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hbredin/wespeaker-voxceleb-resnet34-LM
wespeaker-voxceleb-resnet34-LM is a machine learning model from hbredin. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
This is a copy of voxcelebresnet34LM.onnx speaker embedding model taken from ChengDong Liang's repository.
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Updated Nov 10, 2023
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From the Hugging Face model README
This is a copy of voxceleb_resnet34_LM.onnx speaker embedding model taken from ChengDong Liang's repository.
Copyright 2022 ChengDong Liang
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
from pyannote.audio.pipelines.speaker_verification import PretrainedSpeakerEmbedding
get_embedding = PretrainedSpeakerEmbedding("hbredin/wespeaker-voxceleb-resnet34-LM")
assert waveforms.ndim == 3
batch_size, num_channels, num_samples = waveforms.shape
assert num_channels == 1
embeddings = get_embedding(waveforms)
assert embeddings.ndim == 2
assert embeddings.shape[0] == batch_size