timit-asr/timit_asr
Updated • 340 • 27
How to use hellosimple/wav2vec2-base-timit-demo-colab with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="hellosimple/wav2vec2-base-timit-demo-colab") # Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("hellosimple/wav2vec2-base-timit-demo-colab")
model = AutoModelForCTC.from_pretrained("hellosimple/wav2vec2-base-timit-demo-colab")This model is a fine-tuned version of facebook/wav2vec2-base on the timit_asr 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 |
|---|---|---|---|---|
| 3.5112 | 3.45 | 500 | 1.1699 | 0.8236 |
| 0.5349 | 6.9 | 1000 | 0.3911 | 0.3609 |
| 0.1875 | 10.34 | 1500 | 0.3993 | 0.3170 |
| 0.1113 | 13.79 | 2000 | 0.3870 | 0.3046 |
| 0.0778 | 17.24 | 2500 | 0.4056 | 0.2963 |
| 0.0561 | 20.69 | 3000 | 0.3781 | 0.2918 |
| 0.0461 | 24.14 | 3500 | 0.4186 | 0.2857 |
| 0.0375 | 27.59 | 4000 | 0.4243 | 0.2830 |