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Model: NbAiLab/nb-wav2vec2-1b-bokmaal-v2
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2026-10-20 22:51:22 +08:00
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---
license: apache-2.0
---

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import argparse
from transformers import AutoProcessor
from transformers import Wav2Vec2ProcessorWithLM
from pyctcdecode import build_ctcdecoder
def main(args):
processor = AutoProcessor.from_pretrained(args.model_name_or_path)
vocab_dict = processor.tokenizer.get_vocab()
sorted_vocab_dict = {
k.lower(): v for k, v in sorted(vocab_dict.items(), key=lambda item: item[1])
}
decoder = build_ctcdecoder(
labels=list(sorted_vocab_dict.keys()),
kenlm_model_path=args.kenlm_model_path,
)
processor_with_lm = Wav2Vec2ProcessorWithLM(
feature_extractor=processor.feature_extractor,
tokenizer=processor.tokenizer,
decoder=decoder,
)
processor_with_lm.save_pretrained(args.model_name_or_path)
print(
f"Run: ~/bin/build_binary language_model/*.arpa language_model/5gram.bin -T $(pwd) && rm language_model/*.arpa")
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--model_name_or_path', default="./", help='Model name or path. Defaults to ./')
parser.add_argument('--kenlm_model_path', required=True, help='Path to KenLM arpa file.')
args = parser.parse_args()
return args
if __name__ == "__main__":
main(parse_args())

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{"<s>": 39, "</s>": 40}

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{"labels": [" ", "(", ")", "0", "3", "7", "8", "9", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "\u00e5", "\u00e6", "\u00f8", "\u2047", "", "<s>", "</s>"], "is_bpe": false}

