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kaldi-asr avatar

kaldi-asr/kaldi

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15,415 stars·5,357 forks·Shell·43 viewskaldi-asr.org↗

Kaldi

Kaldi is an automatic speech recognition toolkit used to train and deploy models that convert spoken audio into text. It functions as a framework for designing and evaluating acoustic and language models through a structured pipeline of processing tools.

The system acts as a cross-platform speech engine, capable of compiling recognition logic for Android and WebAssembly to enable execution on mobile devices and web browsers. It also includes a dedicated converter for migrating speech recognition models from the HTK format into a compatible internal structure.

The toolkit covers a broad range of capabilities, including automatic speech recognition training, GPU accelerated speech processing, and the deployment of speech recognition environments across different hardware architectures.

Features

  • Speech Recognition Systems - Provides a comprehensive toolkit to train and deploy models that convert spoken audio into text.
  • HMM-Based Models - Models speech sounds using hidden Markov models and Gaussian mixture models to represent temporal and spectral patterns.
  • Automatic Speech Recognition - Supports training and evaluating models that convert spoken audio into text using structured pipelines and datasets.
  • Speech-to-Text Modeling Toolkits - Ships a toolkit for designing, training, and evaluating acoustic and language models for speech recognition.

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  • Speech Transcription Engines - Provides a high-performance engine for converting spoken audio into text, targetable to Android and WebAssembly.
  • GPU Acceleration Configurations - Configures containerized environments to use GPU hardware for accelerated training and execution of speech models.
  • Beam Search Decoders - Finds the most likely word sequence by pruning low-probability paths through the search graph during decoding.
  • Finite State Transducers - Combines lexicon and language models into a large graph using finite state transducers for efficient word sequence searching.
  • Cross-Platform Deployment Targets - Distributes recognition logic to mobile and web browsers via Android and WebAssembly compilation.
  • Native Execution Engines - Implements core signal processing and decoding logic in compiled C++ for high performance and low latency.
  • Multi-Target Compilers - Cross-compiles recognition logic for mobile and web targets to ensure execution across diverse hardware.
  • Cross-Platform Binary Generation - Generates platform-specific executable binaries for diverse targets including Android and WebAssembly.
  • Linear Algebra - Utilizes optimized BLAS and LAPACK libraries for high-speed computation of acoustic model weights and probabilities.
  • Artificial Intelligence - Toolkit for speech recognition and audio processing.
  • Frameworks And Toolkits - Speech recognition toolkit featuring speaker diarization recipes.
  • Machine Learning and AI - Toolkit for speech recognition research.
  • Speech Recognition - Toolkit for speech recognition and speaker identification tasks.

    • Star history

    Star history chart for kaldi-asr/kaldiStar history chart for kaldi-asr/kaldi

    How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

    Frequently asked questions

    What does kaldi-asr/kaldi do?

    Kaldi is an automatic speech recognition toolkit used to train and deploy models that convert spoken audio into text. It functions as a framework for designing and evaluating acoustic and language models through a structured pipeline of processing tools.

    What are the main features of kaldi-asr/kaldi?

    The main features of kaldi-asr/kaldi are: Speech Recognition Systems, HMM-Based Models, Automatic Speech Recognition, Speech-to-Text Modeling Toolkits, Speech Transcription Engines, GPU Acceleration Configurations, Beam Search Decoders, Finite State Transducers.

    Which projects share features with kaldi-asr/kaldi?

    Projects with overlapping indexed features include: facebookresearch/fairseq — Fairseq is a PyTorch toolkit for sequence-to-sequence modeling, specializing in neural machine translation, automatic… k2-fsa/sherpa-onnx — Sherpa-ONNX is an ONNX-based speech processing toolkit that provides a local speech recognition engine, an on-device… tensorflow/tensorflow — TensorFlow is a comprehensive machine learning framework designed for the construction, training, and deployment of… pytorch/pytorch — PyTorch is a machine learning framework centered on a GPU-ready tensor library that supports multi-dimensional array… openai/whisper — This project is a speech recognition and translation engine that utilizes a sequence-to-sequence transformer… facebookresearch/omnilingual-asr — Omnilingual-ASR is a multilingual automatic speech recognition framework and toolkit designed to transcribe audio…

    Projects sharing features with Kaldi

    These projects share indexed features with Kaldi. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
    • facebookresearch/fairseqfacebookresearch avatar

      facebookresearch/fairseq

      32,228View on GitHub↗

      Fairseq is a PyTorch toolkit for sequence-to-sequence modeling, specializing in neural machine translation, automatic speech recognition, and large-scale language model training. It provides a framework for processing and aligning diverse data sources, including text, audio, and video, to support tasks such as speech-to-text conversion and multimodal sequence learning. The project is distinguished by its distributed training capabilities, which utilize parameter sharding, mixed-precision training, and CPU offloading to handle models that exceed single-device memory. It also includes specializ

      Python
      View on GitHub↗32,228
    • k2-fsa/sherpa-onnxk2-fsa avatar

      k2-fsa/sherpa-onnx

      13,017View on GitHub↗

      Sherpa-ONNX is an ONNX-based speech processing toolkit that provides a local speech recognition engine, an on-device voice synthesis tool, and a speaker identification framework. It is designed as a cross-platform speech API that enables speech-to-text, text-to-speech, and speaker verification tasks to be executed locally on a device without requiring network access. The project is distinguished by its ability to perform zero-shot voice cloning and speaker diarization on-device. It supports a wide range of hardware accelerations, including GPU and various NPU architectures, and provides a Web

      C++aarch64androidarm32
      View on GitHub↗13,017
    • tensorflow/tensorflowtensorflow avatar

      tensorflow/tensorflow

      195,697View on GitHub↗

      TensorFlow is a comprehensive machine learning framework designed for the construction, training, and deployment of complex mathematical models. It utilizes a graph-based execution model that represents operations as directed acyclic graphs, enabling automatic differentiation and efficient parallel processing. The system provides high-level interfaces for defining neural network architectures, alongside a robust engine for managing multidimensional array structures and tensor mathematics. The framework distinguishes itself through a scalable distributed runtime that orchestrates workloads acr

      C++deep-learningdeep-neural-networksdistributed
      View on GitHub↗195,697
    • pytorch/pytorchpytorch avatar

      pytorch/pytorch

      100,814View on GitHub↗

      PyTorch is a machine learning framework centered on a GPU-ready tensor library that supports multi-dimensional array operations across both CPU and accelerator hardware. It provides a foundational infrastructure for mathematical computation and dynamic neural network construction, utilizing a tape-based automatic differentiation system that allows for flexible, non-static graph execution. The framework is designed for deep integration with Python, enabling natural usage alongside standard scientific computing ecosystems. It distinguishes itself through a comprehensive distributed training sui

      Pythonautograddeep-learninggpu
      View on GitHub↗100,814
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