---
library_name: transformers
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
model-index:
- name: wav2vec2-large-mms-1b-kyrgyz-colab
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_17_0
      type: common_voice_17_0
      config: ky
      split: test
      args: ky
    metrics:
    - name: Wer
      type: wer
      value: 0.23120079630802642
---

<!-- 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. -->

# wav2vec2-large-mms-1b-kyrgyz-colab

This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the common_voice_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2384
- Wer: 0.2312

## 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: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 4.9324        | 0.2347 | 100  | 0.3080          | 0.2811 |
| 0.4397        | 0.4695 | 200  | 0.2887          | 0.2666 |
| 0.4336        | 0.7042 | 300  | 0.2816          | 0.2581 |
| 0.4064        | 0.9390 | 400  | 0.2602          | 0.2518 |
| 0.3604        | 1.1737 | 500  | 0.2447          | 0.2433 |
| 0.3752        | 1.4085 | 600  | 0.2603          | 0.2457 |
| 0.3801        | 1.6432 | 700  | 0.2531          | 0.2433 |
| 0.4021        | 1.8779 | 800  | 0.2418          | 0.2441 |
| 0.3807        | 2.1127 | 900  | 0.2625          | 0.2431 |
| 0.3764        | 2.3474 | 1000 | 0.2421          | 0.2410 |
| 0.3669        | 2.5822 | 1100 | 0.2423          | 0.2422 |
| 0.3529        | 2.8169 | 1200 | 0.2447          | 0.2337 |
| 0.3432        | 3.0516 | 1300 | 0.2351          | 0.2345 |
| 0.3535        | 3.2864 | 1400 | 0.2469          | 0.2331 |
| 0.3481        | 3.5211 | 1500 | 0.2353          | 0.2355 |
| 0.3301        | 3.7559 | 1600 | 0.2414          | 0.2309 |
| 0.3696        | 3.9906 | 1700 | 0.2384          | 0.2312 |


### Framework versions

- Transformers 4.49.0.dev0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0