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
| 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") | |
| ``` |