Instructions to use mlx-community/Irodori-TTS-v4-Small-fp16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/Irodori-TTS-v4-Small-fp16 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Irodori-TTS-v4-Small-fp16 mlx-community/Irodori-TTS-v4-Small-fp16
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
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
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.
- Downloads last month
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Model size
0.8B params
Tensor type
F16
·
Hardware compatibility
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Quantized
Model tree for mlx-community/Irodori-TTS-v4-Small-fp16
Base model
Aratako/Irodori-TTS-500M-v2 Finetuned
Aratako/Irodori-TTS-600M-v3-VoiceDesign Finetuned
Aratako/Irodori-TTS-v4-Small