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speaker-segmentation-fine-tuned-callhome-jpn

TensorBoard
Safetensors
Japanese
pyannet
speaker-diarization
speaker-segmentation
Generated from Trainer
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  2. model.safetensors +1 -1
  3. runs/Aug27_06-26-41_3bc17f263cb5/events.out.tfevents.1724740108.3bc17f263cb5.5776.2 +2 -2
README.md ADDED
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+ ---
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+ language:
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+ - jpn
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+ license: mit
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+ base_model: pyannote/speaker-diarization-3.1
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+ tags:
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+ - speaker-diarization
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+ - speaker-segmentation
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+ - generated_from_trainer
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+ datasets:
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+ - diarizers-community/callhome
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+ model-index:
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+ - name: speaker-segmentation-fine-tuned-callhome-jpn
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # speaker-segmentation-fine-tuned-callhome-jpn
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+
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+ 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.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.1719
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+ - Der: 0.2668
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+ - False Alarm: 0.0225
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+ - Missed Detection: 0.0148
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+ - Confusion: 0.2295
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 0.001
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+ - train_batch_size: 32
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+ - eval_batch_size: 32
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: cosine
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+ - num_epochs: 25
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Der | False Alarm | Missed Detection | Confusion |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
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+ | 0.4449 | 1.0 | 44 | 0.9090 | 0.2891 | 0.0225 | 0.0193 | 0.2473 |
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+ | 0.411 | 2.0 | 88 | 0.9007 | 0.2767 | 0.0225 | 0.0088 | 0.2454 |
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+ | 0.3691 | 3.0 | 132 | 0.8465 | 0.2570 | 0.0225 | 0.0115 | 0.2229 |
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+ | 0.3762 | 4.0 | 176 | 0.8855 | 0.2585 | 0.0225 | 0.0088 | 0.2272 |
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+ | 0.337 | 5.0 | 220 | 0.9608 | 0.2721 | 0.0225 | 0.0142 | 0.2354 |
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+ | 0.3203 | 6.0 | 264 | 1.0052 | 0.2636 | 0.0225 | 0.0152 | 0.2259 |
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+ | 0.314 | 7.0 | 308 | 1.0084 | 0.2650 | 0.0225 | 0.0145 | 0.2279 |
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+ | 0.3066 | 8.0 | 352 | 0.9484 | 0.2614 | 0.0225 | 0.0127 | 0.2262 |
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+ | 0.2968 | 9.0 | 396 | 1.0768 | 0.2720 | 0.0225 | 0.0163 | 0.2332 |
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+ | 0.2847 | 10.0 | 440 | 0.9485 | 0.2528 | 0.0225 | 0.0098 | 0.2205 |
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+ | 0.2784 | 11.0 | 484 | 1.0811 | 0.2677 | 0.0225 | 0.0146 | 0.2306 |
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+ | 0.2674 | 12.0 | 528 | 1.0390 | 0.2670 | 0.0225 | 0.0145 | 0.2300 |
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+ | 0.2646 | 13.0 | 572 | 1.1117 | 0.2666 | 0.0225 | 0.0148 | 0.2293 |
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+ | 0.2425 | 14.0 | 616 | 1.1455 | 0.2682 | 0.0225 | 0.0146 | 0.2310 |
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+ | 0.2569 | 15.0 | 660 | 1.1830 | 0.2682 | 0.0225 | 0.0148 | 0.2309 |
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+ | 0.2497 | 16.0 | 704 | 1.1674 | 0.2673 | 0.0225 | 0.0148 | 0.2300 |
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+ | 0.2494 | 17.0 | 748 | 1.1050 | 0.2630 | 0.0225 | 0.0148 | 0.2257 |
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+ | 0.2334 | 18.0 | 792 | 1.1736 | 0.2674 | 0.0225 | 0.0148 | 0.2301 |
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+ | 0.24 | 19.0 | 836 | 1.1566 | 0.2679 | 0.0225 | 0.0148 | 0.2306 |
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+ | 0.2371 | 20.0 | 880 | 1.1571 | 0.2650 | 0.0225 | 0.0148 | 0.2277 |
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+ | 0.2403 | 21.0 | 924 | 1.1472 | 0.2640 | 0.0225 | 0.0148 | 0.2267 |
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+ | 0.2317 | 22.0 | 968 | 1.1751 | 0.2676 | 0.0225 | 0.0148 | 0.2303 |
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+ | 0.2318 | 23.0 | 1012 | 1.1817 | 0.2677 | 0.0225 | 0.0148 | 0.2304 |
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+ | 0.2322 | 24.0 | 1056 | 1.1723 | 0.2669 | 0.0225 | 0.0148 | 0.2296 |
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+ | 0.2418 | 25.0 | 1100 | 1.1719 | 0.2668 | 0.0225 | 0.0148 | 0.2295 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.42.4
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+ - Pytorch 2.4.0+cu121
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+ - Datasets 2.21.0
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+ - Tokenizers 0.19.1
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