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:~$ 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:~$ 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:~$ 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

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:~$ 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:~$ 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:~$ 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:~$ 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

built with gcc 9 (Ubuntu 9.4.0-1ubuntu1~20.04.1)

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

built with gcc 9 (Ubuntu 9.4.0-1ubuntu1~20.04.1)

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:~$ 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

built with gcc 9 (Ubuntu 9.4.0-1ubuntu1~20.04.1)

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

built with gcc 9 (Ubuntu 9.4.0-1ubuntu1~20.04.1)

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:~$ 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:~$ ll *.mp4

-rwx------ 1 rootroot rootroot 3465644 1月 12 01:28 chs.mp4*

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:~$ time(whisper chs.mp4 --model medium --language Chinese)

https://www.toutiao.com/article/7189209812264075835/?app=news_article\&timestamp=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\&timestamp=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^v3^wap_relevant_t0_download\&depth_1-utm_source=distribute.wap_relevant.none-task-blog-2\~default\~CTRLIST\~Rate-2-134897967-blog-130001848.237^v3^wap_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\&timestamp=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\&timestamp=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

20240127在ubuntu20.04.6下配置whisper

load audio and pad/trim it to fit 30 seconds

make log-Mel spectrogram and move to the same device as the model

detect the spoken language

decode the audio

print the recognized text