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+
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+ ---
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+ license: cc-by-nc-4.0
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+ tags:
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+ - mms
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+ - vits
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+ pipeline_tag: text-to-speech
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+ ---
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+
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+ # Massively Multilingual Speech (MMS): Sranan Tongo Text-to-Speech
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+
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+ This repository contains the **Sranan Tongo (srn)** language text-to-speech (TTS) model checkpoint.
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+
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+ This model is part of Facebook's [Massively Multilingual Speech](https://arxiv.org/abs/2305.13516) project, aiming to
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+ provide speech technology across a diverse range of languages. You can find more details about the supported languages
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+ and their ISO 639-3 codes in the [MMS Language Coverage Overview](https://dl.fbaipublicfiles.com/mms/misc/language_coverage_mms.html),
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+ and see all MMS-TTS checkpoints on the Hugging Face Hub: [facebook/mms-tts](https://huggingface.co/models?sort=trending&search=facebook%2Fmms-tts).
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+
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+ MMS-TTS is available in the 🤗 Transformers library from version 4.33 onwards.
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+
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+ ## Model Details
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+
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+ VITS (**V**ariational **I**nference with adversarial learning for end-to-end **T**ext-to-**S**peech) is an end-to-end
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+ speech synthesis model that predicts a speech waveform conditional on an input text sequence. It is a conditional variational
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+ autoencoder (VAE) comprised of a posterior encoder, decoder, and conditional prior.
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+
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+ A set of spectrogram-based acoustic features are predicted by the flow-based module, which is formed of a Transformer-based
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+ text encoder and multiple coupling layers. The spectrogram is decoded using a stack of transposed convolutional layers,
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+ 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
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+ input can be spoken in multiple ways, the model also includes a stochastic duration predictor, which allows the model to
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+ synthesise speech with different rhythms from the same input text.
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+
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+ The model is trained end-to-end with a combination of losses derived from variational lower bound and adversarial training.
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+ To improve the expressiveness of the model, normalizing flows are applied to the conditional prior distribution. During
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+ inference, the text encodings are up-sampled based on the duration prediction module, and then mapped into the
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+ waveform using a cascade of the flow module and HiFi-GAN decoder. Due to the stochastic nature of the duration predictor,
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+ the model is non-deterministic, and thus requires a fixed seed to generate the same speech waveform.
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+
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+ For the MMS project, a separate VITS checkpoint is trained on each langauge.
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+
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+ ## Usage
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+
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+ MMS-TTS is available in the 🤗 Transformers library from version 4.33 onwards. To use this checkpoint,
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+ first install the latest version of the library:
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+
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+ ```
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+ pip install --upgrade transformers accelerate
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+ ```
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+
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+ Then, run inference with the following code-snippet:
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+
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+ ```python
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+ from transformers import VitsModel, AutoTokenizer
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+ import torch
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+
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+ model = VitsModel.from_pretrained("facebook/mms-tts-srn")
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+ tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-srn")
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+
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+ text = "some example text in the Sranan Tongo language"
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+ inputs = tokenizer(text, return_tensors="pt")
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+
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+ with torch.no_grad():
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+ output = model(**inputs).waveform
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+ ```
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+
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+ The resulting waveform can be saved as a `.wav` file:
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+
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+ ```python
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+ import scipy
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+
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+ scipy.io.wavfile.write("techno.wav", rate=model.config.sampling_rate, data=output)
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+ ```
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+
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+ Or displayed in a Jupyter Notebook / Google Colab:
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+
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+ ```python
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+ from IPython.display import Audio
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+
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+ Audio(output, rate=model.config.sampling_rate)
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+ ```
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+
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+
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+
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+ ## BibTex citation
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+
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+ This model was developed by Vineel Pratap et al. from Meta AI. If you use the model, consider citing the MMS paper:
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+
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+ ```
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+ @article{pratap2023mms,
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+ title={Scaling Speech Technology to 1,000+ Languages},
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+ 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},
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+ journal={arXiv},
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+ year={2023}
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+ }
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+ ```
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+
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+ ## License
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+
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+ The model is licensed as **CC-BY-NC 4.0**.