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Speakerbox

Speakerbox is a Python library and application for preparing multi-recording audio datasets and fine-tuning transformer models for few-shot speaker identification. It supports diarization and segment organization, dataset preparation, training, and applying a trained model to new audio.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Speakerbox — OpenViST project profile in Audio & Voice AI.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Speakerbox when you have recordings of the speakers you want to identify and need a workflow from diarization and dataset preparation through model training and inference.

Who it is for

  • Python developers building speaker-identification workflows from recordings containing known speakers.
  • Researchers or audio practitioners who need to prepare labeled segments and fine-tune a speaker model.

Documented uses

  • Organize speech segments from multi-speaker recordings into speaker-labeled datasets.
  • Prepare training, evaluation and test data for speaker identification.
  • Fine-tune a model to identify known speakers in recordings outside the original training set.
  • Apply a trained model to an audio file and receive time-bounded speaker annotations.
Before you choose

Requirements & limits

The public GitHub repository lists an MIT License and documents installation through pip or directly from the development repository. Diarization requires accepting conditions for specified pyannote resources and may require a Hugging Face access token.

  • Diarization quality depends on speaker distinctiveness, speaker count and overlapping speech.
  • The diarization workflow duplicates audio into smaller clips, which requires additional disk space.
  • Manual correction of diarized clusters and labels is part of the documented dataset workflow.
  • The README's reported model results are tied to its described datasets and hardware setup.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Prepare annotated audio for speaker-model training

Use the documented workflow to turn recordings into manually reviewed speaker-labeled segments and prepared training, evaluation and test datasets.

Before you start

  • Multi-speaker audio recordings, preferably named with unique recording IDs.
  • Enough disk space for duplicated, smaller audio clips.
  • For diarization, acceptance of the pyannote speaker-diarization and segmentation conditions; a Hugging Face access token if required after accepting the conditions.

Steps

  1. Install Speakerbox with the command below.

    Documented command
    pip install speakerbox

  2. Complete the pyannote model-condition and, if needed, token prerequisites for diarization.

  3. Use the documented diarize_and_split_audio workflow to cluster and split audio segments into directories.

  4. Manually rename directories to speaker identifiers and remove incorrectly diarized or mislabeled segments.

  5. Prepare training, evaluation and test datasets with expand_labeled_diarized_audio_dir_to_dataset and preprocess_dataset.

Expected outcome

A manually reviewed, labeled dataset prepared for the model-training stage.

Guide limitations

  • Diarization time varies with machine resources and may be lengthy.
  • Clustering accuracy depends on speaker count, voice distinctiveness and overlapping speech.
  • The workflow requires manual correction before dataset preparation.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
16 October 2026
Original website title
CouncilDataProject/speakerbox · Official homepage title · retained in its source language
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