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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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- text-to-speech |
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datasets: |
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- voxpopuli |
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model-index: |
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- name: speecht5_finetuned_voxpopuli_Nederlands |
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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_finetuned_voxpopuli_Nederlands |
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This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4888 |
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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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from transformers import Seq2SeqTrainer |
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trainer = Seq2SeqTrainer( |
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args=training_args, |
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model=model, |
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train_dataset=dataset["train"], |
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eval_dataset=dataset["test"], |
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data_collator=data_collator, |
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tokenizer=processor, |
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) |
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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: 8 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 128 |
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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: 50 |
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- training_steps: 500 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 0.6266 | 1.72 | 100 | 0.5448 | |
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| 0.5533 | 3.44 | 200 | 0.5040 | |
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| 0.5401 | 5.16 | 300 | 0.4930 | |
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| 0.535 | 6.88 | 400 | 0.4898 | |
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| 0.5331 | 8.6 | 500 | 0.4888 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 2.0.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.13.3 |
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