---
base_model: openai/whisper-medium
datasets:
- mozilla-foundation/common_voice_11_0
language:
- id
library_name: transformers
license: apache-2.0
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: whisper-medium-id
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: Common Voice 11.0
      type: mozilla-foundation/common_voice_11_0
      config: id
      split: test
      args: 'config: id, split: test'
    metrics:
    - type: wer
      value: 13.605283966696124
      name: Wer
---

<!-- 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. -->

# whisper-medium-id

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2226
- Wer: 13.6053

## 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-06
- train_batch_size: 16
- 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: 500
- training_steps: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.2022        | 1.9305 | 1000 | 0.1830          | 13.1308 |
| 0.1089        | 3.8610 | 2000 | 0.1824          | 13.0192 |
| 0.0609        | 5.7915 | 3000 | 0.1949          | 13.2657 |
| 0.0327        | 7.7220 | 4000 | 0.2125          | 13.4797 |
| 0.0257        | 9.6525 | 5000 | 0.2226          | 13.6053 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1