Available JSON-RPC methods¶

Attention

Only supported for Python versions 3.6 and above

version¶

Get the version of benchmarkstt

return str: BenchmarkSTT version

list.normalization¶

Get a list of available core normalization

return object: With key being the normalization name, and value its description

normalization.config¶

Use config file notation to define normalization rules. This notation is a list of normalizers, one per line.

Each normalizer that is based needs a file is followed by a file name of a csv, and can be optionally followed by the file encoding (if different than default). All options are loaded in from this csv and applied to the normalizer.

The normalizers can be any of the core normalizers, or you can refer to your own normalizer class (like you would use in a python import, eg. my.own.package.MyNormalizerClass).

Additional rules:

Normalizer names are case-insensitive.
Arguments MAY be wrapped in double quotes.
If an argument contains a space, newline or double quote, it MUST be wrapped in double quotes.
A double quote itself is represented in this quoted argument as two double quotes: "".

The normalization rules are applied top-to-bottom and follow this format:

[normalization]
# This is a comment

# (Normalizer2 has no arguments)
lowercase

# loads regex expressions from regexrules.csv in "utf 8" encoding
regex regexrules.csv "utf 8"

# load another config file, [section1] and [section2]
config configfile.ini section1
config configfile.ini section2

# loads replace expressions from replaces.csv in default encoding
replace     replaces.csv

param file: The config file
param encoding: The file encoding
param section: The subsection of the config file to use, defaults to 'normalization'
example text: "He bravely turned his tail and fled"
example file: "./resources/test/normalizers/configfile.conf"
example encoding: "UTF-8"
example return: "ha bravalY Turnad his tail and flad"
param text: The text to normalize
param bool return_logs: Return normalization logs

normalization.file¶

Read one per line and pass it to the given normalizer

param str|class normalizer: Normalizer name (or class)
param file: The file to read rules from
param encoding: The file encoding
example text: "This is an Ex-Parakeet"
example normalizer: "regex"
example file: "./resources/test/normalizers/regex/en_US"
example encoding: "UTF-8"
example return: "This is an Ex Parrot"
param text: The text to normalize
param bool return_logs: Return normalization logs

normalization.lowercase¶

Lowercase the text

example text: "Easy, Mungo, easy... Mungo..."
example return: "easy, mungo, easy... mungo..."
param text: The text to normalize
param bool return_logs: Return normalization logs

normalization.regex¶

Simple regex replace. By default the pattern is interpreted case-sensitive.

Case-insensitivity is supported by adding inline modifiers.

You might want to use capturing groups to preserve the case. When replacing a character not captured, the information about its case is lost...

Eg. would replace "HAHA! Hahaha!" to "HeHe! Hehehe!":

search

replace

(?i)(h)a

\1e

No regex flags are set by default, you can set them yourself though in the regex, and combine them at will, eg. multiline, dotall and ignorecase.

Eg. would replace "New<CRLF>line" to "newline":

search

replace

(?msi)new.line

newline

example text: "HAHA! Hahaha!"
example search: '(?i)(h)a'
example replace: '\1e'
example return: "HeHe! Hehehe!"
param text: The text to normalize
param bool return_logs: Return normalization logs

normalization.replace¶

Simple search replace

param search: Text to search for
param replace: Text to replace with
example text: "Nudge nudge!"
example search: "nudge"
example replace: "wink"
example return: "Nudge wink!"
param text: The text to normalize
param bool return_logs: Return normalization logs

normalization.replacewords¶

Simple search replace that only replaces "words", the first letter will be checked case insensitive as well with preservation of case..

param search: Word to search for
param replace: Replace with
example text: "She has a heart of formica"
example search: "a"
example replace: "the"
example return: "She has the heart of formica"
param text: The text to normalize
param bool return_logs: Return normalization logs

normalization.unidecode¶

Unidecode characters to ASCII form, see Python's Unidecode package for more info.

example text: "𝖂𝖊𝖓𝖓 𝖎𝖘𝖙 𝖉𝖆𝖘 𝕹𝖚𝖓𝖘𝖙ü𝖈𝖐 𝖌𝖎𝖙 𝖚𝖓𝖉 𝕾𝖑𝖔𝖙𝖊𝖗𝖒𝖊𝖞𝖊𝖗?"
example return: "Wenn ist das Nunstuck git und Slotermeyer?"
param text: The text to normalize
param bool return_logs: Return normalization logs

list.metrics¶

Get a list of available core metrics

return object: With key being the metrics name, and value its description

metrics.beer¶

Bag of Entities Error Rate, BEER, is defined as the error rate per entity with a bag of words approach:

                    abs(ne_hyp - ne_ref)
BEER (entity)   =   ----------------------
                        ne_ref

ne_hyp = number of detections of the entity in the hypothesis file
ne_ref = number of detections of the entity in the reference file

