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update readme and add chinese readme
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wenmeng.zwm
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README.md
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README.md
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<p align="center">
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<br>
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<img src="https://modelscope.oss-cn-beijing.aliyuncs.com/logo.png" width="400"/>
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<br>
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<p>
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<div align="center">
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[](https://pypi.org/project/modelscope/)
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@@ -12,6 +18,13 @@
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<!-- [](https://GitHub.com/modelscope/modelscope/graphs/contributors/) -->
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<!-- [](http://makeapullrequest.com) -->
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<h4 align="center">
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<p>
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<b>English</b> |
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<a href="https://github.com/modelscope/modelscope/blob/master/README_zh.md">中文</a> |
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<p>
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</h4>
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</div>
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@@ -23,20 +36,225 @@ The Python library offers the layered-APIs necessary for model contributors to i
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Apart from harboring implementations of various models, ModelScope library also enables the necessary interactions with ModelScope backend services, particularly with the Model-Hub and Dataset-Hub. Such interactions facilitate management of various entities (models and datasets) to be performed seamlessly under-the-hood, including entity lookup, version control, cache management, and many others.
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# Models and Online Demos
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ModelScope has open-sourced more than 600 models, covering NLP, CV, Audio, Multi-modality, and AI for Science, etc., and also contains hundreds of SOTA models. Users can enter the modelhub of ModelScope through Zero-threshold online experience, or experience the model in the way of developing a cloud environment.
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Here are some examples:
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NLP:
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[nlp_gpt3_text-generation_2.7B](https://modelscope.cn/models/damo/nlp_gpt3_text-generation_2.7B)
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[ChatYuan-large](https://modelscope.cn/models/ClueAI/ChatYuan-large)
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[mengzi-t5-base](https://modelscope.cn/models/langboat/mengzi-t5-base)
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[nlp_csanmt_translation_en2zh](https://modelscope.cn/models/damo/nlp_csanmt_translation_en2zh)
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[nlp_raner_named-entity-recognition_chinese-base-news](https://modelscope.cn/models/damo/nlp_raner_named-entity-recognition_chinese-base-news)
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[nlp_structbert_word-segmentation_chinese-base](https://modelscope.cn/models/damo/nlp_structbert_word-segmentation_chinese-base)
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[Erlangshen-RoBERTa-330M-Sentiment](https://modelscope.cn/models/fengshenbang/Erlangshen-RoBERTa-330M-Sentiment)
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[nlp_convai_text2sql_pretrain_cn](https://modelscope.cn/models/damo/nlp_convai_text2sql_pretrain_cn)
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Audio:
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[speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch](https://modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch)
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[speech_sambert-hifigan_tts_zh-cn_16k](https://modelscope.cn/models/damo/speech_sambert-hifigan_tts_zh-cn_16k)
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[speech_charctc_kws_phone-xiaoyun](https://modelscope.cn/models/damo/speech_charctc_kws_phone-xiaoyun)
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[u2pp_conformer-asr-cn-16k-online](https://modelscope.cn/models/wenet/u2pp_conformer-asr-cn-16k-online)
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[speech_frcrn_ans_cirm_16k](https://modelscope.cn/models/damo/speech_frcrn_ans_cirm_16k)
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[speech_dfsmn_aec_psm_16k](https://modelscope.cn/models/damo/speech_dfsmn_aec_psm_16k)
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CV:
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[cv_tinynas_object-detection_damoyolo](https://modelscope.cn/models/damo/cv_tinynas_object-detection_damoyolo)
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[cv_unet_person-image-cartoon_compound-models](https://modelscope.cn/models/damo/cv_unet_person-image-cartoon_compound-models)
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[cv_convnextTiny_ocr-recognition-general_damo](https://modelscope.cn/models/damo/cv_convnextTiny_ocr-recognition-general_damo)
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[cv_resnet18_human-detection](https://modelscope.cn/models/damo/cv_resnet18_human-detection)
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[cv_resnet50_face-detection_retinaface](https://modelscope.cn/models/damo/cv_resnet50_face-detection_retinaface)
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[cv_unet_image-matting](https://modelscope.cn/models/damo/cv_unet_image-matting)
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[cv_F3Net_product-segmentation](https://modelscope.cn/models/damo/cv_F3Net_product-segmentation)
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[cv_resnest101_general_recognition](https://modelscope.cn/models/damo/cv_resnest101_general_recognition)
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Multi-Modal:
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[multi-modal_clip-vit-base-patch16_zh](https://modelscope.cn/models/damo/multi-modal_clip-vit-base-patch16_zh)
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[ofa_pretrain_base_zh](https://modelscope.cn/models/damo/ofa_pretrain_base_zh)
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[Taiyi-Stable-Diffusion-1B-Chinese-v0.1](https://modelscope.cn/models/fengshenbang/Taiyi-Stable-Diffusion-1B-Chinese-v0.1)
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[mplug_visual-question-answering_coco_large_en](https://modelscope.cn/models/damo/mplug_visual-question-answering_coco_large_en)
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AI for Science:
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[uni-fold-monomer](https://modelscope.cn/models/DPTech/uni-fold-monomer/summary)
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[uni-fold-multimer](https://modelscope.cn/models/DPTech/uni-fold-multimer/summary)
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# QuickTour
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We provide unified interface for inference using `pipeline`, finetuning and evaluation using `Trainer` for different tasks.