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#!/usr/bin/env python
# coding: utf-8
"""From https://github.com/peresolb/number-conversion/"""
import sys
import os
import nltk
# Dict for basic primitive numbers: 1-10
b = {
1: "én",
2: "to",
3: "tre",
4: "fire",
5: "fem",
6: "seks",
7: "sju",
8: "åtte",
9: "ni",
10: "ti",
}
b_nn = {
1: "ein",
2: "to",
3: "tre",
4: "fire",
5: "fem",
6: "seks",
7: "sju",
8: "åtte",
9: "ni",
10: "ti",
}
# Dict for teen primitive numbers: 11-19
t = {
11: "elleve",
12: "tolv",
13: "tretten",
14: "fjorten",
15: "femten",
16: "seksten",
17: "sytten",
18: "atten",
19: "nitten",
}
# Dict for two digit primitive numbers: 20-90
do = {
20: "tjue",
30: "tretti",
40: "førti",
50: "femti",
60: "seksti",
70: "sytti",
80: "åtti",
90: "nitti",
}
# Dict for the 3 digit primitive number: 100
doo = {100: "hundre"}
# Dict for the 4 digit primitive number: 1000
dooo = {1000: "tusen"}
# Reverser function for primitive number dicts
def _revdict(numberdict):
newdict = {}
for k, v in numberdict.items():
newdict[v] = k
return newdict
# The reverse of the primitive number dicts, where strings are keys and integers are values,
# are name of original dict underscore 'r'
b_r, b_nn_r, t_r, do_r, doo_r, dooo_r = (
_revdict(b),
_revdict(b_nn),
_revdict(t),
_revdict(do),
_revdict(doo),
_revdict(dooo),
)
def _oneten(nr, reverse=False, nn=False):
"""Function taking an int from 1-10 and returning the corresponding word,
or, if reverse=True, taking a numberword from 1-10 and returning the digit"""
if reverse == False:
if not type(nr) is int:
return None
if nr <= 10:
if nn == False:
return b[nr]
else:
return b_nn[nr]
else:
if not type(nr) is str:
return None
if nn == False:
if nr in b_r.keys():
return b_r[nr]
else:
if nr in b_nn_r.keys():
return b_nn_r[nr]
def _onedig(nr, reverse=False, nn=False):
if reverse == False:
if not _oneten(nr) == "ti":
if nn == False:
return _oneten(nr)
else:
return _oneten(nr, nn=True)
if reverse == True:
if not _oneten(nr, reverse=True) == 10:
if nn == False:
return _oneten(nr, reverse=True)
else:
return _oneten(nr, reverse=True, nn=True)
def _teen(nr, reverse=False):
"""Function taking a primitive two-digit int in the teen range and returning the
corresponding word, or, if reverse=True, the corresponding number word"""
if reverse == False:
if not type(nr) is int:
return None
if nr in t.keys():
return t[nr]
else:
if not type(nr) is str:
return None
if nr in t_r.keys():
return t_r[nr]
def _twodig(nr, reverse=False):
"""Function taking a primitive two-digit int in the range 20-90 and returning
the corresponding word, or, if reverse=True, the corresponding number word"""
if reverse == False:
if not type(nr) is int:
return None
if nr in do.keys():
return do[nr]
else:
if not type(nr) is str:
return None
if nr in do_r.keys():
return do_r[nr]
def _numparser(numword, nn=False):
"""Parse word to see if they start with a wd in firstnumwords.
If yes, return firstnumword and second part"""
if not type(numword) is str:
return None
firstpart = ""
scndpart = ""
firstnumwords = list(do_r.keys())
for s in firstnumwords:
if numword.startswith(s):
slength = len(s)
firstpart = s
scndpart = numword[slength:]
if nn == False:
if (
scndpart in b_r.keys() and b_r[scndpart] < 10
): # Only return if second part is dig below 10
return (firstpart, scndpart)
else:
if (
scndpart in b_nn_r.keys() and b_nn_r[scndpart] < 10
): # Only return if second part is dig below 10
return (firstpart, scndpart)
def _one_to_nineteen(nr, reverse=False, nn=False):
"""Function taking a primitive two-digit int in the range 1-19
and returning the corresponding word, or, if reverse=True, the corresponding number word"""
if reverse == False:
if not type(nr) is int:
return None
if nr < 11:
if nn == False:
return _oneten(nr)
else:
return _oneten(nr, nn=True)
elif nr < 20:
return _teen(nr)
else:
if not type(nr) is str:
return None
if nn == False:
if type(_oneten(nr, reverse=True)) is int:
return _oneten(nr, reverse=True)
elif type(_teen(nr, reverse=True)):
return _teen(nr, reverse=True)
else:
if type(_oneten(nr, reverse=True, nn=True)) is int:
return _oneten(nr, reverse=True, nn=True)
elif type(_teen(nr, reverse=True)):
return _teen(nr, reverse=True)
def _one_to_nn(nr, reverse=False, nn=False):
"""Function taking an int in the range 1-99 and returning the corresponding word. Reverse as before"""
if reverse == False:
if not type(nr) is int:
return None
if nr > 0:
if nr < 20:
if nn == False:
return _one_to_nineteen(nr)
else:
return _one_to_nineteen(nr, nn=True)
elif nr < 100:
if nr in do.keys():
return _twodig(nr)
else:
nrstring = str(nr)
frstdig = int(nrstring[0]) * 10
scndig = int(nrstring[1])
frstwd = _twodig(frstdig)
if nn == False:
scnwd = _onedig(scndig)
else:
scnwd = _onedig(scndig, nn=True)
nrwd = frstwd + scnwd
return nrwd
else:
if not type(nr) is str:
return None
if nn == False:
if type(_one_to_nineteen(nr, reverse=True)) is int:
return _one_to_nineteen(nr, reverse=True)
elif type(_twodig(nr, reverse=True)) is int:
return _twodig(nr, reverse=True)
else:
if _numparser(nr) == None:
return None
parsed = _numparser(nr)
first = _twodig(parsed[0], reverse=True)
second = _one_to_nineteen(parsed[1], reverse=True)
return first + second
else:
if type(_one_to_nineteen(nr, reverse=True, nn=True)) is int:
return _one_to_nineteen(nr, reverse=True, nn=True)
elif type(_twodig(nr, reverse=True)) is int:
return _twodig(nr, reverse=True)
else:
if _numparser(nr, nn=True) == None:
return None
parsed = _numparser(nr, nn=True)
first = _twodig(parsed[0], reverse=True)
second = _one_to_nineteen(parsed[1], reverse=True, nn=True)
return first + second
def _one_to_nnn(nr, reverse=False, nn=False):
"""Function taking an int in the range 1-999 and returning the corresponding word. Reverse as before"""
if reverse == False:
if not type(nr) is int:
return None
if nr == 0:
return None
if nr < 100: # 1-99
if nn == False:
return _one_to_nn(nr)
else:
return _one_to_nn(nr, nn=True)
elif nr < 1000:
if nr == 100: # 100
return doo[100]
else:
nrstring = str(nr) # 435 181
frstdig = int(nrstring[0]) # 4 1
scndig = int(nrstring[1]) # 3 8
thrdig = int(nrstring[2]) # 5 1
scthdig = int(nrstring[1:]) # 35 81
if nn == False:
frstwd = _onedig(frstdig) # fire
else:
frstwd = _onedig(frstdig, nn=True)
nrwd = ""
if scndig == 0: # 405 or 400
if thrdig == 0: # 400
nrwd = "%s %s" % (frstwd, doo[100]) # fire hundre
else: # 405
if nn == False:
thrdwd = _one_to_nn(thrdig) # fem
else:
thrdwd = _one_to_nn(thrdig, nn=True)
if frstdig != 1:
nrwd = "%s %s og %s" % (
frstwd,
doo[100],
thrdwd,
) # fire hundre og fem
else:
nrwd = "%s og %s" % (doo[100], thrdwd) # hundre og fem
else: # 435
scthwd = ""
if nn == False:
scthwd = _one_to_nn(scthdig) # trettifem
else:
scthwd = _one_to_nn(scthdig, nn=True)
if frstdig != 1:
nrwd = "%s %s og %s" % (frstwd, doo[100], scthwd)
else:
nrwd = "%s og %s" % (doo[100], scthwd) # hundre og trettifem
return nrwd
else:
if not type(nr) is str:
return None
if type(doo_r.get(nr, None)) is int: # hundre - 100
return doo_r[nr]
elif (
len(nr.split(" ")) == 1
and type(_one_to_nn(nr, reverse=True)) is int
and nn == False
):
return _one_to_nn(nr, reverse=True) # 44
elif (
len(nr.split(" ")) == 1
and type(_one_to_nn(nr, reverse=True, nn=True)) is int
and nn == True
):
return _one_to_nn(nr, reverse=True, nn=True) # 44
elif len(nr.split(" ")) == 2: # to hundre
splitwords = nr.split(" ")
if nn == False and nr == "ett hundre":
return 100
elif nn == True and nr == "eitt hundre":
return 100
elif (
type(_one_to_nn(splitwords[0], reverse=True)) is int
and splitwords[1] == "hundre"
):
return _one_to_nn(splitwords[0], reverse=True) * 100
elif len(nr.split(" ")) == 3: # hundre og tre
splitwords = nr.split(" ")
if splitwords[0] == "hundre" and splitwords[1] == "og":
if nn == False:
if type(_one_to_nn(splitwords[2], reverse=True)) is int:
return 100 + _one_to_nn(splitwords[2], reverse=True)
else:
if type(_one_to_nn(splitwords[2], reverse=True, nn=True)) is int:
return 100 + _one_to_nn(splitwords[2], reverse=True, nn=True)
else:
return None
elif len(nr.split(" ")) == 4: # ett hundre og trettifire, fire hundre og åtte
splitwords = nr.split(" ")
if nn == False:
if (
splitwords[0] == "ett"
and splitwords[1] == "hundre"
and splitwords[2] == "og"
and type(_one_to_nn(splitwords[3], reverse=True)) is int
): # ett hundre og trettifire
return 100 + _one_to_nn(splitwords[3], reverse=True)
elif (
type(_one_to_nn(splitwords[0], reverse=True)) is int
and _one_to_nn(splitwords[0], reverse=True) < 10
and splitwords[1] == "hundre"
and splitwords[2] == "og"
and type(_one_to_nn(splitwords[3], reverse=True)) is int
): # fire hundre og trettifire
hundreds = _one_to_nn(splitwords[0], reverse=True) * 100
tens = _one_to_nn(splitwords[3], reverse=True)
return hundreds + tens
else:
return None
else:
if (
splitwords[0] == "eitt"
and splitwords[1] == "hundre"
and splitwords[2] == "og"
and type(_one_to_nn(splitwords[3], reverse=True, nn=True)) is int
): # eit hundre og trettifire
return 100 + _one_to_nn(splitwords[3], reverse=True, nn=True)
elif (
type(_one_to_nn(splitwords[0], reverse=True, nn=True)) is int
and _one_to_nn(splitwords[0], reverse=True, nn=True) < 10
and splitwords[1] == "hundre"
and splitwords[2] == "og"
and type(_one_to_nn(splitwords[3], reverse=True, nn=True)) is int
): # fire hundre og trettifire
hundreds = _one_to_nn(splitwords[0], reverse=True, nn=True) * 100
tens = _one_to_nn(splitwords[3], reverse=True, nn=True)
return hundreds + tens
else:
return None
def _high_hundred(nr, nn=False):
"""In Norwegian, as in English, it is possible to express the numbers 1100-1999 with hundreds,
e.g. "tolv hundre og nittiåtte", /twelve hundred and ninety-eight/. We want to be able to convert
these to integers. However, we don't need to produce them, so this algoritm only goes from strings to integers"""
if not type(nr) is str:
return None
if len(nr.split(" ")) > 1:
frstwd = nr.split(" ")[0]
if not type(_teen(frstwd, reverse=True)) is int:
return None
frstdig = _teen(frstwd, reverse=True)
if len(nr.split(" ")) == 2 and nr.split(" ")[1] == "hundre": # femten hundre
return frstdig * 100
elif (
len(nr.split(" ")) == 4
and nr.split(" ")[1] == "hundre"
and nr.split(" ")[2] == "og"
):
if (
nn == False and type(_one_to_nn(nr.split(" ")[3], reverse=True)) is int
): # femten hundre og førtito
lastdigs = _one_to_nn(nr.split(" ")[3], reverse=True)
return (frstdig * 100) + lastdigs
elif (
nn == True
and type(_one_to_nn(nr.split(" ")[3], reverse=True, nn=True)) is int
): # femten hundre og førtito
lastdigs = _one_to_nn(nr.split(" ")[3], reverse=True, nn=True)
return (frstdig * 100) + lastdigs
def _one_to_nnnnnn(nr, reverse=False, nn=False):
"""Function taking an int in the range 1-999999 and returning the corresponding word. Reverse as before"""
if reverse == False:
if not type(nr) is int:
return None
if nr == 0:
return None
if nr < 1000: # 1-999
if nn == False:
return _one_to_nnn(nr)
else:
return _one_to_nnn(nr, nn=True)
elif nr < 1000000: # 1000-999999
if nr == 1000: # 1000
if nn == False:
return "ett tusen"
else:
return "eitt tusen"
else:
nrstring = str(nr) # Starting with last three digits. e.g. 23[456]
ultdig = int(nrstring[-1]) # 6
penultdig = int(nrstring[-2]) # 5
antepenultdig = int(nrstring[-3]) # 4
ult_and_penultdig = int(nrstring[-2:]) # 56
ult_penult_antepenultdig = int(nrstring[-3:]) # 456
tailstring = ""
if antepenultdig == 0: # 012, 002, 000
if penultdig == 0: # 000, 002
if ultdig == 0: # 000
tailstring = "tusen"
else: # 002
if nn == False:
ultstring = _one_to_nnn(ultdig)
tailstring = "tusen og %s" % ultstring # tusen og to
else:
ultstring = _one_to_nnn(ultdig, nn=True)
tailstring = "tusen og %s" % ultstring # tusen og to
else: # 012
if nn == False:
ult_and_penultstring = _one_to_nnn(ult_and_penultdig)
tailstring = (
"tusen og %s" % ult_and_penultstring
) # tusen og tolv
else:
ult_and_penultstring = _one_to_nnn(
ult_and_penultdig, nn=True
)
tailstring = (
"tusen og %s" % ult_and_penultstring
) # tusen og tolv
else: # 456
if nn == False:
ult_penult_antepenultstring = _one_to_nnn(
ult_penult_antepenultdig
)
if str(ult_penult_antepenultdig)[0] == "1":
tailstring = (
"tusen ett %s" % ult_penult_antepenultstring
) # tusen ett hundre
else:
tailstring = (
"tusen %s" % ult_penult_antepenultstring
) # tusen fire hundre og femtiseks
else:
ult_penult_antepenultstring = _one_to_nnn(
ult_penult_antepenultdig, nn=True
)
if str(ult_penult_antepenultdig)[0] == "1":
tailstring = (
"tusen eitt %s" % ult_penult_antepenultstring
) # tusen ett hundre
else:
tailstring = (
"tusen %s" % ult_penult_antepenultstring
) # tusen fire hundre og femtiseks
startdigs = int(
nrstring[:-3]
) # startstring can consist of the 1, 2 or 3 first digits
startstring = ""
if startdigs == 1: # 1001 starts with "ett"
if nn == False:
startstring = "ett"
else:
startstring = "eitt"
elif startdigs > 99 and startdigs < 200: # 155555 starts with ett
if nn == False:
startnumstring = _one_to_nnn(startdigs)
startstring = "ett %s" % startnumstring
else:
startnumstring = _one_to_nnn(startdigs, nn=True)
startstring = "eitt %s" % startnumstring
else: # the remaining numbers are purely compositional
if nn == False:
startstring = _one_to_nnn(startdigs)
else:
startstring = _one_to_nnn(startdigs, nn=True)
numstring = "%s %s" % (startstring, tailstring)
return numstring
else:
if not type(nr) is str:
return None
if type(_one_to_nnn(nr, reverse=True)) is int and nn == False:
return _one_to_nnn(nr, reverse=True) # 444
elif type(_one_to_nnn(nr, reverse=True, nn=True)) is int and nn == True:
return _one_to_nnn(nr, reverse=True, nn=True) # 444
elif nr == "tusen": # tusen - 1000
return 1000
elif (
len(nr.split(" ")) > 1 and nr.split(" ")[-1] == "tusen"
): # ett tusen, ett hundre tusen etc.
wdlist = nr.split(" ")
firstphrase = " ".join(wdlist[:-1])
if type(_one_to_nnn(firstphrase, reverse=True)) is int and nn == False:
firstdig = _one_to_nnn(firstphrase, reverse=True)
return firstdig * 1000
elif (
type(_one_to_nnn(firstphrase, reverse=True, nn=True)) is int
and nn == True
):
firstdig = _one_to_nnn(firstphrase, reverse=True, nn=True)
return firstdig * 1000
elif (
len(nr.split(" ")) == 2 and nr.split(" ")[0] == "ett" and nn == False
): # ett tusen
return 1000
elif len(nr.split(" ")) == 2 and nr.split(" ")[0] == "eitt" and nn == True:
return 1000
else: # misspellings should not result in return value
return None
else:
if len(nr.split(" ")) > 1: # all other numbers should contain spaces
numwordlist = nr.split(" ")
if (
"tusen" in numwordlist
): # Find last part of numphrase, which starts with "tusen"
tusenind = numwordlist.index("tusen") # find index of "tusen"
lastwords = numwordlist[tusenind:] # words from 'tusen'
firstwords = numwordlist[:tusenind] # words until 'tusen'
lastdigs = 0
if len(lastwords) == 3:
if lastwords[1] == "og": # 'tusen og fire' 'tusen og førtifire'
lastword = lastwords[-1]
if nn == False:
lastdigs = _one_to_nnn(lastword, reverse=True)
elif nn == True:
lastdigs = _one_to_nnn(lastword, reverse=True, nn=True)
elif (
nn == False
and type(_one_to_nnn(" ".join(lastwords[1:]), reverse=True))
is int
and lastwords[2] == "hundre"
): # tusen to hundre
hundredphrase = " ".join(lastwords[1:])
lastdigs = _one_to_nnn(hundredphrase, reverse=True)
elif (
nn == True
and type(
_one_to_nnn(
" ".join(lastwords[1:]), reverse=True, nn=True
)
)
is int
and lastwords[2] == "hundre"
): # tusen to hundre
hundredphrase = " ".join(lastwords[1:])
lastdigs = _one_to_nnn(hundredphrase, reverse=True, nn=True)
else: # misspellings should not result in return value
return None
elif (
len(lastwords) == 5 and lastwords[2] == "hundre"
): # 'tusen fire hundre og fem'
hundredphrase = " ".join(lastwords[1:])
if nn == False:
lastdigs = _one_to_nnn(hundredphrase, reverse=True)
else:
lastdigs = _one_to_nnn(hundredphrase, reverse=True, nn=True)
else: # misspellings should not result in return value
return None
firstdigs = 0
firstphrase = " ".join(firstwords)
if len(firstwords) == 0: # as in 'tusen og tretti'
firstdigs = 1000
elif (
len(firstwords) == 1 and firstwords[0] == "ett" and nn == False
):
firstdigs = 1000
elif (
len(firstwords) == 1 and firstwords[0] == "eitt" and nn == True
):
firstdigs = 1000
elif (
type(_one_to_nnn(firstphrase, reverse=True)) is int
and nn == False
):
firstdigs = _one_to_nnn(firstphrase, reverse=True) * 1000
elif (
type(_one_to_nnn(firstphrase, reverse=True, nn=True)) is int
and nn == True
):
firstdigs = (
_one_to_nnn(firstphrase, reverse=True, nn=True) * 1000
)
else: # misspellings should not result in return value
return None
if type(firstdigs) is int and type(lastdigs) is int:
return firstdigs + lastdigs
def convert_nums(nr, reverse=False, nn=False):
"""Functions for converting numbers. Only works for numbers in range 0-999999 for now"""
if reverse == False:
if type(nr) is int:
returnstring = ""
if nr == 0:
returnstring = "null"
elif nr < 1000000:
if nn == False:
returnstring = _one_to_nnnnnn(nr)
else:
returnstring = _one_to_nnnnnn(nr, nn=True)
else:
return None
return returnstring
else:
if type(nr) is str:
returnint = 0
if nr == "null":
returnint = 0
elif nn == False and type(_one_to_nnnnnn(nr, reverse=True)) is int:
returnint = _one_to_nnnnnn(nr, reverse=True)
elif nn == True and type(_one_to_nnnnnn(nr, reverse=True, nn=True)) is int:
returnint = _one_to_nnnnnn(nr, reverse=True, nn=True)
elif nn == False and type(_high_hundred(nr)) is int:
returnint = _high_hundred(nr)
elif nn == True and type(_high_hundred(nr, nn=True)) is int:
returnint = _high_hundred(nr, nn=True)
else:
return None
return returnint
if __name__ == "__main__":
# testing
mydigit = 243564
mynumstring = "hundre og atten tusen fire hundre og trettién"
mydigit_nn = 34381
mynumstring_nn = "tre hundre og førtiein"
print(
"Digit conversion bm: %s\nString conversion bm: %s"
% (convert_nums(mydigit), convert_nums(mynumstring, reverse=True))
)
print(
"Digit conversion nn: %s\nString conversion nn: %s"
% (
convert_nums(mydigit_nn, nn=True),
convert_nums(mynumstring_nn, nn=True, reverse=True),
)
)

