---
library_name: transformers
language:
- hi
license: mit
base_model: pyannote/speaker-diarization-3.1
tags:
- speaker-diarization
- speaker-segmentation
- generated_from_trainer
datasets:
- diarizers-community/callhome
model-index:
- name: SpeakerSegmentation_Hindi
  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. -->

# SpeakerSegmentation_Hindi

This model is a fine-tuned version of [pyannote/speaker-diarization-3.1](https://huggingface.co/pyannote/speaker-diarization-3.1) on the diarizers-community/callhome dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4294
- Model Preparation Time: 0.0006
- Der: 0.1343
- False Alarm: 0.0233
- Missed Detection: 0.0270
- Confusion: 0.0840

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Der    | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.4597        | 1.0   | 194  | 0.4796          | 0.0006                 | 0.1635 | 0.0254      | 0.0315           | 0.1066    |
| 0.3807        | 2.0   | 388  | 0.4499          | 0.0006                 | 0.1502 | 0.0225      | 0.0312           | 0.0966    |
| 0.379         | 3.0   | 582  | 0.4359          | 0.0006                 | 0.1400 | 0.0217      | 0.0305           | 0.0878    |
| 0.3363        | 4.0   | 776  | 0.4479          | 0.0006                 | 0.1402 | 0.0240      | 0.0278           | 0.0884    |
| 0.3082        | 5.0   | 970  | 0.4358          | 0.0006                 | 0.1371 | 0.0245      | 0.0268           | 0.0859    |
| 0.3125        | 6.0   | 1164 | 0.4287          | 0.0006                 | 0.1361 | 0.0214      | 0.0293           | 0.0855    |
| 0.3143        | 7.0   | 1358 | 0.4247          | 0.0006                 | 0.1344 | 0.0233      | 0.0272           | 0.0839    |
| 0.3081        | 8.0   | 1552 | 0.4211          | 0.0006                 | 0.1328 | 0.0230      | 0.0271           | 0.0827    |
| 0.2999        | 9.0   | 1746 | 0.4298          | 0.0006                 | 0.1341 | 0.0233      | 0.0270           | 0.0838    |
| 0.292         | 10.0  | 1940 | 0.4294          | 0.0006                 | 0.1343 | 0.0233      | 0.0270           | 0.0840    |


### Framework versions

- Transformers 4.49.0.dev0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0