From adcee8828ef5d78b575043954deb662a35e318f7 Mon Sep 17 00:00:00 2001 From: huangmingming <huangmingming@deepscience.cn> Date: 星期一, 30 一月 2023 16:02:54 +0800 Subject: [PATCH] update the minimum size of audio --- README.md | 60 +++++++++++++++++++++++++++++++++++------------------------- 1 files changed, 35 insertions(+), 25 deletions(-) diff --git a/README.md b/README.md index 6dd38b2..25fae21 100644 --- a/README.md +++ b/README.md @@ -2,42 +2,55 @@ # 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� +<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锛乕Model Zoo](docs/modelscope_models.md) -## Highlights -- FunASR supports many types of models, such as, Tranformer, Conformer, [Paraformer](https://arxiv.org/abs/2206.08317). -- A large number of ASR models trained on academic datasets or industrial datasets are open sourced 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 first place on many task in [SpeechIO leaderboard](https://github.com/SpeechColab/Leaderboard) -- FunASR supports large-scale dataset dataloader and multi-GPU training. +## 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... -## Installation(Training and Developing) +## 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. -- Clone the repo: -``` sh -git clone https://github.com/alibaba/FunASR.git -``` +## Installation - Install Conda: ``` sh wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh sh Miniconda3-latest-Linux-x86_64.sh +source ~/.bashrc 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 | - +``` sh +pip3 install torch torchvision torchaudio +``` For more versions, please see [https://pytorch.org/get-started/locally](https://pytorch.org/get-started/locally) - Install ModelScope: + +If you are in the area of China, you could set the source to speedup the downloading. + ``` sh -pip install "modelscope[audio]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html +pip config set global.index-url https://mirror.sjtu.edu.cn/pypi/web/simple +``` + +Install or upgrade modelscope. +``` sh +pip install "modelscope[audio]" --upgrade -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html ``` For more details about modelscope, please see [modelscope installation](https://modelscope.cn/docs/%E7%8E%AF%E5%A2%83%E5%AE%89%E8%A3%85) @@ -45,12 +58,9 @@ - Install FunASR and other packages: ``` sh +git clone https://github.com/alibaba/FunASR.git && cd FunASR pip install --editable ./ ``` - -## Pretrained model hub - -We have trained many academic and industrial models, [model hub](docs/modelscope_models.md) ## Contact @@ -58,8 +68,8 @@ - email: [funasr@list.alibaba-inc.com](funasr@list.alibaba-inc.com) -- Dingding group: -<div align="left"><img src="docs/images/dingding.jpg" width="400"/></div> +- Dingding group and Wechat group: +<div align="left"><img src="docs/images/dingding.jpg" width="250"/> <img src="docs/images/wechat.png" width="222"/></div> ## Acknowledge -- Gitblit v1.9.1