"""WER print functions.

The functions here are used to print the computed statistics

with human-readable formatting.

They have a file argument, but you can also just use

contextlib.redirect_stdout, which may give a nicer syntax.

Authors

* Aku Rouhe 2020

"""

import sys

from speechbrain.utils import edit_distance

def print_wer_summary(wer_details, file=sys.stdout):

"""Prints out WER summary details in human-readable format.

This function essentially mirrors the Kaldi compute-wer output format.

Arguments

---------

wer_details : dict

Dict of wer summary details,

see ``speechbrain.utils.edit_distance.wer_summary``

for format.

file : stream

Where to write. (default: sys.stdout)

"""

print(

"%WER {WER:.2f} [ {num_edits} / {num_scored_tokens}, {insertions} ins, {deletions} del, {substitutions} sub ]".format( # noqa

**wer_details

),

file=file,

end="",

)

print(

(

" [PARTIAL]"

if wer_details["num_scored_sents"] < wer_details["num_ref_sents"]

else ""

),

file=file,

)

print(

"%SER {SER:.2f} [ {num_erroneous_sents} / {num_scored_sents} ]".format(

**wer_details

),

file=file,

)

print(

"Scored {num_scored_sents} sentences, {num_absent_sents} not present in hyp.".format( # noqa

**wer_details

),

file=file,

)

def print_alignments(

details_by_utterance,

file=sys.stdout,

empty_symbol="<eps>",

separator=" ; ",

print_header=True,

sample_separator=None,

):

"""Print WER summary and alignments.

Arguments

---------

details_by_utterance : list

List of wer details by utterance,

see ``speechbrain.utils.edit_distance.wer_details_by_utterance``

for format. Has to have alignments included.

file : stream

Where to write. (default: sys.stdout)

empty_symbol : str

Symbol to use when aligning to nothing.

separator : str

String that separates each token in the output. Note the spaces in the

default.

print_header: bool

Whether to print headers

sample_separator: str

A separator to put between samples (optional)

"""

if print_header:

_print_alignments_global_header(

file=file, empty_symbol=empty_symbol, separator=separator

)

for dets in details_by_utterance:

if dets["scored"]:

if print_header:

_print_alignment_header(dets, file=file)

_print_alignment(

dets["alignment"],

dets["ref_tokens"],

dets["hyp_tokens"],

file=file,

empty_symbol=empty_symbol,

separator=separator,

)

if sample_separator:

print(sample_separator, file=file)

# The following internal functions are used to

# print out more specific things

def _print_top_wer_utts(top_non_empty, top_empty, file=sys.stdout):

print("=" * 80, file=file)

print("UTTERANCES WITH HIGHEST WER", file=file)

if top_non_empty:

print(

"Non-empty hypotheses -- utterances for which output was produced:",

file=file,

)

for dets in top_non_empty:

print("{key} %WER {WER:.2f}".format(**dets), file=file)

else:

print("No utterances which had produced output!", file=file)

if top_empty:

print(

"Empty hypotheses -- utterances for which no output was produced:",

file=file,

)

for dets in top_empty:

print("{key} %WER {WER:.2f}".format(**dets), file=file)

else:

print("No utterances which had not produced output!", file=file)

def _print_top_wer_spks(spks_by_wer, file=sys.stdout):

print("=" * 80, file=file)

print("SPEAKERS WITH HIGHEST WER", file=file)

for dets in spks_by_wer:

print("{speaker} %WER {WER:.2f}".format(**dets), file=file)

def _print_alignment(

alignment, a, b, empty_symbol="<eps>", separator=" ; ", file=sys.stdout

):

# First, get equal length text for all:

a_padded = []

b_padded = []

ops_padded = []

for op, i, j in alignment: # i indexes a, j indexes b

op_string = str(op)

a_string = str(a[i]) if i is not None else empty_symbol

b_string = str(b[j]) if j is not None else empty_symbol

# NOTE: the padding does not actually compute printed length,

# but hopefully we can assume that printed length is

# at most the str len

pad_length = max(len(op_string), len(a_string), len(b_string))

a_padded.append(a_string.center(pad_length))

b_padded.append(b_string.center(pad_length))

ops_padded.append(op_string.center(pad_length))

# Then print, in the order Ref, op, Hyp

print(separator.join(a_padded), file=file)

print(separator.join(ops_padded), file=file)

print(separator.join(b_padded), file=file)

def _print_alignments_global_header(

empty_symbol="<eps>", separator=" ; ", file=sys.stdout

):

print("=" * 80, file=file)

print("ALIGNMENTS", file=file)

print("", file=file)

print("Format:", file=file)

print("<utterance-id>, WER DETAILS", file=file)

# Print the format with the actual

# print_alignment function, using artificial data:

a = ["reference", "on", "the", "first", "line"]

b = ["and", "hypothesis", "on", "the", "third"]

alignment = [

(edit_distance.EDIT_SYMBOLS["ins"], None, 0),

(edit_distance.EDIT_SYMBOLS["sub"], 0, 1),

(edit_distance.EDIT_SYMBOLS["eq"], 1, 2),

(edit_distance.EDIT_SYMBOLS["eq"], 2, 3),

(edit_distance.EDIT_SYMBOLS["sub"], 3, 4),

(edit_distance.EDIT_SYMBOLS["del"], 4, None),

]

_print_alignment(

alignment,

a,

b,

file=file,

empty_symbol=empty_symbol,

separator=separator,

)

def _print_alignment_header(wer_details, file=sys.stdout):

print("=" * 80, file=file)

print(

"{key}, %WER {WER:.2f} [ {num_edits} / {num_ref_tokens}, {insertions} ins, {deletions} del, {substitutions} sub ]".format( # noqa

**wer_details

),

file=file,

)