107
config.json Normal file
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{
"_name_or_path": "facebook/wav2vec2-xls-r-1b",
"activation_dropout": 0.055,
"adapter_kernel_size": 3,
"adapter_stride": 2,
"add_adapter": false,
"apply_spec_augment": true,
"architectures": [
"Wav2Vec2ForCTC"
],
"attention_dropout": 0.094,
"bos_token_id": 1,
"classifier_proj_size": 256,
"codevector_dim": 1024,
"contrastive_logits_temperature": 0.1,
"conv_bias": true,
"conv_dim": [
512,
512,
512,
512,
512,
512,
512
],
"conv_kernel": [
10,
3,
3,
3,
3,
2,
2
],
"conv_stride": [
5,
2,
2,
2,
2,
2,
2
],
"ctc_loss_reduction": "mean",
"ctc_zero_infinity": true,
"diversity_loss_weight": 0.1,
"do_stable_layer_norm": true,
"eos_token_id": 2,
"feat_extract_activation": "gelu",
"feat_extract_dropout": 0.0,
"feat_extract_norm": "layer",
"feat_proj_dropout": 0.04,
"feat_quantizer_dropout": 0.0,
"final_dropout": 0.0,
"hidden_act": "gelu",
"hidden_dropout": 0.047,
"hidden_size": 1280,
"initializer_range": 0.02,
"intermediate_size": 5120,
"layer_norm_eps": 1e-05,
"layerdrop": 0.041,
"mask_feature_length": 64,
"mask_feature_min_masks": 0,
"mask_feature_prob": 0.25,
"mask_time_length": 10,
"mask_time_min_masks": 2,
"mask_time_prob": 0.082,
"model_type": "wav2vec2",
"num_adapter_layers": 3,
"num_attention_heads": 16,
"num_codevector_groups": 2,
"num_codevectors_per_group": 320,
"num_conv_pos_embedding_groups": 16,
"num_conv_pos_embeddings": 128,
"num_feat_extract_layers": 7,
"num_hidden_layers": 48,
"num_negatives": 100,
"output_hidden_size": 1280,
"pad_token_id": 38,
"proj_codevector_dim": 1024,
"tdnn_dilation": [
1,
2,
3,
1,
1
],
"tdnn_dim": [
512,
512,
512,
512,
1500
],
"tdnn_kernel": [
5,
3,
3,
1,
1
],
"torch_dtype": "float32",
"transformers_version": "4.18.0",
"use_weighted_layer_sum": false,
"vocab_size": 41,
"xvector_output_dim": 512
}

