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https://github.com/modelscope/FunASR
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@ -1,3 +1,5 @@
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([简体中文](./docker_zh.md)|English)
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# Docker
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## Install Docker
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72
docs/installation/docker_zh.md
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72
docs/installation/docker_zh.md
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(简体中文|[English](./docker.md))
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# Docker
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## 安装Docker
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### Ubuntu
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```shell
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curl -fsSL https://test.docker.com -o test-docker.sh
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sudo sh test-docker.sh
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```
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### Debian
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```shell
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curl -fsSL https://get.docker.com -o get-docker.sh
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sudo sh get-docker.sh
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```
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### CentOS
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```shell
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curl -fsSL https://get.docker.com | bash -s docker --mirror Aliyun
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```
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### MacOS
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```shell
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brew install --cask --appdir=/Applications docker
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```
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### Windows
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请参考[文档](https://docs.docker.com/desktop/install/windows-install/)
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## 启动Docker
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```shell
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sudo systemctl start docker
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```
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## 下载Docker镜像
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### 镜像仓库
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#### CPU
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`registry.cn-hangzhou.aliyuncs.com/funasr_repo/funasr:funasr-runtime-sdk-cpu-0.1.0`
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#### GPU
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`registry.cn-beijing.aliyuncs.com/modelscope-repo/modelscope:ubuntu20.04-cuda11.7.1-py38-torch2.0.1-tf1.15.5-1.7.0`
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### 拉取镜像
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```shell
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sudo docker pull <image-name>:<tag>
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```
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### 查看镜像
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```shell
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sudo docker images
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```
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## 运行Docker
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```shell
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# cpu
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sudo docker run -itd --name funasr -v <local_dir:dir_in_docker> <image-name>:<tag> /bin/bash
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# gpu
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sudo docker run -itd --gpus all --name funasr -v <local_dir:dir_in_docker> <image-name>:<tag> /bin/bash
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sudo docker exec -it funasr /bin/bash
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```
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## 停止Docker
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```shell
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exit
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sudo docker ps
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sudo docker stop funasr
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```
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@ -1,3 +1,5 @@
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([简体中文](./installation_zh.md)|English)
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<p align="left">
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<a href=""><img src="https://img.shields.io/badge/OS-Linux%2C%20Win%2C%20Mac-brightgreen.svg"></a>
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<a href=""><img src="https://img.shields.io/badge/Python->=3.7,<=3.10-aff.svg"></a>
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@ -13,7 +15,7 @@
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wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
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sh Miniconda3-latest-Linux-x86_64.sh
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source ~/.bashrc
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conda create -n funasr python=3.7
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conda create -n funasr python=3.8
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conda activate funasr
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```
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#### Mac
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@ -60,7 +62,7 @@ If you want to use the pretrained models in ModelScope, you should install the m
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```shell
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pip3 install -U modelscope
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# For the users in China, you could install with the command:
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# pip3 install -U modelscope -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html -i https://mirror.sjtu.edu.cn/pypi/web/simple
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# pip3 install -U modelscope -i https://mirror.sjtu.edu.cn/pypi/web/simple
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```
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### FQA
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75
docs/installation/installation_zh.md
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75
docs/installation/installation_zh.md
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(简体中文|[English](./installation.md))
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<p align="left">
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<a href=""><img src="https://img.shields.io/badge/OS-Linux%2C%20Win%2C%20Mac-brightgreen.svg"></a>
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<a href=""><img src="https://img.shields.io/badge/Python->=3.7,<=3.10-aff.svg"></a>
