🎙️ Parler TTS Story Generator 🎙️

Generate short stories from images and transform them into lifelike audio using Parler TTS, Groq AI, and Hugging Face models, all within an interactive Streamlit app.

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🌟 Features

• Image-to-Text Interpretation: Convert images to textual descriptions with the Salesforce/blip-image-captioning-base model.
• Story Generation: Generate short, creative stories (less than 20 words) based on the interpreted text using the llama3-8b-8192 model from Groq.
• Text-to-Speech: Convert generated stories into lifelike audio using the locally-run parler-tts/parler-tts-large-v1 model, leveraging your GPU/CPU for fast processing.
• Streamlit Integration: Interactive UI for uploading images, generating stories, and downloading audio.
• Error Handling: Automatically switches to CPU if GPU isn't available and handles model loading errors.

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🔧 Setup

Requirements:

• Python 3.8+
• Hugging Face Transformers library
• Parler TTS for conditional generation
• Groq API for story generation
• SoundFile for audio output
• Streamlit for the UI

Installation:

1. Clone the repository:

   git clone https://github.com/rk-vashista/TTS-Story_Generator
   cd TTS-Story_Generator
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Set up environment variables:

   • Create a .env file:

     touch .env
     

   • Add your API keys to .env:

     HUGGING_FACE=<your_hugging_face_api_key>
     GROQ_API=<your_groq_api_key>
     

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🚀 Usage

1. Run the app:

   streamlit run app.py
   

2. Upload an image: Upload a .jpg, .png, or .jpeg image to generate a scenario description.

3. Generate a short story: The app will automatically interpret the uploaded image and generate a short story using Groq's LLaMA model.

4. Convert the story into audio: The app will convert the story into a .wav audio file using Parler TTS, which you can listen to and download.

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📁 Example Workflow:

1. Upload Image ➡️ Text Description
2. Text Description ➡️ Short Story
3. Short Story ➡️ Lifelike Audio

The final audio will be saved as parler_tts_out.wav, available for download directly through the app.

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🔑 Environment Variables

Create a .env file in the project root to store your API keys:

HUGGING_FACE=<your_hugging_face_api_key>
GROQ_API=<your_groq_api_key>

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📌 API Details

• Hugging Face API: Used for image captioning with the Salesforce/blip-image-captioning-base model.
• Groq API: Powers the story generation using llama3-8b-8192.
• Parler TTS: Converts text into expressive speech using the parler-tts-large-v1 model, running on local resources.

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✨ Possible Extensions

• Custom Voices: Experiment with different Parler TTS voice settings or modify the description to adjust the voice's style.
• Longer Stories: Adapt the story generation prompt to produce more detailed narratives.
• Advanced Image Analysis: Try other image-to-text models for deeper interpretation of the uploaded images.

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🛠️ Troubleshooting

• CUDA Errors: If CUDA isn't available, the program will switch to CPU automatically.
• Environment Variables: Ensure the .env file contains correct API keys for Hugging Face and Groq.

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📄 License

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

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🙌 Acknowledgments

• Hugging Face for providing powerful models.
• Groq for real-time AI model execution.
• Parler TTS for enabling high-quality text-to-speech synthesis.
• Streamlit for providing a seamless UI for deployment.