transcribe-anything

USES WHISPER AI

Over 300+⭐'s because this program this app just works! This whisper front-end app is the only one to generate a speaker.json file which partitions the conversation by who doing the speaking.

Easiest whisper implementation to install and use. Just install with pip install transcribe-anything. GPU acceleration is automatic, using the blazingly fast insanely-fast-whisper as the backend for --device insane. This is the only tool to optionally produces a speaker.json file, representing speaker-assigned text that has been de-chunkified.

Hardware acceleration on Windows/Linux/MacOS Arm (M1, M2, +) via --device insane

Input a local file or youtube/rumble url and this tool will transcribe it using Whisper AI into subtitle files and raw text.

Uses whisper AI so this is state of the art translation service - completely free. 🤯🤯🤯

Your data stays private and is not uploaded to any service.

The new version now has state of the art speed in transcriptions, thanks to the new backend --device insane, as well as producing a speaker.json file.

pip install transcribe-anything
# slow cpu mode, works everywhere
transcribe-anything https://www.youtube.com/watch?v=dQw4w9WgXcQ
# insanely fast using the insanely-fast-whisper backend.
transcribe-anything https://www.youtube.com/watch?v=dQw4w9WgXcQ --device insane
# translate from any language to english
transcribe-anything https://www.youtube.com/watch?v=dQw4w9WgXcQ --device insane --task translate

Insanely fast on `cuda` platforms

If you pass in --device insane on a cuda platform then this tool will use this state of the art version of whisper: https://github.com/Vaibhavs10/insanely-fast-whisper, which is MUCH faster and has a pipeline for speaker identification (diarization) using the --hf_token option.

Also note, insanely-fast-whisper (--device insane) included in this project has been fixed to work with python 3.11. The upstream version is still broken on python 3.11 as of 1/22/2024.

Speaker.json

When diarization is enabled via --hf_token (hugging face token) then the output json will contain speaker info labeled as SPEAKER_00, SPEAKER_01 etc. For licensing agreement reasons, you must get your own hugging face token if you want to enable this feature. Also there is an additional step to agree to the user policies for the pyannote.audio located here: https://huggingface.co/pyannote/segmentation-3.0. If you don't do this then you'll see runtime exceptions from pyannote when the --hf_token is used.

What's special to this app is that we also generate a speaker.json which is a de-chunkified version of the output json speaker section.

speaker.json example:

[
  {
    "speaker": "SPEAKER_00",
    "timestamp": [
      0.0,
      7.44
    ],
    "text": "for that. But welcome, Zach Vorhees. Great to have you back on. Thank you, Matt. Craving me back onto your show. Man, we got a lot to talk about.",
    "reason": "beginning"
  },
  {
    "speaker": "SPEAKER_01",
    "timestamp": [
      7.44,
      33.52
    ],
    "text": "Oh, we do. 2023 was the year that OpenAI released, you know, chat GPT-4, which I think most people would say has surpassed average human intelligence, at least in test taking, perhaps not in, you know, reasoning and things like that. But it was a major year for AI. I think that most people are behind the curve on this. What's your take of what just happened in the last 12 months and what it means for the future of human cognition versus machine cognition?",
    "reason": "speaker-switch"
  },
  {
    "speaker": "SPEAKER_00",
    "timestamp": [
      33.52,
      44.08
    ],
    "text": "Yeah. Well, you know, at the beginning of 2023, we had a pretty weak AI system, which was a chat GPT 3.5 turbo was the best that we had. And then between the beginning of last",
    "reason": "speaker-switch"
  }
]

Note that speaker.json is only generated when using --device insane and not for --device cuda nor --device cpu.

`cuda` vs `insane`

Insane mode eats up a lot of memory and it's common to get out of memory errors while transcribing. For example a 3060 12GB nividia card produced out of memory errors are common for big content. If you experience this then pass in --batch-size 8 or smaller. Note that any arguments not recognized by transcribe-anything are passed onto the backend transcriber.

Also, please don't use distil-whisper/distil-large-v2, it produces extremely bad stuttering and it's not entirely clear why this is. I've had to switch it out of production environments because it's so bad. It's also non-deterministic so I think that somehow a fallback non-zero temperature is being used, which produces these stutterings.

cuda is the original AI model supplied by openai. It's more stable but MUCH slower. It also won't produce a speaker.json file which looks like this:

--embed. This app will optionally embed subtitles directly "burned" into an output video.

