FireRedASR: Open-Source Industrial-Grade
Automatic Speech Recognition Models

Kai-Tuo Xu · Feng-Long Xie · Xu Tang · Yao Hu

FireRedASR is a family of open-source industrial-grade automatic speech recognition (ASR) models supporting Mandarin, Chinese dialects and English, achieving a new state-of-the-art (SOTA) on public Mandarin ASR benchmarks, while also offering outstanding singing lyrics recognition capability.

🔥 News

[2025/01/24] We release techincal report, blog, and FireRedASR-AED-L model weights.
[WIP] We plan to release FireRedASR-LLM-L and other model sizes after the Spring Festival.

Method

FireRedASR is designed to meet diverse requirements in superior performance and optimal efficiency across various applications. It comprises two variants:

FireRedASR-LLM: Designed to achieve state-of-the-art (SOTA) performance and to enable seamless end-to-end speech interaction. It adopts an Encoder-Adapter-LLM framework leveraging large language model (LLM) capabilities.
FireRedASR-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.

Evaluation

Results are reported in Character Error Rate (CER%) for Chinese and Word Error Rate (WER%) for English.

Evaluation on Public Mandarin ASR Benchmarks

Model	#Params	aishell1	aishell2	ws_net	ws_meeting	Average-4
FireRedASR-LLM	8.3B	0.76	2.15	4.60	4.67	3.05
FireRedASR-AED	1.1B	0.55	2.52	4.88	4.76	3.18
Seed-ASR	12B+	0.68	2.27	4.66	5.69	3.33
Qwen-Audio	8.4B	1.30	3.10	9.50	10.87	6.19
SenseVoice-L	1.6B	2.09	3.04	6.01	6.73	4.47
Whisper-Large-v3	1.6B	5.14	4.96	10.48	18.87	9.86
Paraformer-Large	0.2B	1.68	2.85	6.74	6.97	4.56

ws means WenetSpeech.

Evaluation on Public Chinese Dialect and English ASR Benchmarks

Test Set	KeSpeech	LibriSpeech test-clean	LibriSpeech test-other
FireRedASR-LLM	3.56	1.73	3.67
FireRedASR-AED	4.48	1.93	4.44
Previous SOTA Results	6.70	1.82	3.50

Usage

Download model files from huggingface and place them in the folder pretrained_models.

Setup

Create a Python environment and install dependencies

$ git clone https://github.com/FireRedTeam/FireRedASR.git
$ conda create --name fireredasr python=3.10
$ pip install -r requirements.txt

Set up Linux PATH and PYTHONPATH

$ export PATH=$PWD/fireredasr/:$PWD/fireredasr/utils/:$PATH
$ export PYTHONPATH=$PWD/:$PYTHONPATH

Convert audio to 16kHz 16-bit PCM format

ffmpeg -i input_audio -ar 16000 -ac 1 -acodec pcm_s16le -f wav output.wav

Quick Start

$ cd examples/
$ bash inference_fireredasr_aed.sh
$ bash inference_fireredasr_llm.sh

Command-line Usage

$ speech2text.py --help
$ speech2text.py --wav_path examples/wav/BAC009S0764W0121.wav --asr_type "aed" --model_dir pretrained_models/FireRedASR-AED-L
$ speech2text.py --wav_path examples/wav/BAC009S0764W0121.wav --asr_type "llm" --model_dir pretrained_models/FireRedASR-LLM-L

Python Usage

from fireredasr.models.fireredasr import FireRedAsr

batch_uttid = ["BAC009S0764W0121"]
batch_wav_path = ["examples/wav/BAC009S0764W0121.wav"]

# FireRedASR-AED
model = FireRedAsr.from_pretrained("aed", "pretrained_models/FireRedASR-AED-L")
results = model.transcribe(
    batch_uttid,
    batch_wav_path,
    {
        "use_gpu": 1,
        "beam_size": 3,
        "nbest": 1,
        "decode_max_len": 0,
        "softmax_smoothing": 1.0,
        "aed_length_penalty": 0.0,
        "eos_penalty": 1.0
    }
)
print(results)


# FireRedASR-LLM
model = FireRedAsr.from_pretrained("llm", "pretrained_models/FireRedASR-LLM-L")
results = model.transcribe(
    batch_uttid,
    batch_wav_path,
    {
        "use_gpu": 1,
        "beam_size": 3,
        "decode_max_len": 0,
        "decode_min_len": 0,
        "repetition_penalty": 1.0,
        "llm_length_penalty": 0.0,
        "temperature": 1.0
    }
)
print(results)

Input Length Limitations

FireRedASR-AED supports audio input up to 60s. Input longer than 60s may cause hallucination issues, and input exceeding 200s will trigger positional encoding errors.
FireRedASR-LLM supports audio input up to 30s. The behavior for longer input is currently unknown.

Acknowledgements

Thanks to the following open-source works:

FireRedASR: Open-Source Industrial-Grade Automatic Speech Recognition Models