Merge pull request #3428 from lissyx/import-ccef
Importer for XML file provided by Conference Centre for Economics, France
This commit is contained in:
lissyx
GitHub
commit
d217369839
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@ -0,0 +1,513 @@
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#!/usr/bin/env python
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"""
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Importer for dataset published from Centre de Conférence Pierre Mendès-France
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Ministère de l'Économie, des Finances et de la Relance
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"""
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import csv
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import sys
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import os
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import progressbar
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import subprocess
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import zipfile
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from glob import glob
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from multiprocessing import Pool
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import hashlib
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import decimal
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import math
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import unicodedata
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import re
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import sox
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import xml.etree.ElementTree as ET
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try:
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from num2words import num2words
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except ImportError as ex:
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print("pip install num2words")
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sys.exit(1)
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import requests
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import json
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from deepspeech_training.util.downloader import SIMPLE_BAR, maybe_download
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from deepspeech_training.util.helpers import secs_to_hours
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from deepspeech_training.util.importers import (
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get_counter,
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get_importers_parser,
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get_imported_samples,
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get_validate_label,
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print_import_report,
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)
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from ds_ctcdecoder import Alphabet
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FIELDNAMES = ["wav_filename", "wav_filesize", "transcript"]
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SAMPLE_RATE = 16000
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CHANNELS = 1
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BIT_DEPTH = 16
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MAX_SECS = 10
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MIN_SECS = 0.85
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DATASET_RELEASE_CSV = "https://data.economie.gouv.fr/explore/dataset/transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020/download/?format=csv&timezone=Europe/Berlin&lang=fr&use_labels_for_header=true&csv_separator=%3B"
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DATASET_RELEASE_SHA = [
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("863d39a06a388c6491c6ff2f6450b151f38f1b57", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.001"),
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("2f3a0305aa04c61220bb00b5a4e553e45dbf12e1", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.002"),
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("5e55e9f1f844097349188ac875947e5a3d7fe9f1", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.003"),
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("8bf54842cf07948ca5915e27a8bd5fa5139c06ae", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.004"),
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("c8963504aadc015ac48f9af80058a0bb3440b94f", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.005"),
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|
("d95e225e908621d83ce4e9795fd108d9d310e244", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.006"),
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("de6ed9c2b0ee80ca879aae8ba7923cc93217d811", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.007"),
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("234283c47dacfcd4450d836c52c25f3e807fc5f2", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.008"),
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("4e6b67a688639bb72f8cd81782eaba604a8d32a6", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.009"),
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("4165a51389777c8af8e6253d87bdacb877e8b3b0", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.010"),
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("34322e7009780d97ef5bd02bf2f2c7a31f00baff", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.011"),
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("48c5be3b2ca9d6108d525da6a03e91d93a95dbac", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.012"),
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("87573172f506a189c2ebc633856fe11a2e9cd213", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.013"),
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("6ab2c9e508e9278d5129f023e018725c4a7c69e8", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.014"),
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("4f84df831ef46dce5d3ab3e21817687a2d8c12d0", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.015"),
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("e69bfb079885c299cb81080ef88b1b8b57158aa6", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.016"),
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("5f764ba788ee273981cf211b242c29b49ca22c5e", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.017"),
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("b6aa81a959525363223494830c1e7307d4c4bae6", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.018"),
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("91ddcf43c7bf113a6f2528b857c7ec22a50a148a", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.019"),
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("fa1b29273dd77b9a7494983a2f9ae52654b931d7", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.020"),
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("1113aef4f5e2be2f7fbf2d54b6c710c1c0e7135f", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.021"),
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("ce6420d5d0b6b5135ba559f83e1a82d4d615c470", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.022"),
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|
("d0976ed292ac24fcf1590d1ea195077c74b05471", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.023"),
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("ec746cd6af066f62d9bf8d3b2f89174783ff4e3c", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.024"),