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For any tasks with any type of input(image, text, audio, video...), you need only 3 lines of code to load model and get the inference result as follows:
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```python
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>>> from modelscope.pipelines import pipeline
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>>> word_segmentation = pipeline('word-segmentation',model='damo/nlp_structbert_word-segmentation_chinese-base')
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>>> word_segmentation('今天天气不错，适合出去游玩')
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{'output': '今天 天气 不错 ， 适合 出去 游玩'}
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```
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Given an image, you can use following code to cut out the human.
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```python
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>>> import cv2
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>>> from modelscope.pipelines import pipeline
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>>> portrait_matting = pipeline('portrait-matting')
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>>> result = portrait_matting('https://modelscope.oss-cn-beijing.aliyuncs.com/test/images/image_matting.png')
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>>> cv2.imwrite('result.png', result['output_img'])
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```
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The output image is
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For finetuning and evaluation, you need ten more lines of code to construct dataset and trainer, and by calling `traner.train()` and
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`trainer.evaluate()` you can finish finetuning and evaluating a certain model.
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```python
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>>> from modelscope.metainfo import Trainers
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>>> from modelscope.msdatasets import MsDataset
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>>> from modelscope.trainers import build_trainer
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>>> train_dataset = MsDataset.load('chinese-poetry-collection', split='train'). remap_columns({'text1': 'src_txt'})
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>>> eval_dataset = MsDataset.load('chinese-poetry-collection', split='test').remap_columns({'text1': 'src_txt'})
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>>> max_epochs = 10
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>>> tmp_dir = './gpt3_poetry'
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>>> kwargs = dict(
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model='damo/nlp_gpt3_text-generation_1.3B',
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train_dataset=train_dataset,
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eval_dataset=eval_dataset,
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max_epochs=max_epochs,
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work_dir=tmp_dir)
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>>> trainer = build_trainer(name=Trainers.gpt3_trainer, default_args=kwargs)
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>>> trainer.train()
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```
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# Why should I use ModelScope library
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1. ModelScope library provides a unified way for model inference, training, and evaluation, which is simple to use.
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2. ModelScope library provides interfaces and implementations for different models to access the ModelScope ecosystem. It is compatible with various machine learning frameworks and seamlessly connects model application and development.
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3. There are more than 600 models in ModelScope community, covering CV, speech, NLP, multi-modality and AI for Science, covering more than 60 tasks. It contains nearly a hundred SOTA (industry-leading) models and more than a dozen pre-trained large models, all of which have been open source or open for use.
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# Installation
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ModelScope Library currently supports tensorflow and pytorch deep learning framework for model training and inference, and it is tested and run on Python 3.7+, Pytorch 1.8+, Tensorflow1.15 or Tensorflow2.0+.
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Please refer to [installation](https://modelscope.cn/docs/%E7%8E%AF%E5%A2%83%E5%AE%89%E8%A3%85).
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In order to allow everyone to directly use all the models on the ModelScope platform without configuring the environment, ModelScope provides official docker image for developers who need it. Based on the official image, you can skip all environment installation and configuration and use it directly. Currently, the latest version of the CPU image and GPU image we provide can be obtained from the following address
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# Get Started
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CPU docker image
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```shell
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registry.cn-hangzhou.aliyuncs.com/modelscope-repo/modelscope:ubuntu20.04-py37-torch1.11.0-tf1.15.5-1.3.0
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```
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You can refer to [quick_start](https://modelscope.cn/docs/%E5%BF%AB%E9%80%9F%E5%BC%80%E5%A7%8B) for quick start.
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GPU docker image
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```shell
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registry.cn-hangzhou.aliyuncs.com/modelscope-repo/modelscope:ubuntu20.04-cuda11.3.0-py37-torch1.11.0-tf1.15.5-1.3.0
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```
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We also provide other documentations including:
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Also you can setup your local python environment using pip and conda. We suggest to use [anaconda](https://docs.anaconda.com/anaconda/install/) to create your python environment:
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```shell
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conda create -n modelscope python=3.7
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conda activate modelscope
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```
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Then you can install pytorch or tensorflow according to your model requirements.
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* Install pytorch [doc](https://pytorch.org/get-started/locally/)
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* Install tensorflow [doc](https://www.tensorflow.org/install/pip)
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After installing the necessary framework, you can install modelscope library as follows:
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If you only want to download models and datasets, install modelscope framework
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```shell
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pip install modelscope
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```
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If you want to use multi-modal models:
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```shell
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pip install modelscope[multi-modal]
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```
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If you want to use nlp models:
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```shell
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pip install modelscope[nlp] -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
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```
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If you want to use cv models:
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```shell
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pip install modelscope[cv] -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
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```
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If you want to use audio models:
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```shell
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pip install modelscope[audio] -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
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```
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If you want to use science models:
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```shell
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pip install modelscope[science] -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
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```
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`Notes`:
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1. Currently, some audio-task models only support python3.7, tensorflow1.15.4 Linux environments. Most other models can be installed and used on windows and Mac (x86).
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2. Some models in the audio field use the third-party library SoundFile for wav file processing. On the Linux system, users need to manually install libsndfile of SoundFile([doc link](https://github.com/bastibe/python-soundfile#installation)). On Windows and MacOS, it will be installed automatically without user operation. For example, on Ubuntu, you can use following commands:
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```shell
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sudo apt-get update
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sudo apt-get install libsndfile1
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```
