christopherthompson81/MOSS-TTS-v1.5-GGUF

MOSS-TTS-v1.5 — GGUF for audio.cpp

Self-contained GGUF packages of OpenMOSS-Team/MOSS-TTS-v1.5, for audio.cpp. Each file carries the 8B delay-pattern backbone, the MOSS-Audio-Tokenizer v1 codec (both encoder and decoder, because cloning needs the encoder), the tokenizer and the model spec — nothing else to download.

file	backbone	codec	size	transcript accuracy
moss_tts_v15_bf16_codec_f16.gguf	bf16	f16	20.5 GB	exact
moss_tts_v15_q8_0_codec_f16.gguf	q8_0	f16	13.2 GB	4/4 takes exact, WER 0.000
moss_tts_v15_q4_k_codec_f16.gguf	q4_k	f16	9.3 GB	3/4 takes exact, WER 0.021

On q4_k

q4_k is usable and clones as well as the others — but it is not lossless on the words. Across four takes of the same sentence at different seeds, one substituted an article ("the" for "this"); q8_0 was exact on all four. Two independent ASR models agree on that substitution, so it is the audio and not a transcription artifact.

Four takes of one sentence is a small sample and the true rate is not pinned down by it. Treat q4_k as the option for when 9.3 GB versus 13.2 GB decides whether the model runs at all, and prefer q8_0 when it fits.

All three clone the reference equally well — median F0 180.8, 194.4 and 185.1 Hz against the reference recording's 189.4 — so the loss shows up in wording, not in the voice.

The codec is left at f16 in every package: it is a small share of the total and it is what turns codes back into a waveform, so quantising it buys little and risks the part you actually hear.

Usage

# Voice cloning from a short reference recording
audiocpp_cli --family moss_tts_v15 --model <dir-containing-the-gguf> --task clon \
  --voice-ref reference.wav \
  --text "This sentence should be spoken in the voice from the reference recording." \
  --out out.wav

# Plain TTS, voice chosen by the model
audiocpp_cli --family moss_tts_v15 --model <dir-containing-the-gguf> --task tts \
  --text "The quick brown fox jumps over the lazy dog." --out out.wav

The reference recording is resampled and downmixed to the codec's 24 kHz mono automatically, so any sample rate and channel count will do.

--tokens <n> sets a duration budget in codec frames at 12.5 a second — the model's own - Tokens: field, and it is honoured: 40 tokens produces about 3.2 seconds.

⚠ Voice-attribute instructions are not reliably followed

--instruct is accepted, but a description of the speaker is followed only loosely. Four prompts differing only in the requested speaker, median F0 of the result, measured on the reference implementation:

"A high-pitched young woman's voice, clearly female."   -> 216.3 Hz   plausible
"A woman speaking softly and warmly."                   -> 184.5 Hz   borderline
"A man speaking in a low register."                     -> 187.8 Hz   miss
"A very deep, low-pitched man's voice."                 -> 180.8 Hz   clear miss

Five samples with median F0 as a proxy for perceived voice is not a rigorous evaluation, and one of the four was plausible — so read this as "unreliable", not "never works". But a "very deep, low-pitched man's voice" at 181 Hz is wrong by any measure.

Use a reference recording when the voice matters. Cloning does work: a clone of a 189.4 Hz reference came back at 184.0 Hz, where the same model without a reference produced 117.3 Hz. For a voice built from a written description rather than a recording, MOSS-VoiceGenerator is the model designed for it.

What works

Cloning, Chinese from an English instruction, long-form (20.4 s from four sentences, and the real-time factor improves with length as the fixed prompt cost is amortised), and the duration budget.

Provenance

Converted with audiocpp_gguf from the upstream safetensors:

audiocpp_gguf \
  --input model_weights=<root>/model.safetensors.index.json \
  --input audio_tokenizer_weights=<root>/audio_tokenizer/model.safetensors.index.json \
  --output moss_tts_v15_<type>_codec_f16.gguf \
  --type <type> --keep-type "audio_tokenizer_weights*=f16" \
  --family moss_tts_v15 --root <root>

Each package was verified by synthesising from it and transcribing the result, not merely by loading it. Licensed Apache-2.0, following both upstream repositories.