254
eval.py Normal file
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#!/usr/bin/env python3
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from num2words import num2words as n2w
from slugify import slugify
from transformers import AutoFeatureExtractor, AutoModelForCTC, pipeline, Wav2Vec2Processor, Wav2Vec2ProcessorWithLM, Wav2Vec2FeatureExtractor
# from pyctcdecode import BeamSearchDecoderCTC
from cardinal_numbers import convert_nums
def log_results(result: Dataset, args: Dict[str, str]):
"""DO NOT CHANGE. This function computes and logs the result metrics."""
log_outputs = args.log_outputs
lm = "withLM" if args.use_lm else "noLM"
model_id = args.model_id.replace("/", "_").replace(".", "")
if args.filter:
extra_args = [args.config, slugify(args.filter), args.split, lm]
else:
extra_args = [args.config, args.split, lm]
dataset_id = "_".join([model_id] + args.dataset.split("/") + extra_args)
# load metric
wer = load_metric("wer")
cer = load_metric("cer")
# compute metrics
wer_result = wer.compute(references=result["target"], predictions=result["prediction"])
cer_result = cer.compute(references=result["target"], predictions=result["prediction"])
# print & log results
result_str = f"{dataset_id}\nWER: {wer_result}\nCER: {cer_result}"
print(result_str)
with open(f"{dataset_id}_eval_results.txt", "w") as f:
f.write(result_str)
with open(f"{dataset_id}_eval_results.tsv", "w") as f:
f.write("\t".join([args.model_id, args.dataset, args.config, args.filter, args.split, str(lm), str(wer_result), str(cer_result)]))
# log all results in text file. Possibly interesting for analysis
if log_outputs is not None:
pred_file = f"log_{dataset_id}_predictions.txt"
target_file = f"log_{dataset_id}_targets.txt"
with open(pred_file, "w") as p, open(target_file, "w") as t:
# mapping function to write output
def write_to_file(batch, i):
p.write(f"{i}" + "\n")
p.write(batch["prediction"] + "\n")
t.write(f"{i}" + "\n")
t.write(batch["target"] + "\n")
result.map(write_to_file, with_indices=True)
def normalize_text(original_text: str, dataset: str) -> str:
"""DO ADAPT FOR YOUR USE CASE. this function normalizes the target text."""
text = original_text.lower()
if dataset.lower().endswith("fleurs"):
replacements = (
(r"\be\.kr", "etter kristus fødsel"),
(r"\bf\.kr", "før kristi fødsel"),
(r"\bca[.]?\b", "circa"),
(r"(\d)\s*km/t", r"\1 kilometer i timen"),
(r"(\d)\s*km", r"\1 kilometer"),
(r"(\d)\s*cm", r"\1 centimeter"),
(r"(\d)\s*mm", r"\1 millimeter"),
(r"kl\.", "klokka"),
(r"f\.eks", "for eksempel"),
)
for abrev, expasion in replacements:
text = re.sub(abrev, expasion, text)
text = re.sub(r'(\d+)[-–](\d+)', r'\1 til \2', text) # 1-89, 70-90
text = re.sub(r'(\d{2}):00', r'\1', text) # 21:00
text = re.sub(r"(\d{2}):0(\d{1})", r"\1 null \2", text) # 17:03
text = re.sub(r"(\d{1,2}):(\d{1,2})", r"\1 \2", text) # 17:23 (time), 4:3 (aspect ratios)
text = re.sub(r"(1[1-9])00", r"\1 hundre", text) # 1800, 1900
text = re.sub(r"(1[1-9])0([1-9])", r"\1 null \2 ", text) # 1901, 1909
text = re.sub(r"(1[1-9])([1-9]\d)", r"\1 \2 ", text) # 1911, 1987
text = re.sub(r"(20)0([1-9])", r"\1 null \2 ", text) # 2009
text = re.sub(r"(20)(\d{2})", r"\1 \2 ", text) # 2009
text = re.sub(r"(\d{1,3})[.](\d{1,2})", r"\1 dot \2 ", text) # 802.11n, 2.5ghz (in English)
text = re.sub(r"(\d{1,2})[ .](\d{3})", r"\1\2", text) # 10 000, 32.000
text = re.sub(r'(\w+)-(\w+)', r'\1 \2', text) # n-standard
# text = re.compile(r"-?0?[1-9][\d.]*").sub(lambda x: n2w(x.group(0), lang="no"), text.replace(".", ""))
text = re.compile(r"-?0?[1-9][\d.]*").sub(lambda x: convert_nums(int(x.group(0)), nn=True), text.replace(".", ""))
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\<EFBFBD>\'\–\_\\\+\#\/]' # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
text = re.sub(chars_to_ignore_regex, "", text) + " "
if dataset.lower().endswith("nst"):
text = text.lower()
text = text.replace("(...vær stille under dette opptaket...)", "")
text = re.sub('[áàâ]', 'a', text)
text = re.sub('[ä]', 'æ', text)
text = re.sub('[éèëê]', 'e', text)
text = re.sub('[íìïî]', 'i', text)
text = re.sub('[óòöô]', 'o', text)
text = re.sub('[ö]', 'ø', text)
text = re.sub('[ç]', 'c', text)
text = re.sub('[úùüû]', 'u', text)
# text = re.sub('\\(?=(Punktum|Komma|Utropstegn|Spørsmålstegn))', ' ', text)
text = re.sub('\s+', ' ', text)
elif dataset.lower().endswith("npsc"):
text = re.sub('[áàâ]', 'a', text)
text = re.sub('[ä]', 'æ', text)
text = re.sub('[éèëê]', 'e', text)
text = re.sub('[íìïî]', 'i', text)
text = re.sub('[óòöô]', 'o', text)
text = re.sub('[ö]', 'ø', text)
text = re.sub('[ç]', 'c', text)
text = re.sub('[úùüû]', 'u', text)
text = re.sub('\s+', ' ', text)
elif dataset.lower().endswith("fleurs"):
text = re.sub('[áàâ]', 'a', text)
text = re.sub('[ä]', 'æ', text)
text = re.sub('[éèëê]', 'e', text)
text = re.sub('[íìïî]', 'i', text)
text = re.sub('[óòöô]', 'o', text)
text = re.sub('[ö]', 'ø', text)
text = re.sub('[ç]', 'c', text)
text = re.sub('[úùüû]', 'u', text)
text = re.sub('[«»]', '', text)
text = re.sub('\s+', ' ', text)
text = re.sub('<ee>', 'eee', text)
text = re.sub('<qq>', 'qqq', text)
text = re.sub('<mm>', 'mmm', text)
text = re.sub('<inaudible>', 'xxx', text)
# # In addition, we can normalize the target text, e.g. removing new lines characters etc...
# # note that order is important here!
# token_sequences_to_ignore = ["\n\n", "\n", " ", " "]
# for t in token_sequences_to_ignore:
# text = " ".join(text.split(t))
return text
def main(args):
# load dataset
dataset = load_dataset(args.dataset, args.config, split=args.split, use_auth_token=True)
if args.filter:
attribute, value = list(map(str.strip, args.filter.split(":")))
dataset = dataset.filter(
lambda x: x[attribute] == value,
desc=f"Filtering on {args.filter}",
)
# for testing: only process the first two examples as a test
# dataset = dataset.select(range(10))
# load processor
feature_extractor = AutoFeatureExtractor.from_pretrained(args.model_id)
sampling_rate = feature_extractor.sampling_rate
# resample audio
dataset = dataset.cast_column("audio", Audio(sampling_rate=sampling_rate))
# load eval pipeline
if args.device is None:
args.device = 0 if torch.cuda.is_available() else -1
# asr = pipeline("automatic-speech-recognition", model=args.model_id, device=args.device)
model_instance = AutoModelForCTC.from_pretrained(args.model_id)
if args.use_lm:
processor = Wav2Vec2ProcessorWithLM.from_pretrained(args.model_id)
decoder = processor.decoder
else:
processor = Wav2Vec2Processor.from_pretrained(args.model_id)
decoder = None
asr = pipeline(
"automatic-speech-recognition",
model=model_instance,
tokenizer=processor.tokenizer,
feature_extractor=processor.feature_extractor,
decoder=decoder,
device=args.device
)
# feature_extractor_dict, _ = Wav2Vec2FeatureExtractor.get_feature_extractor_dict(args.model_id)
# feature_extractor_dict["processor_class"] = "Wav2Vec2Processor" if not args.use_lm else "Wav2Vec2ProcessorWithLM"
# feature_extractor = Wav2Vec2FeatureExtractor.from_dict(feature_extractor_dict)
# asr = pipeline("automatic-speech-recognition", model=args.model_id, feature_extractor=feature_extractor, device=args.device, decoder=BeamSearchDecoderCTC.load_from_dir("./"))
# map function to decode audio
def map_to_pred(batch):
prediction = asr(
batch["audio"]["array"], chunk_length_s=args.chunk_length_s, stride_length_s=args.stride_length_s
)
batch["prediction"] = prediction["text"]
batch["target"] = normalize_text(batch[args.text_column], args.dataset)
return batch
# run inference on all examples
result = dataset.map(map_to_pred, remove_columns=dataset.column_names)
# compute and log_results
# do not change function below
log_results(result, args)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--model_id", type=str, required=True, help="Model identifier. Should be loadable with 🤗 Transformers"
)
parser.add_argument(
"--dataset",
type=str,
required=True,
help="Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets",
)
parser.add_argument(
"--config", type=str, required=True, help="Config of the dataset. *E.g.* `'en'` for Common Voice"
)
parser.add_argument(
"--filter", type=str, default="", help="Simple filter on attributes. *E.g.* `region_of_youth:Troms` would pnly keep those samplesfor which the condition is met"
)
parser.add_argument("--split", type=str, required=True, help="Split of the dataset. *E.g.* `'test'`")
parser.add_argument(
"--text_column", type=str, default="text", help="Column name containing the transcription."
)
parser.add_argument(
"--chunk_length_s", type=float, default=None, help="Chunk length in seconds. Defaults to 5 seconds."
)
parser.add_argument(
"--stride_length_s", type=float, default=None, help="Stride of the audio chunks. Defaults to 1 second."
)
parser.add_argument(
"--log_outputs", action="store_true", help="If defined, write outputs to log file for analysis."
)
parser.add_argument(
"--device",
type=int,
default=None,
help="The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.",
)
parser.add_argument(
"--use_lm", action="store_true", help="If defined, use included language model as the decoder."
)
args = parser.parse_args()
main(args)

