Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Bulgarian
whisper
whisper-event
Generated from Trainer
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use shripadbhat/whisper-medium-bg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shripadbhat/whisper-medium-bg with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="shripadbhat/whisper-medium-bg")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("shripadbhat/whisper-medium-bg") model = AutoModelForSpeechSeq2Seq.from_pretrained("shripadbhat/whisper-medium-bg", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c65467692e3ddb652c1be08847f986377bed59d44f6b0baef7464f135f79a00e
- Size of remote file:
- 3.64 kB
- SHA256:
- 0ba8deb1008b7d0690e9d24bb183c9c9be43f9af1583b7a4ce3ef6e738d6c8e4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.