Automatic Speech Recognition
Transformers
French
English
Arabic
medical
radiology
clinical-dictation
french
healthcare
snomed-ct
wannamed
wannagotech
Eval Results (legacy)
Instructions to use WannaGoTech/WannaMedASR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WannaGoTech/WannaMedASR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="WannaGoTech/WannaMedASR")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("WannaGoTech/WannaMedASR", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update training_metrics.json sovereign naming
Browse files- training_metrics.json +1 -1
training_metrics.json
CHANGED
|
@@ -2,7 +2,7 @@
|
|
| 2 |
"model_version_name": "WannaMedASR-v2-3Stage-LoRA-1.7B",
|
| 3 |
"release_date": "2026-10-29",
|
| 4 |
"base_model": "Generic-ASR-1.7B-Base",
|
| 5 |
-
"architecture": "
|
| 6 |
"adapter_md5": "02de17701ae6aa8b48def079e5de5de4",
|
| 7 |
"audio_format": "Lossless WAV/FLAC PCM_16 16000 Hz Mono (0% lossy)",
|
| 8 |
"zero_garbage_guaranteed": true,
|
|
|
|
| 2 |
"model_version_name": "WannaMedASR-v2-3Stage-LoRA-1.7B",
|
| 3 |
"release_date": "2026-10-29",
|
| 4 |
"base_model": "Generic-ASR-1.7B-Base",
|
| 5 |
+
"architecture": "WannaMedASRForConditionalGeneration (28 layers, 196 LoRA matrices = 392 FP32 tensors, r=16, alpha=32)",
|
| 6 |
"adapter_md5": "02de17701ae6aa8b48def079e5de5de4",
|
| 7 |
"audio_format": "Lossless WAV/FLAC PCM_16 16000 Hz Mono (0% lossy)",
|
| 8 |
"zero_garbage_guaranteed": true,
|