transformers tensorboard safetensors wav2vec2 automatic-speech-recognition generated_from_trainer dataset:common_voice_11_0 base_model:facebook/mms-1b-all license:cc-by-nc-4.0 model-index endpoints_compatible region:us
---
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
- generated_from_trainer
datasets:
- common_voice_11_0
metrics:
- wer
model-index:
- name: wav2vec2-kyrgyz-colab
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_11_0
      type: common_voice_11_0
      config: ky
      split: test
      args: ky
    metrics:
    - name: Wer
      type: wer
      value: 0.26476260446321975
---

<!-- 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-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_11_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2952
- Wer: 0.2648

## 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: 1e-05
- train_batch_size: 2
- 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: 200
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 9.9224        | 0.29  | 500  | 3.9047          | 1.0023 |
| 3.3028        | 0.59  | 1000 | 2.7653          | 0.9997 |
| 2.4414        | 0.88  | 1500 | 1.1551          | 0.6330 |
| 1.5571        | 1.18  | 2000 | 0.5679          | 0.3712 |
| 1.2479        | 1.47  | 2500 | 0.4202          | 0.3291 |
| 1.0857        | 1.76  | 3000 | 0.3655          | 0.3105 |
| 1.0304        | 2.06  | 3500 | 0.3392          | 0.3007 |
| 0.9989        | 2.35  | 4000 | 0.3245          | 0.2909 |
| 0.9711        | 2.65  | 4500 | 0.3157          | 0.2825 |
| 0.9371        | 2.94  | 5000 | 0.3093          | 0.2764 |
| 0.9423        | 3.24  | 5500 | 0.3047          | 0.2732 |
| 0.9226        | 3.53  | 6000 | 0.3017          | 0.2715 |
| 0.9365        | 3.82  | 6500 | 0.2990          | 0.2689 |
| 0.8969        | 4.12  | 7000 | 0.2971          | 0.2668 |
| 0.9101        | 4.41  | 7500 | 0.2961          | 0.2644 |
| 0.9044        | 4.71  | 8000 | 0.2954          | 0.2642 |
| 0.902         | 5.0   | 8500 | 0.2952          | 0.2648 |


### Framework versions

- Transformers 4.37.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
