c 网站开发,有源代码怎么做网站,手机管理网站模板下载,冠县快搜网站建设有限公司20240126请问在ubuntu20.04.6下让GTX1080显卡让whisper工作在large模式下#xff1f; 2024/1/26 21:19
问GTX1080模式使用large该如何配置呢#xff1f; 这个问题没有完成#xff0c;可能需要使用使用显存更大的显卡了#xff01; 比如GTX1080Ti 11GB#xff0c;更猛的可…20240126请问在ubuntu20.04.6下让GTX1080显卡让whisper工作在large模式下 2024/1/26 21:19
问GTX1080模式使用large该如何配置呢 这个问题没有完成可能需要使用使用显存更大的显卡了 比如GTX1080Ti 11GB更猛的可以选择RTX2080TI 22GB了以下四种large模式都异常了 large large-v1 large-v2 large-v3 rootrootrootroot-X99-Turbo:~$ rootrootrootroot-X99-Turbo:~$ watch -n 2 nvidia-smirootrootrootroot-X99-Turbo:~$ whereis whisper whisper: /home/rootroot/.local/bin/whisper rootrootrootroot-X99-Turbo:~$
rootrootroot-X99-Turbo:/# rootrootroot-X99-Turbo:/# find . -name whisper ./usr/lib/x86_64-linux-gnu/espeak-ng-data/voices/!v/whisper ./home/rootroot/.cache/whisper./home/rootroot/.local/bin/whisper ./home/rootroot/.local/lib/python3.8/site-packages/whisper ./home/rootroot/3TB/76Android11.0/out3/.path/whisper ./home/rootroot/3TB/76Android11.0/out/.path/whisper find: ‘./run/user/1000/gvfs’: Permission denied rootrootroot-X99-Turbo:/# rootrootroot-X99-Turbo:/# whereis whisper whisper: rootrootroot-X99-Turbo:/# rootrootroot-X99-Turbo:/# https://www.bilibili.com/read/cv29388784/?jump_opus1 【教程】利用whisper模型自动生成英文粗字幕
运行环境 硬件 NVIDIA GeForce 3090 GPU with 24GB VRAM
该模型理论上也能在CPU环境下运行但极慢。GPU运行也需要占用较大显存。官方提供了多种规模的变体所需显存从1GB-10GB不等如下图
软件 Ubuntu 18.04
理论上来说Windows和MacOS也是支持的不过我没有尝试过
PyTorch 1.11.1
官方说的是在1.10.1上训练的不过这个影响不大
操作步骤 克隆项目仓库 git clone https://github.com/openai/whisper.git 从源码安装Python包 pip install . 命令行使用 whisper audio.aac --model large-v3 --device cuda whisper chs.mp4 --model large-v3 --device cuda
rootrootrootroot-X99-Turbo:~/chs/large$ whisper chs.mp4 --model large-v3 --device cuda Traceback (most recent call last): File /home/rootroot/.local/bin/whisper, line 31, in module sys.exit(cli()) File /home/rootroot/.local/lib/python3.8/site-packages/whisper/transcribe.py, line 458, in cli model load_model(model_name, devicedevice, download_rootmodel_dir) File /home/rootroot/.local/lib/python3.8/site-packages/whisper/__init__.py, line 156, in load_model return model.to(device) File /home/rootroot/.local/lib/python3.8/site-packages/torch/nn/modules/module.py, line 1160, in to return self._apply(convert) File /home/rootroot/.local/lib/python3.8/site-packages/torch/nn/modules/module.py, line 810, in _apply module._apply(fn) File /home/rootroot/.local/lib/python3.8/site-packages/torch/nn/modules/module.py, line 810, in _apply module._apply(fn) File /home/rootroot/.local/lib/python3.8/site-packages/torch/nn/modules/module.py, line 810, in _apply module._apply(fn) [Previous line repeated 2 more times] File /home/rootroot/.local/lib/python3.8/site-packages/torch/nn/modules/module.py, line 833, in _apply param_applied fn(param) File /home/rootroot/.local/lib/python3.8/site-packages/torch/nn/modules/module.py, line 1158, in convert return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking) torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 20.00 MiB. GPU 0 has a total capacty of 7.92 GiB of which 22.75 MiB is free. Including non-PyTorch memory, this process has 7.54 GiB memory in use. Of the allocated memory 7.09 GiB is allocated by PyTorch, and 351.95 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF rootrootrootroot-X99-Turbo:~/chs/large$ rootrootrootroot-X99-Turbo:~/chs/large$ https://www.bilibili.com/read/cv27732514/ OpenAI 发布新版开源语音识别模型 whisper-large-v3 https://zhuanlan.zhihu.com/p/618140077 ChatGPT开源的whisper音频生成字幕可本地搭建环境运行效果质量很棒
Model large-v2 #param [tiny.en, tiny, base.en, base, small.en, small, medium.en, medium, large, large-v2]
https://blog.csdn.net/lusing/article/details/132032965 2023年的深度学习入门指南(24) - 处理音频的大模型 OpenAI Whisper
我们还可以用model参数来选择模型比如有10GB以上显存就可以选择使用large模型 whisper va2.mp3 --model large --language Chinese 默认是small模型。还可以选择tiny, base, medium, large-v1和large-v2.
