ReDimNet2-B6 ONNX Speaker Embeddings
ReDimNet2-B6 produces local speaker embeddings for comparing clean voice samples. It does not diarize audio or assign names by itself.
Model
| Property | Value |
|---|---|
| Parameters | 12.3 million |
| Format | ONNX opset 18, Float32 |
| Model size | 48.9 MiB |
| Input | audio, [1, 96000] mono Float32 samples |
| Sample rate | 16 kHz |
| Window | 6 seconds |
| Output | embedding, [1, 192] L2-normalized Float32 |
Applications should repeat clean two-to-six-second speech to fill the input and center-crop longer samples. Do not use overlapping, mixed, or unalignable speech as identity evidence.
Performance
The export is rejected unless its embedding has cosine similarity at least 0.9999 with the pinned PyTorch checkpoint and remains unit-normalized.
| Measurement | Result |
|---|---|
| Warm six-second CPU inference | 245.5 ms |
Latency is measured on the export host and is not a Windows hardware claim.
C++ usage
The supported native host is speech-core:
#include <speech_core/models/onnx_redimnet_speaker_embedding.h>
speech_core::OnnxReDimNetSpeakerEmbedding model(
"ReDimNet2B6.onnx");
auto embedding = model.embed(samples.data(), samples.size(), 16000);
Files
| File | Description |
|---|---|
ReDimNet2B6.onnx |
Fixed-shape speaker encoder |
config.json |
Graph contract, provenance, hashes, and parity |
README.md |
This model card |
LICENSE |
Upstream MIT license |
Source
Converted from the official
PalabraAI/ReDimNet2 B6
vb2+vox2_v0 large-margin checkpoint. The pinned source revision and
checkpoint SHA-256 are recorded in config.json.
Speaker embeddings are useful for labeling; they are not biometric authentication and do not protect against voice spoofing.
Links
- speech-core — C++ runtime
- C++ docs
- soniqo.audio
- blog