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kamilakesbi commited on Apr 22, 2024

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@@ -18,18 +18,18 @@ The available datasets are the CallHome (Japanese, Chinese, German, Spanish, Eng
 Each model has been fine-tuned on a specific Callhome language subset. They achieve better performances on multilingual data compared to pyannote's pre-trained [segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) model:
-| Callhome dataset subset | Model         |     DER       |  False alarm  | Missed detection|   Confusion   |
 | ------------------------| ------------- | ------------- | ------------- | --------------- | ------------- |
-|          Japanese       | Pretrained    |     25.44     |      2.30     |      17.45      |     5.69      |
-|                         | Fine-tuned    |     18.23     |      6.31     |       6.91      |     5.01      |
-|          Spanish        | Pretrained    |     33.44     |      2.59     |       2.519     |  5.66         |
-|                         | Fine-tuned    |     25.72     |      6.87     |       12.73     | 6.12          |
-|          English        | Pretrained    |  22.16        |      6.29     |       10.97     | 4.90          |
-|                         | Fine-tuned    | 18.40         | 7.10          |       6.98      | 4.32          |
-|          German         | Pretrained    |    21.90      | 3.10          |  14.25          | 4.55          |
-|                         | Fine-tuned    | 16.75         | 5.00          |  7.75           | 4.00          |
-|          Chinese        | Pretrained    | 19.73         | 4.81          | 9.82            | 5.11          |
-|                         | Fine-tuned    | 15.95         | 5.04          | 7.24            | 3.68          |
 Note: Results have been obtained using the [test script](https://github.com/kamilakesbi/diarizers/blob/main/test_segmentation.py) from diarizers.

 Each model has been fine-tuned on a specific Callhome language subset. They achieve better performances on multilingual data compared to pyannote's pre-trained [segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) model:
+| Callhome test dataset subset | Model         |     DER       |  False alarm  | Missed detection|   Confusion   |
 | ------------------------| ------------- | ------------- | ------------- | --------------- | ------------- |
+|          Japanese       | Pretrained    |     25.44     |      **2.30** |      17.45      |     5.69      |
+|                         | Fine-tuned    |    **18.23**  |      6.31     |      **6.91**   |     **5.01**  |
+|          Spanish        | Pretrained    |     33.44     |      **2.59** |      25.19      |  **5.66**     |
+|                         | Fine-tuned    |     **25.72** |      6.87     |      **12.73**  | 6.12          |
+|          English        | Pretrained    |  22.16        |      **6.29** |       10.97     | 4.90          |
+|                         | Fine-tuned    | **18.40**     | 7.10          |       **6.98**  | **4.32**      |
+|          German         | Pretrained    |    21.90      | **3.10**      |  14.25          | 4.55          |
+|                         | Fine-tuned    | **16.75**     | 5.00          |  **7.75**       | **4.00**      |
+|          Chinese        | Pretrained    | 19.73         | **4.81**      | 9.82            | 5.11          |
+|                         | Fine-tuned    | **15.95**     | 5.04          | **7.24**        | **3.68**          |
 Note: Results have been obtained using the [test script](https://github.com/kamilakesbi/diarizers/blob/main/test_segmentation.py) from diarizers.