speechbrain/common_language
Updated • 461 • 43
How to use MicroPhion/wav2vec2-base-lang-id with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("audio-classification", model="MicroPhion/wav2vec2-base-lang-id") # Load model directly
from transformers import AutoProcessor, AutoModelForAudioClassification
processor = AutoProcessor.from_pretrained("MicroPhion/wav2vec2-base-lang-id")
model = AutoModelForAudioClassification.from_pretrained("MicroPhion/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 |
|---|---|---|---|---|
| 2.58 | 0.9989 | 693 | 2.5609 | 0.2899 |
| 1.8581 | 1.9989 | 1386 | 2.1486 | 0.4008 |
| 1.3784 | 2.9989 | 2079 | 1.5906 | 0.5666 |
| 0.976 | 3.9989 | 2772 | 1.4036 | 0.6318 |
| 0.6109 | 4.9989 | 3465 | 1.3022 | 0.6695 |
| 0.4357 | 5.9989 | 4158 | 1.2386 | 0.7138 |
| 0.23 | 6.9989 | 4851 | 1.3078 | 0.7221 |
| 0.1461 | 7.9989 | 5544 | 1.2247 | 0.7534 |
| 0.0567 | 8.9989 | 6237 | 1.3279 | 0.7646 |
| 0.0375 | 9.9989 | 6930 | 1.2554 | 0.7801 |
Base model
facebook/wav2vec2-base