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--- |
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library_name: transformers |
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license: mit |
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base_model: microsoft/speecht5_tts |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: voice_clone |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# voice_clone |
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This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4976 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-06 |
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- train_batch_size: 8 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 64 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- training_steps: 4000 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-------:|:----:|:---------------:| |
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| 0.9069 | 0.8734 | 50 | 0.8148 | |
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| 0.8979 | 1.7336 | 100 | 0.7481 | |
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| 0.8288 | 2.5939 | 150 | 0.7062 | |
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| 0.8054 | 3.4541 | 200 | 0.6817 | |
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| 0.7422 | 4.3144 | 250 | 0.6609 | |
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| 0.7022 | 5.1747 | 300 | 0.6083 | |
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| 0.6318 | 6.0349 | 350 | 0.5642 | |
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| 0.6123 | 6.9083 | 400 | 0.5512 | |
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| 0.5917 | 7.7686 | 450 | 0.5467 | |
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| 0.5866 | 8.6288 | 500 | 0.5385 | |
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| 0.581 | 9.4891 | 550 | 0.5322 | |
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| 0.5605 | 10.3493 | 600 | 0.5318 | |
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| 0.5562 | 11.2096 | 650 | 0.5258 | |
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| 0.5565 | 12.0699 | 700 | 0.5196 | |
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| 0.566 | 12.9432 | 750 | 0.5230 | |
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| 0.561 | 13.8035 | 800 | 0.5204 | |
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| 0.5486 | 14.6638 | 850 | 0.5171 | |
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| 0.554 | 15.5240 | 900 | 0.5192 | |
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| 0.5367 | 16.3843 | 950 | 0.5153 | |
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| 0.5347 | 17.2445 | 1000 | 0.5144 | |
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| 0.5373 | 18.1048 | 1050 | 0.5152 | |
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| 0.5386 | 18.9782 | 1100 | 0.5127 | |
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| 0.5421 | 19.8384 | 1150 | 0.5094 | |
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| 0.5347 | 20.6987 | 1200 | 0.5101 | |
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| 0.5415 | 21.5590 | 1250 | 0.5116 | |
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| 0.5225 | 22.4192 | 1300 | 0.5087 | |
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| 0.5222 | 23.2795 | 1350 | 0.5087 | |
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| 0.5219 | 24.1397 | 1400 | 0.5080 | |
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| 0.5134 | 25.0 | 1450 | 0.5050 | |
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| 0.5342 | 25.8734 | 1500 | 0.5068 | |
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| 0.5265 | 26.7336 | 1550 | 0.5064 | |
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| 0.5279 | 27.5939 | 1600 | 0.5074 | |
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| 0.5304 | 28.4541 | 1650 | 0.5061 | |
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| 0.5132 | 29.3144 | 1700 | 0.5042 | |
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| 0.5091 | 30.1747 | 1750 | 0.5048 | |
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| 0.5152 | 31.0349 | 1800 | 0.5067 | |
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| 0.5192 | 31.9083 | 1850 | 0.5030 | |
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| 0.5232 | 32.7686 | 1900 | 0.5031 | |
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| 0.5247 | 33.6288 | 1950 | 0.5033 | |
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| 0.5261 | 34.4891 | 2000 | 0.5044 | |
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| 0.5116 | 35.3493 | 2050 | 0.5049 | |
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| 0.5049 | 36.2096 | 2100 | 0.5015 | |
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| 0.5044 | 37.0699 | 2150 | 0.5012 | |
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| 0.5195 | 37.9432 | 2200 | 0.5016 | |
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| 0.5186 | 38.8035 | 2250 | 0.5014 | |
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| 0.5245 | 39.6638 | 2300 | 0.5014 | |
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| 0.5248 | 40.5240 | 2350 | 0.5028 | |
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| 0.4969 | 41.3843 | 2400 | 0.5034 | |
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| 0.5009 | 42.2445 | 2450 | 0.5055 | |
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| 0.5019 | 43.1048 | 2500 | 0.5002 | |
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| 0.522 | 43.9782 | 2550 | 0.4999 | |
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| 0.5187 | 44.8384 | 2600 | 0.5020 | |
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| 0.5129 | 45.6987 | 2650 | 0.4994 | |
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| 0.5182 | 46.5590 | 2700 | 0.5009 | |
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| 0.4975 | 47.4192 | 2750 | 0.5014 | |
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| 0.5024 | 48.2795 | 2800 | 0.4971 | |
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| 0.5012 | 49.1397 | 2850 | 0.5004 | |
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| 0.5032 | 50.0 | 2900 | 0.5030 | |
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| 0.517 | 50.8734 | 2950 | 0.5008 | |
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| 0.5139 | 51.7336 | 3000 | 0.4994 | |
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| 0.5107 | 52.5939 | 3050 | 0.5007 | |
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| 0.5127 | 53.4541 | 3100 | 0.4998 | |
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| 0.499 | 54.3144 | 3150 | 0.4975 | |
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| 0.4954 | 55.1747 | 3200 | 0.4994 | |
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| 0.4994 | 56.0349 | 3250 | 0.5000 | |
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| 0.5109 | 56.9083 | 3300 | 0.4986 | |
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| 0.5145 | 57.7686 | 3350 | 0.4994 | |
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| 0.5155 | 58.6288 | 3400 | 0.4990 | |
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| 0.51 | 59.4891 | 3450 | 0.5001 | |
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| 0.501 | 60.3493 | 3500 | 0.5003 | |
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| 0.484 | 61.2096 | 3550 | 0.4989 | |
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| 0.4955 | 62.0699 | 3600 | 0.5006 | |
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| 0.5147 | 62.9432 | 3650 | 0.4992 | |
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| 0.5189 | 63.8035 | 3700 | 0.5009 | |
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| 0.5014 | 64.6638 | 3750 | 0.4994 | |
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| 0.5159 | 65.5240 | 3800 | 0.5020 | |
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| 0.4942 | 66.3843 | 3850 | 0.4989 | |
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| 0.5001 | 67.2445 | 3900 | 0.5002 | |
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| 0.4902 | 68.1048 | 3950 | 0.4981 | |
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| 0.5126 | 68.9782 | 4000 | 0.4976 | |
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### Framework versions |
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- Transformers 4.47.0 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.3.1 |
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- Tokenizers 0.21.0 |
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