wishcribe 1.0.3

Multi-speaker audio/video transcription — Whisper large + pyannote.audio (offline)

Wishcribe

Multi-speaker audio/video transcription — Whisper large + pyannote.audio, fully offline after first run.

[SPEAKER_00] 00:00:01
  Selamat datang di rapat hari ini.

[SPEAKER_01] 00:00:05
  Terima kasih. Mari kita mulai.

[SPEAKER_00] 00:00:10
  Baik, topik pertama adalah anggaran kuartal ini.

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Requirements

• Python 3.9 or higher
• ffmpeg
• 4 GB free disk space (for model weights)
• Internet connection (first run only)

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Installing Python

Windows

1. Go to https://www.python.org/downloads/windows/

2. Click "Download Python 3.x.x" (latest version)

3. Run the installer

4. ⚠️ Important: On the first screen, check "Add Python to PATH" before clicking Install

   

5. Click "Install Now"

6. Once done, open Command Prompt and verify:

   python --version
   pip --version
   

   Both should print a version number.

Tip for Windows: Use Command Prompt or PowerShell to run wishcribe commands.
To open Command Prompt: press Win + R, type cmd, press Enter.

macOS

# Check if Python is already installed
python3 --version

# If not installed, use Homebrew
brew install python

If you don't have Homebrew: https://brew.sh

Ubuntu / Debian Linux

sudo apt update
sudo apt install python3 python3-pip

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Installing ffmpeg

ffmpeg is required to extract audio from video files.

Windows

1. Go to https://ffmpeg.org/download.html
2. Click "Windows" → "Windows builds by BtbN"
3. Download ffmpeg-master-latest-win64-gpl.zip
4. Extract the zip file to C:\ffmpeg
5. Add ffmpeg to PATH:
   • Press Win + S → search "Environment Variables"
   • Click "Edit the system environment variables"
   • Click "Environment Variables"
   • Under "System variables", find Path → click Edit
   • Click New → type C:\ffmpeg\bin
   • Click OK on all windows
6. Open a new Command Prompt and verify:

   ffmpeg -version
   

macOS

brew install ffmpeg

Ubuntu / Debian

sudo apt install ffmpeg

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Installation

Once Python and ffmpeg are installed:

pip install wishcribe

Windows users: If pip is not found, try pip3 or python -m pip install wishcribe

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Quick start

Step 1 — Download all models once

wishcribe download --hf-token hf_xxx

This downloads and caches:

• Whisper large (~2.9 GB) → saved locally
• pyannote diarization (~1 GB) → saved locally

Get your free HuggingFace token at: https://huggingface.co/settings/tokens
⚠️ Accept both licenses before running:
• https://huggingface.co/pyannote/speaker-diarization-3.1
• https://huggingface.co/pyannote/segmentation-3.0

Step 2 — Transcribe (fully offline forever after)

wishcribe --video meeting.mp4

That's it. No token, no internet, no extra flags needed.

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Usage — CLI

Download command

# Download default Whisper large model
wishcribe download --hf-token hf_xxx

# Download a smaller/faster model instead
wishcribe download --hf-token hf_xxx --model medium

# Use a manually downloaded pyannote model folder
wishcribe download --model-path /path/to/pyannote-model

Transcribe command

# Basic — Whisper large by default
wishcribe --video meeting.mp4

# With language + speaker count
wishcribe --video meeting.mp4 --bahasa id --speakers 3

# Override Whisper model
wishcribe --video meeting.mp4 --model medium

# Use OpenAI API for transcription
wishcribe --video meeting.mp4 --use-api --api-key sk-xxx

# Save to a custom folder + include JSON
wishcribe --video meeting.mp4 --output ./results --json

All options

Argument	Description	Default
--video	Path to video or audio file (required)	—
--hf-token	HuggingFace token — first-time only	—
--model-path	Path to local pyannote model folder	—
--model	tiny/base/small/medium/large	large
--bahasa	Language code e.g. id, en	auto-detect
--speakers	Number of speakers (optional)	auto
--output	Output folder	same as input
--use-api	Use OpenAI Whisper API	False
--api-key	OpenAI API key (with --use-api)	—
--json	Also save .json	False
--no-txt	Skip .txt output	False
--no-srt	Skip .srt output	False

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Usage — Python

from wishcribe import download, transcribe

# Step 1 — download models once
download(hf_token="hf_xxx")

# Step 2 — transcribe offline
segments = transcribe("meeting.mp4")

# With options
segments = transcribe(
    "meeting.mp4",
    model="large",     # default — best accuracy
    language="id",
    num_speakers=3,
    output_dir="./out",
)

for seg in segments:
    print(f"[{seg.speaker}] {seg.start:.1f}s  {seg.text}")

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Using a virtual environment (recommended)

To avoid conflicts with other Python packages on your system:

Windows

python -m venv wishcribe-env
wishcribe-env\Scripts\activate
pip install wishcribe

macOS / Linux

python3 -m venv wishcribe-env
source wishcribe-env/bin/activate
pip install wishcribe

Every time you open a new terminal, activate the environment first:

# Windows
wishcribe-env\Scripts\activate

# macOS / Linux
source wishcribe-env/bin/activate

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Whisper model guide

Model	Size	Speed	Accuracy
tiny	75 MB	Very fast	Fair
base	139 MB	Fast	Good
small	461 MB	Moderate	Better
medium	1.4 GB	Slow	Very good
large	2.9 GB	Slowest	Best ⭐ (default)

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Output files

File	Description
<n>_transcript.txt	Plain text grouped by speaker
<n>_transcript.srt	SRT subtitles with speaker labels
<n>_transcript.json	Raw JSON array (opt-in)

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Supported formats

Video: mp4, mkv, avi, mov, webm, and more
Audio: mp3, wav, m4a, flac, ogg, aac, opus, and more
Languages: 90+ (Whisper auto-detects if --bahasa not set)

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Troubleshooting

wishcribe: command not found

pip install wishcribe --upgrade
# or on Windows:
python -m wishcribe --video meeting.mp4

ffmpeg not found
Follow the ffmpeg installation steps above for your OS.

Dependency conflicts (e.g. with tensorflow)
Use a virtual environment (see section above) to isolate wishcribe cleanly.

Out of memory with large model
Switch to a smaller model:

wishcribe --video meeting.mp4 --model medium

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License

MIT — free to use, modify, and distribute.