---
license: apache-2.0
base_model: openai/whisper-small
metrics:
- wer
model-index:
- name: whisper-small-hy-AM
  results: []
datasets:
- mozilla-foundation/common_voice_16_1
language:
- hy
library_name: transformers
tags:
- SpeechToText
- Audio
- Audio Transcription
pipeline_tag: automatic-speech-recognition
---

## Model description

Chillarmo/whisper-small-hy-AM is an AI model designed for speech-to-text conversion specifically tailored for the Armenian language. Leveraging the power of fine-tuning, this model, named whisper-small-hy-AM, is based on [openai/whisper-small](https://huggingface.co/openai/whisper-small) and trained on the common_voice_16_1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2853
- Wer: 38.1160

## Training Data and Future Enhancements

The training data consists of Mozilla Common Voice version 16.1. Plans for future improvements include continuing the training process and integrating an additional 10 hours of data from datasets such as google/fleurs and possibly google/xtreme_s. Despite its current performance, efforts are underway to further reduce the WER.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0989        | 2.48  | 1000 | 0.1948          | 41.5758 |
| 0.03          | 4.95  | 2000 | 0.2165          | 39.1251 |
| 0.0016        | 7.43  | 3000 | 0.2659          | 38.4089 |
| 0.0005        | 9.9   | 4000 | 0.2853          | 38.1160 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1