From 574155be137b7e0af4f874d4025d15c85b265e22 Mon Sep 17 00:00:00 2001
From: 语帆 <yf352572@alibaba-inc.com>
Date: 星期四, 29 二月 2024 16:07:49 +0800
Subject: [PATCH] atsr
---
examples/industrial_data_pretraining/lcbnet/run_bwer_recall.sh | 11
/dev/null | 67 ----
examples/industrial_data_pretraining/lcbnet/compute_wer_details.py | 702 ++++++++++++++++++++++++++++++++++++++++++++++
examples/industrial_data_pretraining/lcbnet/demo.sh | 80 ++++
4 files changed, 782 insertions(+), 78 deletions(-)
diff --git a/examples/industrial_data_pretraining/lcbnet/compute_wer_details.py b/examples/industrial_data_pretraining/lcbnet/compute_wer_details.py
new file mode 100755
index 0000000..e72d871
--- /dev/null
+++ b/examples/industrial_data_pretraining/lcbnet/compute_wer_details.py
@@ -0,0 +1,702 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+
+from enum import Enum
+import re, sys, unicodedata
+import codecs
+import argparse
+from tqdm import tqdm
+import os
+import pdb
+remove_tag = False
+spacelist = [" ", "\t", "\r", "\n"]
+puncts = [
+ "!",
+ ",",
+ "?",
+ "銆�",
+ "銆�",
+ "锛�",
+ "锛�",
+ "锛�",
+ "锛�",
+ "锛�",
+ "銆�",
+ "銆�",
+ "锔�",
+ "銆�",
+ "銆�",
+ "銆�",
+ "銆�",
+]
+
+
+class Code(Enum):
+ match = 1
+ substitution = 2
+ insertion = 3
+ deletion = 4
+
+
+class WordError(object):
+ def __init__(self):
+ self.errors = {
+ Code.substitution: 0,
+ Code.insertion: 0,
+ Code.deletion: 0,
+ }
+ self.ref_words = 0
+
+ def get_wer(self):
+ assert self.ref_words != 0
+ errors = (
+ self.errors[Code.substitution]
+ + self.errors[Code.insertion]
+ + self.errors[Code.deletion]
+ )
+ return 100.0 * errors / self.ref_words
+
+ def get_result_string(self):
+ return (
+ f"error_rate={self.get_wer():.4f}, "
+ f"ref_words={self.ref_words}, "
+ f"subs={self.errors[Code.substitution]}, "
+ f"ins={self.errors[Code.insertion]}, "
+ f"dels={self.errors[Code.deletion]}"
+ )
+
+
+def characterize(string):
+ res = []
+ i = 0
+ while i < len(string):
+ char = string[i]
+ if char in puncts:
+ i += 1
+ continue
+ cat1 = unicodedata.category(char)
+ # https://unicodebook.readthedocs.io/unicode.html#unicode-categories
+ if cat1 == "Zs" or cat1 == "Cn" or char in spacelist: # space or not assigned
+ i += 1
+ continue
+ if cat1 == "Lo": # letter-other
+ res.append(char)
+ i += 1
+ else:
+ # some input looks like: <unk><noise>, we want to separate it to two words.
+ sep = " "
+ if char == "<":
+ sep = ">"
+ j = i + 1
+ while j < len(string):
+ c = string[j]
+ if ord(c) >= 128 or (c in spacelist) or (c == sep):
+ break
+ j += 1
+ if j < len(string) and string[j] == ">":
+ j += 1
+ res.append(string[i:j])
+ i = j
+ return res
+
+
+def stripoff_tags(x):
+ if not x:
+ return ""
+ chars = []
+ i = 0
+ T = len(x)
+ while i < T:
+ if x[i] == "<":
+ while i < T and x[i] != ">":
+ i += 1
+ i += 1
+ else:
+ chars.append(x[i])
+ i += 1
+ return "".join(chars)
+
+
+def normalize(sentence, ignore_words, cs, split=None):
+ """sentence, ignore_words are both in unicode"""
+ new_sentence = []
+ for token in sentence:
+ x = token
+ if not cs:
+ x = x.upper()
+ if x in ignore_words:
+ continue
+ if remove_tag:
+ x = stripoff_tags(x)
+ if not x:
+ continue
+ if split and x in split:
+ new_sentence += split[x]
+ else:
+ new_sentence.append(x)
+ return new_sentence
+
+
+class Calculator:
+ def __init__(self):
+ self.data = {}
+ self.space = []
+ self.cost = {}
+ self.cost["cor"] = 0
+ self.cost["sub"] = 1
+ self.cost["del"] = 1
+ self.cost["ins"] = 1
+
+ def calculate(self, lab, rec):
