alakxender/dhivehi-audio-kn
Viewer • Updated • 4.17k • 118
How to use xklzv/whisper-small-dv with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="xklzv/whisper-small-dv") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("xklzv/whisper-small-dv")
model = AutoModelForSpeechSeq2Seq.from_pretrained("xklzv/whisper-small-dv")This model is a fine-tuned version of openai/whisper-small on the Dhivehi Audio Dataset dataset. It achieves the following results on the evaluation set:
More information needed
More information needed
More information needed
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|---|---|---|---|---|---|
| 0.0605 | 2.3923 | 500 | 0.0790 | 46.1564 | 7.7177 |
| 0.0281 | 4.7847 | 1000 | 0.0854 | 43.5270 | 7.3792 |
| 0.0085 | 7.1770 | 1500 | 0.1165 | 43.3261 | 7.2649 |
| 0.0051 | 9.5694 | 2000 | 0.1230 | 43.4601 | 7.0120 |
| 0.0031 | 11.9617 | 2500 | 0.1358 | 42.3045 | 6.8937 |
| 0.0025 | 14.3541 | 3000 | 0.1438 | 42.9744 | 6.9957 |
| 0.0035 | 16.7464 | 3500 | 0.1413 | 42.3547 | 6.8040 |
| 0.0017 | 19.1388 | 4000 | 0.1480 | 42.2207 | 7.0895 |
Base model
openai/whisper-small