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|
("570d9e1e84178e32fd867171d4b3aaecda1fd4fb", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.025"),
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|
("c29ccc7467a75b2cae3d7f2e9fbbb2ab276cb8ac", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.026"),
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|
("08406a51146d88e208704ce058c060a1e44efa50", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.027"),
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|
("199aedad733a78ea1e7d47def9c71c6fd5795e02", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.028"),
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("db856a068f92fb4f01f410bba42c7271de0f231a", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.029"),
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("e3c0135f16c6c9d25a09dcb4f99a685438a84740", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.030"),
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|
("e51b8bb9c0ae4339f98b4f21e6d29b825109f0ac", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.031"),
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|
("be5e80cbc49b59b31ae33c30576ef0e1a162d84e", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.032"),
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|
("501df58e3ff55fcfd75b93dab57566dc536948b8", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.033"),
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|
("1a114875811a8cdcb8d85a9f6dbee78be3e05131", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.034"),
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("465d824e7ee46448369182c0c28646d155a2249b", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.035"),
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("37f341b1b266d143eb73138c31cfff3201b9d619", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.036"),
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("9e7d8255987a8a77a90e0d4b55c8fd38b9fb5694", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.037"),
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("54886755630cb080a53098cb1b6c951c6714a143", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.038"),
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("4b7cbb0154697be795034f7a49712e882a97197a", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.039"),
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("c8e1e565a0e7a1f6ff1dbfcefe677aa74a41d2f2", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip.040"),
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]
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def _download_and_preprocess_data(csv_url, target_dir):
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dataset_sources = os.path.join(target_dir, "transcriptionsXML_audioMP3_MEFR_CCPMF_2012-2020", "data.txt")
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if os.path.exists(dataset_sources):
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return dataset_sources
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# Making path absolute
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target_dir = os.path.abspath(target_dir)
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csv_ref = requests.get(csv_url).text.split('\r\n')[1:-1]
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for part in csv_ref:
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part_filename = requests.head(part).headers.get("Content-Disposition").split(" ")[1].split("=")[1].replace('"', "")
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if not os.path.exists(os.path.join(target_dir, part_filename)):
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part_path = maybe_download(part_filename, target_dir, part)
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def _big_sha1(fname):
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s = hashlib.sha1()
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buffer_size = 65536
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with open(fname, "rb") as f:
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while True:
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data = f.read(buffer_size)
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if not data:
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break
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s.update(data)
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return s.hexdigest()
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for (sha1, filename) in DATASET_RELEASE_SHA:
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print("Checking {} SHA1:".format(filename))
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csum = _big_sha1(os.path.join(target_dir, filename))
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if csum == sha1:
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print("\t{}: OK {}".format(filename, sha1))
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else:
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print("\t{}: ERROR: expected {}, computed {}".format(filename, sha1, csum))
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assert csum == sha1
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# Conditionally extract data
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_maybe_extract(target_dir, "transcriptionsXML_audioMP3_MEFR_CCPMF_2012-2020", "transcriptionsxml_audiomp3_mefr_ccpmf_2012-2020_2.zip", "transcriptionsXML_audioMP3_MEFR_CCPMF_2012-2020.zip")
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# Produce source text for extraction / conversion
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return _maybe_create_sources(os.path.join(target_dir, "transcriptionsXML_audioMP3_MEFR_CCPMF_2012-2020"))
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def _maybe_extract(target_dir, extracted_data, archive, final):
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# If target_dir/extracted_data does not exist, extract archive in target_dir
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extracted_path = os.path.join(target_dir, extracted_data)
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archive_path = os.path.join(target_dir, archive)
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final_archive = os.path.join(extracted_path, final)
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if not os.path.exists(extracted_path):
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if not os.path.exists(archive_path):
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print('No archive "%s" - building ...' % archive_path)
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all_zip_parts = glob(archive_path + ".*")
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all_zip_parts.sort()
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cmdline = "cat {} > {}".format(" ".join(all_zip_parts), archive_path)
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print('Building with "%s"' % cmdline)
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subprocess.check_call(cmdline, shell=True, cwd=target_dir)
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assert os.path.exists(archive_path)
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print('No directory "%s" - extracting archive %s ...' % (extracted_path, archive_path))
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with zipfile.ZipFile(archive_path) as zip_f:
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zip_f.extractall(extracted_path)
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with zipfile.ZipFile(final_archive) as zip_f:
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zip_f.extractall(target_dir)
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else:
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print('Found directory "%s" - not extracting it from archive.' % extracted_path)
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def _maybe_create_sources(dir):
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dataset_sources = os.path.join(dir, "data.txt")
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MP3 = glob(os.path.join(dir, "**", "*.mp3"))
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XML = glob(os.path.join(dir, "**", "*.xml"))
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MP3_XML_Scores = []
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MP3_XML_Fin = {}
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for f_mp3 in MP3:
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for f_xml in XML:
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b_mp3 = os.path.splitext(os.path.basename(f_mp3))[0]
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b_xml = os.path.splitext(os.path.basename(f_xml))[0]
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a_mp3 = b_mp3.split('_')
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a_xml = b_xml.split('_')
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score = 0
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date_mp3 = a_mp3[0]
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date_xml = a_xml[0]
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if date_mp3 != date_xml:
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continue
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for i in range(min(len(a_mp3), len(a_xml))):
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if (a_mp3[i] == a_xml[i]):
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score += 1
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if score >= 1:
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MP3_XML_Scores.append((f_mp3, f_xml, score))
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# sort by score
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MP3_XML_Scores.sort(key=lambda x: x[2], reverse=True)
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for s_mp3, s_xml, score in MP3_XML_Scores:
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#print(s_mp3, s_xml, score)
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if score not in MP3_XML_Fin:
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MP3_XML_Fin[score] = {}
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if s_mp3 not in MP3_XML_Fin[score]:
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try:
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MP3.index(s_mp3)
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MP3.remove(s_mp3)
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MP3_XML_Fin[score][s_mp3] = s_xml
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except ValueError as ex:
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pass
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else:
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print("here:", MP3_XML_Fin[score][s_mp3], s_xml, file=sys.stderr)
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with open(dataset_sources, "w") as ds:
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for score in MP3_XML_Fin:
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|
for mp3 in MP3_XML_Fin[score]:
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|
xml = MP3_XML_Fin[score][mp3]
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|
if os.path.getsize(mp3) > 0 and os.path.getsize(xml) > 0:
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mp3 = os.path.relpath(mp3, dir)
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xml = os.path.relpath(xml, dir)
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ds.write('{},{},{:0.2e}\n'.format(xml, mp3, 2.5e-4))
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else:
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print("Empty file {} or {}".format(mp3, xml), file=sys.stderr)
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print("Missing XML pairs:", MP3, file=sys.stderr)
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return dataset_sources
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|
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def maybe_normalize_for_digits(label):
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# first, try to identify numbers like "50 000", "260 000"
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if " " in label:
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if any(s.isdigit() for s in label):
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thousands = re.compile(r"(\d{1,3}(?:\s*\d{3})*(?:,\d+)?)")
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maybe_thousands = thousands.findall(label)
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|
if len(maybe_thousands) > 0:
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||||||
|
while True:
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(label, r) = re.subn(r"(\d)\s(\d{3})", "\\1\\2", label)
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|
if r == 0:
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|
break
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||||||
|
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||||||
|
# this might be a time or duration in the form "hh:mm" or "hh:mm:ss"
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|
if ":" in label:
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||||||
|
for s in label.split(" "):
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|
if any(i.isdigit() for i in s):
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||||||
|
date_or_time = re.compile(r"(\d{1,2}):(\d{2}):?(\d{2})?")
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|
maybe_date_or_time = date_or_time.findall(s)
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||||||
|
if len(maybe_date_or_time) > 0:
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||||||
|
maybe_hours = maybe_date_or_time[0][0]
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||||||
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maybe_minutes = maybe_date_or_time[0][1]
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||||||
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maybe_seconds = maybe_date_or_time[0][2]
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||||||
|
if len(maybe_seconds) > 0:
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||||||
|
label = label.replace("{}:{}:{}".format(maybe_hours, maybe_minutes, maybe_seconds), "{} heures {} minutes et {} secondes".format(maybe_hours, maybe_minutes, maybe_seconds))
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||||||
|
else:
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||||||
|
label = label.replace("{}:{}".format(maybe_hours, maybe_minutes), "{} heures et {} minutes".format(maybe_hours, maybe_minutes))
|
||||||
|
|
||||||
|
new_label = []
|
||||||
|
# pylint: disable=too-many-nested-blocks
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||||||
|
for s in label.split(" "):
|
||||||
|
if any(i.isdigit() for i in s):
|
||||||
|
s = s.replace(",", ".") # num2words requires "." for floats
|
||||||
|
s = s.replace("\"", "") # clean some data, num2words would choke on 1959"
|
||||||
|
|
||||||
|
last_c = s[-1]
|
||||||
|
if not last_c.isdigit(): # num2words will choke on "0.6.", "24 ?"