+ # Initialization
+ lab.insert(0, "")
+ rec.insert(0, "")
+ while len(self.space) < len(lab):
+ self.space.append([])
+ for row in self.space:
+ for element in row:
+ element["dist"] = 0
+ element["error"] = "non"
+ while len(row) < len(rec):
+ row.append({"dist": 0, "error": "non"})
+ for i in range(len(lab)):
+ self.space[i][0]["dist"] = i
+ self.space[i][0]["error"] = "del"
+ for j in range(len(rec)):
+ self.space[0][j]["dist"] = j
+ self.space[0][j]["error"] = "ins"
+ self.space[0][0]["error"] = "non"
+ for token in lab:
+ if token not in self.data and len(token) > 0:
+ self.data[token] = {"all": 0, "cor": 0, "sub": 0, "ins": 0, "del": 0}
+ for token in rec:
+ if token not in self.data and len(token) > 0:
+ self.data[token] = {"all": 0, "cor": 0, "sub": 0, "ins": 0, "del": 0}
+ # Computing edit distance
+ for i, lab_token in enumerate(lab):
+ for j, rec_token in enumerate(rec):
+ if i == 0 or j == 0:
+ continue
+ min_dist = sys.maxsize
+ min_error = "none"
+ dist = self.space[i - 1][j]["dist"] + self.cost["del"]
+ error = "del"
+ if dist < min_dist:
+ min_dist = dist
+ min_error = error
+ dist = self.space[i][j - 1]["dist"] + self.cost["ins"]
+ error = "ins"
+ if dist < min_dist:
+ min_dist = dist
+ min_error = error
+ if lab_token == rec_token.replace("<BIAS>", ""):
+ dist = self.space[i - 1][j - 1]["dist"] + self.cost["cor"]
+ error = "cor"
+ else:
+ dist = self.space[i - 1][j - 1]["dist"] + self.cost["sub"]
+ error = "sub"
+ if dist < min_dist:
+ min_dist = dist
+ min_error = error
+ self.space[i][j]["dist"] = min_dist
+ self.space[i][j]["error"] = min_error
+ # Tracing back
+ result = {
+ "lab": [],
+ "rec": [],
+ "code": [],
+ "all": 0,
+ "cor": 0,
+ "sub": 0,
+ "ins": 0,
+ "del": 0,
+ }
+ i = len(lab) - 1
+ j = len(rec) - 1
+ while True:
+ if self.space[i][j]["error"] == "cor": # correct
+ if len(lab[i]) > 0:
+ self.data[lab[i]]["all"] = self.data[lab[i]]["all"] + 1
+ self.data[lab[i]]["cor"] = self.data[lab[i]]["cor"] + 1
+ result["all"] = result["all"] + 1
+ result["cor"] = result["cor"] + 1
+ result["lab"].insert(0, lab[i])
+ result["rec"].insert(0, rec[j])
+ result["code"].insert(0, Code.match)
+ i = i - 1
+ j = j - 1
+ elif self.space[i][j]["error"] == "sub": # substitution
+ if len(lab[i]) > 0:
+ self.data[lab[i]]["all"] = self.data[lab[i]]["all"] + 1
+ self.data[lab[i]]["sub"] = self.data[lab[i]]["sub"] + 1
+ result["all"] = result["all"] + 1
+ result["sub"] = result["sub"] + 1
+ result["lab"].insert(0, lab[i])
+ result["rec"].insert(0, rec[j])
+ result["code"].insert(0, Code.substitution)
+ i = i - 1
+ j = j - 1
+ elif self.space[i][j]["error"] == "del": # deletion
+ if len(lab[i]) > 0:
+ self.data[lab[i]]["all"] = self.data[lab[i]]["all"] + 1
+ self.data[lab[i]]["del"] = self.data[lab[i]]["del"] + 1
+ result["all"] = result["all"] + 1
+ result["del"] = result["del"] + 1
+ result["lab"].insert(0, lab[i])
+ result["rec"].insert(0, "")
+ result["code"].insert(0, Code.deletion)
+ i = i - 1
+ elif self.space[i][j]["error"] == "ins": # insertion
+ if len(rec[j]) > 0:
+ self.data[rec[j]]["ins"] = self.data[rec[j]]["ins"] + 1
+ result["ins"] = result["ins"] + 1
+ result["lab"].insert(0, "")
+ result["rec"].insert(0, rec[j])
+ result["code"].insert(0, Code.insertion)
+ j = j - 1
+ elif self.space[i][j]["error"] == "non": # starting point
+ break
+ else: # shouldn't reach here
+ print(
+ "this should not happen , i = {i} , j = {j} , error = {error}".format(
+ i=i, j=j, error=self.space[i][j]["error"]
