drepic/whisper-small-jp-ct2

This repository contains the CTranslate2 export of the fine-tuned model. • Base Transformers model: drepic/whisper-small-jp • Use with faster-whisper: ``python from faster_whisper import WhisperModel model = WhisperModel("drepic/whisper-small-jp-ct2", device="cuda", compute_type="float16") ``

OTHER FINETUNES

• —Want better accuracy? Try drepic/whisper-medium-jp-ct2

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whisper-small-jp

This model is a fine-tuned version of openai/whisper-small on a Japanese youtube based dataset. It achieves the following results on the evaluation set:

• —Loss: 0.6168
• —Wer: 0.2600
• —Cer: 0.2600

Model description

Better suited for transcribing japanese youtube content.

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: 5e-06
• —trainbatchsize: 8
• —evalbatchsize: 4
• —seed: 42
• —distributed_type: multi-GPU
• —num_devices: 2
• —totaltrainbatch_size: 16
• —totalevalbatch_size: 8
• —optimizer: Use OptimizerNames.ADAMWTORCHFUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• —lrschedulertype: linear
• —lrschedulerwarmup_steps: 300
• —num_epochs: 10
• —mixedprecisiontraining: Native AMP

Training results

Framework versions

• —Transformers 4.56.1
• —Pytorch 2.8.0+cu128
• —Datasets 4.0.0
• —Tokenizers 0.22.0