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
rootroot@rootroot-X99-Turbo:~
rootroot@rootroot-X99-Turbo:\~ python -m pip install --upgrade pip
【可以不安装conda】
rootroot@rootroot-X99-Turbo:~ wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
rootroot@rootroot-X99-Turbo:\~ ffmpeg
rootroot@rootroot-X99-Turbo:~ pip install -U openai-whisper
rootroot@rootroot-X99-Turbo:\~ pip install tiktoken
rootroot@rootroot-X99-Turbo:~ pip install setuptools-rust
rootroot@rootroot-X99-Turbo:\~ whisper audio.mp3 --model medium --language Chinese
rootroot@rootroot-X99-Turbo:~ whisper chi.mp4 --model medium --language Chinese
rootroot@rootroot-X99-Turbo:\~ sudo apt-get install ffmpeg
rootroot@rootroot-X99-Turbo:~$ time(whisper chs.mp4 --model medium --language Chinese)
rootroot@rootroot-X99-Turbo:~$
rootroot@rootroot-X99-Turbo:~$
rootroot@rootroot-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
rootroot@rootroot-X99-Turbo:~$
rootroot@rootroot-X99-Turbo:~$
rootroot@rootroot-X99-Turbo:~$ sudo mkdir /opt/tools
rootroot@rootroot-X99-Turbo:~$ cd /opt/tools/
rootroot@rootroot-X99-Turbo:/opt/tools$
rootroot@rootroot-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 ../
rootroot@rootroot-X99-Turbo:/opt/tools$
rootroot@rootroot-X99-Turbo:/opt/tools$ cd ~
rootroot@rootroot-X99-Turbo:~$
rootroot@rootroot-X99-Turbo:~$
rootroot@rootroot-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]
rootroot@rootroot-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-version=0ubuntu0.1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --enable-avresample --disable-filter=resample --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'
rootroot@rootroot-X99-Turbo:~$
rootroot@rootroot-X99-Turbo:~$
rootroot@rootroot-X99-Turbo:~$
rootroot@rootroot-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: triton<3,>=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 triton<3,>=2.0.0->openai-whisper) (3.13.1)
Requirement already satisfied: llvmlite<0.42,>=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: regex>=2022.1.18 in ./.local/lib/python3.8/site-packages (from tiktoken->openai-whisper) (2023.12.25)
Requirement already satisfied: requests>=2.26.0 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-cu12==12.1.105 in ./.local/lib/python3.8/site-packages (from torch->openai-whisper) (12.1.105)
Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in ./.local/lib/python3.8/site-packages (from torch->openai-whisper) (12.1.105)
Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in ./.local/lib/python3.8/site-packages (from torch->openai-whisper) (12.1.105)
Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in ./.local/lib/python3.8/site-packages (from torch->openai-whisper) (8.9.2.26)
Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in ./.local/lib/python3.8/site-packages (from torch->openai-whisper) (12.1.3.1)
Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in ./.local/lib/python3.8/site-packages (from torch->openai-whisper) (11.0.2.54)
Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in ./.local/lib/python3.8/site-packages (from torch->openai-whisper) (10.3.2.106)
Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in ./.local/lib/python3.8/site-packages (from torch->openai-whisper) (11.4.5.107)
Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in ./.local/lib/python3.8/site-packages (from torch->openai-whisper) (12.1.0.106)
Requirement already satisfied: nvidia-nccl-cu12==2.18.1 in ./.local/lib/python3.8/site-packages (from torch->openai-whisper) (2.18.1)
Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in ./.local/lib/python3.8/site-packages (from torch->openai-whisper) (12.1.105)
Collecting triton<3,>=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-cu12==11.4.5.107->torch->openai-whisper) (12.3.101)
Requirement already satisfied: charset-normalizer<4,>=2 in ./.local/lib/python3.8/site-packages (from requests>=2.26.0->tiktoken->openai-whisper) (3.3.2)
Requirement already satisfied: idna<4,>=2.5 in /usr/lib/python3/dist-packages (from requests>=2.26.0->tiktoken->openai-whisper) (2.8)
Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/lib/python3/dist-packages (from requests>=2.26.0->tiktoken->openai-whisper) (1.25.8)
Requirement already satisfied: certifi>=2017.4.17 in /usr/lib/python3/dist-packages (from requests>=2.26.0->tiktoken->openai-whisper) (2019.11.28)
Requirement already satisfied: zipp>=0.5 in ./.local/lib/python3.8/site-packages (from importlib-metadata->numba->openai-whisper) (3.17.0)
Requirement already satisfied: MarkupSafe>=2.0 in ./.local/lib/python3.8/site-packages (from jinja2->torch->openai-whisper) (2.1.3)
Requirement already satisfied: mpmath>=0.19 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
rootroot@rootroot-X99-Turbo:~$
rootroot@rootroot-X99-Turbo:~$
rootroot@rootroot-X99-Turbo:~$
rootroot@rootroot-X99-Turbo:~$
rootroot@rootroot-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: regex>=2022.1.18 in ./.local/lib/python3.8/site-packages (from tiktoken) (2023.12.25)
Requirement already satisfied: requests>=2.26.0 in ./.local/lib/python3.8/site-packages (from tiktoken) (2.31.0)
Requirement already satisfied: charset-normalizer<4,>=2 in ./.local/lib/python3.8/site-packages (from requests>=2.26.0->tiktoken) (3.3.2)
Requirement already satisfied: idna<4,>=2.5 in /usr/lib/python3/dist-packages (from requests>=2.26.0->tiktoken) (2.8)
Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/lib/python3/dist-packages (from requests>=2.26.0->tiktoken) (1.25.8)
Requirement already satisfied: certifi>=2017.4.17 in /usr/lib/python3/dist-packages (from requests>=2.26.0->tiktoken) (2019.11.28)
rootroot@rootroot-X99-Turbo:~$
rootroot@rootroot-X99-Turbo:~$
rootroot@rootroot-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: setuptools>=62.4 in ./.local/lib/python3.8/site-packages (from setuptools-rust) (69.0.3)
Requirement already satisfied: semantic-version<3,>=2.8.2 in ./.local/lib/python3.8/site-packages (from setuptools-rust) (2.10.0)
Requirement already satisfied: tomli>=1.2.1 in ./.local/lib/python3.8/site-packages (from setuptools-rust) (2.0.1)
rootroot@rootroot-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.
