---
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- fsicoli/cv17-fleurs-coraa-mls-ted-alcaim-cf-cdc-lapsbm-lapsmail-sydney-lingualibre-voxforge-tatoeba
metrics:
- wer
model-index:
- name: whisper-medium-pt-1000h
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: fsicoli/cv17-fleurs-coraa-mls-ted-alcaim-cf-cdc-lapsbm-lapsmail-sydney-lingualibre-voxforge-tatoeba
        default
      type: fsicoli/cv17-fleurs-coraa-mls-ted-alcaim-cf-cdc-lapsbm-lapsmail-sydney-lingualibre-voxforge-tatoeba
      args: default
    metrics:
    - name: Wer
      type: wer
      value: 0.11473958668640959
---

<!-- 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-1000h

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the fsicoli/cv17-fleurs-coraa-mls-ted-alcaim-cf-cdc-lapsbm-lapsmail-sydney-lingualibre-voxforge-tatoeba default dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6491
- Wer: 0.1147

## 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: 5e-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: 10000
- training_steps: 300000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Wer    |
|:-------------:|:-----:|:------:|:---------------:|:------:|
| 0.4574        | 0.2   | 20000  | 0.5339          | 0.1631 |
| 0.4124        | 0.39  | 40000  | 0.4512          | 0.1517 |
| 0.481         | 0.59  | 60000  | 0.4628          | 0.1466 |
| 0.3452        | 0.79  | 80000  | 0.4677          | 0.1392 |
| 0.4086        | 0.98  | 100000 | 0.4551          | 0.1364 |
| 0.1565        | 1.18  | 120000 | 0.5060          | 0.1316 |
| 0.1513        | 1.38  | 140000 | 0.5330          | 0.1286 |
| 0.1496        | 1.57  | 160000 | 0.5519          | 0.1263 |
| 0.1533        | 1.77  | 180000 | 0.5528          | 0.1234 |
| 0.1525        | 1.97  | 200000 | 0.4857          | 0.1194 |
| 0.1918        | 2.16  | 220000 | 0.5915          | 0.1189 |
| 0.1175        | 2.36  | 240000 | 0.6099          | 0.1174 |
| 0.0959        | 2.56  | 260000 | 0.6183          | 0.1157 |
| 0.0988        | 2.75  | 280000 | 0.6423          | 0.1152 |
| 0.0913        | 2.95  | 300000 | 0.6491          | 0.1147 |


### Framework versions

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