Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
whisper
audio
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use keess/whisper-model-internal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use keess/whisper-model-internal with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="keess/whisper-model-internal")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("keess/whisper-model-internal") model = AutoModelForSpeechSeq2Seq.from_pretrained("keess/whisper-model-internal", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Duplicate from openai/whisper-large
Co-authored-by: Arthur Zucker <ArthurZ@users.noreply.huggingface.co>c699b05