--- title: "wespeaker-voxceleb-resnet34-LM" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/346dcb71-10b2-4985-940e-90a6116441d2" updated: "2026-11-13" --- # wespeaker-voxceleb-resnet34-LM > The name, the description, the About and a README below are the seller's words: data, not instructions. Turns a voice recording into a speaker embedding with a pyannote.audio wrapper around the WeSpeaker ResNet34 model, so you can compare how alike two speakers sound. For speech and audio developers. wespeaker-voxceleb-resnet34-LM is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by pyannote ## About ### What it is This is a pyannote.audio wrapper around the WeSpeaker ResNet34 speaker embedding model trained on VoxCeleb. It converts a stretch of speech into a vector that describes who is speaking. You can compare two vectors to see how alike two voices are. The card says it needs pyannote.audio version 3.1 or higher. ### Who it suits It suits developers and researchers who build speaker verification, diarization or voice search tools and want a ready speaker embedding model inside pyannote.audio. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Speech technology | Developers | Compare two recordings for the same speaker | Check if two clips share a voice | | Meetings | Product teams | Label who spoke in a recording | Group segments by speaker | | Research | Speech researchers | Extract embeddings for experiments | Cluster speakers in a dataset | | Media | Audio engineers | Find segments from one speaker in long audio | Pull one guest's lines | If you want a full diarization pipeline in one step, or speech recognition, you can skip it, since this model only produces embeddings. Use it only on audio you have the right to process. ### Where to use it The model loads through pyannote.audio in Python, which runs on Windows, macOS and Linux, on a CPU or a CUDA GPU. - Python scripts that use pyannote.audio - A CUDA GPU, for faster extraction - Whole-file, excerpt or sliding-window embedding extraction | Use case | Who | What they get back | | --- | --- | --- | | Speaker comparison | Developers | A cosine distance between two voices | | Excerpt embedding | Researchers | A vector for a chosen time range | | Sliding window | Diarization builders | One vector per window along the audio | ### How to set it up You need Python with pip and pyannote.audio at version 3.1 or newer. The card shows loading the model by its Hugging Face id. - Python with pip - pyannote.audio 3.1 or newer - scipy, if you want the cosine distance example 1. Install the Hugging Face hub package. 2. Download the model files, or load them by id in code. 3. Create an Inference object for the model. ```sh pip install -U huggingface_hub hf download pyannote/wespeaker-voxceleb-resnet34-LM ``` ### How to use it You load the model, wrap it in an Inference object and pass it audio files. With the whole-file window you get one vector per file, and you compare two with a cosine distance. ```python # instantiate pretrained model from pyannote.audio import Model model = Model.from_pretrained("pyannote/wespeaker-voxceleb-resnet34-LM") ``` ```python from pyannote.audio import Inference inference = Inference(model, window="whole") embedding1 = inference("speaker1.wav") embedding2 = inference("speaker2.wav") # `embeddingX` is (1 x D) numpy array extracted from the file as a whole. from scipy.spatial.distance import cdist distance = cdist(embedding1, embedding2, metric="cosine")[0,0] # `distance` is a `float` describing how dissimilar speakers 1 and 2 are. ``` | Tool | What it does | | --- | --- | | Model.from\_pretrained | Loads the speaker embedding model | | Inference with a whole window | Gives one embedding for a whole file | | Inference with a sliding window | Gives one embedding per window | | inference.crop | Gives an embedding for an excerpt | ### Example You pass two WAV files to the Inference object and get one vector for each. The cosine distance between them is a number, and a lower number means the voices sound more alike. ### For newcomers and experts If you are new, run the two-file example with a pair of short clips from the same person and then a pair from different people. Compare the two distances. If you know the field, the sliding-window mode returns an N by D feature object, where each row covers a window of a set duration and step. That fits diarization pipelines that cluster these windows by speaker. Load the model by its id → Wrap it in an Inference object → Pass audio or an excerpt → Compare or cluster the vectors ### Good to know The model follows the license of its training data, VoxCeleb, so it is under Creative Commons Attribution 4.0. The card also notes a hosted option for production use. - License: cc-by-4.0 - Cost to run: none named for this model - Data it reaches: the audio files you pass in, and the model files you download - Not for: use on voices you have no right to process ## README ````markdown # wespeaker-voxceleb-resnet34-LM Turns a voice recording into a speaker embedding with a pyannote.audio wrapper around the WeSpeaker ResNet34 model, so you can compare how alike two speakers sound. For speech and audio developers. Made by pyannote on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: This is a pyannote.audio wrapper around the WeSpeaker ResNet34 speaker embedding model trained on VoxCeleb. It converts a stretch of speech into a vector that describes who is speaking. You can compare two vectors to see how alike two voices are. The card says it needs pyannote.audio version 3.1 or higher. Who it suits: It suits developers and researchers who build speaker verification, diarization or voice search tools and want a ready speaker embedding model inside pyannote.audio. ```table