--- license: other library_name: pytorch pipeline_tag: text-to-speech tags: - text-to-speech - speech-synthesis - voice - audio - sesame - mimi - llama ---

Quickstart | Model Introduction | Model Summary | Architecture | Links

--- # Miso TTS 8B ## Model Introduction Miso TTS 8B is a text-to-speech model based on the Sesame CSM architecture. It generates Mimi audio codes from text and optional audio context, using a large Llama 3.2-style backbone and a smaller autoregressive audio decoder. The model is designed for high-quality conversational speech generation and voice continuation from prompt audio. This repository contains the inference code, model definition, and setup instructions for running Miso TTS locally. > **Language support:** Miso TTS 8B currently supports **English only**. --- ## Quickstart To run the model, use the inference code at our [public repository](https://github.com/MisoLabsAI/MisoTTS), or try our demo at misolabs.ai. ## Model Summary | Item | Value | | ------------------- | ---------------- | | Model | Miso TTS 8B | | Organization | Miso Labs | | Task | Text-to-speech | | Architecture | Sesame-style CSM | | Backbone | `llama-8B` | | Audio decoder | `llama-300M` | | Text vocabulary | `128,256` | | Audio vocabulary | `2,051` | | Audio codebooks | `32` | | Audio tokenizer | Mimi | | Max sequence length | `2,048` | | Language | `English` | ### Architecture Miso TTS 8B uses two transformer components: - A large backbone transformer that consumes text/audio-frame embeddings. - A smaller decoder transformer that autoregressively predicts higher-order audio codebooks within each frame. Codebook 0 is predicted from the backbone hidden state, while codebooks 1 through 31 are predicted by the audio decoder autoregressively in codebook depth. --- ## Links - Website: [misolabs.ai](https://misolabs.ai) - Hugging Face: [MisoLabs/MisoTTS](https://huggingface.co/MisoLabs/MisoTTS) - GitHub: [MisoLabsAI](https://github.com/MisoLabsAI) - X: [@MisoLabsAI](https://x.com/MisoLabsAI)