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--- |
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library_name: transformers |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: speecht5_finetuned_tr_commonvoice |
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results: [] |
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language: |
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- tr |
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base_model: |
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- microsoft/speecht5_tts |
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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_tr_commonvoice |
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This model was trained from scratch on the None dataset. |
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It achieves the following results on the evaluation set: |
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- eval_loss: 0.5179 |
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- eval_runtime: 361.0936 |
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- eval_samples_per_second: 32.161 |
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- eval_steps_per_second: 16.082 |
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- epoch: 1.6783 |
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- step: 2000 |
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## Model description |
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```python |
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import torch |
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from datasets import load_dataset |
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import soundfile as sf |
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embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation") |
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speaker_embedding = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0) |
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from transformers import pipeline |
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pipe = pipeline("text-to-audio", model="Chan-Y/speecht5_finetuned_tr_commonvoice") |
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text = "bugün okula erken geldim, çalışmam lazım." |
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result = pipe(text, forward_params={"speaker_embeddings": speaker_embedding}) |
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sf.write("speech.wav", result["audio"], samplerate=result["sampling_rate"]) |
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from IPython.display import Audio |
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Audio("speech.wav") |
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``` |
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## Training and evaluation data |
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I used [CommonVoice Turkish Corpus 19.0](https://commonvoice.mozilla.org/tr/datasets) |
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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: 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- training_steps: 4000 |
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- mixed_precision_training: Native AMP |
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### Framework versions |
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- Transformers 4.46.3 |
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- Pytorch 2.5.1+cu124 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
