---
language:
- ur
license: apache-2.0
base_model: openai/whisper-large-v2
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_14_0
metrics:
- wer
model-index:
- name: Whisper Large Ur
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 14.0
      type: mozilla-foundation/common_voice_14_0
      config: ur
      split: test
      args: ur
    metrics:
    - name: Wer
      type: wer
      value: 32.20306217135787
---

<!-- 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 Large Ur

This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co./openai/whisper-large-v2) on the Common Voice 14.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5723
- Wer: 32.2031

## 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: 64
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- 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     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0147        | 9.06  | 1000 | 0.5723          | 32.2031 |


### Framework versions

- Transformers 4.37.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.2.dev0
- Tokenizers 0.15.0