cstr/whisper-large-v3-turbo-german-GGUF

whisper-large-v3-turbo-german — GGUF

GGML conversions and quantisations of primeline/whisper-large-v3-turbo-german for use with CrispStrobe/CrispASR or any whisper.cpp-compatible tool.

Available variants

File	Quant	Size	Notes
ggml-model.bin	F16	1.6 GB	Original conversion, full precision
ggml-model-q5_0.bin	Q5_0	548 MB	Good quality/size tradeoff
ggml-model-q4_k.bin	Q4_K	453 MB	Smallest, fastest on CPU

All variants produce correct German transcription on test audio. Q4_K is recommended for CPU deployment.

Model details

• Architecture: Whisper large-v3 encoder (32 layers) + turbo decoder (4 layers)
• Parameters: 809M
• Languages: German (primary), English
• Base model: primeline/whisper-large-v3-turbo-german
• License: MIT

The "turbo" variant uses only 4 decoder layers (vs 32 in large-v3), making it ~3x faster at inference with minimal quality loss for German.

Usage with CrispASR

# Build CrispASR
git clone https://github.com/CrispStrobe/CrispASR && cd CrispASR
cmake -S . -B build && cmake --build build -j8

# Transcribe German audio
./build/bin/crispasr -m ggml-model-q4_k.bin -f german_audio.wav -l de

# With subtitles
./build/bin/crispasr -m ggml-model-q4_k.bin -f german_audio.wav -l de -osrt --split-on-punct

Conversion

Converted from the original HuggingFace model using whisper.cpp's convert-h5-to-ggml.py, then quantised with whisper-quantize:

python models/convert-h5-to-ggml.py primeline/whisper-large-v3-turbo-german . models
whisper-quantize ggml-model.bin ggml-model-q5_0.bin q5_0
whisper-quantize ggml-model.bin ggml-model-q4_k.bin q4_k