File	Size	Description
`miotts-0.6b-f16.gguf`	1.4 GB	Full precision (F16 weights)
`miotts-0.6b-q8_0.gguf`	793 MB	LLM Q8_0 + codec F16
`miotts-0.6b-q4_k.gguf`	502 MB	Recommended — LLM Q4_K + codec F16
`tokenizer.json`	~14 MB	Qwen3 BPE tokenizer (place next to GGUF)
`en_female.emb.gguf`	<1 KB	English female voice preset
`en_male.emb.gguf`	<1 KB	English male voice preset
`jp_female.emb.gguf`	<1 KB	Japanese female voice preset
`jp_male.emb.gguf`	<1 KB	Japanese male voice preset

Usage

bash

crispasr --backend miotts -m miotts-0.6b-q4_k.gguf \
  --voice en_female.emb.gguf \
  --tts "Hello world, how are you today?" \
  -of output

Important: A voice preset (--voice or -emb) is required for good audio quality. Without it, the codec produces unintelligible audio.

The tokenizer.json must be in the same directory as the GGUF file.

Verified ASR Roundtrip

Quantization	Size	Input	ASR Output
Q4_K+F16	502 MB	"The quick brown fox jumps over the lazy dog."	"The quick brown fox jumps over the lazy dog." ✅
F32	2.6 GB	"Hello world, how are you today?"	"Hello world, how are you today? What?" ✅
F16 (uniform)	1.4 GB	—	Codec too noisy (SnakeBeta precision) ❌

The mixed quantization (LLM=Q4_K, codec=F16) is critical — uniform quantization degrades the UpSampler's SnakeBeta activation.

LLM: Qwen3ForCausalLM (28L, 1024d, GQA 16/8, head_dim=128, vocab=164480)
Codec: MioCodec-25Hz-44.1kHz-v2 (FSQ → wave_prenet → conv_upsample → ResNet → AdaLN-Zero decoder → ResNet → UpSampler with SnakeBeta → iSTFT)
Output: 44.1 kHz mono PCM

Apache 2.0 (MioTTS-0.6B is Qwen3-based). MioCodec weights under MIT.

Upstream model: Aratako/MioTTS-0.6B — published by Aratako.
Upstream licence: apache-2.0. This repository redistributes under the same terms; it grants no rights the upstream licence does not.
What was done here: format conversion and/or quantisation only (GGUF). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs.
Training data: documented — where it is documented at all — by the upstream provider; see the upstream model card. No training data was used, added or selected by this repository.
Provider status: under Regulation (EU) 2024/1689 the upstream authors remain the provider of this model. Converting the serialisation format does not make this repository the provider of a new general-purpose AI model, and no such claim is made. Questions about training content, copyright policy or model capability belong upstream.