---
tags:
- generated_from_trainer
datasets:
- ami
metrics:
- wer
model-index:
- name: model_optimization
  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: 0.24598930481283424
---

<!-- 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. -->

# model_optimization

This model was trained from scratch on the ami dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0220
- Wer: 0.2460

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.2804        | 50.0  | 250  | 1.8094          | 0.3636 |
| 0.637         | 100.0 | 500  | 2.6436          | 0.3155 |
| 0.4223        | 150.0 | 750  | 1.6623          | 0.2406 |
| 0.3273        | 200.0 | 1000 | 2.0220          | 0.2460 |


### Framework versions

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