Instructions to use ml-for-speech/language-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ml-for-speech/language-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="ml-for-speech/language-classification")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("ml-for-speech/language-classification") model = AutoModelForAudioClassification.from_pretrained("ml-for-speech/language-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Create README.md
Browse files
README.md
ADDED
|
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
pipeline_tag: audio-classification
|
| 3 |
+
datasets:
|
| 4 |
+
- common_language
|
| 5 |
+
---
|
| 6 |
+
# Language Classification
|
| 7 |
+
|
| 8 |
+
A model trained for language classification. Thanks to @sanchit-gandhi for [this code](https://huggingface.co/sanchit-gandhi/whisper-base-ft-common-language-id) which was used to train the model.
|
| 9 |
+
|
| 10 |
+
This model was trained for 15 epochs.
|