DeepLearningExamples/PyTorch/SpeechRecognition/QuartzNet/common/metrics.py
2021-09-14 06:03:36 -07:00

60 lines
1.9 KiB
Python

# Copyright (c) 2019, NVIDIA CORPORATION. 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
# limitations under the License.
def __levenshtein(a, b):
"""Calculates the Levenshtein distance between two sequences."""
n, m = len(a), len(b)
if n > m:
# Make sure n <= m, to use O(min(n,m)) space
a, b = b, a
n, m = m, n
current = list(range(n + 1))
for i in range(1, m + 1):
previous, current = current, [i] + [0] * n
for j in range(1, n + 1):
add, delete = previous[j] + 1, current[j - 1] + 1
change = previous[j - 1]
if a[j - 1] != b[i - 1]:
change = change + 1
current[j] = min(add, delete, change)
return current[n]
def word_error_rate(hypotheses, references):
"""Computes average Word Error Rate (WER) between two text lists."""
scores = 0
words = 0
len_diff = len(references) - len(hypotheses)
if len_diff > 0:
raise ValueError("Uneqal number of hypthoses and references: "
"{0} and {1}".format(len(hypotheses), len(references)))
elif len_diff < 0:
hypotheses = hypotheses[:len_diff]
for h, r in zip(hypotheses, references):
h_list = h.split()
r_list = r.split()
words += len(r_list)
scores += __levenshtein(h_list, r_list)
if words!=0:
wer = 1.0*scores/words
else:
wer = float('inf')
return wer, scores, words