The demand for text-to-speech (TTS) technology has significantly increased across various industries, such as accessibility and virtual assistants, propelled by advancements in generating realistic, human-like speech from text. Open-source models such as XTTS-v2, ChatTTS, Dia, Kokoro, Chatterbox, MeloTTS, and OpenVoice v2 provide diverse features including voice cloning, multilingual support, and emotional expression, catering to different applications but also presenting unique limitations. XTTS-v2, despite the shutdown of its original company, remains popular for its multilingual and emotional speech synthesis capabilities. ChatTTS excels in dialogue tasks but is limited to English and Chinese, while Dia offers multi-speaker dialogue generation with emotional control but is English-only. Kokoro is lightweight and efficient, ideal for low-latency applications, whereas Chatterbox offers advanced emotion exaggeration control with low latency. MeloTTS and OpenVoice v2 provide multilingual capabilities, with the latter supporting voice cloning. Deploying these models involves considerations for performance, scalability, and integration with other AI systems, and while TTS models lack standardized benchmarks, the choice between TTS and text-to-audio depends on the specific need for human-like speech versus general audio output.