百度UBUNTU 显存占用 https://www.bmabk.com/index.php/post/162904.html Ubuntu显卡占用情况实时监控
每隔2s刷新一次内存使用情况 watch -n 2 free -m watch -n 1 free -m watch -n 0.5 free -m https://blog.csdn.net/weixin_44554475/article/details/102909308 ubuntu实时显示网速cpu占用和内存占用率
1、ubuntu实时显示网速cpu占用率和内存占用率参考博客 https://www.cnblogs.com/hjw1/p/7901048.html
2、ubuntu实时显示显存使用率 此处的2表示没2秒显示一次显存情况
watch -n 2 nvidia-smi
3、安装htop查看内存情况 安装sudo apt-get install htop 启动 htop
4 ubuntu config clash for windows https://hiif.ong/clash https://blog.csdn.net/N1CROWN/article/details/122662706?utm_mediumdistribute.pc_relevant.none-task-blog-2~default~baidujs_baidulandingword~default-0-122662706-blog-102909308.235^v43^pc_blog_bottom_relevance_base1spm1001.2101.3001.4242.1utm_relevant_index3 Ubuntu16.04 标题栏显示实时网速、CPU使用率
sudo apt-get install python3-psutil curl git gir1.2-appindicator3-0.1
cd indicator-sysmonitor sudo make install nohup indicator-sysmonitor https://www.toutiao.com/article/7315080543987597864/?appnews_articletimestamp1706252345use_new_style1req_id2024012614590561ABBE53940F817BA3B3group_id7315080543987597864tt_frommobile_qqutm_sourcemobile_qqutm_mediumtoutiao_androidutm_campaignclient_shareshare_tokene7d4aa95-92fe-45b6-9dc3-6570888672absourcem_redirect Distil Whisper开源语音识别比Whisper更快更小更准
https://blog.csdn.net/zcxey2911/article/details/134202112?spm1001.2101.3001.4242.3utm_mediumdistribute.wap_relevant.none-task-blog-2~default~baidujs_baidulandingword~default-4-134202112-blog-130588477.237%5Ev3%5Ewap_relevant_t0_downloadshare_token70d15c8b-cc0b-4ca6-8e5b-31a19ce3c062 持续进化快速转录Faster-Whisper对视频进行双语字幕转录实践(Python3.10) https://blog.csdn.net/qq_48424581/article/details/134113540?share_token53aba00d-104f-4b3b-be19-4da75f7897d7 3.6 模型的选择参考如下 _MODELS { tiny.en: https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt, tiny: https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt, base.en: https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt, base: https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt, small.en: https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt, small: https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt, medium.en: https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt, medium: https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt, large-v1: https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large-v1.pt, large-v2: https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt, large: https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt, } https://www.bilibili.com/read/cv20881630/ 免费离线语音识别神器whisper安装教程
补充说明上图中CUDA 11.6和CUDA 11.7都是gpu版本的软件我一开始下载的也是gpu版本的但是因为我的电脑显卡的显存比较低运行whisper模型的时候大模型运行不了。下图是whisper官方给出的运行模型所需显存。
我的显存是4GB一旦使用whisper运行small模式以上的模型就会报显存不足的错误。为了能运行更大的模型以保证语音识别较高的准确率我最终只能选择安装cpu版本。 作者1590856 https://www.bilibili.com/read/cv20881630/ 出处bilibili
当然还有其他的模型可供选择可以在命令行运行whisper --help查看帮助。有以下11种模式可供选择。
[--model {tiny.en,tiny,base.en,base,small.en,small,medium.en,medium,large-v1,large-v2,large}] 作者1590856 https://www.bilibili.com/read/cv20881630/ 出处bilibili
https://blog.csdn.net/nikolay/article/details/128951413?share_token92623f2c-9ed4-483e-9c79-8fcf83f08221 使用openai-whisper 语音转文字
使用CUDA 执行如下指令安装带cuda 的pytorch
pip uninstall torch pip cache purge pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu116 --device cuda 使用device参数 指定 cuda
whisper 屋顶.mp3 --language zh --model small --device cuda --initial_prompt 以下是普通话的句子。
