Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
English
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use rngzhi/cs3264-project-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rngzhi/cs3264-project-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="rngzhi/cs3264-project-v2")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("rngzhi/cs3264-project-v2") model = AutoModelForSpeechSeq2Seq.from_pretrained("rngzhi/cs3264-project-v2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 014a8b64907a038bc2d6173b0edfd084d58f34a01eea3d96052cd7b92fab4c30
- Size of remote file:
- 5.18 kB
- SHA256:
- 5552171a6455f31e4b6286863902bb999f950fb57768060bfb0d3acd2e95cdef
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.