---
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-training_set_variations
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: common_voice_17_0
      type: common_voice_17_0
      config: ta
      split: validation
      args: ta
    metrics:
    - type: wer
      value: 0.3699525493114998
      name: Wer
    - type: bleu
      value: 0.4072321954028345
      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-training_set_variations

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: 0.2281
- Wer: 0.3700
- Cer: 0.0598
- Bleu: 0.4072

## 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: 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: 2000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    | Cer    | Bleu   |
|:-------------:|:-------:|:----:|:---------------:|:------:|:------:|:------:|
| 7.0089        | 1.5625  | 100  | 0.2991          | 0.4260 | 0.0693 | 0.3354 |
| 0.2087        | 3.125   | 200  | 0.2305          | 0.3968 | 0.0634 | 0.3678 |
| 0.1924        | 4.6875  | 300  | 0.2291          | 0.3879 | 0.0624 | 0.3799 |
| 0.1799        | 6.25    | 400  | 0.2290          | 0.3859 | 0.0629 | 0.3830 |
| 0.1698        | 7.8125  | 500  | 0.2224          | 0.3700 | 0.0600 | 0.4119 |
| 0.1587        | 9.375   | 600  | 0.2246          | 0.3672 | 0.0601 | 0.4129 |
| 0.1547        | 10.9375 | 700  | 0.2176          | 0.3855 | 0.0604 | 0.3820 |
| 0.1446        | 12.5    | 800  | 0.2273          | 0.3907 | 0.0619 | 0.3755 |
| 0.1404        | 14.0625 | 900  | 0.2239          | 0.3713 | 0.0605 | 0.4035 |
| 0.1333        | 15.625  | 1000 | 0.2261          | 0.3699 | 0.0602 | 0.4123 |
| 0.1251        | 17.1875 | 1100 | 0.2281          | 0.3700 | 0.0598 | 0.4072 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1