Instructions to use facebook/mms-1b-fl102 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/mms-1b-fl102 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="facebook/mms-1b-fl102")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("facebook/mms-1b-fl102") model = AutoModelForCTC.from_pretrained("facebook/mms-1b-fl102", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f5342caf22c9855f3ce293af99056602b94d3571c6b5b615a7b292658de70a76
- Size of remote file:
- 9.06 MB
- SHA256:
- 83f63c26aee0a49f107064f615472ddfe5de7cd4fd3a103e02196cb65f891727
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.