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Update README.md

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  license: apache-2.0
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  base_model: openai/whisper-large-v3
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  tags:
 
 
 
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  - generated_from_trainer
 
 
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  metrics:
 
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  - wer
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  model-index:
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  - name: whisper-large-v3-translate-zh-v0.1-lt
@@ -16,7 +22,7 @@ This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingf
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  ## Model description
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- 3500小时音声数据微调, 翻译模式直出中文
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  ## Usage
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@@ -32,14 +38,14 @@ The following hyperparameters were used during training:
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  - train_batch_size: 32
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  - eval_batch_size: 32
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  - seed: 42
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- - distributed_type: multi-GPU
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  - gradient_accumulation_steps: 2
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  - total_train_batch_size: 64
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  - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_steps: 4000
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- - num_epochs: 3.0
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- - mixed_precision_training: Native AMP
 
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  ### Training results
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  - Transformers 4.41.1
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  - Pytorch 2.3.1+cu121
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  - Datasets 2.19.1
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- - Tokenizers 0.19.1
 
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  license: apache-2.0
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  base_model: openai/whisper-large-v3
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  tags:
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+ - audio
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+ - automatic-speech-recognition
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+ - translate
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  - generated_from_trainer
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+ language:
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+ - zh
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  metrics:
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+ - cer
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  - wer
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  model-index:
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  - name: whisper-large-v3-translate-zh-v0.1-lt
 
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  ## Model description
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+ 3500小时数据微调, 翻译模式直出中文
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  ## Usage
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  - train_batch_size: 32
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  - eval_batch_size: 32
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  - seed: 42
 
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  - gradient_accumulation_steps: 2
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  - total_train_batch_size: 64
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  - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_steps: 4000
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+ - dropout: 0.1
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+ - mask_time_prob: 0.05
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+ - mask_feature_prob: 0.2
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  ### Training results
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  - Transformers 4.41.1
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  - Pytorch 2.3.1+cu121
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  - Datasets 2.19.1
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+ - Tokenizers 0.19.1