Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
English
whisper
hf-asr-leaderboard
Generated from Trainer
Eval Results (legacy)
Instructions to use boisz/whisper-ameri with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use boisz/whisper-ameri with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="boisz/whisper-ameri")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("boisz/whisper-ameri") model = AutoModelForSpeechSeq2Seq.from_pretrained("boisz/whisper-ameri") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 740e6d05deb89cb5583e9b41ac6af710a2cbcf2e79d1d006d2c2602da7825294
- Size of remote file:
- 5.33 kB
- SHA256:
- 2f4aea57d275c508677d5bdf6666467a1b26b416e49ab9bae1bcbe108e17be2e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.