Install

This front end app for whisper boasts the easiest install in the whisper ecosystem thanks to isolated-environment. You can simply install it with pip, like this:

pip install transcribe-anything

GPU Acceleration

GPU acceleration will be automatically enabled for windows and linux. Mac users are stuck with --device cpu mode. But it's possible that --device insane and --model mps on Mac M1+ will work, but this has been completely untested.

Usage

 transcribe-anything https://www.youtube.com/watch?v=dQw4w9WgXcQ

Will output:

Detecting language using up to the first 30 seconds. Use `--language` to specify the language
Detected language: English
[00:00.000 --> 00:27.000]  We're no strangers to love, you know the rules, and so do I
[00:27.000 --> 00:31.000]  I've built commitments while I'm thinking of
[00:31.000 --> 00:35.000]  You wouldn't get this from any other guy
[00:35.000 --> 00:40.000]  I just wanna tell you how I'm feeling
[00:40.000 --> 00:43.000]  Gotta make you understand
[00:43.000 --> 00:45.000]  Never gonna give you up
[00:45.000 --> 00:47.000]  Never gonna let you down
[00:47.000 --> 00:51.000]  Never gonna run around and desert you
[00:51.000 --> 00:53.000]  Never gonna make you cry
[00:53.000 --> 00:55.000]  Never gonna say goodbye
[00:55.000 --> 00:58.000]  Never gonna tell a lie
[00:58.000 --> 01:00.000]  And hurt you
[01:00.000 --> 01:04.000]  We've known each other for so long
[01:04.000 --> 01:09.000]  Your heart's been aching but you're too shy to say it
[01:09.000 --> 01:13.000]  Inside we both know what's been going on
[01:13.000 --> 01:17.000]  We know the game and we're gonna play it
[01:17.000 --> 01:22.000]  And if you ask me how I'm feeling
[01:22.000 --> 01:25.000]  Don't tell me you're too much to see
[01:25.000 --> 01:27.000]  Never gonna give you up
[01:27.000 --> 01:29.000]  Never gonna let you down
[01:29.000 --> 01:33.000]  Never gonna run around and desert you
[01:33.000 --> 01:35.000]  Never gonna make you cry
[01:35.000 --> 01:38.000]  Never gonna say goodbye
[01:38.000 --> 01:40.000]  Never gonna tell a lie
[01:40.000 --> 01:42.000]  And hurt you
[01:42.000 --> 01:44.000]  Never gonna give you up
[01:44.000 --> 01:46.000]  Never gonna let you down
[01:46.000 --> 01:50.000]  Never gonna run around and desert you
[01:50.000 --> 01:52.000]  Never gonna make you cry
[01:52.000 --> 01:54.000]  Never gonna say goodbye
[01:54.000 --> 01:57.000]  Never gonna tell a lie
[01:57.000 --> 01:59.000]  And hurt you
[02:08.000 --> 02:10.000]  Never gonna give
[02:12.000 --> 02:14.000]  Never gonna give
[02:16.000 --> 02:19.000]  We've known each other for so long
[02:19.000 --> 02:24.000]  Your heart's been aching but you're too shy to say it
[02:24.000 --> 02:28.000]  Inside we both know what's been going on
[02:28.000 --> 02:32.000]  We know the game and we're gonna play it
[02:32.000 --> 02:37.000]  I just wanna tell you how I'm feeling
[02:37.000 --> 02:40.000]  Gotta make you understand
[02:40.000 --> 02:42.000]  Never gonna give you up
[02:42.000 --> 02:44.000]  Never gonna let you down
[02:44.000 --> 02:48.000]  Never gonna run around and desert you
[02:48.000 --> 02:50.000]  Never gonna make you cry
[02:50.000 --> 02:53.000]  Never gonna say goodbye
[02:53.000 --> 02:55.000]  Never gonna tell a lie
[02:55.000 --> 02:57.000]  And hurt you
[02:57.000 --> 02:59.000]  Never gonna give you up
[02:59.000 --> 03:01.000]  Never gonna let you down
[03:01.000 --> 03:05.000]  Never gonna run around and desert you
[03:05.000 --> 03:08.000]  Never gonna make you cry
[03:08.000 --> 03:10.000]  Never gonna say goodbye
[03:10.000 --> 03:12.000]  Never gonna tell a lie
[03:12.000 --> 03:14.000]  And hurt you
[03:14.000 --> 03:16.000]  Never gonna give you up
[03:16.000 --> 03:23.000]  If you want, never gonna let you down Never gonna run around and desert you
[03:23.000 --> 03:28.000]  Never gonna make you hide Never gonna say goodbye
[03:28.000 --> 03:42.000]  Never gonna tell you I ain't ready

