---
license: other
license_name: coqui-public-model-license
license_link: https://coqui.ai/cpml
library_name: coqui
pipeline_tag: text-to-speech
widget:
  - text: "Once when I was six years old I saw a magnificent picture"
---

# ⓍTTS_v2 - C-3PO Fine-Tuned Model


![C-3PO](c3po_1.png)


This repository hosts a fine-tuned version of the ⓍTTS model, utilizing 20 unique voice lines from C-3PO, the iconic Star Wars character. The voice lines were sourced from [Voicy](https://www.voicy.network/official-soundboards/movies/c3po).

## Features
- 🎙️ **Voice Cloning**: Realistic voice cloning with just a short audio clip.
- 🌍 **Multi-Lingual Support**: Generates speech in 17 different languages while maintaining C-3PO's distinct voice.
- 😃 **Emotion & Style Transfer**: Captures the emotional tone and style of the original voice.
- 🔄 **Cross-Language Cloning**: Maintains the unique voice characteristics across different languages.
- 🎧 **High-Quality Audio**: Outputs at a 24kHz sampling rate for clear and high-fidelity audio.

## Supported Languages
The model supports the following 17 languages: English (en), Spanish (es), French (fr), German (de), Italian (it), Portuguese (pt), Polish (pl), Turkish (tr), Russian (ru), Dutch (nl), Czech (cs), Arabic (ar), Chinese (zh-cn), Japanese (ja), Hungarian (hu), Korean (ko), and Hindi (hi).

## Usage in Roll Cage
🤖💬 Boost your AI experience with this Ollama add-on! Enjoy real-time audio 🎙️ and text 🔍 chats, LaTeX rendering 📜, agent automations ⚙️, workflows 🔄, text-to-image 📝➡️🖼️, image-to-text 🖼️➡️🔤, image-to-video 🖼️➡️🎥 transformations. Fine-tune text 📝, voice 🗣️, and image 🖼️ gens. Includes Windows macro controls 🖥️ and DuckDuckGo search.

[ollama_agent_roll_cage (OARC)](https://github.com/Leoleojames1/ollama_agent_roll_cage) is a completely local Python & CMD toolset add-on for the Ollama command line interface. The OARC toolset automates the creation of agents, giving the user more control over the likely output. It provides SYSTEM prompt templates for each ./Modelfile, allowing users to design and deploy custom agents quickly. Users can select which local model file is used in agent construction with the desired system prompt.

## Why This Model for Roll Cage?
The C-3PO fine-tuned model was designed for the Roll Cage chatbot to enhance user interaction with a familiar and beloved voice. By incorporating C-3PO's distinctive speech patterns and tone, Roll Cage becomes more engaging and entertaining. The addition of multi-lingual support and emotion transfer ensures that the chatbot can communicate effectively and expressively across different languages and contexts, providing a more immersive experience for users.

## CoquiTTS and Resources
- 🐸💬 **CoquiTTS**: [Coqui TTS on GitHub](https://github.com/coqui-ai/TTS)
- 📚 **Documentation**: [ReadTheDocs](https://tts.readthedocs.io/en/latest/)
- 👩‍💻 **Questions**: [GitHub Discussions](https://github.com/coqui-ai/TTS/discussions)
- 🗯 **Community**: [Discord](https://discord.gg/5eXr5seRrv)

## License
This model is licensed under the [Coqui Public Model License](https://coqui.ai/cpml). Read more about the origin story of CPML [here](https://coqui.ai/blog/tts/cpml).

## Contact
Join our 🐸Community on [Discord](https://discord.gg/fBC58unbKE) and follow us on [Twitter](https://twitter.com/coqui_ai). For inquiries, email us at info@coqui.ai.

Using 🐸TTS API:

```python
from TTS.api import TTS
tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2", gpu=True)

# generate speech by cloning a voice using default settings
tts.tts_to_file(text="It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
                file_path="output.wav",
                speaker_wav="/path/to/target/speaker.wav",
                language="en")

```

Using 🐸TTS Command line:

```console
 tts --model_name tts_models/multilingual/multi-dataset/xtts_v2 \
     --text "Bugün okula gitmek istemiyorum." \
     --speaker_wav /path/to/target/speaker.wav \
     --language_idx tr \
     --use_cuda true
```

Using the model directly:

```python
from TTS.tts.configs.xtts_config import XttsConfig
from TTS.tts.models.xtts import Xtts

config = XttsConfig()
config.load_json("/path/to/xtts/config.json")
model = Xtts.init_from_config(config)
model.load_checkpoint(config, checkpoint_dir="/path/to/xtts/", eval=True)
model.cuda()

outputs = model.synthesize(
    "It took me quite a long time to develop a voice and now that I have it I am not going to be silent.",
    config,
    speaker_wav="/data/TTS-public/_refclips/3.wav",
    gpt_cond_len=3,
    language="en",
)
```