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nymtheescobar/
ecapa-bengali-finetuned

Name: ecapa-bengali-finetuned
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ECAPA-TDNN Fine-tuned for Bengali Speaker Diarization

BUET DL Sprint 4.0 - Problem 2

Model Description

ECAPA-TDNN speaker embedding model fine-tuned on Bengali talkshow audio for speaker diarization. Base model: speechbrain/spkrec-ecapa-voxceleb.

Usage

python

# In the nuclear diarization notebook, this model is loaded automatically:
from huggingface_hub import hf_hub_download
ckpt = hf_hub_download("nymtheescobar/ecapa-bengali-finetuned", "ecapa_bengali_finetuned.pt")
state_dict = torch.load(ckpt)
ecapa.mods.embedding_model.load_state_dict(state_dict, strict=False)

Training Data

Competition data: 10 files, ~10h, ~170 speakers (ground truth)
Bengali Talkshow Dataset: 207 speakers from 30 files auto-labeled with pyannote 3.1
Total: 318 speakers, 9457 segments

Training

Loss: Triplet (margin=0.3)
Epochs: 5, LR: 0.0001
Segment length: 3.0s

Results

Pretrained DER: 0.6140
Fine-tuned DER: 0.5668
Improvement: 0.0472

Dataset

https://huggingface.co/datasets/nymtheescobar/bengali-talkshow-audio