+ )
+ )
+ return result
+
+ def overall(self):
+ result = {"all": 0, "cor": 0, "sub": 0, "ins": 0, "del": 0}
+ for token in self.data:
+ result["all"] = result["all"] + self.data[token]["all"]
+ result["cor"] = result["cor"] + self.data[token]["cor"]
+ result["sub"] = result["sub"] + self.data[token]["sub"]
+ result["ins"] = result["ins"] + self.data[token]["ins"]
+ result["del"] = result["del"] + self.data[token]["del"]
+ return result
+
+ def cluster(self, data):
+ result = {"all": 0, "cor": 0, "sub": 0, "ins": 0, "del": 0}
+ for token in data:
+ if token in self.data:
+ result["all"] = result["all"] + self.data[token]["all"]
+ result["cor"] = result["cor"] + self.data[token]["cor"]
+ result["sub"] = result["sub"] + self.data[token]["sub"]
+ result["ins"] = result["ins"] + self.data[token]["ins"]
+ result["del"] = result["del"] + self.data[token]["del"]
+ return result
+
+ def keys(self):
+ return list(self.data.keys())
+
+
+def width(string):
+ return sum(1 + (unicodedata.east_asian_width(c) in "AFW") for c in string)
+
+
+def default_cluster(word):
+ unicode_names = [unicodedata.name(char) for char in word]
+ for i in reversed(range(len(unicode_names))):
+ if unicode_names[i].startswith("DIGIT"): # 1
+ unicode_names[i] = "Number" # 'DIGIT'
+ elif unicode_names[i].startswith("CJK UNIFIED IDEOGRAPH") or unicode_names[
+ i
+ ].startswith("CJK COMPATIBILITY IDEOGRAPH"):
+ # 鏄� / 铯�
+ unicode_names[i] = "Mandarin" # 'CJK IDEOGRAPH'
+ elif unicode_names[i].startswith("LATIN CAPITAL LETTER") or unicode_names[
+ i
+ ].startswith("LATIN SMALL LETTER"):
+ # A / a
+ unicode_names[i] = "English" # 'LATIN LETTER'
+ elif unicode_names[i].startswith("HIRAGANA LETTER"): # 銇� 銇� 銈�
+ unicode_names[i] = "Japanese" # 'GANA LETTER'
+ elif (
+ unicode_names[i].startswith("AMPERSAND")
+ or unicode_names[i].startswith("APOSTROPHE")
+ or unicode_names[i].startswith("COMMERCIAL AT")
+ or unicode_names[i].startswith("DEGREE CELSIUS")
+ or unicode_names[i].startswith("EQUALS SIGN")
+ or unicode_names[i].startswith("FULL STOP")
+ or unicode_names[i].startswith("HYPHEN-MINUS")
+ or unicode_names[i].startswith("LOW LINE")
+ or unicode_names[i].startswith("NUMBER SIGN")
+ or unicode_names[i].startswith("PLUS SIGN")
+ or unicode_names[i].startswith("SEMICOLON")
+ ):
+ # & / ' / @ / 鈩� / = / . / - / _ / # / + / ;
+ del unicode_names[i]
+ else:
+ return "Other"
+ if len(unicode_names) == 0:
+ return "Other"
+ if len(unicode_names) == 1:
+ return unicode_names[0]
+ for i in range(len(unicode_names) - 1):
+ if unicode_names[i] != unicode_names[i + 1]:
+ return "Other"
+ return unicode_names[0]
+
+
+def get_args():
+ parser = argparse.ArgumentParser(description="wer cal")
+ parser.add_argument("--ref", type=str, help="Text input path")
+ parser.add_argument("--ref_ocr", type=str, help="Text input path")
+ parser.add_argument("--rec_name", type=str, action="append", default=[])
+ parser.add_argument("--rec_file", type=str, action="append", default=[])
+ parser.add_argument("--verbose", type=int, default=1, help="show")
+ parser.add_argument("--char", type=bool, default=True, help="show")
+ args = parser.parse_args()
+ return args
+
+
+def main(args):
+ cluster_file = ""
+ ignore_words = set()
+ tochar = args.char
+ verbose = args.verbose
+ padding_symbol = " "
+ case_sensitive = False
+ max_words_per_line = sys.maxsize
+ split = None
+
+ if not case_sensitive:
+ ig = set([w.upper() for w in ignore_words])
+ ignore_words = ig
+
