Loved collaborating with @OpenBMB on this.

♻️ Show some love with a repost if you enjoyed the content. Supporting small teams is how we build the future 🤗

Stay ahead with daily drops on LLMs, agents, and workflows by following me → @datachaz https://t.co/tXpDPDlS6G

— Charly Wargnier (@DataChaz) 6 avril 2026

Loved collaborating with @OpenBMB on this. ♻️ Show some love with a repost if you enjoyed the content. Supporting small teams is how we build the future 🤗 Stay ahead with daily drops on LLMs, agents, and workflows by following me → @datachaz Charly Wargnier (@DataChaz) 🚨 The new era of Open-Source TTS is here. @OpenBMB's VoxCPM 2 just dropped and it changes the game for voice synthesis. We are moving past fixed speaker presets to true "Concept-to-Voice" generation. Just describe the voice you want in text, and the 2B model builds it. How does it beat discrete token-based models like Qwen3-TTS? VoxCPM 2 uses a cutting-edge Diffusion-Autoregressive Continuous Representation framework. → Eliminates discrete token data loss → Preserves raw acoustic metadata → Outputs natively in 48,000Hz CD-quality audio The studio-grade expressiveness is phenomenal. I gave it a specific text prompt: "Deep booming male voice, strong resonant vocal, rhythmic hype pace." It dynamically calculates natural breathing, chest vibrations, and micro-pauses. It actually performs the text naturally. Best of all, the entire stack is fully open-source and highly developer-friendly. → Native PyTorch inference workflows → LoRA and full-parameter fine-tuning → Compatible with voxcpm-nanovllm Repo and demos links in 🧵↓ — https://nitter.net/DataChaz/status/2041289800695873546#m

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