google/fleurs
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How to use steja/whisper-small-luxembourgish with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="steja/whisper-small-luxembourgish") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("steja/whisper-small-luxembourgish")
model = AutoModelForSpeechSeq2Seq.from_pretrained("steja/whisper-small-luxembourgish")This model is a fine-tuned version of bofenghuang/whisper-small-cv11-german-punct on the google/fleurs lb_lu 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 |
|---|---|---|---|---|
| 0.0618 | 38.46 | 500 | 1.0104 | 43.2968 |
| 0.0055 | 76.92 | 1000 | 1.0684 | 40.1288 |
| 0.0024 | 115.38 | 1500 | 1.1056 | 40.9447 |
| 0.0014 | 153.85 | 2000 | 1.1280 | 39.7615 |
| 0.0013 | 192.31 | 2500 | 1.1415 | 39.9857 |
| 0.0008 | 230.77 | 3000 | 1.1573 | 39.7996 |
| 0.0006 | 269.23 | 3500 | 1.1682 | 40.0095 |
| 0.0006 | 307.69 | 4000 | 1.1769 | 39.7233 |
| 0.0005 | 346.15 | 4500 | 1.1826 | 39.5134 |
| 0.0004 | 384.62 | 5000 | 1.1857 | 39.4990 |