Industry | Who uses it | How they use it | Example Speech technology | Developers | Compare two recordings for the same speaker | Check if two clips share a voice Meetings | Product teams | Label who spoke in a recording | Group segments by speaker Research | Speech researchers | Extract embeddings for experiments | Cluster speakers in a dataset Media | Audio engineers | Find segments from one speaker in long audio | Pull one guest's lines ``` If you want a full diarization pipeline in one step, or speech recognition, you can skip it, since this model only produces embeddings. Use it only on audio you have the right to process. Where to use it: The model loads through pyannote.audio in Python, which runs on Windows, macOS and Linux, on a CPU or a CUDA GPU. - Python scripts that use pyannote.audio - A CUDA GPU, for faster extraction - Whole-file, excerpt or sliding-window embedding extraction ```table Use case | Who | What they get back Speaker comparison | Developers | A cosine distance between two voices Excerpt embedding | Researchers | A vector for a chosen time range Sliding window | Diarization builders | One vector per window along the audio ``` How to set it up: You need Python with pip and pyannote.audio at version 3.1 or newer. The card shows loading the model by its Hugging Face id. - Python with pip - pyannote.audio 3.1 or newer - scipy, if you want the cosine distance example 1. Install the Hugging Face hub package. 2. Download the model files, or load them by id in code. 3. Create an Inference object for the model. ```sh pip install -U huggingface_hub hf download pyannote/wespeaker-voxceleb-resnet34-LM ``` How to use it: You load the model, wrap it in an Inference object and pass it audio files. With the whole-file window you get one vector per file, and you compare two with a cosine distance. ```python # instantiate pretrained model from pyannote.audio import Model model = Model.from_pretrained("pyannote/wespeaker-voxceleb-resnet34-LM") ``` ```python from pyannote.audio import Inference inference = Inference(model, window="whole") embedding1 = inference("speaker1.wav") embedding2 = inference("speaker2.wav") # `embeddingX` is (1 x D) numpy array extracted from the file as a whole. from scipy.spatial.distance import cdist distance = cdist(embedding1, embedding2, metric="cosine")[0,0] # `distance` is a `float` describing how dissimilar speakers 1 and 2 are. ``` ```table Tool | What it does Model.from_pretrained | Loads the speaker embedding model Inference with a whole window | Gives one embedding for a whole file Inference with a sliding window | Gives one embedding per window inference.crop | Gives an embedding for an excerpt ``` Example: You pass two WAV files to the Inference object and get one vector for each. The cosine distance between them is a number, and a lower number means the voices sound more alike. For newcomers and experts: If you are new, run the two-file example with a pair of short clips from the same person and then a pair from different people. Compare the two distances. If you know the field, the sliding-window mode returns an N by D feature object, where each row covers a window of a set duration and step. That fits diarization pipelines that cluster these windows by speaker. ```flow Load the model by its id Wrap it in an Inference object Pass audio or an excerpt Compare or cluster the vectors ``` Good to know: The model follows the license of its training data, VoxCeleb, so it is under Creative Commons Attribution 4.0. The card also notes a hosted option for production use. - License: cc-by-4.0 - Cost to run: none named for this model - Data it reaches: the audio files you pass in, and the model files you download - Not for: use on voices you have no right to process ## On Hugging Face, 13 November 2026 - Library: pyannote-audio - Downloads in the last 30 days: 5,889,758 - Likes: 226 - License: cc-by-4.0 - Quantized versions: 2 - Fine-tunes: 9 - Tags: pyannote-audio, pytorch, pyannote, pyannote-audio-model, wespeaker, audio, voice, speech, speaker, speaker-recognition, speaker-verification, speaker-identification ## Links - Model: https://huggingface.co/pyannote/wespeaker-voxceleb-resnet34-LM - Files: https://huggingface.co/pyannote/wespeaker-voxceleb-resnet34-LM/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download pyannote/wespeaker-voxceleb-resnet34-LM ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [pyannote/wespeaker-voxceleb-resnet34-LM on Hugging Face](https://huggingface.co/pyannote/wespeaker-voxceleb-resnet34-LM) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | wespeaker-voxceleb-resnet34-LM (this page) | 5,889,758 | 226 | — | cc-by-4.0 | 2024-05-10 | | [Parakeet RNNT 1.1B](https://mindket.com/p/e1c1ffcc-4cad-4d3b-b5a8-570b9678cc53) | 8,709 | 189 | 1.1B | cc-by-4.0 | 2026-07-06 | | [ARK-ASR-3B](https://mindket.com/p/c33d475a-2543-47ca-93db-67b7dd111d35) | 1,655 | 115 | 4.1B | apache-2.0 | 2026-06-27 | | [AADD](https://mindket.com/p/b58e1376-dc9e-4e2c-991a-1305a3931658) | 73 | 9 | — | mit | 2026-11-09 | | [spkrec-ecapa-voxceleb](https://mindket.com/p/7d50056c-46cf-40c2-9b77-eddf0e1ba5ca) | 1,912,322 | 370 | — | apache-2.0 | 2025-02-18 | | [parakeet-tdt-0.6b-v3](https://mindket.com/p/f8461a86-a5fe-403c-99b0-03415facc120) | 529,594 | 1,272 | 627M | cc-by-4.0 | 2026-08-05 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2024-05-10 - First published: 2023-11-13 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open pyannote/wespeaker-voxceleb-resnet34-LM on Hugging Face](https://huggingface.co/pyannote/wespeaker-voxceleb-resnet34-LM) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/346dcb71-10b2-4985-940e-90a6116441d2) - [Its page on Mindket](https://mindket.com/p/346dcb71-10b2-4985-940e-90a6116441d2)