ahishamm/QURANICWhisperDataset
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How to use ahishamm/finetuned-whisper-quranic-large-v3-full with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="ahishamm/finetuned-whisper-quranic-large-v3-full") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("ahishamm/finetuned-whisper-quranic-large-v3-full")
model = AutoModelForSpeechSeq2Seq.from_pretrained("ahishamm/finetuned-whisper-quranic-large-v3-full", device_map="auto")This model is a fine-tuned version of openai/whisper-large-v3 on the QURANICWhisperDataset 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 |
|---|---|---|---|---|
| 0.1349 | 0.2 | 1000 | 0.1227 | 256.8256 |
| 0.1098 | 0.4 | 2000 | 0.0918 | 438.2193 |
| 0.1071 | 0.6 | 3000 | 0.0839 | 286.1663 |
| 0.0837 | 0.8 | 4000 | 0.0737 | 295.5091 |
| 0.0672 | 1.0 | 5000 | 0.0611 | 293.6147 |
| 0.03 | 1.2 | 6000 | 0.0559 | 204.9680 |
| 0.0104 | 1.4 | 7000 | 0.0485 | 189.5761 |
| 0.0245 | 1.6 | 8000 | 0.0456 | 141.0698 |
| 0.0446 | 1.8 | 9000 | 0.0398 | 134.5774 |
| 0.0231 | 2.0 | 10000 | 0.0375 | 121.0055 |
Base model
openai/whisper-large-v3