Instructions to use Ansu/mHubert-basque-ASR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ansu/mHubert-basque-ASR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Ansu/mHubert-basque-ASR")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Ansu/mHubert-basque-ASR") model = AutoModelForCTC.from_pretrained("Ansu/mHubert-basque-ASR") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -153,4 +153,7 @@ with torch.no_grad():
|
|
| 153 |
predicted_ids = torch.argmax(logits, dim=-1)
|
| 154 |
transcription = processor.batch_decode(predicted_ids)
|
| 155 |
print(transcription[0])
|
| 156 |
-
```
|
|
|
|
|
|
|
|
|
|
|
|
| 153 |
predicted_ids = torch.argmax(logits, dim=-1)
|
| 154 |
transcription = processor.batch_decode(predicted_ids)
|
| 155 |
print(transcription[0])
|
| 156 |
+
```
|
| 157 |
+
|
| 158 |
+
# Acknowledgements
|
| 159 |
+
This work has been partially supported by the Basque Government (IKER-GAITU project), the Spanish Ministry for Digital Transformation and of Civil Service, and the EU-funded NextGenerationEU Recovery, Transformation and Resilience Plan (ILENIA project, 2022/TL-22/00215335 & ALIA).
|