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README.md
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---
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language:
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- en
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license: mit
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base_model: microsoft/speecht5_tts
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tags:
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- en_accent,mozilla,t5,common_voice_1_0
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- generated_from_trainer
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datasets:
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- mozilla-foundation/common_voice_1_0
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model-index:
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- name:
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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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#
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This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the
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It achieves the following results on the evaluation set:
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- Loss: 0.
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## Model description
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@@ -46,73 +41,133 @@ The following hyperparameters were used during training:
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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:
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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 | 1.
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### Framework versions
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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: 0.6815
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## Model description
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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 | 1.1437 |
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| 1.3289 | 1.06 | 500 | 0.8521 |
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| 1.3289 | 1.6 | 750 | 0.7901 |
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| 0.8977 | 2.13 | 1000 | 0.7478 |
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| 0.8977 | 2.66 | 1250 | 0.7437 |
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| 0.8131 | 3.19 | 1500 | 0.7243 |
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| 0.8131 | 3.72 | 1750 | 0.7106 |
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| 0.771 | 4.26 | 2000 | 0.7072 |
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| 0.771 | 4.79 | 2250 | 0.7008 |
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| 0.7562 | 5.32 | 2500 | 0.6916 |
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| 0.7562 | 5.85 | 2750 | 0.6850 |
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| 0.7472 | 6.38 | 3000 | 0.6876 |
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| 0.7472 | 6.91 | 3250 | 0.6807 |
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| 0.7266 | 7.45 | 3500 | 0.6804 |
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| 0.7266 | 7.98 | 3750 | 0.6763 |
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| 0.715 | 8.51 | 4000 | 0.6769 |
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| 0.715 | 9.04 | 4250 | 0.6698 |
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| 0.7005 | 9.57 | 4500 | 0.6690 |
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| 0.7005 | 10.11 | 4750 | 0.6653 |
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| 0.6932 | 10.64 | 5000 | 0.6656 |
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| 0.6932 | 11.17 | 5250 | 0.6684 |
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| 0.6854 | 11.7 | 5500 | 0.6645 |
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| 0.6854 | 12.23 | 5750 | 0.6634 |
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| 0.6739 | 12.77 | 6000 | 0.6674 |
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| 0.6739 | 13.3 | 6250 | 0.6606 |
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| 0.6754 | 13.83 | 6500 | 0.6663 |
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| 0.6754 | 14.36 | 6750 | 0.6681 |
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| 0.6592 | 14.89 | 7000 | 0.6589 |
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| 0.6592 | 15.43 | 7250 | 0.6601 |
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| 0.6528 | 15.96 | 7500 | 0.6739 |
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| 0.6528 | 16.49 | 7750 | 0.6643 |
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| 0.6539 | 17.02 | 8000 | 0.6605 |
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| 0.6539 | 17.55 | 8250 | 0.6614 |
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| 0.6437 | 18.09 | 8500 | 0.6551 |
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| 0.6437 | 18.62 | 8750 | 0.6604 |
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| 0.6341 | 19.15 | 9000 | 0.6606 |
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| 0.6341 | 19.68 | 9250 | 0.6582 |
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| 0.6305 | 20.21 | 9500 | 0.6714 |
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| 0.6305 | 20.74 | 9750 | 0.6618 |
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| 0.627 | 21.28 | 10000 | 0.6600 |
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| 0.627 | 21.81 | 10250 | 0.6636 |
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| 0.6244 | 22.34 | 10500 | 0.6692 |
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| 0.6244 | 22.87 | 10750 | 0.6645 |
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| 0.6178 | 23.4 | 11000 | 0.6670 |
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| 0.6178 | 23.94 | 11250 | 0.6611 |
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| 0.6157 | 24.47 | 11500 | 0.6697 |
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| 0.6157 | 25.0 | 11750 | 0.6651 |
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| 0.6108 | 25.53 | 12000 | 0.6642 |
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| 0.6108 | 26.06 | 12250 | 0.6646 |
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| 0.6008 | 26.6 | 12500 | 0.6672 |
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| 0.6008 | 27.13 | 12750 | 0.6601 |