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version https://git-lfs.github.com/spec/v1
oid sha256:7b41c24c63f2f0585bea83666369593f3b3e6d047f327a90f36ebca2c35ef0ff
size 4243671427

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language_model/attrs.json Normal file
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{"alpha": 0.5, "beta": 1.5, "unk_score_offset": -10.0, "score_boundary": true}

3
language_model/unigrams.txt Normal file
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version https://git-lfs.github.com/spec/v1
oid sha256:ac3e71ca49838ca355df6fdcb8d89344a5a9bf9e1a76587cdf5df1367c19b9a9
size 16759269

3
model.safetensors Normal file
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version https://git-lfs.github.com/spec/v1
oid sha256:6ab493ecc739df905d0decdc09ce1d9ee4453987bb1bdbcf2e8b06642b3c47b6
size 3850301028

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preprocessor_config.json Normal file
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{
"do_normalize": true,
"feature_extractor_type": "Wav2Vec2FeatureExtractor",
"feature_size": 1,
"padding_side": "right",
"padding_value": 0,
"processor_class": "Wav2Vec2ProcessorWithLM",
"return_attention_mask": true,
"sampling_rate": 16000
}

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pytorch_model.bin Normal file
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version https://git-lfs.github.com/spec/v1
oid sha256:0de5c9ba91acd5844605a71e621f73b3dd9f9acbfb283d3d835b44a491b189b6
size 3850475057

38
run.sh Executable file
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WANDB_ENTITY=NbAiLab WANDB_PROJECT=wav2vec2 python run_speech_recognition_ctc.py \
--model_name_or_path="facebook/wav2vec2-xls-r-1b" \
--hub_model_id="NbAiLab/wav2vec2-1b-npsc-nst" \
--output_dir="./" \
--num_train_epochs="40" \
--per_device_train_batch_size="12" \
--per_device_eval_batch_size="12" \
--gradient_accumulation_steps="2" \
--learning_rate="2e-5" \
--warmup_steps="2000" \
--length_column_name="input_length" \
--evaluation_strategy="steps" \
--text_column_name="text" \
--save_steps="500" \
--eval_steps="500" \
--logging_steps="100" \
--layerdrop="0.041" \
--attention_dropout="0.094" \
--activation_dropout="0.055" \
--hidden_dropout="0.047" \
--save_total_limit="3" \
--freeze_feature_encoder \
--feat_proj_dropout="0.04" \
--mask_time_prob="0.082" \
--mask_time_length="10" \
--mask_feature_prob="0.25" \
--mask_feature_length="64" \
--gradient_checkpointing \
--min_duration_in_seconds="0.5" \
--max_duration_in_seconds="30.0" \
--use_auth_token \
--seed="42" \
--fp16 \
--group_by_length \
--do_train --do_eval \
--push_to_hub \
--preprocessing_num_workers="32" \
--ctc_zero_infinity

