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How to use anuragshas/whisper-large-v2-bn with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="anuragshas/whisper-large-v2-bn") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("anuragshas/whisper-large-v2-bn")
model = AutoModelForSpeechSeq2Seq.from_pretrained("anuragshas/whisper-large-v2-bn")This model is a fine-tuned version of openai/whisper-large-v2 on the mozilla-foundation/common_voice_11_0 bn dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.046 | 1.21 | 1000 | 0.0746 | 11.1086 |