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--- |
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license: apache-2.0 |
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language: |
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- ja |
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metrics: |
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- cer |
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- wer |
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base_model: |
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- openai/whisper-medium |
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tags: |
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- ctranslate2 |
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- faster-whisper |
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- whisper |
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model-index: |
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- name: whisper-medium-jp-ct2 |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: mozilla-foundation/common_voice_17_0 (ja) |
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type: mozilla-foundation/common_voice_17_0 |
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config: ja |
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split: test |
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args: |
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language: ja |
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metrics: |
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- name: CER |
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type: cer |
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value: 0.18572446886192148 |
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--- |
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> **This repository contains the CTranslate2 export of the fine-tuned model.** |
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> |
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> • Base Transformers model: [drepic/whisper-medium-jp](https://huggingface.co/drepic/whisper-medium-jp) |
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> • Use with `faster-whisper`: |
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> |
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> ```python |
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> from faster_whisper import WhisperModel |
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> model = WhisperModel("drepic/whisper-medium-jp-ct2", device="cuda", compute_type="float16") |
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> ``` |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# OTHER FINETUNES |
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- Want something more lightweight? Try [drepic/whisper-small-jp-ct2](https://huggingface.co/drepic/whisper-small-jp-ct2) |
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# whisper-medium-jp |
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This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on an Japanese youtube based dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4828 |
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- Wer: 0.2254 |
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- Cer: 0.2254 |
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## Model description |
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Better suited for transcribing japanese youtube content. |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 4e-06 |
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- train_batch_size: 4 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 2 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 16 |
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- total_eval_batch_size: 4 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 400 |
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- num_epochs: 15 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:| |
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| 0.5341 | 1.0 | 7155 | 0.5321 | 0.2416 | 0.2416 | |
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| 0.5023 | 2.0 | 14310 | 0.5143 | 0.2369 | 0.2369 | |
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| 0.499 | 3.0 | 21465 | 0.5063 | 0.2337 | 0.2337 | |
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| 0.4773 | 4.0 | 28620 | 0.5010 | 0.2310 | 0.2310 | |
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| 0.4775 | 5.0 | 35775 | 0.4944 | 0.2289 | 0.2289 | |
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| 0.4709 | 6.0 | 42930 | 0.4886 | 0.2288 | 0.2288 | |
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| 0.4907 | 7.0 | 50085 | 0.4870 | 0.2271 | 0.2271 | |
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| 0.4855 | 8.0 | 57240 | 0.4868 | 0.2261 | 0.2261 | |
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| 0.4487 | 9.0 | 64395 | 0.4828 | 0.2254 | 0.2254 | |
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### Framework versions |
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- Transformers 4.56.1 |
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- Pytorch 2.8.0+cu128 |
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- Datasets 4.0.0 |
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- Tokenizers 0.22.0 |
