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---
language: en
tags:
- text-to-speech
- StyleTTS2
- speech-synthesis
license: mit
pipeline_tag: text-to-speech
---
# StyleTTS2 Fine-tuned Model
This model is a fine-tuned version of StyleTTS2, containing all necessary components for inference.
## Model Details
- **Base Model:** StyleTTS2-LibriTTS
- **Architecture:** StyleTTS2
- **Task:** Text-to-Speech
- **Last Checkpoint:** epoch_2nd_00014.pth
## Training Details
- **Total Epochs:** 30
- **Completed Epochs:** 14
- **Total Iterations:** 1169
- **Batch Size:** 2
- **Max Length:** 120
- **Learning Rate:** 0.0001
- **Final Validation Loss:** 0.418901
## Model Components
The repository includes all necessary components for inference:
### Main Model Components:
- bert.pth
- bert_encoder.pth
- predictor.pth
- decoder.pth
- text_encoder.pth
- predictor_encoder.pth
- style_encoder.pth
- diffusion.pth
- text_aligner.pth
- pitch_extractor.pth
- mpd.pth
- msd.pth
- wd.pth
### Utility Components:
- ASR (Automatic Speech Recognition)
- epoch_00080.pth
- config.yml
- models.py
- layers.py
- JDC (F0 Prediction)
- bst.t7
- model.py
- PLBERT
- step_1000000.t7
- config.yml
- util.py
### Additional Files:
- text_utils.py: Text preprocessing utilities
- models.py: Model architecture definitions
- utils.py: Utility functions
- config.yml: Model configuration
- config.json: Detailed configuration and training metrics
## Training Metrics
Training metrics visualization is available in training_metrics.png
## Directory Structure
├── Utils/
│ ├── ASR/
│ ├── JDC/
│ └── PLBERT/
├── model_components/
└── configs/
## Usage Instructions
1. Load the model using the provided config.yml
2. Ensure all utility components (ASR, JDC, PLBERT) are in their respective directories
3. Use text_utils.py for text preprocessing
4. Follow the inference example in the StyleTTS2 documentation
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