whisper-medium-jp-ct2

This repository contains the CTranslate2 export of the fine-tuned model.

• Base Transformers model: drepic/whisper-medium-jp
• Use with faster-whisper:

from faster_whisper import WhisperModel
model = WhisperModel("drepic/whisper-medium-jp-ct2", device="cuda", compute_type="float16")

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->

OTHER FINETUNES

• Want something more lightweight? Try drepic/whisper-small-jp-ct2

whisper-medium-jp

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

• Loss: 0.4828
• Wer: 0.2254
• Cer: 0.2254

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: 4e-06
• train_batch_size: 4
• eval_batch_size: 2
• seed: 42
• distributed_type: multi-GPU
• num_devices: 2
• gradient_accumulation_steps: 2
• total_train_batch_size: 16
• total_eval_batch_size: 4
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 400
• num_epochs: 15
• mixed_precision_training: Native AMP

Training results

Framework versions

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