speakeronnx 0.0.1a2

Pure-onnxruntime speaker embedding library — no torch at runtime

speakeronnx

Pure-onnxruntime speaker embedding library — no torch at runtime.

Extract speaker embeddings, compute cosine similarity, and verify speaker identity using ONNX-exported models downloaded automatically from HuggingFace.

Model collection: OpenVoiceOS/speaker-embeddings-onnx

Install

pip install speakeronnx

Optional high-quality resampling:

pip install speakeronnx soxr

Quick start

from speakeronnx import SpeakerEmbedder, cosine, verify

embedder = SpeakerEmbedder(model="wespeaker-resnet34")

alice1 = embedder.embed("alice_clip1.wav")
alice2 = embedder.embed("alice_clip2.wav")
bob    = embedder.embed("bob_clip1.wav")

print(cosine(alice1, alice2))   # e.g. 0.82  — same speaker
print(cosine(alice1, bob))      # e.g. 0.21  — different speaker

ok, score = verify(alice1, alice2, threshold=0.45)
print(ok, score)  # True 0.82

More examples in examples/.

CLI

speakeronnx list                              # list available models
speakeronnx embed clip.wav                    # extract embedding
speakeronnx verify a.wav b.wav               # same-speaker check (exit 0/1)
speakeronnx verify a.wav b.wav --threshold 0.5
speakeronnx embed clip.wav --model wespeaker-ecapa512

Full CLI reference in docs/cli.md.

Models

All 9 models are registered in MODEL_REGISTRY and downloaded on first use:

Alias	Embed dim	Frontend	License
wespeaker-resnet34	256	fbank80	cc-by-4.0
wespeaker-ecapa512	192	fbank80	cc-by-4.0
wespeaker-resnet293	256	fbank80	cc-by-4.0
campplus	512	fbank80	cc-by-4.0
campplus-zh-en	192	fbank80	apache-2.0
eres2net	192	fbank80	apache-2.0
titanet-small	192	fbank80	cc-by-4.0
titanet-large	192	fbank80	cc-by-4.0
redimnet-b2	192	raw	apache-2.0

Full model comparison and selection guide in docs/models.md.

Documentation

Document	Description
docs/index.md	Full getting-started guide
docs/models.md	Model comparison, selection, frontend/layout details
docs/api.md	Complete API reference
docs/cli.md	CLI usage reference
docs/frontend.md	Feature frontend (fbank80 vs raw) technical details
docs/advanced.md	Custom models, GPU, threshold tuning

Examples

Script	Description
examples/basic_embedding.py	Extract embedding from a single WAV
examples/verify_speakers.py	Verify two clips, try multiple thresholds
examples/compare_models.py	Compare all models on same utterances
examples/batch_enrollment.py	Enroll speakers from directories, match unknown
examples/custom_model.py	Load a custom ONNX model from disk
examples/gpu_inference.py	CUDA / CoreML inference

Tests

# Unit tests (mocked, no downloads, no network)
pytest tests/test_unit.py tests/test_audio.py tests/test_frontend.py \
      tests/test_embedder.py tests/test_cli.py tests/test_model_registry.py -v
| Alias | HF repo | License | Embed dim | Description |
|---|---|---|---|---|
| `wespeaker-resnet34` | [Wespeaker/wespeaker-voxceleb-resnet34-LM](https://huggingface.co/Wespeaker/wespeaker-voxceleb-resnet34-LM) | cc-by-4.0 | 256 | ResNet34 r-vector, VoxCeleb2 Dev — **recommended default** |
| `wespeaker-ecapa512` | [Wespeaker/wespeaker-ecapa-tdnn512-LM](https://huggingface.co/Wespeaker/wespeaker-ecapa-tdnn512-LM) | cc-by-4.0 | 192 | ECAPA-TDNN-512 x-vector, VoxCeleb2 Dev |
| `wespeaker-resnet293` | [Wespeaker/wespeaker-voxceleb-resnet293-LM](https://huggingface.co/Wespeaker/wespeaker-voxceleb-resnet293-LM) | cc-by-4.0 | 256 | ResNet293 r-vector — highest accuracy, 28M params |
| `campplus` | [csukuangfj/speaker-embedding-models](https://huggingface.co/csukuangfj/speaker-embedding-models) | cc-by-4.0 | 512 | CAM++ (D-TDNN backbone), VoxCeleb2 Dev |
| `campplus-zh-en` | [csukuangfj/speaker-embedding-models](https://huggingface.co/csukuangfj/speaker-embedding-models) | apache-2.0 | 192 | 3D-Speaker CAM++ multilingual (zh+en) |
| `eres2net` | [csukuangfj/speaker-embedding-models](https://huggingface.co/csukuangfj/speaker-embedding-models) | apache-2.0 | 192 | ERes2Net, VoxCeleb |
| `titanet-small` | [csukuangfj/speaker-embedding-models](https://huggingface.co/csukuangfj/speaker-embedding-models) | cc-by-4.0 | 192 | NVIDIA NeMo TitaNet-small (~40 MB) |
| `titanet-large` | [csukuangfj/speaker-embedding-models](https://huggingface.co/csukuangfj/speaker-embedding-models) | cc-by-4.0 | 192 | NVIDIA NeMo TitaNet-large (~101 MB) |
| `redimnet-b2` | [OpenVoiceOS/redimnet-b2-vox2-onnx](https://huggingface.co/OpenVoiceOS/redimnet-b2-vox2-onnx) | apache-2.0 | 192 | ReDimNet b2 (1.8M params), raw audio input |

# End-to-end tests (downloads models + generates TTS audio)
pytest tests/test_e2e.py -v -s

Use speakeronnx list to print descriptions and metadata for all registered models.

Feature frontend

• fbank80 models: 80-dim log-Mel filterbank with per-utterance CMN, implemented in pure numpy. See docs/frontend.md.
• raw models (redimnet-b2): raw 16 kHz waveform passed directly to ONNX (internal MelSpectrogram in the model).

Audio requirements

• Mono PCM WAV, any bit depth (8/16/24/32-bit int, 32-bit float)
• Any sample rate (resampled internally to 16 kHz)
• Stereo files are downmixed to mono
• Minimum ~1 second; recommended enrollment 5–30 seconds per speaker

Dependencies

• onnxruntime
• numpy
• huggingface_hub
• soxr (optional, for high-quality resampling)

Project links

• GitHub: TigreGotico/speakeronnx
• PyPI: pip install speakeronnx