audio
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How to use li1212/wav2vec2-base-timit-demo with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="li1212/wav2vec2-base-timit-demo") # Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("li1212/wav2vec2-base-timit-demo")
model = AutoModelForCTC.from_pretrained("li1212/wav2vec2-base-timit-demo", device_map="auto")This model is a fine-tuned version of facebook/wav2vec2-base on an unknown 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 |
|---|---|---|---|---|
| 2.9596 | 3.7975 | 300 | 2.9788 | 0.9977 |
| 0.6615 | 7.5949 | 600 | 0.4876 | 0.4255 |
| 0.2672 | 11.3924 | 900 | 0.2480 | 0.2862 |
| 0.1877 | 15.1899 | 1200 | 0.1968 | 0.2488 |
| 0.1736 | 18.9873 | 1500 | 0.1808 | 0.2372 |
Base model
facebook/wav2vec2-base