https://github.com/malob/article-to-audio-cloud-function

Google Cloud Function that takes a url, converts the article at that url to audio using Cloud Text-To-Speech, then stores it in a Cloud Storage bucket.
https://github.com/malob/article-to-audio-cloud-function

cloud-function google-cloud nodejs podcast podcast-generator text-to-speech

Last synced: 6 months ago
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Google Cloud Function that takes a url, converts the article at that url to audio using Cloud Text-To-Speech, then stores it in a Cloud Storage bucket.

• Host: GitHub
• URL: https://github.com/malob/article-to-audio-cloud-function
• Owner: malob
• Created: 2018-05-14T01:45:41.000Z (almost 8 years ago)
• Default Branch: master
• Last Pushed: 2018-05-16T15:47:54.000Z (almost 8 years ago)
• Last Synced: 2025-04-04T10:52:00.380Z (12 months ago)
• Topics: cloud-function, google-cloud, nodejs, podcast, podcast-generator, text-to-speech
• Language: JavaScript
• Homepage:
• Size: 31.3 KB
• Stars: 23
• Watchers: 3
• Forks: 5
• Open Issues: 1
• Metadata Files:
  • Readme: readme.md

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README

          
# Article to Audio Google Cloud Function

This is a [Google Cloud Function](https://cloud.google.com/functions/) I hacked together that takes a url to an article on the web, and generates an audio file of it using Google's new [Cloud Text-To-Speech](https://cloud.google.com/text-to-speech/) API which has been updated with access to DeepMind's [WaveNet](https://deepmind.com/blog/wavenet-generative-model-raw-audio/) voices.

I created it as part of a project to generate a personal podcast of articles I want to consume. To get the full thing working see my [other repository](https://github.com/malob/generate-podcast-rss-cloud-function) with the Cloud Function that generates the Podcast RSS.

## Sketch of how it works

  * The function accepts a POST request with json in the body.

    * E.g. `{"url": "http://example.com/somearticle"}`

  * It then uses the free [Mercury Web Parser](https://mercury.postlight.com/web-parser/) API to get the body of the article and some metadata.

  * Since the body is returned as HTML it then converts it to plain text. I also add some of the metadata at the top of the article, since I wanted this in the audio.

  * Then it slits up the body into chunks of no larger then 5,000 characters, since that's the limit on what the TTS API can handle per request.

  * From there is then sends each chunk of text to Google's TTS API which returns the audio encoded as MP3, and writes them to a temporary location.

  * Since having multiple files for parts of the article is annoying, it then uses FFMPEG to concatenate the audio chunks into one file.

  * Finally, it stores the audio file as and object in a Google Cloud Storage bucket, along with some of the metadata.

## Configuration details

To get this working you need a Google Cloud Project with a Cloud Storage bucket setup, and the Cloud Text-To-Speech API enabled.

You'll then need to create a new Cloud Function (see configuration details below), and replace the `undefined` global constants in the code, `gcpProjectID`, `gcpBucketName`, and `mercuryApiKey`, with the appropriate values.

### Cloud Function configuration

  * Trigger type: HTTP trigger

  * Memory allocated: 256 MB

  * Timeout: 240s

    * I had to extend this from default of 60s.