End of training

README.md

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+---
+library_name: transformers
+language:
+- eng
+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-eng-forproject
+  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-eng-forproject
+
+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: 0.4600
+- Model Preparation Time: 0.0051
+- Der: 0.1818
+- False Alarm: 0.0578
+- Missed Detection: 0.0721
+- Confusion: 0.0518
+
+## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Der    | False Alarm | Missed Detection | Confusion |
+|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
+| 0.392         | 1.0   | 362  | 0.4730          | 0.0051                 | 0.1926 | 0.0622      | 0.0736           | 0.0568    |
+| 0.4053        | 2.0   | 724  | 0.4586          | 0.0051                 | 0.1838 | 0.0625      | 0.0704           | 0.0509    |
+| 0.3865        | 3.0   | 1086 | 0.4537          | 0.0051                 | 0.1811 | 0.0574      | 0.0723           | 0.0514    |
+| 0.3571        | 4.0   | 1448 | 0.4570          | 0.0051                 | 0.1805 | 0.0551      | 0.0740           | 0.0514    |
+| 0.3409        | 5.0   | 1810 | 0.4600          | 0.0051                 | 0.1818 | 0.0578      | 0.0721           | 0.0518    |
+
+
+### Framework versions
+
+- Transformers 4.44.2
+- Pytorch 2.4.1+cu121
+- Datasets 3.0.1
+- Tokenizers 0.19.1

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": "4.44.2",
+  "warm_up": [
+    0.0,
+    0.0
+  ],
+  "weigh_by_cardinality": false
+}

model.safetensors

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+size 5899124

runs/Oct15_09-31-08_b9e6027e3790/events.out.tfevents.1728984789.b9e6027e3790.5891.0

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+size 23385

training_args.bin

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