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import json
import os
import time
from os.path import join as p_join
from tqdm import tqdm
from typing import Dict
from glob import glob
from soundfile import LibsndfileError
from datasets import Dataset, Audio, DatasetDict
# dataset config
direction = os.getenv("DIRECTION", "enA-jaA")
sides = {i: n for n, i in enumerate(sorted(direction.split("-")), 1)}
sides_rev = {v: k for k, v in sides.items()}
cache_dir_audio = p_join("download", "audio", direction)
cache_dir_feature = p_join("download", "feature", direction)
os.makedirs(cache_dir_audio, exist_ok=True)
os.makedirs(cache_dir_feature, exist_ok=True)
line_no_start = int(os.getenv("LINE_NO_START", 0))
line_no_end = int(os.getenv("LINE_NO_END", 10000))
dataset_id = int(os.getenv("DATASET_ID", 0))
hf_org = "kotoba-tech"
hf_dataset = f"seamless-align-{direction}"
def loader(feature: str) -> Dict:
with open(feature) as f:
return json.load(f)
# create a dataset instance
files = {
int(os.path.basename(i).replace(".json", "")): i for i in glob(p_join(cache_dir_feature, "*.json"))
}
file_ids = [i for i in range(line_no_start, line_no_end) if i in files]
features = [loader(files[i]) for i in file_ids]
print(f"features: {len(features)}")
features = [i for i in features if os.path.exists(i[f"{sides_rev[1]}.path"]) and os.path.exists(i[f"{sides_rev[2]}.path"])]
print(f"features (filtered): {len(features)}")
data_dict = {
f"{sides_rev[1]}.audio": [i.pop(f"{sides_rev[1]}.path") for i in features],
f"{sides_rev[2]}.audio": [i.pop(f"{sides_rev[2]}.path") for i in features]
}
keys = features[0].keys()
data_dict.update(
{k: [i[k] for i in features] for k in keys}
)
audio_dataset = Dataset.from_dict(data_dict)
audio_dataset = audio_dataset.cast_column(f"{sides_rev[1]}.audio", Audio())
audio_dataset = audio_dataset.cast_column(f"{sides_rev[2]}.audio", Audio())
# remove instances with broken audio files
broken_files = []
for i in tqdm(range(len(audio_dataset))):
try:
a = audio_dataset[i]
flag = True
for side_id in sides_rev.keys():
start = a[f"{sides_rev[side_id]}.duration_start"]
end = a[f"{sides_rev[side_id]}.duration_end"]
array = a[f"{sides_rev[side_id]}.audio"]["array"]
flag = 0 < start < end < len(array)
if not flag:
broken_files.append(i)
except LibsndfileError:
broken_files.append(i)
continue
print(f"features (removed broken audio): {len(audio_dataset) - len(broken_files)}")
if len(broken_files) > 0:
print(f"found {len(broken_files)} broken files:")
flag = input("delete the broken files? (y/n): ")
if flag == "y":
# remove broken files
for i in broken_files:
if os.path.exists(files[file_ids[i]]):
os.remove(files[file_ids[i]])
for side_id in sides_rev.keys():
if os.path.exists(data_dict[f"{sides_rev[side_id]}.audio"][i]):
os.remove(data_dict[f"{sides_rev[side_id]}.audio"][i])
valid_data_id = [i for i in range(len(audio_dataset)) if i not in broken_files]
audio_dataset_valid = audio_dataset.select(valid_data_id)
# trim the audio according to the duration
def clip_audio(batch):
for side_id in sides_rev.keys():
start = batch[f"{sides_rev[side_id]}.duration_start"]
end = batch[f"{sides_rev[side_id]}.duration_end"]
audio = batch[f"{sides_rev[side_id]}.audio"]
batch[f"{sides_rev[side_id]}.audio"] = [
{"array": a["array"][s:e], "sampling_rate": a["sampling_rate"]}
for a, s, e in zip(audio, start, end)
]
return batch
audio_dataset_valid = audio_dataset_valid.map(
function=clip_audio,
batched=True,
batch_size=128,
num_proc=1,
desc="clipping audio based on the duration:"
)
dataset_to_push = DatasetDict({"train": audio_dataset_valid})
repo_name = f"{hf_org}/{hf_dataset}"
while True:
try:
dataset_to_push.push_to_hub(repo_name, config_name=f"subset_{dataset_id}")
break
except Exception:
print(f"FAILED: push_to_hub on {repo_name} failed. wait 60 sec and retry soon...")
time.sleep(60)
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