drepic
/

whisper-small-jp-ct2

@@ -1,92 +1,102 @@
----
-library_name: ctranslate2
-license: apache-2.0
-base_model: openai/whisper-small
-tags:
-- audio
-- automatic-speech-recognition
-- ctranslate2
-- faster-whisper
-- generated_from_trainer
-- whisper
-metrics:
-- wer
-model-index:
-- name: whisper-small-jp
-  results: []
----
-> **This repository contains the CTranslate2 export of the fine-tuned model.**
->
-> • Base Transformers model: [drepic/whisper-small-jp](https://huggingface.co/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")
-> ```
-<!-- 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. -->
-# whisper-small-jp
-This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on an unknown 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
-- train_batch_size: 8
-- eval_batch_size: 4
-- seed: 42
-- distributed_type: multi-GPU
-- num_devices: 2
-- total_train_batch_size: 16
-- total_eval_batch_size: 8
-- 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: 300
-- num_epochs: 10
-- mixed_precision_training: Native AMP
-### Training results
-| Training Loss | Epoch | Step  | Validation Loss | Wer    | Cer    |
-|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
-| 0.6589        | 1.0   | 7154  | 0.6615          | 0.2735 | 0.2735 |
-| 0.6273        | 2.0   | 14308 | 0.6457          | 0.2699 | 0.2699 |
-| 0.6251        | 3.0   | 21462 | 0.6359          | 0.2660 | 0.2660 |
-| 0.6427        | 4.0   | 28616 | 0.6283          | 0.2642 | 0.2642 |
-| 0.6389        | 5.0   | 35770 | 0.6243          | 0.2631 | 0.2631 |
-| 0.6078        | 6.0   | 42924 | 0.6242          | 0.2615 | 0.2615 |
-| 0.5788        | 7.0   | 50078 | 0.6195          | 0.2603 | 0.2603 |
-| 0.5801        | 8.0   | 57232 | 0.6180          | 0.2596 | 0.2596 |
-| 0.5866        | 9.0   | 64386 | 0.6145          | 0.2598 | 0.2598 |
-| 0.6052        | 10.0  | 71540 | 0.6168          | 0.2600 | 0.2600 |
-### Framework versions
-- Transformers 4.56.1
-- Pytorch 2.8.0+cu128
-- Datasets 4.0.0
-- Tokenizers 0.22.0

+---
+library_name: ctranslate2
+license: apache-2.0
+base_model: openai/whisper-small
+tags:
+- audio
+- automatic-speech-recognition
+- ctranslate2
+- faster-whisper
+- generated_from_trainer
+- whisper
+metrics:
+- wer
+model-index:
+- name: whisper-small-jp
+  results: []
+---
+> **This repository contains the CTranslate2 export of the fine-tuned model.**
+>
+> • Base Transformers model: [drepic/whisper-small-jp](https://huggingface.co/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")
+> ```
+<!-- 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. -->
+# whisper-small-jp
+This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on an unknown 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
+- train_batch_size: 8
+- eval_batch_size: 4
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 2
+- total_train_batch_size: 16
+- total_eval_batch_size: 8
+- 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: 300
+- num_epochs: 10
+- mixed_precision_training: Native AMP
+### Training results
+| Training Loss | Epoch | Step  | Validation Loss | Wer    | Cer    |
+|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
+| 0.6589        | 1.0   | 7154  | 0.6615          | 0.2735 | 0.2735 |
+| 0.6273        | 2.0   | 14308 | 0.6457          | 0.2699 | 0.2699 |
+| 0.6251        | 3.0   | 21462 | 0.6359          | 0.2660 | 0.2660 |
+| 0.6427        | 4.0   | 28616 | 0.6283          | 0.2642 | 0.2642 |
+| 0.6389        | 5.0   | 35770 | 0.6243          | 0.2631 | 0.2631 |
+| 0.6078        | 6.0   | 42924 | 0.6242          | 0.2615 | 0.2615 |
+| 0.5788        | 7.0   | 50078 | 0.6195          | 0.2603 | 0.2603 |
+| 0.5801        | 8.0   | 57232 | 0.6180          | 0.2596 | 0.2596 |
+| 0.5866        | 9.0   | 64386 | 0.6145          | 0.2598 | 0.2598 |
+| 0.6052        | 10.0  | 71540 | 0.6168          | 0.2600 | 0.2600 |
+### Framework versions
+- Transformers 4.56.1
+- Pytorch 2.8.0+cu128
+- Datasets 4.0.0
+- Tokenizers 0.22.0
+## Evaluation Results
+### mozilla-foundation/common_voice_17_0 (ja) — split: test
+- **CER**: 0.2302
+- **Dataset**: mozilla-foundation/common_voice_17_0
+- **Language/Subset**: ja
+- **Split**: test