Irodori-TTS-v4.1-Small-fp32

mlx-community/Irodori-TTS-v4.1-Small-fp16

This model was converted to MLX format from Aratako/Irodori-TTS-v4.1-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-v4.1-Small-fp16 --text "こんにちは、Irodori TTSのMLX版です。" --ref_audio reference.wav

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-v4.1-Small-fp16",
    ref_audio="reference.wav",
    file_prefix="output",
)

Notes

• Unified v4.1 model: voice cloning, VoiceDesign (caption) and automatic duration prediction in one checkpoint.

• v4.1 differs from v4 only in the duration predictor, which upstream retrained separately with every other parameter frozen. The remaining 683 of 714 tensors are bit-identical to v4.

• 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.

• Short caption-only prompts are shorter than in v4, but still over-predicted. Measured with the same reference clip and caption:

  Text	Tokens	v4 caption only	v4.1 caption only
  こんにちは。	3	3.64s	2.88s
  今日はいい天気ですね。	5	5.60s	4.72s
  MLXへの移植が完了しました。	7	4.08s	3.84s

  Under about seven tokens the model can still fill the surplus by reading the sentence a second time. This matches the reference PyTorch implementation, which predicts 117.41 frames against MLX's 117.56 for the second row, and produces the same repeat. Pass reference audio, or set duration_scale / seconds.