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End of training

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README.md ADDED
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+ ---
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+ language:
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+ - jpn
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+ license: apache-2.0
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+ base_model: openai/whisper-small
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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 [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the diarizers-community/callhome dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.7482
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+ - Der: 0.2201
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+ - False Alarm: 0.0465
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+ - Missed Detection: 0.1319
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+ - Confusion: 0.0417
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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: 5
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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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+ | 0.5488 | 1.0 | 328 | 0.7565 | 0.2280 | 0.0461 | 0.1355 | 0.0465 |
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+ | 0.475 | 2.0 | 656 | 0.7596 | 0.2220 | 0.0467 | 0.1334 | 0.0419 |
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+ | 0.4734 | 3.0 | 984 | 0.7531 | 0.2215 | 0.0437 | 0.1364 | 0.0414 |
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+ | 0.4535 | 4.0 | 1312 | 0.7468 | 0.2194 | 0.0462 | 0.1323 | 0.0409 |
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+ | 0.4764 | 5.0 | 1640 | 0.7482 | 0.2201 | 0.0465 | 0.1319 | 0.0417 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.41.0
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+ - Pytorch 2.3.0+cu121
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+ - Datasets 2.19.1
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+ - Tokenizers 0.19.1
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