transformers tensorboard safetensors whisper automatic-speech-recognition generated_from_trainer dataset:fsicoli/cv16-fleurs-coraa-mls-ted-alcaim-cf-cdc-lapsbm-lapsmail base_model:openai/whisper-medium license:apache-2.0 model-index endpoints_compatible region:us
---
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- fsicoli/cv16-fleurs-coraa-mls-ted-alcaim-cf-cdc-lapsbm-lapsmail
metrics:
- wer
model-index:
- name: whisper-medium-pt-cv16-fleurs-coraa
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: fsicoli/cv16-fleurs-coraa-mls-ted-alcaim-cf-cdc-lapsbm-lapsmail default
      type: fsicoli/cv16-fleurs-coraa-mls-ted-alcaim-cf-cdc-lapsbm-lapsmail
      args: default
    metrics:
    - name: Wer
      type: wer
      value: 0.16517035362506055
---

<!-- 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-coraa

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the fsicoli/cv16-fleurs-coraa-mls-ted-alcaim-cf-cdc-lapsbm-lapsmail default dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4546
- Wer: 0.1652

## 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: 8
- 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: 6000
- training_steps: 20000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.748         | 0.02  | 2000  | 0.3752          | 0.2156 |
| 0.6051        | 0.05  | 4000  | 0.4198          | 0.1885 |
| 0.5578        | 0.07  | 6000  | 0.4245          | 0.1757 |
| 0.4632        | 0.1   | 8000  | 0.4393          | 0.1736 |
| 0.5151        | 0.12  | 10000 | 0.4457          | 0.1701 |
| 0.5158        | 0.15  | 12000 | 0.4314          | 0.1671 |
| 0.55          | 0.17  | 14000 | 0.4566          | 0.1675 |
| 0.4524        | 0.19  | 16000 | 0.4648          | 0.1663 |
| 0.4667        | 0.22  | 18000 | 0.4606          | 0.1656 |
| 0.5133        | 0.24  | 20000 | 0.4546          | 0.1652 |


### Framework versions

- Transformers 4.39.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.1.dev0
- Tokenizers 0.15.0