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<a href=""><img src="https://img.shields.io/badge/Pytorch-%3E%3D1.11-blue"></a>
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</p>
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## 安装
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### 安装Conda（可选）：
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#### Linux
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```sh
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wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
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sh Miniconda3-latest-Linux-x86_64.sh
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source ~/.bashrc
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conda create -n funasr python=3.8
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conda activate funasr
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```
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#### Mac
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```sh
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wget https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh
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# For M1 chip
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# wget https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-arm64.sh
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sh Miniconda3-latest-MacOSX*
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source ~/.zashrc
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conda create -n funasr python=3.8
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conda activate funasr
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```
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#### Windows
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Ref to [docs](https://docs.conda.io/en/latest/miniconda.html#windows-installers)
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### 安装Pytorch（版本 >= 1.11.0）：
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```sh
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pip3 install torch torchaudio
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```
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如果您的环境中存在CUDAs，则应安装与CUDA匹配版本的pytorch，匹配列表可在文档中找到（[文档](https://pytorch.org/get-started/previous-versions/)）。
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### 安装funasr
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#### 从pip安装
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```shell
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pip3 install -U funasr
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# 对于中国大陆用户，可以使用以下命令进行安装：
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# pip3 install -U funasr -i https://mirror.sjtu.edu.cn/pypi/web/simple
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```
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#### 或者从源代码安装
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``` sh
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git clone https://github.com/alibaba/FunASR.git && cd FunASR
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pip3 install -e ./
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# 对于中国大陆用户，可以使用以下命令进行安装：
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# pip3 install -e ./ -i https://mirror.sjtu.edu.cn/pypi/web/simple
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```
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### 安装modelscope（可选）
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如果您想要使用ModelScope中的预训练模型，则应安装modelscope:
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```shell
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pip3 install -U modelscope
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# 对于中国大陆用户，可以使用以下命令进行安装：
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# pip3 install -U modelscope -i https://mirror.sjtu.edu.cn/pypi/web/simple
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```
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### 常见问题解答
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- 在MAC M1芯片上安装时，可能会出现以下错误：
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- - _cffi_backend.cpython-38-darwin.so' (mach-o file, but is an incompatible architecture (have (x86_64), need (arm64e)))
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```shell
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pip uninstall cffi pycparser
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ARCHFLAGS="-arch arm64" pip install cffi pycparser --compile --no-cache-dir
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```
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@ -1,3 +1,5 @@
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([简体中文](./quick_start_zh.md)|English)
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# Quick Start
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> **Note**:
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@ -222,4 +224,3 @@ If you want finetune with multi-GPUs, you could:
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```shell
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CUDA_VISIBLE_DEVICES=1,2 python -m torch.distributed.launch --nproc_per_node 2 finetune.py > log.txt 2>&1
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```
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227
docs/modelscope_pipeline/quick_start_zh.md
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227
docs/modelscope_pipeline/quick_start_zh.md
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(简体中文|[English](./quick_start.md))
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# 快速使用
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> **注意**:
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> modelscope pipeline支持model zoo中的所有模型进行推理和微调。这里我们以typic模型为例来演示用法。
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## 使用pipeline进行推理
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### 语音识别
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#### Paraformer模型
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```python
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from modelscope.pipelines import pipeline
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from modelscope.utils.constant import Tasks
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inference_pipeline = pipeline(
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task=Tasks.auto_speech_recognition,
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model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch',
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)
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rec_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav')
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print(rec_result)
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# {'text': '欢迎大家来体验达摩院推出的语音识别模型'}
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```
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### 语音端点检测
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#### FSMN-VAD模型
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```python
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from modelscope.pipelines import pipeline
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from modelscope.utils.constant import Tasks
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from modelscope.utils.logger import get_logger
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import logging
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logger = get_logger(log_level=logging.CRITICAL)
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logger.setLevel(logging.CRITICAL)
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inference_pipeline = pipeline(
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task=Tasks.voice_activity_detection,
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model='damo/speech_fsmn_vad_zh-cn-16k-common-pytorch',
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)
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segments_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.wav')
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print(segments_result)
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# {'text': [[70, 2340], [2620, 6200], [6480, 23670], [23950, 26250], [26780, 28990], [29950, 31430], [31750, 37600], [38210, 46900], [47310, 49630], [49910, 56460], [56740, 59540], [59820, 70450]]}
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```
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### 标点恢复
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#### CT_Transformer模型
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```python
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from modelscope.pipelines import pipeline
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from modelscope.utils.constant import Tasks
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inference_pipeline = pipeline(
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task=Tasks.punctuation,
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model='damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch',
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)
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rec_result = inference_pipeline(text_in='我们都是木头人不会讲话不会动')
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print(rec_result)
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# {'text': '我们都是木头人，不会讲话，不会动。'}
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```
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### 时间戳预测
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#### TP-Aligner模型
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```python
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from modelscope.pipelines import pipeline
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from modelscope.utils.constant import Tasks
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inference_pipeline = pipeline(
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task=Tasks.speech_timestamp,
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model='damo/speech_timestamp_prediction-v1-16k-offline',)
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rec_result = inference_pipeline(
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audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_timestamps.wav',
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text_in='一 个 东 太 平 洋 国 家 为 什 么 跑 到 西 太 平 洋 来 了 呢',)
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print(rec_result)
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# {'text': '<sil> 0.000 0.380;一 0.380 0.560;个 0.560 0.800;东 0.800 0.980;太 0.980 1.140;平 1.140 1.260;洋 1.260 1.440;国 1.440 1.680;家 1.680 1.920;<sil> 1.920 2.040;为 2.040 2.200;什 2.200 2.320;么 2.320 2.500;跑 2.500 2.680;到 2.680 2.860;西 2.860 3.040;太 3.040 3.200;平 3.200 3.380;洋 3.380 3.500;来 3.500 3.640;了 3.640 3.800;呢 3.800 4.150;<sil> 4.150 4.440;', 'timestamp': [[380, 560], [560, 800], [800, 980], [980, 1140], [1140, 1260], [1260, 1440], [1440, 1680], [1680, 1920], [2040, 2200], [2200, 2320], [2320, 2500], [2500, 2680], [2680, 2860], [2860, 3040], [3040, 3200], [3200, 3380], [3380, 3500], [3500, 3640], [3640, 3800], [3800, 4150]]}
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```
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### 说话人确认
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#### X-vector模型
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```python
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from modelscope.pipelines import pipeline
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from modelscope.utils.constant import Tasks
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import numpy as np
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inference_sv_pipline = pipeline(
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task=Tasks.speaker_verification,
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model='damo/speech_xvector_sv-zh-cn-cnceleb-16k-spk3465-pytorch'
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)
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# embedding extract
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spk_embedding = inference_sv_pipline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/sv_example_enroll.wav')["spk_embedding"]
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# speaker verification
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rec_result = inference_sv_pipline(audio_in=('https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/sv_example_enroll.wav','https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/sv_example_same.wav'))
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print(rec_result["scores"][0])
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# 0.8540499500025098
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```
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### 说话人日志
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#### SOND模型
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```python
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from modelscope.pipelines import pipeline
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from modelscope.utils.constant import Tasks
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inference_diar_pipline = pipeline(
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mode="sond_demo",
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num_workers=0,
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task=Tasks.speaker_diarization,
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diar_model_config="sond.yaml",
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model='damo/speech_diarization_sond-en-us-callhome-8k-n16k4-pytorch',
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model_revision="v1.0.3",
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sv_model="damo/speech_xvector_sv-en-us-callhome-8k-spk6135-pytorch",
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sv_model_revision="v1.0.0",
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)
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audio_list=[
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"https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_data/record.wav",
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"https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_data/spk_A.wav",
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"https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_data/spk_B.wav",
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"https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_data/spk_B1.wav"
