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speaker-segmentation-fine-tuned-CAENNAIS-v3

Transformers
TensorBoard
Safetensors
French
pyannet
speaker-diarization
speaker-segmentation
Generated from Trainer
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 Metrics Community
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  1. README.md +78 -0
  2. model.safetensors +1 -1
  3. runs/Mar12_12-23-53_V301V-JGRCC1.campus.unicaen.fr/events.out.tfevents.1741778638.V301V-JGRCC1.campus.unicaen.fr +2 -2
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+ ---
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+ library_name: transformers
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+ language:
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+ - fr
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+ license: mit
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+ base_model: pyannote/segmentation-3.0
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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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+ - CAENNAIS
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+ model-index:
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+ - name: speaker-segmentation-fine-tuned_CAENNAIS
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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_CAENNAIS
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+
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+ This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the CAENNAIS dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.7201
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+ - Model Preparation Time: 0.0037
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+ - Der: 0.3164
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+ - False Alarm: 0.1145
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+ - Missed Detection: 0.0684
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+ - Confusion: 0.1335
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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: 10
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Der | False Alarm | Missed Detection | Confusion |
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+ |:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
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+ | 0.8687 | 1.0 | 61 | 0.8189 | 0.0037 | 0.3927 | 0.1128 | 0.0900 | 0.1899 |
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+ | 0.7991 | 2.0 | 122 | 0.7751 | 0.0037 | 0.3552 | 0.1158 | 0.0750 | 0.1644 |
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+ | 0.7269 | 3.0 | 183 | 0.7795 | 0.0037 | 0.3571 | 0.1269 | 0.0622 | 0.1680 |
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+ | 0.6891 | 4.0 | 244 | 0.7384 | 0.0037 | 0.3360 | 0.1233 | 0.0642 | 0.1485 |
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+ | 0.6723 | 5.0 | 305 | 0.7053 | 0.0037 | 0.3235 | 0.1017 | 0.0807 | 0.1411 |
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+ | 0.6409 | 6.0 | 366 | 0.7068 | 0.0037 | 0.3193 | 0.1142 | 0.0680 | 0.1372 |
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+ | 0.6345 | 7.0 | 427 | 0.7200 | 0.0037 | 0.3243 | 0.1150 | 0.0695 | 0.1398 |
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+ | 0.6087 | 8.0 | 488 | 0.7483 | 0.0037 | 0.3302 | 0.1241 | 0.0623 | 0.1438 |
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+ | 0.5997 | 9.0 | 549 | 0.7163 | 0.0037 | 0.3158 | 0.1139 | 0.0689 | 0.1330 |
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+ | 0.5976 | 10.0 | 610 | 0.7201 | 0.0037 | 0.3164 | 0.1145 | 0.0684 | 0.1335 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.45.2
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+ - Pytorch 2.4.1+cu121
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+ - Datasets 3.0.1
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+ - Tokenizers 0.20.0
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