whisper-medium-ca

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Whisper Medium Ca

This model is a fine-tuned version of openai/whisper-medium on the Common Voice 11.0, the Fleurs, the SLR69, the tb3_parla and the parlament_parla datasets. It achieves the following results on the evaluation set:

• eval_loss: 0.1905
• eval_wer: 10.0031
• eval_runtime: 10456.4485
• eval_samples_per_second: 1.563
• eval_steps_per_second: 0.195
• epoch: 0.2
• step: 2000

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: 32
• eval_batch_size: 8
• seed: 42
• 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: 1000
• training_steps: 10000
• mixed_precision_training: Native AMP

Framework versions

• Transformers 4.26.0.dev0
• Pytorch 1.13.1+cu117
• Datasets 2.7.1.dev0
• Tokenizers 0.13.2