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.