MOSS-TTS-Local-Transformer-v1.5 — GGUF
GGUF weights for MOSS-TTS-Local-Transformer-v1.5 (48 kHz stereo text-to-speech / voice cloning), converted for use with openmoss — a standalone C++/GGML runtime (CUDA + Vulkan + CPU).
Source models:
- backbone: https://huggingface.co/OpenMOSS-Team/MOSS-TTS-Local-Transformer-v1.5
- codec: https://huggingface.co/OpenMOSS-Team/MOSS-Audio-Tokenizer-v2
License: Apache-2.0, per the source models.
Files
| File | Size | Notes |
|---|---|---|
moss-tts-local-1.5-q8_0.gguf | 4.65 GB | Q8_0 backbone with f16 token embeddings — recommended |
moss-tts-local-1.5-q8_0.extras.gguf | 4.46 GB | sidecar: audio embeddings, local transformer, MOSS-Audio-Tokenizer-v2 |
Both files are required and must sit next to each other — the runtime derives the sidecar path
from the backbone path by replacing .gguf with .extras.gguf.
Usage
./moss-tts-cli \
--model moss-tts-local-1.5-q8_0.gguf \
--text "Hello, world!" \
--language English \
--output out.wav
Voice cloning:
./moss-tts-cli \
--model moss-tts-local-1.5-q8_0.gguf \
--text "This sentence will be spoken in the cloned voice." \
--reference speaker.wav \
--output out.wav
Output is 48 kHz stereo, unlike the 24 kHz mono of the delay-pattern MOSS-TTS models.
How this differs from MOSS-TTS (delay pattern)
| MOSS-TTS-v1.5 | MOSS-TTS-Local-Transformer-v1.5 | |
|---|---|---|
| Backbone | Qwen3-8B, hidden 4096 | Qwen3-4B, hidden 2560 |
| Codebooks | 32, delay-pattern scheduled | 12, emitted per frame |
| Multi-codebook scheme | delay pattern | 1-layer local ("depth") transformer |
| Codec | MOSS-Audio-Tokenizer (24 kHz mono) | MOSS-Audio-Tokenizer-v2 (48 kHz stereo) |
| Frame rate | 12.5 Hz | 12.5 Hz |
Conversion
python scripts/convert_hf_to_gguf.py \
--moss-tts OpenMOSS-Team/MOSS-TTS-Local-Transformer-v1.5 \
--codec OpenMOSS-Team/MOSS-Audio-Tokenizer-v2 \
--output moss-tts-local-1.5.gguf
llama-quantize --token-embedding-type f16 \
moss-tts-local-1.5.gguf moss-tts-local-1.5-q8_0.gguf Q8_0
The converter drops two sets of tied duplicates that the checkpoint stores twice
(text_lm_head ≡ transformer.embed_tokens, and audio_lm_heads[i] ≡ audio_embeddings[i]),
saving ~0.42 B parameters.
Verification
The port was checked against the reference PyTorch implementation:
-
Prompt tokenization — the 69 token ids for a sample prompt match
processor.build_user_message(...)exactly, id for id. (The prompt must be tokenized segment-by-segment; encoding it as one string merges"."+"\n"into a single token and shifts everything downstream.) -
Local transformer — fed the reference's own backbone hidden state, the GGML implementation reproduces all 12 codebook indices exactly, greedily.
-
Full pipeline, greedy — the Q8_0 model reproduces all 12 of the reference's frame-0 codes. The f16 model differs on exactly one codebook whose top-1/top-2 logit margin is 0.009, i.e. a numerical tie.
-
Codec round-trip — 0.995 envelope correlation on both channels at 5 s, 40 s and 120 s, with no degradation in the second half (the per-stage sliding-window attention is correct).
-
Intelligibility — generated samples were transcribed by an audio-capable LLM (Gemma 4 E4B) and came back word-for-word correct:
Prompt text Transcription "The quick brown fox jumps over the lazy dog." exact "Once again, pangram-style text remains the simplest way to exercise a speech synthesis system end to end." exact "This sentence should be spoken in the cloned voice." exact A 120 s codec round-trip of Chinese reference speech also transcribed correctly, confirming the chunked sliding-window attention preserves intelligibility over long inputs.
Speaker similarity under voice cloning was spot-checked but not rigorously verified — the LLM-as-judge used for it was too inconsistent to support a claim either way.