qingen
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results
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# [AMI](https://groups.inf.ed.ac.uk/ami/corpus/)
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The AMI Meeting Corpus is a multi-modal data set consisting of 100 hours of meeting recordings. For a gentle introduction to the corpus, see the [corpus overview](https://groups.inf.ed.ac.uk/ami/corpus/overview.shtml). To access the data, follow the directions given [there](https://groups.inf.ed.ac.uk/ami/download). Around two-thirds of the data has been elicited using a scenario in which the participants play different roles in a design team, taking a design project from kick-off to completion over the course of a day. The rest consists of naturally occurring meetings in a range of domains.
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Detailed information can be found in the [documentation section](http://groups.inf.ed.ac.uk/ami/corpus/datasets.shtml).
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"""
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Data preparation.
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Download: http://groups.inf.ed.ac.uk/ami/download/
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Prepares metadata files (JSON) from manual annotations "segments/" using RTTM format (Oracle VAD).
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Authors
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* qingenz123@126.com (Qingen ZHAO) 2022
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"""
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import os
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import logging
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import argparse
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import xml.etree.ElementTree as et
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import glob
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import json
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from ami_splits import get_AMI_split
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from distutils.util import strtobool
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from utils.dataio import (
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load_pkl,
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save_pkl,
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)
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logger = logging.getLogger(__name__)
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SAMPLERATE = 16000
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def prepare_ami(
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data_folder,
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manual_annot_folder,
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save_folder,
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ref_rttm_dir,
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meta_data_dir,
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split_type="full_corpus_asr",
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skip_TNO=True,
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mic_type="Mix-Headset",
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vad_type="oracle",
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max_subseg_dur=3.0,
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overlap=1.5,
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):
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"""
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Prepares reference RTTM and JSON files for the AMI dataset.
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Arguments
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---------
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data_folder : str
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Path to the folder where the original amicorpus is stored.
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manual_annot_folder : str
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Directory where the manual annotations are stored.
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save_folder : str
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The save directory in results.
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ref_rttm_dir : str
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Directory to store reference RTTM files.
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meta_data_dir : str
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Directory to store the meta data (json) files.
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split_type : str
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Standard dataset split. See ami_splits.py for more information.
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Allowed split_type: "scenario_only", "full_corpus" or "full_corpus_asr"
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skip_TNO: bool
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Skips TNO meeting recordings if True.
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mic_type : str
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Type of microphone to be used.
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vad_type : str
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Type of VAD. Kept for future when VAD will be added.
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max_subseg_dur : float
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Duration in seconds of a subsegments to be prepared from larger segments.
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overlap : float
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Overlap duration in seconds between adjacent subsegments
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Example
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-------
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>>> from recipes.AMI.ami_prepare import prepare_ami
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>>> data_folder = '/network/datasets/ami/amicorpus/'
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>>> manual_annot_folder = '/home/mila/d/dawalatn/nauman/ami_public_manual/'
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>>> save_folder = 'results/save/'
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>>> split_type = 'full_corpus_asr'
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>>> mic_type = 'Lapel'
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>>> prepare_ami(data_folder, manual_annot_folder, save_folder, split_type, mic_type)
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"""
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# Meta files
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meta_files = [
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os.path.join(meta_data_dir, "ami_train." + mic_type + ".subsegs.json"),
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os.path.join(meta_data_dir, "ami_dev." + mic_type + ".subsegs.json"),
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os.path.join(meta_data_dir, "ami_eval." + mic_type + ".subsegs.json"),
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]
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# Create configuration for easily skipping data_preparation stage
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conf = {
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"data_folder": data_folder,
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"save_folder": save_folder,
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"ref_rttm_dir": ref_rttm_dir,
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"meta_data_dir": meta_data_dir,
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"split_type": split_type,
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"skip_TNO": skip_TNO,
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"mic_type": mic_type,
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"vad": vad_type,
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"max_subseg_dur": max_subseg_dur,
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"overlap": overlap,
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"meta_files": meta_files,
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}
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if not os.path.exists(save_folder):
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os.makedirs(save_folder)
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# Setting output option files.
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opt_file = "opt_ami_prepare." + mic_type + ".pkl"
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# Check if this phase is already done (if so, skip it)
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if skip(save_folder, conf, meta_files, opt_file):
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logger.info(
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"Skipping data preparation, as it was completed in previous run."
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)
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return
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msg = "\tCreating meta-data file for the AMI Dataset.."
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logger.debug(msg)
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# Get the split
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train_set, dev_set, eval_set = get_AMI_split(split_type)
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# Prepare RTTM from XML(manual annot) and store are groundtruth
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# Create ref_RTTM directory
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if not os.path.exists(ref_rttm_dir):
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os.makedirs(ref_rttm_dir)
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# Create reference RTTM files
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splits = ["train", "dev", "eval"]
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for i in splits:
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rttm_file = ref_rttm_dir + "/fullref_ami_" + i + ".rttm"
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if i == "train":
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prepare_segs_for_RTTM(
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train_set,
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rttm_file,
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data_folder,
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manual_annot_folder,
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i,
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skip_TNO,
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)
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if i == "dev":
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prepare_segs_for_RTTM(
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dev_set,
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rttm_file,
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data_folder,
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manual_annot_folder,
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i,
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skip_TNO,
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)
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if i == "eval":
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prepare_segs_for_RTTM(
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eval_set,
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rttm_file,
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data_folder,
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manual_annot_folder,
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i,
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skip_TNO,
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)
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# Create meta_files for splits
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meta_data_dir = meta_data_dir
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if not os.path.exists(meta_data_dir):
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os.makedirs(meta_data_dir)
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for i in splits:
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rttm_file = ref_rttm_dir + "/fullref_ami_" + i + ".rttm"
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meta_filename_prefix = "ami_" + i
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prepare_metadata(
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rttm_file,
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meta_data_dir,
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data_folder,
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meta_filename_prefix,
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max_subseg_dur,
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overlap,
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mic_type,
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)
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save_opt_file = os.path.join(save_folder, opt_file)
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save_pkl(conf, save_opt_file)
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def get_RTTM_per_rec(segs, spkrs_list, rec_id):
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"""Prepares rttm for each recording
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"""
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rttm = []
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# Prepare header
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for spkr_id in spkrs_list:
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# e.g. SPKR-INFO ES2008c 0 <NA> <NA> <NA> unknown ES2008c.A_PM <NA> <NA>
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line = (
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"SPKR-INFO "
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+ rec_id
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+ " 0 <NA> <NA> <NA> unknown "
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+ spkr_id
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+ " <NA> <NA>"
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)
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rttm.append(line)
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# Append remaining lines
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for row in segs:
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# e.g. SPEAKER ES2008c 0 37.880 0.590 <NA> <NA> ES2008c.A_PM <NA> <NA>
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if float(row[1]) < float(row[0]):
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msg1 = (
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"Possibly Incorrect Annotation Found!! transcriber_start (%s) > transcriber_end (%s)"
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% (row[0], row[1])
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)
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msg2 = (
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"Excluding this incorrect row from the RTTM : %s, %s, %s, %s"
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% (
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rec_id,
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row[0],
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str(round(float(row[1]) - float(row[0]), 4)),
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str(row[2]),
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)
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)
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logger.info(msg1)
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logger.info(msg2)
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continue
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line = (
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"SPEAKER "
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+ rec_id
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+ " 0 "
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+ str(round(float(row[0]), 4))
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+ " "
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+ str(round(float(row[1]) - float(row[0]), 4))
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+ " <NA> <NA> "
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+ str(row[2])
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+ " <NA> <NA>"
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)
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rttm.append(line)
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return rttm
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def prepare_segs_for_RTTM(
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list_ids, out_rttm_file, audio_dir, annot_dir, split_type, skip_TNO
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):
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RTTM = [] # Stores all RTTMs clubbed together for a given dataset split
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for main_meet_id in list_ids:
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# Skip TNO meetings from dev and eval sets
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if (
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main_meet_id.startswith("TS")
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and split_type != "train"
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and skip_TNO is True
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):
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msg = (
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"Skipping TNO meeting in AMI "
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+ str(split_type)
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+ " set : "
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+ str(main_meet_id)
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)
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logger.info(msg)
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continue
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list_sessions = glob.glob(audio_dir + "/" + main_meet_id + "*")
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list_sessions.sort()
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for sess in list_sessions:
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rec_id = os.path.basename(sess)
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path = annot_dir + "/segments/" + rec_id
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f = path + ".*.segments.xml"
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list_spkr_xmls = glob.glob(f)
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list_spkr_xmls.sort() # A, B, C, D, E etc (Speakers)
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segs = []
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spkrs_list = (
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[]
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) # Since non-scenario recordings contains 3-5 speakers
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for spkr_xml_file in list_spkr_xmls:
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# Speaker ID
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spkr = os.path.basename(spkr_xml_file).split(".")[1]
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spkr_ID = rec_id + "." + spkr
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spkrs_list.append(spkr_ID)
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# Parse xml tree
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tree = et.parse(spkr_xml_file)
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root = tree.getroot()
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# Start, end and speaker_ID from xml file
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segs = segs + [
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[
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elem.attrib["transcriber_start"],
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elem.attrib["transcriber_end"],
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spkr_ID,
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]
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for elem in root.iter("segment")
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]
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# Sort rows as per the start time (per recording)
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segs.sort(key=lambda x: float(x[0]))
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rttm_per_rec = get_RTTM_per_rec(segs, spkrs_list, rec_id)
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RTTM = RTTM + rttm_per_rec
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# Write one RTTM as groundtruth. For example, "fullref_eval.rttm"
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with open(out_rttm_file, "w") as f:
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for item in RTTM:
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f.write("%s\n" % item)
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def is_overlapped(end1, start2):
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"""Returns True if the two segments overlap
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Arguments
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---------
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end1 : float
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End time of the first segment.
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start2 : float
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Start time of the second segment.
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"""
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if start2 > end1:
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return False
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else:
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return True
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def merge_rttm_intervals(rttm_segs):
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"""Merges adjacent segments in rttm if they overlap.
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"""
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# For one recording
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# rec_id = rttm_segs[0][1]
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rttm_segs.sort(key=lambda x: float(x[3]))
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# first_seg = rttm_segs[0] # first interval.. as it is
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merged_segs = [rttm_segs[0]]
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strt = float(rttm_segs[0][3])
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end = float(rttm_segs[0][3]) + float(rttm_segs[0][4])
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for row in rttm_segs[1:]:
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s = float(row[3])
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e = float(row[3]) + float(row[4])
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if is_overlapped(end, s):
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# Update only end. The strt will be same as in last segment
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# Just update last row in the merged_segs
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end = max(end, e)
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merged_segs[-1][3] = str(round(strt, 4))
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merged_segs[-1][4] = str(round((end - strt), 4))
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merged_segs[-1][7] = "overlap" # previous_row[7] + '-'+ row[7]
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else:
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# Add a new disjoint segment
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strt = s
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end = e
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merged_segs.append(row) # this will have 1 spkr ID
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return merged_segs
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def get_subsegments(merged_segs, max_subseg_dur=3.0, overlap=1.5):
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"""Divides bigger segments into smaller sub-segments
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"""
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shift = max_subseg_dur - overlap
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subsegments = []
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# These rows are in RTTM format
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for row in merged_segs:
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seg_dur = float(row[4])
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rec_id = row[1]
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if seg_dur > max_subseg_dur:
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num_subsegs = int(seg_dur / shift)
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# Taking 0.01 sec as small step
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seg_start = float(row[3])
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seg_end = seg_start + seg_dur
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# Now divide this segment (new_row) in smaller subsegments
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for i in range(num_subsegs):
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subseg_start = seg_start + i * shift
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subseg_end = min(subseg_start + max_subseg_dur - 0.01, seg_end)
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subseg_dur = subseg_end - subseg_start
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new_row = [
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"SPEAKER",
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rec_id,
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"0",
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str(round(float(subseg_start), 4)),
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str(round(float(subseg_dur), 4)),
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"<NA>",
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"<NA>",
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row[7],
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"<NA>",
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"<NA>",
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]
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subsegments.append(new_row)
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# Break if exceeding the boundary
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if subseg_end >= seg_end:
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break
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else:
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subsegments.append(row)
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return subsegments
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def prepare_metadata(
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rttm_file, save_dir, data_dir, filename, max_subseg_dur, overlap, mic_type
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):
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# Read RTTM, get unique meeting_IDs (from RTTM headers)
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# For each MeetingID. select that meetID -> merge -> subsegment -> json -> append
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# Read RTTM
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RTTM = []
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with open(rttm_file, "r") as f:
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for line in f:
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entry = line[:-1]
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RTTM.append(entry)
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spkr_info = filter(lambda x: x.startswith("SPKR-INFO"), RTTM)
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rec_ids = list(set([row.split(" ")[1] for row in spkr_info]))
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rec_ids.sort() # sorting just to make JSON look in proper sequence
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# For each recording merge segments and then perform subsegmentation
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MERGED_SEGMENTS = []
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SUBSEGMENTS = []
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for rec_id in rec_ids:
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segs_iter = filter(
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lambda x: x.startswith("SPEAKER " + str(rec_id)), RTTM
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)
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gt_rttm_segs = [row.split(" ") for row in segs_iter]
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# Merge, subsegment and then convert to json format.
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merged_segs = merge_rttm_intervals(
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gt_rttm_segs
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) # We lose speaker_ID after merging
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MERGED_SEGMENTS = MERGED_SEGMENTS + merged_segs
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# Divide segments into smaller sub-segments
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subsegs = get_subsegments(merged_segs, max_subseg_dur, overlap)
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SUBSEGMENTS = SUBSEGMENTS + subsegs
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# Write segment AND sub-segments (in RTTM format)
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segs_file = save_dir + "/" + filename + ".segments.rttm"
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subsegment_file = save_dir + "/" + filename + ".subsegments.rttm"
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with open(segs_file, "w") as f:
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for row in MERGED_SEGMENTS:
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line_str = " ".join(row)
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f.write("%s\n" % line_str)
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with open(subsegment_file, "w") as f:
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for row in SUBSEGMENTS:
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line_str = " ".join(row)
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f.write("%s\n" % line_str)
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# Create JSON from subsegments
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json_dict = {}
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for row in SUBSEGMENTS:
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rec_id = row[1]
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strt = str(round(float(row[3]), 4))
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end = str(round((float(row[3]) + float(row[4])), 4))
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subsegment_ID = rec_id + "_" + strt + "_" + end
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dur = row[4]
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start_sample = int(float(strt) * SAMPLERATE)
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end_sample = int(float(end) * SAMPLERATE)
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# If multi-mic audio is selected
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if mic_type == "Array1":
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wav_file_base_path = (
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data_dir
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+ "/"
|
||||
+ 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()
|
||||
print(args)
|
||||
|
||||
prepare_ami(
|
||||
args.data_folder,
|
||||
args.manual_annot_folder,
|
||||
args.save_folder,
|
||||
args.ref_rttm_dir,
|
||||
args.meta_data_dir
|
||||
)
|
||||
@ -0,0 +1,252 @@
|
||||
"""
|
||||
AMI corpus contained 100 hours of meeting recording.
|
||||
This script returns the standard train, dev and eval split for AMI corpus.
|
||||
For more information on dataset please refer to http://groups.inf.ed.ac.uk/ami/corpus/datasets.shtml
|
||||
|
||||
Authors
|
||||
* qingenz123@126.com (Qingen ZHAO) 2022
|
||||
|
||||
Credits
|
||||
|
||||
"""
|
||||
|
||||
ALLOWED_OPTIONS = ["scenario_only", "full_corpus", "full_corpus_asr"]
|
||||
|
||||
|
||||
def get_AMI_split(split_option):
|
||||
"""
|
||||
Prepares train, dev, and test sets for given split_option
|
||||
|
||||
Arguments
|
||||
---------
|
||||
split_option: str
|
||||
The standard split option.
|
||||
Allowed options: "scenario_only", "full_corpus", "full_corpus_asr"
|
||||
|
||||
Returns
|
||||
-------
|
||||
Meeting IDs for train, dev, and test sets for given split_option
|
||||
"""
|
||||
|
||||
if split_option not in ALLOWED_OPTIONS:
|
||||
print(
|
||||
f'Invalid split "{split_option}" requested!\nValid split_options are: ',
|
||||
ALLOWED_OPTIONS,
|
||||
)
|
||||
return
|
||||
|
||||
if split_option == "scenario_only":
|
||||
|
||||
train_set = [
|
||||
"ES2002",
|
||||
"ES2005",
|
||||
"ES2006",
|
||||
"ES2007",
|
||||
"ES2008",
|
||||
"ES2009",
|
||||
"ES2010",
|
||||
"ES2012",
|
||||
"ES2013",
|
||||
"ES2015",
|
||||
"ES2016",
|
||||
"IS1000",
|
||||
"IS1001",
|
||||
"IS1002",
|
||||
"IS1003",
|
||||
"IS1004",
|
||||
"IS1005",
|
||||
"IS1006",
|
||||
"IS1007",
|
||||
"TS3005",
|
||||
"TS3008",
|
||||
"TS3009",
|
||||
"TS3010",
|
||||
"TS3011",
|
||||
"TS3012",
|
||||
]
|
||||
|
||||
dev_set = [
|
||||
"ES2003",
|
||||
"ES2011",
|
||||
"IS1008",
|
||||
"TS3004",
|
||||
"TS3006",
|
||||
]
|
||||
|
||||
test_set = [
|
||||
"ES2004",
|
||||
"ES2014",
|
||||
"IS1009",
|
||||
"TS3003",
|
||||
"TS3007",
|
||||
]
|
||||
|
||||
if split_option == "full_corpus":
|
||||
# List of train: SA (TRAINING PART OF SEEN DATA)
|
||||
train_set = [
|
||||
"ES2002",
|
||||
"ES2005",
|
||||
"ES2006",
|
||||
"ES2007",
|
||||
"ES2008",
|
||||
"ES2009",
|
||||
"ES2010",
|
||||
"ES2012",
|
||||
"ES2013",
|
||||
"ES2015",
|
||||
"ES2016",
|
||||
"IS1000",
|
||||
"IS1001",
|
||||
"IS1002",
|
||||
"IS1003",
|
||||
"IS1004",
|
||||
"IS1005",
|
||||
"IS1006",
|
||||
"IS1007",
|
||||
"TS3005",
|
||||
"TS3008",
|
||||
"TS3009",
|
||||
"TS3010",
|
||||
"TS3011",
|
||||
"TS3012",
|
||||
"EN2001",
|
||||
"EN2003",
|
||||
"EN2004",
|
||||
"EN2005",
|
||||
"EN2006",
|
||||
"EN2009",
|
||||
"IN1001",
|
||||
"IN1002",
|
||||
"IN1005",
|
||||
"IN1007",
|
||||
"IN1008",
|
||||
"IN1009",
|
||||
"IN1012",
|
||||
"IN1013",
|
||||
"IN1014",
|
||||
"IN1016",
|
||||
]
|
||||
|
||||
# List of dev: SB (DEV PART OF SEEN DATA)
|
||||
dev_set = [
|
||||
"ES2003",
|
||||
"ES2011",
|
||||
"IS1008",
|
||||
"TS3004",
|
||||
"TS3006",
|
||||
"IB4001",
|
||||
"IB4002",
|
||||
"IB4003",
|
||||
"IB4004",
|
||||
"IB4010",
|
||||
"IB4011",
|
||||
]
|
||||
|
||||
# List of test: SC (UNSEEN DATA FOR EVALUATION)
|
||||
# Note that IB4005 does not appear because it has speakers in common with two sets of data.
|
||||
test_set = [
|
||||
"ES2004",
|
||||
"ES2014",
|
||||
"IS1009",
|
||||
"TS3003",
|
||||
"TS3007",
|
||||
"EN2002",
|
||||
]
|
||||
|
||||
if split_option == "full_corpus_asr":
|
||||
train_set = [
|
||||
"ES2002",
|
||||
"ES2003",
|
||||
"ES2005",
|
||||
"ES2006",
|
||||
"ES2007",
|
||||
"ES2008",
|
||||
"ES2009",
|
||||
"ES2010",
|
||||
"ES2012",
|
||||
"ES2013",
|
||||
"ES2014",
|
||||
"ES2015",
|
||||
"ES2016",
|
||||
"IS1000",
|
||||
"IS1001",
|
||||
"IS1002",
|
||||
"IS1003",
|
||||
"IS1004",
|
||||
"IS1005",
|
||||
"IS1006",
|
||||
"IS1007",
|
||||
"TS3005",
|
||||
"TS3006",
|
||||
"TS3007",
|
||||
"TS3008",
|
||||
"TS3009",
|
||||
"TS3010",
|
||||
"TS3011",
|
||||
"TS3012",
|
||||
"EN2001",
|
||||
"EN2003",
|
||||
"EN2004",
|
||||
"EN2005",
|
||||
"EN2006",
|
||||
"EN2009",
|
||||
"IN1001",
|
||||
"IN1002",
|
||||
"IN1005",
|
||||
"IN1007",
|
||||
"IN1008",
|
||||
"IN1009",
|
||||
"IN1012",
|
||||
"IN1013",
|
||||
"IN1014",
|
||||
"IN1016",
|
||||
]
|
||||
|
||||
dev_set0 = [
|
||||
"ES2011",
|
||||
"IS1008",
|
||||
"TS3004",
|
||||
"IB4001",
|
||||
"IB4002",
|
||||
"IB4003",
|
||||
"IB4004",
|
||||
"IB4010",
|
||||
"IB4011",
|
||||
]
|
||||
|
||||
test_set0 = [
|
||||
"ES2004",
|
||||
"IS1009",
|
||||
"TS3003",
|
||||
"EN2002",
|
||||
]
|
||||
|
||||
dev_set1 = [
|
||||
"ES2011a",
|
||||
"IS1008a",
|
||||
"TS3004a",
|
||||
"IB4001",
|
||||
"IB4002",
|
||||
"IB4003",
|
||||
"IB4004",
|
||||
]
|
||||
test_set1 = [
|
||||
"ES2004a",
|
||||
"IS1009a",
|
||||
"TS3003a",
|
||||
"EN2001a",
|
||||
]
|
||||
|
||||
train_set = [
|
||||
"IB4001",
|
||||
"IB4002",
|
||||
"IB4003",
|
||||
"IB4004",
|
||||
]
|
||||
dev_set = [
|
||||
"IB4002",
|
||||
]
|
||||
test_set = [
|
||||
"IB4004",
|
||||
]
|
||||
return train_set, dev_set, test_set
|
||||
@ -0,0 +1,82 @@
|
||||
"""
|
||||
Data reading and writing.
|
||||
|
||||
Authors
|
||||
* qingenz123@126.com (Qingen ZHAO) 2022
|
||||
|
||||
"""
|
||||
|
||||
import os
|
||||
import pickle
|
||||
|
||||
def save_pkl(obj, file):
|
||||
"""Save an object in pkl format.
|
||||
|
||||
Arguments
|
||||
---------
|
||||
obj : object
|
||||
Object to save in pkl format
|
||||
file : str
|
||||
Path to the output file
|
||||
sampling_rate : int
|
||||
Sampling rate of the audio file, TODO: this is not used?
|
||||
|
||||
Example
|
||||
-------
|
||||
>>> tmpfile = os.path.join(getfixture('tmpdir'), "example.pkl")
|
||||
>>> save_pkl([1, 2, 3, 4, 5], tmpfile)
|
||||
>>> load_pkl(tmpfile)
|
||||
[1, 2, 3, 4, 5]
|
||||
"""
|
||||
with open(file, "wb") as f:
|
||||
pickle.dump(obj, f)
|
||||
|
||||
def load_pickle(pickle_path):
|
||||
"""Utility function for loading .pkl pickle files.
|
||||
|
||||
Arguments
|
||||
---------
|
||||
pickle_path : str
|
||||
Path to pickle file.
|
||||
|
||||
Returns
|
||||
-------
|
||||
out : object
|
||||
Python object loaded from pickle.
|
||||
"""
|
||||
with open(pickle_path, "rb") as f:
|
||||
out = pickle.load(f)
|
||||
return out
|
||||
|
||||
def load_pkl(file):
|
||||
"""Loads a pkl file.
|
||||
|
||||
For an example, see `save_pkl`.
|
||||
|
||||
Arguments
|
||||
---------
|
||||
file : str
|
||||
Path to the input pkl file.
|
||||
|
||||
Returns
|
||||
-------
|
||||
The loaded object.
|
||||
"""
|
||||
|
||||
# Deals with the situation where two processes are trying
|
||||
# to access the same label dictionary by creating a lock
|
||||
count = 100
|
||||
while count > 0:
|
||||
if os.path.isfile(file + ".lock"):
|
||||
time.sleep(1)
|
||||
count -= 1
|
||||
else:
|
||||
break
|
||||
|
||||
try:
|
||||
open(file + ".lock", "w").close()
|
||||
with open(file, "rb") as f:
|
||||
return pickle.load(f)
|
||||
finally:
|
||||
if os.path.isfile(file + ".lock"):
|
||||
os.remove(file + ".lock")
|
||||
Loading…
Reference in new issue