mirror of
https://github.com/SWivid/F5-TTS.git
synced 2025-12-12 07:40:43 -08:00
96 lines
3.2 KiB
Python
96 lines
3.2 KiB
Python
# put in src/f5_tts/train/datasets/prepare_emilia_v2.py
|
||
# prepares Emilia dataset with the new format w/ Emilia-YODAS
|
||
|
||
import json
|
||
import os
|
||
from concurrent.futures import ProcessPoolExecutor
|
||
from importlib.resources import files
|
||
from pathlib import Path
|
||
|
||
from datasets.arrow_writer import ArrowWriter
|
||
from tqdm import tqdm
|
||
|
||
from f5_tts.model.utils import repetition_found
|
||
|
||
|
||
# Define filters for exclusion
|
||
out_en = set()
|
||
en_filters = ["ا", "い", "て"]
|
||
|
||
|
||
def process_audio_directory(audio_dir):
|
||
sub_result, durations, vocab_set = [], [], set()
|
||
bad_case_en = 0
|
||
|
||
for file in audio_dir.iterdir():
|
||
if file.suffix == ".json":
|
||
with open(file, "r") as f:
|
||
obj = json.load(f)
|
||
text = obj["text"]
|
||
if any(f in text for f in en_filters) or repetition_found(text, length=4):
|
||
bad_case_en += 1
|
||
continue
|
||
|
||
duration = obj["duration"]
|
||
audio_file = file.with_suffix(".mp3")
|
||
if audio_file.exists():
|
||
sub_result.append({"audio_path": str(audio_file), "text": text, "duration": duration})
|
||
durations.append(duration)
|
||
vocab_set.update(list(text))
|
||
|
||
return sub_result, durations, vocab_set, bad_case_en
|
||
|
||
|
||
def main():
|
||
assert tokenizer in ["pinyin", "char"]
|
||
result, duration_list, text_vocab_set = [], [], set()
|
||
total_bad_case_en = 0
|
||
|
||
executor = ProcessPoolExecutor(max_workers=max_workers)
|
||
futures = []
|
||
dataset_path = Path(dataset_dir)
|
||
for sub_dir in dataset_path.iterdir():
|
||
if sub_dir.is_dir():
|
||
futures.append(executor.submit(process_audio_directory, sub_dir))
|
||
|
||
for future in tqdm(futures, total=len(futures)):
|
||
sub_result, durations, vocab_set, bad_case_en = future.result()
|
||
result.extend(sub_result)
|
||
duration_list.extend(durations)
|
||
text_vocab_set.update(vocab_set)
|
||
total_bad_case_en += bad_case_en
|
||
|
||
executor.shutdown()
|
||
|
||
if not os.path.exists(f"{save_dir}"):
|
||
os.makedirs(f"{save_dir}")
|
||
|
||
with ArrowWriter(path=f"{save_dir}/raw.arrow") as writer:
|
||
for line in tqdm(result, desc="Writing to raw.arrow ..."):
|
||
writer.write(line)
|
||
writer.finalize()
|
||
|
||
with open(f"{save_dir}/duration.json", "w", encoding="utf-8") as f:
|
||
json.dump({"duration": duration_list}, f, ensure_ascii=False)
|
||
|
||
with open(f"{save_dir}/vocab.txt", "w") as f:
|
||
for vocab in sorted(text_vocab_set):
|
||
f.write(vocab + "\n")
|
||
|
||
print(f"For {dataset_name}, sample count: {len(result)}")
|
||
print(f"For {dataset_name}, vocab size is: {len(text_vocab_set)}")
|
||
print(f"For {dataset_name}, total {sum(duration_list) / 3600:.2f} hours")
|
||
print(f"Bad en transcription case: {total_bad_case_en}\n")
|
||
|
||
|
||
if __name__ == "__main__":
|
||
max_workers = 32
|
||
tokenizer = "char"
|
||
dataset_dir = "/home/ubuntu/emilia-dataset/Emilia-YODAS/EN"
|
||
dataset_name = f"Emilia_EN_{tokenizer}"
|
||
# save_dir = os.path.expanduser(f"~/F5-TTS/data/{dataset_name}")
|
||
save_dir = str(files("f5_tts").joinpath("../../")) + f"/data/{dataset_name}"
|
||
|
||
print(f"Prepare for {dataset_name}, will save to {save_dir}\n")
|
||
main()
|