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delete unused file ami_dataset.py, compute_der.py, test=doc

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pull/1651/head
ccrrong 3 years ago
parent bc53f726fe
commit 995436c6f1
3 changed files with 1 additions and 266 deletions
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90
examples/ami/sd0/local/ami_dataset.py
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@ -1,90 +0,0 @@
# Copyright (c) 2022 PaddlePaddle Authors. 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.
import collections
import json
from paddle.io import Dataset
from paddleaudio.backends import load as load_audio
from paddleaudio.datasets.dataset import feat_funcs
class AMIDataset(Dataset):
"""
AMI dataset.
"""
meta_info = collections.namedtuple(
'META_INFO', ('id', 'duration', 'wav', 'start', 'stop', 'record_id'))
def __init__(self, json_file: str, feat_type: str='raw', **kwargs):
"""
Ags:
json_file (:obj:`str`): Data prep JSON file.
labels (:obj:`List[int]`): Labels of audio files.
feat_type (:obj:`str`, `optional`, defaults to `raw`):
It identifies the feature type that user wants to extrace of an audio file.
"""
if feat_type not in feat_funcs.keys():
raise RuntimeError(
f"Unknown feat_type: {feat_type}, it must be one in {list(feat_funcs.keys())}"
)
self.json_file = json_file
self.feat_type = feat_type
self.feat_config = kwargs
self._data = self._get_data()
super(AMIDataset, self).__init__()
def _get_data(self):
with open(self.json_file, "r") as f:
meta_data = json.load(f)
data = []
for key in meta_data:
sub_seg = meta_data[key]["wav"]
wav = sub_seg["file"]
duration = sub_seg["duration"]
start = sub_seg["start"]
stop = sub_seg["stop"]
rec_id = str(key).rsplit("_", 2)[0]
data.append(
self.meta_info(
str(key),
float(duration), wav, int(start), int(stop), str(rec_id)))
return data
def _convert_to_record(self, idx: int):
sample = self._data[idx]
record = {}
# To show all fields in a namedtuple: `type(sample)._fields`
for field in type(sample)._fields:
record[field] = getattr(sample, field)
waveform, sr = load_audio(record['wav'])
waveform = waveform[record['start']:record['stop']]
feat_func = feat_funcs[self.feat_type]
feat = feat_func(
waveform, sr=sr, **self.feat_config) if feat_func else waveform
record.update({'feat': feat})
return record
def __getitem__(self, idx):
return self._convert_to_record(idx)
def __len__(self):
return len(self._data)

2
examples/ami/sd0/local/experiment.py
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@ -25,7 +25,7 @@ from yacs.config import CfgNode
from paddlespeech.s2t.utils.log import Log
from paddlespeech.vector.cluster import diarization as diar
from utils.compute_der import DER
from utils.DER import DER
# Logger setup
logger = Log(__name__).getlog()

