openslr/openslr
Updated • 426 • 29
How to use kiranpantha/whisper-small-np with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="kiranpantha/whisper-small-np") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("kiranpantha/whisper-small-np")
model = AutoModelForSpeechSeq2Seq.from_pretrained("kiranpantha/whisper-small-np", device_map="auto")This model is a fine-tuned version of openai/whisper-small 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 |
|---|---|---|---|---|
| 0.2615 | 0.5995 | 500 | 0.2454 | 47.2685 |
| 0.123 | 1.1990 | 1000 | 0.1994 | 39.3287 |
| 0.1145 | 1.7986 | 1500 | 0.1835 | 36.1574 |
| 0.0547 | 2.3981 | 2000 | 0.1813 | 33.7037 |
| 0.0506 | 2.9976 | 2500 | 0.1730 | 32.2454 |
| 0.0204 | 3.5971 | 3000 | 0.1911 | 32.2454 |
| 0.0079 | 4.1966 | 3500 | 0.2009 | 31.6667 |
| 0.0061 | 4.7962 | 4000 | 0.2022 | 30.0926 |
| 0.0022 | 5.3957 | 4500 | 0.2097 | 30.2546 |
| 0.0022 | 5.9952 | 5000 | 0.2112 | 30.2546 |
Base model
openai/whisper-small