Principle:Speechbrain Speechbrain Speaker Embedding Precomputation

Property	Value
Concept	Pre-extracting speaker identity embeddings from a pretrained model for conditioning multi-speaker TTS
Domains	Speaker_Recognition, Text_to_Speech
Repository	speechbrain/speechbrain
Knowledge Sources	Desplanques et al. 2020 "ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification"
Related Implementation	Implementation:Speechbrain_Speechbrain_EncoderClassifier_Encode_Batch

Overview

Multi-speaker TTS requires a mechanism to condition the acoustic model on speaker identity so that the same text can be synthesized in different voices. Rather than learning speaker representations from scratch during TTS training, a pretrained speaker verification model extracts fixed-dimensional embeddings (192-dimensional vectors) for each utterance. These embeddings are precomputed once and stored, then loaded during TTS training to condition the acoustic model on speaker identity.

Theoretical Foundation

Speaker Embeddings

A speaker embedding is a fixed-length vector that captures the vocal characteristics of a speaker, including pitch range, speaking rate, timbre, and accent. These embeddings are learned by training a speaker verification model on a large-scale speaker recognition dataset (VoxCeleb), where the model must discriminate between thousands of speakers.

The key insight is that speaker identity and speech content are separable. A well-trained speaker encoder projects utterances from the same speaker close together in embedding space, regardless of what they say, while pushing apart utterances from different speakers.

ECAPA-TDNN Architecture

The default speaker encoder used in SpeechBrain's multi-speaker TTS pipeline is ECAPA-TDNN (Emphasized Channel Attention, Propagation and Aggregation in Time Delay Neural Network). Key architectural features include:

1-dimensional Res2Net blocks with Squeeze-and-Excitation (SE) for multi-scale feature extraction
Multi-layer feature aggregation that concatenates frame-level features from all SE-Res2Net blocks
Channel- and context-dependent statistics pooling that produces utterance-level representations
192-dimensional output embedding that is L2-normalized for cosine similarity scoring

Decoupling Speaker and Content

Precomputing speaker embeddings offers several advantages over jointly learning speaker representations:

Training efficiency: The TTS model does not need to learn speaker discrimination, focusing entirely on the text-to-speech mapping
Zero-shot capability: At inference time, a speaker embedding can be extracted from any reference utterance, enabling voice cloning for speakers never seen during TTS training
Stability: The fixed, pretrained embeddings provide a stable conditioning signal that does not change during TTS training, reducing optimization complexity

Embedding Pipeline

The embedding extraction follows a three-stage pipeline within the EncoderClassifier:

waveform -> compute_features (Fbank) -> mean_var_norm -> embedding_model (ECAPA-TDNN) -> 192-dim embedding

Feature extraction (compute_features): Computes log-mel filterbank energies from the raw waveform at 16 kHz sample rate
Normalization (mean_var_norm): Applies mean-variance normalization to the filterbank features for input standardization
Embedding extraction (embedding_model): The ECAPA-TDNN processes normalized features and outputs a 192-dimensional embedding vector

Storage Strategy

Embeddings are stored in pickle files organized by data split (train, valid, test). Each pickle file contains a Python dictionary mapping utterance IDs to their embedding tensors:

# Structure of the pickle file
{
    "116_288045_000003_000002": tensor([0.0234, -0.0156, ...]),  # 192-dim
    "116_288045_000003_000003": tensor([0.0189, -0.0201, ...]),  # 192-dim
    ...
}

This approach enables efficient batch loading during TTS training via a custom TextMelCollate collation function that looks up embeddings by utterance ID.

Resampling Considerations

The speaker encoder may operate at a different sample rate than the TTS model. For example, the ECAPA-TDNN model trained on VoxCeleb expects 16 kHz audio, while some TTS configurations may use 22050 Hz. The embedding computation handles this by resampling audio to the speaker encoder's expected sample rate before extraction.

Alternative Encoders

SpeechBrain supports an alternative mel-spectrogram-based speaker encoder (MelSpectrogramEncoder) that operates on mel-spectrogram representations rather than raw waveforms. This is intended for future work on speaker consistency loss, where the encoder must operate in the same feature space as the TTS acoustic model.

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