The WA_BEER for a set of N entities is defined as the weighted average of the BEER for the set of entities:

WA_BEER ([entity_1, ... entity_N) =  w_1*BEER (entity_1)*L_1/L + ... + w_N*BEER (entity_N))*L_N/L

which is equivalent to:

                                    w_1*abs(ne_hyp_1 - ne_ref_1) + ... + w_N*abs(ne_hyp_N - ne_ref_N)
WA_BEER ([entity_1, ... entity_N) = ------------------------------------------------------------------
                                                                    L

L_1 = number of occurrences of entity 1 in the reference document
L = L_1 + ... + L_N

the weights being normalised by the tool:

w_1 + ... + w_N = 1

The input file defines the list of entities and the weight per entity, w_n. It is processed as a json file with the following structure:

{ "entity_1":W_1, "entity_2" : W_2, "entity_3" :W_3 .. }

W_n being the non-normalized weight, the normalization of the weights is performed by the tool as:

            W_n
w_n =   ---------------
        W_1 + ... +W_N

The minimum value for weight being 0.

param ref: Reference text
param hyp: Hypothesis text

metrics.cer¶

Character Error Rate, basically defined as:

insertions + deletions + substitutions
--------------------------------------
    number of reference characters

Character error rate, CER, compare the differences between reference and hypothesis on a character level. A CER measure is usually lower than WER measure, since words might differ on only one or a few characters, and be classified as fully different.

The CER metric might be useful as a perspective on the WER metric. Word endings might be less relevant if the text will be preprocessed with stemming, or minor spelling mistakes might be acceptable in certain situations. A CER metric might also be used to evaluate a source (an ASR) which output a stream of characters rather than words.

Important: The current implementation of the CER metric ignores whitespace characters. A string like 'aa bb cc' will first be split into words, ['aa','bb','cc'], and then merged into a final string for evaluation: 'aabbcc'.

param mode: 'levenshtein' (default).
param differ_class: For future use.
param ref: Reference text
param hyp: Hypothesis text

metrics.diffcounts¶

Get the amount of differences between reference and hypothesis

param ref: Reference text
param hyp: Hypothesis text

metrics.wer¶

Word Error Rate, basically defined as:

insertions + deletions + substitions
------------------------------------
     number of reference words

See: https://en.wikipedia.org/wiki/Word_error_rate

Calculates the WER using one of two algorithms:

[Mode: 'strict' or 'hunt'] Insertions, deletions and substitutions are identified using the Hunt–McIlroy diff algorithm. The 'hunt' mode applies 0.5 weight to insertions and deletions. This algorithm is the one used internally by Python.

See https://docs.python.org/3/library/difflib.html

[Mode: 'levenshtein'] In the context of WER, Levenshtein distance is the minimum edit distance computed at the word level. This implementation uses the Editdistance c++ implementation by Hiroyuki Tanaka: https://github.com/aflc/editdistance. See: https://en.wikipedia.org/wiki/Levenshtein_distance

param mode: 'strict' (default), 'hunt' or 'levenshtein'.
param differ_class: For future use.
param ref: Reference text
param hyp: Hypothesis text

metrics.worddiffs¶

Present differences on a per-word basis

param dialect: Presentation format. Default is 'ansi'.
example dialect: 'html'
param differ_class: For future use.
param ref: Reference text
param hyp: Hypothesis text

list.benchmark¶

Get a list of available core benchmark

return object: With key being the benchmark name, and value its description

benchmark.beer¶

Bag of Entities Error Rate, BEER, is defined as the error rate per entity with a bag of words approach:

                    abs(ne_hyp - ne_ref)
BEER (entity)   =   ----------------------
                        ne_ref

ne_hyp = number of detections of the entity in the hypothesis file
ne_ref = number of detections of the entity in the reference file

The WA_BEER for a set of N entities is defined as the weighted average of the BEER for the set of entities:

