Feature Extraction
Transformers
Safetensors
English
custom_model
multi-modal
speech-language
custom_code
Eval Results (legacy)
Instructions to use skit-ai/speechllm-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use skit-ai/speechllm-2B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="skit-ai/speechllm-2B", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("skit-ai/speechllm-2B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -136,9 +136,9 @@ model.generate_meta(
|
|
| 136 |
|
| 137 |
| **Dataset** | **Word Error Rate** | **Gender Acc** | **Age Acc** | **Accent Acc** |
|
| 138 |
|:----------------------:|:----------------------:|:-------------:|:----------:|:-------------:|
|
| 139 |
-
| librispeech-test-clean |
|
| 140 |
-
| librispeech-test-other |
|
| 141 |
-
| CommonVoice test |
|
| 142 |
|
| 143 |
|
| 144 |
|
|
|
|
| 136 |
|
| 137 |
| **Dataset** | **Word Error Rate** | **Gender Acc** | **Age Acc** | **Accent Acc** |
|
| 138 |
|:----------------------:|:----------------------:|:-------------:|:----------:|:-------------:|
|
| 139 |
+
| librispeech-test-clean | 7.36 | 0.9490 | | |
|
| 140 |
+
| librispeech-test-other | 10.47 | 0.9099 | | |
|
| 141 |
+
| CommonVoice test | 24.47 | 0.8680 | 0.6061 | 0.6156 |
|
| 142 |
|
| 143 |
|
| 144 |
|