You can deploy this model and start earning money today!

MigoXV/
speech_eres2netv2_sv_zh-cn_16k-common

Name: speech_eres2netv2_sv_zh-cn_16k-common
Rating: 2.5 (18 reviews)

Views

No views yet

ERes2NetV2 Speaker Verification Chinese 16k

This repository contains an ERes2NetV2 speaker verification model exported from the original PyTorch checkpoint to Hugging Face style safetensors format.

Model Details

Architecture: ERes2NetV2 speaker embedding model
Task: speaker verification and speaker embedding extraction
Language/domain: Chinese speech
Sample rate: 16 kHz
Acoustic feature: 80-dimensional Kaldi fbank
Embedding size: 192
Weight file: model.safetensors
Config file: config.json

The exported weights are converted from:

model-bin/speech_eres2netv2_sv_zh-cn_16k-common/pretrained_eres2netv2.ckpt

Repository Files

text

.
|-- config.json
|-- model.safetensors
`-- README.md

config.json keeps the model metadata used by this project and points pretrained_model to model.safetensors.

Loading

Use the project inferencer:

python

from campplus_sid.inferencer.inferencer import Inferencer

inferencer = Inferencer.from_pretrained(
    "model-bin/safetensor/speech_eres2netv2_sv_zh-cn_16k-common",
    device="cpu",
)

For direct weight loading:

python

from safetensors.torch import load_file

state_dict = load_file(
    "model-bin/safetensor/speech_eres2netv2_sv_zh-cn_16k-common/model.safetensors"
)

Input Format

The model expects normalized 80-bin fbank features with shape:

batch, frames, feature_dim

In this project, raw 16 kHz audio is converted to fbank features by the Inferencer before the model forward pass.

Reference

This architecture is associated with the 3D-Speaker speaker recognition work:

https://github.com/alibaba-damo-academy/3D-Speaker

Verification

The exported model.safetensors was validated by loading all tensors and checking the state dict against the ERes2NetV2 model definition with no missing or unexpected keys.

MigoXV/speech_eres2netv2_sv_zh-cn_16k-common