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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.

 


Deepgram-1

 

 

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())
      
    • 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)
      
  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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