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]
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results = inference_diar_pipline(audio_in=audio_list)
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print(results)
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# {'text': 'spk1 [(0.8, 1.84), (2.8, 6.16), (7.04, 10.64), (12.08, 12.8), (14.24, 15.6)]\nspk2 [(0.0, 1.12), (1.68, 3.2), (4.48, 7.12), (8.48, 9.04), (10.56, 14.48), (15.44, 16.0)]'}
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```
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### 常见问题
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#### 使用pipeline进行推理，如何在CPU与GPU进行切换
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The pipeline defaults to decoding with GPU (`ngpu=1`) when GPU is available. If you want to switch to CPU, you could set `ngpu=0`
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```python
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inference_pipeline = pipeline(
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task=Tasks.auto_speech_recognition,
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model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch',
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ngpu=0,
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)
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```
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#### 如何从本地模型进行推理（不联网使用）
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使用modelscope-sdk将模型下载到本地
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```python
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from modelscope.hub.snapshot_download import snapshot_download
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local_dir_root = "./models_from_modelscope"
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model_dir = snapshot_download('damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch', cache_dir=local_dir_root)
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```
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或者使用git将模型下载到本地
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```shell
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git lfs install
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# git clone https://www.modelscope.cn/<namespace>/<model-name>.git
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git clone https://www.modelscope.cn/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch.git
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```
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从下载的本地模型进行推理（可以不联网使用）
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```python
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local_dir_root = "./models_from_modelscope/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
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inference_pipeline = pipeline(
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task=Tasks.auto_speech_recognition,
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model=local_dir_root,
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)
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```
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## 使用pipeline进行微调
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### 语音识别
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#### Paraformer模型
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finetune.py
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```python
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import os
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from modelscope.metainfo import Trainers
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from modelscope.trainers import build_trainer
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from modelscope.msdatasets.audio.asr_dataset import ASRDataset
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def modelscope_finetune(params):
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if not os.path.exists(params.output_dir):
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os.makedirs(params.output_dir, exist_ok=True)
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# dataset split ["train", "validation"]
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ds_dict = ASRDataset.load(params.data_path, namespace='speech_asr')
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kwargs = dict(
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model=params.model,
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data_dir=ds_dict,
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dataset_type=params.dataset_type,
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work_dir=params.output_dir,
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batch_bins=params.batch_bins,
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max_epoch=params.max_epoch,
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lr=params.lr)
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trainer = build_trainer(Trainers.speech_asr_trainer, default_args=kwargs)
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trainer.train()
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if __name__ == '__main__':
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from funasr.utils.modelscope_param import modelscope_args
|
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params = modelscope_args(model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch")
|
||||
params.output_dir = "./checkpoint" # 模型保存路径
|
||||
params.data_path = "speech_asr_aishell1_trainsets" # 数据路径，可以为modelscope中已上传数据，也可以是本地数据
|
||||
params.dataset_type = "small" # 小数据量设置small，若数据量大于1000小时，请使用large
|
||||
params.batch_bins = 2000 # batch size，如果dataset_type="small"，batch_bins单位为fbank特征帧数，如果dataset_type="large"，batch_bins单位为毫秒，
|
||||
params.max_epoch = 50 # 最大训练轮数
|
||||
params.lr = 0.00005 # 设置学习率
|
||||
|
||||
modelscope_finetune(params)
|
||||
```
|
||||
|
||||
```shell
|
||||
python finetune.py &> log.txt &
|
||||
```
|
||||
tail log.txt
|
||||
```
|
||||
[bach-gpu011024008134] 2023-04-23 18:59:13,976 (e2e_asr_paraformer:467) INFO: enable sampler in paraformer, sampling_ratio: 0.75
|
||||
[bach-gpu011024008134] 2023-04-23 18:59:48,924 (trainer:777) INFO: 2epoch:train:1-50batch:50num_updates: iter_time=0.008, forward_time=0.302, loss_att=0.186, acc=0.942, loss_pre=0.005, loss=0.192, backward_time=0.231, optim_step_time=0.117, optim0_lr0=7.484e-06, train_time=0.753
|
||||
[bach-gpu011024008134] 2023-04-23 19:00:23,869 (trainer:777) INFO: 2epoch:train:51-100batch:100num_updates: iter_time=1.152e-04, forward_time=0.275, loss_att=0.184, acc=0.945, loss_pre=0.005, loss=0.189, backward_time=0.234, optim_step_time=0.117, optim0_lr0=7.567e-06, train_time=0.699
|
||||
[bach-gpu011024008134] 2023-04-23 19:00:58,463 (trainer:777) INFO: 2epoch:train:101-150batch:150num_updates: iter_time=1.123e-04, forward_time=0.271, loss_att=0.204, acc=0.942, loss_pre=0.005, loss=0.210, backward_time=0.231, optim_step_time=0.116, optim0_lr0=7.651e-06, train_time=0.692
|
||||
```
|
||||
|
||||
### 常见问题
|
||||
### 多GPU训练
|
||||
|
||||
可以使用下面的指令进行多GPU训练
|
||||
```shell
|
||||
CUDA_VISIBLE_DEVICES=1,2 python -m torch.distributed.launch --nproc_per_node 2 finetune.py > log.txt 2>&1
|
||||
```
|
||||
|
||||
Loading…
Reference in New Issue
Block a user