---
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- fsicoli/cv16-fleurs
- mozilla-foundation/common_voice_16_1
metrics:
- wer
model-index:
- name: whisper-medium-pt-cv16-fleurs
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: fsicoli/cv16-fleurs default
      type: fsicoli/cv16-fleurs
      args: default
    metrics:
    - name: Wer
      type: wer
      value: 0.09421927983206846
language:
- pt
---

<!-- 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-pt-cv16-fleurs

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the fsicoli/cv16-fleurs default dataset.
It achieves the following results on the [mozilla-foundation/common_voice_16_1](https://huggingface.co/datasets/mozilla-foundation/common_voice_16_1) evaluation set:
- Loss: 0.1409
- Wer: 0.0942

## 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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- total_eval_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5000
- training_steps: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2552        | 0.93  | 1000 | 0.2200          | 0.1220 |
| 0.1928        | 1.87  | 2000 | 0.1645          | 0.1062 |
| 0.1646        | 2.8   | 3000 | 0.1508          | 0.1016 |
| 0.1333        | 3.74  | 4000 | 0.1438          | 0.0970 |
| 0.1027        | 4.67  | 5000 | 0.1409          | 0.0942 |


### Framework versions

- Transformers 4.39.0.dev0
- Pytorch 2.2.1
- Datasets 2.16.1
- Tokenizers 0.15.2