openslr/openslr
Updated • 426 • 29
How to use kiranpantha/whisper-base-ne with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="kiranpantha/whisper-base-ne") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("kiranpantha/whisper-base-ne")
model = AutoModelForSpeechSeq2Seq.from_pretrained("kiranpantha/whisper-base-ne", device_map="auto")This model is a fine-tuned version of openai/whisper-base on the OpenSLR54 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 |
|---|---|---|---|---|
| 0.5029 | 0.0750 | 500 | 0.4922 | 77.0205 |
| 0.351 | 0.1499 | 1000 | 0.3561 | 65.6941 |
| 0.3034 | 0.2249 | 1500 | 0.2988 | 57.0618 |
| 0.2689 | 0.2999 | 2000 | 0.2714 | 53.2844 |
| 0.2584 | 0.3749 | 2500 | 0.2537 | 50.8369 |
| 0.2325 | 0.4498 | 3000 | 0.2393 | 48.0282 |
| 0.2238 | 0.5248 | 3500 | 0.2271 | 46.5723 |
| 0.2149 | 0.5998 | 4000 | 0.2149 | 44.4056 |
| 0.2038 | 0.6748 | 4500 | 0.2091 | 43.6834 |
| 0.2026 | 0.7497 | 5000 | 0.2056 | 43.2821 |
Base model
openai/whisper-base