Api

from transcribe_anything.api import transcribe

transcribe(
    url_or_file="https://www.youtube.com/watch?v=dQw4w9WgXcQ",
    output_dir="output_dir",
)

Develop

Works for Ubuntu/MacOS/Win32(in git-bash) This will create a virtual environment

> cd transcribe_anything
> ./install.sh
# Enter the environment:
> source activate.sh

The environment is now active and the next step will only install to the local python. If the terminal is closed then to get back into the environment cd transcribe_anything and execute source activate.sh

Required: Install to current python environment

pip install transcribe-anything
- The command transcribe_anything will magically become available.
transcribe_anything <YOUTUBE_URL>

Tech Stack

OpenAI whisper
insanely-fast-whisper
yt-dlp: https://github.com/yt-dlp/yt-dlp
static-ffmpeg
- github: https://github.com/zackees/static_ffmpeg
- pypi: https://pypi.org/project/static-ffmpeg/

Testing

Every commit is tested for standard linters and a batch of unit tests.

Versions

2.7.37: Fixed breakage due to numpy 2.0 being released.
2.7.36: Fixed some ffmpeg dependencies.
2.7.35: All ffmpeg commands are now static_ffmpeg commands. Fixes issue.
2.7.34: Various fixes.
2.7.33: Fixes linux
2.7.32: Fixes mac m1 and m2.
2.7.31: Adds a warning if using python 3.12, which isn't supported yet in the backend.
2.7.30: adds --query-gpu-json-path
2.7.29: Made to json -> srt more robust for --device insane, bad entries will be skipped but warn.
2.7.28: Fixes bad title fetching with weird characters.
2.7.27: pytorch-audio upgrades broke this package. Upgrade to latest version to resolve.
2.7.26: Add model option distil-whisper/distil-large-v2
2.7.25: Windows (Linux/MacOS) bug with --device insane and python 3.11 installing wrong insanely-fast-whisper version.
2.7.22: Fixes transcribe-anything on Linux.
2.7.21: Tested that Mac Arm can run --device insane. Added tests to ensure this.
2.7.20: Fixes wrong type being returned when speaker.json happens to be empty.
2.7.19: speaker.json is now in plain json format instead of json5 format
2.7.18: Fixes tests
2.7.17: Fixes speaker.json nesting.
2.7.16: Adds --save_hf_token
2.7.15: Fixes 2.7.14 breakage.
2.7.14: (Broken) Now generates speaker.json when diarization is enabled.
2.7.13: Default diarization model is now pyannote/speaker-diarization-3.1
2.7.12: Adds srt_swap for line breaks and improved isolated_environment usage.
2.7.11: --device insane now generates a *.vtt translation file
2.7.10: Better support for namespaced models. Trims text output in output json. Output json is now formatted with indents. SRT file is now printed out for --device insane
2.7.9: All SRT translation errors fixed for --device insane. All tests pass.
2.7.8: During error of --device insane, write out the error.json file into the destination.
2.7.7: Better error messages during failure.
2.7.6: Improved generation of out.txt, removes linebreaks.
2.7.5: --device insane now generates better conforming srt files.
2.7.3: Various fixes for the insane mode backend.
2.7.0: Introduces an insanely-fast-whisper, enable by using --device insane
2.6.0: GPU acceleration now happens automatically on Windows thanks to isolated-environment. This will also prevent interference with different versions of torch for other AI tools.
2.5.0: --model large now aliases to --model large-v3. Use --model large-legacy to use original large model.
2.4.0: pytorch updated to 2.1.2, gpu install script updated to same + cuda version is now 121.
2.3.9: Fallback to cpu device if gpu device is not compatible.
2.3.8: Fix --models arg which
2.3.7: Critical fix: fixes dependency breakage with open-ai. Fixes windows use of embedded tool.
2.3.6: Fixes typo in readme for installation instructions.
2.3.5: Now has --embed to burn the subtitles into the video itself. Only works on local mp4 files at the moment.
2.3.4: Removed out.mp3 and instead use a temporary wav file, as that is faster to process. --no-keep-audio has now been removed.
2.3.3: Fix case where there spaces in name (happens on windows)
2.3.2: Fix windows transcoding error
2.3.1: static-ffmpeg >= 2.5 now specified
2.3.0: Now uses the official version of whisper ai
2.2.1: "test_" is now prepended to all the different output folder names.
2.2.0: Now explictly setting a language will put the file in a folder with that language name, allowing multi language passes without overwriting.
2.1.2: yt-dlp pinned to new minimum version. Fixes downloading issues from old lib. Adds audio normalization by default.
2.1.1: Updates keywords for easier pypi finding.
2.1.0: Unknown args are now assumed to be for whisper and passed to it as-is. Fixes #3
2.0.13: Now works with python 3.9
2.0.12: Adds --device to argument parameters. This will default to CUDA if available, else CPU.
2.0.11: Automatically deletes files in the out directory if they already exist.
2.0.10: fixes local file issue #2
2.0.9: fixes sanitization of path names for some youtube videos
2.0.8: fix --output_dir not being respected.
2.0.7: install_cuda.sh -> install_cuda.py
2.0.6: Fixes twitter video fetching. --keep-audio -> --no-keep-audio
2.0.5: Fix bad filename on trailing urls ending with /, adds --keep-audio
2.0.3: GPU support is now added. Run the install_cuda.sh script to enable.
2.0.2: Minor cleanup of file names (no more out.mp3.txt, it's now out.txt)
2.0.1: Fixes missing dependencies and adds whisper option.
2.0.0: New! Now a front end for Whisper ai!

