Instructions to use anton1561/whisper-small-ph-mhtest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anton1561/whisper-small-ph-mhtest with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="anton1561/whisper-small-ph-mhtest")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("anton1561/whisper-small-ph-mhtest") model = AutoModelForSpeechSeq2Seq.from_pretrained("anton1561/whisper-small-ph-mhtest", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1e61049a8e3c5c2c8b2b960801d8f0522ad122acc1a243e62f296a8765bd21d8
- Size of remote file:
- 967 MB
- SHA256:
- 89cae32f25c71c3de727ae8804b9ec17ab8cb097bb860276d937572c13ae2543
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.