haseong8012/child-50k
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How to use haseong8012/whisper-small_child-50k_cosLR with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="haseong8012/whisper-small_child-50k_cosLR") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("haseong8012/whisper-small_child-50k_cosLR")
model = AutoModelForSpeechSeq2Seq.from_pretrained("haseong8012/whisper-small_child-50k_cosLR")This model is a fine-tuned version of openai/small on the child-50k 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 | Cer |
|---|---|---|---|---|---|
| 0.1058 | 0.36 | 500 | 0.0708 | 8.0241 | 3.2266 |
| 0.0577 | 0.71 | 1000 | 0.0446 | 5.2591 | 2.1891 |
| 0.0296 | 1.07 | 1500 | 0.0309 | 3.2662 | 1.3469 |
| 0.0301 | 1.42 | 2000 | 0.0279 | 2.7286 | 1.1516 |
| 0.0299 | 1.78 | 2500 | 0.0245 | 2.4578 | 0.9920 |
| 0.0219 | 2.13 | 3000 | 0.0262 | 2.6882 | 1.2290 |
| 0.0148 | 2.49 | 3500 | 0.0219 | 2.0899 | 0.9245 |
| 0.0141 | 2.84 | 4000 | 0.0281 | 2.8782 | 1.1615 |
| 0.0097 | 3.2 | 4500 | 0.0210 | 2.0616 | 0.8373 |
| 0.0109 | 3.55 | 5000 | 0.0241 | 2.2556 | 0.9159 |