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

examples/ami/sd0/local/ami_dataset.py

-# 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)

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()

utils/compute_der.py

-# 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()