226 lines
6.2 KiB
Python
226 lines
6.2 KiB
Python
import os
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import numpy as np
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import sys
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import hydra
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from omegaconf import DictConfig, OmegaConf, ListConfig
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def compute_wer(
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ref_file,
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hyp_file,
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cer_file,
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cn_postprocess=False,
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):
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rst = {
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"Wrd": 0,
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"Corr": 0,
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"Ins": 0,
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"Del": 0,
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"Sub": 0,
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"Snt": 0,
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"Err": 0.0,
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"S.Err": 0.0,
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"wrong_words": 0,
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"wrong_sentences": 0,
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}
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hyp_dict = {}
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ref_dict = {}
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with open(hyp_file, "r") as hyp_reader:
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for line in hyp_reader:
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key = line.strip().split()[0]
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value = line.strip().split()[1:]
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if cn_postprocess:
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value = " ".join(value)
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value = value.replace(" ", "")
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if value[0] == "请":
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value = value[1:]
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value = [x for x in value]
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hyp_dict[key] = value
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with open(ref_file, "r") as ref_reader:
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for line in ref_reader:
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key = line.strip().split()[0]
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value = line.strip().split()[1:]
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if cn_postprocess:
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value = " ".join(value)
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value = value.replace(" ", "")
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value = [x for x in value]
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ref_dict[key] = value
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cer_detail_writer = open(cer_file, "w")
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for hyp_key in hyp_dict:
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if hyp_key in ref_dict:
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out_item = compute_wer_by_line(hyp_dict[hyp_key], ref_dict[hyp_key])
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rst["Wrd"] += out_item["nwords"]
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rst["Corr"] += out_item["cor"]
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rst["wrong_words"] += out_item["wrong"]
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rst["Ins"] += out_item["ins"]
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rst["Del"] += out_item["del"]
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rst["Sub"] += out_item["sub"]
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rst["Snt"] += 1
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if out_item["wrong"] > 0:
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rst["wrong_sentences"] += 1
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cer_detail_writer.write(hyp_key + print_cer_detail(out_item) + "\n")
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cer_detail_writer.write(
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"ref:" + "\t" + " ".join(list(map(lambda x: x.lower(), ref_dict[hyp_key]))) + "\n"
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)
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cer_detail_writer.write(
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"hyp:" + "\t" + " ".join(list(map(lambda x: x.lower(), hyp_dict[hyp_key]))) + "\n"
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)
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cer_detail_writer.flush()
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if rst["Wrd"] > 0:
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rst["Err"] = round(rst["wrong_words"] * 100 / rst["Wrd"], 2)
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if rst["Snt"] > 0:
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rst["S.Err"] = round(rst["wrong_sentences"] * 100 / rst["Snt"], 2)
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cer_detail_writer.write("\n")
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cer_detail_writer.write(
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"%WER "
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+ str(rst["Err"])
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+ " [ "
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+ str(rst["wrong_words"])
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+ " / "
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+ str(rst["Wrd"])
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+ ", "
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+ str(rst["Ins"])
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+ " ins, "
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+ str(rst["Del"])
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+ " del, "
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+ str(rst["Sub"])
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+ " sub ]"
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+ "\n"
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)
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cer_detail_writer.write(
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"%SER "
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+ str(rst["S.Err"])
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+ " [ "
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+ str(rst["wrong_sentences"])
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+ " / "
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+ str(rst["Snt"])
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+ " ]"
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+ "\n"
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)
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cer_detail_writer.write(
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"Scored "
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+ str(len(hyp_dict))
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+ " sentences, "
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+ str(len(hyp_dict) - rst["Snt"])
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+ " not present in hyp."
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+ "\n"
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)
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cer_detail_writer.close()
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def compute_wer_by_line(hyp, ref):
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hyp = list(map(lambda x: x.lower(), hyp))
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ref = list(map(lambda x: x.lower(), ref))
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len_hyp = len(hyp)
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len_ref = len(ref)
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cost_matrix = np.zeros((len_hyp + 1, len_ref + 1), dtype=np.int16)
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ops_matrix = np.zeros((len_hyp + 1, len_ref + 1), dtype=np.int8)
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for i in range(len_hyp + 1):
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cost_matrix[i][0] = i
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for j in range(len_ref + 1):
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cost_matrix[0][j] = j
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for i in range(1, len_hyp + 1):
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for j in range(1, len_ref + 1):
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if hyp[i - 1] == ref[j - 1]:
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cost_matrix[i][j] = cost_matrix[i - 1][j - 1]
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else:
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substitution = cost_matrix[i - 1][j - 1] + 1
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insertion = cost_matrix[i - 1][j] + 1
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deletion = cost_matrix[i][j - 1] + 1
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compare_val = [substitution, insertion, deletion]
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min_val = min(compare_val)
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operation_idx = compare_val.index(min_val) + 1
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cost_matrix[i][j] = min_val
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ops_matrix[i][j] = operation_idx
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match_idx = []
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i = len_hyp
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j = len_ref
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rst = {"nwords": len_ref, "cor": 0, "wrong": 0, "ins": 0, "del": 0, "sub": 0}
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while i >= 0 or j >= 0:
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i_idx = max(0, i)
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j_idx = max(0, j)
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if ops_matrix[i_idx][j_idx] == 0: # correct
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if i - 1 >= 0 and j - 1 >= 0:
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match_idx.append((j - 1, i - 1))
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rst["cor"] += 1
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i -= 1
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j -= 1
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elif ops_matrix[i_idx][j_idx] == 2: # insert
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i -= 1
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rst["ins"] += 1
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elif ops_matrix[i_idx][j_idx] == 3: # delete
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j -= 1
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rst["del"] += 1
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elif ops_matrix[i_idx][j_idx] == 1: # substitute
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i -= 1
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j -= 1
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rst["sub"] += 1
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if i < 0 and j >= 0:
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rst["del"] += 1
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elif j < 0 and i >= 0:
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rst["ins"] += 1
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match_idx.reverse()
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wrong_cnt = cost_matrix[len_hyp][len_ref]
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rst["wrong"] = wrong_cnt
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return rst
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def print_cer_detail(rst):
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return (
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"("
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+ "nwords="
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+ str(rst["nwords"])
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+ ",cor="
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+ str(rst["cor"])
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+ ",ins="
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+ str(rst["ins"])
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+ ",del="
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+ str(rst["del"])
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+ ",sub="
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+ str(rst["sub"])
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+ ") corr:"
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+ "{:.2%}".format(rst["cor"] / rst["nwords"])
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+ ",cer:"
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+ "{:.2%}".format(rst["wrong"] / rst["nwords"])
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)
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@hydra.main(config_name=None, version_base=None)
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def main_hydra(cfg: DictConfig):
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ref_file = cfg.get("ref_file", None)
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hyp_file = cfg.get("hyp_file", None)
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cer_file = cfg.get("cer_file", None)
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cn_postprocess = cfg.get("cn_postprocess", False)
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if ref_file is None or hyp_file is None or cer_file is None:
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print(
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"usage : python -m funasr.metrics.wer ++ref_file=test.ref ++hyp_file=test.hyp ++cer_file=test.wer ++cn_postprocess=false"
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)
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sys.exit(0)
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compute_wer(ref_file, hyp_file, cer_file, cn_postprocess)
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if __name__ == "__main__":
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main_hydra()
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