speaker-segmentation-fine-tuned-hindi

Akchunks/speaker-segmentation-fine-tuned-hindi

speaker-segmentation-fine-tuned-hindi is a machine learning model from Akchunks. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as mit.

should probably proofread and complete it, then remove this comment. --

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At a glance

Library: transformers
License: mit
Model type: pyannet
Access: Public
Created: Jun 11, 2025
Updated: Jun 12, 2025
SHA: 3888d1ab

Base models

pyannote/speaker-diarization-3.1

Datasets

Akchunks/synthetic-speaker-diarization-dataset-hindi-short

Model card

From the Hugging Face model README

speaker-segmentation-fine-tuned-hindi-v3

This model is a fine-tuned version of pyannote/speaker-diarization-3.1 on the Akchunks/synthetic-speaker-diarization-dataset-hindi-short dataset. It achieves the following results on the evaluation set:

Loss: 0.3447
Model Preparation Time: 0.007
Der: 0.0985
False Alarm: 0.0375
Missed Detection: 0.0235
Confusion: 0.0375

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: 8
eval_batch_size: 8
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: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Model Preparation Time	Der	False Alarm	Missed Detection	Confusion
No log	1.0	24	0.4600	0.007	0.1443	0.0349	0.0256	0.0837
0.5196	2.0	48	0.3562	0.007	0.1304	0.0325	0.0242	0.0737
0.306	3.0	72	0.3732	0.007	0.1251	0.0402	0.0253	0.0596
0.2116	4.0	96	0.3712	0.007	0.1265	0.0408	0.0242	0.0615
0.1944	5.0	120	0.3846	0.007	0.1223	0.0337	0.0260	0.0627
0.1538	6.0	144	0.3544	0.007	0.1191	0.0375	0.0228	0.0587
0.1417	7.0	168	0.4045	0.007	0.1213	0.0358	0.0241	0.0614
0.1122	8.0	192	0.4213	0.007	0.1267	0.0438	0.0228	0.0601
0.1053	9.0	216	0.4171	0.007	0.1178	0.0368	0.0255	0.0555
0.0897	10.0	240	0.3561	0.007	0.1142	0.0409	0.0228	0.0505
0.1043	11.0	264	0.3738	0.007	0.1122	0.0380	0.0248	0.0495
0.0825	12.0	288	0.3383	0.007	0.1025	0.0377	0.0237	0.0411
0.0894	13.0	312	0.3328	0.007	0.0995	0.0388	0.0237	0.0370
0.0699	14.0	336	0.3272	0.007	0.0988	0.0376	0.0237	0.0375
0.0785	15.0	360	0.3374	0.007	0.0991	0.0378	0.0235	0.0378
0.0759	16.0	384	0.3414	0.007	0.0978	0.0383	0.0233	0.0362
0.0653	17.0	408	0.3417	0.007	0.0973	0.0375	0.0234	0.0364
0.0726	18.0	432	0.3439	0.007	0.0981	0.0374	0.0236	0.0370
0.0684	19.0	456	0.3445	0.007	0.0984	0.0374	0.0235	0.0375
0.0731	20.0	480	0.3447	0.007	0.0985	0.0375	0.0235	0.0375

Framework versions

Transformers 4.52.4
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.1

Library: transformers
Type: pyannet
License: mit
Languages: hi
Created: Jun 11, 2025
Updated: Jun 12, 2025