Automatic Speech Recognition
Transformers
Safetensors
Javanese
whisper
javanese
asr
Generated from Trainer
Eval Results (legacy)
Instructions to use bagasshw/whisper-large-v2-jv-filtered with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bagasshw/whisper-large-v2-jv-filtered with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="bagasshw/whisper-large-v2-jv-filtered")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("bagasshw/whisper-large-v2-jv-filtered") model = AutoModelForSpeechSeq2Seq.from_pretrained("bagasshw/whisper-large-v2-jv-filtered", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 155db0fb9be8c38917ddabfccb470c8e1a69194c1c7385b2752f13cdc5108d2c
- Size of remote file:
- 1.18 GB
- SHA256:
- c6e1f44164f3feb3ca5e3c8b4f1f61f974935037b4f1b96cfdac62f5303f4539
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