NoahDrisort
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speaker-segmentation-fine-tuned-callhome-jpn

speaker-diarization

speaker-segmentation

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NoahDrisort commited on Apr 26

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README.md +10 -10
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@@ -19,11 +19,11 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the diarizers-community/callhome jpn dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.7512
-- Der: 0.2263
-- False Alarm: 0.0467
-- Missed Detection: 0.1359
-- Confusion: 0.0437
 ## Model description
@@ -54,11 +54,11 @@ The following hyperparameters were used during training:
 | Training Loss | Epoch | Step | Validation Loss | Der    | False Alarm | Missed Detection | Confusion |
 |:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
-| 0.5796        | 1.0   | 328  | 0.7613          | 0.2340 | 0.0514      | 0.1341           | 0.0484    |
-| 0.5518        | 2.0   | 656  | 0.7646          | 0.2328 | 0.0480      | 0.1391           | 0.0458    |
-| 0.5304        | 3.0   | 984  | 0.7723          | 0.2321 | 0.0426      | 0.1421           | 0.0474    |
-| 0.5043        | 4.0   | 1312 | 0.7504          | 0.2272 | 0.0490      | 0.1336           | 0.0446    |
-| 0.5086        | 5.0   | 1640 | 0.7512          | 0.2263 | 0.0467      | 0.1359           | 0.0437    |
 ### Framework versions

 This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the diarizers-community/callhome jpn dataset.
 It achieves the following results on the evaluation set:
+- Loss: 0.4828
+- Der: 0.1446
+- False Alarm: 0.0404
+- Missed Detection: 0.0606
+- Confusion: 0.0435
 ## Model description
 | Training Loss | Epoch | Step | Validation Loss | Der    | False Alarm | Missed Detection | Confusion |
 |:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
+| 0.5955        | 1.0   | 394  | 0.5366          | 0.1609 | 0.0435      | 0.0706           | 0.0468    |
+| 0.5648        | 2.0   | 788  | 0.4979          | 0.1509 | 0.0400      | 0.0646           | 0.0462    |
+| 0.5392        | 3.0   | 1182 | 0.4852          | 0.1489 | 0.0447      | 0.0588           | 0.0453    |
+| 0.5283        | 4.0   | 1576 | 0.4756          | 0.1442 | 0.0412      | 0.0607           | 0.0422    |
+| 0.5109        | 5.0   | 1970 | 0.4828          | 0.1446 | 0.0404      | 0.0606           | 0.0435    |
 ### Framework versions

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