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A powerful few-shot/zero-shot voice cloning and TTS WebUI that can produce a quality TTS model from as little as 1 minute of voice data.
No benchmark data available for this model yet.
See which devices can run this model and at what quality level.
GPT-SoVITS is a specialized 0.2B parameter text-to-speech (TTS) and voice conversion framework designed for high-fidelity, few-shot voice cloning. Developed by RVC-Boss, it bridges the gap between complex professional TTS pipelines and accessible local deployment. Unlike traditional TTS models that require hours of studio-quality data, GPT-SoVITS can clone a target voice with as little as 1 minute of training data, or even perform zero-shot inference using a 5-second reference clip.
The model occupies a unique niche in the local AI ecosystem. While massive models like ElevenLabs dominate the cloud-API space, GPT-SoVITS is the primary choice for developers and creators who need to run GPT-SoVITS locally to maintain privacy, avoid per-character costs, or integrate voice synthesis into real-time applications. Its 0.2B parameter architecture is purposefully lean, prioritizing low-latency inference and high throughput on consumer-grade hardware.
GPT-SoVITS utilizes a hybrid architecture that combines a generative pre-trained transformer (GPT) with a Variational Inference with Adversarial Learning (VITS) backbone. This "SoVITS" (Soft-VC + VITS) approach allows the model to handle the nuances of speech—such as prosody, emotion, and rhythm—more effectively than standard concatenative or purely diffusion-based models.
The 0.2B parameter count is deceptive in terms of power; because the model is specialized solely for audio synthesis rather than general-purpose reasoning, it achieves a level of realism that rivals much larger multi-modal LLMs. The dense architecture ensures that every parameter is active during inference, providing a consistent and predictable compute load.
GPT-SoVITS is engineered for high-speed voice cloning and multilingual synthesis. It is particularly effective for workflows where data is scarce or where the user needs to generate large volumes of audio quickly.
The model's standout feature is its ability to perform "zero-shot" synthesis. By providing a 5-second audio prompt, the model can adopt the speaker's identity immediately. For higher fidelity, "few-shot" fine-tuning on 1 minute of data significantly improves the stability of the voice and its ability to handle complex emotional inflections.
GPT-SoVITS supports cross-lingual inference, meaning you can train or prompt the model with a voice speaking Chinese and have it output fluent English, Japanese, Korean, or Cantonese while maintaining the original speaker's vocal characteristics.
The RVC-Boss repository includes a comprehensive WebUI that automates the most difficult parts of the TTS pipeline:
Running GPT-SoVITS locally is highly efficient due to its small parameter footprint. However, because it handles audio waveform generation, the bottleneck is often GPU memory bandwidth and CUDA core availability rather than raw VRAM capacity.
To run GPT-SoVITS with the full WebUI and training capabilities, you should target the following:
Inference speed is measured by the Real-Time Factor (RTF). On a mid-range RTX 4060 Ti, the model achieves an RTF of approximately 0.028. This means it can generate 1 minute of audio in under 2 seconds. On high-end hardware like the RTX 4090, the RTF drops to 0.014, making it suitable for near-instantaneous real-time applications. For Mac users, an M4 CPU handles inference at an RTF of ~0.5, which is still twice as fast as real-time.
While LLMs are often heavily quantized (Q4_K_M, etc.), GPT-SoVITS is typically run in FP16 or BF16 to preserve the nuances of the audio signal. Because the model is only 0.2B parameters, the total VRAM footprint remains under 2GB for the weights themselves, making aggressive quantization unnecessary for most users.
GPT-SoVITS is frequently compared to other local TTS solutions like Fish Speech or Bark.
Fish Speech is a newer competitor that often provides higher biological realism in some languages but typically requires more VRAM and has a more complex setup. GPT-SoVITS remains the "workhorse" of the community because of its integrated WebUI and the sheer speed of its fine-tuning process.
Bark is a GPT-style model that can generate non-verbal sounds (laughter, sighing) but often struggles with "hallucinating" audio or changing the speaker's voice mid-sentence. GPT-SoVITS is significantly more stable for long-form narration and provides much tighter control over the specific voice being used.
For practitioners looking for the best GPU for GPT-SoVITS, an RTX 4070 Super (12GB) offers the best price-to-performance ratio for both training and inference. If you only intend to perform inference, almost any modern consumer GPU with at least 4GB of VRAM will suffice to run this 0.2B model at high speeds.