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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/README.md b/README.md index ec78ad9..1fd1c75 100644 --- a/README.md +++ b/README.md @@ -1,30 +1,91 @@ -# FireRedASR +
+

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

-[[Blog]](https://fireredteam.github.io/demos/firered_asr/) -[[Paper]]() +Kai-Tuo Xu ยท Feng-Long Xie ยท Xu Tang ยท Yao Hu + +
+ +[[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. + + +## ๐Ÿ”ฅ 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. -FireRedASR, a family of large-scale automatic speech recognition (ASR) models for Mandarin, designed to meet diverse requirements in superior performance and optimal efficiency across various applications. FireRedASR comprises two variants: + +## 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. ![Model](/assets/FireRedASR_model.png) -## News -- [2025/01/24] ๐Ÿ”ฅ We release [techincal report]()(under review at arXiv), [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 after the Spring Festival. +## 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 | + -## Setup +## Usage +Download model files from [huggingface](https://huggingface.co/fireredteam) and place them in the folder `pretrained_models`. +After downloading models, your `pretrained_models` folder should be like this: +```doctest +. +โ”œโ”€โ”€ FireRedASR-AED-L +โ”‚ โ”œโ”€โ”€ cmvn.ark +โ”‚ โ”œโ”€โ”€ cmvn.txt +โ”‚ โ”œโ”€โ”€ config.yaml +โ”‚ โ”œโ”€โ”€ dict.txt +โ”‚ โ”œโ”€โ”€ model.pth.tar +โ”‚ โ”œโ”€โ”€ README.md +โ”‚ โ””โ”€โ”€ train_bpe1000.model +โ””โ”€โ”€ README.md +``` +### 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 ``` -## Usage -Download model files from [huggingface](https://huggingface.co/fireredteam) and place them in the folder `pretrained_models` +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 @@ -33,11 +94,8 @@ $ bash inference_fireredasr_aed.sh $ bash inference_fireredasr_llm.sh ``` -### Commond-line Usage +### Command-line Usage ```bash -# Setup PATH & PYTHONPATH -$ export PATH=$PWD/fireredasr/:$PWD/fireredasr/utils/:$PATH -$ export PYTHONPATH=$PWD/:$PYTHONPATH $ 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 @@ -56,13 +114,13 @@ 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 + "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) @@ -74,18 +132,22 @@ 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 + "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: diff --git a/requirements.txt b/requirements.txt index 8cd8a3a..5cf51e6 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,5 +3,5 @@ kaldiio>=2.18.0 kaldi_native_fbank>=1.15 numpy>=1.26.1 peft>=0.13.2 -torch>=2.0.0 +torch>=2.0.0,<2.6.0 transformers>=4.46.3