|
||||||
|
s = s[:-1]
|
||||||
|
|
||||||
|
if any(i.isalpha() for i in s): # So we have any(isdigit()) **and** any(sialpha), like "3D"
|
||||||
|
ns = []
|
||||||
|
for c in s:
|
||||||
|
nc = c
|
||||||
|
if c.isdigit(): # convert "3" to "trois-"
|
||||||
|
try:
|
||||||
|
nc = num2words(c, lang="fr") + "-"
|
||||||
|
except decimal.InvalidOperation as ex:
|
||||||
|
print("decimal.InvalidOperation: '{}'".format(s))
|
||||||
|
raise ex
|
||||||
|
ns.append(nc)
|
||||||
|
s = "".join(s)
|
||||||
|
else:
|
||||||
|
try:
|
||||||
|
s = num2words(s, lang="fr")
|
||||||
|
except decimal.InvalidOperation as ex:
|
||||||
|
print("decimal.InvalidOperation: '{}'".format(s))
|
||||||
|
raise ex
|
||||||
|
new_label.append(s)
|
||||||
|
return " ".join(new_label)
|
||||||
|
|
||||||
|
def maybe_normalize_for_specials_chars(label):
|
||||||
|
label = label.replace("%", "pourcents")
|
||||||
|
label = label.replace("/", ", ") # clean intervals like 2019/2022 to "2019 2022"
|
||||||
|
label = label.replace("-", ", ") # clean intervals like 70-80 to "70 80"
|
||||||
|
label = label.replace("+", " plus ") # clean + and make it speakable
|
||||||
|
label = label.replace("€", " euros ") # clean euro symbol and make it speakable
|
||||||
|
label = label.replace("., ", ", ") # clean some strange "4.0., " (20181017_Innovation.xml)
|
||||||
|
label = label.replace("°", " degré ") # clean some strange "°5" (20181210_EtatsGeneraux-1000_fre_750_und.xml)
|
||||||
|
label = label.replace("...", ".") # remove ellipsis
|
||||||
|
label = label.replace("..", ".") # remove broken ellipsis
|
||||||
|
label = label.replace("m²", "mètre-carrés") # 20150616_Defi_Climat_3_wmv_0_fre_minefi.xml
|
||||||
|
label = label.replace("[end]", "") # broken tag in 20150123_Entretiens_Tresor_PGM_wmv_0_fre_minefi.xml
|
||||||
|
label = label.replace(u'\xB8c', " ç") # strange cedilla in 20150417_Printemps_Economie_2_wmv_0_fre_minefi.xml
|
||||||
|
label = label.replace("C0²", "CO 2") # 20121016_Syteme_sante_copie_wmv_0_fre_minefi.xml
|
||||||
|
return label
|
||||||
|
|
||||||
|
def maybe_normalize_for_anglicisms(label):
|
||||||
|
label = label.replace("B2B", "B to B")
|
||||||
|
label = label.replace("B2C", "B to C")
|
||||||
|
label = label.replace("#", "hashtag ")
|
||||||
|
label = label.replace("@", "at ")
|
||||||
|
return label
|
||||||
|
|
||||||
|
def maybe_normalize(label):
|
||||||
|
label = maybe_normalize_for_specials_chars(label)
|
||||||
|
label = maybe_normalize_for_anglicisms(label)
|
||||||
|
label = maybe_normalize_for_digits(label)
|
||||||
|
return label
|
||||||
|
|
||||||
|
def one_sample(sample):
|
||||||
|
file_size = -1
|
||||||
|
frames = 0
|
||||||
|
|
||||||
|
audio_source = sample[0]
|
||||||
|
target_dir = sample[1]
|
||||||
|
dataset_basename = sample[2]
|
||||||
|
|
||||||
|
start_time = sample[3]
|
||||||
|
duration = sample[4]
|
||||||
|
label = label_filter_fun(sample[5])
|
||||||
|
sample_id = sample[6]
|
||||||
|
|
||||||
|
_wav_filename = os.path.basename(audio_source.replace(".wav", "_{:06}.wav".format(sample_id)))
|
||||||
|
wav_fullname = os.path.join(target_dir, dataset_basename, _wav_filename)
|
||||||
|
|
||||||
|