807
run_speech_recognition_ctc.py Normal file
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#!/usr/bin/env python
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
""" Fine-tuning a 🤗 Transformers CTC model for automatic speech recognition"""
import functools
import json
import logging
import os
import re
import sys
import warnings
from dataclasses import dataclass, field
from typing import Dict, List, Optional, Union
import datasets
import numpy as np
import torch
from datasets import DatasetDict, load_dataset, load_metric
import transformers
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelForCTC,
AutoProcessor,
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainingArguments,
Wav2Vec2Processor,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.16.0.dev0")
require_version("datasets>=1.13.3", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
logger = logging.getLogger(__name__)
def list_field(default=None, metadata=None):
return field(default_factory=lambda: default, metadata=metadata)
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
)
tokenizer_name_or_path: Optional[str] = field(
default=None,
metadata={"help": "Path to pretrained tokenizer or tokenizer identifier from huggingface.co/models"},
)
cache_dir: Optional[str] = field(
default=None,
metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
)
freeze_feature_encoder: bool = field(
default=True, metadata={"help": "Whether to freeze the feature encoder layers of the model."}
)
attention_dropout: float = field(
default=0.0, metadata={"help": "The dropout ratio for the attention probabilities."}
)
activation_dropout: float = field(
default=0.0, metadata={"help": "The dropout ratio for activations inside the fully connected layer."}
)
feat_proj_dropout: float = field(default=0.0, metadata={"help": "The dropout ratio for the projected features."})
hidden_dropout: float = field(
default=0.0,
metadata={
"help": "The dropout probability for all fully connected layers in the embeddings, encoder, and pooler."
},
)
final_dropout: float = field(
default=0.0,
metadata={"help": "The dropout probability for the final projection layer."},
)
mask_time_prob: float = field(
default=0.05,
metadata={
"help": "Probability of each feature vector along the time axis to be chosen as the start of the vector"
"span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature"
"vectors will be masked along the time axis."
},
)
mask_time_length: int = field(
default=10,
metadata={"help": "Length of vector span to mask along the time axis."},
)
mask_feature_prob: float = field(
default=0.0,
metadata={
"help": "Probability of each feature vector along the feature axis to be chosen as the start of the vector"
"span to be masked. Approximately ``mask_feature_prob * sequence_length // mask_feature_length`` feature bins will be masked along the time axis."
},
)
mask_feature_length: int = field(
default=10,
metadata={"help": "Length of vector span to mask along the feature axis."},
)
layerdrop: float = field(default=0.0, metadata={"help": "The LayerDrop probability."})
ctc_loss_reduction: Optional[str] = field(
default="mean", metadata={"help": "The way the ctc loss should be reduced. Should be one of 'mean' or 'sum'."}
)
ctc_zero_infinity: Optional[bool] = field(
default=False, metadata={"help": "If True, will try yo aboud the CTC loss goinf to infinity."}
)
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
Using `HfArgumentParser` we can turn this class
into argparse arguments to be able to specify them on
the command line.
"""
# dataset_name: str = field(
# metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
# )
# dataset_config_name: str = field(
# default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
# )
train_split_name: str = field(
default="train",
metadata={
"help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
},
)
eval_split_name: str = field(
default="test",
metadata={
"help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
},
)
audio_column_name: str = field(
default="audio",
metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"},
)
text_column_name: str = field(
default="text",
metadata={"help": "The name of the dataset column containing the text data. Defaults to 'text'"},
)
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."}
)
preprocessing_num_workers: Optional[int] = field(
default=None,
metadata={"help": "The number of processes to use for the preprocessing."},
)
max_train_samples: Optional[int] = field(
default=None,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
},
)
max_eval_samples: Optional[int] = field(
default=None,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of validation examples to this "
"value if set."
},
)
chars_to_ignore: Optional[List[str]] = list_field(
default=None,
metadata={"help": "A list of characters to remove from the transcripts."},
)
eval_metrics: List[str] = list_field(
default=["wer"],
metadata={"help": "A list of metrics the model should be evaluated on. E.g. `'wer cer'`"},
)
max_duration_in_seconds: float = field(
default=20.0,
metadata={
"help": "Filter audio files that are longer than `max_duration_in_seconds` seconds to 'max_duration_in_seconds`"
},
)
min_duration_in_seconds: float = field(
default=0.0, metadata={"help": "Filter audio files that are shorter than `min_duration_in_seconds` seconds"}
)
preprocessing_only: bool = field(
default=False,
metadata={
"help": "Whether to only do data preprocessing and skip training. "
"This is especially useful when data preprocessing errors out in distributed training due to timeout. "
"In this case, one should run the preprocessing in a non-distributed setup with `preprocessing_only=True` "
"so that the cached datasets can consequently be loaded in distributed training"
},
)
use_auth_token: bool = field(
default=False,
metadata={
"help": "If :obj:`True`, will use the token generated when running"
":obj:`transformers-cli login` as HTTP bearer authorization for remote files."
},
)
unk_token: str = field(
default="[UNK]",
metadata={"help": "The unk token for the tokenizer"},
)
pad_token: str = field(
default="[PAD]",
metadata={"help": "The padding token for the tokenizer"},
)
word_delimiter_token: str = field(
default="|",
metadata={"help": "The word delimiter token for the tokenizer"},
)
phoneme_language: Optional[str] = field(
default=None,
metadata={
"help": "The target language that should be used be"
" passed to the tokenizer for tokenization. Note that"
" this is only relevant if the model classifies the"
" input audio to a sequence of phoneme sequences."
},
)
@dataclass
class DataCollatorCTCWithPadding:
"""
Data collator that will dynamically pad the inputs received.
Args:
processor (:class:`~transformers.AutoProcessor`)
The processor used for proccessing the data.
padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):
Select a strategy to pad the returned sequences (according to the model's padding side and padding index)
among:
* :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).
* :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
maximum acceptable input length for the model if that argument is not provided.
* :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
different lengths).
max_length (:obj:`int`, `optional`):
Maximum length of the ``input_values`` of the returned list and optionally padding length (see above).
max_length_labels (:obj:`int`, `optional`):
Maximum length of the ``labels`` returned list and optionally padding length (see above).
pad_to_multiple_of (:obj:`int`, `optional`):
If set will pad the sequence to a multiple of the provided value.
This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
7.5 (Volta).
"""
processor: AutoProcessor
padding: Union[bool, str] = "longest"
pad_to_multiple_of: Optional[int] = None
pad_to_multiple_of_labels: Optional[int] = None
def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
# split inputs and labels since they have to be of different lenghts and need
# different padding methods
input_features = [{"input_values": feature["input_values"]} for feature in features]
label_features = [{"input_ids": feature["labels"]} for feature in features]
batch = self.processor.pad(
input_features,
padding=self.padding,
pad_to_multiple_of=self.pad_to_multiple_of,
return_tensors="pt",
)
with self.processor.as_target_processor():
labels_batch = self.processor.pad(
label_features,
padding=self.padding,
pad_to_multiple_of=self.pad_to_multiple_of_labels,
return_tensors="pt",
)
# replace padding with -100 to ignore loss correctly
labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100)
batch["labels"] = labels
return batch
def create_vocabulary_from_data(
datasets: DatasetDict,
word_delimiter_token: Optional[str] = None,
unk_token: Optional[str] = None,
pad_token: Optional[str] = None,
):
# Given training and test labels create vocabulary
alphabet = set()
def extract_all_chars(batch):
all_text = " ".join(batch["target_text"])
alphabet.update(all_text)
datasets.map(
extract_all_chars,
batched=True,
batch_size=-1,
keep_in_memory=True,
remove_columns=datasets["train"].column_names,
)
# # take union of all unique characters in each dataset
# vocab_set = functools.reduce(
# lambda vocab_1, vocab_2: {"vocab": list(set(vocab_1["vocab"][0]) | set(vocab_2["vocab"][0]))}, vocabs.values()
# )["vocab"][0]
vocab_dict = {v: k for k, v in enumerate(sorted(list(alphabet)))}
# replace white space with delimiter token
if word_delimiter_token is not None:
vocab_dict[word_delimiter_token] = vocab_dict[" "]
del vocab_dict[" "]
# add unk and pad token
if unk_token is not None:
vocab_dict[unk_token] = len(vocab_dict)
if pad_token is not None:
vocab_dict[pad_token] = len(vocab_dict)
return vocab_dict
def make_dataset(seed=42):
# Pre-processing dataset
import re
def map_nst(entry):
text = entry["text"].lower()
text = text.replace("(...Vær stille under dette opptaket...)", "")
text = re.sub('[áàâ]', 'a', text)
text = re.sub('[ä]', 'æ', text)
text = re.sub('[éèëê]', 'e', text)
text = re.sub('[íìïî]', 'i', text)
text = re.sub('[óòöô]', 'o', text)
text = re.sub('[ö]', 'ø', text)
text = re.sub('[ç]', 'c', text)
text = re.sub('[úùüû]', 'u', text)
# text = re.sub('\\(?=(Punktum|Komma|Utropstegn|Spørsmålstegn))', ' ', text)
text = re.sub('\s+', ' ', text)
return {"text": text}
def filter_nst(entry):
if not ((len(entry["text"]) <= len(entry["audio"]["array"]) // 320) and (len(entry["text"].strip()) >= 3)):
return False # Too short
if re.match(entry["type"], "pIW|CA"):
return False # Spelling out words
return True
def filter_npsc(entry):
# False if there are digits in the text
if not ((len(entry["text"]) <= len(entry["audio"]["array"]) // 320) and (len(entry["text"].strip()) >= 3)):
return False # Too short
if re.search("\d", entry["text"]):
return False
return True
def map_npsc(entry):
batch = {"text": entry["text"].lower()}
batch["text"] = re.sub('[áàâ]', 'a', batch["text"])
batch["text"] = re.sub('[ä]', 'æ', batch["text"])
batch["text"] = re.sub('[éèëê]', 'e', batch["text"])
batch["text"] = re.sub('[íìïî]', 'i', batch["text"])
batch["text"] = re.sub('[óòöô]', 'o', batch["text"])
batch["text"] = re.sub('[ö]', 'ø', batch["text"])
batch["text"] = re.sub('[ç]', 'c', batch["text"])
batch["text"] = re.sub('[úùüû]', 'u', batch["text"])
batch["text"] = re.sub('\s', ' ', batch["text"])
batch["text"] = re.sub('<ee>', 'eee', batch["text"])
batch["text"] = re.sub('<qq>', 'qqq', batch["text"])
batch["text"] = re.sub('<mm>', 'mmm', batch["text"])
batch["text"] = re.sub('<inaudible>', 'xxx', batch["text"])
# batch["text"] = re.sub('<inaudible>', '?', batch["text"])
if "<" in batch["text"]:
raise ValueError(batch["text"])
return batch
nst = datasets.load_dataset("NbAiLab/NST", "no-close")
npsc = datasets.load_dataset("NbAiLab/NPSC", "16K_mp3")
# TODO NST_hesitate
split = len(npsc["train"]) / (len(npsc["train"]) + len(npsc["validation"])) # Use same train/val ratio as NPSC
nst_train = nst["train"].train_test_split(train_size=split, seed=seed)
nst["train"] = nst_train["train"]
nst["validation"] = nst_train["test"]
nst = nst.filter(filter_nst).map(map_nst).shuffle(seed=seed)
npsc = npsc.filter(filter_npsc).map(map_npsc).shuffle(seed=seed)
npsc_base = npsc.remove_columns([col for col in npsc["train"].column_names if col not in ["text", "audio"]])
nst_base = nst.remove_columns([col for col in nst["train"].column_names if col not in ["text", "audio"]])
combined = {}
for split in "train", "validation", "test":
probs = np.array([len(nst_base[split]), len(npsc_base[split])]) # Weight by number of examples
probs = (probs / probs.sum()).tolist()
comb = datasets.interleave_datasets([nst_base[split], npsc_base[split]], probabilities=probs, seed=seed)
combined[split] = comb
return datasets.DatasetDict(**combined)
def main():
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