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3. Some models in computer vision need mmcv-full, you can refer to mmcv [installation guide](https://github.com/open-mmlab/mmcv#installation), a minimal installation is as follows:
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```shell
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pip uninstall mmcv # if you have installed mmcv, uninstall it
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pip install -U openmim
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mim install mmcv-full
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```
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# Learn More
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We provide additional documentations including:
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* [More detailed Installation Guide](https://modelscope.cn/docs/%E7%8E%AF%E5%A2%83%E5%AE%89%E8%A3%85)
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* [Introduction to tasks](https://modelscope.cn/docs/%E4%BB%BB%E5%8A%A1%E7%9A%84%E4%BB%8B%E7%BB%8D)
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* [Use pipeline for model inference](https://modelscope.cn/docs/%E6%A8%A1%E5%9E%8B%E7%9A%84%E6%8E%A8%E7%90%86Pipeline)
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* [Finetune example](https://modelscope.cn/docs/%E6%A8%A1%E5%9E%8B%E7%9A%84%E8%AE%AD%E7%BB%83Train)
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* [Finetuning example](https://modelscope.cn/docs/%E6%A8%A1%E5%9E%8B%E7%9A%84%E8%AE%AD%E7%BB%83Train)
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* [Preprocessing of data](https://modelscope.cn/docs/%E6%95%B0%E6%8D%AE%E7%9A%84%E9%A2%84%E5%A4%84%E7%90%86)
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* [Evaluation metrics](https://modelscope.cn/docs/%E6%A8%A1%E5%9E%8B%E7%9A%84%E8%AF%84%E4%BC%B0)
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* [Evaluation](https://modelscope.cn/docs/%E6%A8%A1%E5%9E%8B%E7%9A%84%E8%AF%84%E4%BC%B0)
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* [Contribute your own model to ModelScope](https://modelscope.cn/docs/ModelScope%E6%A8%A1%E5%9E%8B%E6%8E%A5%E5%85%A5%E6%B5%81%E7%A8%8B%E6%A6%82%E8%A7%88)
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# License
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261
README_zh.md
Normal file
261
README_zh.md
Normal file
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<p align="center">
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<br>
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<img src="https://modelscope.oss-cn-beijing.aliyuncs.com/logo.png" width="400"/>
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<br>
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<p>
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<div align="center">
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||||
|
||||
[](https://pypi.org/project/modelscope/)
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<!-- [](https://easy-cv.readthedocs.io/en/latest/) -->
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[](https://github.com/modelscope/modelscope/blob/master/LICENSE)
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[](https://github.com/modelscope/modelscope/issues)
|
||||
[](https://GitHub.com/modelscope/modelscope/pull/)
|
||||
[](https://GitHub.com/modelscope/modelscope/commit/)
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[](https://opensource.alibaba.com/contribution_leaderboard/details?projectValue=modelscope)
|
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|
||||
<!-- [](https://GitHub.com/modelscope/modelscope/graphs/contributors/) -->
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<!-- [](http://makeapullrequest.com) -->
|
||||
|
||||
<h4 align="center">
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||||
<p>
|
||||
<a href="https://github.com/modelscope/modelscope/blob/master/README.md">English</a> |
|
||||
<b>中文</b> |
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<p>
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</h4>
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</div>
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# 简介
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[ModelScope]( https://www.modelscope.cn) 是一个“模型即服务”(MaaS) 平台，旨在汇集来自 AI 社区的最先进的机器学习模型，并简化流程在实际应用中利用 AI 模型。核心 ModelScope 库使开发人员能够通过丰富的 API 设计层执行推理、训练和评估，从而促进跨不同 AI 领域的最先进模型的统一体验。
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||||
Python 库为模型贡献者提供了必要的分层 API，以便将来自 CV、NLP、语音、多模态以及科学计算的模型集成到 ModelScope 生态系统中。所有这些不同模型的实现都以一种允许轻松统一访问的方式封装在库中。通过这种集成，只需几行代码即可完成模型推理、微调和评估。同时，提供了灵活性，以便在必要时也可以自定义模型应用程序中的不同组件。
