---
license: apache-2.0
---

<div align="center">
<h1>FireRedASR: Open-Source Industrial-Grade
<br>
Automatic Speech Recognition Models</h1>

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

</div>

[[Code]](https://github.com/FireRedTeam/FireRedASR)
[[Paper]](https://arxiv.org/pdf/2501.14350)
[[Model]](https://huggingface.co/fireredteam)
[[Blog]](https://fireredteam.github.io/demos/firered_asr/)

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](https://arxiv.org/pdf/2501.14350), [blog](https://fireredteam.github.io/demos/firered_asr/), and [FireRedASR-AED-L](https://huggingface.co/fireredteam/FireRedASR-AED-L/tree/main) 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](https://huggingface.co/fireredteam) and place them in the folder `pretrained_models`.


### Setup
Create a Python environment and install dependencies
```bash
$ 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
```bash
$ cd examples/
$ bash inference_fireredasr_aed.sh
$ bash inference_fireredasr_llm.sh
```

### Command-line Usage
```bash
$ 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
```python
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:
- [Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct)
- [icefall/ASR_LLM](https://github.com/k2-fsa/icefall/tree/master/egs/speech_llm/ASR_LLM)
- [WeNet](https://github.com/wenet-e2e/wenet)
- [Speech-Transformer](https://github.com/kaituoxu/Speech-Transformer)