What happens here is speech to text conversion in simple words. Google Voice and speech APIs are used by the application software to perform voice recognition and thats why internet connectivity is a must have for this project. Click here to get the details for configuring WiFi in RPi 3. Raspberry Pi 3 has inbuilt WiFi and it fits the application very well, as internet access comes with ease from an access point5 or even from a hotspot. A voice recognition software is installed on the Raspberry Pi 3 which works with the help of internet. Here the focus is towards developing a sort of unsupervised pattern recognition scheme that does not depend on excessive geometry and computations like deformable templates. We are able to recognize the voice of several thousand individuals whom we have met during our lifetime. Our own recognition ability is far more robust than any computer’s software can hope to be. In this tutorial we perform Voice recognition, an extremely complex visual task, almost instantaneously. audio_content ) print ( 'Audio content written to file "output.mp3"' ) file = "output.mp3" # apt install mpg123 With open ( "output.mp3", "wb" ) as out : # Write the response to the output file. synthesize_speech ( input = synthesis_in, voice = voice, audio_config = audio_config ) # The response's audio_content is binary. MP3 ) # Perform the text-to-speech request on the text input with the selected AudioConfig ( audio_encoding = texttospeech. MALE ) # Select the type of audio file you want returnedĪudio_config = texttospeech. VoiceSelectionParams ( language_code = "en-US", name = "en-US-Wavenet-A", ssml_gender = texttospeech. SynthesisInput ( text = synthesis_input ) # Let's make this a premium Wavenet voice in SSML description + ', ' + synthesis_input synthesis_in = texttospeech. label_annotations print ( 'Labels:' ) synthesis_input = '' # Make a simple comma delimited string type sentence.įor label in labels : print ( label. label ( image = image ) labels = response. label_detection ( image = image ) response = client_vision. Image ( content = content ) response = client_vision. If you have not done so already, install mpg123 via the apt install mpg123 command.ĭef main (): takephoto () with open ( 'image.jpg', 'rb' ) as image_file : content = image_file. Once saved, mpg123 is used to play the MP3 over any speaker hooked up to the Raspberry Pi. The Speech is streamed back and stored as an MP3 file on the local drive. I don’t fully use SSML in the below example, but if you would like to see documentation highlighting some of the deeper SSML capabilities, you can do so here.Īs for the voices, I highly recommend Google Wavenet voices for all TTS applications that demand near human quality synthesis. The Text-to-Speech utilizes SSML and Google’s premium Wavenet voices. For some detailed info on what it can do, visit the official Google Cloud Vision AI docs page. The service also has separate functions to recognize the existence of faces, famous logos, and more. In this instance, I chose to use the label_detection feature to help identify objects in the photo. The file will be read into memory and processed by the Cloud Vision SDK, then analyzed by Google’s Cloud Vision AI service. The primary function will execute the takephoto() function and start the process where an image.jpg file populates to the local drive. Enter fullscreen mode Exit fullscreen mode
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