speechbrain/common_language
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How to use anton-l/sew-mid-100k-ft-common-language with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("audio-classification", model="anton-l/sew-mid-100k-ft-common-language") # Load model directly
from transformers import AutoProcessor, AutoModelForAudioClassification
processor = AutoProcessor.from_pretrained("anton-l/sew-mid-100k-ft-common-language")
model = AutoModelForAudioClassification.from_pretrained("anton-l/sew-mid-100k-ft-common-language")This model is a fine-tuned version of asapp/sew-mid-100k on the common_language 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 | Accuracy |
|---|---|---|---|---|
| 3.608 | 1.0 | 173 | 3.7266 | 0.0540 |
| 3.1298 | 2.0 | 346 | 3.2180 | 0.1654 |
| 2.8481 | 3.0 | 519 | 2.9270 | 0.2019 |
| 2.648 | 4.0 | 692 | 2.6991 | 0.2619 |
| 2.5 | 5.0 | 865 | 2.5236 | 0.3004 |
| 2.2578 | 6.0 | 1038 | 2.4019 | 0.3212 |
| 2.2782 | 7.0 | 1211 | 2.1698 | 0.3658 |
| 2.1665 | 8.0 | 1384 | 2.1976 | 0.3631 |
| 2.1626 | 9.0 | 1557 | 2.1473 | 0.3791 |
| 2.1514 | 10.0 | 1730 | 2.1189 | 0.3842 |