mozilla-foundation/common_voice_13_0
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How to use Stopwolf/distil-whisper-large-v2-pt with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="Stopwolf/distil-whisper-large-v2-pt") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("Stopwolf/distil-whisper-large-v2-pt")
model = AutoModelForSpeechSeq2Seq.from_pretrained("Stopwolf/distil-whisper-large-v2-pt")This model is a fine-tuned version of distil-whisper/distil-large-v2 on the mozilla-foundation/common_voice_13_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 Ortho | Wer |
|---|---|---|---|---|---|
| 1.6148 | 0.5 | 900 | 0.4448 | 0.2227 | 0.1690 |
| 0.3709 | 0.99 | 1800 | 0.3524 | 0.1927 | 0.1367 |
| 0.2619 | 1.49 | 2700 | 0.3266 | 0.1751 | 0.1213 |
| 0.2143 | 1.98 | 3600 | 0.3085 | 0.1726 | 0.1168 |
| 0.1219 | 2.48 | 4500 | 0.3070 | 0.1639 | 0.1112 |
| 0.1256 | 2.98 | 5400 | 0.3028 | 0.1649 | 0.1104 |
Base model
distil-whisper/distil-large-v2