Hugging Face's logo

ai4all8
/
VoxCPM2-ONNX

Text-to-Speech
ONNX
Chinese
English
Japanese
onnxruntime
tts
voice-cloning
cpu-inference
cantonese
voxcpm2
Model card Files
xet
 Community
VoxCPM2-ONNX
File size: 2,256 Bytes
ecb511b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
---
license: apache-2.0
tags:
  - tts
  - voice-cloning
  - onnx
  - cpu-inference
  - cantonese
  - voxcpm2
language:
  - zh
  - en
  - ja
library_name: onnxruntime
pipeline_tag: text-to-speech
---

# VoxCPM2-ONNX

ONNX Runtime models for [VoxCPM2](https://github.com/OpenBMB/VoxCPM) — a 2B-parameter text-to-speech model by OpenBMB with voice cloning capability.

**Run VoxCPM2 voice cloning on any CPU — no GPU needed.**

## Models

| File | Size | Description |
|---|---|---|
| `audio_vae_encoder.onnx` + `.data` | ~185 MB | Audio waveform to latent features |
| `audio_vae_decoder.onnx` + `.data` | ~176 MB | Latent features to 48 kHz waveform |
| `voxcpm2_prefill.onnx` + `.data` | ~7.8 GB | Text + reference audio to KV cache + DiT hidden state |
| `voxcpm2_decode_step.onnx` + `.data` | ~8.1 GB | Single autoregressive decode step (10 CFM steps baked in) |

All models use `external_data=True` — keep `.onnx` and `.onnx.data` files together.

## Usage

```bash
# Install
pip install voxcpm>=2.0.2 torch>=2.4.0 onnxruntime>=1.18.0 soundfile numpy tqdm huggingface_hub

# Download models
python -c "
from huggingface_hub import snapshot_download
snapshot_download('ai4all8/VoxCPM2-ONNX', local_dir='./onnx_models', ignore_patterns=['*.md', '*.txt'])
"

# Download VoxCPM2 PyTorch weights (needed for preprocessing)
python -c "from voxcpm import VoxCPM; VoxCPM.from_pretrained('openbmb/VoxCPM2')"

# Run inference (see GitHub repo for full CLI)
git clone https://github.com/ai4all8/VoxCPM2-ONNX.git
cd VoxCPM2-ONNX
python infer.py --text "Hello!" --ref_wav speaker.wav --ref_text "Reference transcript."
```

Full documentation and code: [github.com/ai4all8/VoxCPM2-ONNX](https://github.com/ai4all8/VoxCPM2-ONNX)

## Languages

- Cantonese (粵語)
- Mandarin (普通話)
- English
- Japanese (日本語)

## Performance

| Platform | RTF | Notes |
|---|---|---|
| AMD Ryzen 9 (Windows) | ~4.5x | 8 cores, ORT sequential |
| Intel Core (Linux) | ~9.5x | Single-threaded |

RTF = Real-Time Factor (lower is better; 1.0 = real-time).

## License

Apache License 2.0, same as the original VoxCPM2 model.

## Attribution

Original model: [VoxCPM2 by OpenBMB](https://github.com/OpenBMB/VoxCPM) (Apache 2.0, Copyright OpenBMB / Tsinghua University).