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theoracleguy/MOSS-TTS-Local-Transformer-v1.5-4bit
MOSS-TTS-Local-Transformer-v1.5-4bit is a text-to-speech model from theoracleguy. Use it when you need text read aloud. It is set up for mlx. The card lists the license as apache-2.0.
MLX conversion of OpenMOSS-Team/MOSS-TTS-Local-Transformer-v1.5 (OpenMOSS / MOSI.AI) — the 4B MossTTSLocal speech model (Qwen3 backbone + a local transformer emitting 12 RVQ codebooks per frame), zero-shot voice cloni…
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.safetensors2.6 GB · 99%
How the weights are stored.
U324.5B · 99%
From the Hugging Face model README
MLX conversion of OpenMOSS-Team/MOSS-TTS-Local-Transformer-v1.5
(OpenMOSS / MOSI.AI) — the 4B MossTTSLocal speech model (Qwen3 backbone + a local transformer
emitting 12 RVQ codebooks per frame), zero-shot voice cloning, streaming synthesis, 48 kHz stereo
output through MOSS-Audio-Tokenizer-v2.
Precision: int4, group size 64, affine — 2.4 GB. Converted with mlx_audio.convert
(model type moss_tts_local) from the upstream safetensors, tokenizer files copied verbatim;
the upstream remote-code .py files are not needed by MLX and were dropped.
Sibling tiers: MOSS-TTS-Local-Transformer-v1.5-bf16 · -8bit · -4bit. The codec is
mlx-community/MOSS-Audio-Tokenizer-v2-bf16
(this config's audio_tokenizer_name_or_path still points at the upstream fp32 repo, which
mlx-audio also loads directly).
from mlx_audio.tts import load
from mlx_audio.audio_io import write as audio_write
model = load("mlx-community/MOSS-TTS-Local-Transformer-v1.5-4bit", lazy=True)
result = next(model.generate(
text="Hello, this is MOSS-TTS running on MLX.",
ref_audio="speaker.wav", # optional zero-shot cloning prompt
language="English",
max_tokens=400,
))
audio_write("out.wav", result.audio, result.sample_rate) # 48 kHz stereo
Streaming (stream=True, streaming_interval=2.0) is supported on this checkpoint; see the
mlx-audio moss_tts README. Fixed 12-codebook RVQ depth — do not pass a different
n_vq_for_inference.
Apache-2.0 (upstream weights and this conversion). Please credit OpenMOSS for the model.