> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/elevenlabs/elevenlabs-python/llms.txt
> Use this file to discover all available pages before exploring further.

# Multi-Voice Dialogue

> Generate conversations with multiple distinct voices

Create natural-sounding dialogues and conversations with up to 10 different voices. Perfect for audiobooks, podcasts, training materials, and interactive content.

## Quick Start

```python theme={null}
from elevenlabs import ElevenLabs
from elevenlabs.types import DialogueInput

client = ElevenLabs(api_key="YOUR_API_KEY")

# Create a simple dialogue
dialogue = [
    DialogueInput(
        text="Hello! How can I help you today?",
        voice_id="21m00Tcm4TlvDq8ikWAM"  # Rachel
    ),
    DialogueInput(
        text="I'd like to know more about your services.",
        voice_id="AZnzlk1XvdvUeBnXmlld"  # Domi
    )
]

audio = client.text_to_dialogue.convert(inputs=dialogue)

with open("conversation.mp3", "wb") as f:
    for chunk in audio:
        f.write(chunk)
```

## Features

<CardGroup cols={2}>
  <Card title="Multiple Voices" icon="users">
    Use up to 10 different voices in a single dialogue
  </Card>

  <Card title="Natural Flow" icon="comments">
    Seamless transitions between speakers
  </Card>

  <Card title="Timestamps" icon="clock">
    Get timing information for each voice segment
  </Card>

  <Card title="High Quality" icon="star">
    Professional audio output with natural pacing
  </Card>
</CardGroup>

## Creating Dialogues

### Basic Conversation

```python theme={null}
from elevenlabs import ElevenLabs
from elevenlabs.types import DialogueInput

client = ElevenLabs(api_key="YOUR_API_KEY")

# Define the dialogue
dialogue = [
    DialogueInput(
        text="Welcome to our store! How may I assist you?",
        voice_id="voice_id_1"  # Store employee
    ),
    DialogueInput(
        text="Hi! I'm looking for a birthday gift.",
        voice_id="voice_id_2"  # Customer
    ),
    DialogueInput(
        text="Wonderful! What does the person enjoy?",
        voice_id="voice_id_1"  # Store employee
    ),
    DialogueInput(
        text="They love reading mystery novels.",
        voice_id="voice_id_2"  # Customer
    )
]

# Generate the dialogue
audio = client.text_to_dialogue.convert(
    inputs=dialogue,
    model_id="eleven_multilingual_v2"
)

with open("conversation.mp3", "wb") as f:
    for chunk in audio:
        f.write(chunk)
```

### Audiobook with Character Voices

```python theme={null}
# Audiobook excerpt with narrator and character voices
audiobook_dialogue = [
    DialogueInput(
        text="The detective entered the dimly lit room,",
        voice_id="narrator_voice_id"
    ),
    DialogueInput(
        text="Someone's been here recently,",
        voice_id="detective_voice_id"
    ),
    DialogueInput(
        text="she said, examining the scattered papers.",
        voice_id="narrator_voice_id"
    ),
    DialogueInput(
        text="What are you doing here?",
        voice_id="suspect_voice_id"
    ),
    DialogueInput(
        text="a voice called from the doorway.",
        voice_id="narrator_voice_id"
    )
]

audio = client.text_to_dialogue.convert(
    inputs=audiobook_dialogue,
    model_id="eleven_v3"
)
```

### Podcast with Multiple Hosts

```python theme={null}
# Podcast episode intro
podcast_intro = [
    DialogueInput(
        text="Welcome back to Tech Talk! I'm Sarah.",
        voice_id="host_1_voice_id"
    ),
    DialogueInput(
        text="And I'm Mike. Today we're discussing AI.",
        voice_id="host_2_voice_id"
    ),
    DialogueInput(
        text="That's right! We have an exciting topic.",
        voice_id="host_1_voice_id"
    ),
    DialogueInput(
        text="Let's dive right in!",
        voice_id="host_2_voice_id"
    )
]

audio = client.text_to_dialogue.convert(
    inputs=podcast_intro,
    model_id="eleven_turbo_v2_5"  # Fast generation
)
```

## Advanced Features

### Dialogue with Timestamps

Get timing information for each voice segment:

```python theme={null}
# Generate dialogue with timestamp data
result = client.text_to_dialogue.convert_with_timestamps(
    inputs=dialogue,
    model_id="eleven_multilingual_v2"
)

# Access audio data
audio_data = result.audio

# Access timestamp information
for segment in result.voice_segments:
    print(f"Voice {segment.voice_id}:")
    print(f"  Start: {segment.start_time}s")
    print(f"  End: {segment.end_time}s")
    print(f"  Text: {segment.text}")
```

