GPT-SoVITS

Our Take

A workable 0.2B-parameter dense audio model from RVC-Boss. Treat the modality benchmarks above as the leading indicator of fit — composite scoring across modalities is still maturing.

Generated from this model’s benchmarks and ranking signals. Editor reviews refine it over time.

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:

• UVR5 Integration: Automatically separates vocals from background music for cleaner training data.
• Automatic Segmentation: Slices long audio files into the short clips required for training.
• ASR & Labeling: Uses automated speech recognition to transcribe training data, removing the need for manual text labeling.

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:

• Minimum VRAM: 4GB (Inference only), 8GB (Few-shot fine-tuning).
• Recommended VRAM: 12GB+ (Allows for larger batch sizes during fine-tuning).
• Recommended GPU: NVIDIA RTX 3060, 4060 Ti, or 4090.
• Apple Silicon: Fully supported on M2/M3/M4 chips (8GB RAM minimum).

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.