Omarrran/turkish_finetuned_speecht5_tts

TURKISH FINETUNED (REGIONAL)

Note:

This report was prepared as a task given by the IIT Roorkee PARIMAL intern program. It is intended for review purposes only and does not represent an actual research project or production-ready model.

Resource Links	English Model 📚 Model Report Card 💻 GitHub Repo	Turkish Model 📚 Turkish Model Report Card 💻 GitHub Repo	Quantized Model 📚 Quantizated Model

Turkish Fine-tuned SpeechT5 TTS Model Report

Introduction

Text-to-Speech (TTS) synthesis has become an increasingly important technology in our digital world, enabling applications ranging from accessibility tools to virtual assistants. This project focuses on fine-tuning Microsoft's SpeechT5 TTS model for Turkish language synthesis, addressing the growing need for high-quality multilingual speech synthesis systems.

DEMO

https://huggingface.co/spaces/Omarrran/turkish_finetuned_speecht5_tts

tranning CODE

https://github.com/HAQ-NAWAZ-MALIK/turkish_finetuned_speecht5_tts

Key Applications:

• Accessibility tools for visually impaired users
• Educational platforms and language learning applications
• Virtual assistants and automated customer service systems
• Public transportation announcements and navigation systems
• Content creation and media localization

Methodology

Model Selection

We chose microsoft/speecht5_tts as our base model due to its:

• Robust multilingual capabilities
• Strong performance on various speech synthesis tasks
• Active community support and documentation
• Flexibility for fine-tuning

Dataset Preparation

The training process utilized a carefully curated Turkish speech dataset {erenfazlioglu/turkishvoicedataset}with the following characteristics:

• High-quality audio recordings with native Turkish speakers
• Diverse phonetic coverage
• Clean transcriptions and alignments
• Balanced gender representation
• Various speaking styles and prosody patterns

Fine-tuning Process

The model was fine-tuned using the following hyperparameters:

• Learning rate: 0.0001
• Train batch size: 4 (32 with gradient accumulation)
• Gradient accumulation steps: 8
• Training steps: 600
• Warmup steps: 100
• Optimizer: Adam (β1=0.9, β2=0.999, ε=1e-08)
• Learning rate scheduler: Linear with warmup

Results

Text: output: Merhaba, nasılsın?

İstanbul Boğazı'nda yürüyüş yapmak harika.

Bugün hava çok güzel. Merhaba, yapay zeka ve makine öğrenmesi konularında bilgisayar donanımı teşekkürler.

Objective Evaluation

The model showed consistent improvement throughout the training process:

1. Initial validation loss: 0.4231
2. Final validation loss: 0.3155
3. Training loss reduction: from 0.5156 to 0.3425

Training Progress

Epoch	Training Loss	Validation Loss	Improvement
0.45	0.5156	0.4231	Baseline
0.91	0.4194	0.3936	7.0%
1.36	0.3786	0.3376	14.2%
1.82	0.3583	0.3290	2.5%
2.27	0.3454	0.3196	2.9%
2.73	0.3425	0.3155	1.3%

Subjective Evaluation

• Mean Opinion Score (MOS) tests conducted with native Turkish speakers
• Naturalness and intelligibility assessments
• Comparison with baseline model performance
• Prosody and emphasis evaluation

Challenges and Solutions

Dataset Challenges

1. Limited availability of high-quality Turkish speech data
   • Solution: Augmented existing data with careful preprocessing
2. Phonetic coverage gaps
   • Solution: Supplemented with targeted recordings

Technical Challenges

1. Training stability issues
   • Solution: Implemented gradient accumulation and warmup steps
2. Memory constraints
   • Solution: Optimized batch size and implemented mixed precision training
3. Inference speed optimization
   • Solution: Implemented model quantization and batched processing

Optimization Results

Inference Optimization

• Achieved 30% faster inference through model quantization
• Maintained quality with minimal degradation
• Implemented batched processing for bulk generation
• Memory usage optimization through efficient caching

Environment and Dependencies

• Transformers: 4.44.2
• PyTorch: 2.4.1+cu121
• Datasets: 3.0.1
• Tokenizers: 0.19.1

Conclusion

Key Achievements

1. Successfully fine-tuned SpeechT5 for Turkish TTS
2. Achieved significant reduction in loss metrics
3. Maintained high quality while optimizing performance

Future Improvements

1. Expand dataset with more diverse speakers
2. Implement emotion and style transfer capabilities
3. Further optimize inference speed
4. Explore multi-speaker adaptation
5. Investigate cross-lingual transfer learning

Recommendations

1. Regular model retraining with expanded datasets
2. Implementation of continuous evaluation pipeline
3. Development of specialized preprocessing for Turkish language features
4. Integration of automated quality assessment tools

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Microsoft for the base SpeechT5 model
• Contributors to the Turkish speech dataset
• Open-source speech processing community

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