浏览网站模板,网站菜单怎么做,域名会跳转怎么进原网站,做一个网页的流程目录20240131在ubuntu20.04.6下配置whisper 2024/1/31 15:48 首先你要有一张NVIDIA的显卡#xff0c;比如我用的PDD拼多多的二手GTX1080显卡。【并且极其可能是矿卡#xff01;】800#xffe5; 2、请正确安装好NVIDIA最新的驱动程序和CUDA。可选安装#xff01; 3、配置whispe…20240131在ubuntu20.04.6下配置whisper 2024/1/31 15:48 首先你要有一张NVIDIA的显卡比如我用的PDD拼多多的二手GTX1080显卡。【并且极其可能是矿卡】800 2、请正确安装好NVIDIA最新的驱动程序和CUDA。可选安装 3、配置whisper rootrootrootroot-X99-Turbo:~$ rootrootrootroot-X99-Turbo:~$ python -m pip install --upgrade pip 【可以不安装conda】 rootrootrootroot-X99-Turbo:~$ wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh rootrootrootroot-X99-Turbo:~$ ffmpeg rootrootrootroot-X99-Turbo:~$ pip install -U openai-whisper rootrootrootroot-X99-Turbo:~$ pip install tiktoken rootrootrootroot-X99-Turbo:~$ pip install setuptools-rust rootrootrootroot-X99-Turbo:~$ whisper audio.mp3 --model medium --language Chinese rootrootrootroot-X99-Turbo:~$ whisper chi.mp4 --model medium --language Chinese rootrootrootroot-X99-Turbo:~$ sudo apt-get install ffmpeg rootrootrootroot-X99-Turbo:~$ time(whisper chs.mp4 --model medium --language Chinese) rootrootrootroot-X99-Turbo:~$ rootrootrootroot-X99-Turbo:~$ rootrootrootroot-X99-Turbo:~$ python -m pip install --upgrade pip Collecting pip Downloading pip-23.3.2-py3-none-any.whl (2.1 MB) |████████████████████████████████| 2.1 MB 690 kB/s Installing collected packages: pip Successfully installed pip-23.3.2 rootrootrootroot-X99-Turbo:~$ rootrootrootroot-X99-Turbo:~$ rootrootrootroot-X99-Turbo:~$ sudo mkdir /opt/tools rootrootrootroot-X99-Turbo:~$ cd /opt/tools/ rootrootrootroot-X99-Turbo:/opt/tools$ rootrootrootroot-X99-Turbo:/opt/tools$ ll total 8 drwxr-xr-x 2 root root 4096 1月 26 12:21 ./ drwxr-xr-x 4 root root 4096 1月 26 12:21 ../ rootrootrootroot-X99-Turbo:/opt/tools$ rootrootrootroot-X99-Turbo:/opt/tools$ cd ~ rootrootrootroot-X99-Turbo:~$ rootrootrootroot-X99-Turbo:~$ rootrootrootroot-X99-Turbo:~$ wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh --2024-01-26 12:22:28-- https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh Resolving repo.anaconda.com (repo.anaconda.com)... 104.16.130.3, 104.16.131.3, 2606:4700::6810:8203, ... Connecting to repo.anaconda.com (repo.anaconda.com)|104.16.130.3|:443... connected. HTTP request sent, awaiting response... 200 OK Length: 141613749 (135M) [application/octet-stream] Saving to: ‘Miniconda3-latest-Linux-x86_64.sh’
Miniconda3-latest-Linux-x86_64.sh 100%[] 135.05M 2.82MB/s in 51s
2024-01-26 12:23:20 (2.65 MB/s) - ‘Miniconda3-latest-Linux-x86_64.sh’ saved [141613749/141613749]
rootrootrootroot-X99-Turbo:~$ ffmpeg ffmpeg version 4.2.7-0ubuntu0.1 Copyright (c) 2000-2022 the FFmpeg developers built with gcc 9 (Ubuntu 9.4.0-1ubuntu1~20.04.1) configuration: --prefix/usr --extra-version0ubuntu0.1 --toolchainhardened --libdir/usr/lib/x86_64-linux-gnu --incdir/usr/include/x86_64-linux-gnu --archamd64 --enable-gpl --disable-stripping --enable-avresample --disable-filterresample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librsvg --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opencl --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-nvenc --enable-chromaprint --enable-frei0r --enable-libx264 --enable-shared libavutil 56. 31.100 / 56. 31.100 libavcodec 58. 54.100 / 58. 54.100 libavformat 58. 29.100 / 58. 29.100 libavdevice 58. 8.100 / 58. 8.100 libavfilter 7. 57.100 / 7. 57.100 libavresample 4. 0. 0 / 4. 0. 0 libswscale 5. 5.100 / 5. 5.100 libswresample 3. 5.100 / 3. 5.100 libpostproc 55. 5.100 / 55. 5.100 Hyper fast Audio and Video encoder usage: ffmpeg [options] [[infile options] -i infile]... {[outfile options] outfile}...
