{"categories":[{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/11"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/5"},{"label":"Speech","url":"https://skillfed.io/packages/category/multimedia-sound-audio-speech"}],"enrichment":{"capability":"FunASR is a speech recognition toolkit that transcribes audio offline or via streaming, with integrated voice activity detection, speaker identification, punctuation restoration, and emotion/audio-event tagging across multiple languages and deployment targets.","skillfed_tags":["speech-recognition","speaker-diarization","streaming-asr"],"use_cases":["Transcribe multilingual audio with speaker labels and timestamps for meeting notes, interview processing, or call center analytics.","Deploy a self-hosted OpenAI-compatible speech API endpoint for voice agents and chatbots without cloud vendor lock-in.","Extract emotion and audio events alongside transcription for customer sentiment analysis or accessibility.","Stream live audio transcription via WebSocket for real-time captioning or voice command interfaces.","Run low-latency ASR on CPU or edge devices using SenseVoiceSmall or GGUF checkpoints for embedded systems.","Batch-process large audio corpora with vLLM acceleration for industrial data labeling or training dataset preparation."],"what_it_does":"FunASR is a production-grade speech recognition toolkit that goes beyond single-model transcription. It coordinates multiple specialized models\u2014ASR, voice activity detection (VAD), speaker diarization, punctuation, and emotion tagging\u2014into unified pipelines. You choose the checkpoint that fits your task: Fun-ASR-Nano for Chinese/English/Japanese with GPU acceleration, SenseVoiceSmall for CPU-viable five-language ASR with emotion detection, Paraformer for low-latency streaming, or Qwen3-ASR for 52-language coverage. The toolkit handles offline batch processing, real-time WebSocket streaming, and edge deployment via GGUF or vLLM acceleration.\n\nIt ships with OpenAI-compatible API serving, MCP server integration, and agent hooks for LangChain and AutoGen. The core dependency stack includes transformers, librosa, scipy, and Hydra for configuration; model weights are fetched from ModelScope or Hugging Face on first use. Unlike single-model alternatives, FunASR lets you trade off speed, language coverage, and feature richness per deployment scenario\u2014from CPU-only inference to GPU-accelerated batch processing.","worth_installing":"Yes. FunASR is actively maintained, MIT-licensed, and offers a genuine alternative to single-model approaches. Install it if you need speaker diarization, emotion detection, streaming support, or self-hosted deployment. The 26 runtime dependencies add setup complexity, but the toolkit's flexibility across models and deployment targets justifies it for production use. Start with the Colab quickstart to test before local setup."},"id":"funasr","links":{"html":"https://skillfed.io/packages/funasr","md":"https://skillfed.io/packages/funasr.md","pypi":"https://pypi.org/project/funasr/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-14","license_spdx":null,"license_treatment":"permissive","name":"funasr","python_support":"supports_current","summary":"OpenAI-compatible speech recognition toolkit with WebSocket streaming, vLLM acceleration, and llama.cpp/GGUF edge runtime."},"popularity":{"monthly_downloads":497863,"position":6331,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.4.2"}