|
||||
|
||||
除了包含各种模型的实现之外，ModelScope 库还支持与 ModelScope 后端服务进行必要的交互，特别是与 Model-Hub 和 Dataset-Hub 的交互。这种交互促进了各种实体（模型和数据集）的管理在后台无缝执行，包括实体查找、版本控制、缓存管理等。
|
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|
||||
# 部分模型和在线体验
|
||||
ModelScope开源了600多个模型，涵盖NLP、CV、Audio、多模态、科学计算等，还包含数百个SOTA模型。用户可以进入ModelScope的模型中心零门槛在线体验，或者Notebook方式体验模型。
|
||||
|
||||
示例如下:
|
||||
|
||||
自然语言处理：
|
||||
|
||||
[GPT-3预训练生成模型-中文-2.7B](https://modelscope.cn/models/damo/nlp_gpt3_text-generation_2.7B)
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[元语功能型对话大模型](https://modelscope.cn/models/ClueAI/ChatYuan-large)
|
||||
|
||||
[孟子T5预训练生成模型-中文-base](https://modelscope.cn/models/langboat/mengzi-t5-base)
|
||||
|
||||
[CSANMT连续语义增强机器翻译-英中-通用领域-large](https://modelscope.cn/models/damo/nlp_csanmt_translation_en2zh)
|
||||
|
||||
[RaNER命名实体识别-中文-新闻领域-base](https://modelscope.cn/models/damo/nlp_raner_named-entity-recognition_chinese-base-news)
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||||
|
||||
[BAStructBERT分词-中文-新闻领域-base](https://modelscope.cn/models/damo/nlp_structbert_word-segmentation_chinese-base)
|
||||
|
||||
[二郎神-RoBERTa-330M-情感分类](https://modelscope.cn/models/fengshenbang/Erlangshen-RoBERTa-330M-Sentiment)
|
||||
|
||||
[SPACE-T表格问答预训练模型-中文-通用领域-base](https://modelscope.cn/models/damo/nlp_convai_text2sql_pretrain_cn)
|
||||
|
||||
语音：
|
||||
|
||||
[Paraformer语音识别-中文-通用-16k-离线-large-pytorch](https://modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch)
|
||||
|
||||
[语音合成-中文-多情感领域-16k-多发音人](https://modelscope.cn/models/damo/speech_sambert-hifigan_tts_zh-cn_16k)
|
||||
|
||||
[CTC语音唤醒-移动端-单麦-16k-小云小云](https://modelscope.cn/models/damo/speech_charctc_kws_phone-xiaoyun)
|
||||
|
||||
[WeNet-U2pp_Conformer-语音识别-中文-16k-实时](https://modelscope.cn/models/wenet/u2pp_conformer-asr-cn-16k-online)
|
||||
|
||||
[FRCRN语音降噪-单麦-16k](https://modelscope.cn/models/damo/speech_frcrn_ans_cirm_16k)
|
||||
|
||||
[DFSMN回声消除-单麦单参考-16k](https://modelscope.cn/models/damo/speech_dfsmn_aec_psm_16k)
|
||||
|
||||
|
||||
计算机视觉：
|
||||
|
||||
[DAMOYOLO-高性能通用检测模型-S](https://modelscope.cn/models/damo/cv_tinynas_object-detection_damoyolo)
|
||||
|
||||
[DCT-Net人像卡通化](https://modelscope.cn/models/damo/cv_unet_person-image-cartoon_compound-models)
|
||||
|
||||
[读光-文字识别-行识别模型-中英-通用领域](https://modelscope.cn/models/damo/cv_convnextTiny_ocr-recognition-general_damo)
|
||||
|
||||
[人体检测-通用-Base](https://modelscope.cn/models/damo/cv_resnet18_human-detection)
|
||||
|
||||
[RetinaFace人脸检测关键点模型](https://modelscope.cn/models/damo/cv_resnet50_face-detection_retinaface)
|
||||
|
||||
[BSHM人像抠图](https://modelscope.cn/models/damo/cv_unet_image-matting)
|
||||
|
||||
[图像分割-商品展示图场景的商品分割-电商领域](https://modelscope.cn/models/damo/cv_F3Net_product-segmentation)
|
||||
|
||||
[万物识别-中文-通用领域](https://modelscope.cn/models/damo/cv_resnest101_general_recognition)
|
||||
|
||||
|
||||
多模态：
|
||||
|
||||
[CLIP模型-中文-通用领域-base](https://modelscope.cn/models/damo/multi-modal_clip-vit-base-patch16_zh)
|
||||
|
||||
[OFA预训练模型-中文-通用领域-base](https://modelscope.cn/models/damo/ofa_pretrain_base_zh)
|
||||
|
||||
[太乙-Stable-Diffusion-1B-中文-v0.1](https://modelscope.cn/models/fengshenbang/Taiyi-Stable-Diffusion-1B-Chinese-v0.1)
|
||||
|
||||
[mPLUG视觉问答模型-英文-large](https://modelscope.cn/models/damo/mplug_visual-question-answering_coco_large_en)
|
||||
|
||||
科学计算：
|
||||
|
||||
[Uni-Fold-Monomer 开源的蛋白质单体结构预测模型](https://modelscope.cn/models/DPTech/uni-fold-monomer/summary)
|
||||
|
||||
[Uni-Fold-Multimer 开源的蛋白质复合物结构预测模型](https://modelscope.cn/models/DPTech/uni-fold-multimer/summary)
|
||||
|
||||
# 快速上手
|
||||
|
||||
我们针对不同任务提供了统一的使用接口， 使用`pipeline`进行模型推理、使用`Trainer`进行微调和评估。
|
||||
|
||||
对于任意类型输入（图像、文本、音频、视频...）的任何任务，只需3行代码即可加载模型并获得推理结果，如下所示：
|
||||
```python
|
||||
>>> from modelscope.pipelines import pipeline
|
||||
>>> word_segmentation = pipeline('word-segmentation',model='damo/nlp_structbert_word-segmentation_chinese-base')
|
||||
>>> word_segmentation('今天天气不错，适合出去游玩')
|
||||
{'output': '今天 天气 不错 ， 适合 出去 游玩'}
|
||||
```
|
||||
|
||||
给定一张图片，你可以使用如下代码进行人像抠图.
|
||||
|
||||
|
||||
|
||||
```python
|
||||
>>> import cv2
|
||||
>>> from modelscope.pipelines import pipeline
|
||||
|
||||
>>> portrait_matting = pipeline('portrait-matting')
|
||||
>>> result = portrait_matting('https://modelscope.oss-cn-beijing.aliyuncs.com/test/images/image_matting.png')
|
||||
>>> cv2.imwrite('result.png', result['output_img'])
|
||||
```
|
||||
输出图像如下
|
||||
|
||||
|
||||
对于微调和评估模型， 你需要通过十多行代码构建dataset和trainer，调用`trainer.train()`和`trainer.evaluate()`即可.
