smam/pyannote-segmentation-bengali-finetuned

Bengali Speaker Diarization - Fine-tuned Segmentation Model

Fine-tuned pyannote/segmentation-3.0 for Bengali speaker diarization.

Results

• Best DER: 0.1312

Training Data (~370 hours)

• Synthetic V4: 600 files (~300 hours) from smam/bengali-diarization-synthetic-v4
• DISPLACE24: 67 files (~35 hours)
• DISPLACE26: 125 files (~35 hours)

Training

• Train samples: 600
• Val samples: 67
• Max epochs: 30 (with early stopping on DER, patience=5)
• Optimizer: AdamW (lr=0.0001, weight_decay=0.01)
• Precision: 16-mixed on H100

Task Configuration

• Duration: 10.0s per chunk
• Max speakers per chunk: 3
• Max speakers per frame: 2 (powerset encoding with overlap support)

Usage

from pyannote.audio import Model
import torch

# Load weights
state_dict = torch.load('pytorch_model.bin')
model = Model.from_pretrained('pyannote/segmentation-3.0')
model.load_state_dict(state_dict)