Command line tool¶
usage: benchmarkstt -r REFERENCE -h HYPOTHESIS [-rt {infer,argument,plaintext}] [-ht {infer,argument,plaintext}] [-o {json,markdown,restructuredtext,simpletextbase}] [--diffcounts [differ_class]] [--wer [mode] [differ_class]] [--worddiffs [dialect] [differ_class]] [--config file [section] [encoding]] [--lowercase] [--regex search replace] [--replace search replace] [--replacewords search replace] [--unidecode] [--log] [--version] [--log-level {critical,fatal,error,warn,warning,info,debug,notset}] [--help]named arguments¶
-r, --reference File to use as reference -h, --hypothesis File to use as hypothesis -o, --output-format Possible choices: json, markdown, restructuredtext, simpletextbase
Format of the outputted results
Default: "restructuredtext"
--log show normalization logs (warning: for large files with many normalization rules this will cause a significant performance penalty and a lot of output data)
Default: False
--version Output benchmarkstt version number
Default: False
--log-level Possible choices: critical, fatal, error, warn, warning, info, debug, notset
Set the logging output level
Default: warning
reference and hypothesis types¶
You can specify which file type the --reference/-r and --hypothesis/-h arguments should be treated as.
Available types: 'infer': Load from a given filename. Automatically infer file type from the filename extension. 'argument': Read the argument and treat as plain text (without reading from file) 'plaintext': Load from a given filename. Treat file as plain text
-rt, --reference-type Possible choices: infer, argument, plaintext
Type of reference file
Default: "infer"
-ht, --hypothesis-type Possible choices: infer, argument, plaintext
Type of hypothesis file
Default: "infer"
available metrics¶
A list of metrics to calculate. At least one metric needs to be provided.
--diffcounts Get the amount of differences between reference and hypothesis --wer Word Error Rate, basically defined as:
insertions + deletions + substitions ------------------------------------ number of reference wordsSee: 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. --worddiffs Present differences on a per-word basis
param dialect: Presentation format. Default is 'cli'. example dialect: 'html' param differ_class: For future use. available normalizers¶
A list of normalizers to execute on the input, can be one or more normalizers which are applied sequentially. The program will automatically find the normalizer in benchmarkstt.normalization.core, then benchmarkstt.normalization and finally in the global namespace.
--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" --lowercase Lowercase the text
example text: "Easy, Mungo, easy... Mungo..." example return: "easy, mungo, easy... mungo..." --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!" --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!" --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" --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?"
Additional tools¶
Some additional helpful tools are available through benchmarkstt-tools
, which provides these subcommands: