speechbrain/common_language
Updated • 440 • 43
How to use DongningRao/wav2vec2-base-lang-id with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("audio-classification", model="DongningRao/wav2vec2-base-lang-id") # Load model directly
from transformers import AutoProcessor, AutoModelForAudioClassification
processor = AutoProcessor.from_pretrained("DongningRao/wav2vec2-base-lang-id")
model = AutoModelForAudioClassification.from_pretrained("DongningRao/wav2vec2-base-lang-id")This model is a fine-tuned version of facebook/wav2vec2-base on the common_language dataset. It achieves the following results on the evaluation set:
More information needed
More information needed
More information needed
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.8262 | 1.0 | 347 | 3.1017 | 0.1703 |
| 1.8912 | 2.0 | 694 | 1.9753 | 0.4147 |
| 1.339 | 3.0 | 1041 | 1.6294 | 0.5352 |
| 0.7847 | 4.0 | 1388 | 1.4546 | 0.6189 |
| 0.5866 | 5.0 | 1735 | 1.2889 | 0.6591 |
| 0.3546 | 6.0 | 2082 | 1.3346 | 0.7065 |
| 0.2172 | 7.0 | 2429 | 1.2969 | 0.7291 |
| 0.1056 | 8.0 | 2776 | 1.1767 | 0.7566 |
| 0.0382 | 9.0 | 3123 | 1.2239 | 0.7731 |
| 0.0551 | 10.0 | 3470 | 1.2104 | 0.7855 |
Base model
facebook/wav2vec2-base