Automatic Speech Recognition
Transformers
Safetensors
Swahili
English
whisper
Generated from Trainer
Instructions to use Jacaranda-Health/ASR-STT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jacaranda-Health/ASR-STT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Jacaranda-Health/ASR-STT")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Jacaranda-Health/ASR-STT") model = AutoModelForSpeechSeq2Seq.from_pretrained("Jacaranda-Health/ASR-STT") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -161,12 +161,12 @@ The fine-tuned model demonstrates superior performance in:
|
|
| 161 |
The following charts illustrate the model's training progress and performance improvements:
|
| 162 |
|
| 163 |
### Word Error Rate (WER) Progress
|
| 164 |
-
 Progress
|
| 164 |
+

|
| 165 |
|
| 166 |
The WER chart shows the steady improvement in transcription accuracy throughout the training process. Starting from approximately 21.6% WER at step 500, the model achieves its best performance of 14.7% WER by step 8000, demonstrating consistent learning and convergence.
|
| 167 |
|
| 168 |
### Learning Rate Schedule
|
| 169 |
+

|
| 170 |
|
| 171 |
The learning rate follows a cosine annealing schedule, starting at 1e-05 and gradually decreasing over the 8000 training steps. This schedule helps ensure stable training and prevents overfitting while allowing the model to fine-tune effectively.
|
| 172 |
|