Fast Conformer: Efficient Neural Model

Updated 24 November 2025

Fast Conformer is a neural architecture that improves computational efficiency via aggressive subsampling and scalable attention mechanisms while maintaining high accuracy.
It integrates convolution, multi-head self-attention, and operator fusion techniques to reduce inference time and memory usage, enabling real-time and on-device applications.
Empirical benchmarks show up to 6.8× speedup with minimal accuracy loss, making it ideal for scalable ASR, multilingual AVSR, and self-supervised learning tasks.

A Fast Conformer model refers to any Conformer-based neural network architecture that substantially improves computational efficiency, sequence length scalability, and/or real-time inference throughput relative to the original Conformer, while maintaining or improving modeling accuracy. The Fast Conformer paradigm encompasses a set of design patterns—aggressive subsampling, linear or restricted-context attention mechanisms, structural pruning of blocks and normalization, and optimized compute graph transformations—each grounded in and validated by rigorous empirical benchmarks.

1. Defining Properties and Motivation

The Conformer backbone, incorporating multi-head self-attention (MHSA), depthwise convolution, and macaron-style feedforward modules, achieves state-of-the-art results across ASR and other speech tasks. However, the quadratic complexity of the attention mechanism and the typically modest input downsampling constrain its deployment for long-form input, edge-device inference, and billion-scale model scaling.

Fast Conformer models are characterized by one or more of the following:

Sequence length reduction via aggressive convolutional subsampling (up to ×8–×16), yielding O(T/w) sequence lengths and O(T²/w²) attention cost ( $T$ = input length, $w$ = reduction factor) (Rekesh et al., 2023, Huang et al., 23 Aug 2024, Andrusenko et al., 2022, Burchi et al., 14 Mar 2024).
Linearly scalable attention: local attention windows, linear transformer variants (e.g., Performer, FAVOR+, Hydra, multi-head linear attention) or masked/block-sparse attention, reducing O(T²) to O(T)–O(Tw) (Wang et al., 2020, Li et al., 2021, Seki et al., 4 Nov 2025, Botros et al., 2023).
Block optimizations: block structure simplification, parameter sharing, dynamic block skipping, and U-Net-style down/up architectures to further lower compute or memory cost while maximizing parameter efficiency (Tian et al., 2021, Kim et al., 2022, Andrusenko et al., 2022, Fan et al., 2023, Zhu et al., 13 Mar 2024).
Compute graph rewriting and normalization fusion (e.g., LayerNorm to BatchNorm, operator fusion, low-bit quantization), resulting in hardware-level throughput gains (Song et al., 2022, Xu et al., 2023).

The objectives are to enable real-time or on-device ASR, long-form sequence modeling, and scalable self-supervised representation learning for multitask speech systems.

2. Architectural Enhancements and Variants

Several concrete Fast Conformer instantiations and design strategies have been proposed:

Aggressive Subsampling: Fast Conformer (Rekesh et al.) and NEST stack several depthwise 1D convolutions (strides 2–2–2 or 2×2×2) to achieve 8× reduction, with each subsequent Conformer block operating on a temporal resolution of 80 ms rather than the standard 10–40 ms. Squeezeformer and Uconv-Conformer incorporate even deeper hierarchical downsampling with symmetric upsampling blocks (nearest-neighbor/memory skip connections), balancing information loss and decoding resolution (Rekesh et al., 2023, Huang et al., 23 Aug 2024, Kim et al., 2022, Andrusenko et al., 2022).
Linearly Scalable Attention: Fast Conformer optionally replaces full global attention with hybrid local+global-token attention resembling the Longformer; each token attends to a local window (typically w=128) and to a learned global token, with only the latter attending globally. Other approaches use Performer-style random feature approximations (FAVOR+) or linear kernelizable attention (MHLSA in LAC) to reduce computational and memory overhead (Rekesh et al., 2023, Wang et al., 2020, Seki et al., 4 Nov 2025, Li et al., 2021).
Block Structure Simplification: Squeezeformer decomposes the Conformer macaron block into MF (MHSA+FFN) and CF (Conv+FFN) sub-blocks, applies only post-layernorm with learnable scaling, and unifies all nonlinearities to the Swish activation, facilitating inference kernel fusion and further reducing parameter count (Kim et al., 2022). Convolution-only blocks can be used in the lower encoder layers to minimize memory bandwidth, as shown in "Practical Conformer" (Botros et al., 2023).
Keyframe/Blank-Guided Frame Skipping: Skipformer and key-frame-based Fast Conformer architectures deploy an intermediate CTC loss after an early encoder segment, then use blank label probabilities to select “crucial” frames for further encoding and bypass or discard blank-dominant frames. Merging the outputs restores the original data order. This enables up to 31× sequence compression, 80% reduction in inference time, and, with proper configuration, improved recognition accuracy (Zhu et al., 13 Mar 2024, Fan et al., 2023).
Parameter Sharing and Layer Reduction: By adopting a parameter-sharing strategy (single-parameter set across all Conformer layers) and exploiting Layer Consistency (similarity of internal embedding distributions under parameter sharing), Fast Conformer models can be trained and inferred with a variable or reduced number of blocks without significant representation loss—delivering 7.8× parameter reduction and ~40% speedups (Tian et al., 2021).

