Analyzing Media

The Dolby Media Analyze API is designed to give insight into your media. Our algorithms analyze your media and help identify problems by providing data about your audio's loudness, distortion, noise, silence, and quality. This can be helpful for specialized media processing tasks such as:

  • Quality control
  • Broadcasting recommendations
  • Content segmentation & analysis
  • etc.

This tutorial will show you how to get started quickly using the API to build a range of applications such as podcast tools, online learning platforms, and social video sharing.

⚠️ Not a Developer?

To get started without writing any code, you can use the Try It Now demo to upload a file and view the results.

Getting Started

To get started you'll follow these steps.

  1. Get your API key
  2. Prepare your media
  3. Make an Analyze request
  4. Check the results
  5. Review the output
  6. Learn more

1. Get Your API Key

In order to use the Analyze API you will need to have an API key. When you sign up for an account you are able to retrieve your API key from the Media Processing dashboard. It will be a globally unique identifier (guid) that is passed to all API requests as a header called x-api-key.

2. Prepare Your Media

You have a choice for how to make your media available for processing:

  1. Use your own cloud storage provider.
  2. Use our Dolby Media Input API.

a. Use your own cloud storage provider

You will want to consider this option when you move your applications into production. Our services are able to work with many popular cloud storage services such as AWS S3, Google Cloud Storage, or your own services with basic or token based authentication.

Please see the Media Input and Output guide for more details on the various options.

b. Use our Dolby Media Input API (optional)

The /media/input API was designed to give you a quick way to upload media while evaluating Media Processing services. We can securely store your media temporarily, any media you upload will be removed regularly so shouldn't be used for permanent storage.

Call /media/input to identify a shortcut url. It must begin with dlb:// but otherwise is your own personal unique identifier. Some valid examples:

  • dlb://example.mp4
  • dlb://input/your-favorite-podcast.mp4
  • dlb://usr/home/me/voice-memo.wav

You can think of this like an object key that is used to identify a file for your account. Once you call POST /media/input you'll be returned a new url in the response. This is a pre-signed URL to a cloud storage location you will use to upload the file. You do that by making a PUT request with your media.

The following examples use an environment variable to identify the api key (DOLBYIO_API_KEY) and the file path to your file (INPUT_MEDIA_LOCAL_PATH). You need to either set those variables for your environment or update the code samples with new values.

  • Bash
  • JavaScript
  • Python

curl -X POST https://api.dolby.com/media/input \
  --header "x-api-key: $DOLBYIO_API_KEY" \
  --data '{
      "url": "dlb://in/example.mp4"
  }'

Use the result in a second command replacing $PRE_SIGNED_URL with the response from the previous command:

curl -X PUT $PRE_SIGNED_URL -T ./your-local-media.mp4

const fs = require("fs")
const axios = require("axios").default

// Set or replace these values

const file_path = process.env.INPUT_MEDIA_LOCAL_PATH
const api_key = process.env.DOLBYIO_API_KEY

// Declare your dlb:// location

const config = {
  method: "post",
  url: "https://api.dolby.com/media/input",
  headers: {
    "x-api-key": api_key,
    "Content-Type": "application/json",
    Accept: "application/json",
  },
  data: {
    url: "dlb://in/example.mp4",
  },
}

axios(config)
  .then(function(response) {
    // Upload your media to the pre-signed url response

    const upload_config = {
      method: "put",
      url: response.data.url,
      data: fs.createReadStream(file_path),
      headers: {
        "Content-Type": "application/octet-stream",
        "Content-Length": fs.statSync(file_path).size,
      },
    }
    axios(upload_config)
      .then(function() {
        console.log("File uploaded")
      })
      .catch(function(error) {
        console.log(error)
      })
  })
  .catch(function(error) {
    console.log(error)
  })

import os
import requests

# Set or replace these values

file_path = os.environ["INPUT_MEDIA_LOCAL_PATH"]
api_key = os.environ["DOLBYIO_API_KEY"]

# Declare your dlb:// location

url = "https://api.dolby.com/media/input"
headers = {
    "x-api-key": api_key,
    "Content-Type": "application/json",
    "Accept": "application/json",
}

body = {
    "url": "dlb://in/example.mp4",
}

response = requests.post(url, json=body, headers=headers)
response.raise_for_status()
data = response.json()
presigned_url = data["url"]

# Upload your media to the pre-signed url response

print("Uploading {0} to {1}".format(file_path, presigned_url))
with open(file_path, "rb") as input_file:
  requests.put(presigned_url, data=input_file)

Once the upload is complete, you'll be able to refer to this media with the dlb://in/example.mp4 shortcut.

3. Make an Analyze Request

The Analyze API requires both an input and output parameter to begin analyzing your media. There are additional parameters that can be used to customize the results based on the type of content and preferences you might have but we'll keep this example simple.

Regardless of whether you chose to use your own cloud storage or our /media/input service, our API will need to be able to read the media. See the Media Input and Output guide for a more detailed explanation of the various ways you can provide authentication details.

