Create README.md

README.md

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+---
+license: cc-by-4.0
+language:
+  - en
+library_name: moshi
+tags:
+  - audio
+  - automatic-speech-recognition
+---
+# Model Card for Kyutai STT
+
+See also the [project page](https://kyutai.org/next/stt)
+and the [GitHub repository](https://github.com/kyutai-labs/delayed-streams-modeling/).
+
+This is a model for streaming speech-to-text (STT, also known as automatic speech recognition, ASR).
+Unlike offline speech-to-text, where the model needs the entire audio to produce the transcript,
+our model starts to output the transcript as soon as a few seconds of audio become available.
+
+## Model Details
+
+The model architecture is a Transformer that consumes audio tokenized by Mimi (see [the Moshi paper](https://arxiv.org/abs/2410.00037)) and outputs text tokens.
+The frame rate is 12.5 Hz and each audio frame is represented by 32 audio tokens.
+
+We release two models:
+- `kyutai/stt-1b-en_fr`, an English and French model with ~1B parameters, a 0.5 second delay, and a [semantic VAD](https://kyutai.org/next/stt#semantic-vad).
+- `kyutai/stt-2.6b-en`, an English-only model with ~2.6B parameters and a 2.5 second delay.
+
+## Model Description
+
+Kyutai STT is a decoder-only model for streaming speech-to-text.
+It leverages the multistream architecture of [Moshi](https://moshi.chat/) to model text stream based on the speech stream.
+The text stream is shifted w.r.t. the audio stream to allow the model to predict text tokens based on the input audio.
+
+* Developed by: Kyutai
+* Model type: Streaming Speech-to-Text transcription.
+* Language(s) (NLP): English and French for `kyutai/stt-1b-en_fr`, English for `kyutai/stt-2.6b-en`
+* License: Model weights are licensed under CC-BY 4.0
+* Repository: [GitHub](https://github.com/kyutai-labs/delayed-streams-modeling/)
+
+## Uses
+
+### Direct Use
+
+The model can be used for streaming speech-to-text.
+It is robust to noisy conditions and was found to perform well with audio as long as 2 hours with no additonal changes.
+The model produces transcripts with capitalization and punctuation.
+The predicted text token timestamps can be recovered by subtracting the model's text stream offset (0.5 or 2.5 seconds) from the frame's offset.
+
+## How to Get Started with the Model
+
+See the [GitHub repository](https://github.com/kyutai-labs/delayed-streams-modeling/).
+
+## Training Details
+
+### Training Data
+
+Pretraining stage: For both `kyutai/stt-2.6b-en` and `kyutai/stt-1b-en_fr`, we use an audio collection of 2.5 million hours of publicly available audio content.
+For this dataset, we obtained synthetic transcripts by running [whisper-timestamped](https://github.com/linto-ai/whisper-timestamped).
+
+For `kyutai/stt-2.6b-en`:
+
+- Finetuning stage: We then finetune the model on a collection of public datasets with
+ground-truth transcripts. This dataset contains 24000 hours of audio.
+
+- Long-form finetuning stage: Finally, we finetune the model on a combination of data from the previous stage and long-form audio.
+The long-form audio is obtained from two sources: (a) concatenating LibriSpeech examples (1000 hours), (b) synthesizing dialogs (22000 hours).
+
+For `kyutai/stt-1b-en_fr`:
+
+- Finetuning stage: We finetune on the Fisher dataset of 2000 hours of English audio, plus proprietary data (1000 hours in English, 600 hours in French).
+
+### Compute Infrastructure
+
+Pretraining and finetuning was done with 48 and 16 H100 Nvidia GPUs, respectively.
+
+## Model Card Authors
+
+Neil Zeghidour, Eugene Kharitonov, Manu Orsini, Václav Volhejn, Gabriel de Marmiesse, Edouard Grave, Patrick Perez, Laurent Mazaré, Alexandre Défossez