|
||||
|
||||
```python
|
||||
>>> from modelscope.metainfo import Trainers
|
||||
>>> from modelscope.msdatasets import MsDataset
|
||||
>>> from modelscope.trainers import build_trainer
|
||||
|
||||
>>> train_dataset = MsDataset.load('chinese-poetry-collection', split='train'). remap_columns({'text1': 'src_txt'})
|
||||
>>> eval_dataset = MsDataset.load('chinese-poetry-collection', split='test').remap_columns({'text1': 'src_txt'})
|
||||
>>> max_epochs = 10
|
||||
>>> tmp_dir = './gpt3_poetry'
|
||||
|
||||
>>> kwargs = dict(
|
||||
model='damo/nlp_gpt3_text-generation_1.3B',
|
||||
train_dataset=train_dataset,
|
||||
eval_dataset=eval_dataset,
|
||||
max_epochs=max_epochs,
|
||||
work_dir=tmp_dir)
|
||||
|
||||
>>> trainer = build_trainer(name=Trainers.gpt3_trainer, default_args=kwargs)
|
||||
>>> trainer.train()
|
||||
```
|
||||
|
||||
# 为什么要用ModelScope library
|
||||
|
||||
1. 魔搭ModelScope Library针对模型推理、训练、评估提供了统一的接口，开箱即用。
|
||||
|
||||
2. 魔搭ModelScope Library提供了不同模型接入ModelScope生态的接口和实现，底层能够兼容各种机器学习框架，无缝衔接模型应用与开发。
|
||||
|
||||
3. 魔搭ModelScope社区上架模型超过600个，覆盖了视觉、语音、自然语言处理、多模态等AI主要领域，覆盖任务超过60个。其中包含近百个SOTA（业界领先）模型和十多个预训练大模型，均已开源代码或开放使用。 。
|
||||
|
||||
|
||||
# 安装
|
||||
ModelScope Library目前支持tensorflow，pytorch深度学习框架进行模型训练、推理， 在Python 3.7+, Pytorch 1.8+, Tensorflow1.15/Tensorflow2.0+测试可运行。
|
||||
|
||||
为了让大家能直接用上ModelScope平台上的所有模型，无需配置环境，ModelScope除了在网站上集成了Notebook在线编程环境以外，同时也提供了官方镜像，方便有需要的开发者获取。基于官方镜像，可以跳过所有的环境安装和配置，直接使用，当前我们提供的最新版本的CPU镜像和GPU镜像可从如下地址获取：
|
||||
|
||||
CPU镜像
|
||||
```shell
|
||||
registry.cn-hangzhou.aliyuncs.com/modelscope-repo/modelscope:ubuntu20.04-py37-torch1.11.0-tf1.15.5-1.3.0
|
||||
```
|
||||
|
||||
GPU镜像
|
||||
```shell
|
||||
registry.cn-hangzhou.aliyuncs.com/modelscope-repo/modelscope:ubuntu20.04-cuda11.3.0-py37-torch1.11.0-tf1.15.5-1.3.0
|
||||
```
|
||||
|
||||
你也可以使用pip和conda搭建本地python环境，我们推荐使用[Anaconda](https://docs.anaconda.com/anaconda/install/)，安装完成后，执行如下命令为modelscope library创建对应的python环境：
|
||||
```shell
|
||||
conda create -n modelscope python=3.7
|
||||
conda activate modelscope
|
||||
```
|
||||
|
||||
接下来根据所需使用的模型依赖安装底层计算框架
|
||||
* 安装Pytorch [文档链接](https://pytorch.org/get-started/locally/)
|
||||
* 安装tensorflow [文档链接](https://www.tensorflow.org/install/pip)
|
||||
|
||||
|
||||
安装完前置依赖，你可以按照如下方式安装ModelScope Library。
|
||||
|
||||
ModelScope Libarary由核心框架，以及不同领域模型的对接组件组成。如果只需要ModelScope模型和数据集访问等基础能力，可以只安装ModelScope的核心框架：
|
||||
```shell
|
||||
pip install modelscope
|
||||
```
|
||||
|
||||
如仅需体验多模态领域的模型，可执行如下命令安装领域依赖：
|
||||
```shell
|
||||
pip install modelscope[multi-modal]
|
||||
```
