This model is a fine-tuned version of pyannote/speaker-diarization-3.0 on the talkbank/callhome dataset. It achieves the following results on the evaluation set:

Loss: 0.4725
Model Preparation Time: 0.0071
Der: 0.1767
False Alarm: 0.0593
Missed Detection: 0.0757
Confusion: 0.0417

Model description

This model is a fine-tuned version of pyannote/speaker-diarization-3.0 for speaker segmentation, trained on the talkbank/callhome dataset.

It can be loaded using the code:

python

from pyannote.audio import Pipeline
import torch

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# Load fine-tuned pipeline
pipeline = Pipeline.from_pretrained(
    "Beijuka/speaker-segmentation-fine-tuned-callhome",
    use_auth_token="your_huggingface_token"  # Replace this!
)
pipeline.to(device)

# Run diarization
audio_file = "/path/to/audio.mp3"
diarization = pipeline(audio_file)

# Save RTTM output
with open("finetunemodel.rttm", "w") as f:
    diarization.write_rttm(f)

# Print segments
print(diarization)

You can now use the pipeline on the train dataset:

python

# load dataset example
dataset = load_dataset("talkbank/callhome", 'eng', 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

Intended for use in diarization pipelines for telephone-style audio.
May not generalize to far-field audio or more than two speakers.

Training and evaluation data

Training and validation used the talkbank/callhome dataset.
2-speaker telephone conversations with speaker turn annotations.

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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Model Preparation Time	Der	False Alarm	Missed Detection	Confusion
0.3959	1.0	362	0.4800	0.0071	0.1932	0.0575	0.0781	0.0577
0.4226	2.0	724	0.4797	0.0071	0.1918	0.0640	0.0723	0.0555
0.4117	3.0	1086	0.4726	0.0071	0.1872	0.0530	0.0789	0.0553
0.3875	4.0	1448	0.4671	0.0071	0.1852	0.0549	0.0769	0.0534
0.3646	5.0	1810	0.4710	0.0071	0.1872	0.0571	0.0747	0.0554

Framework versions

Transformers 4.52.3
Pytorch 2.6.0+cu126
Datasets 3.6.0
Tokenizers 0.21.1

mhdp-africa/speaker-segmentation-fine-tuned-callhome

speaker-segmentation-fine-tuned-callhome