Instructions to use shrria/bts-asr-processor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shrria/bts-asr-processor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="shrria/bts-asr-processor")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("shrria/bts-asr-processor") model = AutoModelForCTC.from_pretrained("shrria/bts-asr-processor", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b367e2490922ed3861a648c1e9548fa8e846095c6fae7fee1e261c3f2efa0eb6
- Size of remote file:
- 1.26 GB
- SHA256:
- fd257f1c624a199170263d0bb8649be504cc9a206bb49064384504462682e658
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.