MODEL PROFILE

Speaker-wavLM-tbr

Orange · Speaker-wavLM-tbr

This model produces embeddings that globally represent the timbral traits of a speaker's voice. These embeddings can be used the same way as for a classical speaker verification (ASV): in order to compare two voice signals, an embeddings vector must be computed for each of them. Then the cosine similarity between the two embeddings can be used for comparison. The main difference with classical ASV embeddings is that, here, only the timbral traits are compared. The following code snippet uses the file spk_embeddings.py to build the architecture of the model. Its weights are then downloaded from this repository. The python file also contains the function to compute the timbral embeddings of an audio file. In this tutorial version, the audio file is expected to be sampled at 16kHz. Depending on the available memory (cpu or gpu), you may change the value of the *max_size* parameter, which is used to truncate the long audio signals.

license:cc-by-sa-3.0
Context windowNot disclosed
Input → Output待核验 → 待核验
Model weightsNot verified
Profile updated2026/11/2 19:15:47
OVERVIEW

Model overview

This model produces embeddings that globally represent the timbral traits of a speaker's voice. These embeddings can be used the same way as for a classical speaker verification (ASV): in order to compare two voice signals, an embeddings vector must be computed for each of them. Then the cosine similarity between the two embeddings can be used for comparison. The main difference with classical ASV embeddings is that, here, only the timbral traits are compared. The following code snippet uses the file spk_embeddings.py to build the architecture of the model. Its weights are then downloaded from this repository. The python file also contains the function to compute the timbral embeddings of an audio file. In this tutorial version, the audio file is expected to be sampled at 16kHz. Depending on the available memory (cpu or gpu), you may change the value of the *max_size* parameter, which is used to truncate the long audio signals.

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

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PRICING & COST

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

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Frequently asked questions

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Sources & traceability

许可证:cc-by-sa-3.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。