Workflow:Elevenlabs Elevenlabs python Text to Speech Generation
| Knowledge Sources | |
|---|---|
| Domains | Audio_Generation, Text_to_Speech, Speech_Synthesis |
| Last Updated | 2026-02-15 12:00 GMT |
Overview
End-to-end process for converting text into high-quality speech audio using the ElevenLabs Python SDK, with support for batch generation, streaming output, and multiple voice models.
Description
This workflow covers the standard procedure for generating speech audio from text input using the ElevenLabs API. It supports multiple TTS models (Eleven v3, Multilingual v2, Flash v2.5, Turbo v2.5) with configurable voice selection, output format, and voice settings. The process handles both batch conversion (full audio returned at once) and streaming conversion (audio chunks returned progressively), plus saving and playback of the generated audio.
Usage
Execute this workflow when you need to convert text content into spoken audio. This applies to scenarios such as generating voiceovers, creating audiobook content, producing podcast narration, or building text-to-speech features into applications. The SDK supports over 70 languages and provides multiple quality/latency tradeoffs through different model selections.
Execution Steps
Step 1: Client Initialization
Create an instance of the ElevenLabs client with an API key. The client can be configured with environment-specific base URLs for multi-region support (US, EU, India), custom timeouts, and optional httpx client injection. The API key defaults to the ELEVENLABS_API_KEY environment variable if not provided explicitly.
Key considerations:
- API key is required for all authenticated endpoints
- Default timeout is 240 seconds, which may need adjustment for long-form content
- Four deployment regions are available (Production, US, EU, India)
Step 2: Voice Selection
Select a voice for speech generation. Voices can be retrieved by searching the available voice library using the voices API. Each voice has a unique ID, configurable settings (stability, similarity boost, style, speaker boost), and language capabilities. Pre-made voices are available, or custom cloned voices can be used.
Key considerations:
- Use the voices search endpoint to discover available voices
- Voice settings can be overridden per-request without changing stored defaults
- Different voices have different language and accent strengths
Step 3: Model Selection
Choose the appropriate TTS model based on quality and latency requirements. Eleven v3 offers dramatic delivery with 70+ languages. Multilingual v2 excels in stability and accent accuracy across 29 languages. Flash v2.5 provides ultra-low latency at 50% lower cost. Turbo v2.5 balances quality and speed for developer use cases.
Key considerations:
- Model selection impacts quality, latency, supported languages, and cost
- Eleven v3 supports natural multi-speaker dialogue
- Flash models are optimized for real-time applications
Step 4: Audio Generation
Call the text-to-speech conversion endpoint with the selected voice, model, text content, and output format. For batch mode, the entire audio is returned as an iterator of bytes. For streaming mode, audio chunks are yielded progressively as they are generated, enabling real-time playback before generation completes.
Key considerations:
- Output format options include MP3 (various bitrates), PCM, and mu-law
- Batch mode returns complete audio; streaming mode yields chunks progressively
- Voice settings can be overridden per request for fine-tuning output quality
Step 5: Audio Output
Handle the generated audio by playing it back locally, saving it to a file, or streaming it through an audio player. The SDK provides utility functions for all three operations: play (via ffplay or sounddevice), save (write bytes to file), and stream (via mpv for progressive playback).
Key considerations:
- Local playback requires ffplay (ffmpeg) or sounddevice/soundfile
- Streaming playback requires mpv to be installed
- Jupyter notebook playback is supported via IPython.display.Audio