ⓍTTS_v2 - The San-Ti Fine-Tuned Model

This repository hosts a fine-tuned version of the ⓍTTS model, utilizing 4 minutes of unique voice lines from The San-Ti, The voice lines were sourced from the clip of 3 Body Problem on Youtube, can be found here: The San-Ti Explain how they Stop Science on Earth | 3 Body Problem | Netflix

Just the illustration, we never know their looks.

Listen to a sample of the ⓍTTS_v2 - The San-Ti Fine-Tuned Model:

Here's a The San-Ti mp3 voice line clip from the training data:

Features

🎙️ Voice Cloning: Realistic voice cloning with just a short audio clip.
🌍 Multi-Lingual Support: Generates speech in 17 different languages while maintaining The San-Ti's 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) 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.

CoquiTTS and Resources

🐸💬 CoquiTTS: Coqui TTS on GitHub
📚 Documentation: ReadTheDocs
👩‍💻 Questions: GitHub Discussions
🗯 Community: Discord

License

This model is licensed under the Coqui Public Model License. Read more about the origin story of CPML here.

Contact

Join our 🐸Community on Discord and follow us on Twitter. For inquiries, email us at info@coqui.ai.

Using 🐸TTS API:

from TTS.api import TTS

tts = TTS(model_path="D:/AI/ollama_agent_roll_cage/AgentFiles/Ignored_TTS/XTTS-v2_PeterDrury/", 
          config_path="D:/AI/ollama_agent_roll_cage/AgentFiles/Ignored_TTS/XTTS-v2_PeterDrury/config.json", progress_bar=False, gpu=True).to(self.device)

# 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:

 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:

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",
)