[vector] add AMI data preparation scripts

dataset/ami/ami_prepare.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.
 """
 Data preparation.
 
@@ -21,26 +34,24 @@ from distutils.util import strtobool
 
 from utils.dataio import (
     load_pkl,
-    save_pkl,
-)
+    save_pkl, )
 
 logger = logging.getLogger(__name__)
 SAMPLERATE = 16000
 
 
 def prepare_ami(
-    data_folder,
-    manual_annot_folder,
-    save_folder,
-    ref_rttm_dir,
-    meta_data_dir,
-    split_type="full_corpus_asr",
-    skip_TNO=True,
-    mic_type="Mix-Headset",
-    vad_type="oracle",
-    max_subseg_dur=3.0,
-    overlap=1.5,
-):
+        data_folder,
+        manual_annot_folder,
+        save_folder,
+        ref_rttm_dir,
+        meta_data_dir,
+        split_type="full_corpus_asr",
+        skip_TNO=True,
+        mic_type="Mix-Headset",
+        vad_type="oracle",
+        max_subseg_dur=3.0,
+        overlap=1.5, ):
     """
     Prepares reference RTTM and JSON files for the AMI dataset.
 
@@ -72,12 +83,12 @@ def prepare_ami(
 
     Example
     -------
-    >>> from recipes.AMI.ami_prepare import prepare_ami
-    >>> data_folder = '/network/datasets/ami/amicorpus/'
-    >>> manual_annot_folder = '/home/mila/d/dawalatn/nauman/ami_public_manual/'
-    >>> save_folder = 'results/save/'
+    >>> from dataset.ami.ami_prepare import prepare_ami
+    >>> data_folder = '/home/data/ami/amicorpus/'
+    >>> manual_annot_folder = '/home/data/ami/ami_public_manual/'
+    >>> save_folder = './results/
     >>> split_type = 'full_corpus_asr'
-    >>> mic_type = 'Lapel'
+    >>> mic_type = 'Mix-Headset'
     >>> prepare_ami(data_folder, manual_annot_folder, save_folder, split_type, mic_type)
     """
 
@@ -112,8 +123,7 @@ def prepare_ami(
     # Check if this phase is already done (if so, skip it)
     if skip(save_folder, conf, meta_files, opt_file):
         logger.info(
-            "Skipping data preparation, as it was completed in previous run."
-        )
+            "Skipping data preparation, as it was completed in previous run.")
         return
 
     msg = "\tCreating meta-data file for the AMI Dataset.."
@@ -138,8 +148,7 @@ def prepare_ami(
                 data_folder,
                 manual_annot_folder,
                 i,
-                skip_TNO,
-            )
+                skip_TNO, )
         if i == "dev":
             prepare_segs_for_RTTM(
                 dev_set,
@@ -147,8 +156,7 @@ def prepare_ami(
                 data_folder,
                 manual_annot_folder,
                 i,
-                skip_TNO,
-            )
+                skip_TNO, )
         if i == "eval":
             prepare_segs_for_RTTM(
                 eval_set,
@@ -156,8 +164,7 @@ def prepare_ami(
                 data_folder,
                 manual_annot_folder,
                 i,
-                skip_TNO,
-            )
+                skip_TNO, )
 
     # Create meta_files for splits
     meta_data_dir = meta_data_dir
@@ -174,8 +181,7 @@ def prepare_ami(
             meta_filename_prefix,
             max_subseg_dur,
             overlap,
-            mic_type,
-        )
+            mic_type, )
 
     save_opt_file = os.path.join(save_folder, opt_file)
     save_pkl(conf, save_opt_file)
@@ -190,13 +196,8 @@ def get_RTTM_per_rec(segs, spkrs_list, rec_id):
     # Prepare header
     for spkr_id in spkrs_list:
         # e.g. SPKR-INFO ES2008c 0 <NA> <NA> <NA> unknown ES2008c.A_PM <NA> <NA>
-        line = (
-            "SPKR-INFO "
-            + rec_id
-            + " 0 <NA> <NA> <NA> unknown "
-            + spkr_id
-            + " <NA> <NA>"
-        )
+        line = ("SPKR-INFO " + rec_id + " 0 <NA> <NA> <NA> unknown " + spkr_id +
+                " <NA> <NA>")
         rttm.append(line)
 
