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Running
on
Zero
File size: 3,111 Bytes
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# @ [email protected]
import argparse
import logging
import json
import glob
import os
import numpy as np
import tqdm
import time
import multiprocessing
from g2p_en import G2p
import nltk
nltk.download('averaged_perceptron_tagger_eng')
def parse_args():
parser = argparse.ArgumentParser(description="Encode the gigaspeech phonemes using g2p model")
parser.add_argument('--save_dir', type=str, default=None, help="path to the manifest, phonemes, and encodec codes dirs")
parser.add_argument('--num_cpus', type=int, default=10)
return parser.parse_args()
if __name__ == "__main__":
formatter = (
"%(asctime)s [%(levelname)s] %(filename)s:%(lineno)d || %(message)s"
)
logging.basicConfig(format=formatter, level=logging.INFO)
args = parse_args()
# get the path
phn_save_root = os.path.join(args.save_dir, "g2p")
os.makedirs(phn_save_root, exist_ok=True)
valid_symbols = [
'AA', 'AA0', 'AA1', 'AA2', 'AE', 'AE0', 'AE1', 'AE2', 'AH', 'AH0', 'AH1', 'AH2',
'AO', 'AO0', 'AO1', 'AO2', 'AW', 'AW0', 'AW1', 'AW2', 'AY', 'AY0', 'AY1', 'AY2',
'B', 'CH', 'D', 'DH', 'EH', 'EH0', 'EH1', 'EH2', 'ER', 'ER0', 'ER1', 'ER2', 'EY',
'EY0', 'EY1', 'EY2', 'F', 'G', 'HH', 'IH', 'IH0', 'IH1', 'IH2', 'IY', 'IY0', 'IY1',
'IY2', 'JH', 'K', 'L', 'M', 'N', 'NG', 'OW', 'OW0', 'OW1', 'OW2', 'OY', 'OY0',
'OY1', 'OY2', 'P', 'R', 'S', 'SH', 'T', 'TH', 'UH', 'UH0', 'UH1', 'UH2', 'UW',
'UW0', 'UW1', 'UW2', 'V', 'W', 'Y', 'Z', 'ZH', '<BLK>', ',', '.', '!', '?',
'<B_start>', '<B_end>', '<I_start>', '<I_end>'
]
### phonemization
text_tokenizer = G2p()
stime = time.time()
logging.info(f"phonemizing...")
json_paths = glob.glob(os.path.join(args.save_dir, 'jsons', '*.json'))
for json_path in json_paths:
with open(json_path, 'r') as json_file:
jsondata = json.load(json_file)
df_split = np.array_split(jsondata, args.num_cpus)
print(len(jsondata))
# Optional: Save each part to a separate JSON file
cmds = []
for idx, part in enumerate(df_split):
cmds.append((idx, part))
def process_one(indx, splitdata):
for key in tqdm.tqdm(range(len(splitdata))):
save_fn = os.path.join(phn_save_root, splitdata[key]['segment_id']+".txt")
if not os.path.exists(save_fn):
text = splitdata[key]['text']
phn = text_tokenizer(text)
phn = [item.replace(' ', '<BLK>') for item in phn]
phn = [item for item in phn if item in valid_symbols]
wrong_phn = [item for item in phn if item not in valid_symbols]
if len(wrong_phn) > 0:
print(wrong_phn)
phn_seq = " ".join(phn)
with open(save_fn, "w") as f:
f.write(phn_seq)
with multiprocessing.Pool(processes=args.num_cpus) as pool:
pool.starmap(process_one, cmds) |