Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
English
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use bika5/output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bika5/output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="bika5/output")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("bika5/output") model = AutoModelForSpeechSeq2Seq.from_pretrained("bika5/output", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- da6a7bb5ce428a9ec4fa80275f3adf4cfb537a558f30bc4a34793ad07e657b87
- Size of remote file:
- 5.18 kB
- SHA256:
- 794d9d5aa227b30069208d0b95d75958d18ed29fbce8ac6bc653ccbd692e2a66
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.