willopcbeta/whisper-small-jp-ONNX

whisper-small-jp (ONNX)

This is an ONNX version of drepic/whisper-small-jp. It was automatically converted and uploaded using this Hugging Face Space.

The optimal Q4 quantitative configuration: using decoder_model with q4f16 results in less ambiguous or nonsensical outputs.

quantization: {
    encoder_model: 'q4f16',
    decoder_model_merged: 'q4',
},

Usage with Transformers.js

See the pipeline documentation for automatic-speech-recognition: https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.AutomaticSpeechRecognitionPipeline

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whisper-small-jp

This model is a fine-tuned version of 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

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: 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