File size: 3,177 Bytes
4095301 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 | import os
import csv
import os.path as osp
import argparse
SAMPLE_RATE = 16000
VALID_SID_FIRST_CHARS = [
'0', # General Education
'1', # Liberal Arts
'2', # Science
'3', # Social Science
'4', # MED
'5', # Engineering
'6', # Bio-resource and Agriculture
'7', # Management
'8', # Public Health
'9', # EECS
'A', # Law School 10
'B', # Life Science 11
'E', # Continuing Education Division 12
'K', # Advanced Technology 13
'F', # D-school 14
'H', # D-school 15
'Z', # D-school 16
'P', # Program 17
'Q', # Academic Writing Center 18
]
def normalize_sid(raw_sid):
if raw_sid is None:
return None
items = raw_sid.split(':')
sid = items[-1]
if len(items) == 3:
sid = items[1]
return sid
def is_valid_sid(sid):
if sid is None:
return False
if len(sid) == 0:
return False
items = sid.split('_')
if len(items) != 2:
return False
if items[0][0] not in VALID_SID_FIRST_CHARS:
return False
return True
def frame_diff_to_timestamp(frame_diff, sample_rate=SAMPLE_RATE):
residual = frame_diff % 320
if 320 - residual > 5 and residual > 5:
print(f"Warning: frame_diff {frame_diff} is not very close to a multiple of 320")
# round frame_diff to the nearest 320 frames
frame_diff = round(frame_diff / 320) * 320
sec_diff = frame_diff / sample_rate # if frame_diff is a multiple of 320, then sec_diff is with resolution of 0.02s
# use max min function to ensure sec_diff is within [0.00, 30.00]
sec_diff = max(0.00, min(30.00, sec_diff))
# return token format <|sec_diff:.2f|>
return f"<|{sec_diff:.2f}|>"
def read_vid_to_other_ids_mapping(vid_to_other_ids_csv_fpath, normalized_sid=True):
vid_to_other_ids = {}
with open(vid_to_other_ids_csv_fpath, 'r') as f:
reader = csv.reader(f)
_columns = next(reader)
for row in reader:
if len(row) != 3:
print(f"Error: row {row} has {len(row)} columns, expected 3 columns")
continue
vid = row[0]
cid = row[1]
sid = row[2]
if normalized_sid:
sid = normalize_sid(sid)
vid_to_other_ids[vid] = {'cid': cid, 'sid': sid}
return vid_to_other_ids
def read_sid_to_course_name_mapping(sid_to_course_name_csv_fpath):
sid_to_course_name = {}
with open(sid_to_course_name_csv_fpath, 'r') as f:
reader = csv.reader(f)
_columns = next(reader)
for row in reader:
if len(row) != 3:
print(f"Error: row {row} has {len(row)} columns, expected 3 columns")
continue
sid = row[0]
zh_course_name = row[1]
en_course_name = row[2]
sid_to_course_name[sid] = {'zh': zh_course_name, 'en': en_course_name}
return sid_to_course_name
def main(args):
print(args)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--input",
default="",
help="a sample arg",
)
args = parser.parse_args()
main(args) |