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KALDI ASRMODELSHELP
Kaldi's code lives at https://github.com/kaldi-asr/kaldi.To checkout (i.e. clone in the git terminology) the most recent changes, you can use this command git cloneKALDI ASR
Models. This page contains Kaldi models available for download as .tar.gz archives. They may be downloaded and used for any purpose. Older models can be found on the downloads page. If you have models you would like to share on this page please contact us. A Mandarin ASR system developed by DataTang (Beijing) Co.Ltd. KALDI: DOWNLOADING AND INSTALLING KALDI Dowloading Kaldi. We have now transitioned to GitHub for all future development. You first need to install Git. The most current version of Kaldi, possibly includingKALDI ASR
Librispeech ASR model. The following models are provided: (i) TDNN-F based chain model based on the tdnn_1d_sp recipe, trained on 960h Librispeech data with 3x speed perturbation; (ii) Language models RNNLM trained on Librispeech trainiing transcriptions; and (iii) an i-vector extractor trained on a 200h subset of the data.KALDI ASR
DataTang Mandarin ASR System. This is a Mandarin language ASR system developed by DataTang (Beijing) Co.Ltd. The Mandarin TDNN chain model was trained on 1505 hours Chinese Mandarin corpus released by DataTang. The language model KALDI: THE "NNET3" SETUP Introduction. This documentation covers the latest, "nnet3", DNN setup in Kaldi. For an overview of all deep neural network code in Kaldi, explaining Karel's version, see Deep Neural Networks in Kaldi.. The nnet3 setup is intended to support more general kinds of networks than simple feedforward networks (e.g. things like RNNs and LSTMs) in a natural way that should not require any actual coding. KALDI: ONLINENNET2FEATUREPIPELINE CLASS REFERENCE OnlineNnet2FeaturePipeline is a class that's responsible for putting together the various parts of the feature-processing pipeline for neural networks, in an online setting.. The recipe here does not include fMLLR; instead, it assumes we're giving raw features such as MFCC or PLP or filterbank (with no CMVN) to the neural network, and optionally augmenting these with an iVector that describes KALDI: THE KALDI MATRIX LIBRARYSEE MORE ON KALDI-ASR.ORG KALDI: ONLINESILENCEWEIGHTING CLASS REFERENCE 617 // Don't go further backward into the past then 100 frames beforethe most
KALDI: ONLINEENDPOINTCONFIG STRUCT REFERENCE OnlineEndpointRule rule2. rule2 times out after 0.5 seconds of silence if we reached the final-state with good probability (relative_cost < 2.0) after decoding something. Definition at line 141 of file online-endpoint.h. Referenced by kaldi::EndpointDetected ().KALDI ASRMODELSHELP
Kaldi's code lives at https://github.com/kaldi-asr/kaldi.To checkout (i.e. clone in the git terminology) the most recent changes, you can use this command git cloneKALDI ASR
Models. This page contains Kaldi models available for download as .tar.gz archives. They may be downloaded and used for any purpose. Older models can be found on the downloads page. If you have models you would like to share on this page please contact us. A Mandarin ASR system developed by DataTang (Beijing) Co.Ltd. KALDI: DOWNLOADING AND INSTALLING KALDI Dowloading Kaldi. We have now transitioned to GitHub for all future development. You first need to install Git. The most current version of Kaldi, possibly includingKALDI ASR
Librispeech ASR model. The following models are provided: (i) TDNN-F based chain model based on the tdnn_1d_sp recipe, trained on 960h Librispeech data with 3x speed perturbation; (ii) Language models RNNLM trained on Librispeech trainiing transcriptions; and (iii) an i-vector extractor trained on a 200h subset of the data.KALDI ASR
DataTang Mandarin ASR System. This is a Mandarin language ASR system developed by DataTang (Beijing) Co.Ltd. The Mandarin TDNN chain model was trained on 1505 hours Chinese Mandarin corpus released by DataTang. The language model KALDI: THE "NNET3" SETUP Introduction. This documentation covers the latest, "nnet3", DNN setup in Kaldi. For an overview of all deep neural network code in Kaldi, explaining Karel's version, see Deep Neural Networks in Kaldi.. The nnet3 setup is intended to support more general kinds of networks than simple feedforward networks (e.g. things like RNNs and LSTMs) in a natural way that should not require any actual coding. KALDI: THE KALDI MATRIX LIBRARYSEE MORE ON KALDI-ASR.ORG KALDI: ONLINENNET2FEATUREPIPELINE CLASS REFERENCE OnlineNnet2FeaturePipeline is a class that's responsible for putting together the various parts of the feature-processing pipeline for neural networks, in an online setting.. The recipe here does not include fMLLR; instead, it assumes we're giving raw features such as MFCC or PLP or filterbank (with no CMVN) to the neural network, and optionally augmenting these with an iVector that describes KALDI: ONLINESILENCEWEIGHTING CLASS REFERENCE 617 // Don't go further backward into the past then 100 frames beforethe most
KALDI: ONLINEENDPOINTCONFIG STRUCT REFERENCE OnlineEndpointRule rule2. rule2 times out after 0.5 seconds of silence if we reached the final-state with good probability (relative_cost < 2.0) after decoding something. Definition at line 141 of file online-endpoint.h. Referenced by kaldi::EndpointDetected ().KALDI ASR