Use -h to get full help or, even better, run man ffmpeg rootrootrootroot-X99-Turbo:~$ rootrootrootroot-X99-Turbo:~$ rootrootrootroot-X99-Turbo:~$ rootrootrootroot-X99-Turbo:~$ pip install -U openai-whisper Defaulting to user installation because normal site-packages is not writeable Requirement already satisfied: openai-whisper in ./.local/lib/python3.8/site-packages (20231117) Requirement already satisfied: triton3,2.0.0 in ./.local/lib/python3.8/site-packages (from openai-whisper) (2.2.0) Requirement already satisfied: numba in ./.local/lib/python3.8/site-packages (from openai-whisper) (0.58.1) Requirement already satisfied: numpy in ./.local/lib/python3.8/site-packages (from openai-whisper) (1.24.4) Requirement already satisfied: torch in ./.local/lib/python3.8/site-packages (from openai-whisper) (2.1.2) Requirement already satisfied: tqdm in ./.local/lib/python3.8/site-packages (from openai-whisper) (4.66.1) Requirement already satisfied: more-itertools in ./.local/lib/python3.8/site-packages (from openai-whisper) (10.2.0) Requirement already satisfied: tiktoken in ./.local/lib/python3.8/site-packages (from openai-whisper) (0.5.2) Requirement already satisfied: filelock in ./.local/lib/python3.8/site-packages (from triton3,2.0.0-openai-whisper) (3.13.1) Requirement already satisfied: llvmlite0.42,0.41.0dev0 in ./.local/lib/python3.8/site-packages (from numba-openai-whisper) (0.41.1) Requirement already satisfied: importlib-metadata in ./.local/lib/python3.8/site-packages (from numba-openai-whisper) (7.0.1) Requirement already satisfied: regex2022.1.18 in ./.local/lib/python3.8/site-packages (from tiktoken-openai-whisper) (2023.12.25) Requirement already satisfied: requests2.26.0 in ./.local/lib/python3.8/site-packages (from tiktoken-openai-whisper) (2.31.0) Requirement already satisfied: typing-extensions in ./.local/lib/python3.8/site-packages (from torch-openai-whisper) (4.9.0) Requirement already satisfied: sympy in ./.local/lib/python3.8/site-packages (from torch-openai-whisper) (1.12) Requirement already satisfied: networkx in ./.local/lib/python3.8/site-packages (from torch-openai-whisper) (3.1) Requirement already satisfied: jinja2 in ./.local/lib/python3.8/site-packages (from torch-openai-whisper) (3.1.3) Requirement already satisfied: fsspec in ./.local/lib/python3.8/site-packages (from torch-openai-whisper) (2023.12.2) Requirement already satisfied: nvidia-cuda-nvrtc-cu1212.1.105 in ./.local/lib/python3.8/site-packages (from torch-openai-whisper) (12.1.105) Requirement already satisfied: nvidia-cuda-runtime-cu1212.1.105 in ./.local/lib/python3.8/site-packages (from torch-openai-whisper) (12.1.105) Requirement already satisfied: nvidia-cuda-cupti-cu1212.1.105 in ./.local/lib/python3.8/site-packages (from torch-openai-whisper) (12.1.105) Requirement already satisfied: nvidia-cudnn-cu128.9.2.26 in ./.local/lib/python3.8/site-packages (from torch-openai-whisper) (8.9.2.26) Requirement already satisfied: nvidia-cublas-cu1212.1.3.1 in ./.local/lib/python3.8/site-packages (from torch-openai-whisper) (12.1.3.1) Requirement already satisfied: nvidia-cufft-cu1211.0.2.54 in ./.local/lib/python3.8/site-packages (from torch-openai-whisper) (11.0.2.54) Requirement already satisfied: nvidia-curand-cu1210.3.2.106 in ./.local/lib/python3.8/site-packages (from torch-openai-whisper) (10.3.2.106) Requirement already satisfied: nvidia-cusolver-cu1211.4.5.107 in ./.local/lib/python3.8/site-packages (from torch-openai-whisper) (11.4.5.107) Requirement already satisfied: nvidia-cusparse-cu1212.1.0.106 in ./.local/lib/python3.8/site-packages (from torch-openai-whisper) (12.1.0.106) Requirement already satisfied: nvidia-nccl-cu122.18.1 in ./.local/lib/python3.8/site-packages (from torch-openai-whisper) (2.18.1) Requirement already satisfied: nvidia-nvtx-cu1212.1.105 in ./.local/lib/python3.8/site-packages (from torch-openai-whisper) (12.1.105) Collecting triton3,2.0.0 (from openai-whisper) Downloading triton-2.1.0-0-cp38-cp38-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.3 kB) Requirement already satisfied: nvidia-nvjitlink-cu12 in ./.local/lib/python3.8/site-packages (from nvidia-cusolver-cu1211.4.5.107-torch-openai-whisper) (12.3.101) Requirement already satisfied: charset-normalizer4,2 in ./.local/lib/python3.8/site-packages (from requests2.26.0-tiktoken-openai-whisper) (3.3.2) Requirement already satisfied: idna4,2.5 in /usr/lib/python3/dist-packages (from requests2.26.0-tiktoken-openai-whisper) (2.8) Requirement already satisfied: urllib33,1.21.1 in /usr/lib/python3/dist-packages (from requests2.26.0-tiktoken-openai-whisper) (1.25.8) Requirement already satisfied: certifi2017.4.17 in /usr/lib/python3/dist-packages (from requests2.26.0-tiktoken-openai-whisper) (2019.11.28) Requirement already satisfied: zipp0.5 in ./.local/lib/python3.8/site-packages (from importlib-metadata-numba-openai-whisper) (3.17.0) Requirement already satisfied: MarkupSafe2.0 in ./.local/lib/python3.8/site-packages (from jinja2-torch-openai-whisper) (2.1.3) Requirement already satisfied: mpmath0.19 in ./.local/lib/python3.8/site-packages (from sympy-torch-openai-whisper) (1.3.0) Downloading triton-2.1.0-0-cp38-cp38-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (89.2 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 89.2/89.2 MB 25.9 MB/s eta 0:00:00 Installing collected packages: triton Attempting uninstall: triton Found existing installation: triton 2.2.0 Uninstalling triton-2.2.0: Successfully uninstalled triton-2.2.0 Successfully installed triton-2.1.0 rootrootrootroot-X99-Turbo:~$ rootrootrootroot-X99-Turbo:~$ rootrootrootroot-X99-Turbo:~$ rootrootrootroot-X99-Turbo:~$ rootrootrootroot-X99-Turbo:~$ pip install tiktoken Defaulting to user installation because normal site-packages is not writeable Requirement already satisfied: tiktoken in ./.local/lib/python3.8/site-packages (0.5.2) Requirement already satisfied: regex2022.1.18 in ./.local/lib/python3.8/site-packages (from tiktoken) (2023.12.25) Requirement already satisfied: requests2.26.0 in ./.local/lib/python3.8/site-packages (from tiktoken) (2.31.0) Requirement already satisfied: charset-normalizer4,2 in ./.local/lib/python3.8/site-packages (from