Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Safetensors
English
whisper
hf-asr-leaderboard
Generated from Trainer
Instructions to use genevera/whisper-medium.en-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use genevera/whisper-medium.en-ft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="genevera/whisper-medium.en-ft")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("genevera/whisper-medium.en-ft") model = AutoModelForSpeechSeq2Seq.from_pretrained("genevera/whisper-medium.en-ft", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bdea9a1b41f1452674b94cfaeaa3214ee02506734ba818c9834d95aeef0e03f3
- Size of remote file:
- 3.06 GB
- SHA256:
- d899fc994b713fd88efa6eaf9190351fbfa30a0e713bb7eb1f344a62b3cc6b90
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