/audio/speech

LiteLLM Python SDK Usage

Quick Start

from pathlib import Path
from litellm import speech
import os 

os.environ["OPENAI_API_KEY"] = "sk-.."

speech_file_path = Path(__file__).parent / "speech.mp3"
response = speech(
        model="openai/tts-1",
        voice="alloy",
        input="the quick brown fox jumped over the lazy dogs",
    )
response.stream_to_file(speech_file_path)

Async Usage

from litellm import aspeech
from pathlib import Path
import os, asyncio

os.environ["OPENAI_API_KEY"] = "sk-.."

async def test_async_speech(): 
    speech_file_path = Path(__file__).parent / "speech.mp3"
    response = await litellm.aspeech(
            model="openai/tts-1",
            voice="alloy",
            input="the quick brown fox jumped over the lazy dogs",
            api_base=None,
            api_key=None,
            organization=None,
            project=None,
            max_retries=1,
            timeout=600,
            client=None,
            optional_params={},
        )
    response.stream_to_file(speech_file_path)

asyncio.run(test_async_speech())

LiteLLM Proxy Usage

LiteLLM provides an openai-compatible /audio/speech endpoint for Text-to-speech calls.

curl http://0.0.0.0:4000/v1/audio/speech \
  -H "Authorization: Bearer sk-1234" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "tts-1",
    "input": "The quick brown fox jumped over the lazy dog.",
    "voice": "alloy"
  }' \
  --output speech.mp3

Setup

- model_name: tts
  litellm_params:
    model: openai/tts-1
    api_key: os.environ/OPENAI_API_KEY

litellm --config /path/to/config.yaml

# RUNNING on http://0.0.0.0:4000

Supported Providers

Provider	Link to Usage
OpenAI	Usage
Azure OpenAI	Usage
Vertex AI	Usage
Gemini	Usage

/audio/speech to /chat/completions Bridge

LiteLLM allows you to use /chat/completions models to generate speech through the /audio/speech endpoint. This is useful for models like Gemini's TTS-enabled models that are only accessible via /chat/completions.

Gemini Text-to-Speech

Python SDK Usage

Gemini Text-to-Speech SDK Usage

import litellm
import os

# Set your Gemini API key
os.environ["GEMINI_API_KEY"] = "your-gemini-api-key"

def test_audio_speech_gemini():
    result = litellm.speech(
        model="gemini/gemini-2.5-flash-preview-tts",
        input="the quick brown fox jumped over the lazy dogs",
        api_key=os.getenv("GEMINI_API_KEY"),
    )
    
    # Save to file
    from pathlib import Path
    speech_file_path = Path(__file__).parent / "gemini_speech.mp3"
    result.stream_to_file(speech_file_path)
    print(f"Audio saved to {speech_file_path}")

test_audio_speech_gemini()

Async Usage

Gemini Text-to-Speech Async Usage

import litellm
import asyncio
import os
from pathlib import Path

os.environ["GEMINI_API_KEY"] = "your-gemini-api-key"

async def test_async_gemini_speech():
    speech_file_path = Path(__file__).parent / "gemini_speech.mp3"
    response = await litellm.aspeech(
        model="gemini/gemini-2.5-flash-preview-tts",
        input="the quick brown fox jumped over the lazy dogs",
        api_key=os.getenv("GEMINI_API_KEY"),
    )
    response.stream_to_file(speech_file_path)
    print(f"Audio saved to {speech_file_path}")

asyncio.run(test_async_gemini_speech())

LiteLLM Proxy Usage

Setup Config:

Gemini Proxy Configuration

model_list:
- model_name: gemini-tts
  litellm_params:
    model: gemini/gemini-2.5-flash-preview-tts
    api_key: os.environ/GEMINI_API_KEY

Start Proxy:

Start LiteLLM Proxy

litellm --config /path/to/config.yaml

# RUNNING on http://0.0.0.0:4000

Make Request:

Gemini TTS Request

curl http://0.0.0.0:4000/v1/audio/speech \
  -H "Authorization: Bearer sk-1234" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gemini-tts",
    "input": "The quick brown fox jumped over the lazy dog.",
    "voice": "alloy"
  }' \
  --output gemini_speech.mp3

Vertex AI Text-to-Speech

Python SDK Usage

Vertex AI Text-to-Speech SDK Usage

import litellm
import os
from pathlib import Path

# Set your Google credentials
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "path/to/service-account.json"

def test_audio_speech_vertex():
    result = litellm.speech(
        model="vertex_ai/gemini-2.5-flash-preview-tts",
        input="the quick brown fox jumped over the lazy dogs",
    )
    
    # Save to file
    speech_file_path = Path(__file__).parent / "vertex_speech.mp3"
    result.stream_to_file(speech_file_path)
    print(f"Audio saved to {speech_file_path}")

test_audio_speech_vertex()

LiteLLM Proxy Usage

Setup Config:

Vertex AI Proxy Configuration

model_list:
- model_name: vertex-tts
  litellm_params:
    model: vertex_ai/gemini-2.5-flash-preview-tts
    vertex_project: your-project-id
    vertex_location: us-central1

Make Request:

Vertex AI TTS Request

curl http://0.0.0.0:4000/v1/audio/speech \
  -H "Authorization: Bearer sk-1234" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "vertex-tts",
    "input": "The quick brown fox jumped over the lazy dog.",
    "voice": "en-US-Wavenet-D"
  }' \
  --output vertex_speech.mp3

✨ Enterprise LiteLLM Proxy - Set Max Request File Size

Use this when you want to limit the file size for requests sent to audio/transcriptions

- model_name: whisper
  litellm_params:
    model: whisper-1
    api_key: sk-*******
    max_file_size_mb: 0.00001 # 👈 max file size in MB  (Set this intentionally very small for testing)
  model_info:
    mode: audio_transcription

Make a test Request with a valid file

curl --location 'http://localhost:4000/v1/audio/transcriptions' \
--header 'Authorization: Bearer sk-1234' \
--form 'file=@"/Users/ishaanjaffer/Github/litellm/tests/gettysburg.wav"' \
--form 'model="whisper"'

Expect to see the follow response

{"error":{"message":"File size is too large. Please check your file size. Passed file size: 0.7392807006835938 MB. Max file size: 0.0001 MB","type":"bad_request","param":"file","code":500}}%