s3prl/superb
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How to use michaelung/wav2vec2-base-ft-keyword-spotting with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("audio-classification", model="michaelung/wav2vec2-base-ft-keyword-spotting") # Load model directly
from transformers import AutoProcessor, AutoModelForAudioClassification
processor = AutoProcessor.from_pretrained("michaelung/wav2vec2-base-ft-keyword-spotting")
model = AutoModelForAudioClassification.from_pretrained("michaelung/wav2vec2-base-ft-keyword-spotting", device_map="auto")This model is a fine-tuned version of facebook/wav2vec2-base on the superb 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 |
|---|---|---|---|---|
| 0.4888 | 1.0 | 384 | 0.3631 | 0.9701 |
| 0.2033 | 2.0 | 768 | 0.1407 | 0.9721 |
| 0.1843 | 3.0 | 1152 | 0.1012 | 0.9782 |
| 0.1435 | 4.0 | 1537 | 0.0895 | 0.9807 |
| 0.116 | 5.0 | 1920 | 0.0866 | 0.9816 |
Base model
facebook/wav2vec2-base