{"id":4159,"date":"2023-11-04T23:14:06","date_gmt":"2023-11-04T23:14:06","guid":{"rendered":"http:\/\/localhost:10003\/how-to-create-a-speech-recognition-app-with-python-and-google-cloud-api\/"},"modified":"2023-11-05T05:47:58","modified_gmt":"2023-11-05T05:47:58","slug":"how-to-create-a-speech-recognition-app-with-python-and-google-cloud-api","status":"publish","type":"post","link":"http:\/\/localhost:10003\/how-to-create-a-speech-recognition-app-with-python-and-google-cloud-api\/","title":{"rendered":"How to Create a Speech Recognition App with Python and Google Cloud API"},"content":{"rendered":"
Speech recognition is the ability of a computer to convert spoken language into written text. With the help of Python and the Google Cloud API, you can easily create a speech recognition app that can transcribe audio files or live speech.<\/p>\n
In this tutorial, you will learn how to create a speech recognition app using Python and the Google Cloud API. By the end of this tutorial, you will be able to transcribe audio files and perform live speech recognition.<\/p>\n
Before you start, you will need the following:<\/p>\n
To use the Google Cloud API for speech recognition, you first need to set up the API and obtain the necessary credentials.<\/p>\n
To interact with the Google Cloud API from Python, you need to install the Open a terminal window and run the following command to install the library:<\/p>\n To authenticate your Python script with the Google Cloud API, you need to set the environment variable If you are using Windows, run the following command in the terminal:<\/p>\n If you are using macOS or Linux, run the following command instead:<\/p>\n Replace To transcribe an audio file using the Speech-to-Text API, you first need to upload the audio file to Google Cloud Storage.<\/p>\n Import the necessary libraries:<\/p>\n Replace Test the script by calling the Replace Run the script by executing the following command in the terminal:<\/p>\n If successful, you will see a message indicating that the audio file has been uploaded to the specified bucket.<\/p>\n<\/li>\n<\/ol>\n Now that you have uploaded the audio file to Google Cloud Storage, you can transcribe it using the Speech-to-Text API.<\/p>\n In addition to transcribing audio files, you can also perform live speech recognition using the Speech-to-Text API.<\/p>\n To perform live speech recognition, you need to install the The script will continuously listen for your speech input and display the recognized text on the console.<\/p>\n<\/li>\n<\/ol>\n In this tutorial, you learned how to create a speech recognition app using Python and the Google Cloud API. You learned how to set up the Google Cloud API, install the required libraries, configure authentication, upload an audio file to Google Cloud Storage, transcribe the audio using the Speech-to-Text API, and perform live speech recognition.<\/p>\n With this knowledge, you can now create your own speech recognition apps and integrate them into your projects.<\/p>\n","protected":false},"excerpt":{"rendered":" Speech recognition is the ability of a computer to convert spoken language into written text. With the help of Python and the Google Cloud API, you can easily create a speech recognition app that can transcribe audio files or live speech. In this tutorial, you will learn how to create Continue Reading<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_import_markdown_pro_load_document_selector":0,"_import_markdown_pro_submit_text_textarea":"","footnotes":""},"categories":[1],"tags":[261,10,39,30,1313,41,40,75,54,1507],"yoast_head":"\ngoogle-cloud-speech<\/code> library.<\/p>\n
pip install google-cloud-speech\n<\/code><\/pre>\n
Step 3: Configuring Authentication<\/h2>\n
GOOGLE_APPLICATION_CREDENTIALS<\/code> to the path of your credentials JSON file.<\/p>\n
set GOOGLE_APPLICATION_CREDENTIALS=\/path\/to\/credentials.json\n<\/code><\/pre>\n
export GOOGLE_APPLICATION_CREDENTIALS=\/path\/to\/credentials.json\n<\/code><\/pre>\n
\/path\/to\/credentials.json<\/code> with the actual path to your credentials JSON file.<\/p>\n
Step 4: Uploading Audio File to Google Cloud Storage<\/h2>\n
4.1 Create a Bucket in Google Cloud Storage<\/h3>\n
\n
4.2 Upload the Audio File<\/h3>\n
\n
google-cloud-storage<\/code> library:\n
pip install google-cloud-storage\n<\/code><\/pre>\n<\/li>\n
upload_audio.py<\/code> and open it in your preferred code editor.<\/p>\n<\/li>\n
from google.cloud import storage\n<\/code><\/pre>\n<\/li>\n
def upload_audio(bucket_name, audio_file_path):\n client = storage.Client()\n bucket = client.bucket(bucket_name)\n blob = bucket.blob(audio_file_path)\n\n blob.upload_from_filename(audio_file_path)\n\n print(f\"Audio file {audio_file_path} uploaded to {bucket_name}.\")\n<\/code><\/pre>\n
bucket_name<\/code> with the name of your bucket and
audio_file_path<\/code> with the path to your audio file.<\/p>\n<\/li>\n
upload_audio<\/code> function:<\/p>\n
if __name__ == \"__main__\":\n bucket_name = \"your-bucket-name\"\n audio_file_path = \"path-to-your-audio-file\"\n\n upload_audio(bucket_name, audio_file_path)\n<\/code><\/pre>\n
your-bucket-name<\/code> with the name of your bucket and
path-to-your-audio-file<\/code> with the actual path to your audio file.<\/p>\n<\/li>\n
python upload_audio.py\n<\/code><\/pre>\n
Step 5: Transcribing Audio using the Speech-to-Text API<\/h2>\n
5.1 Create a Speech-to-Text Transcription Job<\/h3>\n
\n
5.2 Check Transcription Job Status<\/h3>\n
\n
5.3 Download the Transcription Result<\/h3>\n
\n
Step 6: Performing Live Speech Recognition<\/h2>\n
6.1 Install the Required Libraries<\/h3>\n
pyaudio<\/code> library. Open a terminal window and run the following command:<\/p>\n
pip install pyaudio\n<\/code><\/pre>\n
6.2 Create a Live Speech Recognition Script<\/h3>\n
\n
live_speech_recognition.py<\/code> and open it in your preferred code editor.<\/li>\n
import speech_recognition as sr\n<\/code><\/pre>\n<\/li>\n
def live_speech_recognition():\n recognizer = sr.Recognizer()\n microphone = sr.Microphone()\n\n with microphone as source:\n recognizer.adjust_for_ambient_noise(source)\n\n while True:\n print(\"Say something...\")\n audio = recognizer.listen(source)\n\n try:\n text = recognizer.recognize_google_cloud(audio)\n print(\"You said:\", text)\n except sr.UnknownValueError:\n print(\"Could not understand audio\")\n except sr.RequestError as e:\n print(\"Error:\", str(e))\n<\/code><\/pre>\n<\/li>\n
live_speech_recognition<\/code> function:\n
if __name__ == \"__main__\":\n live_speech_recognition()\n<\/code><\/pre>\n<\/li>\n
python live_speech_recognition.py\n<\/code><\/pre>\n
Conclusion<\/h2>\n