# Detecting last checkpoint.
last_checkpoint = None
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
last_checkpoint = get_last_checkpoint(training_args.output_dir)
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
"Use --overwrite_output_dir to overcome."
)
elif last_checkpoint is not None:
logger.info(
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
)
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
handlers=[logging.StreamHandler(sys.stdout)],
)
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN)
# Log on each process the small summary:
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
)
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank):
transformers.utils.logging.set_verbosity_info()
logger.info("Training/evaluation parameters %s", training_args)
# Set seed before initializing model.
set_seed(training_args.seed)
# 1. First, let's load the dataset
raw_datasets = make_dataset(seed=training_args.seed)
if training_args.do_train:
if data_args.audio_column_name not in raw_datasets["train"].column_names:
raise ValueError(
f"--audio_column_name '{data_args.audio_column_name}' not found in dataset '{data_args.dataset_name}'. "
"Make sure to set `--audio_column_name` to the correct audio column - one of "
f"{', '.join(raw_datasets['train'].column_names)}."
)
if data_args.text_column_name not in raw_datasets["train"].column_names:
raise ValueError(
f"--text_column_name {data_args.text_column_name} not found in dataset '{data_args.dataset_name}'. "
"Make sure to set `--text_column_name` to the correct text column - one of "
f"{', '.join(raw_datasets['train'].column_names)}."
)
if data_args.max_train_samples is not None:
raw_datasets["train"] = raw_datasets["train"].select(range(data_args.max_train_samples))
if training_args.do_eval:
if data_args.max_eval_samples is not None:
raw_datasets["eval"] = raw_datasets["eval"].select(range(data_args.max_eval_samples))
# 2. We remove some special characters from the datasets
# that make training complicated and do not help in transcribing the speech
# E.g. characters, such as `,` and `.` do not really have an acoustic characteristic
# that could be easily picked up by the model
# chars_to_ignore_regex = (
# f'[{"".join(data_args.chars_to_ignore)}]' if data_args.chars_to_ignore is not None else None
# )
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\<EFBFBD>\'\–\_\\\+\#\/]'
text_column_name = data_args.text_column_name
def remove_special_characters(batch):
if chars_to_ignore_regex is not None:
batch["target_text"] = re.sub(chars_to_ignore_regex, "", batch[text_column_name]).lower() + " "
else:
batch["target_text"] = batch[text_column_name].lower() + " "
return batch
with training_args.main_process_first(desc="dataset map special characters removal"):
raw_datasets = raw_datasets.map(
remove_special_characters,
remove_columns=[text_column_name],
desc="remove special characters from datasets",
)
# save special tokens for tokenizer
word_delimiter_token = data_args.word_delimiter_token
unk_token = data_args.unk_token
pad_token = data_args.pad_token
# 3. Next, let's load the config as we might need it to create
# the tokenizer
# load config
config = AutoConfig.from_pretrained(
model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_auth_token=data_args.use_auth_token
)
# 4. Next, if no tokenizer file is defined,
# we create the vocabulary of the model by extracting all unique characters from
# the training and evaluation datasets
# We need to make sure that only first rank saves vocabulary
# make sure all processes wait until vocab is created
tokenizer_name_or_path = model_args.tokenizer_name_or_path
tokenizer_kwargs = {}
if tokenizer_name_or_path is None:
# save vocab in training output dir
tokenizer_name_or_path = training_args.output_dir
vocab_file = os.path.join(tokenizer_name_or_path, "vocab.json")
with training_args.main_process_first():
if training_args.overwrite_output_dir and os.path.isfile(vocab_file):
os.remove(vocab_file)
with training_args.main_process_first(desc="dataset map vocabulary creation"):
if not os.path.isfile(vocab_file):
os.makedirs(tokenizer_name_or_path, exist_ok=True)
vocab_dict = create_vocabulary_from_data(
raw_datasets,
word_delimiter_token=word_delimiter_token,
unk_token=unk_token,
pad_token=pad_token,
)
# save vocab dict to be loaded into tokenizer
with open(vocab_file, "w") as file:
json.dump(vocab_dict, file)
# if tokenizer has just been created
# it is defined by `tokenizer_class` if present in config else by `model_type`
tokenizer_kwargs = {
"config": config if config.tokenizer_class is not None else None,
"tokenizer_type": config.model_type if config.tokenizer_class is None else None,
"unk_token": unk_token,
"pad_token": pad_token,
"word_delimiter_token": word_delimiter_token,
}
# 5. Now we can instantiate the feature extractor, tokenizer and model
# Note for distributed training, the .from_pretrained methods guarantee that only
# one local process can concurrently download model & vocab.
# load feature_extractor and tokenizer
tokenizer = AutoTokenizer.from_pretrained(
tokenizer_name_or_path,
use_auth_token=data_args.use_auth_token,
**tokenizer_kwargs,
)
feature_extractor = AutoFeatureExtractor.from_pretrained(
model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_auth_token=data_args.use_auth_token
)
# adapt config
config.update(
{
"feat_proj_dropout": model_args.feat_proj_dropout,
"attention_dropout": model_args.attention_dropout,
"hidden_dropout": model_args.hidden_dropout,
"final_dropout": model_args.final_dropout,
"mask_time_prob": model_args.mask_time_prob,
"mask_time_length": model_args.mask_time_length,
"mask_feature_prob": model_args.mask_feature_prob,
"mask_feature_length": model_args.mask_feature_length,
"gradient_checkpointing": training_args.gradient_checkpointing,
"layerdrop": model_args.layerdrop,
"ctc_loss_reduction": model_args.ctc_loss_reduction,
"ctc_zero_infinity": model_args.ctc_zero_infinity,
"pad_token_id": tokenizer.pad_token_id,
"vocab_size": len(tokenizer),
"activation_dropout": model_args.activation_dropout,
}
)
# create model
model = AutoModelForCTC.from_pretrained(
model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
config=config,
use_auth_token=data_args.use_auth_token,
)
# freeze encoder
if model_args.freeze_feature_encoder:
model.freeze_feature_encoder()
# 6. Now we preprocess the datasets including loading the audio, resampling and normalization
# Thankfully, `datasets` takes care of automatically loading and resampling the audio,
# so that we just need to set the correct target sampling rate and normalize the input
# via the `feature_extractor`
# make sure that dataset decodes audio with correct sampling rate
dataset_sampling_rate = next(iter(raw_datasets.values())).features[data_args.audio_column_name].sampling_rate
if dataset_sampling_rate != feature_extractor.sampling_rate:
raw_datasets = raw_datasets.cast_column(
data_args.audio_column_name, datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate)
)
# derive max & min input length for sample rate & max duration
max_input_length = data_args.max_duration_in_seconds * feature_extractor.sampling_rate
min_input_length = data_args.min_duration_in_seconds * feature_extractor.sampling_rate
audio_column_name = data_args.audio_column_name
num_workers = data_args.preprocessing_num_workers
# `phoneme_language` is only relevant if the model is fine-tuned on phoneme classification
phoneme_language = data_args.phoneme_language
# Preprocessing the datasets.
# We need to read the audio files as arrays and tokenize the targets.
def prepare_dataset(batch):
# load audio
sample = batch[audio_column_name]
inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"])
batch["input_values"] = inputs.input_values[0]
batch["input_length"] = len(batch["input_values"])
# encode targets
additional_kwargs = {}
if phoneme_language is not None:
additional_kwargs["phonemizer_lang"] = phoneme_language
batch["labels"] = tokenizer(batch["target_text"], **additional_kwargs).input_ids
return batch
with training_args.main_process_first(desc="dataset map preprocessing"):
vectorized_datasets = raw_datasets.map(
prepare_dataset,
remove_columns=next(iter(raw_datasets.values())).column_names,
num_proc=num_workers,
desc="preprocess datasets",
)
def is_audio_in_length_range(length):
return length > min_input_length and length < max_input_length
# filter data that is shorter than min_input_length
vectorized_datasets = vectorized_datasets.filter(
is_audio_in_length_range,
num_proc=num_workers,
input_columns=["input_length"],
)
# 7. Next, we can prepare the training.
# Let's use word error rate (WER) as our evaluation metric,
# instantiate a data collator and the trainer
# Define evaluation metrics during training, *i.e.* word error rate, character error rate
eval_metrics = {metric: load_metric(metric) for metric in data_args.eval_metrics}
# for large datasets it is advised to run the preprocessing on a
# single machine first with ``args.preprocessing_only`` since there will mostly likely
# be a timeout when running the script in distributed mode.
# In a second step ``args.preprocessing_only`` can then be set to `False` to load the
# cached dataset
if data_args.preprocessing_only:
logger.info(f"Data preprocessing finished. Files cached at {vectorized_datasets.cache_files}")
return
def compute_metrics(pred):
pred_logits = pred.predictions
pred_ids = np.argmax(pred_logits, axis=-1)
pred.label_ids[pred.label_ids == -100] = tokenizer.pad_token_id
pred_str = tokenizer.batch_decode(pred_ids)
# we do not want to group tokens when computing the metrics
label_str = tokenizer.batch_decode(pred.label_ids, group_tokens=False)
metrics = {k: v.compute(predictions=pred_str, references=label_str) for k, v in eval_metrics.items()}
return metrics
# Now save everything to be able to create a single processor later
if is_main_process(training_args.local_rank):
# save feature extractor, tokenizer and config
feature_extractor.save_pretrained(training_args.output_dir)
tokenizer.save_pretrained(training_args.output_dir)
config.save_pretrained(training_args.output_dir)
try:
processor = AutoProcessor.from_pretrained(training_args.output_dir)
except (OSError, KeyError):
warnings.warn(
"Loading a processor from a feature extractor config that does not"
" include a `processor_class` attribute is deprecated and will be removed in v5. Please add the following "
" attribute to your `preprocessor_config.json` file to suppress this warning: "
" `'processor_class': 'Wav2Vec2Processor'`",
FutureWarning,
)
processor = Wav2Vec2Processor.from_pretrained(training_args.output_dir)
# Instantiate custom data collator
data_collator = DataCollatorCTCWithPadding(processor=processor)
# Initialize Trainer
trainer = Trainer(
model=model,
data_collator=data_collator,
args=training_args,
compute_metrics=compute_metrics,
train_dataset=vectorized_datasets["train"] if training_args.do_train else None,
eval_dataset=vectorized_datasets["validation"] if training_args.do_eval else None,
tokenizer=feature_extractor,
)
# 8. Finally, we can start training
# Training
if training_args.do_train:
# use last checkpoint if exist
if last_checkpoint is not None:
checkpoint = last_checkpoint
elif os.path.isdir(model_args.model_name_or_path):
checkpoint = model_args.model_name_or_path
else:
checkpoint = None
train_result = trainer.train(resume_from_checkpoint=checkpoint)
trainer.save_model()
metrics = train_result.metrics
max_train_samples = (
data_args.max_train_samples
if data_args.max_train_samples is not None
else len(vectorized_datasets["train"])
)
metrics["train_samples"] = min(max_train_samples, len(vectorized_datasets["train"]))
trainer.log_metrics("train", metrics)
trainer.save_metrics("train", metrics)
trainer.save_state()
# Evaluation
results = {}
if training_args.do_eval:
logger.info("*** Evaluate ***")
metrics = trainer.evaluate()
max_eval_samples = (
data_args.max_eval_samples if data_args.max_eval_samples is not None else len(vectorized_datasets["eval"])
)
metrics["eval_samples"] = min(max_eval_samples, len(vectorized_datasets["eval"]))
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
# Write model card and (optionally) push to hub
config_name = data_args.dataset_config_name if data_args.dataset_config_name is not None else "na"
kwargs = {
"finetuned_from": model_args.model_name_or_path,
"tasks": "speech-recognition",
"tags": ["automatic-speech-recognition", data_args.dataset_name],
"dataset_args": f"Config: {config_name}, Training split: {data_args.train_split_name}, Eval split: {data_args.eval_split_name}",
"dataset": f"{data_args.dataset_name.upper()} - {config_name.upper()}",
}
if "common_voice" in data_args.dataset_name:
kwargs["language"] = config_name
if training_args.push_to_hub:
trainer.push_to_hub(**kwargs)
else:
trainer.create_model_card(**kwargs)
return results
if __name__ == "__main__":
main()

