SPHINX_FE(1)
π SPHINX_FE π (that's sphinx_fe, section 1 of the General Commands Manual, bestie)
β¨ NAME β¨
sphinx_fe β convert audio files to acoustic feature files π§β¨
πΈ SYNOPSIS πΈ
sphinx_fe [ options ]...
π¦ DESCRIPTION π¦
Okay so picture this: you've got audio files. This little icon right here takes those audio files β and they can be in Microsoft WAV, NIST Sphere, or raw format, she's not picky π β and turns them into acoustic feature files, which is what you feed into batch-mode speech recognition. Slay.
And lowkey? The resulting files are also useful for various other things. Versatile queen. π
A list of options follows, and bestie there are so many, but we're going through every single one, no skipping, periodt.
π THE OPTIONS (all of them, fr fr) π
-alphaβ Preemphasis parameter-argfileβ file (e.g.feat.paramsfrom an acoustic model) to read parameters from. This will override anything set in other command line arguments. π yeah, she overrides everything else, so watch it-blocksizeβ Number of samples to read at a time.-build_outdirsβ Create missing subdirectories in output directory-cβ file for batch processing-cep2specβ Input is cepstral files, output is log spectral files-diβ directory, input file names are relative to this, if defined-ditherβ Add 1/2-bit noise-doβ directory, output files are relative to this-doublebwβ Use double bandwidth filters (same center freq)-eiβ extension to be applied to all input files-eoβ extension to be applied to all output files-exampleβ Shows example of how to use the tool-frateβ Frame rate-helpβ Shows the usage of the tool-iβ audio input file-input_endianβ Endianness of input data, big or little, ignored if NIST or MS Wav-lifterβ Length of sin-curve for liftering, or0for no liftering.-logspecβ Write out logspectral files instead of cepstra-lowerfβ Lower edge of filters-mach_endianβ Endianness of machine, big or little-mswavβ Defines input format as Microsoft Wav (RIFF)-ncepβ Number of cep coefficients-nchansβ Number of channels of data (interlaced samples assumed)-nfftβ Size of FFT-nfiltβ Number of filter banks-nistβ Defines input format as NIST sphere-npartβ Number of parts to run in (supersedes-nskipand-runlenif non-zero)-nskipβ If a control file was specified, the number of utterances to skip at the head of the file-oβ cepstral output file-ofmtβ Format of output files - one ofsphinx,htk,text.-partβ Index of the part to run (supersedes-nskipand-runlenif non-zero)-rawβ Defines input format as raw binary data-remove_dcβ Remove DC offset from each frame-remove_noiseβ Remove noise with spectral subtraction in mel-energies-remove_silenceβ Enables VAD, removes silence frames from processing-round_filtersβ Round mel filter frequencies to DFT points-runlenβ If a control file was specified, the number of utterances to process, or-1for all-samprateβ Sampling rate-seedβ Seed for random number generator; if less than zero, pick our own-smoothspecβ Write out cepstral-smoothed logspectral files-spec2cepβ Input is log spectral files, output is cepstral files-sph2pipeβ Input is NIST sphere (possibly with Shorten), usesph2pipeto convert-transformβ Which type of transform to use to calculate cepstra (legacy,dct, orhtk)-unit_areaβ Normalize mel filters to unit area-upperfβ Upper edge of filters-vad_postspeechβ Num of silence frames to keep after from speech to silence.-vad_prespeechβ Num of speech frames to keep before silence to speech.-vad_startspeechβ Num of speech frames to trigger vad from silence to speech.-vad_thresholdβ Threshold for decision between noise and silence frames. Log-ratio between signal level and noise level.-verboseβ Show input filenames-warp_paramsβ defining the warping function-warp_typeβ Warping function type (or shape)-whichchanβ Channel to process (numbered from 1), or0to mix all channels-wlenβ Hamming window length
π¨ THE PART WHERE I NEED YOU TO LOCK IN π¨
Currently the only kind of features supported are MFCCs (mel-frequency cepstral coefficients). There are numerous options which control the properties of the output features. π§
It is VERY important that you document the specific set of flags used to create any given set of feature files, since this information is NOT recorded in the files themselves, and any mismatch between the parameters used to extract features for recognition and those used to extract features for training will cause recognition to fail. π
Like bestie I am not joking, write down your flags. The files will not remember for you. Recognition will simply fail. Not me watching you lose a whole training run because you forgot which -nfilt you used π
π AUTHOR π
Written by numerous people at CMU from 1994 onwards. This manual page by David Huggins-Daines <dhuggins@cs.cmu.edu> π
π COPYRIGHT π
Copyright Β© 1994-2007 Carnegie Mellon University. See the file COPYING included with this package for more information.
Okay that's the whole page, you're all caught up π«Ά this little cutie is dated 2007-08-27 and she lives in the General Commands Manual, so go forth and extract those features, mwah π β¨πΈ
π¦ where this page comes from
- debian bookworm: sphinxbase-utils 0.8+5prealpha+1-16 Β· BSD-2-Clause
- debian trixie: sphinxbase-utils 0.8+5prealpha+1-21+b1 Β· BSD-2-Clause
- ubuntu jammy: sphinxbase-utils 0.8+5prealpha+1-13build1 Β· BSD-2-Clause
- ubuntu noble: sphinxbase-utils 0.8+5prealpha+1-17build2 Β· BSD-2-Clause
- ubuntu questing: sphinxbase-utils 0.8+5prealpha+1-21build1
- ubuntu resolute: sphinxbase-utils 0.8+5prealpha+1-21ubuntu2 Β· BSD-2-Clause
- π girlified by deepseek-ai/DeepSeek-V4.1-Flash on : a language model's rewrite of the original, under its terms