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说明文档
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Medical_Whisper_large_1.5b
此模型是基于 openai/whisper-large-v3 在 primock_data 数据集上进行微调的版本。
模型描述
这是 whisper-large-v3 通过迁移学习在医生/患者问诊数据上进行微调的版本。此版本为 ONNX 格式，采用 fp32 精度。敬请期待如何在 OnnxRuntime 中运行此流程的说明！
预期用途与限制
医疗转录
训练与评估数据
Na0s/Primock_med
训练流程
全面的迁移学习
训练超参数
训练过程中使用了以下超参数：
- learning_rate: 1e-05
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 24
- optimizer: Adam，betas=(0.9,0.999)，epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 500
- mixed_precision_training: Native AMP
性能概览：
| 模型名称 | WER | CER | 参数量 |
|---|---|---|---|
| Whisper Tiny | 0.46 | 0.27 | 39M |
| Whisper Base | 0.42 | 0.26 | 74M |
| Whisper Small | 0.39 | 0.26 | 244M |
| Whisper Medium | 0.37 | 0.23 | 769M |
| Whisper Large v3 | 0.33 | 0.18 | 1.55B |
| Whisper Medical | 0.19 | 0.10 | 1.55B |
基础 Whisper 模型与 Medical Whisper 在验证集上的性能对比。
| 模型名称 | WER | CER | 参数量 |
|---|---|---|---|
| Whisper Medical | 0.24 | 0.13 | 1.55B |
表格：Medical Whisper 在测试集上的性能。
框架版本
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
Esperanto/Medical-Whisper-large-kvc-fp32-onnx
作者 Esperanto
automatic-speech-recognition
↓ 1
♥ 0
创建时间: 2024-11-13 15:39:46+00:00
更新时间: 2024-12-05 09:32:39+00:00
在 Hugging Face 上查看文件 (1266)
.gitattributes
README.md
config.json
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