mozilla-foundation/common_voice_17_0
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How to use xbilek25/whisper-medium-en-cv-6.2 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="xbilek25/whisper-medium-en-cv-6.2") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("xbilek25/whisper-medium-en-cv-6.2")
model = AutoModelForSpeechSeq2Seq.from_pretrained("xbilek25/whisper-medium-en-cv-6.2", device_map="auto")This model is a fine-tuned version of openai/whisper-medium.en on the Common Voice 17.0 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 |
|---|---|---|---|---|
| No log | 0 | 0 | 2.4185 | 46.5401 |
| 0.6822 | 0.1 | 750 | 0.9972 | 36.9871 |
| 0.2058 | 1.1 | 1500 | 1.0039 | 48.4997 |
| 0.0635 | 2.1 | 2250 | 1.0966 | 42.9884 |
| 0.0275 | 3.1 | 3000 | 1.1136 | 35.3950 |
| 0.0149 | 4.1 | 3750 | 1.1359 | 33.1598 |
| 0.0075 | 5.1 | 4500 | 1.1148 | 37.3546 |
| 0.0043 | 6.1 | 5250 | 1.1232 | 33.9865 |
| 0.0008 | 7.1 | 6000 | 1.1331 | 35.3644 |
| 0.0005 | 8.1 | 6750 | 1.1354 | 31.4452 |
| 0.0004 | 9.1 | 7500 | 1.1366 | 31.6595 |
Base model
openai/whisper-medium.en