Instructions to use avalonai/whisper-small-jv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use avalonai/whisper-small-jv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="avalonai/whisper-small-jv")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("avalonai/whisper-small-jv") model = AutoModelForSpeechSeq2Seq.from_pretrained("avalonai/whisper-small-jv") - Notebooks
- Google Colab
- Kaggle
Model ini adalah hasil dari proses fine-tuning model Whisper Small pada dataset OpenSLR untuk bahasa Jawa. Anda dapat menggunakan model ini dengan menjalankan syntax berikut.
from transformers import WhisperForConditionalGeneration, WhisperProcessor
model = WhisperForConditionalGeneration.from_pretrained("avalonai/whisper-small-jv")
processor = WhisperProcessor.from_pretrained("avalonai/whisper-small-jv")
Berdasarkan hasil eksperimen kami, model yang telah kami fine-tune berhasil memperoleh WER sebesar 23.22%.
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