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This repo contains the fully trained ByT5 that was used to estimate per-character entropies. Using it, you can also recreate the illustration in the paper.
Citation
If you use this for research, please cite:
@misc{https://doi.org/10.48550/arxiv.2206.12693,
doi = {10.48550/ARXIV.2206.12693},
url = {https://arxiv.org/abs/2206.12693},
author = {Krabbenhöft, Hajo Nils and Barth, Erhardt},
keywords = {Computation and Language (cs.CL), Sound (cs.SD), Audio and Speech Processing (eess.AS), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering, F.2.1; I.2.6; I.2.7},
title = {TEVR: Improving Speech Recognition by Token Entropy Variance Reduction},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
Generate TEVR Tokenizer from Text corpus
(copy of Generate TEVR Tokenizer.ipynb)
# TODO: load large text dataset like OSCAR
all_sentences_de = ["Über vier Jahrzehnte gehörte er zu den führenden Bildhauern Niederbayerns", "die katze ist niedlich"] * 1000
from huggingface_hub import snapshot_download
data_folder = snapshot_download("fxtentacle/tevr-token-entropy-predictor-de")
from transformers import T5ForConditionalGeneration
model = T5ForConditionalGeneration.from_pretrained(data_folder)
model.to('cuda')
model.eval()
None
import torch
def text_to_cross_entropy(text):
ttext = torch.tensor([[0]+list(text.encode('UTF-8'))],dtype=torch.int64).to('cuda')
tone = torch.tensor([[1]],dtype=torch.int32).to('cuda')
logits = model.forward(input_ids=tone, attention_mask=tone, decoder_input_ids=ttext, return_dict=False)[0].detach()
cross_entropy = torch.nn.functional.cross_entropy(input=logits[0][:-1], target=ttext[0][1:], reduction='none').detach().cpu().numpy()
return cross_entropy
text = all_sentences_de[0]
cross_entropy = text_to_cross_entropy(text)
print(text)
for i in range(len(text)):
print(text[i], cross_entropy[i])
Über vier Jahrzehnte gehörte er zu den führenden Bildhauern Niederbayerns
Ü 7.254014
b 0.17521738
e 0.00046933602
r 0.01929327
0.0003675739
v 0.20927554
i 6.13207
e 0.3896482
r 0.009583538
2.07364
J 0.02978594
a 2.483246
h 0.1591908
r 0.0045124847
z 0.00028653807
e 4.0242333
h 0.031035878
n 0.028907888
t 0.003264101
e 0.0018929198
0.05816966
g 1.2782481
e 3.5076692
h 0.694337
ö 0.5319732
r 0.48336726
t 0.0050443523
e 0.0017187123
0.14511283
e 1.0435015
r 0.18165778
1.0247636
z 0.3594512
u 0.0077577736
2.072764
d 0.17377533
e 1.0727838
n 1.2805216
0.24939628
f 0.27717885
ü 0.012466482
h 4.4356546
r 1.7371752
e 0.051492628
n 2.99407
d 0.009648594
e 0.19667451
n 0.007495021
0.2529005
B 0.004451485
i 0.024661187
l 0.0028436247
d 2.6620464
h 2.825038
a 0.8215449
u 0.011406565
e 2.9599652
r 0.45834702
n 0.11848967
0.5955992
N 0.010709903
i 1.5338714
e 0.1834471
d 5.668945
e 2.052247
r 0.7692907
b 0.0675718
a 0.028234791
y 0.0045266068
e 4.1125383
r 1.2630856
n 5.436057
s 0.46446246
from tqdm import tqdm
sentence_data = all_sentences_de
text_and_entropies = []
for text in tqdm(sentence_data):
text_and_entropies.append([text,text_to_cross_entropy(text)])
100%|██████████| 2000/2000 [00:09<00:00, 219.00it/s]
from collections import Counter
# 4s
#target_lengths = [1]
#token_budgets = [36]
# 4m
target_lengths = [4,3,2,1]
token_budgets = [40,80,96,36]
# 4l
#target_lengths = [4,3,2,1]
#token_budgets = [384,320,160,36]
ngrams = [Counter() for l in target_lengths]
tokens = []
for tgi,tgl in enumerate(target_lengths):
for row in tqdm(text_and_entropies[1:]):
use_text = row[0]
use_scores = row[1]
for t in tokens:
use_text = use_text.replace(t[0],'#')
candidates = []
for i in range(len(use_text)-(tgl-1)):
part = use_text[i:i+tgl].lower()
if '#' in part: continue
if ' ' in part: continue
if '-' in part: continue
score = sum(use_scores[i:i+tgl])
# print(part, score)
candidates.append([score, part])
candidates.sort(reverse=False)
candidates = candidates[:max(1,int(len(candidates)/5))]
#print(candidates)
ngrams[tgi].update([c[1] for c in candidates])
new_tokens = ngrams[tgi].most_common(token_budgets[tgi])
print(new_tokens)
tokens += new_tokens
#break
100%|██████████| 1999/1999 [00:00<00:00, 14645.88it/s]
[('lich', 1000), ('hnte', 999), ('rbay', 999), ('örte', 999), ('hört', 999), ('ahrz', 999), ('jahr', 999), ('bild', 999)]
100%|██████████| 1999/1999 [00:00<00:00, 18574.04it/s]
[('ist', 1000), ('den', 999), ('ber', 999), ('aue', 999), ('ern', 999), ('uer', 999)]
100%|██████████| 1999/1999 [00:00<00:00, 20827.32it/s]
[('ni', 1000), ('ge', 999), ('er', 999), ('fü', 999), ('vi', 999)]
100%|██████████| 1999/1999 [00:00<00:00, 19927.45it/s]
[('e', 2999), ('u', 999), ('n', 999), ('h', 999)]
all_tokens = ['<pad>','<eos>',' ']+[t[0] for t in tokens]+['?']
print(len(all_tokens), all_tokens)
27 ['<pad>', '<eos>', ' ', 'lich', 'hnte', 'rbay', 'örte', 'hört', 'ahrz', 'jahr', 'bild', 'ist', 'den', 'ber', 'aue', 'ern', 'uer', 'ni', 'ge', 'er', 'fü', 'vi', 'e', 'u', 'n', 'h', '?']
import json
with open('./tevr-tokenizer.txt','wt') as f:
json.dump(all_tokens, f)
import sys
import os
sys.path.append(data_folder)
from text_tokenizer import HajoTextTokenizer
text_tokenizer = HajoTextTokenizer('./tevr-tokenizer.txt')
sentence = "gehörte"
print(sentence)
encoded = text_tokenizer.encode(sentence)
print(encoded)
print([text_tokenizer.all_tokens[i] for i in encoded])
print([text_tokenizer.decode(encoded)])
gehörte
[18, 25, 6]
['ge', 'h', 'örte']
['gehörte']
Testing Tokenizer File
(copy of TEVR Explanation.ipynb)
from huggingface_hub import snapshot_download
data_folder = snapshot_download("fxtentacle/tevr-token-entropy-predictor-de")
from transformers import T5ForConditionalGeneration
model = T5ForConditionalGeneration.from_pretrained(data_folder)
model.to('cuda')
model.eval()
None
import torch
def text_to_cross_entropy(text):
ttext = torch.tensor([[0]+list(text.encode('UTF-8'))],dtype=torch.int64).to('cuda')
tone = torch.tensor([[1]],dtype=torch.int32).to('cuda')
logits = model.forward(input_ids=tone, attention_mask=tone, decoder_input_ids=ttext, return_dict=False)[0].detach()
cross_entropy = torch.nn.functional.cross_entropy(input=logits[0][:-1], target=ttext[0][1:], reduction='none').detach().cpu().numpy()
return cross_entropy
import sys
import os
sys.path.append(data_folder)
from text_tokenizer import HajoTextTokenizer
tokenizer_file = 'text-tokenizer-de-4m.txt'
text_tokenizer = HajoTextTokenizer(data_folder+'/'+tokenizer_file)
text = "die katze ist niedlich"
cross_entropy = text_to_cross_entropy(text)
tokens = text_tokenizer.encode(text)
tokens = [text_tokenizer.all_tokens[t] for t in tokens]
print(tokens)
token_sums = []
token_sums2 = []
for t in tokens:
ce = sum(cross_entropy[len(token_sums):len(token_sums)+len(t)])
for r in range(len(t)): token_sums.append(ce / len(t))
token_sums2.append(ce)
print(token_sums)
['die', ' ', 'k', 'at', 'ze', ' ', 'ist', ' ', 'n', 'ied', 'lich']
[3.3762913048267365, 3.3762913048267365, 3.3762913048267365, 0.29695791006088257, 4.193424224853516, 2.3430762887001038, 2.3430762887001038, 2.8417416363954544, 2.8417416363954544, 1.1227068901062012, 2.017452405144771, 2.017452405144771, 2.017452405144771, 0.0016304069431498647, 2.580254554748535, 2.3091587026913962, 2.3091587026913962, 2.3091587026913962, 1.0126478232632508, 1.0126478232632508, 1.0126478232632508, 1.0126478232632508]
import numpy as np
html = '<table style="font-size: 20px; font-family: Roboto">'
html += '<tr><td><b>(1)</b></td>'+''.join([f'<td style="text-align:left">{c}</td>' for c in list(text)])+'</tr>'
