mim-asr-interviews-full-small-no-augmented

This model is a fine-tuned version of openai/whisper-small on the None dataset.It achieves the following results on the evaluation set:

• Loss: 0.8421
• Wer: 75.7006

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: 1e-05
• train_batch_size: 32
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 64
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 1000
• training_steps: 10000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.0003	52.63	1000	0.6102	55.2783
0.0001	105.26	2000	0.6581	76.8522
0.0	157.89	3000	0.6936	62.1113
0.0	210.53	4000	0.7215	64.2994
0.0	263.16	5000	0.7532	65.4127
0.0	315.79	6000	0.7775	65.1440
0.0	368.42	7000	0.8028	65.0288
0.0	421.05	8000	0.8207	69.8656
0.0	473.68	9000	0.8364	70.2495
0.0	526.32	10000	0.8421	75.7006

Framework versions

• Transformers 4.28.1
• Pytorch 2.0.0+cu117
• Datasets 2.11.0
• Tokenizers 0.13.3

以上模型是在None数据集上，根据 openai/whisper-small 进行微调的版本。它在评估集上实现了以下结果： - 损失（Loss）：0.8421 - 词错误率（WER）：75.7006 模型描述： 需要更多信息 拟合用途和限制： 需要更多信息 训练和评估数据： 需要更多信息 训练过程： 训练超参数： 以下是训练过程中使用的超参数： - 学习率（learning_rate）: 1e-05 - 训练批次大小（train_batch_size）: 32 - 评估批次大小（eval_batch_size）: 16 - 随机种子（seed）: 42 - 梯度累积步数（gradient_accumulation_steps）: 2 - 总训练批次大小（total_train_batch_size）: 64 - 优化器（optimizer）: Adam，参数为betas=(0.9, 0.999)和epsilon=1e-08 - 学习率调度器类型（lr_scheduler_type）: 线性（linear） - 学习率调度器预热步数（lr_scheduler_warmup_steps）: 1000 - 训练步数（training_steps）: 10000 - 混合精度训练（mixed_precision_training）: Native AMP 训练结果：

Training Loss	Epoch	Step	Validation Loss	Wer
0.0003	52.63	1000	0.6102	55.2783
0.0001	105.26	2000	0.6581	76.8522
0.0	157.89	3000	0.6936	62.1113
0.0	210.53	4000	0.7215	64.2994
0.0	263.16	5000	0.7532	65.4127
0.0	315.79	6000	0.7775	65.1440
0.0	368.42	7000	0.8028	65.0288
0.0	421.05	8000	0.8207	69.8656
0.0	473.68	9000	0.8364	70.2495
0.0	526.32	10000	0.8421	75.7006

框架版本： 以下是所用框架的版本： - Transformers 4.28.1 - Pytorch 2.0.0+cu117 - Datasets 2.11.0 - Tokenizers 0.13.3