End of training

README.md

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+---
+library_name: transformers
+language:
+- en
+license: mit
+base_model: pyannote/speaker-diarization-3.1
+tags:
+- speaker-diarization
+- speaker-segmentation
+- generated_from_trainer
+datasets:
+- diarizers-community/voxconverse
+model-index:
+- name: JSWOOK/pyannote_3_fine_tuning
+  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. -->
+
+# JSWOOK/pyannote_3_fine_tuning
+
+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/voxconverse dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3134
+- Model Preparation Time: 0.0048
+- Der: 0.0888
+- False Alarm: 0.0134
+- Missed Detection: 0.0337
+- Confusion: 0.0417
+
+## 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: 5e-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Der    | False Alarm | Missed Detection | Confusion |
+|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
+| No log        | 1.0   | 24   | 0.3180          | 0.0048                 | 0.0915 | 0.0119      | 0.0385           | 0.0410    |
+| 0.1903        | 2.0   | 48   | 0.3116          | 0.0048                 | 0.0903 | 0.0125      | 0.0369           | 0.0409    |
+| 0.1839        | 3.0   | 72   | 0.3089          | 0.0048                 | 0.0896 | 0.0128      | 0.0357           | 0.0411    |
+| 0.1825        | 4.0   | 96   | 0.3176          | 0.0048                 | 0.0896 | 0.0131      | 0.0352           | 0.0413    |
+| 0.1797        | 5.0   | 120  | 0.3148          | 0.0048                 | 0.0892 | 0.0132      | 0.0346           | 0.0413    |
+| 0.1801        | 6.0   | 144  | 0.3141          | 0.0048                 | 0.0890 | 0.0133      | 0.0342           | 0.0415    |
+| 0.1735        | 7.0   | 168  | 0.3137          | 0.0048                 | 0.0887 | 0.0134      | 0.0338           | 0.0416    |
+| 0.1705        | 8.0   | 192  | 0.3133          | 0.0048                 | 0.0887 | 0.0134      | 0.0337           | 0.0416    |
+| 0.1796        | 9.0   | 216  | 0.3133          | 0.0048                 | 0.0887 | 0.0134      | 0.0337           | 0.0417    |
+| 0.1644        | 10.0  | 240  | 0.3134          | 0.0048                 | 0.0888 | 0.0134      | 0.0337           | 0.0417    |
+
+
+### Framework versions
+
+- Transformers 4.44.2
+- Pytorch 2.5.0+cu121
+- Datasets 3.1.0
+- Tokenizers 0.19.1

config.yaml

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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: 4.44.2
+warm_up:
+- 0.0
+- 0.0
+weigh_by_cardinality: false

model.safetensors

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 size 5899124

pytorch_model.bin

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