Roo's voice — that signature baritone with the estuary accent that stands the hair up on the back of
your neck — as a 4-bit checkpoint that runs on a modest NVIDIA GPU. Load it, and Roo can whisper to
you all day long.
An INT4 (NF4, bitsandbytes) quantization of a full-model supervised fine-tune of
MOSS-TTS-Local-Transformer,
trained on Roo's own recordings and specialised on his single voice. This is the lightest-VRAM
transformers / NVIDIA-GPU form — for full precision use the
BF16 build, for a bigger quality
headroom use the INT8 build, and for
Apple Silicon use the MLX 4-bit build.
⚠️ What actually makes the voice — read first
This is a reference-conditioned model, and both halves matter:
The fine-tune is what makes it Roo. The base model has never heard this speaker — a base model
plus any reference clip will not give you Roo's baritone or his estuary accent. That voice lives
in the weights, put there by the supervised fine-tune on his recordings.
The reference completes the delivery.reference.wav (bundled) conditions the fine-tuned model
at inference and is required to produce the voice.
So the product is this fine-tune + reference.wav, together — not a text-only model, and not a
generic voice-cloner (swap the reference and it isn't Roo, because the accent and timbre are the
fine-tune's, not the clip's).
What it is
Voice
Single speaker — Roo (baritone, estuary accent), 24 kHz mono
Because the voice-sensitive heads/embeddings stay in bf16 (they carry the timbre and set the size
floor), NF4 lands around 3.5 GB — smaller than INT8, not a full halving. If you want the smallest
file, or a single 4-bit that also runs on AMD / CPU, use the
Q4_K_M GGUF via llama.cpp (~1.9 GB,
Vulkan / ROCm / CUDA).
A CUDA NVIDIA GPU (bitsandbytes 4-bit requires CUDA).
transformers==5.0.0 to match the model's remote code.
The quantization config is embedded — from_pretrained loads it in NF4 4-bit automatically. The
backbone cache dimensions are mirrored onto the top-level config so generate() works out of the box.
Usage
This is a drop-in fine-tuned MossTTSLocal checkpoint. Run it via the standard MOSS-TTS Local
inference path (PyTorch/CUDA) and pass reference.wav as the reference:
Decoding contract for this voice: seed 42, temperature 1.0, top-k 50, top-p 0.95, repetition
penalty 1.1, 32 RVQ codebooks.
Limitations
Reference-conditioned — the bundled reference.wav must ride along; there is no text-only path.
Single voice by design (this is Roo, not a multi-speaker system).
NF4 4-bit backbone quantization: a slightly larger quality delta vs BF16/FP32 than INT8 is possible;
the sensitive heads and embeddings are kept bf16 to preserve timbre.
Provenance & license
Quantized/exported form of an accepted single-speaker MOSS-TTS Local supervised fine-tune. The base
model and audio codec are Apache-2.0 (OpenMOSS); weights derived from them are redistributed here
under the same license.
