What is Muyan-TTS?
Creating natural-sounding, long-form audio like podcasts often requires specialized tools. Muyan-TTS offers a robust, open-source solution specifically developed for these scenarios. If you need to generate high-fidelity speech, customize voices, or build applications requiring efficient text-to-speech synthesis for extended content, Muyan-TTS provides the foundation and flexibility you need. It's built upon extensive podcast audio data and allows for further training and adaptation.
Key Features
🎙️ Optimized for Long-Form Audio: Pre-trained on over 100,000 hours of diverse podcast audio, Muyan-TTS excels at generating expressive and coherent speech suitable for podcasts, audiobooks, and other extended narrations. This extensive training ensures high fidelity and natural prosody.
🔧 Fully Open-Source & Trainable: Access the complete model, including both the pre-trained base model for zero-shot synthesis and a supervised fine-tuned (SFT) version for enhanced single-speaker performance. This allows you to inspect, modify, and retrain the model for your specific requirements.
🔊 Efficient Voice Adaptation: Customize voice outputs effectively. Muyan-TTS supports speaker adaptation using just dozens of minutes of target speech data, enabling you to create personalized voice experiences without needing massive datasets.
⚡ Class-Leading Inference Speed: Generate audio quickly. Muyan-TTS achieves an inference time of just 0.33 seconds for every 1 second of synthesized audio (tested on an NVIDIA A100 GPU), making it the fastest among the compared open-source TTS models. This efficiency is crucial for real-time applications or large-scale content generation.
🏗️ Robust Two-Stage Architecture: The model combines a Llama-3.2-3B language model backbone for strong semantic understanding with a SoVITS-based decoder fine-tuned on high-quality podcast data. This design balances linguistic accuracy with high audio fidelity and stability, mitigating common LLM hallucination issues in speech synthesis.
Use Cases
Explore how Muyan-TTS can be applied in various technical contexts:
Custom Podcast Production Tools: Integrate Muyan-TTS into content creation platforms to offer podcasters personalized narration voices, automate voiceover generation for summaries, or create consistent host voices for recurring segments.
Accessible Audio Content Generation: Build services that convert long-form text articles or books into natural-sounding audiobooks or accessible podcast formats, leveraging the model's speed and quality for efficient large-scale synthesis.
Speech Synthesis Research & Development: Utilize the open-source models and architecture as a baseline for research into long-form TTS, speaker adaptation techniques, or exploring efficient TTS model training and deployment strategies.
Conclusion
Muyan-TTS stands out as a powerful, open-source text-to-speech model tailored for the demands of podcasting and long-form audio generation. Its foundation on extensive podcast data, combined with a robust architecture based on Llama-3.2-3B and SoVITS, delivers high-quality, natural-sounding speech. Key advantages include its efficient speaker adaptation capabilities, leading inference speed, and the flexibility offered by its fully open-source nature. For developers and creators seeking a customizable and performant TTS solution for extended audio content, Muyan-TTS provides a compelling and accessible option.
More information on Muyan-TTS
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Muyan-TTS was manually vetted by our editorial team and was first featured on 2025-05-06.
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