VoxCPM

🎙️ VoxCPM: Tokenizer-Free TTS for Context-Aware Speech Generation and True-to-Life Voice Cloning

Overview

VoxCPM is a novel tokenizer-free Text-to-Speech (TTS) system that redefines realism in speech synthesis. By modeling speech in a continuous space, it overcomes the limitations of discrete tokenization and enables two flagship capabilities: context-aware speech generation and true-to-life zero-shot voice cloning.

Unlike mainstream approaches that convert speech to discrete tokens, VoxCPM uses an end-to-end diffusion autoregressive architecture that directly generates continuous speech representations from text. Built on MiniCPM-4 backbone, it achieves implicit semantic-acoustic decoupling through hierachical language modeling and FSQ constraints, greatly enhancing both expressiveness and generation stability.

🚀 Key Features

• Context-Aware, Expressive Speech Generation - VoxCPM comprehends text to infer and generate appropriate prosody, delivering speech with remarkable expressiveness and natural flow. It spontaneously adapts speaking style based on content, producing highly fitting vocal expression trained on a massive 1.8 million-hour bilingual corpus.
• True-to-Life Voice Cloning - With only a short reference audio clip, VoxCPM performs accurate zero-shot voice cloning, capturing not only the speaker’s timbre but also fine-grained characteristics such as accent, emotional tone, rhythm, and pacing to create a faithful and natural replica.
• High-Efficiency Synthesis - VoxCPM supports streaming synthesis with a Real-Time Factor (RTF) as low as 0.17 on a consumer-grade NVIDIA RTX 4090 GPU, making it possible for real-time applications.

Quick Start

🔧 Install from PyPI

pip install voxcpm

1. Model Download (Optional)

By default, when you first run the script, the model will be downloaded automatically, but you can also download the model in advance.

• Download VoxCPM-0.5B

  from huggingface_hub import snapshot_download
  snapshot_download("openbmb/VoxCPM-0.5B",local_files_only=local_files_only)
  

• Download ZipEnhancer and SenseVoice-Small. We use ZipEnhancer to enhance speech prompts and SenseVoice-Small for speech prompt ASR in the web demo.

  from modelscope import snapshot_download
  snapshot_download('iic/speech_zipenhancer_ans_multiloss_16k_base')
  snapshot_download('iic/SenseVoiceSmall')
  

2. Basic Usage

import soundfile as sf
from voxcpm import VoxCPM

model = VoxCPM.from_pretrained("openbmb/VoxCPM-0.5B")

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")

3. CLI Usage

After installation, the entry point is voxcpm (or use python -m voxcpm.cli).

# 1) Direct synthesis (single text)
voxcpm --text "Hello VoxCPM" --output out.wav

# 2) Voice cloning (reference audio + transcript)
voxcpm --text "Hello" \
  --prompt-audio path/to/voice.wav \
  --prompt-text "reference transcript" \
  --output out.wav \
  --denoise

# 3) Batch processing (one text per line)
voxcpm --input examples/input.txt --output-dir outs
# (optional) Batch + cloning
voxcpm --input examples/input.txt --output-dir outs \
  --prompt-audio path/to/voice.wav \
  --prompt-text "reference transcript" \
  --denoise

# 4) Inference parameters (quality/speed)
voxcpm --text "..." --output out.wav \
  --cfg-value 2.0 --inference-timesteps 10 --normalize

# 5) Model loading
# Prefer local path
voxcpm --text "..." --output out.wav --model-path /path/to/VoxCPM_model_dir
# Or from Hugging Face (auto download/cache)
voxcpm --text "..." --output out.wav \
  --hf-model-id openbmb/VoxCPM-0.5B --cache-dir ~/.cache/huggingface --local-files-only

# 6) Denoiser control
voxcpm --text "..." --output out.wav \
  --no-denoiser --zipenhancer-path iic/speech_zipenhancer_ans_multiloss_16k_base

# 7) Help
voxcpm --help
python -m voxcpm.cli --help

4. Start web demo

You can start the UI interface by running python app.py, which allows you to perform Voice Cloning and Voice Creation.

👩‍🍳 A Voice Chef's Guide

Welcome to the VoxCPM kitchen! Follow this recipe to cook up perfect generated speech. Let’s begin.

🥚 Step 1: Prepare Your Base Ingredients (Content)

First, choose how you’d like to input your text:.

1. Regular Text (Classic Mode)

• ✅ Keep "Text Normalization" ON. Type naturally (e.g., "Hello, world! 123"). The system will automatically process numbers, abbreviations, and punctuation using WeTextProcessing library.

2. Phoneme Input (Native Mode)

• ❌ Turn "Text Normalization" OFF. Enter phoneme text like {HH AH0 L OW1} (EN) or {ni3}{hao3} (ZH) for precise pronunciation control. In this mode, VoxCPM also supports native understanding of other complex non-normalized text—try it o