Best Self-Hosted TTS Models in 2025

The demand for high-quality, human-like text-to-speech (TTS) solutions has surged, driven by applications like virtual assistants, audiobooks, and accessibility tools. Below, we break down the best self-hosted TTS models, with insights from recent research and community feedback.

MaskGCT

maskgct.github.io

  • Overview: A fully non-autoregressive model that eliminates dependency on text-audio alignment and phoneme-level duration prediction. It uses a two-stage self-hosted TTS process: predicting semantic tokens from text and generating acoustic tokens for speech synthesis.
  • Strengths:
    • Parallel generation for real-time applications (0.15 RTF).
    • Trained on 100K+ hours of multilingual data, supporting English and Chinese with potential for expansion.
    • Open-source and modular, ideal for customization.
  • Use Case: Best for developers needing enterprise-grade quality without latency.

FishSpeech

https://github.com/fishaudio/fish-speech

  • Features:
    • Zero/few-shot cloning with 10–30 seconds of audio input.
    • Supports 8 languages (English, Japanese, Chinese, etc.) and handles mixed-language inputs.
    • Achieves low CER (2%) and WER for accurate speech generation.
  • Advantage: No phoneme dependency, making it robust for scripts like Chinese characters.
  • Limitation: Requires GPU acceleration for optimal performance.

MeloTTS

https://github.com/myshell-ai/MeloTTS

  • Highlights:
    • Multilingual support (English, Spanish, Chinese, etc.) with mixed-language input handling.
    • Optimized for CPU real-time inference, ideal for low-resource environments.
    • Based on VITS/VITS2 architectures for natural prosody.
  • Community Tip: Use speaker embeddings to fine-tune emotional expressiveness.

F5-TTS

https://github.com/SWivid/F5-TTS

  • Innovations:
    • Sway Sampling: Improves inference efficiency without retraining.
    • Zero-shot multilingual capabilities trained on 100K hours of data.
  • Performance: Outperforms diffusion-based models in speed (0.15 RTF).

Spark-TTS

https://github.com/SparkAudio/Spark-TTS

  • Unique Approach:
    • Preserves paralinguistic features (timbre, emotion) through bidirectional acoustic-semantic correlations.
    • Uses ASR transcripts and LLM-generated continuations for training robustness.
  • Applications: Effective for multilingual, emotion-aware synthesis in call centers or virtual agents.

Key Considerations

  • Self-Hosting: MaskGCT and FishSpeech lead in flexibility and quality but require technical expertise. For low-resource setups, MeloTTS is CPU-friendly.
  • Cloud APIs: A2E TTS API balances cost and performance for enterprises, while Neets.ai suits budget projects.
  • Hybrid Workflows: Combine self-hosted models like SparkTTS (self-hosted) with A2E Avatar API for custom, high-quality video clone pipelines.
  • Developers: Start with FishSpeech or MaskGCT for open-source adaptability.
  • Enterprises: Deploy A2E API scalable, low-latency solutions.
  • Hobbyists: Try MeloTTS or Spark TTS for user-friendly setups.

Last updated: April 26, 2025