https://www.youtube.com/watch?v=IiYeppojd_Y&list=PLxefhmF0pcPlMKc8tPgYmYXXShh8P_8al&index=9 python model - flutter App To use a deep learning model trained in Python within a Flutter app, you can follow these general steps: Train and Save the Model in Python: Train your speech-to-text model using Python. Save the model to a file format that can be loaded later (e.g., TensorFlow SavedModel, PyTorch model, etc.). Set Up a Backend Server: Set up a backend server using a framework like Flask, Django, or FastAPI. The backend server will load the trained model and provide an API endpoint to handle requests from the Flutter app. The server will process audio files sent from the Flutter app, apply the speech-to-text model, and return the transcriptions. Flutter App: Develop the Flutter app to record audio, send the audio file to the backend server, and display the transcription results. Here is a step-by-step guide: Step 1: Train and Save the Model in Python Assuming you have already trained your model, save it to a file. For example, using TensorFlow: Step 2: Set Up the Backend Server Set up a Flask server as an example: Create a server.py file: Step 3: Develop the Flutter App Add dependencies to pubspec.yaml: Create the Flutter UI to record audio and send it to the backend server: