VoxCPM Text-to-Speech

Synthesize speech from Chinese/English/multilingual text using OpenBMB's VoxCPM. Two model tiers, three input modes, thirty languages, nine Chinese dialects. Apache-2.0 licensed.

Verified 2026-07-04: voxcpm 2.0.3 on PyPI, dependency graph resolves cleanly (torch ≥ 2.5, transformers ≥ 4.36, gradio 6.x). PyPI reachable. Model weights host on Hugging Face (openbmb/VoxCPM2, openbmb/VoxCPM-0.5B).

This environment (QoderWork sandbox) has no GPU and cannot reach huggingface.co directly — audio generation is not exercised here. Deploy on a machine with CUDA / Apple Silicon / a HF mirror to actually synthesize.

Prerequisites

Component	Requirement
Python	≥ 3.10, < 3.13 (officially tested)
PyTorch	≥ 2.5.0
CUDA	≥ 12.0 (recommended); MPS or CPU also work (slower)
GPU VRAM	5 GB for VoxCPM-0.5B, 8 GB for VoxCPM2 (2B)
Disk	~2 GB per model (weights auto-download from HF)
Network	Access to `huggingface.co` (or set `HF_ENDPOINT` to a mirror)

Install:

pip install voxcpm

For China deployments where HF is slow, use ModelScope mirror:

export HF_ENDPOINT=https://hf-mirror.com
# or use ModelScope directly:
pip install modelscope

Quick Start

CLI (simplest)

# Basic synthesis to WAV
voxcpm design --text "你好，欢迎收听三分钟 AI 快报。" --output greeting.wav

# With voice-style natural-language description
voxcpm design --text "(年轻女声，温柔甜美) 今天天气真好，我们来聊聊 AI。" --output warm.wav

# English
voxcpm design --text "Welcome to today's AI briefing." --output en.wav

# Batch
voxcpm batch --input-file lines.txt --output-dir out/

Python SDK (programmatic)

from voxcpm import VoxCPM
import soundfile as sf

model = VoxCPM.from_pretrained("openbmb/VoxCPM2", load_denoiser=False)

wav = model.generate(
    text="你好，这是一段测试语音。",
    cfg_value=2.0,           # classifier-free guidance strength (higher = more expressive)
    inference_timesteps=10,  # diffusion steps (more = better quality, slower)
    seed=42                  # reproducibility
)
sf.write("demo.wav", wav, model.tts_model.sample_rate)  # 24 kHz output

Streaming synthesis (for long text)

for chunk in model.generate_streaming(text="很长的一段文本..."):
    # chunk is np.ndarray of PCM samples
    process(chunk)

Input Modes

1. Design (natural-language voice control)

Wrap voice description in () at the start of the text:

"(年轻女声，语气甜美，稍快语速) 今天要给大家介绍一款有意思的 AI 工具。"
"(中年男声，稳重、专业) 欢迎收听本期节目。"
"(带四川口音的女生，轻松) 这个咋回事嘛！"

Available cues (composable):

Voice type: 男声 / 女声 / 童声 / 老年男声 / 中年女声 ...
Tone: 温柔 / 严肃 / 兴奋 / 平静 / 忧郁 / 慵懒 ...
Pace: 快语速 / 慢语速 / 稍快 / 稍慢
Style: 播音腔 / 口语化 / 说书人 / 儿童剧 / 广告腔
Dialect: 四川话 / 粤语 / 吴语 / 东北话 / 河南话 / 陕西话 / 山东话 / 天津话 / 闽南话

2. Voice cloning (zero-shot)

Provide a 3-30 second reference audio + its transcript:

wav = model.generate(
    text="要合成的新文本。",
    prompt_wav_path="reference.wav",
    prompt_text="参考音频对应的原文（准确抄写）"
)

The generated voice matches the reference speaker.

