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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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# Massively Multilingual Speech (MMS): Hindi Text-to-Speech |
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This repository contains the **Hindi (hin)** language text-to-speech (TTS) model checkpoint. |
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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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MMS-TTS is available in the 🤗 Transformers library from version 4.33 onwards. |
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## Model Details |
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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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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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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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For the MMS project, a separate VITS checkpoint is trained on each langauge. |
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## Usage |
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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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pip install --upgrade transformers accelerate |
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``` |
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Then, run inference with the following code-snippet: |
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```python |
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from transformers import VitsModel, AutoTokenizer |
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import torch |
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model = VitsModel.from_pretrained("facebook/mms-tts-hin") |
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tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-hin") |
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text = "some example text in the Hindi language" |
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inputs = tokenizer(text, return_tensors="pt") |
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with torch.no_grad(): |
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output = model(**inputs).waveform |
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``` |
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The resulting waveform can be saved as a `.wav` file: |
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```python |
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import scipy |
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scipy.io.wavfile.write("techno.wav", rate=model.config.sampling_rate, data=output.float().numpy()) |
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``` |
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Or displayed in a Jupyter Notebook / Google Colab: |
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```python |
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from IPython.display import Audio |
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Audio(output.numpy(), rate=model.config.sampling_rate) |
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``` |
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## BibTex citation |
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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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@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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## License |
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The model is licensed as **CC-BY-NC 4.0**. |
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