DeepLearningExamples/PyTorch/SpeechRecognition/Jasper/metrics.py
Przemek Strzelczyk fa400a7367 Adding Jasper/PyT
2019-07-26 20:08:16 +02:00

69 lines
2.3 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.
from typing import List
def __levenshtein(a: List, b: List) -> int:
"""Calculates the Levenshtein distance between a and b.
"""
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: List[str], references: List[str]) -> float:
"""
Computes Average Word Error rate between two texts represented as
corresponding lists of string. Hypotheses and references must have same length.
Args:
hypotheses: list of hypotheses
references: list of references
Returns:
(float) average word error rate
"""
scores = 0
words = 0
if len(hypotheses) != len(references):
raise ValueError("In word error rate calculation, hypotheses and reference"
" lists must have the same number of elements. But I got:"
"{0} and {1} correspondingly".format(len(hypotheses), len(references)))
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