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