mozilla-foundation/common_voice_13_0
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How to use Sleepyp00/whisper-small-Swedish with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="Sleepyp00/whisper-small-Swedish") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("Sleepyp00/whisper-small-Swedish")
model = AutoModelForSpeechSeq2Seq.from_pretrained("Sleepyp00/whisper-small-Swedish")This model is a fine-tuned version of openai/whisper-small 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 |
|---|---|---|---|---|
| 0.5133 | 0.64 | 500 | 0.3286 | 24.1571 |
| 0.3701 | 1.28 | 1000 | 0.2979 | 22.7444 |
| 0.3324 | 1.92 | 1500 | 0.2818 | 21.2813 |
| 0.2016 | 2.55 | 2000 | 0.2802 | 21.4191 |
| 0.1428 | 3.19 | 2500 | 0.2805 | 20.5921 |
| 0.1455 | 3.83 | 3000 | 0.2740 | 20.3059 |
| 0.1162 | 4.47 | 3500 | 0.2797 | 20.2343 |
| 0.0854 | 5.11 | 4000 | 0.2793 | 19.8526 |
| 0.0859 | 5.75 | 4500 | 0.2809 | 20.0302 |
| 0.0574 | 6.39 | 5000 | 0.2852 | 19.8367 |
| 0.0644 | 7.02 | 5500 | 0.2851 | 19.6644 |
| 0.0637 | 7.66 | 6000 | 0.2860 | 19.5690 |
Base model
openai/whisper-small