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PaddleSpeech/examples/ami/sd0/local/ami_prepare.py

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# 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.
Download: http://groups.inf.ed.ac.uk/ami/download/
Prepares metadata files (JSON) from manual annotations "segments/" using RTTM format (Oracle VAD).
"""
import argparse
import glob
import json
import logging
import os
import xml.etree.ElementTree as et
from distutils.util import strtobool
from ami_splits import get_AMI_split
from dataio import load_pkl
from dataio import 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, ):
"""
Prepares reference RTTM and JSON files for the AMI dataset.
Arguments
---------
data_folder : str
Path to the folder where the original amicorpus is stored.
manual_annot_folder : str
Directory where the manual annotations are stored.
save_folder : str
The save directory in results.
ref_rttm_dir : str
Directory to store reference RTTM files.
meta_data_dir : str
Directory to store the meta data (json) files.
split_type : str
Standard dataset split. See ami_splits.py for more information.
Allowed split_type: "scenario_only", "full_corpus" or "full_corpus_asr"
skip_TNO: bool
Skips TNO meeting recordings if True.
mic_type : str
Type of microphone to be used.
vad_type : str
Type of VAD. Kept for future when VAD will be added.
max_subseg_dur : float
Duration in seconds of a subsegments to be prepared from larger segments.
overlap : float
Overlap duration in seconds between adjacent subsegments
Example
-------
>>> 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 = 'Mix-Headset'
>>> prepare_ami(data_folder, manual_annot_folder, save_folder, split_type, mic_type)
"""
# Meta files
meta_files = [
os.path.join(meta_data_dir, "ami_train." + mic_type + ".subsegs.json"),
os.path.join(meta_data_dir, "ami_dev." + mic_type + ".subsegs.json"),
os.path.join(meta_data_dir, "ami_eval." + mic_type + ".subsegs.json"),
]
# Create configuration for easily skipping data_preparation stage
conf = {
"data_folder": data_folder,
"save_folder": save_folder,
"ref_rttm_dir": ref_rttm_dir,
"meta_data_dir": meta_data_dir,
"split_type": split_type,
"skip_TNO": skip_TNO,
"mic_type": mic_type,
"vad": vad_type,
"max_subseg_dur": max_subseg_dur,
"overlap": overlap,
"meta_files": meta_files,
}
if not os.path.exists(save_folder):
os.makedirs(save_folder)
# Setting output option files.
opt_file = "opt_ami_prepare." + mic_type + ".pkl"
# 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.")
return
msg = "\tCreating meta-data file for the AMI Dataset.."
logger.debug(msg)
# Get the split
train_set, dev_set, eval_set = get_AMI_split(split_type)
# Prepare RTTM from XML(manual annot) and store are groundtruth
# Create ref_RTTM directory
if not os.path.exists(ref_rttm_dir):
os.makedirs(ref_rttm_dir)
# Create reference RTTM files
splits = ["train", "dev", "eval"]
for i in splits:
rttm_file = ref_rttm_dir + "/fullref_ami_" + i + ".rttm"
if i == "train":
prepare_segs_for_RTTM(
train_set,
rttm_file,
data_folder,
manual_annot_folder,
i,
skip_TNO, )
if i == "dev":
prepare_segs_for_RTTM(
dev_set,
rttm_file,
data_folder,
manual_annot_folder,
i,
skip_TNO, )
if i == "eval":
prepare_segs_for_RTTM(
eval_set,
rttm_file,
data_folder,
manual_annot_folder,
i,
skip_TNO, )
# Create meta_files for splits
meta_data_dir = meta_data_dir
if not os.path.exists(meta_data_dir):
os.makedirs(meta_data_dir)
for i in splits:
rttm_file = ref_rttm_dir + "/fullref_ami_" + i + ".rttm"
meta_filename_prefix = "ami_" + i
prepare_metadata(
rttm_file,
meta_data_dir,
data_folder,
meta_filename_prefix,
max_subseg_dur,
overlap,
mic_type, )
save_opt_file = os.path.join(save_folder, opt_file)
save_pkl(conf, save_opt_file)
def get_RTTM_per_rec(segs, spkrs_list, rec_id):
"""Prepares rttm for each recording
"""
rttm = []
# 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>")
rttm.append(line)
# Append remaining lines
for row in segs:
# e.g. SPEAKER ES2008c 0 37.880 0.590 <NA> <NA> ES2008c.A_PM <NA> <NA>
if float(row[1]) < float(row[0]):
msg1 = (
"Possibly Incorrect Annotation Found!! transcriber_start (%s) > transcriber_end (%s)"
% (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]), ))
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>")
rttm.append(line)
return rttm
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))
logger.info(msg)
continue
list_sessions = glob.glob(audio_dir + "/" + main_meet_id + "*")
list_sessions.sort()
for sess in list_sessions:
rec_id = os.path.basename(sess)
path = annot_dir + "/segments/" + rec_id
f = path + ".*.segments.xml"
list_spkr_xmls = glob.glob(f)
list_spkr_xmls.sort() # A, B, C, D, E etc (Speakers)
segs = []
spkrs_list = (
[]) # Since non-scenario recordings contains 3-5 speakers
for spkr_xml_file in list_spkr_xmls:
# Speaker ID
spkr = os.path.basename(spkr_xml_file).split(".")[1]
spkr_ID = rec_id + "." + spkr
spkrs_list.append(spkr_ID)
# Parse xml tree
tree = et.parse(spkr_xml_file)
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")]
# Sort rows as per the start time (per recording)
segs.sort(key=lambda x: float(x[0]))
rttm_per_rec = get_RTTM_per_rec(segs, spkrs_list, rec_id)
RTTM = RTTM + rttm_per_rec
# Write one RTTM as groundtruth. For example, "fullref_eval.rttm"
with open(out_rttm_file, "w") as f:
for item in RTTM:
f.write("%s\n" % item)
def is_overlapped(end1, start2):
"""Returns True if the two segments overlap
Arguments
---------
end1 : float
End time of the first segment.
start2 : float
Start time of the second segment.
"""
if start2 > end1:
return False
else:
return True
def merge_rttm_intervals(rttm_segs):
"""Merges adjacent segments in rttm if they overlap.
"""
# For one recording
# rec_id = rttm_segs[0][1]
rttm_segs.sort(key=lambda x: float(x[3]))
# first_seg = rttm_segs[0] # first interval.. as it is
merged_segs = [rttm_segs[0]]
strt = float(rttm_segs[0][3])
end = float(rttm_segs[0][3]) + float(rttm_segs[0][4])
for row in rttm_segs[1:]:
s = float(row[3])
e = float(row[3]) + float(row[4])
if is_overlapped(end, s):
# Update only end. The strt will be same as in last segment
# Just update last row in the merged_segs
end = max(end, e)
merged_segs[-1][3] = str(round(strt, 4))
merged_segs[-1][4] = str(round((end - strt), 4))
merged_segs[-1][7] = "overlap" # previous_row[7] + '-'+ row[7]
else:
# Add a new disjoint segment
strt = s
end = e
merged_segs.append(row) # this will have 1 spkr ID
return merged_segs
def get_subsegments(merged_segs, max_subseg_dur=3.0, overlap=1.5):
"""Divides bigger segments into smaller sub-segments
"""
shift = max_subseg_dur - overlap
subsegments = []
# These rows are in RTTM format
for row in merged_segs:
seg_dur = float(row[4])
rec_id = row[1]
if seg_dur > max_subseg_dur:
num_subsegs = int(seg_dur / shift)
# Taking 0.01 sec as small step
seg_start = float(row[3])
seg_end = seg_start + seg_dur
# Now divide this segment (new_row) in smaller subsegments
for i in range(num_subsegs):
subseg_start = seg_start + i * shift
subseg_end = min(subseg_start + max_subseg_dur - 0.01, seg_end)
subseg_dur = subseg_end - subseg_start
new_row = [
"SPEAKER",
rec_id,
"0",
str(round(float(subseg_start), 4)),
str(round(float(subseg_dur), 4)),
"<NA>",
"<NA>",
row[7],
"<NA>",
"<NA>",
]
subsegments.append(new_row)
# Break if exceeding the boundary
if subseg_end >= seg_end:
break
else:
subsegments.append(row)
return subsegments
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
# Read RTTM
RTTM = []
with open(rttm_file, "r") as f:
for line in f:
entry = line[:-1]
RTTM.append(entry)
spkr_info = filter(lambda x: x.startswith("SPKR-INFO"), RTTM)
rec_ids = list(set([row.split(" ")[1] for row in spkr_info]))
rec_ids.sort() # sorting just to make JSON look in proper sequence
# For each recording merge segments and then perform subsegmentation
MERGED_SEGMENTS = []
SUBSEGMENTS = []
for rec_id in rec_ids:
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
MERGED_SEGMENTS = MERGED_SEGMENTS + merged_segs
# Divide segments into smaller sub-segments
subsegs = get_subsegments(merged_segs, max_subseg_dur, overlap)
SUBSEGMENTS = SUBSEGMENTS + subsegs
# Write segment AND sub-segments (in RTTM format)
segs_file = save_dir + "/" + filename + ".segments.rttm"
subsegment_file = save_dir + "/" + filename + ".subsegments.rttm"
with open(segs_file, "w") as f:
for row in MERGED_SEGMENTS:
line_str = " ".join(row)
f.write("%s\n" % line_str)
with open(subsegment_file, "w") as f:
for row in SUBSEGMENTS:
line_str = " ".join(row)
f.write("%s\n" % line_str)
# Create JSON from subsegments
json_dict = {}
for row in SUBSEGMENTS:
rec_id = row[1]
strt = str(round(float(row[3]), 4))
end = str(round((float(row[3]) + float(row[4])), 4))
subsegment_ID = rec_id + "_" + strt + "_" + end
dur = row[4]
start_sample = int(float(strt) * SAMPLERATE)
end_sample = int(float(end) * SAMPLERATE)
# If multi-mic audio is selected
if mic_type == "Array1":
wav_file_base_path = (data_dir + "/" + rec_id + "/audio/" + rec_id +
"." + mic_type + "-")
f = [] # adding all 8 mics
for i in range(8):
f.append(wav_file_base_path + str(i + 1).zfill(2) + ".wav")
audio_files_path_list = f
# Note: key "files" with 's' is used for multi-mic
json_dict[subsegment_ID] = {
"wav": {
"files": audio_files_path_list,
"duration": float(dur),
"start": int(start_sample),
"stop": int(end_sample),
},
}
else:
# Single mic audio
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] = {
"wav": {
"file": wav_file_path,
"duration": float(dur),
"start": int(start_sample),
"stop": int(end_sample),
},
}
out_json_file = save_dir + "/" + filename + "." + mic_type + ".subsegs.json"
with open(out_json_file, mode="w") as json_f:
json.dump(json_dict, json_f, indent=2)
msg = "%s JSON prepared" % (out_json_file)
logger.debug(msg)
def skip(save_folder, conf, meta_files, opt_file):
"""
Detects if the AMI data_preparation has been already done.
If the preparation has been done, we can skip it.
Returns
-------
bool
if True, the preparation phase can be skipped.
if False, it must be done.
"""
# Checking if meta (json) files are available
skip = True
for file_path in meta_files:
if not os.path.isfile(file_path):
skip = False
# Checking saved options
save_opt_file = os.path.join(save_folder, opt_file)
if skip is True:
if os.path.isfile(save_opt_file):
opts_old = load_pkl(save_opt_file)
if opts_old == conf:
skip = True
else:
skip = False
else:
skip = False
return skip
if __name__ == '__main__':
parser = argparse.ArgumentParser(
prog='python ami_prepare.py --data_folder /home/data/ami/amicorpus \
--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')
parser.add_argument(
'--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')
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')
parser.add_argument(
'--meta_data_dir',
required=True,
help='Directory to store the meta data (json) files')
parser.add_argument(
'--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')
parser.add_argument(
'--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')
parser.add_argument(
'--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')
args = parser.parse_args()
prepare_ami(args.data_folder, args.manual_annot_folder, args.save_folder,
args.ref_rttm_dir, args.meta_data_dir)
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