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README.md

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-
----
-license: cc-by-nc-4.0
-tags:
-- mms
-- vits
-pipeline_tag: text-to-speech
----
-
-
-## Model Details
-
-VITS (**V**ariational **I**nference with adversarial learning for end-to-end **T**ext-to-**S**peech) is an end-to-end 
-speech synthesis model that predicts a speech waveform conditional on an input text sequence. It is a conditional variational 
-autoencoder (VAE) comprised of a posterior encoder, decoder, and conditional prior.
-
-A set of spectrogram-based acoustic features are predicted by the flow-based module, which is formed of a Transformer-based
-text encoder and multiple coupling layers. The spectrogram is decoded using a stack of transposed convolutional layers,
-much in the same style as the HiFi-GAN vocoder. Motivated by the one-to-many nature of the TTS problem, where the same text 
-input can be spoken in multiple ways, the model also includes a stochastic duration predictor, which allows the model to 
-synthesise speech with different rhythms from the same input text. 
-
-The model is trained end-to-end with a combination of losses derived from variational lower bound and adversarial training. 
-To improve the expressiveness of the model, normalizing flows are applied to the conditional prior distribution. During 
-inference, the text encodings are up-sampled based on the duration prediction module, and then mapped into the 
-waveform using a cascade of the flow module and HiFi-GAN decoder. Due to the stochastic nature of the duration predictor,
-the model is non-deterministic, and thus requires a fixed seed to generate the same speech waveform.
-
-For the MMS project, a separate VITS checkpoint is trained on each langauge.
-
-## Usage
-
-MMS-TTS is available in the 🤗 Transformers library from version 4.33 onwards. To use this checkpoint, 
-first install the latest version of the library:
-
-```
-pip install --upgrade transformers accelerate
-```
-
-Then, run inference with the following code-snippet:
-
-```python
-from transformers import VitsModel, AutoTokenizer
-import torch
-
-model = VitsModel.from_pretrained("facebook/mms-tts-lug")
-tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-lug")
-
-text = "some example text in the Ganda language"
-inputs = tokenizer(text, return_tensors="pt")
-
-with torch.no_grad():
-    output = model(**inputs).waveform
-```
-
-The resulting waveform can be saved as a `.wav` file:
-
-```python
-import scipy
-
-scipy.io.wavfile.write("techno.wav", rate=model.config.sampling_rate, data=output)
-```
-
-Or displayed in a Jupyter Notebook / Google Colab:
-
-```python
-from IPython.display import Audio
-
-Audio(output, rate=model.config.sampling_rate)
-```
-
-
-## License
-
-The model is licensed as **CC-BY-NC 4.0**.