Principle:Speechbrain Speechbrain VoxCeleb Data Preparation
| Property | Value |
|---|---|
| Principle Name | VoxCeleb Data Preparation |
| Domains | Data_Engineering, Speaker_Recognition |
| Related Implementation | Implementation:Speechbrain_Speechbrain_Prepare_Voxceleb |
| Repository | speechbrain/speechbrain |
| Source Context | recipes/VoxCeleb/voxceleb_prepare.py
|
Overview
Preparing speaker recognition datasets with segment-level audio and speaker identity labels. Speaker recognition requires carefully structured datasets where each sample is associated with a speaker identity label and represents a controlled-length audio segment. The VoxCeleb dataset provides in-the-wild speaker data extracted from YouTube celebrity interviews, containing over 7,000 speakers across diverse acoustic conditions.
Theoretical Foundations
Dataset Requirements for Speaker Recognition
Training speaker embedding models demands datasets with specific properties:
- Speaker identity labels: Each audio segment must be labeled with its corresponding speaker identity to enable discriminative training via classification objectives.
- Controlled-length segments: Variable-length utterances are divided into fixed-duration chunks (e.g., 3 seconds) so that each training sample represents a consistent temporal context. This ensures uniform mini-batches and stable gradient computation.
- Sufficient speaker diversity: The training set must contain a large number of distinct speakers (thousands) to learn a generalizable speaker embedding space.
- Acoustic variability: In-the-wild recordings with natural background noise, reverberation, and channel effects produce more robust models than studio-quality data.
Segment Extraction Strategy
Given an utterance of duration D seconds, the preparation extracts non-overlapping chunks of duration seg_dur seconds:
num_chunks = floor(D / seg_dur)
chunk_i: start = i * seg_dur * sample_rate, stop = (i + 1) * seg_dur * sample_rate
Each chunk becomes an independent training sample sharing the speaker label of its parent utterance.
Amplitude Filtering
Segments with average amplitude below a threshold (amp_th, typically 5e-04) are discarded to remove silence or near-silent regions that provide no speaker-discriminative information:
if mean(|signal[start:stop]|) < amp_th:
discard segment
Train/Dev Splitting
The dataset is split into training and development (validation) partitions. Two strategies exist:
- Utterance-level split: Randomly assigns utterances to train/dev according to the split_ratio (e.g., [90, 10]). The same speaker may appear in both sets.
- Speaker-level split: Assigns entire speakers to either train or dev, ensuring no speaker overlap between partitions. This is more rigorous for evaluating generalization.
In both cases, utterances from speakers that appear in the verification test pairs file are excluded from both train and dev sets to prevent data leakage.
Verification Trial Pairs
For evaluation, a verification pairs file specifies trial pairs in the format:
label enrol_utterance_path test_utterance_path
where label is 1 (same speaker) or 0 (different speaker). Separate enrollment and test CSV files are generated from these pairs.
VoxCeleb Dataset Structure
The VoxCeleb dataset is organized hierarchically:
data_folder/
wav/
id10001/ # speaker ID
session01/ # recording session
utterance01.wav
utterance02.wav
session02/
...
id10002/
...
VoxCeleb1 contains 1,211 speakers and VoxCeleb2 contains 5,994 speakers. The combination provides sufficient diversity for training robust speaker embeddings.
Output Format
The preparation produces CSV files with the following columns:
| Column | Description |
|---|---|
| ID | Unique segment identifier (speaker--session--utterance_start_stop) |
| duration | Total utterance duration in seconds |
| wav | Absolute path to the wav file |
| start | Start sample index of the segment |
| stop | Stop sample index of the segment |
| spk_id | Speaker identity string (e.g., id10001) |
Key Design Decisions
- Fixed-duration chunking rather than variable-length: Simplifies batching and ensures the model sees consistent temporal context during training.
- Amplitude thresholding: Low-energy segments carry no speaker information and can degrade training if included.
- Verification file exclusion: Speakers in the verification set must not appear in training data to ensure unbiased evaluation.
- Separate enrollment/test CSVs: The verification pipeline needs distinct data loaders for enrollment and test utterances.