Instructions to use Auralis/NatHACKS_Auralis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Auralis/NatHACKS_Auralis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Auralis/NatHACKS_Auralis")# Load model directly from transformers import AutoProcessor, Wav2Vec2ForSpeechClassification processor = AutoProcessor.from_pretrained("Auralis/NatHACKS_Auralis") model = Wav2Vec2ForSpeechClassification.from_pretrained("Auralis/NatHACKS_Auralis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Create tokenizer_config.json
#3
by braingel - opened
- tokenizer_config.json +7 -0
tokenizer_config.json
ADDED
|
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"tokenizer_class": "Wav2Vec2CTCTokenizer",
|
| 3 |
+
"vocab_size": 32,
|
| 4 |
+
"pad_token_id": 0,
|
| 5 |
+
"bos_token_id": 1,
|
| 6 |
+
"eos_token_id": 2
|
| 7 |
+
}
|