DataTang Mandarin ASR System. This is a Mandarin language ASR system developed by DataTang (Beijing) Co.Ltd. The Mandarin TDNN chain model was trained on 1505 hours Chinese Mandarin corpus released by DataTang. The language model KALDI: EXAMPLES INCLUDED WITH KALDI Examples included with Kaldi. When you check out the Kaldi source tree (see Downloading and installing Kaldi ), you will find many sets of example scripts in the egs/ directory. This table summarizes some key facts about some of those example scripts; however, it it not an exhaustive list. Name. KALDI: THE BUILD PROCESS (HOW KALDI IS COMPILED) The build process for Windows is separate from the build process for UNIX-like systems, and is described in windows/INSTALL (tested some time ago with Windows 7 and Microsoft Visual Studio 2013). We use scripts to create the Visual Studio 10.0 solution file. There are two options for the math library on Windows: either Intel MKL, or useCygwin
KALDI: ACOUSTIC MODELING CODE Introduction. We will start with a few words about the general philosophy of our modeling code, and why we chose this path. Our aim is for Kaldi to support conventional models (i.e. diagonal GMMs) and Subspace Gaussian Mixture Models (SGMMs), but also to be easily extensible to new kinds of model. KALDI: RUNNING THE EXAMPLE SCRIPTS (40 MINUTES) The next stage of the tutorial is to start running the example scripts for Resource Management. Change directory to the top level (we called it kaldi-1), and then to egs/. Look at the README.txt file in that directory, and specifically look at the Resource Management section. It mentions the LDC catalog number corresponding to the corpus. KALDI: FEATURE EXTRACTION Introduction. Our feature extraction and waveform-reading code aims to create standard MFCC and PLP features, setting reasonable defaults but leaving available the options that people are most likely to want to tweak (for example, the number of mel bins, minimum and maximum frequency cutoffs, and so on). KALDI: FEATUREEXTRACTION The frame-extraction options class. flush. True if we are asserting that this number of samples is 'all there is', false if we expecting more data to possibly come in. This only makes a difference to the answer if opts.snips_edges == false. For offline feature extraction you always want flush == true.KALDI: MEMBER LIST
This is the complete list of members for ConstantSumDescriptor, including all inherited members. KALDI: THE CUDA MATRIX LIBRARY The CUDA matrix library provides access to GPU-based matrix operations with an interface similar to The Kaldi Matrix library.. The general principle is that if you want to be able to run a particular part of the computation the GPU, you would declare the relevant quantities as type CuMatrix or CuVector instead of Matrix or Vector.Then, if you have configured Kaldi to use the GPU and if the KALDI: TRAININGGRAPHCOMPILER CLASS REFERENCE Modifies an FST so that it transuces the same paths, but the input side of the paths can all have theKALDI ASRMODELSHELP
Kaldi's code lives at https://github.com/kaldi-asr/kaldi.To checkout (i.e. clone in the git terminology) the most recent changes, you can use this command git cloneKALDI ASR
Models. This page contains Kaldi models available for download as .tar.gz archives. They may be downloaded and used for any purpose. Older models can be found on the downloads page. If you have models you would like to share on this page please contact us. A Mandarin ASR system developed by DataTang (Beijing) Co.Ltd. KALDI: DOWNLOADING AND INSTALLING KALDI Dowloading Kaldi. We have now transitioned to GitHub for all future development. You first need to install Git. The most current version of Kaldi, possibly includingKALDI ASR
Librispeech ASR model. The following models are provided: (i) TDNN-F based chain model based on the tdnn_1d_sp recipe, trained on 960h Librispeech data with 3x speed perturbation; (ii) Language models RNNLM trained on Librispeech trainiing transcriptions; and (iii) an i-vector extractor trained on a 200h subset of the data. KALDI: EXAMPLES INCLUDED WITH KALDI Examples included with Kaldi. When you check out the Kaldi source tree (see Downloading and installing Kaldi ), you will find many sets of example scripts in the egs/ directory. This table summarizes some key facts about some of those example scripts; however, it it not an exhaustive list. Name.KALDI ASR
DataTang Mandarin ASR System. This is a Mandarin language ASR system developed by DataTang (Beijing) Co.Ltd. The Mandarin TDNN chain model was trained on 1505 hours Chinese Mandarin corpus released by DataTang. The language model KALDI: THE "NNET3" SETUP Introduction. This documentation covers the latest, "nnet3", DNN setup in Kaldi. For an overview of all deep neural network code in Kaldi, explaining Karel's version, see Deep Neural Networks in Kaldi.. The nnet3 setup is intended to support more general kinds of networks than simple feedforward networks (e.g. things like RNNs and LSTMs) in a natural way that should not require any actual coding. KALDI: THE KALDI MATRIX LIBRARYSEE MORE ON KALDI-ASR.ORG KALDI: ONLINENNET2FEATUREPIPELINE CLASS REFERENCE OnlineNnet2FeaturePipeline is a class that's responsible for putting together the various parts of the feature-processing pipeline for neural networks, in an online setting.. The recipe here does not include fMLLR; instead, it assumes we're giving raw features such as MFCC or PLP or filterbank (with no CMVN) to the neural network, and optionally augmenting these with an iVector that describes KALDI: ONLINESILENCEWEIGHTING CLASS REFERENCE 617 // Don't go further backward into the past then 100 frames beforethe most
