---
license: apache-2.0
language:
- zh
- en
tags:
- speaker-recognition
- speaker-embedding
- speaker-verification
- speaker-diarization
- onnx
- campplus
- 3d-speaker
library_name: onnxruntime
---

# CAM++ 3D-Speaker (200k speakers, 192-dim) — ONNX

ONNX Runtime export of [3D-Speaker](https://github.com/modelscope/3D-Speaker) `CAM++` speaker embedding model **`speech_campplus_sv_zh_en_16k-common_advanced`** (192-dim, trained on 200k speakers across VoxCeleb + CNCeleb + 3D-Speaker corpus).

This is the only public ONNX export of this specific 200k-speaker variant — the upstream HF mirror [funasr/campplus](https://huggingface.co/funasr/campplus/tree/main) only ships the smaller PyTorch `.bin`, and the ModelScope ONNX is not always accessible outside China.

| | Spec |
|---|---|
| Architecture | CAM++ (FCM + TDNN + DenseTDNN + StatsPool + Dense) |
| Input | 80-dim fbank, `(batch, frames, 80)` float32 |
| Output | 192-dim L2-normalized speaker embedding, `(batch, 192)` |
| Sample rate | 16 kHz |
| File size | 27 MB |
| Speakers (training) | ~200k (VoxCeleb 1+2 + CNCeleb 1+2 + 3D-Speaker) |
| Languages | Chinese + English (works well cross-lingual incl. Vietnamese) |

## Quick start

```python
import numpy as np
import onnxruntime as ort
import kaldi_native_fbank as knf
import soundfile as sf
from huggingface_hub import hf_hub_download

# 1. Load model
model_path = hf_hub_download("welcomyou/campplus-3dspeaker-200k-onnx",
                              "campplus_cn_en_common_200k.onnx")
sess = ort.InferenceSession(model_path, providers=["CPUExecutionProvider"])

# 2. Compute 80-dim fbank from 16kHz mono audio
audio, sr = sf.read("speaker.wav", dtype="float32")  # must be 16 kHz mono
opts = knf.FbankOptions()
opts.frame_opts.samp_freq = 16000
opts.frame_opts.dither = 0.0
opts.mel_opts.num_bins = 80
fbank_extractor = knf.OnlineFbank(opts)
fbank_extractor.accept_waveform(16000, audio * 32768.0)
fbank_extractor.input_finished()
frames = np.stack([fbank_extractor.get_frame(i)
                   for i in range(fbank_extractor.num_frames_ready)])
frames -= frames.mean(axis=0, keepdims=True)  # CMVN

# 3. Run inference
embedding = sess.run(None, {"input": frames[np.newaxis]})[0][0]
print(embedding.shape)  # (192,)

# 4. Cosine similarity for verification
emb_a = embedding / np.linalg.norm(embedding)
# emb_b = ... (another speaker)
# similarity = emb_a @ emb_b
```

## Reproducing the export

```bash
# 1. Download PyTorch checkpoint from ModelScope
pip install modelscope
python -c "from modelscope import snapshot_download; \
           snapshot_download('iic/speech_campplus_sv_zh_en_16k-common_advanced')"

# 2. Run export script (re-implements CAM++ architecture + loads weights + exports ONNX)
python convert_onnx/export_campplus_onnx.py \
    --pt_path   modelscope_cache/iic/speech_campplus_sv_zh_en_16k-common_advanced/campplus_cn_en_common.pt \
    --onnx_path models/campp-3dspeaker/campplus_cn_en_common_200k.onnx
```

Script: [`convert_onnx/export_campplus_onnx.py`](https://github.com/welcomyou/sherpa-vietnamese-asr/blob/main/convert_onnx/export_campplus_onnx.py) — copies `FCM`, `TDNN`, `DenseTDNN`, `StatsPool`, `CAMPPlus` modules from the [3D-Speaker repo](https://github.com/modelscope/3D-Speaker), loads `campplus_cn_en_common.pt`, traces with `torch.onnx.export`.

## Credits & License

- **Original model**: 3D-Speaker `speech_campplus_sv_zh_en_16k-common_advanced` ([ModelScope](https://www.modelscope.cn/models/iic/speech_campplus_sv_zh_en_16k-common_advanced))
- **Architecture**: [3D-Speaker](https://github.com/modelscope/3D-Speaker) by Alibaba DAMO Academy
- **Paper**: [CAM++: A Fast and Efficient Network for Speaker Verification using Context-aware Masking](https://arxiv.org/abs/2303.00332)

License: **Apache 2.0** (inherited from 3D-Speaker).

## Used by

- [Sherpa Vietnamese ASR](https://github.com/welcomyou/sherpa-vietnamese-asr) — Senko speaker diarization pipeline (~2.5× faster than ResNet34 on CPU).