---

library_name: transcribe.cpp

pipeline_tag: automatic-speech-recognition

license: other

license_name: swissdial-cc-by-nc-4.0-no-reidentification

license_link: https://form.jotform.com/223344961502048

language:

- gsw

- de

tags:

- gguf

- transcribe.cpp

- whisper

- f16

- swiss-german

- automatic-speech-recognition

base_model:

- openai/whisper-large-v3

- Flurin17/whisper-large-v3-peft-swiss-german

---

Whisper Large-v3 Swiss German – GGUF F16

GGUF F16 conversion of openai/whisper-large-v3 with the LoRA adapter

Flurin17/whisper-large-v3-peft-swiss-german merged into the weights. This is

a standalone checkpoint for

handy-computer/transcribe.cpp.

Usage

Build transcribe.cpp, convert the input to 16 kHz mono WAV, and run:

ffmpeg -i input.m4a -ar 16000 -ac 1 input.wav

build/bin/transcribe-cli \
  -m whisper-large-v3-swiss-german-F16.gguf \
  --language de \
  input.wav

The model is multilingual. For Swiss German transcription, de is the

recommended Whisper language token and transcribe is the intended task.

Compatibility

• Format: GGUF, mostly F16 with required small tensors stored in F32
• Runtime: handy-computer/transcribe.cpp
• Not compatible with oMLX, which expects MLX Safetensors rather than Whisper

GGUF. Use

gcoli/whisper-large-v3-swiss-german-mlx-fp16

for oMLX.

Provenance

• Base revision: 1ecca609f9a5ae2cd97a576a9725bc714c022a93
• Adapter revision: 2ae117cf342bc57d6068066181a9d359e98a2961
• transcribe.cpp converter revision:

856d7c10a1a864b900e066b7c9801edf373f5148

Conversion procedure:

1. load the base checkpoint as FP16;
2. merge the PEFT adapter with merge_and_unload(safe_merge=True);
3. save one standalone FP16 Safetensors checkpoint;
4. convert it with scripts/convert-whisper.py from the pinned

transcribe.cpp revision;

1. verify the GGUF container, provenance, SHA-256 checksums, and runtime model

loading through transcribe-cli.

The GGUF embeds the tokenizer, special tokens, language metadata, Whisper

frontend parameters, Mel filterbank, Hann window, and model tensors. No weight

quantization below F16 is applied.

Evaluation

The CoreML conversion of the same merged checkpoint was verified against its

PyTorch source during conversion. This GGUF build performs a runtime loading

and transcription smoke test. Independent Swiss German WER, dialect coverage,

and controlled performance benchmarks for this GGUF conversion have not yet

been published.

Limitations

Whisper can hallucinate or omit text, particularly with noise, silence,

overlapping speakers, uncommon dialects, or specialized vocabulary. Do not use

its output as the sole basis for high-impact decisions. Obtain consent before

transcribing people.

License and usage conditions

SwissDial CC BY-NC 4.0 with no-reidentification condition

The Swiss German adaptation was trained on the

SwissDial dataset.

Use of this model is subject to the following inherited conditions:

• non-commercial use under

Creative Commons Attribution-NonCommercial 4.0;

• no attempt to determine the identity of any SwissDial speaker;
• attribution of SwissDial and citation of its publication in research use.

This checkpoint is a modified and converted derivative. The Whisper base model

remains subject to Apache-2.0. This repository grants no additional rights to

upstream models, training data, or software and does not imply endorsement by

ETH Zurich, OpenAI, the adapter authors, or the transcribe.cpp authors.