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--- |
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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: speecht5_tts |
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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 |
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This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7806 |
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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: 0.0001 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- training_steps: 30000 |
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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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| No log | 0.53 | 250 | 0.8506 | |
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| 1.0736 | 1.06 | 500 | 0.8219 | |
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| 1.0736 | 1.6 | 750 | 0.7713 | |
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| 0.8607 | 2.13 | 1000 | 0.7947 | |
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| 0.8607 | 2.66 | 1250 | 0.7537 | |
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| 0.802 | 3.19 | 1500 | 0.7304 | |
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| 0.802 | 3.72 | 1750 | 0.7409 | |
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| 0.7627 | 4.26 | 2000 | 0.7282 | |
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| 0.7627 | 4.79 | 2250 | 0.7224 | |
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| 0.7442 | 5.32 | 2500 | 0.7132 | |
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| 0.7442 | 5.85 | 2750 | 0.7718 | |
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| 0.736 | 6.38 | 3000 | 0.7362 | |
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| 0.736 | 6.91 | 3250 | 0.7283 | |
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| 0.7234 | 7.45 | 3500 | 0.7377 | |
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| 0.7234 | 7.98 | 3750 | 0.7226 | |
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| 0.6968 | 8.51 | 4000 | 0.7285 | |
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| 0.6968 | 9.04 | 4250 | 0.7395 | |
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| 0.692 | 9.57 | 4500 | 0.7306 | |
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| 0.692 | 10.11 | 4750 | 0.7221 | |
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| 0.6807 | 10.64 | 5000 | 0.7349 | |
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| 0.6807 | 11.17 | 5250 | 0.7310 | |
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| 0.6702 | 11.7 | 5500 | 0.7391 | |
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| 0.6702 | 12.23 | 5750 | 0.7299 | |
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| 0.6559 | 12.77 | 6000 | 0.7277 | |
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| 0.6559 | 13.3 | 6250 | 0.7453 | |
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| 0.6511 | 13.83 | 6500 | 0.7303 | |
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| 0.6511 | 14.36 | 6750 | 0.7451 | |
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| 0.6335 | 14.89 | 7000 | 0.7209 | |
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| 0.6335 | 15.43 | 7250 | 0.7421 | |
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| 0.6282 | 15.96 | 7500 | 0.7277 | |
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| 0.6282 | 16.49 | 7750 | 0.7426 | |
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| 0.6286 | 17.02 | 8000 | 0.7724 | |
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| 0.6286 | 17.55 | 8250 | 0.7310 | |
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| 0.6164 | 18.09 | 8500 | 0.7414 | |
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| 0.6164 | 18.62 | 8750 | 0.7411 | |
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| 0.6029 | 19.15 | 9000 | 0.7466 | |
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| 0.6029 | 19.68 | 9250 | 0.7267 | |
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| 0.5986 | 20.21 | 9500 | 0.7593 | |
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| 0.5986 | 20.74 | 9750 | 0.7544 | |
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| 0.595 | 21.28 | 10000 | 0.7441 | |
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| 0.595 | 21.81 | 10250 | 0.7422 | |
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| 0.5905 | 22.34 | 10500 | 0.7399 | |
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| 0.5905 | 22.87 | 10750 | 0.7494 | |
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| 0.5792 | 23.4 | 11000 | 0.7311 | |
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| 0.5792 | 23.94 | 11250 | 0.7479 | |
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| 0.5774 | 24.47 | 11500 | 0.7615 | |
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| 0.5774 | 25.0 | 11750 | 0.7578 | |
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| 0.5684 | 25.53 | 12000 | 0.7603 | |
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| 0.5684 | 26.06 | 12250 | 0.7300 | |
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| 0.5621 | 26.6 | 12500 | 0.7385 | |
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| 0.5621 | 27.13 | 12750 | 0.7447 | |
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| 0.5666 | 27.66 | 13000 | 0.7400 | |
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| 0.5666 | 28.19 | 13250 | 0.7518 | |
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| 0.5525 | 28.72 | 13500 | 0.7462 | |
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| 0.5525 | 29.26 | 13750 | 0.7351 | |
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| 0.5471 | 29.79 | 14000 | 0.7673 | |
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| 0.5471 | 30.32 | 14250 | 0.7325 | |
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| 0.5449 | 30.85 | 14500 | 0.7455 | |
