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공부 내용
flutter speech to text
Y
yeji Kim
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python model - flutter App
To use a deep learning model trained in Python within a Flutter app, you can follow these general steps:
1.
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.).
2.
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.
3.
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:
import tensorflow as tf # Assuming `model` is your trained model model.save('path/to/saved_model')
Step 2: Set Up the Backend Server
Set up a Flask server as an example:
pip install flask tensorflow
Create a server.py file:
from flask import Flask, request, jsonify import tensorflow as tf import numpy as np import librosa app = Flask(__name__) # Load the saved model model = tf.keras.models.load_model('path/to/saved_model') def preprocess_audio(file_path): # Load and preprocess the audio file audio, sr = librosa.load(file_path, sr=16000) # Assume the model expects input shape (None, 16000) audio = librosa.util.fix_length(audio, 16000) audio = np.expand_dims(audio, axis=0) return audio @app.route('/predict', methods=['POST']) def predict(): if 'file' not in request.files: return jsonify({"error": "No file provided"}), 400 file = request.files['file'] file_path = 'temp.wav' file.save(file_path) audio = preprocess_audio(file_path) prediction = model.predict(audio) transcription = decode_prediction(prediction) return jsonify({"transcription": transcription}) def decode_prediction(prediction): # Decode the model's output to text return "decoded text" if __name__ == '__main__': app.run(debug=True)
Step 3: Develop the Flutter App
Add dependencies to pubspec.yaml:
dependencies: flutter: sdk: flutter http: ^0.13.3 audioplayers: ^0.20.1 path_provider: ^2.0.1
Create the Flutter UI to record audio and send it to the backend server:
import 'package:flutter/material.dart'; import 'package:http/http.dart' as http; import 'package:path_provider/path_provider.dart'; import 'package:audioplayers/audioplayers.dart'; import 'dart:io'; void main() { runApp(MyApp()); } class MyApp extends StatelessWidget { @override Widget build(BuildContext context) { return MaterialApp( home: SpeechToTextScreen(), ); } } class SpeechToTextScreen extends StatefulWidget { @override _SpeechToTextScreenState createState() => _SpeechToTextScreenState(); } class _SpeechToTextScreenState extends State<SpeechToTextScreen> { final AudioPlayer _audioPlayer = AudioPlayer(); bool _isRecording = false; String _transcription = ""; Future<void> _recordAudio() async { // Code to record audio using your preferred method // Save the audio file to a temporary directory Directory tempDir = await getTemporaryDirectory(); String tempPath = '${tempDir.path}/temp.wav'; // Start recording // This is a placeholder. Use a suitable package to record audio in Flutter. await _audioPlayer.startRecorder(toFile: tempPath); setState(() { _isRecording = true; }); } Future<void> _stopRecording() async { // Stop recording // This is a placeholder. Use a suitable package to stop recording in Flutter. await _audioPlayer.stopRecorder(); setState(() { _isRecording = false; }); // Send the recorded file to the backend server Directory tempDir = await getTemporaryDirectory(); String tempPath = '${tempDir.path}/temp.wav'; File audioFile = File(tempPath); await _sendToServer(audioFile); } Future<void> _sendToServer(File audioFile) async { var request = http.MultipartRequest( 'POST', Uri.parse('http://your_server_ip:your_server_port/predict'), ); request.files.add(await http.MultipartFile.fromPath('file', audioFile.path)); var response = await request.send(); if (response.statusCode == 200) { var responseData = await response.stream.bytesToString(); setState(() { _transcription = responseData; // Update this according to your server's response format }); } else { setState(() { _transcription = "Error: ${response.statusCode}"; }); } } @override Widget build(BuildContext context) { return Scaffold( appBar: AppBar(title: Text("Speech to Text")), body: Center( child: Column( mainAxisAlignment: MainAxisAlignment.center, children: [ _isRecording ? ElevatedButton( onPressed: _stopRecording, child: Text("Stop Recording"), ) : ElevatedButton( onPressed: _recordAudio, child: Text("Start Recording"), ), SizedBox(height: 20), Text(_transcription), ], ), ), ); } }
Explanation
1.
Python Server:
A Flask server loads the trained model and provides an API to handle audio file uploads.
The server processes the audio file, runs the model, and returns the transcription.
2.
Flutter App:
The Flutter app records audio and saves it to a temporary file.
The app sends the audio file to the backend server and displays the transcription returned by the server.
Note
Ensure the Flutter app and the backend server are on the same network or the server is publicly accessible.
Use a suitable Flutter package for audio recording, like flutter_sound or audioplayers, as per your requirements.
Handle errors and edge cases appropriately in both the backend server and Flutter app.
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