The guide provides a comprehensive overview of implementing open-source text-to-speech (TTS) systems in production environments, emphasizing the importance of understanding licensing, cost implications, and testing requirements. It highlights the challenges of deploying these models, such as GPU constraints, latency under real concurrency levels, and the need for robust queueing and failover strategies. The document underscores the necessity of evaluating models like XTTS v2, Bark, ChatTTS, MeloTTS, and Chatterbox for their suitability in real-world applications, taking into account factors like naturalness, latency, pronunciation accuracy, and resource demands. It also addresses the operational realities of maintaining TTS systems post-launch, including GPU memory management, monitoring, and compliance with regulations such as HIPAA and GDPR. The guide suggests that while open-source TTS offers control and transparency, managed platforms like Deepgram Aura may offer more reliable performance for high-volume, compliance-sensitive deployments, reducing the operational overhead associated with managing GPUs and infrastructure.