WA_BEER ([entity_1, ... entity_N) =  w_1*BEER (entity_1)*L_1/L + ... + w_N*BEER (entity_N))*L_N/L

which is equivalent to:

                                    w_1*abs(ne_hyp_1 - ne_ref_1) + ... + w_N*abs(ne_hyp_N - ne_ref_N)
WA_BEER ([entity_1, ... entity_N) = ------------------------------------------------------------------
                                                                    L

L_1 = number of occurrences of entity 1 in the reference document
L = L_1 + ... + L_N

the weights being normalised by the tool:

w_1 + ... + w_N = 1

The input file defines the list of entities and the weight per entity, w_n. It is processed as a json file with the following structure:

{ "entity_1":W_1, "entity_2" : W_2, "entity_3" :W_3 .. }

W_n being the non-normalized weight, the normalization of the weights is performed by the tool as:

            W_n
w_n =   ---------------
        W_1 + ... +W_N

The minimum value for weight being 0.

param ref

Reference text

param hyp

Hypothesis text

param config

The config to use

param bool return_logs

Return normalization logs

example ref

'Hello darkness my OLD friend'

example hyp

'Hello darkness my old foe'

example config

[normalization]
# using a simple config file
Lowercase

example result

""

benchmark.cer¶

Character Error Rate, basically defined as:

insertions + deletions + substitutions
--------------------------------------
    number of reference characters

Character error rate, CER, compare the differences between reference and hypothesis on a character level. A CER measure is usually lower than WER measure, since words might differ on only one or a few characters, and be classified as fully different.

The CER metric might be useful as a perspective on the WER metric. Word endings might be less relevant if the text will be preprocessed with stemming, or minor spelling mistakes might be acceptable in certain situations. A CER metric might also be used to evaluate a source (an ASR) which output a stream of characters rather than words.

Important: The current implementation of the CER metric ignores whitespace characters. A string like 'aa bb cc' will first be split into words, ['aa','bb','cc'], and then merged into a final string for evaluation: 'aabbcc'.

param mode

'levenshtein' (default).

param differ_class

For future use.

param ref

Reference text

param hyp

Hypothesis text

param config

The config to use

param bool return_logs

Return normalization logs

example ref

'Hello darkness my OLD friend'

example hyp

'Hello darkness my old foe'

example config

[normalization]
# using a simple config file
Lowercase

example result

""

benchmark.diffcounts¶

Get the amount of differences between reference and hypothesis

param ref

Reference text

param hyp

Hypothesis text

param config

The config to use

param bool return_logs

Return normalization logs

example ref

'Hello darkness my OLD friend'

example hyp

'Hello darkness my old foe'

example config

[normalization]
# using a simple config file
Lowercase

example result

""

benchmark.wer¶

Word Error Rate, basically defined as:

insertions + deletions + substitions
------------------------------------
     number of reference words

See: https://en.wikipedia.org/wiki/Word_error_rate

Calculates the WER using one of two algorithms:

[Mode: 'strict' or 'hunt'] Insertions, deletions and substitutions are identified using the Hunt–McIlroy diff algorithm. The 'hunt' mode applies 0.5 weight to insertions and deletions. This algorithm is the one used internally by Python.

See https://docs.python.org/3/library/difflib.html

[Mode: 'levenshtein'] In the context of WER, Levenshtein distance is the minimum edit distance computed at the word level. This implementation uses the Editdistance c++ implementation by Hiroyuki Tanaka: https://github.com/aflc/editdistance. See: https://en.wikipedia.org/wiki/Levenshtein_distance

param mode

'strict' (default), 'hunt' or 'levenshtein'.

param differ_class

For future use.

param ref

Reference text

param hyp

Hypothesis text

param config

The config to use

param bool return_logs

Return normalization logs

example ref

'Hello darkness my OLD friend'

example hyp

'Hello darkness my old foe'

example config

[normalization]
# using a simple config file
Lowercase

example result

""

benchmark.worddiffs¶

Present differences on a per-word basis

param dialect

Presentation format. Default is 'ansi'.

example dialect

'html'

param differ_class

For future use.

param ref

Reference text

param hyp

Hypothesis text

param config

The config to use

param bool return_logs

Return normalization logs

example ref

'Hello darkness my OLD friend'

example hyp

'Hello darkness my old foe'

example config

[normalization]
# using a simple config file
Lowercase

example result

""

help¶

Returns available api methods

return object: With key being the method name, and value its description