---
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-3000h
  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: 0.11007210455159983
---

<!-- 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-3000h

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: 0.9306
- Wer: 0.1101

## 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
- num_epochs: 10.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step    | Validation Loss | Wer    |
|:-------------:|:-----:|:-------:|:---------------:|:------:|
| 0.4423        | 0.2   | 20000   | 0.4723          | 0.1633 |
| 0.4963        | 0.39  | 40000   | 0.4921          | 0.1547 |
| 0.3853        | 0.59  | 60000   | 0.5099          | 0.1470 |
| 0.37          | 0.79  | 80000   | 0.4753          | 0.1439 |
| 0.3615        | 0.98  | 100000  | 0.5074          | 0.1386 |
| 0.2394        | 1.18  | 120000  | 0.4858          | 0.1341 |
| 0.227         | 1.38  | 140000  | 0.5758          | 0.1323 |
| 0.2461        | 1.57  | 160000  | 0.5067          | 0.1322 |
| 0.2078        | 1.77  | 180000  | 0.5087          | 0.1291 |
| 0.2138        | 1.97  | 200000  | 0.5201          | 0.1273 |
| 0.1188        | 2.16  | 220000  | 0.6359          | 0.1265 |
| 0.1009        | 2.36  | 240000  | 0.6229          | 0.1253 |
| 0.1394        | 2.56  | 260000  | 0.5734          | 0.1231 |
| 0.1383        | 2.75  | 280000  | 0.5914          | 0.1213 |
| 0.1332        | 2.95  | 300000  | 0.6174          | 0.1212 |
| 0.0634        | 3.15  | 320000  | 0.6461          | 0.1190 |
| 0.0667        | 3.34  | 340000  | 0.6330          | 0.1211 |
| 0.0546        | 3.54  | 360000  | 0.6927          | 0.1190 |
| 0.1029        | 3.74  | 380000  | 0.6777          | 0.1184 |
| 0.0664        | 3.93  | 400000  | 0.6367          | 0.1161 |
| 0.0665        | 4.13  | 420000  | 0.7467          | 0.1171 |
| 0.0695        | 4.33  | 440000  | 0.7332          | 0.1164 |
| 0.0708        | 4.52  | 460000  | 0.7141          | 0.1171 |
| 0.0695        | 4.72  | 480000  | 0.6869          | 0.1169 |
| 0.0758        | 4.92  | 500000  | 0.7360          | 0.1153 |
| 0.061         | 5.11  | 520000  | 0.7594          | 0.1161 |
| 0.0804        | 5.31  | 540000  | 0.7640          | 0.1158 |
| 0.0963        | 5.51  | 560000  | 0.7848          | 0.1157 |
| 0.0815        | 5.7   | 580000  | 0.7635          | 0.1145 |
| 0.0794        | 5.9   | 600000  | 0.7566          | 0.1134 |
| 0.0907        | 6.1   | 620000  | 0.8152          | 0.1147 |
| 0.0664        | 6.29  | 640000  | 0.8405          | 0.1123 |
| 0.0654        | 6.49  | 660000  | 0.8278          | 0.1119 |
| 0.0652        | 6.69  | 680000  | 0.8267          | 0.1134 |
| 0.1043        | 6.88  | 700000  | 0.8254          | 0.1122 |
| 0.0383        | 7.08  | 720000  | 0.8719          | 0.1122 |
| 0.0461        | 7.28  | 740000  | 0.8640          | 0.1130 |
| 0.0791        | 7.47  | 760000  | 0.8990          | 0.1122 |
| 0.0587        | 7.67  | 780000  | 0.9107          | 0.1122 |
| 0.0578        | 7.87  | 800000  | 0.9060          | 0.1124 |
| 0.0218        | 8.06  | 820000  | 0.8845          | 0.1111 |
| 0.0125        | 8.26  | 840000  | 0.9072          | 0.1112 |
| 0.0172        | 8.46  | 860000  | 0.8899          | 0.1107 |
| 0.0204        | 8.65  | 880000  | 0.9149          | 0.1108 |
| 0.0145        | 8.85  | 900000  | 0.9097          | 0.1103 |
| 0.0146        | 9.05  | 920000  | 0.9084          | 0.1107 |
| 0.0166        | 9.24  | 940000  | 0.9053          | 0.1103 |
| 0.0177        | 9.44  | 960000  | 0.9193          | 0.1100 |
| 0.0157        | 9.64  | 980000  | 0.9212          | 0.1101 |
| 0.0096        | 9.83  | 1000000 | 0.9313          | 0.1103 |


### Framework versions

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