+ default_clusters = {}
+ default_words = {}
+ ref_file = args.ref
+ ref_ocr = args.ref_ocr
+ rec_files = args.rec_file
+ rec_names = args.rec_name
+ assert len(rec_files) == len(rec_names)
+
+ # load ocr
+ ref_ocr_dict = {}
+ with codecs.open(ref_ocr, "r", "utf-8") as fh:
+ for line in fh:
+ if "$" in line:
+ line = line.replace("$", " ")
+ if tochar:
+ array = characterize(line)
+ else:
+ array = line.strip().split()
+ if len(array) == 0:
+ continue
+ fid = array[0]
+ ref_ocr_dict[fid] = normalize(array[1:], ignore_words, case_sensitive, split)
+
+ if split and not case_sensitive:
+ newsplit = dict()
+ for w in split:
+ words = split[w]
+ for i in range(len(words)):
+ words[i] = words[i].upper()
+ newsplit[w.upper()] = words
+ split = newsplit
+
+ rec_sets = {}
+ calculators_dict = dict()
+ ub_wer_dict = dict()
+ hotwords_related_dict = dict() # 璁板綍recall鐩稿叧鐨勫唴瀹�
+ for i, hyp_file in enumerate(rec_files):
+ rec_sets[rec_names[i]] = dict()
+ with codecs.open(hyp_file, "r", "utf-8") as fh:
+ for line in fh:
+ if tochar:
+ array = characterize(line)
+ else:
+ array = line.strip().split()
+ if len(array) == 0:
+ continue
+ fid = array[0]
+ rec_sets[rec_names[i]][fid] = normalize(array[1:], ignore_words, case_sensitive, split)
+
+ calculators_dict[rec_names[i]] = Calculator()
+ ub_wer_dict[rec_names[i]] = {"u_wer": WordError(), "b_wer": WordError(), "wer": WordError()}
+ hotwords_related_dict[rec_names[i]] = {'tp': 0, 'tn': 0, 'fp': 0, 'fn': 0}
+ # tp: 鐑瘝鍦╨abel閲岋紝鍚屾椂鍦╮ec閲�
+ # tn: 鐑瘝涓嶅湪label閲岋紝鍚屾椂涓嶅湪rec閲�
+ # fp: 鐑瘝涓嶅湪label閲岋紝浣嗘槸鍦╮ec閲�
+ # fn: 鐑瘝鍦╨abel閲岋紝浣嗘槸涓嶅湪rec閲�
+
+ # record wrong label but in ocr
+ wrong_rec_but_in_ocr_dict = {}
+ for rec_name in rec_names:
+ wrong_rec_but_in_ocr_dict[rec_name] = 0
+
+ _file_total_len = 0
+ with os.popen("cat {} | wc -l".format(ref_file)) as pipe:
+ _file_total_len = int(pipe.read().strip())
+
+ # compute error rate on the interaction of reference file and hyp file
+ for line in tqdm(open(ref_file, 'r', encoding='utf-8'), total=_file_total_len):
+ if tochar:
+ array = characterize(line)
+ else:
+ array = line.rstrip('\n').split()
+ if len(array) == 0: continue
+ fid = array[0]
+ lab = normalize(array[1:], ignore_words, case_sensitive, split)
+
+ if verbose:
+ print('\nutt: %s' % fid)
+
+ ocr_text = ref_ocr_dict[fid]
+ ocr_set = set(ocr_text)
+ print('ocr: {}'.format(" ".join(ocr_text)))
+ list_match = [] # 鎸噇abel閲岄潰鍦╫cr閲岄潰鐨勫唴瀹�
+ list_not_mathch = []
+ tmp_error = 0
+ tmp_match = 0
+ for index in range(len(lab)):
+ # text_list.append(uttlist[index+1])
+ if lab[index] not in ocr_set:
+ tmp_error += 1
+ list_not_mathch.append(lab[index])
+ else:
+ tmp_match += 1
+ list_match.append(lab[index])
+ print('label in ocr: {}'.format(" ".join(list_match)))
+
+ # for each reco file
+ base_wrong_ocr_wer = None
+ ocr_wrong_ocr_wer = None
+
+ for rec_name in rec_names:
+ rec_set = rec_sets[rec_name]
+ if fid not in rec_set:
+ continue
+ rec = rec_set[fid]
+
+ # print(rec)
+ for word in rec + lab:
+ if word not in default_words:
+ default_cluster_name = default_cluster(word)
+ if default_cluster_name not in default_clusters:
+ default_clusters[default_cluster_name] = {}
+ if word not in default_clusters[default_cluster_name]:
+ default_clusters[default_cluster_name][word] = 1
+ default_words[word] = default_cluster_name
+
+ result = calculators_dict[rec_name].calculate(lab.copy(), rec.copy())
+ if verbose:
+ if result['all'] != 0:
+ wer = float(result['ins'] + result['sub'] + result['del']) * 100.0 / result['all']
