Instructions to use mlboydaisuke/VoxCPM2-CoreAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- VoxCPM
How to use mlboydaisuke/VoxCPM2-CoreAI with VoxCPM:
import soundfile as sf from voxcpm import VoxCPM model = VoxCPM.from_pretrained("mlboydaisuke/VoxCPM2-CoreAI") wav = model.generate( text="VoxCPM is an innovative end-to-end TTS model from ModelBest, designed to generate highly expressive speech.", prompt_wav_path=None, # optional: path to a prompt speech for voice cloning prompt_text=None, # optional: reference text cfg_value=2.0, # LM guidance on LocDiT, higher for better adherence to the prompt, but maybe worse inference_timesteps=10, # LocDiT inference timesteps, higher for better result, lower for fast speed normalize=True, # enable external TN tool denoise=True, # enable external Denoise tool retry_badcase=True, # enable retrying mode for some bad cases (unstoppable) retry_badcase_max_times=3, # maximum retrying times retry_badcase_ratio_threshold=6.0, # maximum length restriction for bad case detection (simple but effective), it could be adjusted for slow pace speech ) sf.write("output.wav", wav, 16000) print("saved: output.wav") - Notebooks
- Google Colab
- Kaggle
gen-cards: add Use-it markers
Browse files
README.md
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@@ -24,6 +24,9 @@ A tokenizer-free diffusion TTS: a **MiniCPM4 28-layer** text-semantic LM + an **
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LM drive a **12-layer LocDiT** flow-matching diffusion head, decoded by a **48 kHz AudioVAE**. Five Core AI
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bundles + a few host-side projections.
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## What's inside
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LM drive a **12-layer LocDiT** flow-matching diffusion head, decoded by a **48 kHz AudioVAE**. Five Core AI
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bundles + a few host-side projections.
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<!-- gen-cards:use-it begin id=voxcpm2-2b (managed by scripts/gen-cards — edit cards.json / QuickStart.swift, not this block) -->
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<!-- gen-cards:use-it end -->
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## What's inside
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| dir | contents |
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