if not os.path.exists(wav_fullname):
|
||||||
|
subprocess.check_output(["ffmpeg", "-i", audio_source, "-ss", str(start_time), "-t", str(duration), "-c", "copy", wav_fullname], stdin=subprocess.DEVNULL, stderr=subprocess.STDOUT)
|
||||||
|
|
||||||
|
file_size = os.path.getsize(wav_fullname)
|
||||||
|
frames = int(subprocess.check_output(["soxi", "-s", wav_fullname], stderr=subprocess.STDOUT))
|
||||||
|
|
||||||
|
_counter = get_counter()
|
||||||
|
_rows = []
|
||||||
|
|
||||||
|
if file_size == -1:
|
||||||
|
# Excluding samples that failed upon conversion
|
||||||
|
_counter["failed"] += 1
|
||||||
|
elif label is None:
|
||||||
|
# Excluding samples that failed on label validation
|
||||||
|
_counter["invalid_label"] += 1
|
||||||
|
elif int(frames/SAMPLE_RATE*1000/10/2) < len(str(label)):
|
||||||
|
# Excluding samples that are too short to fit the transcript
|
||||||
|
_counter["too_short"] += 1
|
||||||
|
elif frames/SAMPLE_RATE < MIN_SECS:
|
||||||
|
# Excluding samples that are too short
|
||||||
|
_counter["too_short"] += 1
|
||||||
|
elif frames/SAMPLE_RATE > MAX_SECS:
|
||||||
|
# Excluding very long samples to keep a reasonable batch-size
|
||||||
|
_counter["too_long"] += 1
|
||||||
|
else:
|
||||||
|
# This one is good - keep it for the target CSV
|
||||||
|
_rows.append((os.path.join(dataset_basename, _wav_filename), file_size, label))
|
||||||
|
_counter["imported_time"] += frames
|
||||||
|
_counter["all"] += 1
|
||||||
|
_counter["total_time"] += frames
|
||||||
|
|
||||||
|
return (_counter, _rows)
|
||||||
|
|
||||||
|
def _maybe_import_data(xml_file, audio_source, target_dir, rel_tol=1e-1):
|
||||||
|
dataset_basename = os.path.splitext(os.path.split(xml_file)[1])[0]
|
||||||
|
wav_root = os.path.join(target_dir, dataset_basename)
|
||||||
|
if not os.path.exists(wav_root):
|
||||||
|
os.makedirs(wav_root)
|
||||||
|
|
||||||
|
source_frames = int(subprocess.check_output(["soxi", "-s", audio_source], stderr=subprocess.STDOUT))
|
||||||
|
print("Source audio length: %s" % secs_to_hours(source_frames / SAMPLE_RATE))
|
||||||
|
|
||||||
|
# Get audiofile path and transcript for each sentence in tsv
|
||||||
|
samples = []
|
||||||
|
tree = ET.parse(xml_file)
|
||||||
|
root = tree.getroot()
|
||||||
|
seq_id = 0
|
||||||
|
this_time = 0.0
|
||||||
|
this_duration = 0.0
|
||||||
|
prev_time = 0.0
|
||||||
|
prev_duration = 0.0
|
||||||
|
this_text = ""
|
||||||
|
for child in root:
|
||||||
|
if child.tag == "row":
|
||||||
|
cur_time = float(child.attrib["timestamp"])
|
||||||
|
cur_duration = float(child.attrib["timedur"])
|
||||||
|
cur_text = child.text
|
||||||
|
|
||||||
|
if this_time == 0.0:
|
||||||
|
this_time = cur_time
|
||||||
|
|
||||||
|
delta = cur_time - (prev_time + prev_duration)
|
||||||
|
# rel_tol value is made from trial/error to try and compromise between:
|
||||||
|
# - cutting enough to skip missing words
|
||||||
|
# - not too short, not too long sentences
|
||||||
|
is_close = math.isclose(cur_time, this_time + this_duration, rel_tol=rel_tol)
|
||||||
|
is_short = ((this_duration + cur_duration + delta) < MAX_SECS)
|
||||||
|
|
||||||
|
# when the previous element is close enough **and** this does not
|
