---
title: fireredasr-aed-large-zh-en-onnx-offline-20250124
canonical_url: "https://www.modelscope.cn/models/manyeyes/fireredasr-aed-large-zh-en-onnx-offline-20250124"
md_url: "https://www.modelscope.cn/models/manyeyes/fireredasr-aed-large-zh-en-onnx-offline-20250124.md"
repository: manyeyes/fireredasr-aed-large-zh-en-onnx-offline-20250124
chinese_name: "FireRedASR-AED-L-语音识别-中文普通话-中文方言-英语-onnx模型"
last_updated: 2025-06-10
license: "Apache License 2.0"
pipeline_tag: auto-speech-recognition
tasks:
- auto-speech-recognition
library_name:
- onnx
- pytorch
frameworks:
- Pytorch
downloads: 294
stars: 11
language:
- en
- zh
tags:
- audio
- automatic-speech-recognition
- asr
---
# fireredasr-aed-large-zh-en-onnx-offline-20250124
> fireredasr-aed-large-zh-en-onnx-offline-20250124 - manyeyes 在 ModelScope 开源的模型。FireRedASR是一个开源的工业级自动语音识别（ASR）模型系列，支持普通话、中国方言和英语，在公共普通话ASR基准上实现了最新的技术水平（SOTA），同时还提供了出色的歌词识别能力。FireRedASR AED：旨在平衡高性能和计算效率，并作为基于LLM的语音模型中的有效语音表示模块。它采用基于注意力的编码器-解码器（AED）架构。
manyeyes/fireredasr-aed-large-zh-en-onnx-offline-20250124 是 ModelScope 魔搭社区上的auto-speech-recognition模型，采用 Apache License 2.0 许可。
- **Repository**: manyeyes/fireredasr-aed-large-zh-en-onnx-offline-20250124
- **License**: Apache License 2.0
- **Tasks**: auto-speech-recognition
- **Downloads**: 294
- **Stars**: 11
- **Last updated**: 2025-06-10
Source: https://www.modelscope.cn/models/manyeyes/fireredasr-aed-large-zh-en-onnx-offline-20250124
---
FireRedASR: Open-Source Industrial-Grade
Automatic Speech Recognition Models
[Kai-Tuo Xu](https://github.com/kaituoxu) · [Feng-Long Xie](https://scholar.google.com/citations?user=bi8ExI4AAAAJ&hl=zh-CN&oi=sra) · [Xu Tang](https://scholar.google.com/citations?user=grP24aAAAAAJ&hl=zh-CN&oi=sra) · [Yao Hu](https://scholar.google.com/citations?user=LIu7k7wAAAAJ&hl=zh-CN)
[[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
- [2025/02/17] We release [FireRedASR-LLM-L](https://huggingface.co/fireredteam/FireRedASR-LLM-L/tree/main) model weights.
- [2025/01/24] We release [technical 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.
## 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`.
If you want to use `FireRedASR-LLM-L`, you also need to download [Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct) and place it in the folder `pretrained_models`. Then, go to folder `FireRedASR-LLM-L` and run `$ ln -s ../Qwen2-7B-Instruct`
### 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)
```
## Usage Tips
### Batch Beam Search
- When performing batch beam search with FireRedASR-LLM, please ensure that the input lengths of the utterances are similar. If there are significant differences in utterance lengths, shorter utterances may experience repetition issues. You can either sort your dataset by length or set `batch_size` to 1 to avoid the repetition issue.
### 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)
#### 您可以通过如下git clone命令，或者ModelScope SDK来下载模型
SDK下载
```bash
#安装ModelScope
pip install modelscope
```
```python
#SDK模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('manyeyes/fireredasr-aed-large-zh-en-onnx-offline-20250124')
```
Git下载
```
#Git模型下载
git clone https://www.modelscope.cn/manyeyes/fireredasr-aed-large-zh-en-onnx-offline-20250124.git
```
如果您是本模型的贡献者，我们邀请您根据模型贡献文档，及时完善模型卡片内容。