---
license: apache-2.0
base_model: openai/whisper-large
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
metrics:
- wer
model-index:
- name: openai/whisper-large
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_13_0
      type: common_voice_13_0
      config: pt
      split: test
      args: pt
    metrics:
    - name: Wer
      type: wer
      value: 6.399303387769856
---

<!-- 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. -->

# openai/whisper-large

This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co./openai/whisper-large) on the common_voice_13_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4799
- Wer: 6.3993

## 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: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 20000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.077         | 3.53  | 1000  | 0.1616          | 5.4957 |
| 0.0155        | 7.05  | 2000  | 0.2549          | 6.1956 |
| 0.0045        | 10.58 | 3000  | 0.3122          | 5.9261 |
| 0.0017        | 14.11 | 4000  | 0.3317          | 6.0099 |
| 0.0018        | 17.64 | 5000  | 0.3604          | 6.0099 |
| 0.0009        | 21.16 | 6000  | 0.3779          | 6.1791 |
| 0.0012        | 24.69 | 7000  | 0.3470          | 6.0066 |
| 0.0013        | 28.22 | 8000  | 0.3838          | 6.1479 |
| 0.0007        | 31.75 | 9000  | 0.3839          | 6.0395 |
| 0.0003        | 35.27 | 10000 | 0.4090          | 6.2054 |
| 0.0003        | 38.8  | 11000 | 0.4053          | 6.2859 |
| 0.0002        | 42.33 | 12000 | 0.4235          | 6.3467 |
| 0.0002        | 45.86 | 13000 | 0.4326          | 6.3500 |
| 0.0001        | 49.38 | 14000 | 0.4415          | 6.3714 |
| 0.0001        | 52.91 | 15000 | 0.4506          | 6.3878 |
| 0.0001        | 56.44 | 16000 | 0.4586          | 6.4092 |
| 0.0001        | 59.96 | 17000 | 0.4663          | 6.3944 |
| 0.0001        | 63.49 | 18000 | 0.4730          | 6.3911 |
| 0.0001        | 67.02 | 19000 | 0.4778          | 6.3944 |
| 0.0001        | 70.55 | 20000 | 0.4799          | 6.3993 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.15.1