||||||
|
# go over MAX_SECS, we append content
|
||||||
|
if (is_close and is_short):
|
||||||
|
this_duration += cur_duration + delta
|
||||||
|
this_text += cur_text
|
||||||
|
else:
|
||||||
|
samples.append((audio_source, target_dir, dataset_basename, this_time, this_duration, this_text, seq_id))
|
||||||
|
|
||||||
|
this_time = cur_time
|
||||||
|
this_duration = cur_duration
|
||||||
|
this_text = cur_text
|
||||||
|
|
||||||
|
seq_id += 1
|
||||||
|
|
||||||
|
prev_time = cur_time
|
||||||
|
prev_duration = cur_duration
|
||||||
|
|
||||||
|
# Keep track of how many samples are good vs. problematic
|
||||||
|
_counter = get_counter()
|
||||||
|
num_samples = len(samples)
|
||||||
|
_rows = []
|
||||||
|
|
||||||
|
print("Processing XML data: {}".format(xml_file))
|
||||||
|
pool = Pool()
|
||||||
|
bar = progressbar.ProgressBar(max_value=num_samples, widgets=SIMPLE_BAR)
|
||||||
|
for i, processed in enumerate(pool.imap_unordered(one_sample, samples), start=1):
|
||||||
|
_counter += processed[0]
|
||||||
|
_rows += processed[1]
|
||||||
|
bar.update(i)
|
||||||
|
bar.update(num_samples)
|
||||||
|
pool.close()
|
||||||
|
pool.join()
|
||||||
|
|
||||||
|
imported_samples = get_imported_samples(_counter)
|
||||||
|
assert _counter["all"] == num_samples
|
||||||
|
assert len(_rows) == imported_samples
|
||||||
|
|
||||||
|
print_import_report(_counter, SAMPLE_RATE, MAX_SECS)
|
||||||
|
print("Import efficiency: %.1f%%" % ((_counter["total_time"] / source_frames)*100))
|
||||||
|
print("")
|
||||||
|
|
||||||
|
return _counter, _rows
|
||||||
|
|
||||||
|
def _maybe_convert_wav(mp3_filename, _wav_filename):
|
||||||
|
if not os.path.exists(_wav_filename):
|
||||||
|
print("Converting {} to WAV file: {}".format(mp3_filename, _wav_filename))
|
||||||
|
transformer = sox.Transformer()
|
||||||
|
transformer.convert(samplerate=SAMPLE_RATE, n_channels=CHANNELS, bitdepth=BIT_DEPTH)
|
||||||
|
try:
|
||||||
|
transformer.build(mp3_filename, _wav_filename)
|
||||||
|
except sox.core.SoxError:
|
||||||
|
pass
|
||||||
|
|
||||||
|
def write_general_csv(target_dir, _rows, _counter):
|
||||||
|
target_csv_template = os.path.join(target_dir, "ccpmf_{}.csv")
|
||||||
|
with open(target_csv_template.format("train"), "w") as train_csv_file: # 80%
|
||||||
|
with open(target_csv_template.format("dev"), "w") as dev_csv_file: # 10%
|
||||||
|
with open(target_csv_template.format("test"), "w") as test_csv_file: # 10%
|
||||||
|
train_writer = csv.DictWriter(train_csv_file, fieldnames=FIELDNAMES)
|
||||||
|
train_writer.writeheader()
|
||||||
|
dev_writer = csv.DictWriter(dev_csv_file, fieldnames=FIELDNAMES)
|
||||||
|
dev_writer.writeheader()
|
||||||
|
test_writer = csv.DictWriter(test_csv_file, fieldnames=FIELDNAMES)
|
||||||
|
test_writer.writeheader()
|
||||||
|
|
||||||
|
bar = progressbar.ProgressBar(max_value=len(_rows), widgets=SIMPLE_BAR)
|
||||||
|
for i, item in enumerate(bar(_rows)):
|
||||||
|
i_mod = i % 10
|
||||||
|
if i_mod == 0:
|
||||||
|
writer = test_writer
|
||||||
|
elif i_mod == 1:
|
||||||
|
writer = dev_writer
|
||||||
|
else:
|
||||||
|
writer = train_writer
|
||||||
|
