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---
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
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