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impaired-v3-independent-all

Automatic Speech Recognition
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whisper
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impaired-v3-independent-all
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---
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
datasets:
- impaired_v3_independent_all
metrics:
- wer
model-index:
- name: impaired-v3-independent-all
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: impaired_v3_independent_all
      type: impaired_v3_independent_all
      config: cs
      split: test
      args: cs
    metrics:
    - name: Wer
      type: wer
      value: 0.4068825910931174
---

<!-- 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. -->

# impaired-v3-independent-all

This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the impaired_v3_independent_all dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4531
- Wer: 0.4069

## 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: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0077        | 13.99 | 1000 | 1.0277          | 0.3968 |
| 0.0008        | 27.97 | 2000 | 1.2058          | 0.4008 |
| 0.0001        | 41.96 | 3000 | 1.3848          | 0.4069 |
| 0.0001        | 55.94 | 4000 | 1.4363          | 0.3998 |
| 0.0001        | 69.93 | 5000 | 1.4531          | 0.4069 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1