FireRedPunc

• FireRedASR2: Automatic Speech Recognition (ASR) supporting peech and singing transcription for Chinese (Mandarin, 20+ dialects/accents), English, code-switching. 2.89% average CER on 4 public Mandarin benchmarks, 11.55% on 19 Chinese dialects and accents benchmarks, outperforming Doubao-ASR, Qwen3-ASR-1.7B, Fun-ASR, and Fun-ASR-Nano-2512. FireRedASR2-AED also supports word-level timestamps and confidence scores.
• FireRedVAD: Voice Activity Detection (VAD) supporting speech/singing/music in 100+ languages. 97.57% F1, outperforming Silero-VAD, TEN-VAD, FunASR-VAD and WebRTC-VAD. Supports non-streaming/streaming VAD and Multi-label VAD (mVAD).
• FireRedLID: Spoken Language Identification (LID) supporting 100+ languages and 20+ Chinese dialects/accents. 97.18% accuracy, outperforming Whisper and SpeechBrain.
• FireRedPunc: Punctuation Prediction (Punc) for Chinese and English. 78.90% average F1, outperforming FunASR-Punc (62.77%).

2S: 2nd-generation FireRedASR, now expanded to an all-in-one ASR System

🔥 News

• [2026.03.12] 🔥 We release FireRedASR2S technical report. See arXiv.
• [2026.03.05] 🚀 vLLM supports FireRedASR2-LLM. See vLLM Usage part.
• [2026.02.25] 🔥 We release FireRedASR2-LLM model weights. 🤗 🤖
• [2026.02.13] 🚀 Support TensorRT-LLM inference acceleration for FireRedASR2-AED (contributed by NVIDIA). Benchmark on AISHELL-1 test set shows 12.7x speedup over PyTorch baseline (single H20).
• [2026.02.12] 🔥 We release FireRedASR2S (FireRedASR2-AED, FireRedVAD, FireRedLID, and FireRedPunc) with model weights and inference code. Download links below. Technical report and finetuning code coming soon.

Available Models and Languages

Method

FireRedASR2S: System Overview

FireRedASR2

FireRedASR2 builds upon FireRedASR with improved accuracy, designed to meet diverse requirements in superior performance and optimal efficiency across various applications. It comprises two variants:

• FireRedASR2-LLM: Designed to achieve state-of-the-art performance and to enable seamless end-to-end speech interaction. It adopts an Encoder-Adapter-LLM framework leveraging large language model (LLM) capabilities.
• FireRedASR2-AED: Designed to balance high performance and computational efficiency and to serve as an effective speech representation module in LLM-based speech models. It utilizes an Attention-based Encoder-Decoder (AED) architecture.

Other Modules

• FireRedVAD: DFSMN-based non-streaming/streaming Voice Activity Detection and Multi-label VAD (mVAD). mVAD can be viewed as a lightweight Audio Event Detection (AED) system specialized for a small set of sound categories (speech/singing/music).
• FireRedLID: Encoder-Decoder-based Spoken Language Identification. See FireRedLID README for language details.
• FireRedPunc: BERT-based Punctuation Prediction.

Quick Start

Setup

1. Create a clean Python environment:

$ conda create --name fireredasr2s python=3.10
$ conda activate fireredasr2s
$ git clone https://github.com/FireRedTeam/FireRedASR2S.git
$ cd FireRedASR2S  # or fireredasr2s

2. Install dependencies and set up PATH and PYTHONPATH:

$ pip install -r requirements.txt
$ export PATH=$PWD/fireredasr2s/:$PATH
$ export PYTHONPATH=$PWD/:$PYTHONPATH

3. Download models:

# Download via ModelScope (recommended for users in China)
pip install -U modelscope
modelscope download --model xukaituo/FireRedASR2-AED --local_dir ./pretrained_models/FireRedASR2-AED
modelscope download --model xukaituo/FireRedVAD --local_dir ./pretrained_models/FireRedVAD
modelscope download --model xukaituo/FireRedLID --local_dir ./pretrained_models/FireRedLID
modelscope download --model xukaituo/FireRedPunc --local_dir ./pretrained_models/FireRedPunc
modelscope download --model xukaituo/FireRedASR2-LLM --local_dir ./pretrained_models/FireRedASR2-LLM

# Download via Hugging Face
pip install -U "huggingface_hub[cli]"
huggingface-cli download FireRedTeam/FireRedASR2-AED --local-dir ./pretrained_models/FireRedASR2