writer.writerow({"wav_filename": item[0], "wav_filesize": item[1], "transcript": item[2]})
|
||||||
|
|
||||||
|
print("")
|
||||||
|
print("~~~~ FINAL STATISTICS ~~~~")
|
||||||
|
print_import_report(_counter, SAMPLE_RATE, MAX_SECS)
|
||||||
|
print("~~~~ (FINAL STATISTICS) ~~~~")
|
||||||
|
print("")
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
PARSER = get_importers_parser(description="Import XML from Conference Centre for Economics, France")
|
||||||
|
PARSER.add_argument("target_dir", help="Destination directory")
|
||||||
|
PARSER.add_argument("--filter_alphabet", help="Exclude samples with characters not in provided alphabet")
|
||||||
|
PARSER.add_argument("--normalize", action="store_true", help="Converts diacritic characters to their base ones")
|
||||||
|
|
||||||
|
PARAMS = PARSER.parse_args()
|
||||||
|
validate_label = get_validate_label(PARAMS)
|
||||||
|
ALPHABET = Alphabet(PARAMS.filter_alphabet) if PARAMS.filter_alphabet else None
|
||||||
|
|
||||||
|
def label_filter_fun(label):
|
||||||
|
if PARAMS.normalize:
|
||||||
|
label = unicodedata.normalize("NFKD", label.strip()) \
|
||||||
|
.encode("ascii", "ignore") \
|
||||||
|
.decode("ascii", "ignore")
|
||||||
|
label = maybe_normalize(label)
|
||||||
|
label = validate_label(label)
|
||||||
|
if ALPHABET and label:
|
||||||
|
try:
|
||||||
|
ALPHABET.encode(label)
|
||||||
|
except KeyError:
|
||||||
|
label = None
|
||||||
|
return label
|
||||||
|
|
||||||
|
dataset_sources = _download_and_preprocess_data(csv_url=DATASET_RELEASE_CSV, target_dir=PARAMS.target_dir)
|
||||||
|
sources_root_dir = os.path.dirname(dataset_sources)
|
||||||
|
all_counter = get_counter()
|
||||||
|
all_rows = []
|
||||||
|
with open(dataset_sources, "r") as sources:
|
||||||
|
for line in sources.readlines():
|
||||||
|
d = line.split(",")
|
||||||
|
this_xml = os.path.join(sources_root_dir, d[0])
|
||||||
|
this_mp3 = os.path.join(sources_root_dir, d[1])
|
||||||
|
this_rel = float(d[2])
|
||||||
|
|
||||||
|
wav_filename = os.path.join(sources_root_dir, os.path.splitext(os.path.basename(this_mp3))[0] + ".wav")
|
||||||
|
_maybe_convert_wav(this_mp3, wav_filename)
|
||||||
|
counter, rows = _maybe_import_data(this_xml, wav_filename, sources_root_dir, this_rel)
|
||||||
|
|
||||||
|
all_counter += counter
|
||||||
|
all_rows += rows
|
||||||
|
write_general_csv(sources_root_dir, _counter=all_counter, _rows=all_rows)
|
||||||
|
|
@ -19,7 +19,7 @@ def maybe_download(archive_name, target_dir, archive_url):
|
||||||
total_size = int(req.headers.get('content-length', 0))
|
total_size = int(req.headers.get('content-length', 0))
|
||||||
done = 0
|
done = 0
|
||||||
with open(archive_path, 'wb') as f:
|
with open(archive_path, 'wb') as f:
|
||||||
bar = progressbar.ProgressBar(max_value=total_size, widgets=SIMPLE_BAR)
|
bar = progressbar.ProgressBar(max_value=total_size if total_size > 0 else progressbar.UnknownLength, widgets=SIMPLE_BAR)
|
||||||
for data in req.iter_content(1024*1024):
|
for data in req.iter_content(1024*1024):
|
||||||
done += len(data)
|
done += len(data)
|
||||||
f.write(data)
|
f.write(data)
|
||||||
|
|
|
||||||
Loading…
Reference in New Issue