metadata

license: apache-2.0
base_model: openai/whisper-large-v3
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-large-v3-pt-1000h-ct2
    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: 0.11132023872721715

whisper-large-v3-pt-1000h-ct2

This model is a fine-tuned version of openai/whisper-large-v3 on the fsicoli/cv17-fleurs-coraa-mls-ted-alcaim-cf-cdc-lapsbm-lapsmail-sydney-lingualibre-voxforge-tatoeba default dataset. It was converted to the CTranslate2 format. It achieves the following results on the evaluation set:

Loss: 0.5576
Wer: 0.1113

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: 16
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 2
total_train_batch_size: 32
total_eval_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 10000
training_steps: 82000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.2717	0.39	10000	0.4143	0.1341
0.2646	0.79	20000	0.4141	0.1284
0.2244	1.18	30000	0.5361	0.1253
0.2056	1.57	40000	0.4714	0.1223
0.2034	1.97	50000	0.4937	0.1195
0.1717	2.36	60000	0.5127	0.1178
0.1692	2.75	70000	0.6040	0.1146
0.121	3.15	80000	0.5361	0.1130

Framework versions

Transformers 4.39.0.dev0
Pytorch 2.2.1
Datasets 2.18.1.dev0
Tokenizers 0.15.2