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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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- turkish |
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- tr |
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model-index: |
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- name: speecht5_finetuned_emirhan_tr |
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results: [] |
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language: |
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- tr |
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pipeline_tag: text-to-speech |
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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_emirhan_tr |
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This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on [erenfazlioglu/turkishvoicedataset](https://huggingface.co/datasets/erenfazlioglu/turkishvoicedataset). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3135 |
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## Model description |
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The base model uses a transformer-based approach, specifically Transfer Transformer, to generate high-quality speech from text. The fine-tuning process on the Turkish Voice Dataset enables the model to produce more natural and accurate speech in Turkish. |
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## Intended uses & limitations |
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This model is intended for text-to-speech (TTS) applications specifically tailored for the Turkish language. It can be used in various scenarios, such as voice assistants, automated announcements, and accessibility tools for Turkish speakers. |
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## Training and evaluation data |
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The model's performance is optimized for Turkish and may not generalize well to other languages. |
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The model might not handle rare or domain-specific vocabulary as effectively as more common words. |
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## Training procedure |
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The model was fine-tuned on the Turkish Voice Dataset, which consists of high-quality synthetic Turkish voice recordings from Microsoft Azure. The dataset was split into training and evaluation subsets, with the evaluation set used to measure the model's loss and overall performance. |
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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: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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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: 100 |
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- training_steps: 660 |
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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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| 0.514 | 0.4545 | 100 | 0.4372 | |
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| 0.4226 | 0.9091 | 200 | 0.3626 | |
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| 0.3771 | 1.3636 | 300 | 0.3417 | |
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| 0.3562 | 1.8182 | 400 | 0.3278 | |
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| 0.3472 | 2.2727 | 500 | 0.3217 | |
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| 0.3402 | 2.7273 | 600 | 0.3135 | |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
