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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: SpeechT5 TTS English Accented |
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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 English Accented |
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This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the Common Voice dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5854 |
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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: 10000 |
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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 | 1.41 | 250 | 0.5448 | |
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| 0.6715 | 2.82 | 500 | 0.5147 | |
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| 0.6715 | 4.24 | 750 | 0.5225 | |
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| 0.5532 | 5.65 | 1000 | 0.5096 | |
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| 0.5532 | 7.06 | 1250 | 0.5293 | |
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| 0.5156 | 8.47 | 1500 | 0.5310 | |
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| 0.5156 | 9.89 | 1750 | 0.5417 | |
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| 0.4874 | 11.3 | 2000 | 0.5185 | |
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| 0.4874 | 12.71 | 2250 | 0.5112 | |
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| 0.4693 | 14.12 | 2500 | 0.5154 | |
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| 0.4693 | 15.54 | 2750 | 0.5148 | |
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| 0.4619 | 16.95 | 3000 | 0.5367 | |
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| 0.4619 | 18.36 | 3250 | 0.5207 | |
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| 0.447 | 19.77 | 3500 | 0.5318 | |
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| 0.447 | 21.19 | 3750 | 0.5286 | |
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| 0.4348 | 22.6 | 4000 | 0.5345 | |
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| 0.4348 | 24.01 | 4250 | 0.5362 | |
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| 0.4237 | 25.42 | 4500 | 0.5568 | |
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| 0.4237 | 26.84 | 4750 | 0.5352 | |
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| 0.4195 | 28.25 | 5000 | 0.5395 | |
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| 0.4195 | 29.66 | 5250 | 0.5487 | |
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| 0.4132 | 31.07 | 5500 | 0.5443 | |
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| 0.4132 | 32.49 | 5750 | 0.5491 | |
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| 0.3975 | 33.9 | 6000 | 0.5465 | |
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| 0.3975 | 35.31 | 6250 | 0.5505 | |
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| 0.396 | 36.72 | 6500 | 0.5450 | |
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| 0.396 | 38.14 | 6750 | 0.5510 | |
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| 0.3884 | 39.55 | 7000 | 0.5517 | |
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| 0.3884 | 40.96 | 7250 | 0.5685 | |
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| 0.383 | 42.37 | 7500 | 0.5622 | |
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| 0.383 | 43.79 | 7750 | 0.5659 | |
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| 0.3806 | 45.2 | 8000 | 0.5636 | |
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| 0.3806 | 46.61 | 8250 | 0.5681 | |
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| 0.3738 | 48.02 | 8500 | 0.5797 | |
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| 0.3738 | 49.44 | 8750 | 0.5741 | |
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| 0.3705 | 50.85 | 9000 | 0.5765 | |
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| 0.3705 | 52.26 | 9250 | 0.5770 | |
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| 0.364 | 53.67 | 9500 | 0.5854 | |
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| 0.364 | 55.08 | 9750 | 0.5806 | |
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| 0.36 | 56.5 | 10000 | 0.5854 | |
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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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