massscriber 0.1.0

Local, free, multilingual audio transcription app powered by faster-whisper.

Massscriber

Massscriber is a local-first audio transcription app built on top of faster-whisper. It is designed for people who want:

• no paid API dependency
• no upload requirement for private recordings
• unlimited local processing
• strong multilingual transcription quality
• both a simple UI and a scriptable CLI

Highlights

• Local transcription with faster-whisper
• Multilingual speech-to-text with automatic language detection
• Batch processing for multiple audio or video files
• Gradio UI for drag-and-drop use
• CLI mode for automation and power users
• Export formats: txt, srt, vtt, json
• Quality-first model option: large-v3
• Speed-first model option: turbo

Supported Inputs

The UI currently accepts:

• .mp3
• .wav
• .m4a
• .flac
• .ogg
• .aac
• .wma
• .mp4
• .mkv

faster-whisper handles media decoding through its own stack, so a manual ffmpeg install is usually not required.

Model Guidance

Model	Best for	Notes
large-v3	Highest accuracy	Best default for serious transcription work
turbo	Fastest practical transcription	Great speed/quality balance
medium	Mid-range systems	Useful fallback for lower VRAM devices
small, base, tiny	Lightweight testing	Faster, but lower accuracy

Important note: no speech recognition model is perfectly error-free. For a fully free and local workflow, large-v3 is one of the strongest practical choices available today.

Quick Start

Windows helper scripts

install.bat
start_ui.bat

Manual setup

python -m venv .venv
.venv\Scripts\activate
python -m pip install --upgrade pip
python -m pip install -e .

Run the App

Launch the UI

.venv\Scripts\activate
python app.py

The default UI address is http://127.0.0.1:7860.

Use the CLI

.venv\Scripts\activate
python app.py transcribe "C:\audio\meeting.mp3" --model large-v3 --formats txt srt json

You can also use the installed console entry point:

massscriber transcribe "C:\audio\meeting.mp3" --model turbo --formats txt srt

Recommended Settings

Best quality

• Model: large-v3
• Beam size: 5
• VAD: enabled
• Word timestamps: enabled

Best speed

• Model: turbo
• Device: cuda when available
• Compute type: float16
• Batch size: 8 or 16

Outputs

By default, transcripts are written to the outputs directory:

• txt
• srt
• json

You can also enable vtt from the UI or CLI.

GPU Notes

If you use an NVIDIA GPU, faster-whisper may require CUDA runtime libraries on your machine. CPU mode works too; it is simply slower.

According to the faster-whisper recommendations:

• int8 is a good default for CPU execution
• float16 or int8_float16 are good GPU options

If you hit CUDA DLL issues on Windows, follow the Windows notes in the official faster-whisper documentation.

First Run Behavior

The selected model is downloaded automatically on first use. For large-v3, the first run can take a while because the model is large.

Development

Run tests

python -m unittest discover -s tests -v

Local verification

python -m py_compile app.py massscriber\__init__.py massscriber\types.py massscriber\exporters.py massscriber\transcriber.py massscriber\ui.py

Versioning and Releases

• Project version is defined in massscriber.__version__
• Packaging reads the version dynamically from the package
• Human-readable release history lives in CHANGELOG.md
• Release steps are documented in RELEASING.md
• Pushing a tag like v0.1.0 triggers the GitHub release workflow

Roadmap

• Speaker diarization
• Better subtitle segmentation options
• Packaged desktop builds
• Folder watch / auto-transcribe workflows

License

This project is released under the MIT License. See LICENSE.

References

• SYSTRAN/faster-whisper
• openai/whisper