Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
jw
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use bagasshw/whisper-large-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bagasshw/whisper-large-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="bagasshw/whisper-large-v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("bagasshw/whisper-large-v2") model = AutoModelForSpeechSeq2Seq.from_pretrained("bagasshw/whisper-large-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from bagasshw/whisper-large-v2: direct link, hf CLI and curl.
- Browser
- Download file 5.84 kB
-
https://huggingface.co/bagasshw/whisper-large-v2/resolve/main/training_args.bin
- Command line
-
hf download hf://bagasshw/whisper-large-v2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/bagasshw/whisper-large-v2/resolve/main/training_args.bin
5.84 kB
- Xet hash:
- f806e165e942c3d4f8d3a2ce7912e1e5dc5589f3c4ae916186240b2c6f62e731
- Size of remote file:
- 5.84 kB
- SHA256:
- 72595d462cda1db234be248b768101f0acdef17718f00507829d56bba7c635f2
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