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:
- 3f61e1a7f32afa007bc3abef74a51c663ee167a64b6a05a0e94e552d72291aab
- Size of remote file:
- 4.99 GB
- SHA256:
- 4da1134538b87c7c459b851cfca013747d8a831d28688a143a23009dfa338cfe
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