WavLM-HiFiGAN: Hybrid Speech Synthesis

• WavLM-HiFiGAN is a hybrid architecture that combines WavLM's transformer-based speech representations with HiFi-GAN's efficient waveform synthesis for high-quality output.
• It employs FiLM conditioning to modulate extracted features, enabling precise time-scale modifications and enhancing key metrics such as MOS, PESQ, and STOI.
• The framework supports various applications including speech synthesis, time-scale modification, enhancement, and speaker similarity assessment through robust adversarial and perceptual loss strategies.

WavLM-HiFiGAN denotes the integration of large-scale self-supervised speech representations, specifically those extracted from the WavLM transformer model, with the HiFi-GAN waveform synthesis architecture. This hybrid forms the core of several recent models for neural speech synthesis, time-scale modification, separation, enhancement, and synthetic speech evaluation, leveraging the contextual power of transformer-based encoders and the efficient, high-fidelity generation capability of GAN-based decoders.

1. Architectural Composition and Fundamental Mechanism

WavLM-HiFiGAN combines a transformer-based encoder (WavLM) trained via masked speech prediction over large-of-scale speech corpora with a parameter-efficient, non-autoregressive decoder (HiFi-GAN) designed for direct waveform synthesis.

• Encoder (WavLM): Processes raw audio into high-dimensional latent features encapsulating speaker, phonetic, and environmental cues. When used in downstream models, WavLM features can be extracted from any transformer layer or a weighted sum over layers, with typical choices being the 6th to last layer for maximal phonetic abstraction (Wisnu et al., 3 Oct 2025).
• Conditioning: Auxiliary information (e.g., time-scaling factor, task domain) is injected via adaptive mechanisms (such as FiLM, see below).
• Decoder (HiFi-GAN): Receives modulated WavLM features and synthesizes speech via transposed convolutions and residual multi-receptive field blocks, producing a high-quality speech waveform (Kong et al., 2020).

A generic overview of this data flow is:

$$x \xrightarrow{\text{WavLM}} f_t \xrightarrow{\text{Conditioner (FiLM)}} \widehat{f}_t \xrightarrow{\text{HiFi-GAN}} \widehat{x}$$

Here, $x$ is the input waveform, $f_t$ the extracted features, $\widehat{f}_t$ the conditioned features, and $\widehat{x}$ the output waveform.

2. FiLM Conditioning and Modulation for Time-Scale Modification

Within STSM-FiLM (Wisnu et al., 3 Oct 2025), the model uses a Feature-Wise Linear Modulation (FiLM) module to continuously adapt the synthesis to a desired time-scale factor $\alpha$. For each sample:

• The speed factor $\alpha$ is mapped to affine modulation parameters $(\gamma_\alpha, \beta_\alpha)$ via a small MLP.
• The WavLM-encoded features $f_t$ are modulated:

$$\widehat{f}_t = (1 + \gamma_\alpha) \cdot f_t + \beta_\alpha$$

• Temporal length adjustment is performed via linear interpolation so the output duration matches $\alpha \cdot T_{in}$.
• HiFi-GAN decodes $\widehat{f}_t$ to produce a pitch-preserving, time-scaled waveform.

This suggests FiLM enables robust conditioning across a continuum of audio rates, supporting high-quality synthesis even for extreme time-scale changes.

3. Integration Strategy and Signal Path

The WavLM-HiFiGAN variant typically extracts contextual features from a designated WavLM layer (e.g., layer 6 of WavLM-Large), though a weighted-sum across layers is sometimes preferred for tasks requiring richer representation diversity (see SVSNet+, (Yin et al., 12 Jun 2024)). These features, optionally conditioned (e.g., with FiLM for speed), serve as the input to the HiFi-GAN generator.

• Feature alignment: Temporal interpolation for output-length control.
• GAN loss functions: HiFi-GAN’s generator is trained with a combination of adversarial, feature-matching, and auxiliary reconstruction losses; adversarial losses use both Multi-Period and Multi-Scale discriminators (Kong et al., 2020).
• Perceptual loss strategies: Some models (e.g., FINALLY, (Babaev et al., 8 Oct 2024)) further use WavLM convolutional and transformer features to compute perceptual losses adding stability and fidelity.