### Streaming Dialogue

Stream dialogue generation in real-time:

```python theme={null}
from elevenlabs import stream

# Stream the dialogue
audio_stream = client.text_to_dialogue.stream(
    inputs=dialogue,
    model_id="eleven_turbo_v2_5"
)

# Play as it generates
stream(audio_stream)

# Or process chunks manually
for chunk in audio_stream:
    # Process each audio chunk
    pass
```

## Customization

### Model Selection

```python theme={null}
# Use Eleven v3 for dramatic performances
audio = client.text_to_dialogue.convert(
    inputs=dialogue,
    model_id="eleven_v3"
)

# Use Turbo for faster generation
audio = client.text_to_dialogue.convert(
    inputs=dialogue,
    model_id="eleven_turbo_v2_5"
)

# Use Multilingual v2 for best stability
audio = client.text_to_dialogue.convert(
    inputs=dialogue,
    model_id="eleven_multilingual_v2"
)
```

### Language Support

```python theme={null}
# Specify language for better pronunciation
audio = client.text_to_dialogue.convert(
    inputs=dialogue,
    language_code="es",  # Spanish
    model_id="eleven_multilingual_v2"
)
```

### Custom Voice Settings

```python theme={null}
from elevenlabs.types import ModelSettingsResponseModel

# Apply custom settings
settings = ModelSettingsResponseModel(
    stability=0.7,
    similarity_boost=0.8
)

audio = client.text_to_dialogue.convert(
    inputs=dialogue,
    settings=settings,
    model_id="eleven_multilingual_v2"
)
```

### Pronunciation Dictionaries

```python theme={null}
from elevenlabs.types import PronunciationDictionaryVersionLocator

# Use pronunciation dictionary for technical terms
dictionaries = [
    PronunciationDictionaryVersionLocator(
        pronunciation_dictionary_id="dict_id",
        version_id="version_id"
    )
]

audio = client.text_to_dialogue.convert(
    inputs=dialogue,
    pronunciation_dictionary_locators=dictionaries
)
```

## Async Usage

```python theme={null}
from elevenlabs import AsyncElevenLabs
import asyncio

async def generate_dialogue():
    client = AsyncElevenLabs(api_key="YOUR_API_KEY")
    
    dialogue = [
        {"text": "Hello there!", "voice_id": "voice_1"},
        {"text": "Hi! Nice to meet you.", "voice_id": "voice_2"}
    ]
    
    audio = await client.text_to_dialogue.convert(inputs=dialogue)
    
    chunks = []
    async for chunk in audio:
        chunks.append(chunk)
    
    return b"".join(chunks)

audio_data = asyncio.run(generate_dialogue())
```

## Use Cases

### Educational Content

```python theme={null}
# Create interactive learning content
educational_dialogue = [
    DialogueInput(
        text="Today we'll learn about photosynthesis.",
        voice_id="teacher_voice"
    ),
    DialogueInput(
        text="What is photosynthesis?",
        voice_id="student_voice"
    ),
    DialogueInput(
        text="Great question! It's how plants make food.",
        voice_id="teacher_voice"
    )
]
```

### Customer Service Training

```python theme={null}
# Training scenarios
training_scenario = [
    DialogueInput(
        text="Thank you for calling. How can I help you?",
        voice_id="agent_voice"
    ),
    DialogueInput(
        text="I have a problem with my recent order.",
        voice_id="customer_voice"
    ),
    DialogueInput(
        text="I'm sorry to hear that. Let me help you.",
        voice_id="agent_voice"
    )
]
```

### Interactive Stories

```python theme={null}
# Choose-your-own-adventure style content
story_dialogue = [
    DialogueInput(
        text="You find yourself at a crossroads.",
        voice_id="narrator_voice"
    ),
    DialogueInput(
        text="Which path will you take?",
        voice_id="narrator_voice"
    )
]
```

## Best Practices

<AccordionGroup>
  <Accordion title="Voice Selection">
    * Choose distinct voices for each character
    * Match voice characteristics to character traits
    * Use consistent voices throughout the dialogue
    * Test voice combinations before production
  </Accordion>

  <Accordion title="Script Writing">
    * Write natural, conversational text
    * Include appropriate pauses with punctuation
    * Keep individual segments concise
    * Vary sentence structure for natural flow
  </Accordion>

  <Accordion title="Performance Optimization">
    * Use Turbo models for faster generation
    * Consider streaming for long dialogues
    * Batch process multiple dialogues with async
    * Cache frequently used voice combinations
  </Accordion>
</AccordionGroup>

## Limitations

<Warning>
  * Maximum of 10 unique voice IDs per dialogue
  * Total text length may be limited by your subscription tier
  * Generation time increases with dialogue length and number of voices
</Warning>

## Related Features

* [Text to Speech](/text-to-speech) - Single voice generation
* [Voice Cloning](/advanced/voice-cloning) - Create custom voices
* [Voices](/voices) - Browse available voices