3. Mathematical Foundations and Computational Complexity

All Fast Conformer models retain the joint attention-convolutional structure of the original architecture, but strategically transform the scaling of the two dominant complexity bottlenecks:

Attention Complexity: Standard MHSA computes O(T²d) for sequence length $T$ and model dimension $d$ . Subsampling reduces T→T/w, yielding O(T²/w²). Linear attention and keyframe-guided approaches reduce this further toward O(Td) or O(U²) where U≪T is number of surviving tokens (Wang et al., 2020, Li et al., 2021, Rekesh et al., 2023, Fan et al., 2023).
Feedforward/Convolution: Downsampling and block partitioning concentrate compute in a small number of high-resolution layers; the remainder run at low resolution with O(T/w) cost (Kim et al., 2022, Andrusenko et al., 2022). Fusion of normalization and activation kernels removes runtime overhead (Song et al., 2022).

Empirical speedups reach 2.8–6.8× for end-to-end throughput with negligible (≤0.2%) absolute WER change in all major speech recognition benchmarks (Rekesh et al., 2023, Huang et al., 23 Aug 2024, Kim et al., 2022, Botros et al., 2023, Moser et al., 13 Dec 2024).

4. Empirical Performance and Benchmarks

The architectural and algorithmic advances above have consistently delivered:

Word Error Rate (WER) and Character Error Rate (CER) Gains: Fast Conformer achieves 4.99% WER vs. 5.19% for baseline Conformer on LibriSpeech test-other (RNNT, 120M params), and can reach 2.52% (1.1B params, FC-XXL). Squeezeformer, Uconv-Conformer, and Skipformer report 0.6–9.2% relative WER/CER reduction over baseline at up to 58% CPU speedup (Rekesh et al., 2023, Kim et al., 2022, Andrusenko et al., 2022, Zhu et al., 13 Mar 2024).
Sequence Compression and Inference Speed: Skipformer compresses input by 22–31× and attains 47–56% higher throughput versus traditional SqueezeFormer/Conformer. Key-Frame approaches discard 60–65% of input frames and reduce encoder compute and wall time by ~3× (Zhu et al., 13 Mar 2024, Fan et al., 2023).
On-Device and Edge Inference: Through model and graph optimizations (e.g., depthwise-separable convolution, memory alignment, operator fusion), Fast Conformer-based systems achieve <0.2 RTF on smartwatches and iPhones, with 10× reduced energy consumption and within 0.1–0.2% WER of full-precision models (Xu et al., 2023, Song et al., 2022, Botros et al., 2023).
Downstream Task Transfer: NEST demonstrates that the FastConformer encoder, when self-supervised with random-projection quantization and noise augmentation, yields state-of-the-art in PR, ASR, KS, ER, SID, SV, SD across the SUPERB benchmark, and outperforms larger Whisper and SeamlessM4T models in multilingual ASR (Huang et al., 23 Aug 2024).