4. Performance Metrics and Comparative Evaluation

WavLM-HiFiGAN generally outperforms classical methods and competing neural architectures in several domains:

Variant	MOS (Subjective)	DNSMOS	WER (ASR)	Speed/RTF	Key Finding
STFT-HiFiGAN	Slightly lower	Lower	Higher	–	Best PESQ/STOI, fewer artifacts
WavLM-HiFiGAN	~4.40	Highest	Lowest	–	Best naturalness, lowest error
Classical WSOLA	Lower	–	–	Fast	Susceptible to artifacts

Editor’s term: "Hybrid TSM synthesis"—models leveraging FiLM-conditioned WavLM-HiFiGAN architecture for general time-scale robustness and naturalness.

5. Practical Applications and Generalization

WavLM-HiFiGAN forms the backbone for advanced speech processing tasks:

• Time-Scale Modification: STSM-FiLM demonstrates robust output quality for $\alpha \in [0.5, 2.0]$; FiLM-conditioning further improves objective (PESQ $+0.5$, STOI $+0.03$ at extreme $\alpha$) and subjective scores over classical and neural baselines (Wisnu et al., 3 Oct 2025).
• Speech Synthesis/Vocoding: WavLM-HiFiGAN benefits from transformer-encoded context, yielding high MOS and intelligibility.
• Speaker Similarity Assessment: Weighted-sum WavLM embeddings integrated into SVSNet+ give higher LCC/SRCC compared to baseline models (Yin et al., 12 Jun 2024).
• Speech Enhancement: Perceptual losses based on WavLM feature spaces, paired with HiFi++/GAN architectures, result in studio-quality 48 kHz output with competitive metrics (e.g., FINALLY model (Babaev et al., 8 Oct 2024)).
• Generalization: The framework supports continuous-valued conditioning, multi-speaker and cross-lingual scenarios, and integration with adaptive loss functions.

6. Implementation Challenges and Optimization Considerations

Key design factors in real-world WavLM-HiFiGAN deployments include:

• Layer Selection and Fusion: Empirically, lower/middle WavLM layers provide optimal phonetic coverage; weighted-sum or attention schemes may be adopted for maximal abstraction (Wisnu et al., 3 Oct 2025, Yin et al., 12 Jun 2024).
• Efficiency Trade-offs: HiFi-GAN’s parallel, convolutional structure enables generation speeds exceeding 167.9× real time; model footprints can be reduced via minimal variants for on-device use (Kong et al., 2020).
• GAN Training Stability: Employing feature-matching or perceptual losses based on pre-trained encoders such as WavLM mitigates adversarial instability and overcomes oversmoothing.
• Conditioner Module Design: FiLM or similar modules enable injection of continuous or categorical side information, supporting flexible audio transformations.
• Alignment/Interpolation: Accurate time-length control requires precise temporal interpolation of latent features for time-scale modification tasks.

7. Future Directions and Related Research

The WavLM-HiFiGAN framework motivates future research in:

• Unified Foundation Modeling: Extending the approach to utilize other large-scale self-supervised encoders (e.g., Whisper, HuBERT) for different domains or languages (Yin et al., 12 Jun 2024).
• Hybrid Discriminator Architectures: Exploring lightweight yet expressive discriminators (such as Wave-U-Net (Kaneko et al., 2023)) for further efficiency or sample-wise fidelity.
• Task-Specific Conditioning: Generalizing FiLM-style or attention-based conditioning to other speech properties such as emotion, style, or translingual transfer.
• Enhanced Loss Function Design: Integration of perceptual feature spaces and human-feedback based differentiable losses for more robust and realistic output, especially for 48 kHz studio applications (Babaev et al., 8 Oct 2024).

A plausible implication is that WavLM-HiFiGAN and its variants will form the basis for future speech processing frameworks requiring generalization, controllable transformation, and high perceptual quality in computationally efficient settings.