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| 0.5449 | 31.38 | 14750 | 0.7473 | |
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| 0.5349 | 31.91 | 15000 | 0.7549 | |
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| 0.5349 | 32.45 | 15250 | 0.7513 | |
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| 0.5345 | 32.98 | 15500 | 0.7472 | |
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| 0.5345 | 33.51 | 15750 | 0.7542 | |
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| 0.5285 | 34.04 | 16000 | 0.7513 | |
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| 0.5285 | 34.57 | 16250 | 0.7466 | |
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| 0.522 | 35.11 | 16500 | 0.7627 | |
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| 0.522 | 35.64 | 16750 | 0.7609 | |
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| 0.5209 | 36.17 | 17000 | 0.7616 | |
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| 0.5209 | 36.7 | 17250 | 0.7612 | |
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| 0.5151 | 37.23 | 17500 | 0.7601 | |
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| 0.5151 | 37.77 | 17750 | 0.7590 | |
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| 0.5088 | 38.3 | 18000 | 0.7568 | |
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| 0.5088 | 38.83 | 18250 | 0.7551 | |
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| 0.5105 | 39.36 | 18500 | 0.7688 | |
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| 0.5105 | 39.89 | 18750 | 0.7631 | |
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| 0.5046 | 40.43 | 19000 | 0.7654 | |
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| 0.5046 | 40.96 | 19250 | 0.7749 | |
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| 0.5029 | 41.49 | 19500 | 0.7617 | |
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| 0.5029 | 42.02 | 19750 | 0.7735 | |
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| 0.4969 | 42.55 | 20000 | 0.7763 | |
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| 0.4969 | 43.09 | 20250 | 0.7484 | |
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| 0.497 | 43.62 | 20500 | 0.7606 | |
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| 0.497 | 44.15 | 20750 | 0.7726 | |
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| 0.4889 | 44.68 | 21000 | 0.7564 | |
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| 0.4889 | 45.21 | 21250 | 0.7694 | |
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| 0.4842 | 45.74 | 21500 | 0.7639 | |
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| 0.4842 | 46.28 | 21750 | 0.7784 | |
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| 0.4829 | 46.81 | 22000 | 0.7817 | |
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| 0.4829 | 47.34 | 22250 | 0.7727 | |
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| 0.4772 | 47.87 | 22500 | 0.7661 | |
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| 0.4772 | 48.4 | 22750 | 0.7630 | |
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| 0.477 | 48.94 | 23000 | 0.7640 | |
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| 0.477 | 49.47 | 23250 | 0.7730 | |
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| 0.4766 | 50.0 | 23500 | 0.7708 | |
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| 0.4766 | 50.53 | 23750 | 0.7716 | |
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| 0.4717 | 51.06 | 24000 | 0.7670 | |
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| 0.4717 | 51.6 | 24250 | 0.7671 | |
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| 0.4686 | 52.13 | 24500 | 0.7711 | |
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| 0.4686 | 52.66 | 24750 | 0.7704 | |
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| 0.4685 | 53.19 | 25000 | 0.7775 | |
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| 0.4685 | 53.72 | 25250 | 0.7690 | |
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| 0.4635 | 54.26 | 25500 | 0.7839 | |
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| 0.4635 | 54.79 | 25750 | 0.7746 | |
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| 0.4617 | 55.32 | 26000 | 0.7738 | |
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| 0.4617 | 55.85 | 26250 | 0.7753 | |
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| 0.4549 | 56.38 | 26500 | 0.7830 | |
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| 0.4549 | 56.91 | 26750 | 0.7777 | |
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| 0.4564 | 57.45 | 27000 | 0.7758 | |
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| 0.4564 | 57.98 | 27250 | 0.7728 | |
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| 0.4546 | 58.51 | 27500 | 0.7772 | |
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| 0.4546 | 59.04 | 27750 | 0.7795 | |
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| 0.4511 | 59.57 | 28000 | 0.7754 | |
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| 0.4511 | 60.11 | 28250 | 0.7867 | |
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| 0.4467 | 60.64 | 28500 | 0.7838 | |
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| 0.4467 | 61.17 | 28750 | 0.7858 | |
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| 0.4512 | 61.7 | 29000 | 0.7758 | |
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| 0.4512 | 62.23 | 29250 | 0.7819 | |
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| 0.4497 | 62.77 | 29500 | 0.7871 | |
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| 0.4497 | 63.3 | 29750 | 0.7817 | |
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| 0.4463 | 63.83 | 30000 | 0.7806 | |
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### Framework versions |
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- Transformers 4.36.0.dev0 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.14.1 |
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