fsicoli/common_voice_15_0
Updated • 102k • 6
How to use npallewela/whisper-base-dv-1 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="npallewela/whisper-base-dv-1") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("npallewela/whisper-base-dv-1")
model = AutoModelForSpeechSeq2Seq.from_pretrained("npallewela/whisper-base-dv-1")This model is a fine-tuned version of openai/whisper-base on the Common Voice 15 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 Ortho | Wer |
|---|---|---|---|---|---|
| 2.3429 | 2.3669 | 400 | 2.2946 | 202.8266 | 113.8993 |
| 1.2184 | 4.7337 | 800 | 1.2136 | 203.0430 | 112.1693 |
| 1.0668 | 7.1006 | 1200 | 1.0778 | 201.4657 | 111.3182 |
| 1.0197 | 9.4675 | 1600 | 1.0351 | 199.5743 | 111.5619 |
| 0.9854 | 11.8343 | 2000 | 1.0064 | 194.9470 | 110.4323 |
| 0.9512 | 14.2012 | 2400 | 0.9839 | 189.6776 | 109.2157 |
| 0.9335 | 16.5680 | 2800 | 0.9635 | 189.0215 | 109.1896 |
| 0.9074 | 18.9349 | 3200 | 0.9436 | 188.9866 | 109.4072 |
| 0.8867 | 21.3018 | 3600 | 0.9265 | 193.4394 | 110.2983 |
| 0.8529 | 23.6686 | 4000 | 0.9098 | 194.4305 | 110.5542 |
Base model
openai/whisper-base