html += '<tr><td><b>(2)</b></td>'+''.join(['<td>1.0</td>'.format(v) for v in cross_entropy])+'<td>σ²={:3.1f}</td>'.format(np.var([1.0 for v in cross_entropy]))+'</tr>'
html += '<tr><td><b>(3)</b></td>'+''.join(['<td>{:3.1f}</td>'.format(v) for v in cross_entropy])+'<td>σ²={:3.1f}</td>'.format(np.var(cross_entropy))+'</tr>'
html += '<tr><td><b>(4)</b></td>'+''.join([f'<td style="text-align:center" colspan={len(t)}>{t}</td>' for t in tokens])+'</tr>'
html += '<tr><td><b>(5)</b></td>'+''.join([f'<td style="text-align:center" colspan={len(t)}>{"{:3.1f}".format(token_sums2[i])}</td>' for i,t in enumerate(tokens)])+'</tr>'
html += '<tr><td><b>(6)</b></td>'+''.join(['<td>{:3.1f}</td>'.format(v) for v in token_sums])+'<td>σ²={:3.1f}</td>'.format(np.var(token_sums))+'</tr>'
html += '</table>'
import IPython
IPython.display.HTML(html)
| (1) | d | i | e | k | a | t | z | e | i | s | t | n | i | e | d | l | i | c | h | ||||
| (2) | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | σ²=0.0 |
| (3) | 8.9 | 1.0 | 0.2 | 0.3 | 4.2 | 1.6 | 3.1 | 5.4 | 0.3 | 1.1 | 3.0 | 3.0 | 0.0 | 0.0 | 2.6 | 0.6 | 4.4 | 1.9 | 4.0 | 0.0 | 0.0 | 0.0 | σ²=5.0 |
| (4) | die | k | at | ze | ist | n | ied | lich | |||||||||||||||
| (5) | 10.1 | 0.3 | 4.2 | 4.7 | 5.7 | 1.1 | 6.1 | 0.0 | 2.6 | 6.9 | 4.1 | ||||||||||||
| (6) | 3.4 | 3.4 | 3.4 | 0.3 | 4.2 | 2.3 | 2.3 | 2.8 | 2.8 | 1.1 | 2.0 | 2.0 | 2.0 | 0.0 | 2.6 | 2.3 | 2.3 | 2.3 | 1.0 | 1.0 | 1.0 | 1.0 | σ²=1.1 |
from text_tokenizer import HajoTextTokenizer
text_tokenizer = HajoTextTokenizer(data_folder+'/'+tokenizer_file)
tt = text_tokenizer.all_tokens
print(', '.join(tt))
<pad>, <eos>, , chen, sche, lich, isch, icht, iche, eine, rden, tion, urde, haft, eich, rung, chte, ssen, chaf, nder, tlic, tung, eite, iert, sich, ngen, erde, scha, nden, unge, lung, mmen, eren, ende, inde, erun, sten, iese, igen, erte, iner, tsch, keit, der, die, ter, und, ein, ist, den, ten, ber, ver, sch, ung, ste, ent, ach, nte, auf, ben, eit, des, ers, aus, das, von, ren, gen, nen, lle, hre, mit, iel, uch, lte, ann, lie, men, dem, and, ind, als, sta, elt, ges, tte, ern, wir, ell, war, ere, rch, abe, len, ige, ied, ger, nnt, wei, ele, och, sse, end, all, ahr, bei, sie, ede, ion, ieg, ege, auc, che, rie, eis, vor, her, ang, für, ass, uss, tel, er, in, ge, en, st, ie, an, te, be, re, zu, ar, es, ra, al, or, ch, et, ei, un, le, rt, se, is, ha, we, at, me, ne, ur, he, au, ro, ti, li, ri, eh, im, ma, tr, ig, el, um, la, am, de, so, ol, tz, il, on, it, sc, sp, ko, na, pr, ni, si, fe, wi, ns, ke, ut, da, gr, eu, mi, hr, ze, hi, ta, ss, ng, sa, us, ba, ck, em, kt, ka, ve, fr, bi, wa, ah, gt, di, ab, fo, to, rk, as, ag, gi, hn, s, t, n, m, r, l, f, e, a, b, d, h, k, g, o, i, u, w, p, z, ä, ü, v, ö, j, c, y, x, q, á, í, ō, ó, š, é, č, ?
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