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speaker-segmentation-fine-tuned-callhome-jpn

speaker-diarization

speaker-segmentation

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kamilakesbi commited on Apr 21

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README.md +10 -10
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 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: 0.5146
-- Der: 0.1869
-- False Alarm: 0.0933
-- Missed Detection: 0.0709
-- Confusion: 0.0227
 ## 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 |
 |:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
-| 0.6043        | 1.0   | 340  | 0.5075          | 0.1789 | 0.0682      | 0.0789           | 0.0318    |
-| 0.5766        | 2.0   | 680  | 0.5207          | 0.1951 | 0.1012      | 0.0708           | 0.0230    |
-| 0.5345        | 3.0   | 1020 | 0.5011          | 0.1798 | 0.0852      | 0.0716           | 0.0231    |
-| 0.518         | 4.0   | 1360 | 0.5344          | 0.1934 | 0.1009      | 0.0700           | 0.0225    |
-| 0.5147        | 5.0   | 1700 | 0.5146          | 0.1869 | 0.0933      | 0.0709           | 0.0227    |
 ### 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: 0.7653
+- Der: 0.2311
+- False Alarm: 0.0477
+- Missed Detection: 0.1352
+- Confusion: 0.0482
 ## Model description
 | Training Loss | Epoch | Step | Validation Loss | Der    | False Alarm | Missed Detection | Confusion |
 |:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
+| 0.5917        | 1.0   | 328  | 0.7859          | 0.2409 | 0.0507      | 0.1369           | 0.0533    |
+| 0.5616        | 2.0   | 656  | 0.7738          | 0.2350 | 0.0530      | 0.1350           | 0.0471    |
+| 0.5364        | 3.0   | 984  | 0.7737          | 0.2358 | 0.0484      | 0.1368           | 0.0506    |
+| 0.5121        | 4.0   | 1312 | 0.7626          | 0.2317 | 0.0483      | 0.1358           | 0.0475    |
+| 0.5166        | 5.0   | 1640 | 0.7653          | 0.2311 | 0.0477      | 0.1352           | 0.0482    |
 ### Framework versions

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