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Whisper Medium TW - Augmented

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

• eval_loss: 0.0951
• eval_wer: 7.4865
• eval_runtime: 2823.6824
• eval_samples_per_second: 1.668
• eval_steps_per_second: 0.834
• epoch: 1.7
• step: 600

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

Training:

• mozilla-foundation/common_voice_11_0 (train+validation)

Evaluation:

• mozilla-foundation/common_voice_11_0 (test)

Training procedure

• Datasets were augmented on-the-fly using audiomentations via PitchShift and TimeStretch transformations at p=0.3.
• A space is added between each Chinese character, as demonstrated in the original paper. Effectively, WER == CER in this case.

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 1e-05
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• gradient_accumulation_steps: 16
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 100
• training_steps: 1000
• mixed_precision_training: Native AMP

Framework versions

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