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import sys,os,random
import pyalveo
import time
from glob import glob
import csv
# disable insecure HTTPS warnings from the staging servers
import requests
requests.packages.urllib3.disable_warnings()
COLLECTION_NAME = "sc-cw-children"
#COLLECTION_NAME = "sctestcollection2"
EXT_MAP = {
'.wav': "Audio",
'.txt': "Text",
'.sf0': "Pitch Track",
'.sfb': "Formant Track",
'.lab': "Annotation",
'.trg': "Annotation",
'.hlb': "Annotation",
}
DEMOGRAPHIC_MAP = {
'Speakerid': 'olac:speaker',
'Gender': 'foaf:gender',
'ParentsLanguage': 'austalk:parents_first_langage',
'OtherLanguage': 'austalk:other_languages',
'Age': 'foaf:age',
'POB': 'austalk:pob_town',
'State': 'austalk:pob_state',
'Country': 'austalk:pob_country',
'Sibling': 'sibling'
}
SPKR_DICT = None
def read_demographic(basedir):
"""Read the demographic spreadsheet and return a dict of dicts"""
global SPKR_DICT
if SPKR_DICT == None:
SPKR_DICT = dict()
with open(os.path.join(basedir, "sp1-sp8-demographic.csv")) as fd:
reader = csv.DictReader(fd)
for row in reader:
SPKR_DICT[row['Speakerid']] = row
def speaker_meta(speaker):
"""Return a dictionary of metadata for this speaker"""
if speaker in SPKR_DICT:
result = dict()
for key in SPKR_DICT[speaker].keys():
metakey = DEMOGRAPHIC_MAP[key]
if SPKR_DICT[speaker][key] != '':
result[metakey] = SPKR_DICT[speaker][key]
return result
else:
return dict()
def process(client, basedir):
"""Process the files in this corpus"""
collection_uri = client.api_url + "catalog/" + COLLECTION_NAME
# delete any existing items
print "Deleting items: ", list(client.get_items(collection_uri))
for itemuri in client.get_items(collection_uri):
client.delete_item(itemuri)
#count = 0
for itemid, meta, files in corpus_items(basedir):
start = time.time()
item = client.add_item(collection_uri, itemid, meta)
print "Item: ", itemid, time.time()-start
for file in files:
docname = os.path.basename(file)
root, ext = os.path.splitext(docname)
if ext in EXT_MAP:
doctype = EXT_MAP[ext]
else:
doctype = "Other"
docmeta = {
"dcterms:title": docname,
"dcterms:type": doctype
}
try:
client.add_document(item, docname, docmeta, file=file)
print "\tDocument: ", docname, time.time()-start
except pyalveo.pyalveo.APIError as e:
print "Error: ", e
# count += 1
# if count > 10:
# return
def corpus_items(basedir):
"""Return an iterator over items in the corpus,
each item is returned as a tuple: (itemid, metadata, [file1, file2, file3])
where itemid is the identifier
metadata is a dictionary of metadata
fileN are the files to attach to the item
"""
base_meta = {
'dcterms:creator': 'C. Watson and S. Cassidy',
"ausnc:mode": "spoken",
"ausnc:communication_context": "face-to-face",
"olac:language": "eng",
"ausnc:interactivity": "read",
"ausnc:audience": "individual",
}
read_demographic(basedir)
for spkr in os.listdir(basedir):
meta = base_meta.copy()
meta['olac:speaker'] = spkr
meta.update(speaker_meta(spkr))
# iterate over wav files
for wav in os.listdir(os.path.join(basedir, spkr, 'data')):
files = []
(sp, prompt, ext) = wav.split('.')
if ext == 'wav':
meta['dcterms:title'] = prompt
meta['austalk:prompt'] = prompt
# gather the files
files.extend(glob(os.path.join(basedir, spkr, 'data', sp+'.'+prompt+'.*')))
files.extend(glob(os.path.join(basedir, spkr, 'labels', sp+'.'+prompt+'.*')))
yield (spkr + "." + prompt, meta, files)
if __name__=='__main__':
basedir = sys.argv[1]
config = sys.argv[2]
client = pyalveo.Client(configfile=config, verifySSL=False)
process(client, basedir)
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