Campplus Zh En Onnx
ONNX speaker-embedding model for on-device, CPU-only speaker diarization in a multilingual (Mandarin/Taiwanese + English, code-switched) setting. Repackaged for sherpa-onnx with fp16 and int8 variants added for...
Source du modèle
- Type
- Other
- Taille du fichier
- 8,20–27,0 MB
- Version
- vCampplus zh en fp16
- Mis à jour
- 25 oct. 2026
- Source
- Hugging Face
Description de la source
CAM++ zh+en speaker embedding (ONNX: fp32 / fp16 / int8)
ONNX speaker-embedding model for on-device, CPU-only speaker diarization in a multilingual (Mandarin/Taiwanese + English, code-switched) setting. Repackaged for sherpa-onnx with fp16 and int8 variants added for size/latency comparison.
- Base model: 3D-Speaker
speech_campplus_sv_zh_en_16k-common_advanced(CAM++, D-TDNN), trained on code-switched Mandarin+English. Embedding dim 192, 16 kHz, 80-dim fbank input. - fp32 is the official sherpa-onnx artifact; fp16 / int8 were produced from it with
onnxconverter_common.float16(stats sub-graph kept in fp32) andonnxruntime.quantize_dynamic. - License: Apache-2.0 (inherited from 3D-Speaker / ModelScope).
| file | precision | size |
|---|---|---|
campplus_zh_en_fp32.onnx | fp32 | 27 MB |
campplus_zh_en_fp16.onnx | fp16 | 14 MB |
campplus_zh_en_int8.onnx | int8 (dynamic) | 8.2 MB |
On-device benchmark (Pixel 6, ARM64 CPU, sherpa-onnx, 4 threads)
Per-utterance embedding over real clips; accuracy = speaker separation margin
(mean inter-speaker − mean intra-speaker cosine distance, higher = better) and clustering outcome;
speed = ms per utterance (5-run average). Baseline = the previous embedder, 3D-Speaker
eres2net_base (zh).
Clean 2-speaker clip (EN + ZH):
| model | size | ms/utt | accuracy (margin) | clustering |
|---|---|---|---|---|
| eres2net_base (old) | 37.8 MB | 256 | 0.715 | 2 spk ✓ |
| CAM++ fp32 | 27.0 MB | 102 | 0.814 | 2 spk ✓ |
| CAM++ int8 | 8.2 MB | 287 | 0.738 | 2 spk ✓ |
| CAM++ fp16 | 14.0 MB | 70 | 0.808 | 2 spk ✓ |
3-speaker cross-lingual news clip:
| model | size | ms/utt | accuracy (margin) | clustering |
|---|---|---|---|---|
| eres2net_base (old) | 37.8 MB | 316 | 0.275 | 3 spk ✓ |
| CAM++ fp32 | 27.0 MB | 120 | 0.305 | 3 spk ✓ |
| CAM++ int8 | 8.2 MB | 330 | 0.284 | 3 spk ✓ |
| CAM++ fp16 | 14.0 MB | 78 | 0.304 | 3 spk ✓ |
Takeaways
- fp16 is the best on-device choice: fastest (~1.5× faster than fp32, ~3.5× faster than the old eres2net), half the size of fp32, and accuracy indistinguishable from fp32. The Pixel 6's ARMv8.2 fp16 SIMD runs it in hardware.
- int8 is counter-productive on this CPU: slower than fp32 (sherpa-onnx's ORT build has no
optimized
MatMulIntegerkernel for this graph) and slightly less accurate — it only wins on disk. - CAM++ beats the previous eres2net_base baseline on accuracy, speed, and size at every precision.
Usage (sherpa-onnx)
import sherpa_onnx
ext = sherpa_onnx.SpeakerEmbeddingExtractor(
sherpa_onnx.SpeakerEmbeddingExtractorConfig(
model="campplus_zh_en_fp16.onnx", num_threads=4, provider="cpu"))
Produced for VoxSumDroid (offline Android transcribe + diarize + summarize). Original model © 3D-Speaker (ModelScope), Apache-2.0.
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Extraits de sources
2 extraits--- license: apache-2.0 tags: - speaker-recognition - speaker-diarization - speaker-embedding - onnx - sherpa-onnx - campplus - 3d-speaker language: - zh - en library_name: sherpa-onnx pipeline_tag: audio-classification --- # CAM++ zh+en speaker embedding (ONNX: fp32 / fp16 / int8) ONNX speaker-embedding model for **on-device, CPU-only** speaker diarization in a multilingual (Mandarin/Taiwanese + English, code-switched) setting. Repackaged for [sherpa-onnx](https://github.com/k2-fsa/sherpa-onnx) with **fp16** and **int8** variants added for size/latency comparison. - **Base model:** 3D-Speaker `speech_campplus_sv_zh_en_16k-common_advanced` (CAM++, D-TDNN), trained on code-switched Mandarin+English. Embedding dim **192**, 16 kHz, 80-dim fbank input. - **fp32** is the official sherpa-onnx artifact; **fp16** / **int8** were produced from it with `onnxconverter_common.float16` (stats sub-graph kept in fp32) and `onnxruntime.quantize_dynamic`. - License: **Apache-2.0** (inherited from 3D-Speaker / ModelScope). | file | precision | size | |---|---|---| | `campplus_zh_en_fp32.onnx` | fp32 | 27 MB | | `campplus_zh_en_fp16.onnx` | fp16 | 14 MB | | `campplus_zh_en_int8.onnx` | int8 (dynamic) | 8.2 MB | ## On-device benchmark (Pixel 6, ARM64 CPU, sherpa-onnx, 4 threads) Per-utterance embedding over real clips; **accuracy** = speaker separation margin (mean inter-speaker − mean intra-speaker cosine distance, higher = better) and clustering outcome; **speed** = ms per utterance (5-run average). Baseline = the previous embedder, 3D-Speaker `eres2net_base` (zh). **Clean 2-speaker clip (EN + ZH):** | model | size | ms/utt | accuracy (margin) | clustering | |---|---|---|---|---| | eres2net_base (old) | 37.8 MB | 256 | 0.715 | 2 spk ✓ | | CAM++ **fp32** | 27.0 MB | 102 | **0.814** | 2 spk ✓ | | CAM++ **int8** | 8.2 MB | 287 | 0.738 | 2 spk ✓ | | CAM++ **fp16** | 14.0 MB | **70** | 0.808 | 2 spk ✓ | **3-speaker cross-lingual news clip:** | model | size | ms/utt | accuracy (margin) | clustering | |---|---|---|---|---| | eres2net_base (old) | 37.8 MB | 316 | 0.275 | 3 spk ✓ | | CAM++ **fp32** | 27.0 MB | 120 | 0.305 | 3 spk ✓ | | CAM++ **int8** | 8.2 MB | 330 | 0.284 | 3 spk ✓ | | CAM++ **fp16** | 14.0 MB | **78** | 0.304 | 3 spk ✓ | ### Takeaways - **fp16 is the best on-device choice:** fastest (~1.5× faster than fp32, ~3.5× faster than the old eres2net), half...
Source context: 0 downloads · 0 likes · Pipeline audio-classification · Library sherpa-onnx · Repo Luigi/campplus-zh-en-onnx
