos.decode(mel) |
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``` |
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### Training Data |
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The model was trained on private 800+ hours dataset, made from Ukrainian audio books, using [narizaka](https://github.com/patriotyk/narizaka) tool. |
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### Training Procedure |
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The model was trained for 2.0M steps and 210 epochs with a batch size of 20. We used a Cosine scheduler with a initial learning rate of 3e-4. |
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We where using two RTX-3090 video cards for training, and it took about one month of continuous training. |
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#### Training Hyperparameters |
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* initial_learning_rate: 3e-4 |
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* scheduler: cosine without warmup or restarts |
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* mel_loss_coeff: 45 |
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* mrd_loss_coeff: 1.0 |
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* batch_size: 20 |
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* num_samples: 32768 |
