speecht5_finetuned_emirhan_tr

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

• Loss: 0.3135

Model description

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.

Intended uses & limitations

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.

Training and evaluation data

The model's performance is optimized for Turkish and may not generalize well to other languages. The model might not handle rare or domain-specific vocabulary as effectively as more common words.

Training procedure

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.

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0001
• 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: 100
• training_steps: 660
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
0.514	0.4545	100	0.4372
0.4226	0.9091	200	0.3626
0.3771	1.3636	300	0.3417
0.3562	1.8182	400	0.3278
0.3472	2.2727	500	0.3217
0.3402	2.7273	600	0.3135

Framework versions

• Transformers 4.42.4
• Pytorch 2.4.0+cu121
• Datasets 2.21.0
• Tokenizers 0.19.1