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| 0.6067 | 27.66 | 13000 | 0.6760 |
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| 0.6067 | 28.19 | 13250 | 0.6639 |
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| 0.5985 | 28.72 | 13500 | 0.6662 |
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| 0.5985 | 29.26 | 13750 | 0.6720 |
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| 0.5957 | 29.79 | 14000 | 0.6710 |
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| 0.5957 | 30.32 | 14250 | 0.6688 |
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| 0.5944 | 30.85 | 14500 | 0.6714 |
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| 0.5944 | 31.38 | 14750 | 0.6760 |
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| 0.5886 | 31.91 | 15000 | 0.6639 |
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| 0.5886 | 32.45 | 15250 | 0.6714 |
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| 0.5868 | 32.98 | 15500 | 0.6722 |
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| 0.5868 | 33.51 | 15750 | 0.6790 |
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| 0.5851 | 34.04 | 16000 | 0.6728 |
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| 0.5851 | 34.57 | 16250 | 0.6812 |
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| 0.5819 | 35.11 | 16500 | 0.6756 |
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| 0.5819 | 35.64 | 16750 | 0.6679 |
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| 0.5811 | 36.17 | 17000 | 0.6719 |
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| 0.5811 | 36.7 | 17250 | 0.6684 |
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| 0.5759 | 37.23 | 17500 | 0.6776 |
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| 0.5759 | 37.77 | 17750 | 0.6743 |
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| 0.5743 | 38.3 | 18000 | 0.6725 |
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| 0.5743 | 38.83 | 18250 | 0.6730 |
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| 0.5761 | 39.36 | 18500 | 0.6712 |
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| 0.5761 | 39.89 | 18750 | 0.6765 |
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| 0.576 | 40.43 | 19000 | 0.6779 |
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| 0.576 | 40.96 | 19250 | 0.6801 |
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| 0.5734 | 41.49 | 19500 | 0.6756 |
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| 0.5734 | 42.02 | 19750 | 0.6761 |
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| 0.5743 | 42.55 | 20000 | 0.6857 |
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| 0.5743 | 43.09 | 20250 | 0.6734 |
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| 0.5732 | 43.62 | 20500 | 0.6753 |
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| 0.5732 | 44.15 | 20750 | 0.6803 |
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| 0.5657 | 44.68 | 21000 | 0.6743 |
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| 0.5657 | 45.21 | 21250 | 0.6831 |
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| 0.565 | 45.74 | 21500 | 0.6799 |
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| 0.565 | 46.28 | 21750 | 0.6769 |
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| 0.565 | 46.81 | 22000 | 0.6786 |
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| 0.565 | 47.34 | 22250 | 0.6788 |
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| 0.5583 | 47.87 | 22500 | 0.6830 |
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| 0.5583 | 48.4 | 22750 | 0.6884 |
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| 0.5652 | 48.94 | 23000 | 0.6827 |
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| 0.5652 | 49.47 | 23250 | 0.6795 |
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| 0.5625 | 50.0 | 23500 | 0.6807 |
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| 0.5625 | 50.53 | 23750 | 0.6788 |
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| 0.5605 | 51.06 | 24000 | 0.6862 |
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| 0.5605 | 51.6 | 24250 | 0.6822 |
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| 0.5571 | 52.13 | 24500 | 0.6819 |
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| 0.5571 | 52.66 | 24750 | 0.6797 |
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| 0.5633 | 53.19 | 25000 | 0.6835 |
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| 0.5633 | 53.72 | 25250 | 0.6835 |
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| 0.5572 | 54.26 | 25500 | 0.6881 |
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| 0.5572 | 54.79 | 25750 | 0.6791 |
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| 0.5571 | 55.32 | 26000 | 0.6815 |
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| 0.5571 | 55.85 | 26250 | 0.6868 |
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| 0.5534 | 56.38 | 26500 | 0.6876 |
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| 0.5534 | 56.91 | 26750 | 0.6871 |
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| 0.5525 | 57.45 | 27000 | 0.6836 |
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| 0.5525 | 57.98 | 27250 | 0.6841 |
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| 0.5542 | 58.51 | 27500 | 0.6911 |
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| 0.5542 | 59.04 | 27750 | 0.6835 |
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| 0.5512 | 59.57 | 28000 | 0.6806 |
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| 0.5512 | 60.11 | 28250 | 0.6805 |
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| 0.5474 | 60.64 | 28500 | 0.6858 |
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| 0.5474 | 61.17 | 28750 | 0.6874 |
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| 0.5548 | 61.7 | 29000 | 0.6811 |
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| 0.5548 | 62.23 | 29250 | 0.6808 |
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| 0.5545 | 62.77 | 29500 | 0.6868 |
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| 0.5545 | 63.3 | 29750 | 0.6894 |
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| 0.5522 | 63.83 | 30000 | 0.6815 |
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### Framework versions
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runs/Dec10_14-09-53_Threadripper/events.out.tfevents.1702235393.Threadripper
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:d8f57f5a6f7038b5599f5b8304e86fe4acdf994607bbbca3175d9fb2faf65604
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size 48487
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