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README.md

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+
+---
+license: cc-by-nc-4.0
+tags:
+- mms
+- vits
+pipeline_tag: text-to-speech
+---
+
+# Massively Multilingual Speech (MMS): Daba Text-to-Speech
+
+This repository contains the **Daba (dbq)** language text-to-speech (TTS) model checkpoint.
+
+This model is part of Facebook's [Massively Multilingual Speech](https://arxiv.org/abs/2305.13516) project, aiming to
+provide speech technology across a diverse range of languages. You can find more details about the supported languages
+and their ISO 639-3 codes in the [MMS Language Coverage Overview](https://dl.fbaipublicfiles.com/mms/misc/language_coverage_mms.html),
+and see all MMS-TTS checkpoints on the Hugging Face Hub: [facebook/mms-tts](https://huggingface.co/models?sort=trending&search=facebook%2Fmms-tts).
+
+MMS-TTS is available in the 🤗 Transformers library from version 4.33 onwards.
+
+## 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-dbq")
+tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-dbq")
+
+text = "some example text in the Daba 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)
+```
+
+
+
+## BibTex citation
+
+This model was developed by Vineel Pratap et al. from Meta AI. If you use the model, consider citing the MMS paper:
+
+```
+@article{pratap2023mms,
+    title={Scaling Speech Technology to 1,000+ Languages},
+    author={Vineel Pratap and Andros Tjandra and Bowen Shi and Paden Tomasello and Arun Babu and Sayani Kundu and Ali Elkahky and Zhaoheng Ni and Apoorv Vyas and Maryam Fazel-Zarandi and Alexei Baevski and Yossi Adi and Xiaohui Zhang and Wei-Ning Hsu and Alexis Conneau and Michael Auli},
+    journal={arXiv},
+    year={2023}
+}
+```
+
+## License
+
+The model is licensed as **CC-BY-NC 4.0**.