Generates conversational speech and voice continuation from text and optional audio context, outputting Mimi audio codes. Built on a Sesame-style CSM with an 8B Llama-like backbone plus a smaller autoregressive audio decoder. Suited for local TTS inference and voice-cloning workflows.
A 12B unified, encoder-free multimodal model that directly ingests text, images and audio and returns text; supports very long contexts (up to 256K tokens), native function-calling/thinking modes, and small-model deployment for local or on-device use.
Large streaming-audio dataset for training and evaluating audio-LLMs and audio agents. About 2.28M clips grouped into multi-turn “streams” across six task subsets (ASR, speech translation, audio understanding, voice chat, proactive response, environment-aware); audio shipped as tar shards.
Generates multilingual text-to-speech with zero-shot voice cloning, token-level duration control, and inline pause markers. v1.5 improves multilingual fidelity (with language tags), cloning stability, and long-reference handling—suitable for research and production TTS pipelines.
Provides ~100 hours of expert-annotated, multi-channel Chinese conversational speech with per-segment timestamps, speaker IDs and paralinguistic labels for turn-taking, overlap/interruption detection and full‑duplex dialogue research. Licensed for academic/non-commercial use (CC BY‑NC 4.0).
Zero-shot TTS for expressive long-form monologue and multi-speaker dialogue, designed to preserve acoustic consistency, conversational coherence, and affective continuity. Trained on SwanData-Speech and using a 25 Hz VAE, pause-aware text conditioning, and a flow-matching DiT with DiffusionNFT fine-tuning.
Generates synchronized, streaming spatial audio from panoramic video and text prompts using a causal autoregressive diffusion transformer. Combines Spatial Video-Audio Contrastive (SVAC) alignment and online direct preference optimization (ODPO) to improve spatial perception, plus an automated annotation pipeline and public demos.
Around 80K short audio clips paired with transcripts in JSON, organized for easy loading with the Hugging Face datasets ecosystem—designed for short-form speech tasks (ASR, TTS, fine-tuning) and quick prototyping with common Python data tools.
Converts text into expressive conversational speech across 100+ languages with zero-shot voice cloning and inline control tokens for emotion, style, prosody, pauses, and sound effects. Released under a research/non-commercial license; commercial use requires separate licensing.
End-to-end evaluation framework for conversational voice agents that runs bot-to-bot audio simulations and scores agents on task accuracy (EVA-A) and interaction experience (EVA-X). Includes per-scenario backend state, accent/noise perturbations, and 213 scenarios across airline, healthcare HR, and enterprise IT domains.
Provides a comprehensive benchmark for instruction-based audio editing across seven audio modalities and eight operation types, with 2,000 high-fidelity samples and a rubric that decomposes tasks into 17,741 verifiable criteria for multi-dimensional evaluation.
Adds discrete audio tokens and an audio encoder to a 30B MoE text LLM so a single model can perform ASR, speech translation, TTS, text-to-audio and speech-to-speech while preserving text reasoning and long-context capabilities; supports thinking/instruct modes and up to 1M-token context.