Automatic Speech Recognition
Transformers
PyTorch
whisper
Generated from Trainer
whisper-event
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use AigizK/whisper-medium-ba with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AigizK/whisper-medium-ba with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="AigizK/whisper-medium-ba")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("AigizK/whisper-medium-ba") model = AutoModelForSpeechSeq2Seq.from_pretrained("AigizK/whisper-medium-ba", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from AigizK/whisper-medium-ba: direct link, hf CLI and curl.
- Browser
- Download file 3.06 GB
-
https://huggingface.co/AigizK/whisper-medium-ba/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://AigizK/whisper-medium-ba/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/AigizK/whisper-medium-ba/resolve/main/pytorch_model.bin
3.06 GB
- Xet hash:
- 6aaef16f03383ea8fee59f55a1b277f9f84b98c39b9e90fe06be5604dcec9091
- Size of remote file:
- 3.06 GB
- SHA256:
- 63221c837364015910b8725d4929f1837433ffa27b4f2653edece0aec0e13b3d
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