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mlx-community/
Irodori-TTS-v4-Small-fp16

Name: Irodori-TTS-v4-Small-fp16
Availability: InStock

Aratako/Irodori-TTS-v4-Small

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mlx-community/Irodori-TTS-v4-Small-fp16

This model was converted to MLX format from Aratako/Irodori-TTS-v4-Small using mlx-audio version 0.4.7.

Use with mlx-audio

pip install -U mlx-audio

Command line

mlx_audio.tts.generate --model mlx-community/Irodori-TTS-v4-Small-fp16 --text "こんにちは、Irodori TTSのMLX版です。" --ref_audio reference.wav

Python

python

from mlx_audio.tts.generate import generate_audio

generate_audio(
    text="こんにちは、Irodori TTSのMLX版です。",
    model="mlx-community/Irodori-TTS-v4-Small-fp16",
    ref_audio="reference.wav",
    file_prefix="output",
)

Notes

Unified v4 model: voice cloning, VoiceDesign (caption) and automatic duration prediction in one checkpoint.
The ModernBERT-ja-310m text encoder weights and its tokenizer are bundled, and the Semantic-DACVAE-Japanese-32dim codec ships in dacvae/, so inference needs no additional downloads.
Reference audio: up to 120s. Passing a list of clips encodes each separately and concatenates them, which matches training better than one long recording.
With a caption but no reference audio, the duration predictor roughly doubles the length of short texts (under ~7 tokens) and the model reads the sentence twice to fill the window. This matches the reference PyTorch implementation. Pass reference audio, or set duration_scale / seconds.