5. Application Domains and Multi-Task Extension

Fast Conformer has proven applicable not only in ASR but in a range of speech-centric modalities:

Multilingual Audio-Visual Speech Recognition (AVSR): Fast Conformer enables a two-branch (audio-visual) hybrid CTC/RNN-T model, scaling to six languages and massive unlabeled data. AVSR SOTA is achieved on LRS3 (0.8% WER) and MuAViC (12.5% WER, −11.9% absolute vs baseline), along with robustness to severe audio-noise via multimodal fusion (Burchi et al., 14 Mar 2024).
Self-supervised Representation Learning: The NEST framework (FastConformer backbone, 8× subsampling, random-projection quantization, generalized noisy augmentation) provides a unified SSL pretraining solution for downstream ASR, SLU, speaker diarization, and translation, consistently advancing SOTA accuracy at substantially reduced compute (Huang et al., 23 Aug 2024).
Speech Enhancement and Generative Models: Favor+ and Hydra-based Fast Conformer variants (DF-Conformer, DC-Hydra) replace quadratic attention with linear, state-space, or bidirectional SSM layers (Hydra/Mamba-based mixers) to preserve modeling fidelity for long sequences with linear memory usage. This is especially impactful for codec-based generative speech denoising and enhancement (Seki et al., 4 Nov 2025).

6. Trade-Offs, Limitations, and Open Directions

Sequence Redundancy Exploitation: Most Fast Conformer variants leverage empirical redundancy in deep sequence representations or acoustic labeling (blank frame dominance). Over-aggressive reduction (e.g., >16× downsampling, blank thresholds too high) harms subword coverage and boundary precision (Zhu et al., 13 Mar 2024, Andrusenko et al., 2022).
Block/Attention Approximation: Performer, FAVOR+, or linear-attention approximations entail stochastic or low-rank error bounds. Hydra-style state-space models offer richer global mixing but require careful optimization to avoid overfitting or gradient drift (Seki et al., 4 Nov 2025, Li et al., 2021, Fan et al., 2023).
Training Instability in Operator-Fused Models: Naive removal of LayerNorm or uncalibrated batch normalization can induce output explosion/collapse—append-only post-normalization/fusion is necessary for stable learning (Song et al., 2022).
Edge-Device Engineering: Extensive graph transformation—data-layout alignment, kernel fusion, chunked computation, quantization—are mandatory to achieve actual hardware-level throughput gains (e.g., <0.2 RTF on Apple Neural Engine-accelerated devices) (Xu et al., 2023).

Potential directions include dynamic/learned skipping thresholds, further integration with block-sparse or hierarchical attention, and synergy with state-space and SSM-based sequence models for transformer replacement at extreme scale or latency requirements.

7. Representative Implementations and Benchmarks

Model / Approach	Key Efficiency Feature(s)	Reported Speedup	Accuracy Change	arXiv Reference
Fast Conformer (Rekesh et al.)	8x subsampling, linearly scalable attention	2.8× encoder throughput	4.99%→4.99% WER (↓)	(Rekesh et al., 2023)
NEST	8x FastConformer, random proj, SSL	×1.5 speed, batch scaling	SOTA on SUPERB, WER↓	(Huang et al., 23 Aug 2024)
Skipformer	Keyframe CTC skip-and-recover	CPU/GPU speed ↑47–56%	4.64%→4.27% CER (↓)	(Zhu et al., 13 Mar 2024)
Squeezeformer	Temporal U-Net, block simplification	40% FLOPs drop	WER↓ 1.4%	(Kim et al., 2022)
DC-Hydra	Hydra SSM (linear, exact global)	Linear memory/seq length	↑ CAcc, UTMOS	(Seki et al., 4 Nov 2025)
Practical Conformer	Performer attention, conv-only	6.8× latency drop	6.5%→7.7% WER (↑)*	(Botros et al., 2023)
Uconv-Conformer	U-Net skip, ×16 reduction	23–47% CPU/GPU speedup	10.9%→9.9% WER (↓)	(Andrusenko et al., 2022)
FusionFormer	BN+ReLU fusion, operator merge	10–15% inference speedup	~0.15–0.2% WER loss	(Song et al., 2022)

*WER restored by second-pass decoding.

Fast Conformer models collectively form the current Pareto frontier for efficient, sequence-length-scalable, and high-accuracy attention–convolutional encoders in speech and multimodal tasks. Their design space—spanning sequence reduction, local-global and kernelized attention, block/module simplification, normalization fusion, on-device execution, and unified self-supervised learning—continues to accelerate research and production deployment across speech processing, AVSR, diarization, enhancement, and beyond (Rekesh et al., 2023, Huang et al., 23 Aug 2024, Seki et al., 4 Nov 2025, Zhu et al., 13 Mar 2024, Kim et al., 2022, Xu et al., 2023, Song et al., 2022).