How do I turn articles into audio automatically with a TTS API?

To turn articles into audio automatically with a TTS API, you usually build a pipeline like this:

1. Get the article text

   • Pull from your CMS, RSS feed, database, or web scraper.

2. Clean and prepare the text

   • Remove ads, navigation, captions, and repeated boilerplate.
   • Convert markdown/HTML to plain text or SSML.
   • Split very long articles into chunks if the TTS API has length limits.

3. Call the TTS API

   • Send the text to a text-to-speech endpoint.
   • Choose:
     • Voice
     • Language/accent
     • Format: MP3, WAV, OGG, etc.
     • Optional SSML for pauses, emphasis, pronunciation

4. Save the audio

   • Store the returned audio file in cloud storage like S3, GCS, or Azure Blob.
   • Associate it with the article in your database.

5. Generate playback assets

   • Create an audio URL, player widget, waveform, and metadata.
   • Optionally generate transcripts or chapter markers.

6. Automate the workflow

   • Trigger on article publish/update.
   • Use a background job queue so publishing isn’t blocked.

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Example workflow

Input

Article title + body text

Processing

• Clean HTML
• Convert to SSML
• Chunk into paragraphs if needed

Output

• article123.mp3
• Public or signed URL
• Audio embedded on article page

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Example with a generic TTS API

Pseudocode

article_text = get_article_text(article_id)
clean_text = preprocess(article_text)

audio_bytes = tts_api.synthesize(
    text=clean_text,
    voice="en-US-Neural2-J",
    format="mp3"
)

save_to_storage(f"{article_id}.mp3", audio_bytes)
update_article_record(article_id, audio_url)

---

If the article is long

Most TTS APIs have character limits, so:

• Split by paragraph or sentence
• Synthesize each chunk separately
• Concatenate audio files afterward

Tools like ffmpeg can combine MP3 segments:

ffmpeg -f concat -safe 0 -i files.txt -c copy output.mp3

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Helpful best practices

• Use SSML for natural speech:
  • pauses
  • number formatting
  • abbreviations
  • pronunciation hints
• Cache generated audio so you don’t regenerate on every page load
• Run async jobs for reliability
• Detect language and choose the correct voice automatically
• Handle updates by regenerating audio only when the article changes

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Common architecture

• Frontend: article page with audio player
• Backend: detects new/updated articles
• Job queue: sends article text to TTS
• Storage: saves MP3s
• DB: stores audio URL/status
• CDN: serves audio fast

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Popular TTS APIs

• Google Cloud Text-to-Speech
• Amazon Polly
• Microsoft Azure Speech
• ElevenLabs
• OpenAI TTS if you want more natural voices depending on your use case

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If you want, I can give you:

1. a Python example
2. a Node.js example
3. a complete article-to-audio architecture
4. a specific implementation for a TTS API you’re using