3. Combined (clone + style tweak)

wav = model.generate(
    text="(稍快，兴奋) 要合成的新文本。",
    reference_wav_path="reference.wav"  # softer form of clone; keeps timbre but style overrides
)

Common Workflows

End-to-end: script → audio

Pair with podcast-scriptwriter. Given a script:

A（轻松）：欢迎收听《三分钟 AI 快报》。
B（认真）：本周动作不小。

import re
from voxcpm import VoxCPM
import soundfile as sf, numpy as np

model = VoxCPM.from_pretrained("openbmb/VoxCPM2", load_denoiser=False)
voices = {
    'A': "reference_female.wav",   # or use "(年轻女声，语气理性犀利)" as prefix
    'B': "reference_male.wav",
}

segments = []
for line in open('script.txt', encoding='utf-8'):
    m = re.match(r'^([AB])（([^）]+)）：(.+)$', line.strip())
    if not m: continue
    speaker, tone, text = m.groups()
    styled = f"({tone}) {text}"
    wav = model.generate(text=styled, prompt_wav_path=voices[speaker], cfg_value=2.0, inference_timesteps=10)
    segments.append(wav)
    segments.append(np.zeros(int(0.4 * model.tts_model.sample_rate)))  # 0.4s pause

full = np.concatenate(segments)
sf.write('episode.wav', full, model.tts_model.sample_rate)

Multi-language podcast

Feed the same script translated to en/ja/ko/fr; VoxCPM auto-detects language per line. Keep the same reference audio to preserve host identity across languages.

High-throughput vLLM serving

For volume, use the OpenAI-compatible vLLM Omni server:

vllm serve openbmb/VoxCPM2 --omni --port 8000

Then hit /v1/audio/speech like OpenAI TTS.

Fallback: edge-tts (no GPU)

If VoxCPM is impossible in the environment (no GPU, HF blocked, tight time), fall back to Microsoft Edge TTS (free, no API key, 400+ voices):

pip install edge-tts
edge-tts --voice zh-CN-XiaoxiaoNeural --text "你好世界" --write-media hello.mp3

Trade-offs: no voice cloning, fixed voice pool, network-dependent, not open-source. Acceptable stop-gap.

Limitations

GPU strongly recommended — CPU works but ~10-50× slower.
First-run downloads ~2 GB from HF; use HF_ENDPOINT for a mirror if slow.
CUDA 12+ requires recent driver — older drivers may need CUDA 11 build; check torch.cuda.is_available().
Long text (> 2 min): use generate_streaming and stitch chunks; pure generate may hit VRAM ceiling.
English-only clone: works but sample quality slightly lower than Chinese-native.
Dialects require prefix format — (粤语) 早晨。 not 粤语：早晨。.
No word-level timing output — for karaoke use whisper-like STT alignment on the produced WAV.
Sample rate is fixed at 24 kHz — resample downstream if needed.
Python 3.13+ untested — pin to 3.10-3.12.

Failure Modes

Symptom	Cause	Action
`torch.cuda.OutOfMemoryError`	Model too big for GPU	Switch to `openbmb/VoxCPM-0.5B`, or use CPU
Model download stalls	HF connectivity	Set `HF_ENDPOINT=https://hf-mirror.com`
Robotic / metallic voice	`cfg_value` too high	Try 1.5-2.5; sweet spot is 2.0
Voice clone doesn't match	Reference too short/noisy	Use 5-15 s clean sample; enable `load_denoiser=True`
Language auto-detected wrong	Text mixed	Split by language, generate per-segment
CLI `voxcpm: command not found`	Not in venv PATH	`pip install voxcpm` in the active env; or `python -m voxcpm ...`

References

Repo: https://github.com/OpenBMB/VoxCPM ⭐ 32.4k
Docs: https://voxcpm.readthedocs.io/
HF model: https://huggingface.co/openbmb/VoxCPM2 (2B) or https://huggingface.co/openbmb/VoxCPM-0.5B (small)
Demo: https://openbmb.github.io/VoxCPM-demopage/
License: Apache-2.0
Fallback: Microsoft Edge TTS (pip install edge-tts) — no API key, no clone

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