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preprocess_v2.py
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preprocess_v2.py
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import os
import argparse
import json
import sys
sys.setrecursionlimit(500000) # Fix the error message of RecursionError: maximum recursion depth exceeded while calling a Python object. You can change the number as you want.
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--add_auxiliary_data", type=bool, help="Whether to add extra data as fine-tuning helper")
parser.add_argument("--languages", default="CJE")
args = parser.parse_args()
if args.languages == "CJE":
langs = ["[ZH]", "[JA]", "[EN]"]
elif args.languages == "CJ":
langs = ["[ZH]", "[JA]"]
elif args.languages == "C":
langs = ["[ZH]"]
new_annos = []
# Source 1: transcribed short audios
if os.path.exists("short_character_anno.txt"):
with open("short_character_anno.txt", 'r', encoding='utf-8') as f:
short_character_anno = f.readlines()
new_annos += short_character_anno
# Source 2: transcribed long audio segments
if os.path.exists("./long_character_anno.txt"):
with open("./long_character_anno.txt", 'r', encoding='utf-8') as f:
long_character_anno = f.readlines()
new_annos += long_character_anno
# Get all speaker names
speakers = []
for line in new_annos:
path, speaker, text = line.split("|")
if speaker not in speakers:
speakers.append(speaker)
assert (len(speakers) != 0), "No audio file found. Please check your uploaded file structure."
# Source 3 (Optional): sampled audios as extra training helpers
if args.add_auxiliary_data:
with open("./sampled_audio4ft.txt", 'r', encoding='utf-8') as f:
old_annos = f.readlines()
# filter old_annos according to supported languages
filtered_old_annos = []
for line in old_annos:
for lang in langs:
if lang in line:
filtered_old_annos.append(line)
old_annos = filtered_old_annos
for line in old_annos:
path, speaker, text = line.split("|")
if speaker not in speakers:
speakers.append(speaker)
num_old_voices = len(old_annos)
num_new_voices = len(new_annos)
# STEP 1: balance number of new & old voices
cc_duplicate = num_old_voices // num_new_voices
if cc_duplicate == 0:
cc_duplicate = 1
# STEP 2: modify config file
with open("./configs/finetune_speaker.json", 'r', encoding='utf-8') as f:
hps = json.load(f)
# assign ids to new speakers
speaker2id = {}
for i, speaker in enumerate(speakers):
speaker2id[speaker] = i
# modify n_speakers
hps['data']["n_speakers"] = len(speakers)
# overwrite speaker names
hps['speakers'] = speaker2id
hps['train']['log_interval'] = 10
hps['train']['eval_interval'] = 100
hps['train']['batch_size'] = 16
hps['data']['training_files'] = "final_annotation_train.txt"
hps['data']['validation_files'] = "final_annotation_val.txt"
# save modified config
with open("./configs/modified_finetune_speaker.json", 'w', encoding='utf-8') as f:
json.dump(hps, f, indent=2)
# STEP 3: clean annotations, replace speaker names with assigned speaker IDs
import text
cleaned_new_annos = []
for i, line in enumerate(new_annos):
path, speaker, txt = line.split("|")
if len(txt) > 150:
continue
cleaned_text = text._clean_text(txt, hps['data']['text_cleaners'])
cleaned_text += "\n" if not cleaned_text.endswith("\n") else ""
cleaned_new_annos.append(path + "|" + str(speaker2id[speaker]) + "|" + cleaned_text)
cleaned_old_annos = []
for i, line in enumerate(old_annos):
path, speaker, txt = line.split("|")
if len(txt) > 150:
continue
cleaned_text = text._clean_text(txt, hps['data']['text_cleaners'])
cleaned_text += "\n" if not cleaned_text.endswith("\n") else ""
cleaned_old_annos.append(path + "|" + str(speaker2id[speaker]) + "|" + cleaned_text)
# merge with old annotation
final_annos = cleaned_old_annos + cc_duplicate * cleaned_new_annos
# save annotation file
with open("./final_annotation_train.txt", 'w', encoding='utf-8') as f:
for line in final_annos:
f.write(line)
# save annotation file for validation
with open("./final_annotation_val.txt", 'w', encoding='utf-8') as f:
for line in cleaned_new_annos:
f.write(line)
print("finished")
else:
# Do not add extra helper data
# STEP 1: modify config file
with open("./configs/finetune_speaker.json", 'r', encoding='utf-8') as f:
hps = json.load(f)
# assign ids to new speakers
speaker2id = {}
for i, speaker in enumerate(speakers):
speaker2id[speaker] = i
# modify n_speakers
hps['data']["n_speakers"] = len(speakers)
# overwrite speaker names
hps['speakers'] = speaker2id
hps['train']['log_interval'] = 10
hps['train']['eval_interval'] = 100
hps['train']['batch_size'] = 16
hps['data']['training_files'] = "final_annotation_train.txt"
hps['data']['validation_files'] = "final_annotation_val.txt"
# save modified config
with open("./configs/modified_finetune_speaker.json", 'w', encoding='utf-8') as f:
json.dump(hps, f, indent=2)
# STEP 2: clean annotations, replace speaker names with assigned speaker IDs
import text
cleaned_new_annos = []
for i, line in enumerate(new_annos):
path, speaker, txt = line.split("|")
if len(txt) > 150:
continue
cleaned_text = text._clean_text(txt, hps['data']['text_cleaners']).replace("[ZH]", "")
cleaned_text += "\n" if not cleaned_text.endswith("\n") else ""
cleaned_new_annos.append(path + "|" + str(speaker2id[speaker]) + "|" + cleaned_text)
final_annos = cleaned_new_annos
# save annotation file
with open("./final_annotation_train.txt", 'w', encoding='utf-8') as f:
for line in final_annos:
f.write(line)
# save annotation file for validation
with open("./final_annotation_val.txt", 'w', encoding='utf-8') as f:
for line in cleaned_new_annos:
f.write(line)
print("finished")