---
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: 0.4958060228262364
      name: Wer
    - type: bleu
      value: 0.2629057639184852
      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: 0.5099
- Wer: 0.4958
- Cer: 0.0885
- Bleu: 0.2629

## 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   |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| 11.4061       | 50.0  | 50   | 4.6415          | 1.0007 | 0.9640 | 0.0    |
| 1.789         | 100.0 | 100  | 0.3026          | 0.4457 | 0.0734 | 0.3064 |
| 0.08          | 150.0 | 150  | 0.3223          | 0.4304 | 0.0711 | 0.3275 |
| 0.0473        | 200.0 | 200  | 0.3547          | 0.4426 | 0.0742 | 0.3156 |
| 0.0364        | 250.0 | 250  | 0.3786          | 0.4556 | 0.0761 | 0.2972 |
| 0.0298        | 300.0 | 300  | 0.4070          | 0.4629 | 0.0800 | 0.2875 |
| 0.0279        | 350.0 | 350  | 0.4190          | 0.4688 | 0.0799 | 0.2864 |
| 0.0253        | 400.0 | 400  | 0.4353          | 0.4755 | 0.0818 | 0.2757 |
| 0.0198        | 450.0 | 450  | 0.4808          | 0.5066 | 0.0887 | 0.2432 |
| 0.0216        | 500.0 | 500  | 0.4699          | 0.4780 | 0.0815 | 0.2777 |
| 0.0194        | 550.0 | 550  | 0.4745          | 0.4895 | 0.0877 | 0.2643 |
| 0.0201        | 600.0 | 600  | 0.5035          | 0.4971 | 0.0881 | 0.2647 |
| 0.0153        | 650.0 | 650  | 0.5099          | 0.4958 | 0.0885 | 0.2629 |


### Framework versions

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