ⓍTTS

ⓍTTS is a super cool Text-to-Speech model that lets you clone voices in different languages by using just a quick 3-second audio clip. Built on the 🐢Tortoise, ⓍTTS has important model changes that make cross-language voice cloning and multi-lingual speech generation super easy. There is no need for an excessive amount of training data that spans countless hours.

This is the same model that powers Coqui Studio, and Coqui API, however we apply a few tricks to make it faster and support streaming inference.

Features

Voice cloning with just a 3-second audio clip.
Cross-language voice cloning.
Multi-lingual speech generation.
24khz sampling rate.

Code

Current implementation only supports inference.

Languages

As of now, XTTS-v1 supports 13 languages: English, Spanish, French, German, Italian, Portuguese, Polish, Turkish, Russian, Dutch, Czech, Arabic, and Chinese (Simplified).

Stay tuned as we continue to add support for more languages. If you have any language requests, please feel free to reach out.

License

This model is licensed under Coqui Public Model License.

Contact

Come and join in our 🐸Community. We're active on Discord and Twitter. You can also mail us at info@coqui.ai.

Using 🐸TTS API:

from TTS.api import TTS
tts = TTS("tts_models/multilingual/multi-dataset/xtts_v1", 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")

# generate speech by cloning a voice using custom 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",
                decoder_iterations=30)

Using 🐸TTS Command line:

 tts --model_name tts_models/multilingual/multi-dataset/xtts_v1 \
     --text "Bugün okula gitmek istemiyorum." \
     --speaker_wav /path/to/target/speaker.wav \
     --language_idx tr \
     --use_cuda true

Using 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",
)

Important resources & papers

VallE: https://arxiv.org/abs/2301.02111
Tortoise Repo: https://github.com/neonbjb/tortoise-tts
Faster implementation: https://github.com/152334H/tortoise-tts-fast
Univnet: https://arxiv.org/abs/2106.07889
Latent Diffusion:https://arxiv.org/abs/2112.10752
DALL-E: https://arxiv.org/abs/2102.12092

XttsConfig

.. autoclass:: TTS.tts.configs.xtts_config.XttsConfig
    :members:

XttsArgs

.. autoclass:: TTS.tts.models.xtts.XttsArgs
    :members:

XTTS Model

.. autoclass:: TTS.tts.models.xtts.XTTS
    :members: