FunASR - End-to-End Speech Recognition Toolkit
4.0Industrial-grade speech recognition toolkit. 170x realtime on GPU, 50+ languages, speaker diarization, emotion detection, OpenAI-compatible API, vLLM acceleration, and agent integration.
About
FunASR is an open-source end-to-end speech recognition toolkit designed for industrial-grade applications, developed and continuously maintained by the speech lab at Alibaba DAMO Academy. It integrates a complete set of speech processing capabilities, ranging from speech recognition and speaker diarization to emotion detection. The toolkit achieves inference speeds up to 170 times real-time on GPU and supports recognition in more than 50 languages. FunASR offers an OpenAI-compatible API interface and leverages vLLM acceleration to significantly boost large-model inference efficiency, enabling seamless integration into agent systems. It is an ideal choice for building high-accuracy, low-latency speech interaction applications.
Key Features
Pricing & Fees
- Pricing: Not publicly listed on the pages we reviewed; check the official site for current plans.
- Open-source toolkit: FunASR is hosted on GitHub and can be installed and run locally, so core inference carries no per-call service fee.
- Flexible deployment: Supports native vLLM, a split-engine vLLM mode, and llama.cpp with GGUF models, letting users pick infrastructure that matches their budget and hardware.
- Whisper-compatible API: Offers an OpenAI-compatible interface, so existing OpenAI/Whisper integrations can be switched over without code rewrites.
Who It's For
FunASR is well suited for AI application developers who want to quickly add speech recognition, speaker diarization, or emotion analysis to their products while reducing self-development costs. It also serves enterprise technical teams that need to deploy high-concurrency, low-latency speech services for call centers, meeting transcription, live captioning, and similar scenarios. Developers working on agents and RPA solutions can integrate voice interaction into agent workflows for voice-driven automation. Academic researchers will find it a flexible, reproducible baseline system for speech technology experiments and comparisons.