ChatTTS is an innovative text-to-speech (TTS) generation model specifically designed for everyday conversational scenarios. This groundbreaking technology aims to provide natural and expressive voice synthesis for dialogue-based applications, particularly suited for Large Language Model (LLM) assistants and similar conversational contexts.
Developed with a focus on bilingual capabilities, ChatTTS supports both Chinese and English languages. The model has undergone extensive training, utilizing over 100,000 hours of Chinese and English speech data to achieve its remarkable performance.
For those eager to explore this technology, an open-source version of ChatTTS is available on HuggingFace. This version is a pre-trained model based on 40,000 hours of data, though it’s worth noting that it has not undergone Super Fine-Tuning (SFT).
Key Features of ChatTTS
Conversational TTS Excellence
ChatTTS stands out for its optimization in dialogue-based tasks. The model supports multiple speakers, facilitating interactive and dynamic conversations that closely mimic natural human interactions.
Fine-Grained Prosodic Control
One of the most impressive aspects of ChatTTS is its ability to predict and control fine-grained prosodic features. This includes the incorporation of laughter, pauses, and interjections, adding a layer of realism and expressiveness to the synthesized speech.
Superior Prosody
In terms of prosody, ChatTTS outperforms many existing open-source TTS models. This advancement contributes significantly to the natural flow and rhythm of the generated speech, making it more engaging and lifelike.
Utilizing ChatTTS: From Basic to Advanced
ChatTTS offers flexibility in its usage, catering to both novice users and those seeking more advanced control. Let’s explore both the basic and advanced methods of implementing this powerful tool.
Basic Usage
For those looking to get started quickly, here’s a simple implementation of ChatTTS:
import ChatTTS
from IPython.display import Audio
chat = ChatTTS.Chat()
chat.load_models(compile=False) # Set to True for enhanced performance
texts =
wavs = chat.infer(texts)
torchaudio.save("output1.wav", torch.from_numpy(wavs), 24000)
This basic setup allows users to generate speech from text with minimal configuration.
Advanced Usage
For users seeking more control over the generated speech, ChatTTS offers advanced features:
Speaker Sampling and Parameter Customization
rand_spk = chat.sample_random_speaker()
params_infer_code = {
'spk_emb': rand_spk,
'temperature': .3,
'top_P': 0.7,
'top_K': 20,
}
This code snippet demonstrates how to sample a speaker from a Gaussian distribution and set custom parameters for inference.
Sentence-Level Control
paramsrefinetext = {
'prompt': ''
}
wav = chat.infer(texts, paramsrefinetext=paramsrefinetext, paramsinfercode=paramsinfercode)
Here, users can manually control sentence-level attributes using special tokens.
Word-Level Control
text = 'What is your favorite english food?'
wav = chat.infer(text, skiprefinetext=True, paramsrefinetext=paramsrefinetext, paramsinfercode=paramsinfercode)
This example showcases how to implement word-level control for even more precise speech generation.
Practical Example: ChatTTS Self-Introduction
To illustrate the capabilities of ChatTTS, consider this self-introduction example:
inputs_en = """
chat T T S is a text to speech model designed for dialogue applications.
it supports mixed language input and offers multi speaker
capabilities with precise control over prosodic elements like like
laughter, pauses, and intonation.
it delivers natural and expressive speech,so please
use the project responsibly at your own risk.
""".replace('n', '')
params_refine_text = {
'prompt': ''
}
audio_array_en = chat.infer(inputs_en, params_refine_text=params_refine_text)
torchaudio.save("output3.wav", torch.from_numpy(audio_array_en), 24000)
This script generates a self-introduction for ChatTTS, showcasing its ability to handle mixed language input and control prosodic elements.
Frequently Asked Questions
Hardware Requirements and Performance
Q: What are the GPU memory requirements and inference speed? A: For a 30-second audio clip, a minimum of 4GB GPU memory is necessary. Using a 4090 GPU, the model can generate audio for approximately 7 semantic tokens per second, with a real-time factor (RTF) of about 0.3.
Model Stability
Q: What if I encounter issues with multiple speakers or poor audio quality? A: These challenges are common in autoregressive models like bark and valle. While difficult to avoid entirely, trying multiple sampling attempts may help in finding suitable results.
Emotional Control Capabilities
Q: Besides laughter, what other emotions can be controlled? Can we control other emotions? A: In the current release, the only token-level control units are , , and . Future versions may introduce models with additional emotional control capabilities.
Ethical Considerations and Limitations
It’s crucial to note that ChatTTS is currently intended for academic purposes only, specifically for education and research. The developers do not guarantee the accuracy, completeness, or reliability of the information provided.
To mitigate potential misuse, the developers have incorporated high-frequency noise during the 40,000-hour model training and compressed the audio quality using MP3 format where possible. These measures aim to prevent malicious use of the technology.
Conclusion
ChatTTS represents a significant advancement in text-to-speech technology, particularly for conversational applications. Its ability to generate natural, expressive speech with fine-grained control over prosodic features makes it a powerful tool for researchers and developers in the field of artificial intelligence and natural language processing.
As the technology continues to evolve, we can expect even more sophisticated capabilities and applications. However, it’s essential to approach its use responsibly, keeping in mind the ethical considerations and limitations outlined by the developers.
For the most up-to-date information and detailed instructions, please refer to the official GitHub page of the ChatTTS project.
