End of training

README.md

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+---
+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: 0.5138
+- Model Preparation Time: 0.004
+- Der: 0.1829
+- False Alarm: 0.0160
+- Missed Detection: 0.0109
+- Confusion: 0.1560
+
+## 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 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 |
+|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
+| 0.6088        | 1.0   | 168  | 0.5709          | 0.004                  | 0.1955 | 0.0160      | 0.0091           | 0.1705    |
+| 0.5435        | 2.0   | 336  | 0.5429          | 0.004                  | 0.1906 | 0.0160      | 0.0135           | 0.1611    |
+| 0.5076        | 3.0   | 504  | 0.5202          | 0.004                  | 0.1835 | 0.0160      | 0.0091           | 0.1585    |
+| 0.4867        | 4.0   | 672  | 0.5083          | 0.004                  | 0.1799 | 0.0160      | 0.0091           | 0.1549    |
+| 0.4795        | 5.0   | 840  | 0.5138          | 0.004                  | 0.1829 | 0.0160      | 0.0109           | 0.1560    |
+
+
+### Framework versions
+
+- Transformers 4.47.1
+- Pytorch 2.5.1+cu121
+- Datasets 3.2.0
+- Tokenizers 0.21.0

config.json

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+{
+  "architectures": [
+    "SegmentationModel"
+  ],
+  "chunk_duration": 10.0,
+  "max_speakers_per_chunk": 3,
+  "max_speakers_per_frame": 2,
+  "min_duration": null,
+  "model_type": "pyannet",
+  "sample_rate": 16000,
+  "torch_dtype": "float32",
+  "transformers_version": "4.47.1",
+  "warm_up": [
+    0.0,
+    0.0
+  ],
+  "weigh_by_cardinality": false
+}

model.safetensors

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+size 5899124

runs/Dec30_15-57-48_6cfbcabd5c79/events.out.tfevents.1735574329.6cfbcabd5c79.777.0

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training_args.bin

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