Kaldi is a state-of-the-art speech recognition toolkit written in C++. It’s intended to be used mainly for acoustic modelling research.
Kaldi provides a speech recognition system based on finite-state transducers (using the freely available OpenFst), together with detailed documentation and scripts for building complete recognition systems. Its core library supports modelling of arbitrary phonetic-context sizes, acoustic modelling with subspace Gaussian mixture models (SGMM) as well as standard Gaussian mixture models, together with all commonly used linear and affine transforms.
Even though the software has been in development for some years, it’s in an early stage of development; there are no official releases of the software.
Key Features
- Code-level integration with Finite State Transducers (FSTs) – compiles against the OpenFst toolkit.
- Tools for converting LMs in the standard ARPA format to FSTs.
- Extensive linear algebra support including a matrix library that wraps standard Basic Linear Algebra Subroutines (BLAS) and Linear Algebra Package (LAPACK) routines.
- An extensible design.
- MMI.
- Boosted MMU.
- MCE discriminative training.
- Feature-space discriminative training (similar to fMPE, but based on boosted MMI).
- Deep neural networks.
- Supports Gaussian mixture modules with diagonal and full covariance structures.
- Support both model-space adaptation using maximum likelihood linear regression (MLLR) and feature-space adaptation using feature-space MLLR (fMLLR), also known as constrained MLL.
- Complete recipes.
OpenFst is a library for constructing, combining, optimizing, and searching weighted finite-state transducers (FSTs). Weighted finite-state transducers are automata where each transition has an input label, an output label, and a weight. The more familiar finite-state acceptor is represented as a transducer with each transition’s input and output label equal. Finite-state acceptors are used to represent sets of strings (specifically, regular or rational sets); finite-state transducers are used to represent binary relations between pairs of strings (specifically, rational transductions). The weights can be used to represent the cost of taking a particular transition.
Website: kaldi-asr.org
Support: GitHub Code Repository
Developer: Daniel Povey, Arnab Ghoshal, Gilles Boulianne, and many others
License: Apache License v2.0
Kaldi is written in C++. Learn C++ with our recommended free books and free tutorials.
Related Software
| Speech Recognition Tools | |
|---|---|
| Whisper | Automatic speech recognition (system trained on 680,000 hours of data |
| whisper.cpp | Run Whisper locally with fast, efficient C/C++ speech recognition |
| Flashlight | Fast, flexible machine learning library written entirely in C++. |
| sherpa-onnx | Offline speech recognition for many platforms and languages |
| Kaldi | C++ toolkit designed for speech recognition researchers. |
| FunASR | Versatile speech recognition toolkit for real-world audio |
| faster-whisper | Faster Whisper transcription with reduced memory use |
| SpeechBrain | All-in-one conversational AI toolkit based on PyTorch |
| Handy | Offline speech-to-text application |
| ESPnet | End-to-End speech processing toolkit |
| PocketSphinx | Lightweight speech recognition for embedded and mobile use |
| deepspeech.pytorch | Implementation of DeepSpeech2 using Baidu Warp-CTC. |
| Epicenter | Transcription application with global speech-to-text functionality |
| Julius | Two-pass large vocabulary continuous speech recognition engine |
| Speech Note | Write notes, transcribe speech, translate text and read content aloud |
| aTrain | Private local transcription of recorded speech through a polished GUI |
| hyprwhspr | Native speech-to-text designed for Arch / Omarchy |
| Vocalinux | Local voice dictation through a clean desktop interface |
| ostt | Open Speech-to-Text |
| Simon | Flexible speech recognition software |
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