speecht5_finetuned_cv_sq

This model is a fine-tuned version of microsoft/speecht5_tts on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.4970

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 1e-05
• train_batch_size: 4
• eval_batch_size: 2
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• training_steps: 4000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
0.5899	9.3787	1000	0.5392
0.5424	18.7573	2000	0.5075
0.531	28.1360	3000	0.4995
0.5359	37.5147	4000	0.4970

Framework versions

• Transformers 4.43.1
• Pytorch 2.3.1+cu121
• Datasets 2.20.0
• Tokenizers 0.19.1

Development and attribution

• Original SpeechT5 architecture and base weights: Microsoft, through microsoft/speecht5_tts
• Albanian speech adaptation, fine-tuning, evaluation, packaging, and release: Kushtrim Visoka
• Model card and ongoing maintenance: Kushtrim Visoka

Acknowledgements

This work builds on microsoft/speecht5_tts and the work of its original authors. The upstream architecture and base weights remain the work of their respective creators. This repository documents the task-specific adaptation and release work carried out by Kushtrim Visoka and does not claim authorship of the original model. Dataset creators and tool authors referenced elsewhere in this card should also be cited according to their licenses and documentation.

Citation

If you use this fine-tuned model, please cite this repository and the original upstream model.

@misc{visoka_speecht5_albanian_2026,
  author       = {Visoka, Kushtrim},
  title        = {SpeechT5 Fine-tuned for Albanian},
  year         = {2026},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/Kushtrim/speecht5_finetuned_cv_sq},
  note         = {Fine-tuned derivative of microsoft/speecht5_tts}
}