transformers tensorboard safetensors wav2vec2 automatic-speech-recognition generated_from_trainer dataset:audiofolder base_model:Nachuwu/wav2vec2-fleur-mms-batch6-epoch16-finetunning license:cc-by-nc-4.0 model-index endpoints_compatible region:us
---
license: cc-by-nc-4.0
base_model: Nachuwu/wav2vec2-fleur-mms-batch6-epoch16-finetunning
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- wer
model-index:
- name: wav2vec2-fleur-mms-batch6-epoch16-finetunning-2
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: audiofolder
      type: audiofolder
      config: default
      split: test
      args: default
    metrics:
    - name: Wer
      type: wer
      value: 0.5148173673328739
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# wav2vec2-fleur-mms-batch6-epoch16-finetunning-2

This model is a fine-tuned version of [Nachuwu/wav2vec2-fleur-mms-batch6-epoch16-finetunning](https://huggingface.co/Nachuwu/wav2vec2-fleur-mms-batch6-epoch16-finetunning) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7540
- Wer: 0.5148

## 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: 6
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 14
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.7213        | 0.42  | 100  | 0.8689          | 0.5824 |
| 0.8363        | 0.84  | 200  | 0.9158          | 0.6085 |
| 0.8347        | 1.27  | 300  | 0.9091          | 0.5362 |
| 0.874         | 1.69  | 400  | 0.8487          | 0.5307 |
| 0.7452        | 2.11  | 500  | 0.8507          | 0.5472 |
| 0.791         | 2.53  | 600  | 0.8075          | 0.5410 |
| 0.7681        | 2.95  | 700  | 0.8389          | 0.5575 |
| 0.7645        | 3.38  | 800  | 0.8122          | 0.5610 |
| 0.739         | 3.8   | 900  | 0.8625          | 0.5569 |
| 0.7989        | 4.22  | 1000 | 0.8166          | 0.5624 |
| 0.7618        | 4.64  | 1100 | 0.8646          | 0.5796 |
| 0.7689        | 5.06  | 1200 | 0.8462          | 0.5445 |
| 0.738         | 5.49  | 1300 | 0.8014          | 0.5431 |
| 0.6966        | 5.91  | 1400 | 0.7993          | 0.5217 |
| 0.7275        | 6.33  | 1500 | 0.8049          | 0.5252 |
| 0.7304        | 6.75  | 1600 | 0.7870          | 0.5231 |
| 0.7225        | 7.17  | 1700 | 0.8037          | 0.5472 |
| 0.7148        | 7.59  | 1800 | 0.7913          | 0.5389 |
| 0.7225        | 8.02  | 1900 | 0.7908          | 0.5183 |
| 0.7123        | 8.44  | 2000 | 0.7894          | 0.5245 |
| 0.692         | 8.86  | 2100 | 0.7693          | 0.5224 |
| 0.7855        | 9.28  | 2200 | 0.7614          | 0.5252 |
| 0.6421        | 9.7   | 2300 | 0.7870          | 0.5169 |
| 0.6831        | 10.13 | 2400 | 0.7820          | 0.5121 |
| 0.7293        | 10.55 | 2500 | 0.7640          | 0.5245 |
| 0.6625        | 10.97 | 2600 | 0.7912          | 0.5575 |
| 0.6612        | 11.39 | 2700 | 0.7578          | 0.5203 |
| 0.6873        | 11.81 | 2800 | 0.7579          | 0.5231 |
| 0.7113        | 12.24 | 2900 | 0.7595          | 0.5258 |
| 0.7393        | 12.66 | 3000 | 0.7532          | 0.5148 |
| 0.7297        | 13.08 | 3100 | 0.7532          | 0.5183 |
| 0.6303        | 13.5  | 3200 | 0.7514          | 0.5162 |
| 0.6444        | 13.92 | 3300 | 0.7540          | 0.5148 |


### Framework versions

- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