175
utils/compute_der.py
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@ -1,175 +0,0 @@
# Copyright (c) 2022 PaddlePaddle Authors. 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.
"""Calculates Diarization Error Rate (DER) which is the sum of Missed Speaker (MS),
False Alarm (FA), and Speaker Error Rate (SER) using md-eval-22.pl from NIST RT Evaluation.
Credits
This code is adapted from https://github.com/speechbrain/speechbrain
"""
import argparse
import os
import re
import subprocess
import numpy as np
FILE_IDS = re.compile(r"(?<=Speaker Diarization for).+(?=\*\*\*)")
SCORED_SPEAKER_TIME = re.compile(r"(?<=SCORED SPEAKER TIME =)[\d.]+")
MISS_SPEAKER_TIME = re.compile(r"(?<=MISSED SPEAKER TIME =)[\d.]+")
FA_SPEAKER_TIME = re.compile(r"(?<=FALARM SPEAKER TIME =)[\d.]+")
ERROR_SPEAKER_TIME = re.compile(r"(?<=SPEAKER ERROR TIME =)[\d.]+")
def rectify(arr):
"""Corrects corner cases and converts scores into percentage.
"""
# Numerator and denominator both 0.
arr[np.isnan(arr)] = 0
# Numerator > 0, but denominator = 0.
arr[np.isinf(arr)] = 1
arr *= 100.0
return arr
def DER(
ref_rttm,
sys_rttm,
ignore_overlap=False,
collar=0.25,
individual_file_scores=False, ):
"""Computes Missed Speaker percentage (MS), False Alarm (FA),
Speaker Error Rate (SER), and Diarization Error Rate (DER).
Arguments
---------
ref_rttm : str
The path of reference/groundtruth RTTM file.
sys_rttm : str
The path of the system generated RTTM file.
individual_file_scores : bool
If True, returns scores for each file in order.
collar : float
Forgiveness collar.
ignore_overlap : bool
If True, ignores overlapping speech during evaluation.
Returns
-------
MS : float array
Missed Speech.
FA : float array
False Alarms.
SER : float array
Speaker Error Rates.
DER : float array
Diarization Error Rates.
"""
curr = os.path.abspath(os.path.dirname(__file__))
mdEval = os.path.join(curr, "./md-eval.pl")
cmd = [
mdEval,
"-af",
"-r",
ref_rttm,
"-s",
sys_rttm,
"-c",
str(collar),
]
print(cmd)
if ignore_overlap:
cmd.append("-1")
try:
stdout = subprocess.check_output(cmd, stderr=subprocess.STDOUT)
except subprocess.CalledProcessError as ex:
stdout = ex.output
else:
stdout = stdout.decode("utf-8")
# Get all recording IDs
file_ids = [m.strip() for m in FILE_IDS.findall(stdout)]
file_ids = [
file_id[2:] if file_id.startswith("f=") else file_id
for file_id in file_ids
]
scored_speaker_times = np.array(
[float(m) for m in SCORED_SPEAKER_TIME.findall(stdout)])
miss_speaker_times = np.array(
[float(m) for m in MISS_SPEAKER_TIME.findall(stdout)])
fa_speaker_times = np.array(
[float(m) for m in FA_SPEAKER_TIME.findall(stdout)])
error_speaker_times = np.array(
[float(m) for m in ERROR_SPEAKER_TIME.findall(stdout)])
with np.errstate(invalid="ignore", divide="ignore"):
tot_error_times = (
miss_speaker_times + fa_speaker_times + error_speaker_times)
miss_speaker_frac = miss_speaker_times / scored_speaker_times
fa_speaker_frac = fa_speaker_times / scored_speaker_times
sers_frac = error_speaker_times / scored_speaker_times
ders_frac = tot_error_times / scored_speaker_times
# Values in percentage of scored_speaker_time
miss_speaker = rectify(miss_speaker_frac)
fa_speaker = rectify(fa_speaker_frac)
sers = rectify(sers_frac)
ders = rectify(ders_frac)
if individual_file_scores:
return miss_speaker, fa_speaker, sers, ders
else:
return miss_speaker[-1], fa_speaker[-1], sers[-1], ders[-1]
def main():
parser = argparse.ArgumentParser(description="Compute DER")
parser.add_argument(
"--ref_rttm",
type=str,
help="the path of reference/groundtruth RTTM file")
parser.add_argument(
"--sys_rttm",
type=str,
help="the path of the system generated RTTM file.")
parser.add_argument(
"--individual_file_scores",
type=bool,
help="whether returns scores for each file in order.")
parser.add_argument("--collar", type=float, help="forgiveness collar.")
parser.add_argument(
"--ignore_overlap",
type=bool,
help="whether ignores overlapping speech during evaluation.")
args = parser.parse_args()
Scores = DER(args.ref_rttm, args.sys_rttm, args.ignore_overlap, args.collar,
args.individual_file_scores)
print(Scores)
if __name__ == "__main__":
main()
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