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---
license: mit
base_model: pyannote/segmentation-3.0
tags:
- speaker-diarization
- speaker-segmentation
- generated_from_trainer
datasets:
- diarizers-community/callhome
model-index:
- name: speaker-segmentation-fine-tuned-callhome-eng
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
This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the diarizers-community/callhome eng dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4602
- Der: 0.1828
- False Alarm: 0.0584
- Missed Detection: 0.0717
- Confusion: 0.0528
## Model description
This segmentation model has been trained on English data (Callhome) using [diarizers](https://github.com/huggingface/diarizers/tree/main).
It can be loaded with two lines of code:
```python
from diarizers import SegmentationModel
segmentation_model = SegmentationModel().from_pretrained('diarizers-community/speaker-segmentation-fine-tuned-callhome-eng')
```
To use it within a pyannote speaker diarization pipeline, load the [pyannote/speaker-diarization-3.1](https://huggingface.co/pyannote/speaker-diarization-3.1) pipeline, and convert the model to a pyannote compatible format:
```python
from pyannote.audio import Pipeline
import torch
device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")
# load the pre-trained pyannote pipeline
pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization-3.1")
pipeline.to(device)
# replace the segmentation model with your fine-tuned one
model = segmentation_model.to_pyannote_model()
pipeline._segmentation.model = model.to(device)
```
You can now use the pipeline on audio examples:
```python
# load dataset example
dataset = load_dataset("diarizers-community/callhome", "jpn", split="data")
sample = dataset[0]["audio"]
# pre-process inputs
sample["waveform"] = torch.from_numpy(sample.pop("array")[None, :]).to(device, dtype=model.dtype)
sample["sample_rate"] = sample.pop("sampling_rate")
# perform inference
diarization = pipeline(sample)
# dump the diarization output to disk using RTTM format
with open("audio.rttm", "w") as rttm:
diarization.write_rttm(rttm)
```
## 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: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Der | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.4123 | 1.0 | 362 | 0.4801 | 0.1930 | 0.0627 | 0.0741 | 0.0563 |
| 0.3906 | 2.0 | 724 | 0.4558 | 0.1836 | 0.0589 | 0.0727 | 0.0519 |
| 0.3753 | 3.0 | 1086 | 0.4643 | 0.1830 | 0.0557 | 0.0746 | 0.0527 |
| 0.3632 | 4.0 | 1448 | 0.4566 | 0.1821 | 0.0564 | 0.0728 | 0.0529 |
| 0.3475 | 5.0 | 1810 | 0.4602 | 0.1828 | 0.0584 | 0.0717 | 0.0528 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1
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