Create README.md

README.md

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+---
+license: cc-by-4.0
+language:
+- fr
+- en
+library_name: hibiki
+tags:
+- speech
+- translation
+- streaming
+metrics:
+- bleu
+---
+
+# Model Card for Hibiki
+
+[Hibiki](https://github.com/kyutai-labs/hibiki) is a model for streaming speech translation (also known as *simultaneous* translation). Unlike offline translation—where one waits for the end of the source utterance to start translating--- Hibiki adapts its flow to accumulate just enough context to produce a correct translation in real-time, chunk by chunk. As the user speaks, Hibiki generates natural speech in the target language, optionally with voice transfer, along with a text translation.
+Hibiki currently only supports French-to-English translation.
+
+## Model Details
+
+This is the model simply referred to as *Hibiki* in our [paper](https://arxiv.org/abs/2502.03382), a 2.7B parameter
+hierarchical Transformer producing speech and text tokens at a framerate of 12.5Hz, with audio being generated at a
+2.2kbps bitrate.
+
+### Model Description
+
+Hibiki is a decoder-only model for simultaneous speech translation. Hibiki leverages the multistream architecture of [Moshi](https://arxiv.org/abs/2410.00037)
+to model source and target speech jointly. This allows Hibiki to continuously process the input stream while generating
+the target speech. Hibiki produces text and audio tokens at a constant framerate of 12.5Hz. This allows for a continuous
+output audio stream, along with timestamped text tranlsation. Since Hibiki relies on simple temperature sampling,
+it is compatible with batching unlike models that rely on complex inference policies. Moreover, the fidelity of Hibiki's
+voice transfer can be controlled by changing the coefficient of the Classifier-Free Guidance: a larger coefficient will
+increase voice similarity, but excessive coefficients can lead to worse translations.
+
+
+- **Developed by:**  Kyutai
+- **Model type:** Simultaneous speech-to-speech and speech-to-text translation.
+- **Language(s) (NLP):** French-to-English
+- **License:** CC-BY
+
+### Model Sources 
+
+
+- **Repository:** [repo](https://github.com/kyutai-labs/hibiki) 
+- **Paper:** [paper](https://arxiv.org/abs/2502.03382) 
+- **Examples:** [demo](https://hf.co/spaces/kyutai/hibiki-samples) 
+
+## Uses
+
+### Direct Use
+
+The model can be used for streaming translation from French to English in real-time settings, or for batched
+simultaneous translation of many input sequences. It is robust to noisy conditions and is trained on sequences up
+to 120 seconds. 
+
+
+### Downstream Use
+
+Some components of the model can be used independently or repurposed relatively easily. 
+For instance the Mimi codec is a state-of-the-art audio neural codec that combines semantic and acoustic information into audio tokens running at 12Hz and a bitrate of 1.1kbps, which make it particularly adapted to train speech language models or text-to-speech systems. Regarding the main Hibiki architecture,
+supporting other pairs of languages would require finetuning. 
+
+
+### Out-of-Scope Use
+
+The model is not intended to be used to impersonate other people or any malicious use of any kind. . 
+
+
+## How to Get Started with the Model
+
+See the main [README](https://github.com/kyutai-labs/hibiki) file. 
+
+## Training Details
+
+### Training Data
+
+- Textual data: The underlying [Helium](https://huggingface.co/kyutai/helium-1-preview-2b) model is trained on a mix of
+data including: Wikipedia, Stack Exchange, open-access scientific articles (from peS2o) and Common Crawl.
+
+- Audio data
+
+  - **Unsupervised audio dataset:** used for pre-training, this is a collection of 7M hours of readily available audio content in English and 450k hours in French, following the preprocessing and recipe of [Moshi](https://arxiv.org/abs/2410.00037).
+  - **Synthetic translation dataset**: Around 40k hours of parallel French-English data synthesized with *contextual alignment* (see [Section 3.2](https://arxiv.org/pdf/2502.03382)) with various levels of speaker similarity.
+  - **Translation finetuning:** A 900 hours mixture of a resynthesized version of [CVSS-T](https://github.com/google-research-datasets/cvss) and synthetic long-form utterances.
+
+### Training procedure and hyper-parameters
+
+The different stages of the training procedure are detailled in the paper along with the hyper-parameters. 
+
+### Compute Infrastructure
+
+The final model was trained on 48 H100 Nvidia GPUs. 
+
+## Citation 
+
+```
+@misc{labiausse2025hibiki,
+      title={High-Fidelity Simultaneous Speech-To-Speech Translation}, 
+      author={Tom Labiausse and Laurent Mazaré and Edouard Grave and Patrick Pérez and Alexandre Défossez and Neil Zeghidour},
+      year={2025},
+      eprint={2502.03382},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2502.03382}, 
+}
+```
+
+
+## Model Card Authors
+
+Tom Labiausse, Laurent Mazaré, Edouard Grave, Patrick Pérez, Alexandre Défossez, Neil Zeghidour