End of training

README.md

@@ -11,23 +11,23 @@ tags:
 datasets:
 - diarizers-community/callhome
 model-index:
-- name: speaker-segmentation-fine-tuned-hindi1
+- name: speaker-segmentation-fine-tuned-callhome-jpn
   results: []
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
 
-# speaker-segmentation-fine-tuned-hindi1
+# speaker-segmentation-fine-tuned-callhome-jpn
 
 This model is a fine-tuned version of [pyannote/speaker-diarization-3.1](https://huggingface.co/pyannote/speaker-diarization-3.1) on the diarizers-community/callhome dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.4409
+- Loss: 0.4370
 - Model Preparation Time: 0.0038
-- Der: 0.1421
-- False Alarm: 0.0241
-- Missed Detection: 0.0277
-- Confusion: 0.0903
+- Der: 0.1404
+- False Alarm: 0.0234
+- Missed Detection: 0.0272
+- Confusion: 0.0898
 
 ## Model description
 
@@ -58,11 +58,11 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Der    | False Alarm | Missed Detection | Confusion |
 |:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
-| 0.4605        | 1.0   | 194  | 0.4802          | 0.0038                 | 0.1609 | 0.0247      | 0.0332           | 0.1030    |
-| 0.386         | 2.0   | 388  | 0.4500          | 0.0038                 | 0.1516 | 0.0221      | 0.0318           | 0.0976    |
-| 0.3711        | 3.0   | 582  | 0.4384          | 0.0038                 | 0.1447 | 0.0225      | 0.0291           | 0.0931    |
-| 0.3674        | 4.0   | 776  | 0.4407          | 0.0038                 | 0.1430 | 0.0240      | 0.0279           | 0.0911    |
-| 0.3413        | 5.0   | 970  | 0.4409          | 0.0038                 | 0.1421 | 0.0241      | 0.0277           | 0.0903    |
+| 0.4567        | 1.0   | 194  | 0.4782          | 0.0038                 | 0.1572 | 0.0219      | 0.0351           | 0.1003    |
+| 0.384         | 2.0   | 388  | 0.4576          | 0.0038                 | 0.1483 | 0.0221      | 0.0295           | 0.0968    |
+| 0.3582        | 3.0   | 582  | 0.4375          | 0.0038                 | 0.1402 | 0.0230      | 0.0284           | 0.0888    |
+| 0.357         | 4.0   | 776  | 0.4406          | 0.0038                 | 0.1413 | 0.0229      | 0.0277           | 0.0907    |
+| 0.3406        | 5.0   | 970  | 0.4370          | 0.0038                 | 0.1404 | 0.0234      | 0.0272           | 0.0898    |
 
 
 ### Framework versions