Instructions to use RecCode/whisper_final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RecCode/whisper_final with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="RecCode/whisper_final")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("RecCode/whisper_final") model = AutoModelForSpeechSeq2Seq.from_pretrained("RecCode/whisper_final") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -44,7 +44,6 @@ should probably proofread and complete it, then remove this comment. -->
|
|
| 44 |
- **optimizer**: Adam with betas=(0.9,0.999) and epsilon=1e-08
|
| 45 |
- **lr_scheduler_type**: linear
|
| 46 |
- **lr_scheduler_warmup_steps**: 10
|
| 47 |
-
- **num_epochs**: 1
|
| 48 |
- **mixed_precision_training**: Native AMP
|
| 49 |
|
| 50 |
### 학습 결과
|
|
|
|
| 44 |
- **optimizer**: Adam with betas=(0.9,0.999) and epsilon=1e-08
|
| 45 |
- **lr_scheduler_type**: linear
|
| 46 |
- **lr_scheduler_warmup_steps**: 10
|
|
|
|
| 47 |
- **mixed_precision_training**: Native AMP
|
| 48 |
|
| 49 |
### 학습 결과
|