These are all valid input values:

Similarly, for output you can use any location which our APIs will be able to write to. As this is less common, specifying a dlb:// output location will create one on the fly. Here are some examples:

  • Bash
  • JavaScript
  • Python

curl -X POST https://api.dolby.com/media/analyze \
     --header "x-api-key: $DOLBYIO_API_KEY" \
     --data '{
         "input": "s3://dolbyio/public/shelby/airplane.original.mp4",
         "output": "dlb://out/airplane.analysis.json"
     }'

const axios = require("axios").default

const config = {
  method: "post",
  url: "https://api.dolby.com/media/analyze",
  headers: {
    "x-api-key": process.env.DOLBYIO_API_KEY,
    "Content-Type": "application/json",
    "Accept": "application/json",
  },
  data: {
    input: "s3://dolbyio/public/shelby/airplane.original.mp4",
    output: "dlb://out/airplane.analysis.json",
  },
}

axios(config)
  .then(function(response) {
    console.log(response.data.job_id)
  })
  .catch(function(error) {
    console.log(error)
  })

import os
import requests

# Set or replace these values
api_key = os.environ['DOLBYIO_API_KEY']
body = {
  "input" : "s3://dolbyio/public/shelby/airplane.original.mp4",
  "output" : "dlb://out/airplane.analysis.json"
}

url = "https://api.dolby.com/media/analyze"
headers = {
  "x-api-key": api_key,
  "Content-Type": "application/json",
  "Accept": "application/json"
}

response = requests.post(url, json=body, headers=headers)
response.raise_for_status()
print(response.json())

The JSON response will include a unique job_id that you'll need to use to check on the status of media processing.

{"job_id":"b49955b4-9b64-4d8b-a4c6-2e3550472a33"}

You can explore the API Reference to learn more about other options to customize the behavior of the media processing.

4. Check the Results

It will take a few moments for the API to analyze your file. You'll need to check the status of the job. You can learn more about this in the How it Works section of the Introduction to learn more.

For this GET /media/analyze request you'll need to use the job id returned from the previous step. In these examples it is specified as an environment variable you'll need to set or replace in the code samples.

  • Bash
  • JavaScript
  • Python

curl -X GET "https://api.dolby.com/media/analyze?job_id=$DOLBYIO_JOB_ID" \
     --header "x-api-key: $DOLBYIO_API_KEY"

const axios = require("axios").default

const config = {
  method: "get",
  url: "https://api.dolby.com/media/analyze",
  headers: {
    "x-api-key": process.env.DOLBYIO_API_KEY,
    "Content-Type": "application/json",
    "Accept": "application/json",
  },
  params: {
    job_id: process.env.DOLBYIO_JOB_ID,
  },
}

axios(config)
  .then(function(response) {
    console.log(JSON.stringify(response.data, null, 4))
  })
  .catch(function(error) {
    console.log(error)
  })

import os
import requests

url = "https://api.dolby.com/media/analyze"
headers = {
  "x-api-key": os.environ["DOLBYIO_API_KEY"],
  "Content-Type": "application/json",
  "Accept": "application/json"
}

params = {
  "job_id": os.environ["DOLBYIO_JOB_ID"]
}

response = requests.get(url, params=params, headers=headers)
response.raise_for_status()
print(response.json())

While the job is still in progress, you will be able to see the status and progress values returned.

{
  "path": "/media/analyze",
  "status": "Running",
  "progress": 42
}

If you re-run and call again after a period of time you'll see the status changes and the output you originally specified will be ready for downloading.

{
  "path": "/media/analyze",
  "progress": 100,
  "result": {},
  "status": "Success"
}

5. Review the Output

Once media processing is complete, the result file will be PUT in the output location specified when the job was started. If you used the optional dlb://out/example.analysis.json location you can use the /media/output API to retrieve the file. It takes a url as the only parameter.

  • Bash
  • JavaScript
  • Python

curl  -X GET https://api.dolby.com/media/output?url=dlb://out/example.analysis.json \
      -O -L \
      --header "x-api-key: $DOLBYIO_API_KEY"

You specify the -L because the service will redirect you to a cloud storage location. You shouldn't try to retrieve directly from the cloud storage as the location may change, so use /media/output with the shortcut instead. The -O is to just output the file to your local system with the same filename.

const fs = require("fs")
const axios = require("axios").default

const output_path = process.env.OUTPUT_MEDIA_LOCAL_PATH

const config = {
  method: "get",
  url: "https://api.dolby.com/media/output",
  headers: {
    "x-api-key": process.env.DOLBYIO_API_KEY,
    "Content-Type": "application/json",
    "Accept": "application/json",
  },
  responseType: "stream",
  params: {
    url: "dlb://out/example.analysis.json",
  },
}

axios(config)
  .then(function(response) {
    response.data.pipe(fs.createWriteStream(output_path))
    response.data.on("error", function(error) {
      console.log(error)
    })
    response.data.on("end", function() {
      console.log("File downloaded!")
    })
  })
  .catch(function(error) {
    console.log(error)
  })

import os
import shutil
import requests

output_path = os.environ["OUTPUT_MEDIA_LOCAL_PATH"]

url = "https://api.dolby.com/media/output"
headers = {
    "x-api-key": os.environ["DOLBYIO_API_KEY"],
    "Content-Type": "application/json",
    "Accept": "application/json",
}

args = {
    "url": "dlb://out/example.analysis.json",
}

with requests.get(url, params=args, headers=headers, stream=True) as response:
    response.raise_for_status()
    response.raw.decode_content = True
    print("Downloading from {0} into {1}".format(response.url, output_path))
    with open(output_path, "wb") as output_file:
        shutil.copyfileobj(response.raw, output_file)

Review the results and check out the API documentation if you have any questions about the data returned.

Learn More

That's just the start of what you can do with the Analyze API. For more information, these resources may be helpful: