google/fleurs
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How to use octava/whisper-small-disaster with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="octava/whisper-small-disaster") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("octava/whisper-small-disaster")
model = AutoModelForSpeechSeq2Seq.from_pretrained("octava/whisper-small-disaster", device_map="auto")This model is a fine-tuned version of openai/whisper-small on the Extracted Youtube with self developed dataset 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 |
|---|---|---|---|---|
| 0.2816 | 0.9346 | 1000 | 0.2899 | 16.8831 |
| 0.1195 | 1.8692 | 2000 | 0.2892 | 15.5519 |
| 0.0453 | 2.8037 | 3000 | 0.2804 | 14.4481 |
| 0.0205 | 3.7383 | 4000 | 0.3164 | 13.9286 |
| 0.007 | 4.6729 | 5000 | 0.3248 | 13.5065 |
| 0.0045 | 5.6075 | 6000 | 0.3295 | 13.2468 |
| 0.0015 | 6.5421 | 7000 | 0.3354 | 12.6299 |
| 0.001 | 7.4766 | 8000 | 0.3398 | 12.5325 |
| 0.0008 | 8.4112 | 9000 | 0.3416 | 12.8247 |
| 0.0007 | 9.3458 | 10000 | 0.3443 | 12.7922 |
Base model
openai/whisper-small