Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Afrikaans
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use M2LabOrg/whisper-small-af with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use M2LabOrg/whisper-small-af with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="M2LabOrg/whisper-small-af")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("M2LabOrg/whisper-small-af") model = AutoModelForSpeechSeq2Seq.from_pretrained("M2LabOrg/whisper-small-af", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a8e9cd5d787486dc361a14db7987a8ace80508491e8a695cc96609485b761103
- Size of remote file:
- 5.24 kB
- SHA256:
- 43a04b4cd83496a3e7556bf55aa2ccd78f0f6221a38110ea13c76045f3f77438
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.