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--- |
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library_name: transformers |
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license: mit |
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base_model: Moustapha91/speecht5_finetuned_wo_v1 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: speecht5_tts_wolof |
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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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# speecht5_tts_wolof |
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This model is a fine-tuned version of [Moustapha91/speecht5_finetuned_wo_v1](https://huggingface.co/Moustapha91/speecht5_finetuned_wo_v1) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2943 |
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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: 1e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 16 |
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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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| 0.9404 | 0.5952 | 50 | 0.4362 | |
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| 0.8342 | 1.1905 | 100 | 0.3784 | |
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| 0.7869 | 1.7857 | 150 | 0.3627 | |
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| 0.7841 | 2.3810 | 200 | 0.3546 | |
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| 0.762 | 2.9762 | 250 | 0.3489 | |
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| 0.7487 | 3.5714 | 300 | 0.3431 | |
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| 0.7423 | 4.1667 | 350 | 0.3392 | |
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| 0.7211 | 4.7619 | 400 | 0.3362 | |
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| 0.7147 | 5.3571 | 450 | 0.3304 | |
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| 0.7097 | 5.9524 | 500 | 0.3266 | |
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| 0.7058 | 6.5476 | 550 | 0.3223 | |
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| 0.6929 | 7.1429 | 600 | 0.3198 | |
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| 0.6887 | 7.7381 | 650 | 0.3152 | |
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| 0.664 | 8.3333 | 700 | 0.3131 | |
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| 0.6736 | 8.9286 | 750 | 0.3115 | |
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| 0.6767 | 9.5238 | 800 | 0.3105 | |
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| 0.6722 | 10.1190 | 850 | 0.3095 | |
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| 0.6702 | 10.7143 | 900 | 0.3075 | |
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| 0.6615 | 11.3095 | 950 | 0.3058 | |
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| 0.6654 | 11.9048 | 1000 | 0.3063 | |
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| 0.6682 | 12.5 | 1050 | 0.3083 | |
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| 0.6607 | 13.0952 | 1100 | 0.3051 | |
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| 0.6514 | 13.6905 | 1150 | 0.3042 | |
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| 0.6605 | 14.2857 | 1200 | 0.3041 | |
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| 0.6509 | 14.8810 | 1250 | 0.3028 | |
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| 0.6556 | 15.4762 | 1300 | 0.3025 | |
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| 0.6477 | 16.0714 | 1350 | 0.3019 | |
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| 0.6489 | 16.6667 | 1400 | 0.3011 | |
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| 0.6567 | 17.2619 | 1450 | 0.3007 | |
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| 0.6533 | 17.8571 | 1500 | 0.3016 | |
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| 0.6489 | 18.4524 | 1550 | 0.3009 | |
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| 0.6454 | 19.0476 | 1600 | 0.3002 | |
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| 0.6354 | 19.6429 | 1650 | 0.2992 | |
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| 0.645 | 20.2381 | 1700 | 0.2996 | |
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| 0.6376 | 20.8333 | 1750 | 0.2993 | |
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| 0.6472 | 21.4286 | 1800 | 0.2991 | |
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| 0.6571 | 22.0238 | 1850 | 0.2995 | |
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| 0.6333 | 22.6190 | 1900 | 0.2986 | |
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| 0.6323 | 23.2143 | 1950 | 0.2973 | |
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| 0.6314 | 23.8095 | 2000 | 0.2980 | |
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| 0.6437 | 24.4048 | 2050 | 0.2980 | |
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| 0.6383 | 25.0 | 2100 | 0.2977 | |
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| 0.6314 | 25.5952 | 2150 | 0.2978 | |
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| 0.6309 | 26.1905 | 2200 | 0.2965 | |
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| 0.6365 | 26.7857 | 2250 | 0.2965 | |
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| 0.6406 | 27.3810 | 2300 | 0.2966 | |
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| 0.6286 | 27.9762 | 2350 | 0.2968 | |
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| 0.6279 | 28.5714 | 2400 | 0.2963 | |
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| 0.6304 | 29.1667 | 2450 | 0.2967 | |
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| 0.6457 | 29.7619 | 2500 | 0.2960 | |
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| 0.6372 | 30.3571 | 2550 | 0.2958 | |
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| 0.6338 | 30.9524 | 2600 | 0.2952 | |
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| 0.6325 | 31.5476 | 2650 | 0.2956 | |
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| 0.6313 | 32.1429 | 2700 | 0.2951 | |
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| 0.6345 | 32.7381 | 2750 | 0.2956 | |
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| 0.6289 | 33.3333 | 2800 | 0.2949 | |
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| 0.6264 | 33.9286 | 2850 | 0.2947 | |
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| 0.6302 | 34.5238 | 2900 | 0.2952 | |
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| 0.6248 | 35.1190 | 2950 | 0.2945 | |
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| 0.626 | 35.7143 | 3000 | 0.2945 | |
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| 0.6248 | 36.3095 | 3050 | 0.2947 | |
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| 0.6306 | 36.9048 | 3100 | 0.2943 | |
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| 0.6258 | 37.5 | 3150 | 0.2944 | |
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| 0.6318 | 38.0952 | 3200 | 0.2947 | |
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| 0.6279 | 38.6905 | 3250 | 0.2947 | |
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| 0.628 | 39.2857 | 3300 | 0.2940 | |
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| 0.632 | 39.8810 | 3350 | 0.2947 | |
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| 0.6259 | 40.4762 | 3400 | 0.2939 | |
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| 0.6305 | 41.0714 | 3450 | 0.2943 | |
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| 0.6381 | 41.6667 | 3500 | 0.2939 | |
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| 0.6341 | 42.2619 | 3550 | 0.2942 | |
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| 0.6163 | 42.8571 | 3600 | 0.2937 | |
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| 0.6256 | 43.4524 | 3650 | 0.2934 | |
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| 0.628 | 44.0476 | 3700 | 0.2934 | |
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| 0.6371 | 44.6429 | 3750 | 0.2945 | |
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| 0.6209 | 45.2381 | 3800 | 0.2930 | |
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| 0.6285 | 45.8333 | 3850 | 0.2939 | |
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| 0.6309 | 46.4286 | 3900 | 0.2938 | |
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| 0.6216 | 47.0238 | 3950 | 0.2935 | |
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| 0.6352 | 47.6190 | 4000 | 0.2943 | |
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### Framework versions |
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- Transformers 4.47.0.dev0 |
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- Pytorch 2.4.0 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.0 |
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