Principle:Elevenlabs Elevenlabs python TTS Model Configuration
| Knowledge Sources | |
|---|---|
| Domains | Speech_Synthesis, Configuration |
| Last Updated | 2026-02-15 00:00 GMT |
Overview
A configuration pattern for selecting the TTS model and audio output format to control quality, latency, language support, and file encoding of synthesized speech.
Description
TTS Model Configuration involves two related choices that affect every TTS operation:
- Model selection: Choosing which neural TTS model to use, each with different capabilities (multilingual, turbo/low-latency, flash) and quality/speed tradeoffs
- Output format selection: Choosing the audio encoding format (MP3, PCM, WAV, mu-law) with specific sample rates and bitrates
These are string parameters passed to TTS methods, not standalone API calls. The model_id is a string like "eleven_multilingual_v2" and output_format is a string like "mp3_44100_128".
Usage
Configure model and output format for every TTS call. Choose higher-quality models (multilingual_v2) for production audio, turbo models (turbo_v2_5) for low-latency applications, and appropriate output formats for the target playback system (MP3 for files, PCM for real-time processing, mu-law for telephony).
Theoretical Basis
Model and format selection follows a configuration pattern:
# Model IDs (string parameters)
model_id = "eleven_multilingual_v2" # High quality, multilingual
model_id = "eleven_turbo_v2_5" # Low latency
model_id = "eleven_flash_v2_5" # Fastest
# Output format pattern: codec_samplerate_bitrate
output_format = "mp3_44100_128" # MP3, 44.1kHz, 128kbps
output_format = "pcm_16000" # Raw PCM, 16kHz
output_format = "ulaw_8000" # mu-law, 8kHz (telephony)