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--- |
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language: |
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- ca |
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license: mit |
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base_model: microsoft/speecht5_tts |
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tags: |
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- TTS |
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- generated_from_trainer |
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- text-to-speech |
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datasets: |
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- openslr |
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model-index: |
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- name: SpeechT5 TTS Catalan |
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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 Catalan |
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This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the OpenSLR dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4360 |
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## Model description |
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This model was trained using the instructions provided on this [notebook](https://colab.research.google.com/drive/1i7I5pzBcU3WDFarDnzweIj4-sVVoIUFJ) |
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but using the catalan subset of OpenSLR dataset. The main change is the use of trimming to delete large parts of silence that this |
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dataset originally have. You can check the notebook used for this training [here](https://colab.research.google.com/drive/1B4idPGWxtAftOft6I47UjOB_l1yoiXzn) |
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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: 16 |
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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: 32 |
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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: 8000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 0.5039 | 8.37 | 1000 | 0.4530 | |
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| 0.4723 | 16.74 | 2000 | 0.4345 | |
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| 0.4583 | 25.1 | 3000 | 0.4316 | |
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| 0.4565 | 33.47 | 4000 | 0.4294 | |
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| 0.4363 | 41.84 | 5000 | 0.4329 | |
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| 0.446 | 50.21 | 6000 | 0.4331 | |
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| 0.4508 | 58.58 | 7000 | 0.4336 | |
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| 0.4529 | 66.95 | 8000 | 0.4360 | |
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### Framework versions |
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- Transformers 4.32.0.dev0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
