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🎤 SpeechT5 TTS Technical Train2

This model is a fine-tuned version of microsoft/speecht5_tts using a custom dataset, specifically trained for Text-to-Speech (TTS) tasks.

🎯 Key Metric:

Loss on the evaluation set: 0.3763

📢 Listen to the generated sample:

The text is " Hello ,few technical terms i used while fine tuning are API and REST and CUDA and TTS."

📝 Model Description

The SpeechT5 TTS Technical Train2 is built on the SpeechT5 architecture and was fine-tuned for speech synthesis (TTS). The fine-tuning focused on improving the naturalness and clarity of the generated audio from text.

🛠 Base Model: Microsoft SpeechT5
📚 Dataset: Custom (specific details to be provided)

🔧 Intended Uses & Limitations

✅ Primary Use Cases:

Text-to-Speech (TTS) for technical Interview Texts .
Virtual Assistants:

⚠ Limitations:

Best suited for English TTS tasks.
Require further fine-tuning on Large dataset .

📅 Training Data

The model was fine-tuned on a custom dataset, curated for enhancing TTS outputs. This dataset consists of various types of text that help the model generate more natural speech, making it suitable for TTS applications.

⚙ Hyperparameters:

The model was trained with the following hyperparameters:

Learning Rate: 1e-05
Train Batch Size: 16
Eval Batch Size: 8
Seed: 42
Gradient Accumulation Steps: 2
Total Train Batch Size: 32
Optimizer: AdamW (betas=(0.9, 0.999), epsilon=1e-08)
LR Scheduler Type: Linear
Warmup Steps: 50
Training Steps: 500
Mixed Precision Training: Native AMP

⚙ 📊 Training Results::

🏋‍♂ Training Loss	🕑 Epoch	🛤 Step	📉 Validation Loss
1.1921	100.0	100	0.4136
0.8435	200.0	200	0.3791
0.8294	300.0	300	0.3766
0.7959	400.0	400	0.3744
0.7918	500.0	500	0.3763

📦 Framework Versions

Transformers: 4.46.0.dev0
PyTorch: 2.4.1+cu121
Datasets: 3.0.2
Tokenizers: 0.20.1

pranavdaware
/

speecht5_tts_technical_train2