---
license: mit
language:
- de
metrics:
- wer
pipeline_tag: automatic-speech-recognition
---
## Model Description

Fine-tuned Whisper-tiny on SwissDial-ZH dataset for Swiss German dialects.

## Model Details

- **Model Name**: nizarmichaud/whisper-tiny-swiss-german
- **Base Model**: Whisper-tiny-v3
- **Dataset**: SwissDial-ZH (8 Swiss German dialects): https://mtc.ethz.ch/publications/open-source/swiss-dial.html
- **Languages**: Swiss German

## Training

- **Duration**: 4 hours
- **Hardware**: NVIDIA RTX 3080
- **Batch Size**: 32
- **Train/Test Split**: 90%/10% (specific sentence selection)

## Performance

- **WER**: ~37% on test set

## Usage

```python
from transformers import WhisperForConditionalGeneration, WhisperProcessor

model_name = "nizarmichaud/whisper-tiny-swiss-german"
model = WhisperForConditionalGeneration.from_pretrained(model_name)
processor = WhisperProcessor.from_pretrained(model_name)

audio_input = ...  # Your audio input here
inputs = processor(audio_input, return_tensors="pt", sampling_rate=16000)
generated_ids = model.generate(inputs["input_features"])
transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)

print(transcription)
```




---
license: mit
---