ahishamm/whisperQURANIC
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How to use ahishamm/finetuned-whisper-quranic-large-v3 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") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("ahishamm/finetuned-whisper-quranic-large-v3")
model = AutoModelForSpeechSeq2Seq.from_pretrained("ahishamm/finetuned-whisper-quranic-large-v3", device_map="auto")# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("ahishamm/finetuned-whisper-quranic-large-v3")
model = AutoModelForSpeechSeq2Seq.from_pretrained("ahishamm/finetuned-whisper-quranic-large-v3", device_map="auto")This model is a fine-tuned version of openai/whisper-large-v3 on the whisperQURANIC 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 |
|---|---|---|---|---|
| 0.1467 | 0.4 | 200 | 0.1302 | 42.9071 |
| 0.1226 | 0.8 | 400 | 0.0958 | 156.6683 |
| 0.0746 | 1.2 | 600 | 0.0772 | 494.4510 |
| 0.0868 | 1.6 | 800 | 0.0678 | 252.8552 |
| 0.0801 | 2.0 | 1000 | 0.0560 | 361.0673 |
| 0.0552 | 2.4 | 1200 | 0.0473 | 153.8658 |
| 0.053 | 2.8 | 1400 | 0.0399 | 310.5204 |
| 0.0421 | 3.2 | 1600 | 0.0308 | 305.3961 |
| 0.0291 | 3.6 | 1800 | 0.0266 | 242.5182 |
| 0.0303 | 4.0 | 2000 | 0.0238 | 268.8141 |
Base model
openai/whisper-large-v3
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="ahishamm/finetuned-whisper-quranic-large-v3")