Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
English
whisper
Generated from Trainer
Instructions to use navin-kumar-j/whisper-base-en-w-pcd-10-4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use navin-kumar-j/whisper-base-en-w-pcd-10-4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="navin-kumar-j/whisper-base-en-w-pcd-10-4", device_map="auto")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("navin-kumar-j/whisper-base-en-w-pcd-10-4") model = AutoModelForSpeechSeq2Seq.from_pretrained("navin-kumar-j/whisper-base-en-w-pcd-10-4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4136c3c3ca89bc7036d8a03340a1a28171561ad354006aba6288d7c7c1f34823
- Size of remote file:
- 290 MB
- SHA256:
- 934e98f947080f35141248c4b7f3cf22f858a7539ded522885a40be189a8e326
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.