README.md

---
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: 0.35028609973948643
      name: Wer
    - type: bleu
      value: 0.4335646189418293
      name: Bleu
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# 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: 0.1970
- Wer: 0.3503
- Cer: 0.0557
- Bleu: 0.4336

## 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   |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|:------:|
| 7.329         | 0.0977 | 100  | 0.3217          | 0.4338 | 0.0716 | 0.3272 |
| 0.2154        | 0.1953 | 200  | 0.2314          | 0.3794 | 0.0634 | 0.3999 |
| 0.189         | 0.2930 | 300  | 0.2188          | 0.3656 | 0.0592 | 0.4166 |
| 0.1975        | 0.3906 | 400  | 0.2212          | 0.3763 | 0.0608 | 0.4032 |
| 0.1813        | 0.4883 | 500  | 0.2117          | 0.3634 | 0.0585 | 0.4171 |
| 0.1791        | 0.5859 | 600  | 0.2074          | 0.3590 | 0.0578 | 0.4220 |
| 0.187         | 0.6836 | 700  | 0.2087          | 0.3607 | 0.0582 | 0.4188 |
| 0.1789        | 0.7812 | 800  | 0.2064          | 0.3542 | 0.0568 | 0.4327 |
| 0.1704        | 0.8789 | 900  | 0.2076          | 0.3661 | 0.0587 | 0.4095 |
| 0.1813        | 0.9766 | 1000 | 0.2044          | 0.3589 | 0.0574 | 0.4200 |
| 0.1633        | 1.0742 | 1100 | 0.2029          | 0.3582 | 0.0575 | 0.4220 |
| 0.1699        | 1.1719 | 1200 | 0.2034          | 0.3537 | 0.0566 | 0.4335 |
| 0.1822        | 1.2695 | 1300 | 0.2037          | 0.3589 | 0.0578 | 0.4227 |
| 0.1654        | 1.3672 | 1400 | 0.2028          | 0.3549 | 0.0568 | 0.4288 |
| 0.1696        | 1.4648 | 1500 | 0.2011          | 0.3579 | 0.0567 | 0.4199 |
| 0.1622        | 1.5625 | 1600 | 0.1999          | 0.3568 | 0.0570 | 0.4228 |
| 0.1742        | 1.6602 | 1700 | 0.1983          | 0.3490 | 0.0559 | 0.4365 |
| 0.1581        | 1.7578 | 1800 | 0.1973          | 0.3511 | 0.0558 | 0.4329 |
| 0.1616        | 1.8555 | 1900 | 0.1970          | 0.3482 | 0.0556 | 0.4381 |
| 0.1607        | 1.9531 | 2000 | 0.1970          | 0.3503 | 0.0557 | 0.4336 |


### Framework versions

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