metadata

license: apache-2.0
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - facebook/multilingual_librispeech
metrics:
  - wer
model-index:
  - name: Whisper largeV2 German MLS
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: facebook/multilingual_librispeech german
          type: facebook/multilingual_librispeech
          config: german
          split: test
          args: german
        metrics:
          - name: Wer
            type: wer
            value: 6.048320913895545

Whisper largeV2 German MLS

This model is a fine-tuned version of openai/whisper-large-v2 on the facebook/multilingual_librispeech german dataset. It achieves the following results on the evaluation set:

Loss: 0.1370
Wer: 6.0483

Model description

The model is fine-tuned for 4000 updates/steps on multilingual librispeech German train data.

Zero-shot - 5.5 (MLS German test)
Fine-tune MLS German train - 6.04 (MLS German test)

Even after fine-tuning the model is doing slightly worse than the zero-shot.

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: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 64
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
0.1755	0.25	1000	0.1844	7.7118
0.1185	0.5	2000	0.1636	7.0659
0.1081	0.75	3000	0.1396	6.0844
0.1222	1.0	4000	0.1370	6.0483

Framework versions

Transformers 4.26.0.dev0
Pytorch 1.13.0+cu117
Datasets 2.7.1.dev0
Tokenizers 0.13.2