     # Append remaining lines
@@ -206,57 +207,35 @@ def get_RTTM_per_rec(segs, spkrs_list, rec_id):
         if float(row[1]) < float(row[0]):
             msg1 = (
                 "Possibly Incorrect Annotation Found!! transcriber_start (%s) > transcriber_end (%s)"
-                % (row[0], row[1])
-            )
+                % (row[0], row[1]))
             msg2 = (
-                "Excluding this incorrect row from the RTTM : %s, %s, %s, %s"
-                % (
-                    rec_id,
-                    row[0],
-                    str(round(float(row[1]) - float(row[0]), 4)),
-                    str(row[2]),
-                )
-            )
+                "Excluding this incorrect row from the RTTM : %s, %s, %s, %s" %
+                (rec_id, row[0], str(round(float(row[1]) - float(row[0]), 4)),
+                 str(row[2]), ))
             logger.info(msg1)
             logger.info(msg2)
             continue
 
-        line = (
-            "SPEAKER "
-            + rec_id
-            + " 0 "
-            + str(round(float(row[0]), 4))
-            + " "
-            + str(round(float(row[1]) - float(row[0]), 4))
-            + " <NA> <NA> "
-            + str(row[2])
-            + " <NA> <NA>"
-        )
+        line = ("SPEAKER " + rec_id + " 0 " + str(round(float(row[0]), 4)) + " "
+                + str(round(float(row[1]) - float(row[0]), 4)) + " <NA> <NA> " +
+                str(row[2]) + " <NA> <NA>")
         rttm.append(line)
 
     return rttm
 
 
-def prepare_segs_for_RTTM(
-    list_ids, out_rttm_file, audio_dir, annot_dir, split_type, skip_TNO
-):
+def prepare_segs_for_RTTM(list_ids, out_rttm_file, audio_dir, annot_dir,
+                          split_type, skip_TNO):
 
     RTTM = []  # Stores all RTTMs clubbed together for a given dataset split
 
     for main_meet_id in list_ids:
 
         # Skip TNO meetings from dev and eval sets
-        if (
-            main_meet_id.startswith("TS")
-            and split_type != "train"
-            and skip_TNO is True
-        ):
-            msg = (
-                "Skipping TNO meeting in AMI "
-                + str(split_type)
-                + " set : "
-                + str(main_meet_id)
-            )
+        if (main_meet_id.startswith("TS") and split_type != "train" and
+                skip_TNO is True):
+            msg = ("Skipping TNO meeting in AMI " + str(split_type) + " set : "
+                   + str(main_meet_id))
             logger.info(msg)
             continue
 
@@ -271,8 +250,7 @@ def prepare_segs_for_RTTM(
             list_spkr_xmls.sort()  # A, B, C, D, E etc (Speakers)
             segs = []
             spkrs_list = (
-                []
-            )  # Since non-scenario recordings contains 3-5 speakers
+                [])  # Since non-scenario recordings contains 3-5 speakers
 
             for spkr_xml_file in list_spkr_xmls:
 
@@ -286,14 +264,11 @@ def prepare_segs_for_RTTM(
                 root = tree.getroot()
 
                 # Start, end and speaker_ID from xml file
-                segs = segs + [
-                    [
-                        elem.attrib["transcriber_start"],
-                        elem.attrib["transcriber_end"],
-                        spkr_ID,
-                    ]
-                    for elem in root.iter("segment")
-                ]
+                segs = segs + [[
+                    elem.attrib["transcriber_start"],
+                    elem.attrib["transcriber_end"],
+                    spkr_ID,
+                ] for elem in root.iter("segment")]
 
             # Sort rows as per the start time (per recording)
             segs.sort(key=lambda x: float(x[0]))
@@ -404,9 +379,8 @@ def get_subsegments(merged_segs, max_subseg_dur=3.0, overlap=1.5):
     return subsegments
 
 
-def prepare_metadata(
-    rttm_file, save_dir, data_dir, filename, max_subseg_dur, overlap, mic_type
-):
+def prepare_metadata(rttm_file, save_dir, data_dir, filename, max_subseg_dur,
+                     overlap, mic_type):
     # Read RTTM, get unique meeting_IDs (from RTTM headers)
     # For each MeetingID. select that meetID -> merge -> subsegment -> json -> append
 
@@ -425,15 +399,13 @@ def prepare_metadata(
     MERGED_SEGMENTS = []
     SUBSEGMENTS = []
     for rec_id in rec_ids:
-        segs_iter = filter(
-            lambda x: x.startswith("SPEAKER " + str(rec_id)), RTTM
-        )
+        segs_iter = filter(lambda x: x.startswith("SPEAKER " + str(rec_id)),
+                           RTTM)
         gt_rttm_segs = [row.split(" ") for row in segs_iter]
 
         # Merge, subsegment and then convert to json format.
         merged_segs = merge_rttm_intervals(
-            gt_rttm_segs
-        )  # We lose speaker_ID after merging
+            gt_rttm_segs)  # We lose speaker_ID after merging
         MERGED_SEGMENTS = MERGED_SEGMENTS + merged_segs
 
         # Divide segments into smaller sub-segments
@@ -467,16 +439,8 @@ def prepare_metadata(
 
         # If multi-mic audio is selected
         if mic_type == "Array1":
-            wav_file_base_path = (
-                data_dir
-                + "/"
-                + rec_id
-                + "/audio/"
-                + rec_id
-                + "."
-                + mic_type
-                + "-"
-            )
+            wav_file_base_path = (data_dir + "/" + rec_id + "/audio/" + rec_id +
+                                  "." + mic_type + "-")
 
             f = []  # adding all 8 mics
             for i in range(8):
@@ -494,16 +458,8 @@ def prepare_metadata(
             }
         else:
             # Single mic audio
-            wav_file_path = (
-                data_dir
-                + "/"
-                + rec_id
-                + "/audio/"
-                + rec_id
-                + "."
-                + mic_type
-                + ".wav"
-            )
+            wav_file_path = (data_dir + "/" + rec_id + "/audio/" + rec_id + "."
+                             + mic_type + ".wav")
 
             # Note: key "file" without 's' is used for single-mic
             json_dict[subsegment_ID] = {
@@ -554,6 +510,7 @@ def skip(save_folder, conf, meta_files, opt_file):
 
     return skip
 
+
 if __name__ == '__main__':
 
     parser = argparse.ArgumentParser(
@@ -561,42 +518,56 @@ if __name__ == '__main__':
             --manual_annot_folder /home/data/ami/ami_public_manual_1.6.2 \
             --save_folder ./results/ --ref_rttm_dir ./results/ref_rttms \
             --meta_data_dir ./results/metadata',
-        description='AMI Data preparation') 
+        description='AMI Data preparation')
     parser.add_argument(
-        '--data_folder', required=True, help='Path to the folder where the original amicorpus is stored')
+        '--data_folder',
+        required=True,
+        help='Path to the folder where the original amicorpus is stored')
     parser.add_argument(
-        '--manual_annot_folder', required=True, help='Directory where the manual annotations are stored')
+        '--manual_annot_folder',
+        required=True,
+        help='Directory where the manual annotations are stored')
     parser.add_argument(
         '--save_folder', required=True, help='The save directory in results')
     parser.add_argument(
-        '--ref_rttm_dir', required=True, help='Directory to store reference RTTM files')
+        '--ref_rttm_dir',
+        required=True,
+        help='Directory to store reference RTTM files')
     parser.add_argument(
-        '--meta_data_dir', required=True, help='Directory to store the meta data (json) files')
+        '--meta_data_dir',
+        required=True,
+        help='Directory to store the meta data (json) files')
     parser.add_argument(
-        '--split_type', 
-        default="full_corpus_asr", 
+        '--split_type',
+        default="full_corpus_asr",
         help='Standard dataset split. See ami_splits.py for more information')
     parser.add_argument(
-        '--skip_TNO', default=True, type=strtobool, help='Skips TNO meeting recordings if True')
+        '--skip_TNO',
+        default=True,
+        type=strtobool,
+        help='Skips TNO meeting recordings if True')
     parser.add_argument(
-        '--mic_type', default="Mix-Headset", help='Type of microphone to be used')
+        '--mic_type',
+        default="Mix-Headset",
+        help='Type of microphone to be used')
     parser.add_argument(
-        '--vad_type', default="oracle", help='Type of VAD. Kept for future when VAD will be added')
+        '--vad_type',
+        default="oracle",
+        help='Type of VAD. Kept for future when VAD will be added')
     parser.add_argument(
-        '--max_subseg_dur', 
-        default=3.0, 
-        type=float, 
-        help='Duration in seconds of a subsegments to be prepared from larger segments')
+        '--max_subseg_dur',
+        default=3.0,
+        type=float,
+        help='Duration in seconds of a subsegments to be prepared from larger segments'
+    )
     parser.add_argument(
-        '--overlap', default=1.5, type=float, help='Overlap duration in seconds between adjacent subsegments')
+        '--overlap',
+        default=1.5,
+        type=float,
+        help='Overlap duration in seconds between adjacent subsegments')
 
     args = parser.parse_args()
     print(args)
 
-    prepare_ami(
-        args.data_folder, 
-        args.manual_annot_folder, 
-        args.save_folder, 
-        args.ref_rttm_dir, 
-        args.meta_data_dir
-        )
+    prepare_ami(args.data_folder, args.manual_annot_folder, args.save_folder,
+                args.ref_rttm_dir, args.meta_data_dir)
\ No newline at end of file

dataset/ami/ami_splits.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.
 """
 AMI corpus contained 100 hours of meeting recording.
 This script returns the standard train, dev and eval split for AMI corpus.
@@ -29,8 +42,7 @@ def get_AMI_split(split_option):
     if split_option not in ALLOWED_OPTIONS:
         print(
             f'Invalid split "{split_option}" requested!\nValid split_options are: ',
-            ALLOWED_OPTIONS,
-        )
+            ALLOWED_OPTIONS, )
         return
 
     if split_option == "scenario_only":

utils/dataio.py → dataset/ami/dataio.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.
 """
 Data reading and writing.
 
@@ -5,10 +18,10 @@ Authors
  * qingenz123@126.com (Qingen ZHAO) 2022
  
 """
-
 import os
 import pickle
 
+
 def save_pkl(obj, file):
     """Save an object in pkl format.
 
@@ -31,6 +44,7 @@ def save_pkl(obj, file):
     with open(file, "wb") as f:
         pickle.dump(obj, f)
 
+
 def load_pickle(pickle_path):
     """Utility function for loading .pkl pickle files.
 
@@ -48,6 +62,7 @@ def load_pickle(pickle_path):
         out = pickle.load(f)
     return out
 
+
 def load_pkl(file):
     """Loads a pkl file.
 
@@ -79,4 +94,4 @@ def load_pkl(file):
             return pickle.load(f)
     finally:
         if os.path.isfile(file + ".lock"):
-            os.remove(file + ".lock")
+            os.remove(file + ".lock")
\ No newline at end of file

utils/DER.py → paddlespeech/vector/utils/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.
 
@@ -26,7 +39,6 @@ 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
 
@@ -38,12 +50,11 @@ def rectify(arr):
 
 
 def DER(
-    ref_rttm,
-    sys_rttm,
-    ignore_overlap=False,
-    collar=0.25,
-    individual_file_scores=False,
-):
+        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).
 
@@ -118,25 +129,20 @@ def DER(
         ]
 
         scored_speaker_times = np.array(
-            [float(m) for m in SCORED_SPEAKER_TIME.findall(stdout)]
-        )
+            [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)]
-        )
+            [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)]
-        )
+            [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)]
-        )
+            [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_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
@@ -153,13 +159,19 @@ def DER(
         else:
             return miss_speaker[-1], fa_speaker[-1], sers[-1], ders[-1]
 
+
 if __name__ == '__main__':
 
-    parser = argparse.ArgumentParser(description='Compute Diarization Error Rate')
+    parser = argparse.ArgumentParser(
+        description='Compute Diarization Error Rate')
     parser.add_argument(
-        '--ref_rttm', required=True, help='the path of reference/groundtruth RTTM file')
+        '--ref_rttm',
+        required=True,
+        help='the path of reference/groundtruth RTTM file')
     parser.add_argument(
-        '--sys_rttm', required=True, help='the path of the system generated RTTM file')
+        '--sys_rttm',
+        required=True,
+        help='the path of the system generated RTTM file')
     parser.add_argument(
         '--individual_file',
         default=False,
@@ -176,4 +188,5 @@ if __name__ == '__main__':
     print(args)
 
     der = DER(args.ref_rttm, args.sys_rttm)
-    print("miss_speaker: %.3f%% fa_speaker: %.3f%% sers: %.3f%% ders: %.3f%%" % (der[0], der[1], der[2], der[-1]))
+    print("miss_speaker: %.3f%% fa_speaker: %.3f%% sers: %.3f%% ders: %.3f%%" %
\ No newline at end of file
+          (der[0], der[1], der[2], der[-1]))