transformers safetensors wav2vec2 automatic-speech-recognition generated_from_trainer dataset:common_voice_6_1 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_6_1
metrics:
- wer
model-index:
- name: wav2vec2-large-mms-1b-russian-colab
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_6_1
      type: common_voice_6_1
      config: ru
      split: test
      args: ru
    metrics:
    - name: Wer
      type: wer
      value: 0.1797582065867861
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# wav2vec2-large-mms-1b-russian-colab

This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the common_voice_6_1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1305
- Wer: 0.1798

## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.4789        | 0.21  | 100  | 0.1676          | 0.2126 |
| 0.2042        | 0.41  | 200  | 0.1507          | 0.2010 |
| 0.1991        | 0.62  | 300  | 0.1460          | 0.1989 |
| 0.1882        | 0.83  | 400  | 0.1464          | 0.1992 |
| 0.1804        | 1.03  | 500  | 0.1402          | 0.1900 |
| 0.1809        | 1.24  | 600  | 0.1404          | 0.1890 |
| 0.1787        | 1.45  | 700  | 0.1392          | 0.1873 |
| 0.1781        | 1.65  | 800  | 0.1413          | 0.1926 |
| 0.1767        | 1.86  | 900  | 0.1411          | 0.1883 |
| 0.1779        | 2.07  | 1000 | 0.1375          | 0.1853 |
| 0.1749        | 2.27  | 1100 | 0.1356          | 0.1845 |
| 0.1749        | 2.48  | 1200 | 0.1343          | 0.1836 |
| 0.1652        | 2.69  | 1300 | 0.1339          | 0.1826 |
| 0.1677        | 2.89  | 1400 | 0.1334          | 0.1826 |
| 0.1764        | 3.1   | 1500 | 0.1333          | 0.1832 |
| 0.1697        | 3.31  | 1600 | 0.1320          | 0.1811 |
| 0.1678        | 3.51  | 1700 | 0.1313          | 0.1804 |
| 0.165         | 3.72  | 1800 | 0.1308          | 0.1799 |
| 0.1641        | 3.93  | 1900 | 0.1305          | 0.1798 |


### Framework versions

- Transformers 4.39.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