|
||||
|
||||
如仅需体验NLP领域模型，可执行如下命令安装领域依赖（因部分依赖由ModelScope独立host，所以需要使用"-f"参数）：
|
||||
```shell
|
||||
pip install modelscope[nlp] -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
|
||||
```
|
||||
|
||||
If you want to use cv models:
|
||||
```shell
|
||||
pip install modelscope[cv] -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
|
||||
```
|
||||
|
||||
如仅需体验语音领域模型，可执行如下命令安装领域依赖（因部分依赖由ModelScope独立host，所以需要使用"-f"参数）：
|
||||
```shell
|
||||
pip install modelscope[audio] -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
|
||||
```
|
||||
|
||||
`注意`：当前大部分语音模型需要在Linux环境上使用，并且推荐使用python3.7 + tensorflow 1.x的组合。
|
||||
|
||||
如仅需体验科学计算领域模型，可执行如下命令安装领域依赖（因部分依赖由ModelScope独立host，所以需要使用"-f"参数）：
|
||||
```shell
|
||||
pip install modelscope[science] -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
|
||||
```
|
||||
|
||||
`注`:
|
||||
1. 目前部分语音相关的模型仅支持 python3.7,tensorflow1.15.4的Linux环境使用。 其他绝大部分模型可以在windows、mac（x86）上安装使用。.
|
||||
|
||||
2. 语音领域中一部分模型使用了三方库SoundFile进行wav文件处理，在Linux系统上用户需要手动安装SoundFile的底层依赖库libsndfile，在Windows和MacOS上会自动安装不需要用户操作。详细信息可参考[SoundFile 官网](https://github.com/bastibe/python-soundfile#installation)。以Ubuntu系统为例，用户需要执行如下命令:
|
||||
```shell
|
||||
sudo apt-get update
|
||||
sudo apt-get install libsndfile1
|
||||
```
|
||||
|
||||
3. CV领域的少数模型，需要安装mmcv-full， 如果运行过程中提示缺少mmcv，请参考mmcv[安装手册](https://github.com/open-mmlab/mmcv#installation)进行安装。 这里提供一个最简版的mmcv-full安装步骤，但是要达到最优的mmcv-full的安装效果（包括对于cuda版本的兼容），请根据自己的实际机器环境，以mmcv官方安装手册为准。
|
||||
```shell
|
||||
pip uninstall mmcv # if you have installed mmcv, uninstall it
|
||||
pip install -U openmim
|
||||
mim install mmcv-full
|
||||
```
|
||||
|
||||
|
||||
# 更多教程
|
||||
|
||||
除了上述内容，我们还提供如下信息：
|
||||
* [更加详细的安装文档](https://modelscope.cn/docs/%E7%8E%AF%E5%A2%83%E5%AE%89%E8%A3%85)
|
||||
* [任务的介绍](https://modelscope.cn/docs/%E4%BB%BB%E5%8A%A1%E7%9A%84%E4%BB%8B%E7%BB%8D)
|
||||
* [模型推理](https://modelscope.cn/docs/%E6%A8%A1%E5%9E%8B%E7%9A%84%E6%8E%A8%E7%90%86Pipeline)
|
||||
* [模型微调](https://modelscope.cn/docs/%E6%A8%A1%E5%9E%8B%E7%9A%84%E8%AE%AD%E7%BB%83Train)
|
||||
* [数据预处理](https://modelscope.cn/docs/%E6%95%B0%E6%8D%AE%E7%9A%84%E9%A2%84%E5%A4%84%E7%90%86)
|
||||
* [模型评估](https://modelscope.cn/docs/%E6%A8%A1%E5%9E%8B%E7%9A%84%E8%AF%84%E4%BC%B0)
|
||||
* [贡献模型到ModelScope](https://modelscope.cn/docs/ModelScope%E6%A8%A1%E5%9E%8B%E6%8E%A5%E5%85%A5%E6%B5%81%E7%A8%8B%E6%A6%82%E8%A7%88)
|
||||
|
||||
# License
|
||||
|
||||
本项目使用[Apache License (Version 2.0)](https://github.com/modelscope/modelscope/blob/master/LICENSE).
|
||||
Reference in New Issue
Block a user