Notes:

Insanely Fast whisper for GPU
- https://github.com/Vaibhavs10/insanely-fast-whisper
Fast Whisper for CPU
- https://github.com/SYSTRAN/faster-whisper?tab=readme-ov-file
A better whisper CLI that supports more options but has a manual install.
- https://github.com/ochen1/insanely-fast-whisper-cli/blob/main/requirements.txt
Subtitles translator:
- https://github.com/TDHM/Subtitles-Translator
Forum post on how to avoid stuttering
- https://community.openai.com/t/how-to-avoid-hallucinations-in-whisper-transcriptions/125300/23
More stable transcriptions:
- https://github.com/jianfch/stable-ts?tab=readme-ov-file

Name		Name	Last commit message	Last commit date
Latest commit History 398 Commits
.github/workflows		.github/workflows
.vscode		.vscode
tests		tests
transcribe_anything		transcribe_anything
.aiderignore		.aiderignore
.gitignore		.gitignore
LICENSE		LICENSE
MANIFEST.in		MANIFEST.in
README.md		README.md
install.sh		install.sh
install_cuda.py		install_cuda.py
lint		lint
make_venv.py		make_venv.py
notes.txt		notes.txt
requirements.testing.txt		requirements.testing.txt
requirements.txt		requirements.txt
setup.py		setup.py
test		test
tox.ini		tox.ini
upload_package.sh		upload_package.sh

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transcribe-anything

USES WHISPER AI

Insanely fast on `cuda` platforms

Speaker.json

speaker.json example:

`cuda` vs `insane`

Install

GPU Acceleration

Usage

Api

Develop

Required: Install to current python environment

Tech Stack

Testing

Versions

Notes:

About

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Contributors 2

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zackees/transcribe-anything

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transcribe-anything

USES WHISPER AI

Insanely fast on cuda platforms

Speaker.json

speaker.json example:

cuda vs insane

Install

GPU Acceleration

Usage

Api

Develop

Required: Install to current python environment

Tech Stack

Testing

Versions

Notes:

About

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Contributors 2

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Insanely fast on `cuda` platforms

`cuda` vs `insane`

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