metadata

language:
  - de
license: apache-2.0
base_model: openai/whisper-tiny
tags:
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_11_0
metrics:
  - wer
model-index:
  - name: whisper-tiny-french-HanNeurAI
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 11.0
          type: mozilla-foundation/common_voice_11_0
          config: fr
          split: test
          args: 'config: de, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 38.84530607837283

whisper-tiny-french-HanNeurAI

This model is a fine-tuned version of openai/whisper-tiny on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set:

Loss: 0.6998
Wer: 38.8453

This fine-tuning model is part of my school project. With limitation of my compute, I scaled down the dataset

Additional information and demo code can be found in this github: HanCreation/Whisper-Tiny-German

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-05
train_batch_size: 16
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: 500
training_steps: 4000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.6833	0.16	1000	0.8090	43.6285
0.6272	0.32	2000	0.7441	41.3900
0.5671	0.48	3000	0.7124	40.0427
0.5593	0.64	4000	0.6998	38.8453

Framework versions

Transformers 4.40.2
Pytorch 2.3.0
Datasets 2.19.1
Tokenizers 0.19.1