A multi-voice text-to-speech system emphasizing quality and realistic prosody.
A multi-voice text-to-speech system emphasizing quality and realistic prosody.
Tortoise TTS is an open-source, multi-voice text-to-speech system leveraging both autoregressive and diffusion decoders for high-quality speech synthesis. The architecture prioritizes realistic prosody and intonation, producing natural-sounding speech. It requires an NVIDIA GPU for local installation and is designed for inference mode. The system can be installed via pip and offers Docker support for simplified deployment. It supports various interfaces, including command-line scripts and a Python API. While initially noted for its slow sampling rates, recent optimizations have improved performance significantly. The project emphasizes voice customization and provides tools for reading large amounts of text, making it suitable for applications requiring personalized and expressive speech synthesis.
A multi-voice text-to-speech system emphasizing quality and realistic prosody.
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Tortoise TTS is an open-source, multi-voice text-to-speech system leveraging both autoregressive and diffusion decoders for high-quality speech synthesis.
Explore all tools that specialize in convert text to audio. This domain focus ensures Tortoise TTS delivers optimized results for this specific requirement.
Explore all tools that specialize in voice cloning. This domain focus ensures Tortoise TTS delivers optimized results for this specific requirement.
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Supports combining multiple voices in a single output, enabling complex and nuanced speech patterns. Achieved through advanced voice mixing algorithms within the TTS pipeline.
Employs deep learning models to accurately model and reproduce human-like prosody and intonation patterns. Uses contextual analysis to vary speech delivery.
Enables users to fine-tune and customize voice characteristics. Voice cloning using reference clips.
Optional integration with DeepSpeed for optimized performance on supported hardware. This improves throughput via efficient memory utilization and distributed processing.
Key-value cache optimization technique to improve inference speed. Reduces redundant computations by storing and reusing intermediate results.
Install Miniconda (recommended for Windows).
Create a Conda environment: `conda create --name tortoise python=3.9 numba inflect`.
Activate the environment: `conda activate tortoise`.
Install PyTorch: `conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia`.
Clone the Tortoise TTS repository: `git clone https://github.com/neonbjb/tortoise-tts.git`.
Change directory: `cd tortoise-tts`.
Install Tortoise TTS: `python setup.py install`.
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“Generally positive reviews regarding voice quality, but performance can be a bottleneck.”
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