mikr/whisper-audio-concat-test

Whisper Large-v2 Czech CV11 audio concatenation test

This model is a fine-tuned version of openai/whisper-large-v2 on the mozilla-foundation/common_voice_11_0 cs dataset. It achieves the following results on the evaluation set:

• Loss: 0.2563
• Wer: 8.3774

Model description

First test of audio concatenation few short samples to one training sample together.

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: 32
• eval_batch_size: 8
• seed: 42
• distributed_type: multi-GPU
• gradient_accumulation_steps: 2
• 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: 5000

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.0022	24.39	1000	0.2181	8.7807
0.0002	48.77	2000	0.2563	8.3774
0.0001	73.17	3000	0.2756	8.4510
0.0001	97.55	4000	0.2871	8.4823
0.0001	121.94	5000	0.2913	8.4731

Framework versions

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