README.md

metadata

title: xtts2 + Bark TTS
emoji: 🎙️
colorFrom: purple
colorTo: pink
sdk: gradio
sdk_version: 6.3.0
app_file: app.py
pinned: false
license: apache-2.0
tags:
  - text-to-speech
  - voice-cloning
  - xtts
  - bark
  - mcp-server
short_description: XTTS2 voice cloning + Bark TTS in one space

TTS Hub: XTTS2 + Bark

Two powerful TTS models in one space, optimized for CPU.

Models

Model	Voice Source	Languages	Special Features
XTTS2 (default)	Your audio sample	16 languages	Voice cloning
Bark	Preset voices	EN, DE, FR, ES, ZH, JA, KO	Non-speech sounds, temperature control

Usage

XTTS2 (Voice Cloning)

1. Upload 3-30 seconds of reference voice audio
2. Enter text to synthesize
3. Select language and speed
4. Click "Generate Speech"

Bark (Preset Voices)

1. Select "Bark (Preset Voices)"
2. Choose a voice preset (e.g., v2/en_speaker_6)
3. Adjust temperature controls (optional):
   • Text Temperature (0.1-1.0): Controls semantic variation
   • Waveform Temperature (0.1-1.0): Controls audio variation
4. Set seed for reproducibility (optional, -1 for random)
5. Enter text with optional special tokens
6. Click "Generate Speech"

Bark special tokens:

• [laughter] [laughs] [sighs] [music] [gasps] [clears throat]
• ♪ la la la ♪ for singing
• MAN: WOMAN: for speaker labels

Long text handling: Text is automatically split into chunks and processed sequentially with natural pauses between segments.

---

API

Python Client

from gradio_client import Client, handle_file

client = Client("Luminia/xtts2-Bark")

# XTTS2 (voice cloning)
result = client.predict(
    text="Hello, this is a voice cloning test.",
    model_choice="XTTS2 (Voice Cloning)",
    reference_audio=handle_file("voice_sample.wav"),
    language="English",
    speed=1.0,
    voice_preset="v2/en_speaker_6",
    text_temp=0.7,       # Bark only (ignored for XTTS2)
    waveform_temp=0.7,   # Bark only (ignored for XTTS2)
    seed=-1,             # Bark only (ignored for XTTS2)
    api_name="/synthesize"
)
print(result)  # (audio_path, status)

# Bark (preset voice) with temperature control
result = client.predict(
    text="Hello! [laughter] This is Bark speaking.",
    model_choice="Bark (Preset Voices)",
    reference_audio=None,
    language="English",
    speed=1.0,
    voice_preset="v2/en_speaker_6",
    text_temp=0.7,       # Semantic temperature (0.1-1.0)
    waveform_temp=0.7,   # Audio waveform temperature (0.1-1.0)
    seed=42,             # Set seed for reproducibility (-1 for random)
    api_name="/synthesize"
)
print(result)

REST API (curl)

# XTTS2 with voice cloning
curl -X POST "https://luminia-xtts2-bark.hf.space/gradio_api/call/synthesize" \
  -H "Content-Type: application/json" \
  -d '{
    "data": [
      "Hello world",
      "XTTS2 (Voice Cloning)",
      {"path": "https://example.com/voice.wav"},
      "English",
      1.0,
      "v2/en_speaker_6",
      0.7,
      0.7,
      -1
    ]
  }'

# Bark with preset voice and temperature control
curl -X POST "https://luminia-xtts2-bark.hf.space/gradio_api/call/synthesize" \
  -H "Content-Type: application/json" \
  -d '{
    "data": [
      "Hello [laughter] world",
      "Bark (Preset Voices)",
      null,
      "English",
      1.0,
      "v2/en_speaker_3",
      0.7,
      0.7,
      42
    ]
  }'

MCP (Model Context Protocol)

This Space supports MCP for AI assistants.

Tool schema:

{
  "name": "synthesize",
  "parameters": {
    "text": {"type": "string", "description": "Text to synthesize"},
    "model_choice": {"type": "string", "enum": ["XTTS2 (Voice Cloning)", "Bark (Preset Voices)"]},
    "reference_audio": {"type": "file", "description": "Reference audio for XTTS2 (optional for Bark)"},
    "language": {"type": "string", "default": "English"},
    "speed": {"type": "number", "default": 1.0},
    "voice_preset": {"type": "string", "default": "v2/en_speaker_6"},
    "text_temp": {"type": "number", "default": 0.7, "description": "Bark text/semantic temperature (0.1-1.0)"},
    "waveform_temp": {"type": "number", "default": 0.7, "description": "Bark waveform temperature (0.1-1.0)"},
    "seed": {"type": "integer", "default": -1, "description": "Bark seed for reproducibility (-1 for random)"}
  },
  "returns": ["audio", "string"]
}

MCP Config:

{
  "mcpServers": {
    "tts-hub": {"url": "https://luminia-xtts2-bark.hf.space/gradio_api/mcp/"}
  }
}

---

CLI Usage

# XTTS2 voice cloning
python app.py tts -t "Hello world" -o output.wav -m xtts2 -r voice_sample.wav -l English -s 1.0

# Bark preset voice (basic)
python app.py tts -t "Hello [laughter] world" -o output.wav -m bark -v "v2/en_speaker_6"

# Bark with temperature control and seed
python app.py tts -t "Hello world" -o output.wav -m bark -v "v2/en_speaker_6" \
  --text-temp 0.7 --waveform-temp 0.7 --seed 42

Bark Voice Presets

Preset	Language
v2/en_speaker_0 - v2/en_speaker_9	English
v2/de_speaker_0 - v2/de_speaker_2	German
v2/fr_speaker_0 - v2/fr_speaker_1	French
v2/es_speaker_0 - v2/es_speaker_1	Spanish
v2/zh_speaker_0 - v2/zh_speaker_1	Chinese
v2/ja_speaker_0	Japanese
v2/ko_speaker_0	Korean

Bark Temperature Guide

Setting	Low (0.1-0.3)	Medium (0.5-0.7)	High (0.8-1.0)
Text Temp	More predictable, robotic	Natural, balanced	Creative, variable
Waveform Temp	Cleaner audio	Natural variation	More expressive

Recommended: Start with 0.7 for both temperatures for natural-sounding speech.

---

Credits

• XTTS2: Coqui TTS (Apache 2.0)
• Bark: Suno AI (MIT)

Licensed under Apache 2.0.