requests2.26.0-tiktoken) (3.3.2) Requirement already satisfied: idna4,2.5 in /usr/lib/python3/dist-packages (from requests2.26.0-tiktoken) (2.8) Requirement already satisfied: urllib33,1.21.1 in /usr/lib/python3/dist-packages (from requests2.26.0-tiktoken) (1.25.8) Requirement already satisfied: certifi2017.4.17 in /usr/lib/python3/dist-packages (from requests2.26.0-tiktoken) (2019.11.28) rootrootrootroot-X99-Turbo:~$ rootrootrootroot-X99-Turbo:~$ rootrootrootroot-X99-Turbo:~$ pip install setuptools-rust Defaulting to user installation because normal site-packages is not writeable Requirement already satisfied: setuptools-rust in ./.local/lib/python3.8/site-packages (1.8.1) Requirement already satisfied: setuptools62.4 in ./.local/lib/python3.8/site-packages (from setuptools-rust) (69.0.3) Requirement already satisfied: semantic-version3,2.8.2 in ./.local/lib/python3.8/site-packages (from setuptools-rust) (2.10.0) Requirement already satisfied: tomli1.2.1 in ./.local/lib/python3.8/site-packages (from setuptools-rust) (2.0.1) rootrootrootroot-X99-Turbo:~$ sudo apt update sudo apt install ffmpeg Get:1 file:/var/cuda-repo-ubuntu2004-12-0-local InRelease [1,575 B] Get:2 file:/var/cuda-repo-ubuntu2004-12-3-local InRelease [1,572 B] Get:1 file:/var/cuda-repo-ubuntu2004-12-0-local InRelease [1,575 B] Get:2 file:/var/cuda-repo-ubuntu2004-12-3-local InRelease [1,572 B] Hit:3 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal InRelease Hit:4 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal-updates InRelease Hit:5 http://mirrors.tuna.tsinghua.edu.cn/ubuntu focal-backports InRelease Hit:6 http://security.ubuntu.com/ubuntu focal-security InRelease Hit:7 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu focal InRelease Reading package lists... Done Building dependency tree Reading state information... Done 30 packages can be upgraded. Run apt list --upgradable to see them. Reading package lists... Done Building dependency tree Reading state information... Done ffmpeg is already the newest version (7:4.2.7-0ubuntu0.1). 0 upgraded, 0 newly installed, 0 to remove and 30 not upgraded. rootrootrootroot-X99-Turbo:~$ rootrootrootroot-X99-Turbo:~$ rootrootrootroot-X99-Turbo:~$ rootrootrootroot-X99-Turbo:~$ rootrootrootroot-X99-Turbo:~$ whisper audio.mp3 --model medium --language Chinese 100%|█████████████████████████████████████| 1.42G/1.42G [03:2400:00, 7.48MiB/s] Traceback (most recent call last): File /home/rootroot/.local/lib/python3.8/site-packages/whisper/audio.py, line 58, in load_audio out run(cmd, capture_outputTrue, checkTrue).stdout File /usr/lib/python3.8/subprocess.py, line 516, in run raise CalledProcessError(retcode, process.args, subprocess.CalledProcessError: Command [ffmpeg, -nostdin, -threads, 0, -i, audio.mp3, -f, s16le, -ac, 1, -acodec, pcm_s16le, -ar, 16000, -] returned non-zero exit status 1.
The above exception was the direct cause of the following exception:
Traceback (most recent call last): File /home/rootroot/.local/lib/python3.8/site-packages/whisper/transcribe.py, line 478, in cli result transcribe(model, audio_path, temperaturetemperature, **args) File /home/rootroot/.local/lib/python3.8/site-packages/whisper/transcribe.py, line 122, in transcribe mel log_mel_spectrogram(audio, model.dims.n_mels, paddingN_SAMPLES) File /home/rootroot/.local/lib/python3.8/site-packages/whisper/audio.py, line 140, in log_mel_spectrogram audio load_audio(audio) File /home/rootroot/.local/lib/python3.8/site-packages/whisper/audio.py, line 60, in load_audio raise RuntimeError(fFailed to load audio: {e.stderr.decode()}) from e RuntimeError: Failed to load audio: ffmpeg version 4.2.7-0ubuntu0.1 Copyright (c) 2000-2022 the FFmpeg developers built with gcc 9 (Ubuntu 9.4.0-1ubuntu1~20.04.1) configuration: --prefix/usr --extra-version0ubuntu0.1 --toolchainhardened --libdir/usr/lib/x86_64-linux-gnu --incdir/usr/include/x86_64-linux-gnu --archamd64 --enable-gpl --disable-stripping --enable-avresample --disable-filterresample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librsvg --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opencl --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-nvenc --enable-chromaprint --enable-frei0r --enable-libx264 --enable-shared libavutil 56. 31.100 / 56. 31.100 libavcodec 58. 54.100 / 58. 54.100 libavformat 58. 29.100 / 58. 29.100 libavdevice 58. 8.100 / 58. 8.100 libavfilter 7. 57.100 / 7. 57.100 libavresample 4. 0. 0 / 4. 0. 0 libswscale 5. 5.100 / 5. 5.100 libswresample 3. 5.100 / 3. 5.100 libpostproc 55. 5.100 / 55. 5.100 audio.mp3: No such file or directory
Skipping audio.mp3 due to RuntimeError: Failed to load audio: ffmpeg version 4.2.7-0ubuntu0.1 Copyright (c) 2000-2022 the FFmpeg developers built with gcc 9 (Ubuntu 9.4.0-1ubuntu1~20.04.1) configuration: --prefix/usr --extra-version0ubuntu0.1 --toolchainhardened --libdir/usr/lib/x86_64-linux-gnu --incdir/usr/include/x86_64-linux-gnu --archamd64 --enable-gpl --disable-stripping --enable-avresample --disable-filterresample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librsvg --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opencl --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-nvenc --enable-chromaprint --enable-frei0r --enable-libx264 --enable-shared libavutil 56. 31.100 / 56. 31.100 libavcodec 58. 54.100 / 58. 54.100 libavformat 58. 29.100 / 58. 29.100 libavdevice 58. 8.100 / 58. 8.100 libavfilter 7. 57.100 / 7. 57.100 libavresample 4. 0. 0 / 4. 0. 0 libswscale 5. 5.100 / 5. 5.100 libswresample 3. 5.100 / 3. 5.100 libpostproc 55. 5.100 / 55. 5.100 audio.mp3: No such file or directory
rootrootrootroot-X99-Turbo:~$ rootrootrootroot-X99-Turbo:~$ rootrootrootroot-X99-Turbo:~$ whisper chi.mp4 --model medium --language Chinese Traceback (most recent call last): File /home/rootroot/.local/lib/python3.8/site-packages/whisper/audio.py, line 58, in load_audio out run(cmd, capture_outputTrue, checkTrue).stdout File /usr/lib/python3.8/subprocess.py, line 516, in run raise CalledProcessError(retcode, process.args, subprocess.CalledProcessError: Command [ffmpeg, -nostdin, -threads, 0, -i, chi.mp4, -f, s16le, -ac, 1, -acodec, pcm_s16le, -ar, 16000, -] returned non-zero exit status 1.
