From fdf74bb85cfe3dd0ce6cbaf51ec8d5b3ca3d2039 Mon Sep 17 00:00:00 2001 From: 仁迷 <haoneng.lhn@alibaba-inc.com> Date: 星期四, 09 二月 2023 17:18:43 +0800 Subject: [PATCH] update persian model recipe --- README.md | 64 ++++++++++++++++---------------- 1 files changed, 32 insertions(+), 32 deletions(-) diff --git a/README.md b/README.md index b140a20..7b16f58 100644 --- a/README.md +++ b/README.md @@ -1,44 +1,38 @@ -<div align="left"><img src="image/funasr_logo.jpg" width="400"/></div> +<div align="left"><img src="docs/images/funasr_logo.jpg" width="400"/></div> # FunASR: A Fundamental End-to-End Speech Recognition Toolkit <strong>FunASR</strong> hopes to build a bridge between academic research and industrial applications on speech recognition. By supporting the training & finetuning of the industrial-grade speech recognition model released on [ModelScope](https://www.modelscope.cn/models?page=1&tasks=auto-speech-recognition), researchers and developers can conduct research and production of speech recognition models more conveniently, and promote the development of speech recognition ecology. ASR for Fun锛� -## Installation(Training and Developing) +## Release Notes: +### 2023.1.16, funasr-0.1.6 +- We release a new version model [Paraformer-large-long](https://modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary), which integrate the [VAD](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/summary) model, [ASR](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary), + [Punctuation](https://www.modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/summary) model and timestamp together. The model could take in several hours long inputs. +- We release a new type model, [VAD](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/summary), which could predict the duration of none-silence speech. It could be freely integrated with any ASR models in [Model Zoo](docs/modelscope_models.md). +- We release a new type model, [Punctuation](https://www.modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/summary), which could predict the punctuation of ASR models's results. It could be freely integrated with any ASR models in [Model Zoo](docs/modelscope_models.md). +- We release a new model, [Data2vec](https://www.modelscope.cn/models/damo/speech_data2vec_pretrain-zh-cn-aishell2-16k-pytorch/summary), an unsupervised pretraining model which could be finetuned on ASR and other downstream tasks. +- We release a new model, [Paraformer-Tiny](https://www.modelscope.cn/models/damo/speech_paraformer-tiny-commandword_asr_nat-zh-cn-16k-vocab544-pytorch/summary), a lightweight Paraformer model which supports Mandarin command words recognition. +- We release a new type model, [SV](https://www.modelscope.cn/models/damo/speech_xvector_sv-zh-cn-cnceleb-16k-spk3465-pytorch/summary), which could extract speaker embeddings and further perform speaker verification on paired utterances. It will be supported for speaker diarization in the future version. +- We improve the pipeline of modelscope to speedup the inference, by integrating the process of build model into build pipeline. +- Various new types of audio input types are now supported by modelscope inference pipeline, including wav.scp, wav format, audio bytes, wave samples... -- Clone the repo: -``` sh -git clone https://github.com/alibaba/FunASR.git -``` +## Key Features +- Many types of typical models are supported, e.g., [Tranformer](https://arxiv.org/abs/1706.03762), [Conformer](https://arxiv.org/abs/2005.08100), [Paraformer](https://arxiv.org/abs/2206.08317). +- We have released large number of academic and industrial pretrained models on [ModelScope](https://www.modelscope.cn/models?page=1&tasks=auto-speech-recognition) +- The pretrained model [Paraformer-large](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary) obtains the best performance on many tasks in [SpeechIO leaderboard](https://github.com/SpeechColab/Leaderboard) +- FunASR supplies a easy-to-use pipeline to finetune pretrained models from [ModelScope](https://www.modelscope.cn/models?page=1&tasks=auto-speech-recognition) +- Compared to [Espnet](https://github.com/espnet/espnet) framework, the training speed of large-scale datasets in FunASR is much faster owning to the optimized dataloader. -- Install Conda: -``` sh -wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -sh Miniconda3-latest-Linux-x86_64.sh -conda create -n funasr python=3.7 -conda activate funasr -``` - -- Install Pytorch (version >= 1.7.0): - -| cuda | | -|:-----:| --- | -| 9.2 | conda install pytorch==1.7.0 torchvision==0.8.0 torchaudio==0.7.0 cudatoolkit=9.2 -c pytorch | -| 10.2 | conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=10.2 -c pytorch | -| 11.1 | conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=11.1 -c pytorch | - -For more versions, please see https://pytorch.org/get-started/locally/ - -- Install ModelScope: -``` sh -pip install "modelscope[audio]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html -``` - -- Install other packages: +## Installation ``` sh +git clone https://github.com/alibaba/FunASR.git && cd FunASR pip install --editable ./ ``` +For more details, please ref to [installation](https://github.com/alibaba-damo-academy/FunASR/wiki) + +## Usage +For users who are new to FunASR and ModelScope, please refer to [FunASR Docs](https://alibaba-damo-academy.github.io/FunASR/index.html). ## Contact @@ -46,15 +40,21 @@ - email: [funasr@list.alibaba-inc.com](funasr@list.alibaba-inc.com) -- Dingding group: -<div align="left"><img src="image/dingding.jpg" width="400"/></div> +|Dingding group | Wechat group| +|:---:|:---:| +|<div align="left"><img src="docs/images/dingding.jpg" width="250"/> |<img src="docs/images/wechat.png" width="222"/></div>| +## Contributors + +| <div align="left"><img src="docs/images/DeepScience.png" width="250"/> | +|:---:| ## Acknowledge 1. We borrowed a lot of code from [Kaldi](http://kaldi-asr.org/) for data preparation. 2. We borrowed a lot of code from [ESPnet](https://github.com/espnet/espnet). FunASR follows up the training and finetuning pipelines of ESPnet. 3. We referred [Wenet](https://github.com/wenet-e2e/wenet) for building dataloader for large scale data training. +4. We acknowledge [DeepScience](https://www.deepscience.cn) for contributing the grpc service. ## License This project is licensed under the [The MIT License](https://opensource.org/licenses/MIT). FunASR also contains various third-party components and some code modified from other repos under other open source licenses. -- Gitblit v1.9.1