MOSS-TTS-Local-Transformer-v1.5-GGUF

Brought to you by the LocalAI team. GGUF conversion of [`OpenMOSS-Team/MOSS-TTS-Local-Transformer-v1.5`](https://huggingface.co/OpenMOSS-Team/MOSS-TTS-Local-Transformer-v1.5) for [moss-tts.cpp](https://github.com/mudler/moss-tts.cpp): a C++17/ggml inference port of the OpenMOSS MOSS-TTS family that runs entirely on stock ggml with no Python, ONNX or torch at inference time. MossTTSLocal v1.5 is an RQ-Transformer text-to-speech model: - **Global backbone:** a 36-layer Qwen3 transformer over time (the text + speech prefix), hidden size 2560. - **Local depth transformer:** a 1-layer GPT-J block (LayerNorm, fused QKV, interleaved RoPE, SiLU MLP) over the codebooks within each frame. - **Binary decision head:** a direct 2-wide `local_text_head` on channel 0 that chooses continue (audio slot) vs stop (audio end) per frame. - **12 RVQ codebooks** decoded through **MOSS-Audio-Tokenizer-v2** at **48 kHz stereo** (interleaved output). The non-matmul tensors (all `lc.*` heads and embeddings, every norm and bias) are kept in f32 in every quant; only the Qwen3 global and GPT-J local attention/FFN matmuls are quantized, so the CPU gather/head paths stay exact. This port is numerically verified against the reference PyTorch implementation: the language model matches to about 1e-5 with an exact greedy code sequence, and the MOSS-Audio-Tokenizer-v2 decode matches at about 115 dB SNR (bit-exact in f32). On CPU it runs roughly twice as fast per frame as the reference PyTorch at the same f32 precision. The non-matmul path (heads, embeddings, norms, biases) stays f32 in both quants. This repo also ships the codec (`moss-audio-tokenizer-v2-f32`) and tokenizer (`moss-tokenizer-v1_5`) GGUFs required to run the model. With [moss-tts.cpp](https://github.com/mudler/moss-tts.cpp) built: moss-tts-cli tts-local \ --model moss-tts-local-v1_5-q8_0.gguf \ --codec moss-audio-tokenizer-v2-f32.gguf \ --tokenizer moss-tokenizer-v1_5.gguf \ --text "Hello from the LocalAI team." \ --out out.wav The output is a 48 kHz stereo WAV. Swap `--model` for any of the quants above. - Inference engine: https://github.com/mudler/moss-tts.cpp - Base model: https://huggingface.co/OpenMOSS-Team/MOSS-TTS-Local-Transformer-v1.5 - Codec: https://huggingface.co/OpenMOSS-Team/MOSS-Audio-Tokenizer-v2 Released under the Apache-2.0 license, following the upstream base model.