rootroot@rootroot-X99-Turbo:~$
rootroot@rootroot-X99-Turbo:~$
rootroot@rootroot-X99-Turbo:~$
rootroot@rootroot-X99-Turbo:~$
rootroot@rootroot-X99-Turbo:~$ whisper audio.mp3 --model medium --language Chinese
100%|█████████████████████████████████████| 1.42G/1.42G [03:24<00:00, 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_output=True, check=True).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, temperature=temperature, **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, padding=N_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(f"Failed 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-version=0ubuntu0.1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --enable-avresample --disable-filter=resample --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-version=0ubuntu0.1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --enable-avresample --disable-filter=resample --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
rootroot@rootroot-X99-Turbo:~$
rootroot@rootroot-X99-Turbo:~$
rootroot@rootroot-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_output=True, check=True).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, temperature=temperature, **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, padding=N_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(f"Failed 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-version=0ubuntu0.1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --enable-avresample --disable-filter=resample --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-version=0ubuntu0.1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --enable-avresample --disable-filter=resample --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
rootroot@rootroot-X99-Turbo:~$
rootroot@rootroot-X99-Turbo:~$
rootroot@rootroot-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.
rootroot@rootroot-X99-Turbo:~$
rootroot@rootroot-X99-Turbo:~$
rootroot@rootroot-X99-Turbo:~$ ll *.mp4
-rwx------ 1 rootroot rootroot 3465644 1月 12 01:28 chs.mp4*
rootroot@rootroot-X99-Turbo:~$
rootroot@rootroot-X99-Turbo:~$
rootroot@rootroot-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 rootroot@rootroot-X99-Turbo:\~$ rootroot@rootroot-X99-Turbo:\~$ rootroot@rootroot-X99-Turbo:\~$ time(whisper chs.mp4 --model medium --language Chinese) https://www.toutiao.com/article/7189209812264075835/?app=news_article\×tamp=1706203570\&use_new_style=1\&req_id=20240126012609901ACEF7F5666533AA21\&group_id=7189209812264075835\&tt_from=mobile_qq\&utm_source=mobile_qq\&utm_medium=toutiao_android\&utm_campaign=client_share\&share_token=5e0cda89-00c5-40fe-afa0-c3c88dd056c4\&source=m_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(f"Detected language: {max(probs, key=probs.get)}") # decode the audio options = whisper.DecodingOptions(language='Chinese') result = whisper.decode(model, mel, options) # print the recognized text print(result.text) 参考资料： https://www.toutiao.com/article/7229151806801248807/?app=news_article\×tamp=1706203733\&use_new_style=1\&req_id=20240126012853D9D3D4539BEF1333DBCC\&group_id=7229151806801248807\&tt_from=mobile_qq\&utm_source=mobile_qq\&utm_medium=toutiao_android\&utm_campaign=client_share\&share_token=085ce76c-b23a-4609-b2d0-d18c8d7ab8f8\&source=m_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?spm=1001.2101.3001.6650.2\&utm_medium=distribute.wap_relevant.none-task-blog-2\~default\~CTRLIST\~Rate-2-134897967-blog-130001848.237%5Ev3%5Ewap_relevant_t0_download\&depth_1-utm_source=distribute.wap_relevant.none-task-blog-2\~default\~CTRLIST\~Rate-2-134897967-blog-130001848.237%5Ev3%5Ewap_relevant_t0_download\&share_token=845e69c5-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_token=f48bfb40-9399-4375-894e-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_token=e6ad6f74-2fab-45c5-bdb5-40b48fe2cd79 whisper 语音识别项目部署 https://www.toutiao.com/article/7327918175801164325/?app=news_article\×tamp=1706203446\&use_new_style=1\&req_id=202401260124058D2D3B0452AC9B3435B3\&group_id=7327918175801164325\&tt_from=mobile_qq\&utm_source=mobile_qq\&utm_medium=toutiao_android\&utm_campaign=client_share\&share_token=ad4cdc74-1590-4a7b-b020-14f9186f9ef2\&source=m_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/?app=news_article\×tamp=1706203504\&use_new_style=1\&req_id=2024012601250342BCD0F3D434AA335380\&group_id=7276749520275456572\&tt_from=mobile_qq\&utm_source=mobile_qq\&utm_medium=toutiao_android\&utm_campaign=client_share\&share_token=5bc13cbe-db1d-4883-bff4-b01f258dd1c2\&source=m_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 