KALDI ASRMODELSHELP
Kaldi's code lives at https://github.com/kaldi-asr/kaldi.To checkout (i.e. clone in the git terminology) the most recent changes, you can use this command git cloneKALDI ASR
Models. This page contains Kaldi models available for download as .tar.gz archives. They may be downloaded and used for any purpose. Older models can be found on the downloads page. If you have models you would like to share on this page please contact us. A Mandarin ASR system developed by DataTang (Beijing) Co.Ltd. KALDI: DOWNLOADING AND INSTALLING KALDI Dowloading Kaldi. We have now transitioned to GitHub for all future development. You first need to install Git. The most current version of Kaldi, possibly includingKALDI ASR
Librispeech ASR model. The following models are provided: (i) TDNN-F based chain model based on the tdnn_1d_sp recipe, trained on 960h Librispeech data with 3x speed perturbation; (ii) Language models RNNLM trained on Librispeech trainiing transcriptions; and (iii) an i-vector extractor trained on a 200h subset of the data. KALDI: EXAMPLES INCLUDED WITH KALDI Examples included with Kaldi. When you check out the Kaldi source tree (see Downloading and installing Kaldi ), you will find many sets of example scripts in the egs/ directory. This table summarizes some key facts about some of those example scripts; however, it it not an exhaustive list. Name.KALDI ASR
DataTang Mandarin ASR System. This is a Mandarin language ASR system developed by DataTang (Beijing) Co.Ltd. The Mandarin TDNN chain model was trained on 1505 hours Chinese Mandarin corpus released by DataTang. The language model KALDI: THE "NNET3" SETUP Introduction. This documentation covers the latest, "nnet3", DNN setup in Kaldi. For an overview of all deep neural network code in Kaldi, explaining Karel's version, see Deep Neural Networks in Kaldi.. The nnet3 setup is intended to support more general kinds of networks than simple feedforward networks (e.g. things like RNNs and LSTMs) in a natural way that should not require any actual coding. KALDI: THE KALDI MATRIX LIBRARYSEE MORE ON KALDI-ASR.ORG KALDI: ONLINENNET2FEATUREPIPELINE CLASS REFERENCE OnlineNnet2FeaturePipeline is a class that's responsible for putting together the various parts of the feature-processing pipeline for neural networks, in an online setting.. The recipe here does not include fMLLR; instead, it assumes we're giving raw features such as MFCC or PLP or filterbank (with no CMVN) to the neural network, and optionally augmenting these with an iVector that describes KALDI: ONLINESILENCEWEIGHTING CLASS REFERENCE 617 // Don't go further backward into the past then 100 frames beforethe most
KALDI ASR
Librispeech ASR model. The following models are provided: (i) TDNN-F based chain model based on the tdnn_1d_sp recipe, trained on 960h Librispeech data with 3x speed perturbation; (ii) Language models RNNLM trained on Librispeech trainiing transcriptions; and (iii) an i-vector extractor trained on a 200h subset of the data.KALDI ASR
DataTang Mandarin ASR System. This is a Mandarin language ASR system developed by DataTang (Beijing) Co.Ltd. The Mandarin TDNN chain model was trained on 1505 hours Chinese Mandarin corpus released by DataTang. The language model KALDI: THE BUILD PROCESS (HOW KALDI IS COMPILED) The build process for Windows is separate from the build process for UNIX-like systems, and is described in windows/INSTALL (tested some time ago with Windows 7 and Microsoft Visual Studio 2013). We use scripts to create the Visual Studio 10.0 solution file. There are two options for the math library on Windows: either Intel MKL, or useCygwin
KALDI: ACOUSTIC MODELING CODE Introduction. We will start with a few words about the general philosophy of our modeling code, and why we chose this path. Our aim is for Kaldi to support conventional models (i.e. diagonal GMMs) and Subspace Gaussian Mixture Models (SGMMs), but also to be easily extensible to new kinds of model. KALDI: RUNNING THE EXAMPLE SCRIPTS (40 MINUTES) The next stage of the tutorial is to start running the example scripts for Resource Management. Change directory to the top level (we called it kaldi-1), and then to egs/. Look at the README.txt file in that directory, and specifically look at the Resource Management section. It mentions the LDC catalog number corresponding to the corpus. KALDI: FEATURE EXTRACTION Introduction. Our feature extraction and waveform-reading code aims to create standard MFCC and PLP features, setting reasonable defaults but leaving available the options that people are most likely to want to tweak (for example, the number of mel bins, minimum and maximum frequency cutoffs, and so on). KALDI: THE CUDA MATRIX LIBRARY The CUDA matrix library provides access to GPU-based matrix operations with an interface similar to The Kaldi Matrix library.. The general principle is that if you want to be able to run a particular part of the computation the GPU, you would declare the relevant quantities as type CuMatrix or CuVector instead of Matrix or Vector.Then, if you have configured Kaldi to use the GPU and if theKALDI: MEMBER LIST
