End of training

README.md

@@ -0,0 +1,73 @@
+---
+library_name: transformers
+language:
+- jpn
+license: mit
+base_model: pyannote/speaker-diarization-3.1
+tags:
+- speaker-diarization
+- speaker-segmentation
+- generated_from_trainer
+datasets:
+- diarizers-community/callhome
+model-index:
+- 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-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: 0.7600
+- Model Preparation Time: 0.0042
+- Der: 0.2253
+- False Alarm: 0.0463
+- Missed Detection: 0.1355
+- Confusion: 0.0434
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.001
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: cosine
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Der    | False Alarm | Missed Detection | Confusion |
+|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
+| 0.5792        | 1.0   | 328  | 0.7544          | 0.0042                 | 0.2330 | 0.0530      | 0.1318           | 0.0482    |
+| 0.5219        | 2.0   | 656  | 0.7515          | 0.0042                 | 0.2265 | 0.0483      | 0.1352           | 0.0430    |
+| 0.5221        | 3.0   | 984  | 0.7581          | 0.0042                 | 0.2261 | 0.0489      | 0.1340           | 0.0432    |
+| 0.5033        | 4.0   | 1312 | 0.7550          | 0.0042                 | 0.2248 | 0.0489      | 0.1329           | 0.0429    |
+| 0.5249        | 5.0   | 1640 | 0.7600          | 0.0042                 | 0.2253 | 0.0463      | 0.1355           | 0.0434    |
+
+
+### Framework versions
+
+- Transformers 4.48.3
+- Pytorch 2.5.1+cu124
+- Datasets 3.3.2
+- Tokenizers 0.21.0

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:a22731a5e8b52f319620d80b2d14d4a475a48c287a681c5e630fa4279d0d9475
+oid sha256:bed9cc859aa6d3a21f5f9c0bf8a9214f76a3136049f59ca152f6cb91161f8fc2
 size 5899124

runs/Mar11_07-38-08_e7c53d7bb846/events.out.tfevents.1741678715.e7c53d7bb846.23345.0

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:0b57ed0f79be48483f0565bf51fc13f07cce56d1ec62eb5f8ad776281ab9e117
-size 21153
+oid sha256:02b7b6a9e625f9e1ae91c6e5ef3619f585db38bbdc86da4ec9bab4657a9dfdb1
+size 22059