inclusionAI / Ming-3.0-audio-flash

Ming-3.0-audio-flash 
### Hear clearly. Understand deeply. Act intelligently.
A native audio model built for real-world speech recognition, audio understanding, reasoning, and end-to-end spoken interaction.
🤗 Hugging Face · Flash · 🤗 Hugging Face · Tiny · 🤖 ModelScope · Flash · 📓 Cookbook · 💻 Examples
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https://github.com/user-attachments/assets/2accce07-c451-4132-9fc4-1ed375888a24
## 🧩 Model Variants
- **Ming-3.0-audio-flash** — A 126B-parameter Mixture-of-Experts model with 6.5B activated parameters. This is the model used by the examples and benchmark results below.
- **Ming-3.0-audio-tiny** — A compact 9.3B-parameter Mixture-of-Experts model with 2.7B activated parameters. See its model card for checkpoint details and usage guidance.
## ✨ Highlights
- 🎙️ **Complex speech recognition** — Recognizes Chinese and English speech across dialects, accents, noisy environments, and multi-speaker meetings.
- ⏱️ **Long-audio understanding** — Comprehends and analyzes long-form recordings.
- 🎧 **Multi-dimensional perception** — Understands fine-grained speech, non-speech sounds, acoustic scenes, and music.
- 💡 **Audio reasoning** — Analyzes and reasons directly over audio content.
- 🗣️ **Audio interaction** — Supports end-to-end spoken dialogue with optional synthesized speech output.
Ming-3.0-audio-flash closes the loop from **hearing the sound**, to **understanding the information**, to **getting the task done**.
## 🏗️ Architecture
## 📊 Benchmarks
All scores are reported as percentages. ↑ means higher is better, while ↓ means lower is better. Ming-3.0-audio-flash is evaluated in non-thinking mode. **Bold** indicates the best result, underlined indicates the second-best result, and an asterisk (`*`) indicates results obtained in our own evaluation.
### Speech Recognition
### Audio Intelligence
### Text Intelligence
> [!NOTE]
> Results for comparison models are reproduced under our evaluation setup and may differ from their official reports due to differences in prompts, decoding settings, model versions, and evaluation pipelines.
## 🧭 Inference Options
| | vLLM-Omni | SGLang | Transformers |
| --- | --- | --- | --- |
| Best for | Serving and spoken interaction | High-throughput text serving | Local evaluation and research |
| Input | Audio + text instruction | Audio + text instruction | Audio + text instruction |
| Output | Text, or text + speech | Text | Text, or text + speech |
| Interface | OpenAI-compatible API | OpenAI-compatible API | Hugging Face AutoClass |
| Dependencies | `requirements-vllm-omni.txt` | `requirements-sglang.txt` | `requirements-transformers.txt` |
## 🚀 Quick Start
### 1️⃣ Download the Model
 [code] 
 Download from ModelScope 
 [code] 
The model directory contains the tokenizer, processor, model implementation, checkpoint shards, and Talker assets. Large model files are distributed through Hugging Face and ModelScope rather than this GitHub repository.
### 2️⃣ Start a Server
#### vLLM-Omni
Install the Ming-3.0-audio-flash fixe