# wav2letter++: The Fastest Open-source Speech Recognition System

arXiv: https://arxiv.org/abs/1812.07625 · HTML: https://mlsanity.com/paper/1812.07625/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2018.

## Built on by (most cited first, 30 of 42)

- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [SUPERB: Speech processing Universal PERformance Benchmark](https://mlsanity.com/paper/2105.01051/) (2021)
- [VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://mlsanity.com/paper/2101.00390/) (2021)
- [Scaling Speech Technology to 1,000+ Languages](https://mlsanity.com/paper/2305.13516/) (2023)
- [Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training](https://mlsanity.com/paper/2104.01027/) (2021)
- [Unsupervised Speech Recognition](https://mlsanity.com/paper/2105.11084/) (2021)
- [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://mlsanity.com/paper/2109.11680/) (2021)
- [Towards Building ASR Systems for the Next Billion Users](https://mlsanity.com/paper/2111.03945/) (2021)
- [Wav2Seq: Pre-training Speech-to-Text Encoder-Decoder Models Using Pseudo Languages](https://mlsanity.com/paper/2205.01086/) (2022)
- [On Neural Architecture Inductive Biases for Relational Tasks](https://mlsanity.com/paper/2206.05056/) (2022)
- [Flashlight: Enabling Innovation in Tools for Machine Learning](https://mlsanity.com/paper/2201.12465/) (2022)
- [SPIRAL: Self-supervised Perturbation-Invariant Representation Learning for Speech Pre-Training](https://mlsanity.com/paper/2201.10207/) (2022)
- [emg2qwerty: A Large Dataset with Baselines for Touch Typing using Surface Electromyography](https://mlsanity.com/paper/2410.20081/) (2024)
- [Supervision-Guided Codebooks for Masked Prediction in Speech Pre-training](https://mlsanity.com/paper/2206.10125/) (2022)
- [Assem-VC: Realistic Voice Conversion by Assembling Modern Speech Synthesis Techniques](https://mlsanity.com/paper/2104.00931/) (2021)
- [Star Temporal Classification: Sequence Classification with Partially Labeled Data](https://mlsanity.com/paper/2201.12208/) (2022)
- [On-demand compute reduction with stochastic wav2vec 2.0](https://mlsanity.com/paper/2204.11934/) (2022)
- [What do End-to-End Speech Models Learn about Speaker, Language and Channel Information? A Layer-wise and Neuron-level Analysis](https://mlsanity.com/paper/2107.00439/) (2021)
- [Wav2Vec-Aug: Improved self-supervised training with limited data](https://mlsanity.com/paper/2206.13654/) (2022)
- [Scaling A Simple Approach to Zero-Shot Speech Recognition](https://mlsanity.com/paper/2407.17852/) (2024)
- [ATCSpeechNet: A multilingual end-to-end speech recognition framework for air traffic control systems](https://mlsanity.com/paper/2102.08535/) (2021)
- [Comparing CTC and LFMMI for out-of-domain adaptation of wav2vec 2.0 acoustic model](https://mlsanity.com/paper/2104.02558/) (2021)
- [Text-To-Speech Data Augmentation for Low Resource Speech Recognition](https://mlsanity.com/paper/2204.00291/) (2022)
- [Improving Medical Speech-to-Text Accuracy with Vision-Language Pre-training Model](https://mlsanity.com/paper/2303.00091/) (2023)
- [Audiomer: A Convolutional Transformer For Keyword Spotting](https://mlsanity.com/paper/2109.10252/) (2021)
- [Huqariq: A Multilingual Speech Corpus of Native Languages of Peru for Speech Recognition](https://mlsanity.com/paper/2207.05498/) (2022)
- [Evaluating OpenAI's Whisper ASR for Punctuation Prediction and Topic Modeling of life histories of the Museum of the Person](https://mlsanity.com/paper/2305.14580/) (2023)
- [TS-SUPERB: A Target Speech Processing Benchmark for Speech Self-Supervised Learning Models](https://mlsanity.com/paper/2505.06660/) (2025)
- [Are E2E ASR models ready for an industrial usage?](https://mlsanity.com/paper/2112.12572/) (2021)
- [A Comparison of Methods for OOV-word Recognition on a New Public Dataset](https://mlsanity.com/paper/2107.08091/) (2021)

## Abstract (arXiv)

This paper introduces wav2letter++, the fastest open-source deep learning speech recognition framework. wav2letter++ is written entirely in C++, and uses the ArrayFire tensor library for maximum efficiency. Here we explain the architecture and design of the wav2letter++ system and compare it to other major open-source speech recognition systems. In some cases wav2letter++ is more than 2x faster than other optimized frameworks for training end-to-end neural networks for speech recognition. We also show that wav2letter++'s training times scale linearly to 64 GPUs, the highest we tested, for models with 100 million parameters. High-performance frameworks enable fast iteration, which is often a crucial factor in successful research and model tuning on new datasets and tasks.