+ else:
+ wer = 0.0
+ print('WER(%s): %4.2f %%' % (rec_name, wer), end=' ')
+ print('N=%d C=%d S=%d D=%d I=%d' %
+ (result['all'], result['cor'], result['sub'], result['del'], result['ins']))
+
+
+ # print(result['rec'])
+ wrong_rec_but_in_ocr = []
+ for idx in range(len(result['lab'])):
+ if result['lab'][idx] != "":
+ if result['lab'][idx] != result['rec'][idx].replace("<BIAS>", ""):
+ if result['lab'][idx] in list_match:
+ wrong_rec_but_in_ocr.append(result['lab'][idx])
+ wrong_rec_but_in_ocr_dict[rec_name] += 1
+ print('wrong_rec_but_in_ocr: {}'.format(" ".join(wrong_rec_but_in_ocr)))
+
+ if rec_name == "base":
+ base_wrong_ocr_wer = len(wrong_rec_but_in_ocr)
+ if "ocr" in rec_name or "hot" in rec_name:
+ ocr_wrong_ocr_wer = len(wrong_rec_but_in_ocr)
+ if ocr_wrong_ocr_wer < base_wrong_ocr_wer:
+ print("{} {} helps, {} -> {}".format(fid, rec_name, base_wrong_ocr_wer, ocr_wrong_ocr_wer))
+ elif ocr_wrong_ocr_wer > base_wrong_ocr_wer:
+ print("{} {} hurts, {} -> {}".format(fid, rec_name, base_wrong_ocr_wer, ocr_wrong_ocr_wer))
+
+ # recall = 0
+ # false_alarm = 0
+ # for idx in range(len(result['lab'])):
+ # if "<BIAS>" in result['rec'][idx]:
+ # if result['rec'][idx].replace("<BIAS>", "") in list_match:
+ # recall += 1
+ # else:
+ # false_alarm += 1
+ # print("bias hotwords recall: {}, fa: {}, list_match {}, recall: {:.2f}, fa: {:.2f}".format(
+ # recall, false_alarm, len(list_match), recall / len(list_match) if len(list_match) != 0 else 0, false_alarm / len(list_match) if len(list_match) != 0 else 0
+ # ))
+ # tp: 鐑瘝鍦╨abel閲岋紝鍚屾椂鍦╮ec閲�
+ # tn: 鐑瘝涓嶅湪label閲岋紝鍚屾椂涓嶅湪rec閲�
+ # fp: 鐑瘝涓嶅湪label閲岋紝浣嗘槸鍦╮ec閲�
+ # fn: 鐑瘝鍦╨abel閲岋紝浣嗘槸涓嶅湪rec閲�
+ _rec_list = [word.replace("<BIAS>", "") for word in rec]
+ _label_list = [word for word in lab]
+ _tp = _tn = _fp = _fn = 0
+ hot_true_list = [hotword for hotword in ocr_text if hotword in _label_list]
+ hot_bad_list = [hotword for hotword in ocr_text if hotword not in _label_list]
+ for badhotword in hot_bad_list:
+ count = len([word for word in _rec_list if word == badhotword])
+ # print(f"bad {badhotword} count: {count}")
+ # for word in _rec_list:
+ # if badhotword == word:
+ # count += 1
+ if count == 0:
+ hotwords_related_dict[rec_name]['tn'] += 1
+ _tn += 1
+ # fp: 0
+ else:
+ hotwords_related_dict[rec_name]['fp'] += count
+ _fp += count
+ # tn: 0
+ # if badhotword in _rec_list:
+ # hotwords_related_dict[rec_name]['fp'] += 1
+ # else:
+ # hotwords_related_dict[rec_name]['tn'] += 1
+ for hotword in hot_true_list:
+ true_count = len([word for word in _label_list if hotword == word])
+ rec_count = len([word for word in _rec_list if hotword == word])
+ # print(f"good {hotword} true_count: {true_count}, rec_count: {rec_count}")
+ if rec_count == true_count:
+ hotwords_related_dict[rec_name]['tp'] += true_count
+ _tp += true_count
+ elif rec_count > true_count:
+ hotwords_related_dict[rec_name]['tp'] += true_count
+ # fp: 涓嶅湪label閲岋紝浣嗘槸鍦╮ec閲�
+ hotwords_related_dict[rec_name]['fp'] += rec_count - true_count
+ _tp += true_count
+ _fp += rec_count - true_count
+ else:
+ hotwords_related_dict[rec_name]['tp'] += rec_count
+ # fn: 鐑瘝鍦╨abel閲岋紝浣嗘槸涓嶅湪rec閲�
+ hotwords_related_dict[rec_name]['fn'] += true_count - rec_count
+ _tp += rec_count
+ _fn += true_count - rec_count
+ print("hotword: tp: {}, tn: {}, fp: {}, fn: {}, all: {}, recall: {:.2f}%".format(
+ _tp, _tn, _fp, _fn, sum([_tp, _tn, _fp, _fn]), _tp / (_tp + _fn) * 100 if (_tp + _fn) != 0 else 0
+ ))
+
+ # if hotword in _rec_list:
+ # hotwords_related_dict[rec_name]['tp'] += 1
+ # else:
+ # hotwords_related_dict[rec_name]['fn'] += 1
+ # 璁＄畻uwer, bwer, wer
+ for code, rec_word, lab_word in zip(result["code"], result["rec"], result["lab"]):
+ if code == Code.match:
+ ub_wer_dict[rec_name]["wer"].ref_words += 1
+ if lab_word in hot_true_list:
+ # tmp_ref.append(ref_tokens[ref_idx])
+ ub_wer_dict[rec_name]["b_wer"].ref_words += 1
+ else:
+ ub_wer_dict[rec_name]["u_wer"].ref_words += 1
+ elif code == Code.substitution:
+ ub_wer_dict[rec_name]["wer"].ref_words += 1
+ ub_wer_dict[rec_name]["wer"].errors[Code.substitution] += 1
+ if lab_word in hot_true_list:
+ # tmp_ref.append(ref_tokens[ref_idx])
+ ub_wer_dict[rec_name]["b_wer"].ref_words += 1
+ ub_wer_dict[rec_name]["b_wer"].errors[Code.substitution] += 1
+ else:
+ ub_wer_dict[rec_name]["u_wer"].ref_words += 1
+ ub_wer_dict[rec_name]["u_wer"].errors[Code.substitution] += 1
+ elif code == Code.deletion:
+ ub_wer_dict[rec_name]["wer"].ref_words += 1
+ ub_wer_dict[rec_name]["wer"].errors[Code.deletion] += 1
+ if lab_word in hot_true_list:
+ # tmp_ref.append(ref_tokens[ref_idx])
+ ub_wer_dict[rec_name]["b_wer"].ref_words += 1
+ ub_wer_dict[rec_name]["b_wer"].errors[Code.deletion] += 1
+ else:
+ ub_wer_dict[rec_name]["u_wer"].ref_words += 1
+ ub_wer_dict[rec_name]["u_wer"].errors[Code.deletion] += 1
+ elif code == Code.insertion:
+ ub_wer_dict[rec_name]["wer"].errors[Code.insertion] += 1
+ if rec_word in hot_true_list:
+ ub_wer_dict[rec_name]["b_wer"].errors[Code.insertion] += 1
+ else:
+ ub_wer_dict[rec_name]["u_wer"].errors[Code.insertion] += 1
+
+ space = {}
+ space['lab'] = []
+ space['rec'] = []
+ for idx in range(len(result['lab'])):
+ len_lab = width(result['lab'][idx])
+ len_rec = width(result['rec'][idx])
+ length = max(len_lab, len_rec)
+ space['lab'].append(length - len_lab)
+ space['rec'].append(length - len_rec)
+ upper_lab = len(result['lab'])
+ upper_rec = len(result['rec'])
+ lab1, rec1 = 0, 0
+ while lab1 < upper_lab or rec1 < upper_rec:
+ if verbose > 1:
+ print('lab(%s):' % fid.encode('utf-8'), end=' ')
+ else:
+ print('lab:', end=' ')
+ lab2 = min(upper_lab, lab1 + max_words_per_line)
+ for idx in range(lab1, lab2):
+ token = result['lab'][idx]
+ print('{token}'.format(token=token), end='')
+ for n in range(space['lab'][idx]):
+ print(padding_symbol, end='')
+ print(' ', end='')
+ print()
+ if verbose > 1:
+ print('rec(%s):' % fid.encode('utf-8'), end=' ')
+ else:
+ print('rec:', end=' ')
+
+ rec2 = min(upper_rec, rec1 + max_words_per_line)
+ for idx in range(rec1, rec2):
+ token = result['rec'][idx]
+ print('{token}'.format(token=token), end='')
+ for n in range(space['rec'][idx]):
+ print(padding_symbol, end='')
+ print(' ', end='')
+ print()
+ # print('\n', end='\n')
+ lab1 = lab2
+ rec1 = rec2
+ print('\n', end='\n')
+ # break
+ if verbose:
+ print('===========================================================================')
+ print()
+
+ print(wrong_rec_but_in_ocr_dict)
+ for rec_name in rec_names:
+ result = calculators_dict[rec_name].overall()
+
+ if result['all'] != 0:
+ wer = float(result['ins'] + result['sub'] + result['del']) * 100.0 / result['all']
+ else:
+ wer = 0.0
+ print('{} Overall -> {:4.2f} %'.format(rec_name, wer), end=' ')
+ print('N=%d C=%d S=%d D=%d I=%d' %
+ (result['all'], result['cor'], result['sub'], result['del'], result['ins']))
+ print(f"WER: {ub_wer_dict[rec_name]['wer'].get_result_string()}")
+ print(f"U-WER: {ub_wer_dict[rec_name]['u_wer'].get_result_string()}")
+ print(f"B-WER: {ub_wer_dict[rec_name]['b_wer'].get_result_string()}")
+
+ print('hotword: tp: {}, tn: {}, fp: {}, fn: {}, all: {}, recall: {:.2f}%'.format(
+ hotwords_related_dict[rec_name]['tp'],
+ hotwords_related_dict[rec_name]['tn'],
+ hotwords_related_dict[rec_name]['fp'],
+ hotwords_related_dict[rec_name]['fn'],
+ sum([v for k, v in hotwords_related_dict[rec_name].items()]),
+ hotwords_related_dict[rec_name]['tp'] / (
+ hotwords_related_dict[rec_name]['tp'] + hotwords_related_dict[rec_name]['fn']
+ ) * 100 if hotwords_related_dict[rec_name]['tp'] + hotwords_related_dict[rec_name]['fn'] != 0 else 0
+ ))
+
+ # tp: 鐑瘝鍦╨abel閲岋紝鍚屾椂鍦╮ec閲�
+ # tn: 鐑瘝涓嶅湪label閲岋紝鍚屾椂涓嶅湪rec閲�
+ # fp: 鐑瘝涓嶅湪label閲岋紝浣嗘槸鍦╮ec閲�
+ # fn: 鐑瘝鍦╨abel閲岋紝浣嗘槸涓嶅湪rec閲�
+ if not verbose:
+ print()
+ print()
+
+
+if __name__ == "__main__":
+ args = get_args()
+
+ # print("")
+ print(args)
+ main(args)
+
diff --git a/examples/industrial_data_pretraining/lcbnet/demo.sh b/examples/industrial_data_pretraining/lcbnet/demo.sh
index 9515f98..f90b8e2 100755
--- a/examples/industrial_data_pretraining/lcbnet/demo.sh
+++ b/examples/industrial_data_pretraining/lcbnet/demo.sh
@@ -1,13 +1,71 @@
file_dir="/nfs/yufan.yf/workspace/github/FunASR/examples/industrial_data_pretraining/lcbnet/exp/speech_lcbnet_contextual_asr-en-16k-bpe-vocab5002-pytorch"
+CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
+inference_device="cuda"
-#CUDA_VISIBLE_DEVICES="" \
-python -m funasr.bin.inference \
---config-path=${file_dir} \
---config-name="config.yaml" \
-++init_param=${file_dir}/model.pb \
-++tokenizer_conf.token_list=${file_dir}/tokens.txt \
-++input=[${file_dir}/wav.scp,${file_dir}/ocr.txt] \
-+data_type='["kaldi_ark", "text"]' \
-++tokenizer_conf.bpemodel=${file_dir}/bpe.model \
-++output_dir="./outputs/debug" \
-++device="cpu" \
+if [ ${inference_device} == "cuda" ]; then
+ nj=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+else
+ inference_batch_size=1
+ CUDA_VISIBLE_DEVICES=""
+ for JOB in $(seq ${nj}); do
+ CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES"-1,"
+ done
+fi
+
+inference_dir="outputs/slidespeech_dev_beamsearch"
+_logdir="${inference_dir}/logdir"
+echo "inference_dir: ${inference_dir}"
+
+mkdir -p "${_logdir}"
+key_file1=${file_dir}/dev/wav.scp
+key_file2=${file_dir}/dev/ocr.txt
+split_scps1=
+split_scps2=
+for JOB in $(seq "${nj}"); do
+ split_scps1+=" ${_logdir}/wav.${JOB}.scp"
+ split_scps2+=" ${_logdir}/ocr.${JOB}.txt"
+done
+utils/split_scp.pl "${key_file1}" ${split_scps1}
+utils/split_scp.pl "${key_file2}" ${split_scps2}
+
+gpuid_list_array=(${CUDA_VISIBLE_DEVICES//,/ })
+for JOB in $(seq ${nj}); do
+ {
+ id=$((JOB-1))
+ gpuid=${gpuid_list_array[$id]}
+
+ export CUDA_VISIBLE_DEVICES=${gpuid}
+
+ python -m funasr.bin.inference \
+ --config-path=${file_dir} \
+ --config-name="config.yaml" \
+ ++init_param=${file_dir}/model.pb \
+ ++tokenizer_conf.token_list=${file_dir}/tokens.txt \
+ ++input=[${_logdir}/wav.${JOB}.scp,${_logdir}/ocr.${JOB}.txt] \
+ +data_type='["kaldi_ark", "text"]' \
+ ++tokenizer_conf.bpemodel=${file_dir}/bpe.model \
+ ++output_dir="${inference_dir}/${JOB}" \
+ ++device="${inference_device}" \
+ ++ncpu=1 \
+ ++disable_log=true &> ${_logdir}/log.${JOB}.txt
+
+ }&
+done
+wait
+
+
+mkdir -p ${inference_dir}/1best_recog
+
+for JOB in $(seq "${nj}"); do
+ cat "${inference_dir}/${JOB}/1best_recog/token" >> "${inference_dir}/1best_recog/token"
+done
+
+echo "Computing WER ..."
+sed -e 's/ /\t/' -e 's/ //g' -e 's/鈻�/ /g' -e 's/\t /\t/' ${inference_dir}/1best_recog/token > ${inference_dir}/1best_recog/token.proc
+cp ${file_dir}/dev/text ${inference_dir}/1best_recog/token.ref
+cp ${file_dir}/dev/ocr.list ${inference_dir}/1best_recog/ocr.list
+python utils/compute_wer.py ${inference_dir}/1best_recog/token.ref ${inference_dir}/1best_recog/token.proc ${inference_dir}/1best_recog/token.cer
+tail -n 3 ${inference_dir}/1best_recog/token.cer
+
+./run_bwer_recall.sh ${inference_dir}/1best_recog/
+tail -n 6 ${inference_dir}/1best_recog/BWER-UWER.results |head -n 5
diff --git a/examples/industrial_data_pretraining/lcbnet/demo_nj.sh b/examples/industrial_data_pretraining/lcbnet/demo_nj.sh
deleted file mode 100755
index 4aae9e5..0000000
--- a/examples/industrial_data_pretraining/lcbnet/demo_nj.sh
+++ /dev/null
@@ -1,67 +0,0 @@
-file_dir="/nfs/yufan.yf/workspace/github/FunASR/examples/industrial_data_pretraining/lcbnet/exp/speech_lcbnet_contextual_asr-en-16k-bpe-vocab5002-pytorch"
-CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
-inference_device="cuda"
-
-if [ ${inference_device} == "cuda" ]; then
- nj=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
-else
- inference_batch_size=1
- CUDA_VISIBLE_DEVICES=""
- for JOB in $(seq ${nj}); do
- CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES"-1,"
- done
-fi
-
-inference_dir="outputs/test"
-_logdir="${inference_dir}/logdir"
-echo "inference_dir: ${inference_dir}"
-
-mkdir -p "${_logdir}"
-key_file1=${file_dir}/wav.scp
-key_file2=${file_dir}/ocr.txt
-split_scps1=
-split_scps2=
-for JOB in $(seq "${nj}"); do
- split_scps1+=" ${_logdir}/wav.${JOB}.scp"
- split_scps2+=" ${_logdir}/ocr.${JOB}.txt"
-done
-utils/split_scp.pl "${key_file1}" ${split_scps1}
-utils/split_scp.pl "${key_file2}" ${split_scps2}
-
-gpuid_list_array=(${CUDA_VISIBLE_DEVICES//,/ })
-for JOB in $(seq ${nj}); do
- {
- id=$((JOB-1))
- gpuid=${gpuid_list_array[$id]}
-
- export CUDA_VISIBLE_DEVICES=${gpuid}
-
- python -m funasr.bin.inference \
- --config-path=${file_dir} \
- --config-name="config.yaml" \
- ++init_param=${file_dir}/model.pb \
- ++tokenizer_conf.token_list=${file_dir}/tokens.txt \
- ++input=[${_logdir}/wav.${JOB}.scp,${_logdir}/ocr.${JOB}.txt] \
- +data_type='["kaldi_ark", "text"]' \
- ++tokenizer_conf.bpemodel=${file_dir}/bpe.model \
- ++output_dir="${inference_dir}/${JOB}" \
- ++device="${inference_device}" \
- ++ncpu=1 \
- ++disable_log=true &> ${_logdir}/log.${JOB}.txt
-
- }&
-done
-wait
-
-
-mkdir -p ${inference_dir}/1best_recog
-
-for JOB in $(seq "${nj}"); do
- cat "${inference_dir}/${JOB}/1best_recog/token" >> "${inference_dir}/1best_recog/token"
-done
-
-echo "Computing WER ..."
-sed -e 's/ /\t/' -e 's/ //g' -e 's/鈻�/ /g' -e 's/\t /\t/' ${inference_dir}/1best_recog/token > ${inference_dir}/1best_recog/token.proc
-cp ${file_dir}/text ${inference_dir}/1best_recog/token.ref
-python utils/compute_wer.py ${inference_dir}/1best_recog/token.ref ${inference_dir}/1best_recog/token.proc ${inference_dir}/1best_recog/token.cer
-tail -n 3 ${inference_dir}/1best_recog/token.cer
\ No newline at end of file
diff --git a/examples/industrial_data_pretraining/lcbnet/run_bwer_recall.sh b/examples/industrial_data_pretraining/lcbnet/run_bwer_recall.sh
new file mode 100755
index 0000000..7d6b6ff
--- /dev/null
+++ b/examples/industrial_data_pretraining/lcbnet/run_bwer_recall.sh
@@ -0,0 +1,11 @@
+#now_result_name=asr_conformer_acc1_lr002_warm20000/decode_asr_asr_model_valid.acc.ave
+#hotword_type=ocr_1ngram_top10_hotwords_list
+hot_exp_suf=$1
+
+
+python compute_wer_details.py --v 1 \
+ --ref ${hot_exp_suf}/token.ref \
+ --ref_ocr ${hot_exp_suf}/ocr.list \
+ --rec_name base \
+ --rec_file ${hot_exp_suf}/token.proc \
+ > ${hot_exp_suf}/BWER-UWER.results
--
Gitblit v1.9.1