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[:5%]+validation[20%:25%]+validation[60%:65%]+validation[90%:]
      args: ta
    metrics:
    - type: wer
      value: 0.5119016249451032
      name: Wer
    - type: bleu
      value: 0.24178033350654143
      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: 0.4958
- Wer: 0.5119
- Cer: 0.0973
- Bleu: 0.2418

## 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   |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| 6.9079        | 100.0 | 100  | 0.3463          | 0.4393 | 0.0786 | 0.3177 |
| 0.0658        | 200.0 | 200  | 0.3912          | 0.4538 | 0.0839 | 0.3158 |
| 0.0346        | 300.0 | 300  | 0.4707          | 0.5046 | 0.0947 | 0.2477 |
| 0.0265        | 400.0 | 400  | 0.4906          | 0.5137 | 0.0967 | 0.2393 |
| 0.0184        | 500.0 | 500  | 0.5407          | 0.5240 | 0.1005 | 0.2301 |
| 0.0158        | 600.0 | 600  | 0.4958          | 0.5119 | 0.0973 | 0.2418 |


### Framework versions

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