This is the complete list of members for ConstantSumDescriptor, including all inherited members. KALDI: TRAININGGRAPHCOMPILER CLASS REFERENCE Modifies an FST so that it transuces the same paths, but the input side of the paths can all have the KALDI: ONLINEENDPOINTCONFIG STRUCT REFERENCE OnlineEndpointRule rule2. rule2 times out after 0.5 seconds of silence if we reached the final-state with good probability (relative_cost < 2.0) after decoding something. Definition at line 141 of file online-endpoint.h. Referenced by kaldi::EndpointDetected ().KALDI ASRMODELSHELP
Kaldi's code lives at https://github.com/kaldi-asr/kaldi.To checkout (i.e. clone in the git terminology) the most recent changes, you can use this command git cloneKALDI ASR
Models. This page contains Kaldi models available for download as .tar.gz archives. They may be downloaded and used for any purpose. Older models can be found on the downloads page. If you have models you would like to share on this page please contact us. A Mandarin ASR system developed by DataTang (Beijing) Co.Ltd. KALDI: ABOUT THE KALDI PROJECTSEE MORE ON KALDI-ASR.ORG KALDI: DOWNLOADING AND INSTALLING KALDI Dowloading Kaldi. We have now transitioned to GitHub for all future development. You first need to install Git. The most current version of Kaldi, possibly includingKALDI ASR
Librispeech ASR model. The following models are provided: (i) TDNN-F based chain model based on the tdnn_1d_sp recipe, trained on 960h Librispeech data with 3x speed perturbation; (ii) Language models RNNLM trained on Librispeech trainiing transcriptions; and (iii) an i-vector extractor trained on a 200h subset of the data. KALDI: EXAMPLES INCLUDED WITH KALDI Examples included with Kaldi. When you check out the Kaldi source tree (see Downloading and installing Kaldi ), you will find many sets of example scripts in the egs/ directory. This table summarizes some key facts about some of those example scripts; however, it it not an exhaustive list. Name. KALDI: FEATURE AND MODEL-SPACE TRANSFORMS IN KALDI Kaldi code currently supports a number of feature and model-space transformations and projections. Feature-space transforms and projections are treated in a consistent way by the tools (they are essientially just matrices), and the following sections relate to the commonalities: Applying global linear or affine feature transforms. KALDI: DECODING GRAPH CONSTRUCTION IN KALDISEE MORE ON KALDI-ASR.ORG KALDI: ONLINENNET2FEATUREPIPELINE CLASS REFERENCE OnlineNnet2FeaturePipeline is a class that's responsible for putting together the various parts of the feature-processing pipeline for neural networks, in an online setting.. The recipe here does not include fMLLR; instead, it assumes we're giving raw features such as MFCC or PLP or filterbank (with no CMVN) to the neural network, and optionally augmenting these with an iVector that describes KALDI: DEEP NEURAL NETWORKS IN KALDI We currently have three separate codebases for deep neural nets in Kaldi. All are still active in the sense that the up-to-date recipes refer to all of them. The first one ("nnet1" ( is located in code subdirectories nnet/ and nnetbin/, and is primarily maintained by Karel Vesely. The second is located in code subdirectories nnet2/ andnnet2bin
KALDI ASRMODELSHELP
Kaldi's code lives at https://github.com/kaldi-asr/kaldi.To checkout (i.e. clone in the git terminology) the most recent changes, you can use this command git cloneKALDI ASR
Models. This page contains Kaldi models available for download as .tar.gz archives. They may be downloaded and used for any purpose. Older models can be found on the downloads page. If you have models you would like to share on this page please contact us. A Mandarin ASR system developed by DataTang (Beijing) Co.Ltd. KALDI: ABOUT THE KALDI PROJECTSEE MORE ON KALDI-ASR.ORG KALDI: DOWNLOADING AND INSTALLING KALDI Dowloading Kaldi. We have now transitioned to GitHub for all future development. You first need to install Git. The most current version of Kaldi, possibly includingKALDI ASR
Librispeech ASR model. The following models are provided: (i) TDNN-F based chain model based on the tdnn_1d_sp recipe, trained on 960h Librispeech data with 3x speed perturbation; (ii) Language models RNNLM trained on Librispeech trainiing transcriptions; and (iii) an i-vector extractor trained on a 200h subset of the data. KALDI: EXAMPLES INCLUDED WITH KALDI Examples included with Kaldi. When you check out the Kaldi source tree (see Downloading and installing Kaldi ), you will find many sets of example scripts in the egs/ directory. This table summarizes some key facts about some of those example scripts; however, it it not an exhaustive list. Name. KALDI: FEATURE AND MODEL-SPACE TRANSFORMS IN KALDI Kaldi code currently supports a number of feature and model-space transformations and projections. Feature-space transforms and projections are treated in a consistent way by the tools (they are essientially just matrices), and the following sections relate to the commonalities: Applying global linear or affine feature transforms. KALDI: DECODING GRAPH CONSTRUCTION IN KALDISEE MORE ON KALDI-ASR.ORG KALDI: ONLINENNET2FEATUREPIPELINE CLASS REFERENCE OnlineNnet2FeaturePipeline is a class that's responsible for putting together the various parts of the feature-processing pipeline for neural networks, in an online setting.. The recipe here does not include fMLLR; instead, it assumes we're giving raw features such as MFCC or PLP or filterbank (with no CMVN) to the neural network, and optionally augmenting these with an iVector that describes KALDI: DEEP NEURAL NETWORKS IN KALDI We currently have three separate codebases for deep neural nets in Kaldi. All are still active in the sense that the up-to-date recipes refer to all of them. The first one ("nnet1" ( is located in code subdirectories nnet/ and nnetbin/, and is primarily maintained by Karel Vesely. The second is located in code subdirectories nnet2/ andnnet2bin
