If the speech input cannot be understood or recognized, you will see the following as output − Listening. The recognized text, which in this case is "Hello, how are you?", is then printed to the console as the output. After capturing the audio, it recognizes the speech and converts it to text using the Google Speech Recognition engine. In this example, the program listens for speech input using the microphone. You can explore the documentation to learn more about the available options and configuration possibilities.įor the code provided in the previous section, here's what you can expect as output if the speech input is successfully recognized − Listening. It's worth noting that pyttsx3 supports multiple speech synthesis engines, including Windows SAPI5, macOS NSSpeechSynthesizer, and Linux eSpeak. Finally, the say method is used to convert the specified text into speech, and the runAndWait method ensures that the speech is synthesized and played back. Then, properties such as speech rate and volume level can be set to customize the output. In the preceding procedure, firstly, the library is initialized with pyttsx3.init(), creating an instance of the speech synthesis engine. Use the say method to convert text to speech − Set the properties of the speech synthesis engine (optional) −ĮtProperty("rate", 150) # Speed of speech (words per minute)ĮtProperty("volume", 0.8) # Volume level (0.0 to 1.0) Import the library and initialize the speech synthesis engine − Install the pyttsx3 library by running the following command − Follow the steps below to convert text to speech: Python offers several libraries for this purpose, such as pyttsx3, which is a cross-platform text-to-speech library. Converting Text to SpeechĬonverting text to speech involves synthesizing natural-sounding speech from text input. Now that we have successfully converted speech to text, let's move on to the next step: converting text to speech. Feel free to explore the library's documentation for more advanced usage. The SpeechRecognition library provides several configuration options, such as specifying the language, adjusting the speech recognition engine, or even working with audio files instead of live audio input. It's important to handle potential errors, such as when the speech cannot be understood or recognized. The recognized text is then printed to the console. The recognize_google method is used to perform the actual speech recognition, and it takes the captured audio as input. ![]() The above procedure demonstrates a basic implementation of speech-to-text conversion using the Google Speech Recognition engine. Text = recognizer.recognize_google(audio) Use the recognizer object to recognize the speech and convert it to text − Import the library and initialize a recognizer object −Ĭapture audio input using a microphone or load an audio file − ![]() Install the SpeechRecognition library by running the following command − Follow the steps below to convert speech to text − Python offers the SpeechRecognition library, which provides a simple interface to various speech recognition engines, including Google Speech Recognition, CMU Sphinx, and Wit.ai. The first step in converting speech to text is to recognize and transcribe the spoken words. ![]() In this blog post, we will explore how to leverage Python to convert speech to text and text to speech, empowering developers to create innovative applications that bridge the gap between spoken and written communication. Python, with its extensive library ecosystem, offers powerful tools and APIs that make it relatively straightforward to implement speech-to-text and text-to-speech conversions. From voice-controlled assistants to transcription services, this functionality is in high demand across a wide range of applications. In today's digital age, the ability to seamlessly convert between speech and text has become increasingly important.
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