The above exception was the direct cause of the following exception:
Traceback (most recent call last): File /home/rootroot/.local/lib/python3.8/site-packages/whisper/transcribe.py, line 478, in cli result transcribe(model, audio_path, temperaturetemperature, **args) File /home/rootroot/.local/lib/python3.8/site-packages/whisper/transcribe.py, line 122, in transcribe mel log_mel_spectrogram(audio, model.dims.n_mels, paddingN_SAMPLES) File /home/rootroot/.local/lib/python3.8/site-packages/whisper/audio.py, line 140, in log_mel_spectrogram audio load_audio(audio) File /home/rootroot/.local/lib/python3.8/site-packages/whisper/audio.py, line 60, in load_audio raise RuntimeError(fFailed to load audio: {e.stderr.decode()}) from e RuntimeError: Failed to load audio: ffmpeg version 4.2.7-0ubuntu0.1 Copyright (c) 2000-2022 the FFmpeg developers built with gcc 9 (Ubuntu 9.4.0-1ubuntu1~20.04.1) configuration: --prefix/usr --extra-version0ubuntu0.1 --toolchainhardened --libdir/usr/lib/x86_64-linux-gnu --incdir/usr/include/x86_64-linux-gnu --archamd64 --enable-gpl --disable-stripping --enable-avresample --disable-filterresample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librsvg --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opencl --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-nvenc --enable-chromaprint --enable-frei0r --enable-libx264 --enable-shared libavutil 56. 31.100 / 56. 31.100 libavcodec 58. 54.100 / 58. 54.100 libavformat 58. 29.100 / 58. 29.100 libavdevice 58. 8.100 / 58. 8.100 libavfilter 7. 57.100 / 7. 57.100 libavresample 4. 0. 0 / 4. 0. 0 libswscale 5. 5.100 / 5. 5.100 libswresample 3. 5.100 / 3. 5.100 libpostproc 55. 5.100 / 55. 5.100 chi.mp4: No such file or directory
Skipping chi.mp4 due to RuntimeError: Failed to load audio: ffmpeg version 4.2.7-0ubuntu0.1 Copyright (c) 2000-2022 the FFmpeg developers built with gcc 9 (Ubuntu 9.4.0-1ubuntu1~20.04.1) configuration: --prefix/usr --extra-version0ubuntu0.1 --toolchainhardened --libdir/usr/lib/x86_64-linux-gnu --incdir/usr/include/x86_64-linux-gnu --archamd64 --enable-gpl --disable-stripping --enable-avresample --disable-filterresample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librsvg --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opencl --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-nvenc --enable-chromaprint --enable-frei0r --enable-libx264 --enable-shared libavutil 56. 31.100 / 56. 31.100 libavcodec 58. 54.100 / 58. 54.100 libavformat 58. 29.100 / 58. 29.100 libavdevice 58. 8.100 / 58. 8.100 libavfilter 7. 57.100 / 7. 57.100 libavresample 4. 0. 0 / 4. 0. 0 libswscale 5. 5.100 / 5. 5.100 libswresample 3. 5.100 / 3. 5.100 libpostproc 55. 5.100 / 55. 5.100 chi.mp4: No such file or directory
rootrootrootroot-X99-Turbo:~$ rootrootrootroot-X99-Turbo:~$ rootrootrootroot-X99-Turbo:~$ sudo apt-get install ffmpeg Reading package lists... Done Building dependency tree Reading state information... Done ffmpeg is already the newest version (7:4.2.7-0ubuntu0.1). 0 upgraded, 0 newly installed, 0 to remove and 30 not upgraded. rootrootrootroot-X99-Turbo:~$ rootrootrootroot-X99-Turbo:~$ rootrootrootroot-X99-Turbo:~$ ll *.mp4 -rwx------ 1 rootroot rootroot 3465644 1月 12 01:28 chs.mp4* rootrootrootroot-X99-Turbo:~$ rootrootrootroot-X99-Turbo:~$ rootrootrootroot-X99-Turbo:~$ whisper chs.mp4 --model medium --language Chinese [00:00.000 -- 00:01.400] 前段時間有個巨石鴻吼 [00:01.400 -- 00:03.000] 某某是男人最好的衣妹 [00:03.000 -- 00:04.800] 這裡的某某可以替換為減肥 [00:04.800 -- 00:07.800] 長髮 西裝 考研 術唱 永潔無間等等等等 [00:07.800 -- 00:09.200] 我聽到最新的一個說法是 [00:09.200 -- 00:12.000] 微分碎蓋加口罩加半框眼鏡加春風衣 [00:12.000 -- 00:13.400] 等於男人最好的衣妹 [00:13.400 -- 00:14.400] 大概也就前幾年 [00:14.400 -- 00:17.400] 春風衣還和格子襯衫並列為程序員穿搭精華 [00:17.400 -- 00:20.000] 紫紅色春風衣還被譽為廣場舞大媽標配 [00:20.000 -- 00:21.600] 路透牌還是我爹這個年紀的人 [00:21.600 -- 00:22.800] 才會願意買的牌子 [00:22.800 -- 00:24.400] 不知道風向為啥變得這麼快 [00:24.400 -- 00:26.800] 為啥這東西突然變成男生逆襲神器 [00:26.800 -- 00:27.800] 時尚潮流單品 [00:27.800 -- 00:29.400] 後來我翻了一下小紅書就懂了 [00:29.400 -- 00:30.400] 時尚這個時期 [00:30.400 -- 00:31.600] 重點不在於衣服 [00:31.600 -- 00:32.200] 在於人 [00:32.200 -- 00:34.600] 先在小紅書上面和春風衣相關的筆記 [00:34.600 -- 00:36.200] 照片裡的男生都是這樣的 [00:36.200 -- 00:37.000] 這樣的 [00:37.000 -- 00:38.000] 還有這樣的 [00:38.000 -- 00:39.400] 你們哪裡是看穿搭的 [00:39.400 -- 00:40.600] 你們明明是看臉 [00:40.600 -- 00:41.800] 就這個造型 這個年齡 [00:41.800 -- 00:44.000] 你換上老頭衫也能穿出氛圍感好嗎 [00:44.000 -- 00:46.600] 我又想起了當年郭德綱老師穿季凡西的殘劇 [00:46.600 -- 00:48.600] 這個世界對我們這些長得不好看的人 [00:48.600 -- 00:49.600] 還真是苛刻的 [00:49.600 -- 00:52.000] 所以說我總結了一下春風衣傳達的要領 [00:52.200 -- 00:54.400] 大概就是一張白鏡且人畜無憾的臉 [00:54.400 -- 00:55.200] 充足的髮量 [00:55.200 -- 00:56.200] 纖細的體型 [00:56.200 -- 00:58.200] 當然身上的春風衣還得是駱駝的 [00:58.200 -- 00:59.400] 去年在戶外用品界 [00:59.400 -- 01:00.200] 最頂流的 [01:00.200 -- 01:01.200] 既不是鳥橡樹 [01:01.200 -- 01:02.800] 也不是有校服之稱的北面 [01:02.800 -- 01:04.200] 或者老臺頂流哥倫比亞 [01:04.200 -- 01:05.000] 而是駱駝 [01:05.000 -- 01:07.200] 雙11 駱駝在天貓戶外服飾品類 [01:07.200 -- 01:09.000] 拿下銷售額和銷量雙料冠軍 [01:09.000 -- 01:10.200] 銷量達到百萬幾 [01:10.200 -- 01:10.800] 再抖音 [01:10.800 -- 01:13.400] 駱駝銷售同比增幅高達296% [01:13.400 -- 01:16.200] 旗下主打的三合一高性價比春風衣成為爆品 [01:22.600 -- 01:23.200] 至於線下 [01:23.200 -- 01:24.400] 還是網友總覺得好 [01:24.400 -- 01:26.800] 如今在南方街頭的駱駝比沙漠裡的都多 [01:30.000 -- 01:31.200] 至於駱駝為啥這麼火 [01:31.200 -- 01:32.000] 便宜啊 [01:32.000 -- 01:33.600] 拿賣得最好的丁珍同款 [01:33.600 -- 01:35.600] 幻影黑三合一春風衣舉個例子 [01:35.600 -- 01:36.000] 線下買 [01:36.000 -- 01:37.600] 標牌價格2198 [01:37.600 -- 01:39.200] 但是跑到網上看一下 [01:39.200 -- 01:40.800] 標價就變成了699 [01:40.800 -- 01:41.400] 至於折扣 [01:41.400 -- 01:42.400] 日常也都是有的 [01:42.400 -- 01:43.600] 400出頭就能買到 [01:43.600 -- 01:45.200] 甚至有時候能递到300價 [01:45.200 -- 01:46.200] 要是你還嫌貴 [01:46.200 -- 01:48.400] 駱駝還有200塊出頭的單層春風衣 [01:48.400 -- 01:49.200] 就這個價格 [01:49.200 -- 01:51.800] 哥上海恐怕還不夠兩次City Walk的報名費 [01:51.800 -- 01:52.600] 看來這個價格 [01:52.600 -- 01:54.800] 再對比一下北面1000塊錢起步 [01:54.800 -- 01:56.000] 你就能理解為啥北面 [01:56.000 -- 01:58.200] 這麼快就被大學生踢出了校服序列了 [01:58.200 -- 02:00.400] 我不知道現在大學生每個月生活費多少 [02:00.400 -- 02:02.200] 反正按照我上學時候的生活費 [02:02.200 -- 02:03.200] 一個月不吃不喝 [02:03.200 -- 02:05.000] 也就買得起倆袖子加一個帽子 [02:05.000 -- 02:06.400] 難怪當年全是假北面 [02:06.400 -- 02:07.400] 現在都是真駱駝 [02:07.400 -- 02:08.800] 至少人家是正品啊 [02:08.800 -- 02:10.000] 我翻了一下社交媒體 [02:10.000 -- 02:11.200] 發現對駱駝的吐槽 [02:11.200 -- 02:12.000] 和買了駱駝的 [02:12.000 -- 02:13.400] 基本上是1比1的比例 [02:13.400 -- 02:15.000] 吐槽最多的就是衣服會掉色 [02:15.000 -- 02:15.800] 還會串色 [02:15.800 -- 02:17.000] 比如圖層洗個幾次 [02:17.000 -- 02:18.200] 穿個兩天就掉光了 [02:18.200 -- 02:19.600] 比如不同倉庫發的貨 [02:19.600 -- 02:20.600] 質量參差不齊 [02:20.600 -- 02:21.600] 買衣服還得看戶口 [02:21.600 -- 02:22.400] 聽出聲 [02:22.400 -- 02:23.600] 至於什麼做工比較差 [02:23.600 -- 02:24.800] 內膽多 走線操 [02:24.800 -- 02:26.400] 不防水之類的就更多了 [02:26.400 -- 02:27.400] 但是這些吐槽 [02:27.400 -- 02:29.200] 並不意味著會影響駱駝的銷量 [02:29.200 -- 02:30.800] 甚至還會有不少自來水表示 [02:30.800 -- 02:32.600] 就這價格要啥子行車啊 [02:32.600 -- 02:34.000] 所謂性價比性價比 [02:34.000 -- 02:35.200] 脫離價位談性能 [02:35.200 -- 02:37.000] 這就不符合消費者的需求嘛 [02:37.000 -- 02:38.400] 無數次價格戰告訴我們 [02:38.400 -- 02:39.400] 只要肯降價 [02:39.400 -- 02:41.000] 就沒有賣不出去的產品 [02:41.000 -- 02:42.400] 一件衝鋒衣1000多 [02:42.400 -- 02:43.600] 你覺得平平無奇 [02:43.600 -- 02:45.000] 500多你覺得差點意思 [02:45.000 -- 02:46.400] 200塊你就秒下單了 [02:46.400 -- 02:47.000] 到99 [02:47.000 -- 02:48.400] 恐怕就要拼點手速了 [02:48.400 -- 02:49.600] 像衝鋒衣這個品類 [02:49.600 -- 02:50.800] 本來價格跨度就大 [02:50.800 -- 02:52.800] 北面最便宜的GORTEX衝鋒衣 [02:52.800 -- 02:53.800] 價格3000起步 [02:53.800 -- 02:55.200] 大概是同品牌最便宜 [02:55.200 -- 02:56.200] 衝鋒衣的三倍價格 [02:56.200 -- 02:57.200] 至於十足那樣 [02:57.200 -- 02:59.000] 搭載了GORTEX的硬殼起步價 [02:59.000 -- 03:00.000] 就要到4500 [03:00.000 -- 03:01.200] 而且同樣是GORTEX [03:01.200 -- 03:02.800] 內部也有不同的系列和檔次 [03:02.800 -- 03:03.600] 做成衣服 [03:03.600 -- 03:05.600] 中間的差價恐怕就夠買兩件駱駝了 [03:05.600 -- 03:06.600] 至於智能控溫 [03:06.600 -- 03:07.400] 防水拉鍊 [03:07.400 -- 03:08.000] 全壓膠 [03:08.000 -- 03:09.800] 更加不可能出現在駱駝這裡了 [03:09.800 -- 03:11.800] 至少不會是300 400的駱駝身上會有的 [03:11.800 -- 03:12.800] 有的價外的衣服 [03:12.800 -- 03:14.200] 買的就是一個放棄幻想 [03:14.200 -- 03:15.800] 吃到肚子裡的科技魚很活 [03:15.800 -- 03:17.000] 是能給你省錢的 [03:17.000 -- 03:18.400] 穿在身上的科技魚很活 [03:18.400 -- 03:20.000] 裝裝件件都是要加錢的 [03:20.000 -- 03:21.600] 所以正如羅曼羅蘭所說 [03:21.600 -- 03:23.200] 這世界上只有一種英雄主義 [03:23.200 -- 03:24.800] 就是在認清了駱駝的本質以後 [03:24.800 -- 03:26.000] 依然選擇買駱駝 [03:26.000 -- 03:27.000] 關於駱駝的火爆 [03:27.000 -- 03:28.200] 我有一些小小的看法 [03:28.200 -- 03:29.000] 駱駝這個東西 [03:29.000 -- 03:30.400] 它其實就是個潮牌 [03:30.400 -- 03:32.000] 看看它的營銷方式就知道了 [03:32.000 -- 03:33.000] 現在打開小黃書 [03:33.000 -- 03:35.000] 日常可以看到駱駝穿搭是這樣的 [03:35.000 -- 03:36.600] 加一點氛圍感是這樣的 [03:36.600 -- 03:37.400] 對比一下 [03:37.400 -- 03:39.000] 其他品牌的風格是這樣的 [03:39.000 -- 03:39.800] 這樣的 [03:39.800 -- 03:41.200] 其實對比一下就知道了 [03:41.200 -- 03:42.600] 其他品牌突出一個時程 [03:42.600 -- 03:44.200] 能防風就一定要講防風 [03:44.200 -- 03:46.000] 能扛動就一定要講扛動 [03:46.000 -- 03:47.400] 但駱駝在營銷的時候 [03:47.400 -- 03:49.200] 主打的就是一個城市戶外風 [03:49.200 -- 03:50.400] 雖然造型是春風衣 [03:50.400 -- 03:52.200] 但場景往往是在城市裡 [03:52.200 -- 03:54.200] 哪怕在野外也要突出一個風和日麗 [03:54.200 -- 03:55.000] 陽光美媚 [03:55.000 -- 03:56.400] 至少不會在明顯的嚴寒 [03:56.400 -- 03:58.000] 高海拔或是惡劣氣候下 [03:58.200 -- 04:00.200] 如果用一個詞形容駱駝的營銷風格 [04:00.200 -- 04:01.000] 那就是清洗 [04:01.000 -- 04:03.000] 或者說他很理解自己的消費者是誰 [04:03.000 -- 04:04.000] 需要什麼產品 [04:04.000 -- 04:05.200] 從使用場景來說 [04:05.200 -- 04:06.600] 駱駝的消費者買春風衣 [04:06.600 -- 04:08.800] 不是真的有什麼大風大雨要去應對 [04:08.800 -- 04:11.000] 春風衣的作用是下雨沒帶傘的時候 [04:11.000 -- 04:12.000] 臨時頂個幾分鐘 [04:12.000 -- 04:13.600] 讓你能圖書館跑回宿舍 [04:13.600 -- 04:15.000] 或者是冬天騎電動車 [04:15.000 -- 04:16.200] 被風吹得不行的時候 [04:16.200 -- 04:17.200] 稍微扛一下風 [04:17.200 -- 04:18.400] 不至於體感太冷 [04:18.400 -- 04:19.800] 當然他們也會出門 [04:19.800 -- 04:21.800] 但大部分時候也都是去別的城市 [04:21.800 -- 04:24.000] 或者在城市周邊搞搞簡單的徒步 [04:24.000 -- 04:26.000] 這種情況下穿個駱駝已經夠了 [04:26.000 -- 04:27.200] 從購買動機來說 [04:27.200 -- 04:29.200] 駱駝就更沒有必要上那些應回科技了 [04:29.200 -- 04:31.000] 消費者買駱駝買的是個什麼呢 [04:31.000 -- 04:32.200] 不是春風衣的功能性 [04:32.200 -- 04:33.400] 而是春風衣的造型 [04:33.400 -- 04:34.400] 寬鬆的版型 [04:34.400 -- 04:36.400] 能精準遮住微微隆起的小肚子 [04:36.400 -- 04:37.400] 棱角分明的質感 [04:37.400 -- 04:39.400] 能隱藏一切不完美的身體線條 [04:39.400 -- 04:41.400] 顯瘦的副作用就是顯年輕 [04:41.400 -- 04:42.600] 再配上一條牛仔褲 [04:42.600 -- 04:43.800] 配上一雙大黃靴 [04:43.800 -- 04:45.200] 大學生的氣質就出來了 [04:45.200 -- 04:46.200] 要是自拍的時候 [04:46.200 -- 04:47.800] 再配上大學宿舍洗素臺 [04:47.800 -- 04:49.200] 那永遠擦不乾淨的鏡子 [04:49.200 -- 04:50.600] 瞬間青春無敵了 [04:50.800 -- 04:51.800] 說的更直白一點 [04:51.800 -- 04:53.200] 人家買的是個簡靈神器 [04:53.200 -- 04:53.800] 所以說 [04:53.800 -- 04:56.000] 吐槽穿駱駝都是假戶外愛好者的人 [04:56.000 -- 04:57.600] 其實並沒有理解駱駝的定位 [04:57.600 -- 04:59.800] 駱駝其實是給了想要入門山系穿搭 [04:59.800 -- 05:01.800] 想要追逐流行的人一個最平價 [05:01.800 -- 05:03.000] 決策成本最低的選擇 [05:03.000 -- 05:04.800] 至於那些真正的硬核戶外愛好者 [05:04.800 -- 05:05.800] 駱駝既沒有能力 [05:05.800 -- 05:07.200] 也沒有打算觸打他們 [05:07.200 -- 05:08.000] 反過來說 [05:08.000 -- 05:09.600] 那些自駕穿越邊疆國道 [05:09.600 -- 05:11.800] 或者去奧爾卑斯山區登山探險的人 [05:11.800 -- 05:13.600] 也不太可能在戶外服飾上省錢 [05:13.600 -- 05:15.000] 畢竟光是交通住宿 [05:15.400 -- 05:16.400] 成本就不低了 [05:16.400 -- 05:17.200] 對他們來說 [05:17.200 -- 05:19.000] 戶外裝備很多時候是保命用的 [05:19.000 -- 05:21.000] 也就不存在跟風奧造型的必要了 [05:21.000 -- 05:22.200] 最後我再說個題外話 [05:22.200 -- 05:24.200] 年輕人追捧駱駝一個隱藏的原因 [05:24.200 -- 05:25.800] 其實是羽絨服越來越貴了 [05:25.800 -- 05:26.600] 有媒體統計 [05:26.600 -- 05:30.000] 現在國產羽絨服的平均售價已經高達881元 [05:30.000 -- 05:32.000] 波斯登均價最高接近2000元 [05:32.000 -- 05:32.800] 而且過去幾年 [05:32.800 -- 05:34.800] 國產羽絨服品牌都在轉向高端化 [05:34.800 -- 05:37.000] 羽絨服市場分為8000元以上的奢侈級 [05:37.000 -- 05:38.400] 2000元以下的大眾級 [05:38.400 -- 05:39.800] 而在中間的高端級 [05:39.800 -- 05:41.200] 國產品牌一直沒有存在感 [05:41.200 -- 05:42.200] 所以過去幾年 [05:42.200 -- 05:43.600] 波斯登天工人這些品牌 [05:43.600 -- 05:45.200] 都把2000元到8000元這個市場 [05:45.200 -- 05:46.600] 當成未來的發展趨勢 [05:46.600 -- 05:48.000] 東新證券研報顯示 [05:48.000 -- 05:49.600] 從2018到2021年 [05:49.600 -- 05:52.200] 波斯登均價4年漲幅達到60%以上 [05:52.200 -- 05:53.200] 過去5個菜年 [05:53.200 -- 05:55.000] 這個品牌的營銷開支從20多億 [05:55.000 -- 05:56.000] 漲到了60多億 [05:56.000 -- 05:57.200] 羽絨服價格往上走 [05:57.200 -- 05:59.200] 年輕消費者就開始拋棄羽絨服 [05:59.200 -- 06:00.400] 購買平價衝鋒衣 [06:00.400 -- 06:02.200] 裡面再穿個普通價外的瑤麗絨 [06:02.200 -- 06:03.400] 或者羽絨小夾克 [06:03.400 -- 06:05.200] 也不比大幾千的羽絨服差多少 [06:05.200 -- 06:05.800] 說到底 [06:05.800 -- 06:07.000] 現在消費社會發達了 [06:07.000 -- 06:08.000] 沒有什麼需求是 [06:08.000 -- 06:09.600] 一定要某種特定的解決方案 [06:09.600 -- 06:11.600] 特定價位的商品才能實現的 [06:11.600 -- 06:12.200] 要保暖 [06:12.200 -- 06:13.200] 羽絨服固然很好 [06:13.200 -- 06:15.200] 但衝鋒衣加一些內搭也很暖和 [06:15.200 -- 06:16.000] 要時尚 [06:16.000 -- 06:18.000] 大幾千塊錢的設計師品牌非常不錯 [06:18.000 -- 06:19.400] 但350的拼多多服飾 [06:19.400 -- 06:20.600] 搭得好也能出彩 [06:20.600 -- 06:21.600] 要去野外徒步 [06:21.600 -- 06:23.000] 花五六千買鳥也可以 [06:23.000 -- 06:25.200] 但迪卡農也足以應付大多數狀況 [06:25.200 -- 06:25.800] 所以說 [06:25.800 -- 06:27.600] 花高價買衝鋒衣當然也OK [06:27.600 -- 06:28.600] 三四百買件駱駝 [06:28.600 -- 06:29.800] 也是可以接受的選擇 [06:29.800 -- 06:32.000] 何況駱駝也多多少少有一些功能性 [06:32.000 -- 06:33.800] 畢竟它再怎麼樣還是個衝鋒衣 [06:33.800 -- 06:34.800] 理解了這個事情 [06:34.800 -- 06:36.800] 就很容易分辨什麼是智商稅的 [06:36.800 -- 06:38.800] 那些向你灌輸非某個品牌不用 [06:38.800 -- 06:39.800] 告訴你某個需求 [06:39.800 -- 06:41.400] 只有某個產品才能滿足 [06:41.400 -- 06:42.200] 某個品牌 [06:42.200 -- 06:44.400] 就是某個品牌絕對的比試鏈頂端 [06:44.400 -- 06:46.800] 這類銀銷的智商稅含量必然是很高的 [06:46.800 -- 06:48.800] 它的目的是剝奪你選擇的權利 [06:48.800 -- 06:51.200] 讓你主動放棄比價和尋找平梯的想法 [06:51.200 -- 06:53.000] 從而避免與其他品牌競爭 [06:53.000 -- 06:54.200] 而沒有競爭的市場 [06:54.200 -- 06:56.200] 才是智商稅含量最高的市場 [06:56.200 -- 06:57.400] 消費商業洞穴 [06:57.400 -- 06:58.400] 禁在IC實驗室 [06:58.400 -- 06:59.000] 我是館長 [06:59.000 -- 07:00.000] 我們下期再見 rootrootrootroot-X99-Turbo:~$ rootrootrootroot-X99-Turbo:~$ rootrootrootroot-X99-Turbo:~$ time(whisper chs.mp4 --model medium --language Chinese) https://www.toutiao.com/article/7189209812264075835/?appnews_articletimestamp1706203570use_new_style1req_id20240126012609901ACEF7F5666533AA21group_id7189209812264075835tt_frommobile_qqutm_sourcemobile_qqutm_mediumtoutiao_androidutm_campaignclient_shareshare_token5e0cda89-00c5-40fe-afa0-c3c88dd056c4sourcem_redirect 已达到人类水准语音识别模型的whisper真的有这么厉害吗
transcribe函数的language目前支持99种语言如下
en: english,zh: chinese, de: german,es: spanish, ru: russian,ko: korean, fr: french,ja: japanese, pt: portuguese,tr: turkish, pl: polish,ca: catalan, nl: dutch,ar: arabic, sv: swedish,it: italian, id: indonesian,hi: hindi, fi: finnish,vi: vietnamese, he: hebrew,uk: ukrainian, el: greek,ms: malay, cs: czech,ro: romanian, da: danish,hu: hungarian, ta: tamil,no: norwegian, th: thai,ur: urdu, hr: croatian,bg: bulgarian, lt: lithuanian,la: latin, mi: maori,ml: malayalam, cy: welsh,sk: slovak, te: telugu,fa: persian, lv: latvian,bn: bengali, sr: serbian,az: azerbaijani, sl: slovenian,kn: kannada, et: estonian,mk: macedonian, br: breton,eu: basque, is: icelandic,hy: armenian, ne: nepali,mn: mongolian, bs: bosnian,kk: kazakh, sq: albanian,sw: swahili, gl: galician,mr: marathi, pa: punjabi,si: sinhala, km: khmer,sn: shona, yo: yoruba,so: somali, af: afrikaans,oc: occitan, ka: georgian,be: belarusian, tg: tajik,sd: sindhi, gu: gujarati,am: amharic, yi: yiddish,lo: lao, uz: uzbek,fo: faroese, ht: haitian creole,ps: pashto, tk: turkmen,nn: nynorsk, mt: maltese,sa: sanskrit, lb: luxembourgish,my: myanmar, bo: tibetan,tl: tagalog, mg: malagasy,as: assamese, tt: tatar,haw: hawaiian, ln: lingala,ha: hausa, ba: bashkir,jw: javanese,su: sundanese, 官方还提供了另外一种调用方案
import whisper model whisper.load_model(base) # load audio and pad/trim it to fit 30 seconds audio whisper.load_audio(audio.mp3) audio whisper.pad_or_trim(audio) # make log-Mel spectrogram and move to the same device as the model mel whisper.log_mel_spectrogram(audio).to(model.device) # detect the spoken language _, probs model.detect_language(mel) print(fDetected language: {max(probs, keyprobs.get)}) # decode the audio options whisper.DecodingOptions(languageChinese) result whisper.decode(model, mel, options) # print the recognized text print(result.text)
参考资料 https://www.toutiao.com/article/7229151806801248807/?appnews_articletimestamp1706203733use_new_style1req_id20240126012853D9D3D4539BEF1333DBCCgroup_id7229151806801248807tt_frommobile_qqutm_sourcemobile_qqutm_mediumtoutiao_androidutm_campaignclient_shareshare_token085ce76c-b23a-4609-b2d0-d18c8d7ab8f8sourcem_redirect C版本人工智能实时语音转文字(字幕/语音识别)Whisper.cpp实践 【WINDOWS大模型需要10GB】 https://blog.csdn.net/hhy321/article/details/134897967?spm1001.2101.3001.6650.2utm_mediumdistribute.wap_relevant.none-task-blog-2~default~CTRLIST~Rate-2-134897967-blog-130001848.237%5Ev3%5Ewap_relevant_t0_downloaddepth_1-utm_sourcedistribute.wap_relevant.none-task-blog-2~default~CTRLIST~Rate-2-134897967-blog-130001848.237%5Ev3%5Ewap_relevant_t0_downloadshare_token845e69c5-c625-4834-8faa-08f1f29f55b2 【小沐学Python】Python实现语音识别Whisper https://blog.csdn.net/xkukeer/article/details/130227944?share_tokenf48bfb40-9399-4375-894e-3ecf96d1c51d openai的whisper语音识别介绍
第三步选择使用的模型。 官方说有5种模型其中4种是English-only模型但是实测english-only也可以支持中文只测了base可以支持中文其他的没测但应该也可以 虽说支持中文但是也有不理想的地方中文的识别错误率WER (Word Error Rate)还不低在所有支持语言的大概排中游水平。
第四步具体使用 有好几种方法 1、命令行模式 whisper audio.flac audio.mp3 audio.wav --model medium
对于非英文语言加上–language参数例如日语 whisper japanese.wav --language Japanese
支持的语言类型还挺多的 【WINDOWS】 https://blog.csdn.net/liaoqingjian/article/details/132474687?share_tokene6ad6f74-2fab-45c5-bdb5-40b48fe2cd79 whisper 语音识别项目部署 https://www.toutiao.com/article/7327918175801164325/?appnews_articletimestamp1706203446use_new_style1req_id202401260124058D2D3B0452AC9B3435B3group_id7327918175801164325tt_frommobile_qqutm_sourcemobile_qqutm_mediumtoutiao_androidutm_campaignclient_shareshare_tokenad4cdc74-1590-4a7b-b020-14f9186f9ef2sourcem_redirect Whisper对于中文语音识别与转写中文文本优化的实践(Python3.10) 【WINDOWS】 https://www.toutiao.com/article/7276749520275456572/?appnews_articletimestamp1706203504use_new_style1req_id2024012601250342BCD0F3D434AA335380group_id7276749520275456572tt_frommobile_qqutm_sourcemobile_qqutm_mediumtoutiao_androidutm_campaignclient_shareshare_token5bc13cbe-db1d-4883-bff4-b01f258dd1c2sourcem_redirect 语音转文字软件Whisper实时自动语音识别音频视频文案提取
