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Getting Started with Pre-recorded Audio

In this guide, you'll learn how to automatically transcribe pre-recorded audio files using Deepgram's SDKs, which are supported for use with the Deepgram API.

Before You Begin

Before you run the code, you'll need to do a few things.

Create a Deepgram Account

Before you can use Deepgram products, you'll need to create a Deepgram account. Signup is free and includes:

Create a Deepgram API Key

To access Deepgram’s API, you'll need to create a Deepgram API Key. Make note of your API Key; you will need it later.

Configure Environment

We provide sample scripts in Python and Node.js and assume you have already configured either a Python or Node development environment. System requirements will vary depending on the programming language you use:

  • Node.js: node >= 14.14.37
  • Python: python >= 3.7

If you get stuck at any point, help is just a click away! Contact Support.

Transcribe Audio

Once you have your API Key, it's time to transcribe audio! The instructions below will guide you through the process of creating a sample application, installing the Deepgram SDK, configuring code with your own Deepgram API Key and pre-recorded audio to transcribe, and finally, building and running the application.

Choose an Audio File

Download our sample audio file, or record your own using your device’s microphone.

Install the SDK

Open your terminal, navigate to the location on your drive where you want to create your project, and install the Deepgram SDK.

Example
# Initialize a new application
npm init

# Install the Deepgram Node.js SDK
# https://github.com/deepgram/node-sdk
npm install @deepgram/sdk
# Install the Deepgram Python SDK
# https://github.com/deepgram/python-sdk
pip install deepgram-sdk

Write the Code

In your terminal, create a new file in your project's location, and populate it with code.

Example
// Example filename: index.js

const fs = require('fs');
const { Deepgram } = require('@deepgram/sdk');

// Your Deepgram API Key
const deepgramApiKey = 'YOUR_DEEPGRAM_API_KEY';

// Location of the file you want to transcribe. Should include filename and extension.
// Example of a local file: ../../Audio/life-moves-pretty-fast.wav
// Example of a remote file: https://static.deepgram.com/examples/interview_speech-analytics.wav
const file = 'YOUR_FILE_LOCATION';

// Mimetype for the file you want to transcribe
// Only necessary if transcribing a local file
// Example: audio/wav
const mimetype = 'YOUR_FILE_MIME_TYPE';

// Initialize the Deepgram SDK
const deepgram = new Deepgram(deepgramApiKey);

// Check whether requested file is local or remote, and prepare accordingly
if (file.startsWith('http')) {
  // File is remote
  // Set the source
    source = {
      url: file
    }
}
else {
  // File is local
  // Open the audio file
  const audio = fs.readFileSync(file);

  // Set the source
  source = {
    buffer: audio,
    mimetype: mimetype
  }
}

// Send the audio to Deepgram and get the response
deepgram.transcription.preRecorded(
  source,
  {
    punctuate: true
  }
)
.then((response) => {
  // Write the response to the console
  console.dir(response, {depth: null});

  // Write only the transcript to the console
  //console.dir(response.results.channels[0].alternatives[0].transcript, { depth: null });
})
.catch((err) => {
  console.log(err);
})
# Example filename: deepgram_test.py

from deepgram import Deepgram
import asyncio, json

# Your Deepgram API Key
DEEPGRAM_API_KEY = 'YOUR_DEEPGRAM_API_KEY'

# Location of the file you want to transcribe. Should include filename and extension.
# Example of a local file: ../../Audio/life-moves-pretty-fast.wav
# Example of a remote file: https://static.deepgram.com/examples/interview_speech-analytics.wav
FILE = 'YOUR_FILE_LOCATION'

# Mimetype for the file you want to transcribe
# Include this line only if transcribing a local file
# Example: audio/wav
MIMETYPE = 'YOUR_FILE_MIME_TYPE'

async def main():

  # Initialize the Deepgram SDK
  deepgram = Deepgram(DEEPGRAM_API_KEY)
  
  # Check whether requested file is local or remote, and prepare source
  if FILE.startswith('http'):
    # file is remote
    # Set the source
    source = {
      'url': FILE
    }
  else:
    # file is local
    # Open the audio file
    audio = open(FILE, 'rb')

    # Set the source
    source = {
      'buffer': audio,
      'mimetype': MIMETYPE
    }

  # Send the audio to Deepgram and get the response
  response = await asyncio.create_task(
    deepgram.transcription.prerecorded(
      source, 
      {
        'punctuate': True
      }
    )
  )

  # Write the response to the console
  print(json.dumps(response, indent=4))

  # Write only the transcript to the console
  #print(response["results"]["channels"][0]["alternatives"][0]["transcript"])

try:
  # If running in a Jupyter notebook, Jupyter is already running an event loop, so run main with this line instead:
  #await main()
  asyncio.run(main())
except Exception as e:
  exception_type, exception_object, exception_traceback = sys.exc_info()
  line_number = exception_traceback.tb_lineno
  print(f'line {line_number}: {exception_type} - {e}')

Be sure to replace YOUR_DEEPGRAM_API_KEY, YOUR_FILE_LOCATION, AND YOUR_FILE_MIME_TYPE with your Deepgram API Key, the location of the file you want to transcribe, and the mime type of the file you want to transcribe, respectively.

Start the application

Run your application from the terminal.

Example
# Run your application using the file you created in the previous step
# Example: node index.js
node YOUR_PROJECT_NAME.js
# Run your application using the file you created in the previous step
# Example: python deepgram_test.py
python YOUR_PROJECT_NAME.py

Be sure to replace YOUR_PROJECT_NAME with the name of the file to which you saved the code in the previous step.

See results

Your transcripts will appear in your browser's developer console.

Analyze the Response

When the file is finished processing (often after only a few seconds), you’ll receive a JSON response:

{
  "metadata":{
    "transaction_key":"Ha0aVG...",
    "request_id":"se24UY...",
    "sha256":"2d5b81...",
    "created":"2021-07-08T09:11:38.593Z",
    "duration":19.0,
    "channels":1
  },
  "results":{
    "channels":[
      {
        "alternatives":[
          {
            "transcript":"Yep. I said it before, and I'll say it again. Life moves pretty fast. You don't stop and look around once in a while. You could miss it. Thank.",
            "confidence":0.9757011,
            "words":[
              {
                "word":"yep",
                "start":5.66,
                "end":5.94,
                "confidence":0.994987,
                "punctuated_word":"Yep."
              },
              {
                "word":"i",
                "start":7.2344832,
                "end":7.434014,
                "confidence":0.8217165,
                "punctuated_word":"I"
              },
              {
                "word":"said",
                "start":7.434014,
                "end":7.5537324,
                "confidence":0.979774,
                "punctuated_word":"said"
              },
              ...
            ]
          }
        ]
      }
    ]
  }
}

In this default response, we see:

  • transcript: the transcript for the audio segment being processed.

  • confidence: a floating point value between 0 and 1 that indicates overall transcript reliability. Larger values indicate higher confidence.

  • words: an object containing each word in the transcript, along with its start time and end time (in seconds) from the beginning of the audio stream, and a confidence value.

    Because we passed the punctuate: true option to the transcription.prerecorded method, each word object also includes its punctuated_word value, which contains the transformed word after punctuation and capitalization are applied.

By default, Deepgram applies its general AI model, which is a good, general purpose model for everyday situations.

What's Next?

Now that you've gotten a transcript for pre-recorded audio, enhance your knowledge by exploring the following areas.

Customize Transcripts

To customize the transcripts you receive, you can send a variety of parameters to the Deepgram API.

For example, if your audio is in Spanish rather than English, you can pass the language: parameter with the es option to the transcription.prerecorded method in the previous examples:

Example
deepgram.transcription.preRecorded(
  source,
  {
    punctuate: true,
    language: es
  }
)
    response = await asyncio.create_task(
      deepgram.transcription.prerecorded(
        source, 
        {
          'punctuate': True,
          'language': 'es'
        }
      )
    )

To learn more about the languages available with Deepgram, see the Language feature guide. To learn more about the many ways you can customize your results with Deepgram's API, check out the Deepgram API Reference.

Explore Use Cases

Time to learn about the different ways you can use Deepgram products to help you meet your business objectives. Explore Deepgram's use cases.

Transcribe Streaming Audio

Now that you know how to transcribe pre-recorded audio, check out how you can use Deepgram to transcribe streaming audio in real time. To learn more, see Getting Started with Streaming Audio.

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