We just released Tontaube V1.1, a text-to-speech model built for long-form audio (audiobooks, articles, voice agents) with zero-shot voice cloning. Our previous V1 release needed a lot of VRAM because of its vLLM setup, so this version is about making it more accessible: the 4-bit weights run in about 4 GB of VRAM, or at about real time on a desktop CPU. In a blind listening study with 400 pairwise comparisons, listeners preferred our V1 model's prosody over Fish Audio S2 Pro in about 70% of cases (details are in the paper). V1.1 is a post-trained version of this model. What it is ~2.9B parameters, autoregressive, streams audio as it generates Clones a voice from 5–60 seconds of reference audio; 46 voices are bundled English and German are the main languages (German pronunciation is not perfect yet). Spanish, French, Italian, Dutch and Portuguese work but have not been checked by native speakers Long texts are split at sentence boundaries and joined, so you can feed it whole chapters Running it locally The default is a 4-bit (GPTQ) version: 1.8 GB download, loads in about 4 GB of VRAM, peaks at about 6.5 GB on long texts GPU speed: about 13x real time on an RTX 5090 for a single request (21–23x batched), ~190 ms to first audio when streaming. About 6–7x on an RTX 3090 CPU: no GPU needed. About real time on a 12-core desktop CPU (Ryzen 9 5900X, dual-channel DDR4). It is limited by memory bandwidth and needs AVX2 No vLLM or llama.cpp needed: it ships its own server with Triton kernels for the GPU and AVX2 kernels for the CPU. Full-precision weights and a vLLM backend are there too if you have 24 GB+ and want throughput ​ git clone --branch v1.1.0 --depth 1 https://github.com/craitech/tontaube.git cd tontaube uv run --python 3.12 --frozen tontaube serve That gives you an HTTP API on port 8080 (plain, SSE and WebSocket streaming). uv run tontaube ui starts a browser interface. New in V1.1 Five style sliders per request: speaking rate, pauses, intonation (flat to very expressive), pitch (deep to high) and articulation (crisp to normal) The 4-bit version and the CPU path An adapter that cleans up the decoded audio Things to know before you try it Linux x86-64 only. NVIDIA GPUs (RTX 30 series or newer; GTX 10 series also works) or CPU. No macOS, no AMD/Intel GPUs; Windows through WSL2 The first start downloads about 8 GB of models (ours plus the codec and enhancer it depends on) and builds a ~9 GB Python environment Audiobook-style reading is its strongest mode; conversational and agent-style lines can be less reliable Licence: the code is Apache 2.0. The weights are under our own community licence, not an OSI one. Research and personal use is free. Commercial use is free below EUR 200k yearly revenue, with a “Powered by TontaubeV1” credit. Links Code: https://github.com/craitech/tontaube 4-bit weights: https://huggingface.co/TontaubeAI/TontaubeV1.1-int4 Full-precision weights: https://huggingface.co/TontaubeAI/TontaubeV1.1 Audio samples: in the README and on the model card Browser demo: https://huggingface.co/spaces/TontaubeAI/tontaube-v1-tts-demo or https://tontaube.ai Let me know if you have any questions!   submitted by   /u/EAVDR [link]   [comments]