KALDI: DECODING GRAPH CONSTRUCTION IN KALDI The overall picture for decoding-graph creation is that we are constructing the graph HCLG = H o C o L o G. Here. G is an acceptor (i.e. its input and output symbols are the same) that encodes the grammar or language model. L is the lexicon; its output symbols are words and its input symbols are phones. C represents the context-dependency: itsKALDI: KALDI TOOLS
Determinize lattices, keeping only the best path (sequence of acoustic states) for each input-symbol sequence. This is a version of lattice-determnize-pruned that accepts the --num-threads option. These programs do pruning as part of the determinization algorithm, which isKALDI ASR
The same acoustic models, only added compiled decoding graph. WER evaluated on eval2000 (entire test set, not just Switchboard subset). notes on how to use this and extend the lexicon KALDI: RUNNING THE EXAMPLE SCRIPTS (40 MINUTES) The next stage of the tutorial is to start running the example scripts for Resource Management. Change directory to the top level (we called it kaldi-1), and then to egs/. Look at the README.txt file in that directory, and specifically look at the Resource Management section. It mentions the LDC catalog number corresponding to the corpus. KALDI: KALDI FOR DUMMIES TUTORIAL Introduction. This is a step by step tutorial for absolute beginners on how to create a simple ASR (Automatic Speech Recognition) system in Kaldi toolkit using your own set of data. I really would have liked to read something like this when I was starting to deal with Kaldi. This is all based on my experience as an amateur in case of speechKALDI ASR
DataTang Mandarin ASR System. This is a Mandarin language ASR system developed by DataTang (Beijing) Co.Ltd. The Mandarin TDNN chain model was trained on 1505 hours Chinese Mandarin corpus released by DataTang. The language model KALDI: KEYWORD SEARCH IN KALDI Introduction. This page describes the keyword search module in Kaldi. Our implementation includes the following features: Lattice indexing for fast keyword retrieval. Proxy keywords to handle out-of-vocabulary (OOV) problem. In the following document, we will focus on word level keyword search for simplicity purpose, but our implementation KALDI: DEEP NEURAL NETWORKS IN KALDI We currently have three separate codebases for deep neural nets in Kaldi. All are still active in the sense that the up-to-date recipes refer to all of them. The first one ("nnet1" ( is located in code subdirectories nnet/ and nnetbin/, and is primarily maintained by Karel Vesely. The second is located in code subdirectories nnet2/ andnnet2bin
KALDI: ONLINE DECODING IN KALDI In the Kaldi scripts, cepstral mean and variance normalization (CMVN) is generally done on a per-speaker basis. Obviously in an online-decoding context, this is impossible to do because it is "non-causal" (the current feature depends on future features). The basic solution we use is to do "moving-window" cepstral meannormalization.
KALDI: MEMBER LIST
This is the complete list of members for ConstantSumDescriptor, including all inherited members.KALDI ASRMODELSHELP
Kaldi's code lives at https://github.com/kaldi-asr/kaldi.To checkout (i.e. clone in the git terminology) the most recent changes, you can use this command git cloneKALDI ASR
Models. This page contains Kaldi models available for download as .tar.gz archives. They may be downloaded and used for any purpose. Older models can be found on the downloads page. If you have models you would like to share on this page please contact us. A Mandarin ASR system developed by DataTang (Beijing) Co.Ltd. KALDI: DOWNLOADING AND INSTALLING KALDI Dowloading Kaldi. We have now transitioned to GitHub for all future development. You first need to install Git. The most current version of Kaldi, possibly includingKALDI ASR
Librispeech ASR model. The following models are provided: (i) TDNN-F based chain model based on the tdnn_1d_sp recipe, trained on 960h Librispeech data with 3x speed perturbation; (ii) Language models RNNLM trained on Librispeech trainiing transcriptions; and (iii) an i-vector extractor trained on a 200h subset of the data. KALDI: THE "NNET3" SETUP Introduction. This documentation covers the latest, "nnet3", DNN setup in Kaldi. For an overview of all deep neural network code in Kaldi, explaining Karel's version, see Deep Neural Networks in Kaldi.. The nnet3 setup is intended to support more general kinds of networks than simple feedforward networks (e.g. things like RNNs and LSTMs) in a natural way that should not require any actual coding. KALDI: FEATURE EXTRACTIONSEE MORE ON KALDI-ASR.ORG KALDI: THE KALDI MATRIX LIBRARYSEE MORE ON KALDI-ASR.ORGKALDI ASR
SRE16 Xvector Model. An xvector DNN trained on augmented Switchboard, Mixer 6, and NIST SREs. The directory also contains a PLDA backend forscoring.
KALDI: ONLINENNET2FEATUREPIPELINE CLASS REFERENCE OnlineNnet2FeaturePipeline is a class that's responsible for putting together the various parts of the feature-processing pipeline for neural networks, in an online setting.. The recipe here does not include fMLLR; instead, it assumes we're giving raw features such as MFCC or PLP or filterbank (with no CMVN) to the neural network, and optionally augmenting these with an iVector that describes KALDI: ONLINESILENCEWEIGHTING CLASS REFERENCE 617 // Don't go further backward into the past then 100 frames beforethe most
KALDI ASRMODELSHELP
Kaldi's code lives at https://github.com/kaldi-asr/kaldi.To checkout (i.e. clone in the git terminology) the most recent changes, you can use this command git cloneKALDI ASR
Models. This page contains Kaldi models available for download as .tar.gz archives. They may be downloaded and used for any purpose. Older models can be found on the downloads page. If you have models you would like to share on this page please contact us. A Mandarin ASR system developed by DataTang (Beijing) Co.Ltd. KALDI: DOWNLOADING AND INSTALLING KALDI Dowloading Kaldi. We have now transitioned to GitHub for all future development. You first need to install Git. The most current version of Kaldi, possibly includingKALDI ASR
Librispeech ASR model. The following models are provided: (i) TDNN-F based chain model based on the tdnn_1d_sp recipe, trained on 960h Librispeech data with 3x speed perturbation; (ii) Language models RNNLM trained on Librispeech trainiing transcriptions; and (iii) an i-vector extractor trained on a 200h subset of the data. KALDI: THE "NNET3" SETUP Introduction. This documentation covers the latest, "nnet3", DNN setup in Kaldi. For an overview of all deep neural network code in Kaldi, explaining Karel's version, see Deep Neural Networks in Kaldi.. The nnet3 setup is intended to support more general kinds of networks than simple feedforward networks (e.g. things like RNNs and LSTMs) in a natural way that should not require any actual coding. KALDI: FEATURE EXTRACTIONSEE MORE ON KALDI-ASR.ORG KALDI: THE KALDI MATRIX LIBRARYSEE MORE ON KALDI-ASR.ORGKALDI ASR
SRE16 Xvector Model. An xvector DNN trained on augmented Switchboard, Mixer 6, and NIST SREs. The directory also contains a PLDA backend forscoring.
KALDI: ONLINENNET2FEATUREPIPELINE CLASS REFERENCE OnlineNnet2FeaturePipeline is a class that's responsible for putting together the various parts of the feature-processing pipeline for neural networks, in an online setting.. The recipe here does not include fMLLR; instead, it assumes we're giving raw features such as MFCC or PLP or filterbank (with no CMVN) to the neural network, and optionally augmenting these with an iVector that describes KALDI: ONLINESILENCEWEIGHTING CLASS REFERENCE 617 // Don't go further backward into the past then 100 frames beforethe most
KALDI ASR
Kaldi's code lives at https://github.com/kaldi-asr/kaldi.To checkout (i.e. clone in the git terminology) the most recent changes, you can use this command git clone KALDI: EXAMPLES INCLUDED WITH KALDI Examples included with Kaldi. When you check out the Kaldi source tree (see Downloading and installing Kaldi ), you will find many sets of example scripts in the egs/ directory. This table summarizes some key facts about some of those example scripts; however, it it not an exhaustive list. Name. KALDI: ACOUSTIC MODELING CODE Introduction. We will start with a few words about the general philosophy of our modeling code, and why we chose this path. Our aim is for Kaldi to support conventional models (i.e. diagonal GMMs) and Subspace Gaussian Mixture Models (SGMMs), but also to be easily extensible to new kinds of model. KALDI: RUNNING THE EXAMPLE SCRIPTS (40 MINUTES) The next stage of the tutorial is to start running the example scripts for Resource Management. Change directory to the top level (we called it kaldi-1), and then to egs/. Look at the README.txt file in that directory, and specifically look at the Resource Management section. It mentions the LDC catalog number corresponding to the corpus. KALDI: THE BUILD PROCESS (HOW KALDI IS COMPILED) The build process for Windows is separate from the build process for UNIX-like systems, and is described in windows/INSTALL (tested some time ago with Windows 7 and Microsoft Visual Studio 2013). We use scripts to create the Visual Studio 10.0 solution file. There are two options for the math library on Windows: either Intel MKL, or useCygwin
KALDI ASR
SRE16 Xvector Model. An xvector DNN trained on augmented Switchboard, Mixer 6, and NIST SREs. The directory also contains a PLDA backend forscoring.
KALDI: THE CUDA MATRIX LIBRARY The CUDA matrix library provides access to GPU-based matrix operations with an interface similar to The Kaldi Matrix library.. The general principle is that if you want to be able to run a particular part of the computation the GPU, you would declare the relevant quantities as type CuMatrix or CuVector instead of Matrix or Vector.Then, if you have configured Kaldi to use the GPU and if theKALDI: MEMBER LIST
This is the complete list of members for ConstantSumDescriptor, including all inherited members. KALDI: VECTOR< Real > CLASS TEMPLATE REFERENCE Init assumes the current contents of the class are invalid (i.e. junk or has already been freed), and it sets the vector to newly allocated memory with the specified dimension. dim == 0 is acceptable. The memory contents pointed to by data_ will be undefined. Definition at line 167 of file kaldi-vector.cc. 167 {. 168 KALDI_ASSERT (dim >= 0); KALDI: TRAININGGRAPHCOMPILER CLASS REFERENCE Modifies an FST so that it transuces the same paths, but the input side of the paths can all have theKALDI ASRMODELSHELP
Kaldi's code lives at https://github.com/kaldi-asr/kaldi.To checkout (i.e. clone in the git terminology) the most recent changes, you can use this command git cloneKALDI ASR
Models. This page contains Kaldi models available for download as .tar.gz archives. They may be downloaded and used for any purpose. Older models can be found on the downloads page. If you have models you would like to share on this page please contact us. A Mandarin ASR system developed by DataTang (Beijing) Co.Ltd. KALDI: DOWNLOADING AND INSTALLING KALDI Dowloading Kaldi. We have now transitioned to GitHub for all future development. You first need to install Git. The most current version of Kaldi, possibly including KALDI ASRKALDI ASR TOOLKITKALDI SPEECH Librispeech ASR model. The following models are provided: (i) TDNN-F based chain model based on the tdnn_1d_sp recipe, trained on 960h Librispeech data with 3x speed perturbation; (ii) Language models RNNLM trained on Librispeech trainiing transcriptions; and (iii) an i-vector extractor trained on a 200h subset of the data. KALDI: FEATURE AND MODEL-SPACE TRANSFORMS IN KALDI Kaldi code currently supports a number of feature and model-space transformations and projections. Feature-space transforms and projections are treated in a consistent way by the tools (they are essientially just matrices), and the following sections relate to the commonalities: Applying global linear or affine feature transforms. KALDI: KALDI FOR DUMMIES TUTORIALKALDI FOR DUMMIESCODING FOR DUMMIESFOR DUMMIES BOOKSPOWER TOOLS FOR DUMMIESPRO TOOLS FOR DUMMIES PDFYOUTUBE FOR DUMMIES PDF FREE Introduction. This is a step by step tutorial for absolute beginners on how to create a simple ASR (Automatic Speech Recognition) system in Kaldi toolkit using your own set of data. I really would have liked to read something like this when I was starting to deal with Kaldi. This is all based on my experience as an amateur in case of speech KALDI: THE "NNET3" SETUP Introduction. This documentation covers the latest, "nnet3", DNN setup in Kaldi. For an overview of all deep neural network code in Kaldi, explaining Karel's version, see Deep Neural Networks in Kaldi.. The nnet3 setup is intended to support more general kinds of networks than simple feedforward networks (e.g. things like RNNs and LSTMs) in a natural way that should not require any actual coding. KALDI: ACOUSTIC MODELING CODE Introduction. We will start with a few words about the general philosophy of our modeling code, and why we chose this path. Our aim is for Kaldi to support conventional models (i.e. diagonal GMMs) and Subspace Gaussian Mixture Models (SGMMs), but also to be easily extensible to new kinds of model. KALDI: KALDI I/O MECHANISMS Input/output mechanisms for fundamental types and STL types. See "Low-level I/O functions" for a list of functions involved in this. We have provided thse functions to make it easier to read and write fundamental types; they are mostly called from the Read and Write functions of Kaldi classes. The Kaldi classes are under no obligationto use
KALDI: VECTOR< Real > CLASS TEMPLATE REFERENCE Init assumes the current contents of the class are invalid (i.e. junk or has already been freed), and it sets the vector to newly allocated memory with the specified dimension. dim == 0 is acceptable. The memory contents pointed to by data_ will be undefined. Definition at line 167 of file kaldi-vector.cc. 167 {. 168 KALDI_ASSERT (dim >= 0);KALDI ASRMODELSHELP
Kaldi's code lives at https://github.com/kaldi-asr/kaldi.To checkout (i.e. clone in the git terminology) the most recent changes, you can use this command git cloneKALDI ASR
Models. This page contains Kaldi models available for download as .tar.gz archives. They may be downloaded and used for any purpose. Older models can be found on the downloads page. If you have models you would like to share on this page please contact us. A Mandarin ASR system developed by DataTang (Beijing) Co.Ltd. KALDI: DOWNLOADING AND INSTALLING KALDI Dowloading Kaldi. We have now transitioned to GitHub for all future development. You first need to install Git. The most current version of Kaldi, possibly including KALDI ASRKALDI ASR TOOLKITKALDI SPEECH Librispeech ASR model. The following models are provided: (i) TDNN-F based chain model based on the tdnn_1d_sp recipe, trained on 960h Librispeech data with 3x speed perturbation; (ii) Language models RNNLM trained on Librispeech trainiing transcriptions; and (iii) an i-vector extractor trained on a 200h subset of the data. KALDI: FEATURE AND MODEL-SPACE TRANSFORMS IN KALDI Kaldi code currently supports a number of feature and model-space transformations and projections. Feature-space transforms and projections are treated in a consistent way by the tools (they are essientially just matrices), and the following sections relate to the commonalities: Applying global linear or affine feature transforms. KALDI: KALDI FOR DUMMIES TUTORIALKALDI FOR DUMMIESCODING FOR DUMMIESFOR DUMMIES BOOKSPOWER TOOLS FOR DUMMIESPRO TOOLS FOR DUMMIES PDFYOUTUBE FOR DUMMIES PDF FREE Introduction. This is a step by step tutorial for absolute beginners on how to create a simple ASR (Automatic Speech Recognition) system in Kaldi toolkit using your own set of data. I really would have liked to read something like this when I was starting to deal with Kaldi. This is all based on my experience as an amateur in case of speech KALDI: THE "NNET3" SETUP Introduction. This documentation covers the latest, "nnet3", DNN setup in Kaldi. For an overview of all deep neural network code in Kaldi, explaining Karel's version, see Deep Neural Networks in Kaldi.. The nnet3 setup is intended to support more general kinds of networks than simple feedforward networks (e.g. things like RNNs and LSTMs) in a natural way that should not require any actual coding. KALDI: ACOUSTIC MODELING CODE Introduction. We will start with a few words about the general philosophy of our modeling code, and why we chose this path. Our aim is for Kaldi to support conventional models (i.e. diagonal GMMs) and Subspace Gaussian Mixture Models (SGMMs), but also to be easily extensible to new kinds of model. KALDI: KALDI I/O MECHANISMS Input/output mechanisms for fundamental types and STL types. See "Low-level I/O functions" for a list of functions involved in this. We have provided thse functions to make it easier to read and write fundamental types; they are mostly called from the Read and Write functions of Kaldi classes. The Kaldi classes are under no obligationto use
KALDI: VECTOR< Real > CLASS TEMPLATE REFERENCE Init assumes the current contents of the class are invalid (i.e. junk or has already been freed), and it sets the vector to newly allocated memory with the specified dimension. dim == 0 is acceptable. The memory contents pointed to by data_ will be undefined. Definition at line 167 of file kaldi-vector.cc. 167 {. 168 KALDI_ASSERT (dim >= 0); KALDI: ABOUT THE KALDI PROJECT Kaldi is similar in aims and scope to HTK. The goal is to have modern and flexible code, written in C++, that is easy to modify and extend. Important features include: Code-level integration with Finite State Transducers (FSTs) We compile against the OpenFst toolkit (using KALDI: DOWNLOADING AND INSTALLING KALDI Dowloading Kaldi. We have now transitioned to GitHub for all future development. You first need to install Git. The most current version of Kaldi, possibly including KALDI: ACOUSTIC MODELING CODE Introduction. We will start with a few words about the general philosophy of our modeling code, and why we chose this path. Our aim is for Kaldi to support conventional models (i.e. diagonal GMMs) and Subspace Gaussian Mixture Models (SGMMs), but also to be easily extensible to new kinds of model. KALDI: KALDI TUTORIAL: OVERVIEW OF THE DISTRIBUTION (20 The src/ directory (10 minutes) Change directory back up to the top level (kaldi-1) and into src/. List the directory. You will see a few files and a large number of subdirectories. Look at the Makefile. At the top it sets the variable SUBDIRS. This is a list of the subdirectories containing code. KALDI: EXAMPLES INCLUDED WITH KALDI Examples included with Kaldi. When you check out the Kaldi source tree (see Downloading and installing Kaldi ), you will find many sets of example scripts in the egs/ directory. This table summarizes some key facts about some of those example scripts; however, it it not an exhaustive list. Name. KALDI: THE BUILD PROCESS (HOW KALDI IS COMPILED) The build process for Windows is separate from the build process for UNIX-like systems, and is described in windows/INSTALL (tested some time ago with Windows 7 and Microsoft Visual Studio 2013). We use scripts to create the Visual Studio 10.0 solution file. There are two options for the math library on Windows: either Intel MKL, or useCygwin
KALDI: FEATUREEXTRACTION The frame-extraction options class. flush. True if we are asserting that this number of samples is 'all there is', false if we expecting more data to possibly come in. This only makes a difference to the answer if opts.snips_edges == false. For offline feature extraction you always want flush == true.KALDI ASR
DataTang Mandarin ASR System. This is a Mandarin language ASR system developed by DataTang (Beijing) Co.Ltd. The Mandarin TDNN chain model was trained on 1505 hours Chinese Mandarin corpus released by DataTang. The language model KALDI: THE KALDI MATRIX LIBRARY The Kaldi matrix library is mostly a C++ wrapper for standard BLAS and LAPACK linear algebra routines. This documentation page provides an overview of how to use the library. See Matrix and vector classes for code-level documentation, and see External matrix libraries for an explanation of how the matrix code makes use of external libraries. KALDI: ONLINEENDPOINTCONFIG STRUCT REFERENCE OnlineEndpointRule rule2. rule2 times out after 0.5 seconds of silence if we reached the final-state with good probability (relative_cost < 2.0) after decoding something. Definition at line 141 of file online-endpoint.h. Referenced by kaldi::EndpointDetected (). ------------------------- Home Documentation Help! Models ------------------------- Contact dpovey@gmail.comPhone: 425 247 4129
(Daniel Povey)
Kaldi's code lives at https://github.com/kaldi-asr/kaldi. To checkout (i.e. clone in the git terminology) the most recent changes, you can use this command git clone https://github.com/kaldi-asr/kaldi or follow the github link and click "Download in zip" on the github page (right hand side of the web page) To browse the model builds that are available (not many), please clickon models .
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