Deepgram: service API for high-precision speech recognition and synthesis solutions

General Introduction

Deepgram is a company focused on speech recognition and natural language processing technologies, offering powerful Speech-to-Text and Text-to-Speech APIs.The platform utilizes advanced artificial intelligence technologies to help developers integrate speech transcription and comprehension capabilities into their applications and services. Deepgram's solutions are used in a wide range of fields, including medical transcription, automated customer service, podcast transcription, and more, and are dedicated to improving the efficiency and experience of human-computer interaction.

Function List

• Speech-to-Text (Speech-to-Text): Provides high-precision, low-latency speech-to-text services that support multiple languages and accents.
• Text-to-Speech (TTS): Generates natural and smooth speech output for real-time AI and high-throughput applications.
• Audio Intelligence (AI): Provides audio analysis and comprehension capabilities to help organizations analyze audio data at scale.
• Voice Agent API (Voice Agent API): A unified speech API that supports natural human-machine dialog for a variety of automation application scenarios.

Using Help

Installation and use

1. register an account: Visit Deepgram's official website and sign up for a new account.
2. Getting the API key: After logging into your account, get the API key in the console.
3. Integrated API::
   • Speech to text (STT)::
     Python

     import requests
     
     url = "https://api.deepgram.com/v1/listen"
     headers = {
         "Authorization": "Token YOUR_API_KEY",
         "Content-Type": "application/json"
     }
     data = {
         "url": "https://path.to/your/audio/file.wav"
     }
     response = requests.post(url, headers=headers, json=data)
     print(response.json())
     

     AI generated code. View and use carefully. For more information on FAQs.
   • Text-to-speech (TTS)::
     Python

     import requests
     
     url = "https://api.deepgram.com/v1/speak"
     headers = {
         "Authorization": "Token YOUR_API_KEY",
         "Content-Type": "application/json"
     }
     data = {
         "text": "Hello, this is a test.",
         "voice": "en_us_male"
     }
     response = requests.post(url, headers=headers, json=data)
     with open("output.wav", "wb") as f.
         f.write(response.content)
     

     AI generated code. View and use carefully. For more information on FAQs.
4. Real-Time Speech Processing: Real-time speech recognition using WebSocket connections.
   Python

   import websocket
   import json
   
   def on_message(ws, message):
       print(json.loads(message))
   
   ws = websocket.WebSocketApp(
       "wss://api.deepgram.com/v1/listen",
       header={"Authorization": "Token YOUR_API_KEY"},
       on_message=on_message
   )
   ws.run_forever()
   

Speech-to-Text User Guide

1. Integrated API: Integrate Deepgram's Speech-to-Text API in your application. you can refer to the sample code in the official documentation for integration.
2. Uploading audio files: Upload audio files to be transcribed via API, support multiple audio formats.
3. Get Transcription Results: The API returns transcribed text results that you can further process and display in your application.

Text-to-Speech User's Guide

1. Integrated API: Integrate Deepgram's Text-to-Speech API in your application.
2. input text: Enter text content to be converted to speech via the API.
3. Getting Voice Output: The API returns the generated speech file, which you can play or store in your application.

Audio Intelligence User's Guide

1. Integrated API: Integrate Deepgram's Audio Intelligence API in your application.
2. Uploading audio files: Upload audio files to be analyzed through the API.
3. Get analysis results: The API returns audio analysis results, including sentiment analysis, keyword extraction, and other information.

Voice Agent API (Voice Agent API) User Guide

1. Integrated API: Integrate Deepgram's Voice Agent API in your application.
2. Configuring the dialog model: Configure the appropriate dialog model according to the application scenario.
3. Realization of man-machine dialogue: Enable natural and smooth human-machine dialog through APIs to enhance user experience.

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