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 |
| Flashlight | Fast, flexible machine learning library written entirely in C++. |
| Coqui STT | Deep-learning toolkit for training and deploying speech-to-text models |
| Kaldi | C++ toolkit designed for speech recognition researchers. |
| SpeechBrain | All-in-one conversational AI toolkit based on PyTorch |
| Handy | Offline speech-to-text application |
| ESPnet | End-to-End speech processing toolkit |
| deepspeech.pytorch | Implementation of DeepSpeech2 using Baidu Warp-CTC. |
| Whispering | Transcription application with global speech-to-text functionality |
| Julius | Two-pass large vocabulary continuous speech recognition engine |
| CMUSphinx | Speech recognition system for mobile and server applications |
| Simon | Flexible speech recognition software |
| hyprwhspr | Native speech-to-text designed for Arch / Omarchy |
| ostt | Open Speech-to-Text |
| DeepSpeech | TensorFlow implementation of Baidu's DeepSpeech architecture. |
| OpenSeq2Seq | TensorFlow-based toolkit for sequence-to-sequence models |
| Eesen | End-to-End Speech Recognition |
Read our verdict in the software roundup.
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