---
base_model: facebook/mms-1b-all
datasets:
- common_voice_17_0
library_name: transformers
license: cc-by-nc-4.0
metrics:
- wer
- bleu
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-mms-1b-CV17.0
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: common_voice_17_0
      type: common_voice_17_0
      config: ta
      split: test[:5%]+test[20%:25%]+test[60%:65%]+test[90%:]
      args: ta
    metrics:
    - type: wer
      value: 0.39458150446496143
      name: Wer
    - type: bleu
      value: 0.3894086227361947
      name: Bleu
---

<!-- 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-mms-1b-CV17.0

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: 0.2560
- Wer: 0.3946
- Cer: 0.0667
- Bleu: 0.3894

## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.15
- training_steps: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    | Cer    | Bleu   |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|:------:|
| 5.2362        | 0.3509 | 500  | 0.3039          | 0.4233 | 0.0716 | 0.3579 |
| 0.2003        | 0.7018 | 1000 | 0.2726          | 0.4072 | 0.0694 | 0.3806 |
| 0.1821        | 1.0526 | 1500 | 0.2632          | 0.3993 | 0.0677 | 0.3890 |
| 0.1792        | 1.4035 | 2000 | 0.2602          | 0.4011 | 0.0677 | 0.3850 |
| 0.1764        | 1.7544 | 2500 | 0.2572          | 0.3984 | 0.0674 | 0.3850 |
| 0.1771        | 2.1053 | 3000 | 0.2570          | 0.3955 | 0.0672 | 0.3918 |
| 0.1777        | 2.4561 | 3500 | 0.2562          | 0.3970 | 0.0670 | 0.3866 |
| 0.1721        | 2.8070 | 4000 | 0.2547          | 0.3975 | 0.0669 | 0.3841 |
| 0.177         | 3.1579 | 4500 | 0.2560          | 0.3949 | 0.0668 | 0.3910 |
| 0.1765        | 3.5088 | 5000 | 0.2560          | 0.3946 | 0.0667 | 0.3894 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1