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{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "[UNK]", "pad_token": "[PAD]", "additional_special_tokens": [{"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}]}

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{"unk_token": "[UNK]", "bos_token": "<s>", "eos_token": "</s>", "pad_token": "[PAD]", "do_lower_case": false, "word_delimiter_token": "|", "replace_word_delimiter_char": " ", "special_tokens_map_file": null, "name_or_path": "./", "tokenizer_class": "Wav2Vec2CTCTokenizer", "processor_class": "Wav2Vec2ProcessorWithLM"}

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version https://git-lfs.github.com/spec/v1
oid sha256:d63c31e6cdc4ad990144c981422252a8ee34040e3eb92deeb7c6f36e75ccdee2
size 3055

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{"(": 1, ")": 2, "0": 3, "3": 4, "7": 5, "8": 6, "9": 7, "a": 8, "b": 9, "c": 10, "d": 11, "e": 12, "f": 13, "g": 14, "h": 15, "i": 16, "j": 17, "k": 18, "l": 19, "m": 20, "n": 21, "o": 22, "p": 23, "q": 24, "r": 25, "s": 26, "t": 27, "u": 28, "v": 29, "w": 30, "x": 31, "y": 32, "z": 33, "å": 34, "æ": 35, "ø": 36, "|": 0, "[UNK]": 37, "[PAD]": 38}