From 995436c6f17307c91030f7fc2cfb189f0532f4a8 Mon Sep 17 00:00:00 2001 From: ccrrong <1039058843@qq.com> Date: Fri, 8 Apr 2022 21:43:06 +0800 Subject: [PATCH] delete unused file ami_dataset.py, compute_der.py, test=doc --- examples/ami/sd0/local/ami_dataset.py | 90 ------------- examples/ami/sd0/local/experiment.py | 2 +- utils/compute_der.py | 175 -------------------------- 3 files changed, 1 insertion(+), 266 deletions(-) delete mode 100644 examples/ami/sd0/local/ami_dataset.py delete mode 100755 utils/compute_der.py diff --git a/examples/ami/sd0/local/ami_dataset.py b/examples/ami/sd0/local/ami_dataset.py deleted file mode 100644 index c44329c8..00000000 --- a/examples/ami/sd0/local/ami_dataset.py +++ /dev/null @@ -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) diff --git a/examples/ami/sd0/local/experiment.py b/examples/ami/sd0/local/experiment.py index 5bb406d1..29822837 100755 --- a/examples/ami/sd0/local/experiment.py +++ b/examples/ami/sd0/local/experiment.py @@ -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() diff --git a/utils/compute_der.py b/utils/compute_der.py deleted file mode 100755 index d22f6a7d..00000000 --- a/utils/compute_der.py +++ /dev/null @@ -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()