---
license: apache-2.0
base_model: facebook/wav2vec2-base-960h
tags:
- generated_from_trainer
datasets:
- ami
metrics:
- wer
model-index:
- name: my_awesome_asr_mind_model6e-5
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: ami
      type: ami
      config: ihm
      split: None
      args: ihm
    metrics:
    - name: Wer
      type: wer
      value: 0.26252597552528284
---

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should probably proofread and complete it, then remove this comment. -->

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/jadorantes2-utep/%3Cmy-amazing-projecttokenizer6e-5%3E/runs/ujcw1zru)
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/jadorantes2-utep/%3Cmy-amazing-projecttokenizer6e-5%3E/runs/ujcw1zru)
# my_awesome_asr_mind_model6e-5

This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on the ami dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9652
- Wer: 0.2625

## 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: 6e-05
- train_batch_size: 32
- 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: 1000
- training_steps: 3000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 3.1601        | 15.1515 | 500  | 3.1815          | 1.0    |
| 3.0665        | 30.3030 | 1000 | 3.5100          | 1.0    |
| 2.1863        | 45.4545 | 1500 | 1.2838          | 0.3812 |
| 0.9609        | 60.6061 | 2000 | 0.9112          | 0.2863 |
| 0.6826        | 75.7576 | 2500 | 0.9450          | 0.2667 |
| 0.5687        | 90.9091 | 3000 | 0.9652          | 0.2625 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1