google/speech_commands
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How to use 0xb1/wav2vec2-base-finetuned-speech_commands-v0.02 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("audio-classification", model="0xb1/wav2vec2-base-finetuned-speech_commands-v0.02") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForAudioClassification
processor = AutoProcessor.from_pretrained("0xb1/wav2vec2-base-finetuned-speech_commands-v0.02")
model = AutoModelForAudioClassification.from_pretrained("0xb1/wav2vec2-base-finetuned-speech_commands-v0.02", device_map="auto")This model is a fine-tuned version of facebook/wav2vec2-base on the speech_commands 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 |
|---|---|---|---|---|
| 0.9963 | 1.0 | 663 | 0.7316 | 0.9612 |
| 0.4965 | 2.0 | 1326 | 0.2656 | 0.9672 |
| 0.4306 | 3.0 | 1989 | 0.1630 | 0.9720 |
| 0.2901 | 4.0 | 2652 | 0.1283 | 0.9753 |
| 0.2963 | 5.0 | 3315 | 0.1170 | 0.9759 |