Instructions to use rasgaard/whisper-tiny.da with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rasgaard/whisper-tiny.da with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="rasgaard/whisper-tiny.da")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("rasgaard/whisper-tiny.da") model = AutoModelForSpeechSeq2Seq.from_pretrained("rasgaard/whisper-tiny.da", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 575 Bytes
6d31c19 b5e5c2b 9f49998 15e272a 9f49998 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ---
library_name: transformers
datasets:
- CoRal-project/coral
language:
- da
base_model:
- openai/whisper-tiny
pipeline_tag: automatic-speech-recognition
---
A small hobby project trained in a Kaggle notebook using their free P100 GPUs. Was curious about if you could train whisper-tiny to perform decently if you specialized it for a single language, i.e. danish in this case. The TL;DR is that the results are not great :)
```python
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition",
model="rasgaard/whisper-tiny.da")
``` |