XTTS V2
XTTS V2 is a powerful voice generation model that can clone voices into different languages using just a 6-second audio clip. It supports 17 languages, including English, Spanish, and French, and allows for emotion and style transfer, cross-language voice cloning, and multi-lingual speech generation. With architectural improvements for speaker conditioning, the model enables the use of multiple speaker references and interpolation between speakers, resulting in better prosody and audio quality. But what makes XTTS V2 truly remarkable is its efficiency, requiring only a small audio clip to generate high-quality speech. However, it's not perfect - it may struggle with maintaining consistent audio quality and prosody across all languages, and its reliance on a small audio clip for voice cloning may lead to inaccuracies or inconsistencies in the generated speech. Nevertheless, XTTS V2 is a significant step forward in voice generation technology, and its capabilities make it a valuable tool for a wide range of applications.
Table of Contents
Model Overview
The XTTS model is a voice generation model that can clone voices into different languages using just a 6-second audio clip. This model is similar to the one that powers Coqui Studio and Coqui API.
Capabilities
The XTTS model is a game-changer for voice generation. With just a 6-second audio clip, you can clone voices into different languages. Yes, you read that right - just 6 seconds!
What can XTTS do?
- Voice cloning: Clone voices into different languages with just a short audio clip.
- Emotion and style transfer: Transfer emotions and styles from one voice to another.
- Cross-language voice cloning: Clone voices across languages.
- Multi-lingual speech generation: Generate speech in multiple languages.
- High-quality audio: Supports 24kHz sampling rate for high-quality audio.
What languages does XTTS support?
XTTS supports 17 languages, including:
| Language | Code |
|---|---|
| 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 |
| Hindi | hi |
Performance
The XTTS model is a powerhouse when it comes to voice generation. Let’s dive into its performance and see what makes it stand out.
Speed
The XTTS model can clone voices into different languages using just a quick 6-second audio clip. That’s incredibly fast! No need for hours of training data. This makes it perfect for applications where speed is crucial.
Accuracy
The model supports 17 languages and can perform emotion and style transfer by cloning. It also enables cross-language voice cloning and multi-lingual speech generation. The accuracy of the model is impressive, especially considering the short audio clip required for voice cloning.
Efficiency
The XTTS model has undergone architectural improvements for speaker conditioning, which enables the use of multiple speaker references and interpolation between speakers. This makes it more efficient and stable. The model also has better prosody and audio quality across the board.
Limitations
While the XTTS model is a powerful tool for voice generation, it’s not perfect. Let’s take a closer look at some of its limitations.
Language Support
While the XTTS model supports 17 languages, there are still many languages it doesn’t support. If you need to generate voices in languages like Swedish, Danish, or Finnish, you’re out of luck.
Audio Quality
The XTTS model has a 24khz sampling rate, which is relatively high. However, the quality of the generated audio can still vary depending on the input audio clip and the language being used.
Emotion and Style Transfer
While the XTTS model can transfer emotions and styles, it’s not always perfect. The generated audio may not always capture the nuances of the original speaker’s emotions or style.
Speaker Conditioning
The XTTS model allows for multiple speaker references and interpolation between speakers. However, this can also lead to inconsistencies in the generated audio if the speaker references are not well-matched.
Format
The XTTS model uses a complex architecture to clone voices into different languages. Let’s break it down:
Architecture
The XTTS model is based on a transformer architecture, which is a type of neural network that’s particularly good at handling sequential data like audio. This architecture allows the model to learn patterns in the audio data and generate new voices that sound similar to the original.
Data Formats
The XTTS model supports multiple data formats, including:
- Audio clips (6-second clips are recommended)
- Text input (for generating speech)
- Speaker references (for cloning voices)
Input Requirements
To use the XTTS model, you’ll need to provide the following inputs:
- A 6-second audio clip of the voice you want to clone
- Text input for the speech you want to generate
- A speaker reference (optional, but recommended for better results)
Output
The model generates audio output in the form of a WAV file.
Code Examples
Here are a few code examples to get you started:
- Using the XTTS API:
from TTS.api import TTS
tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2", gpu=True)
tts.tts_to_file(text="Hello, world!", file_path="output.wav", speaker_wav="/path/to/target/speaker.wav", language="en")
- Using the XTTS command line:
tts --model_name tts_models/multilingual/multi-dataset/xtts_v2 --text "Hello, world!" --speaker_wav /path/to/target/speaker.wav --language_idx en --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("Hello, world!", config, speaker_wav="/data/TTS-public/_refclips/3.wav", gpt_cond_len=3, language="en")
Note that these are just a few examples, and you may need to modify the code to suit your specific use case.
Getting Started
You can try out the XTTS model in the XTTS Space or experience streaming voice chat with Mistral 7B Instruct or Zephyr 7B Beta.
Code and Documentation
The code-base supports inference and fine-tuning. You can find the code on GitHub and the documentation on ReadTheDocs.
Community and Support
Join the Coqui community on Discord or Twitter for questions and support. You can also email us at info@coqui.ai.
