Offline Speech Recognition Demo¶

This demo provides a command-line interface for automatic speech recognition using OpenVINO. Components used by this executable:

lspeech_s5_ext model - Example pre-trained LibriSpeech DNN
speech_library.dll (.so) - Open source speech recognition library that uses OpenVINO Inference Engine, Intel Speech Feature Extraction and Intel Speech Decoder libraries

How It Works¶

The application transcribes speech from a given WAV file and outputs the text to the console.

Running¶

The application requires two command-line parameters, which point to an audio file with speech to transcribe and a configuration file describing the resources to use for transcription.

Parameters for Executable¶

-wave - Path to input WAV to process. WAV file needs to be in the following format: RIFF WAVE PCM 16bit, 16kHz, 1 channel, with header.
-c, --config - Path to configuration file with paths to resources and other parameters.

Example usage:

offline_speech_recognition_app.exe -wave="<path_to_audio>/inputAudio.wav" -c="<path_to_config>/configFile.cfg"

Configuration File Description¶

The configuration file is an ASCII text file where:

Parameter name and its value are separated with the space character
Parameter and value pair ends with the end of line character

Parameter Description¶

Parameter	Description	Value used for demo
`-fe:rt:numCeps`	Number of MFCC cepstrums	13
`-fe:rt:contextLeft`	Numbers of past frames that are stacked to form input vector for neural network inference	5
`-fe:rt:contextRight`	Numbers of future frames that are stacked to form input vector for neural network inference	5
`-fe:rt:hpfBeta`	High pass filter beta coefficient, where 0.0f means no filtering	0.0f
`-fe:rt:inputDataType`	Feature extraction input format description	INT16_16KHZ
`-fe:rt:cepstralLifter`	Lifting factor	22.0f
`-fe:rt:noDct`	Flag: use DCT as final step or not	0
`-fe:rt:featureTransform`	Kaldi feature transform file that normalizes stacked features for neural network inference
`-dec:wfst:acousticModelFName`	Full path to the acoustic model file, for example `openvino_ir.xml`
`-dec:wfst:acousticScaleFactor`	The acoustic log likelihood scaling factor	0.1f
`-dec:wfst:beamWidth`	Viterbi search beam width	14.0f
`-dec:wfst:latticeWidth`	Lattice beam width (extends the beam width)	0.0f
`-dec:wfst:nbest`	Number of n-best hypothesis to be generated	1
`-dec:wfst:confidenceAcousticScaleFactor`	Scaling parameter to factor in acoustic scores in confidence computations	1.0f
`-dec:wfst:confidenceLMScaleFactor`	Scaling parameter to factor in language model in confidence computations	1.0f
`-dec:wfst:hmmModelFName`	Full path to HMM model
`-dec:wfst:fsmFName`	Full path to pronunciation model or full statically composed LM, if static composition is used
`-dec:wfstotf:gramFsmFName`	Full path to grammar model
`-dec:wfst:outSymsFName`	Full path to the output symbols (lexicon) filename
`-dec:wfst:tokenBufferSize`	Token pool size expressed in number of DWORDs	150000
`-dec:wfstotf:traceBackLogSize`	Number of entries in traceback expressed as log2(N)	19
`-dec:wfstotf:minStableFrames`	The time expressed in frames, after which the winning hypothesis is recognized as stable and the final result can be printed	45
`-dec:wfst:maxCumulativeTokenSize`	Maximum fill rate of token buffer before token beam is adjusted to keep token buffer fill constant. Expressed as factor of buffer size (0.0, 1.0)	0.2f
`-dec:wfst:maxTokenBufferFill`	Active token count number triggering beam tightening expressed as factor of buffer size	0.6f
`-dec:wfst:maxAvgTokenBufferFill`	Average active token count number for utterance, which triggers beam tightening when exceeded. Expressed as factor of buffer size	1.0f
`-dec:wfst:tokenBufferMinFill`	Minimum fill rate of token buffer	0.1f
`-dec:wfst:pruningTighteningDelta`	Beam tightening value when token pool usage reaches the pool capacity	1.0f
`-dec:wfst:pruningRelaxationDelta`	Beam relaxation value when token pool is not meeting minimum fill ratio criterion	0.5f
`-dec:wfst:useScoreTrendForEndpointing`	Extend end pointing with acoustic feedback	1
`-dec:wfstotf:cacheLogSize`	Number of entries in LM cache expressed as log2(N)	16
`-eng:output:format`	Format of the speech recognition output	text
`-inference:contextLeft`	IE: Additional stacking option, independent from feature extraction	0
`-inference:contextRight`	IE: Additional stacking option, independent from feature extraction	0
`-inference:device`	IE: Device used for neural computations	CPU
`-inference:numThreads`	IE: Number of threads used by GNA in SW mode	1
`-inference:scaleFactor`	IE: Scale factor used for static quantization	3000.0
`-inference:quantizationBits`	IE: Quantization resolution in bits	16 or 8

Demo Output¶

The resulting transcription for the sample audio file:

[ INFO ] Using feature transformation
[ INFO ] InferenceEngine API
[ INFO ] Device info:
[ INFO ]        CPU: MKLDNNPlugin
[ INFO ] Batch size: 1
[ INFO ] Model loading time: 61.01 ms
Recognition result:
HOW ARE YOU DOING

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