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import json
import os
import tarfile
import zipfile
import gzip
import subprocess
from os.path import join as p_join
from math import ceil, floor
from tqdm import tqdm
from multiprocessing import Pool
from typing import Optional, Dict
from glob import glob
# import librosa
import pandas as pd
import soundfile as sf
from datasets import Dataset, Audio, DatasetDict
audio_loader = Audio()
# dataset config
url_metadata_dict = {
"enA-jaA": "https://dl.fbaipublicfiles.com/seamless/data/seamless_align_nov2023_extension/seamless.dataset.metadata.public.enA-jaA.tsv.gz",
"enA-zhA": "https://dl.fbaipublicfiles.com/seamless/data/seamless_align_nov2023_extension/seamless.dataset.metadata.public.enA-zhA.tsv.gz",
"enA-viA": "https://dl.fbaipublicfiles.com/seamless/data/seamless_align_nov2023_extension/seamless.dataset.metadata.public.enA-viA.tsv.gz",
}
direction = os.getenv("DIRECTION", "enA-jaA")
if direction not in url_metadata_dict:
a, b = direction.split("-")
url_metadata_dict[direction] = f"https://dl.fbaipublicfiles.com/seamless/data/seamless_align_nov2023_extension/seamless.dataset.metadata.public.{a}-{b}.tsv.gz"
sides = set(direction.split("-"))
cache_dir_audio = p_join("download", "audio", direction)
cache_dir_feature = p_join("download", "feature", direction)
os.makedirs(cache_dir_feature, exist_ok=True)
for s in sides:
os.makedirs(p_join(cache_dir_audio, s), exist_ok=True)
# processor config
n_pool = int(os.getenv("N_POOL", 1))
wget_max_retry = os.getenv("MAX_RETRY", "2")
wget_timeout = os.getenv("TIMEOUT", "20")
line_no_start = int(os.getenv("LINE_NO_START", 0))
line_no_end = int(os.getenv("LINE_NO_END", 10000))
dataset_id = os.getenv("DATASET_ID", 0)
hf_org = os.getenv("HF_ORG", "asahi417")
hf_dataset = os.getenv("HF_DATASET", f"seamless-align-{direction}")
skip_download = bool(int(os.getenv("SKIP_DOWNLOAD", 0)))
sampling_rate = 16000 # seamless-align aligns audio in 16kHz
def wget(url: str, output_file: Optional[str] = None):
os.makedirs(os.path.dirname(output_file), exist_ok=True)
subprocess.run(["wget", url, "-O", output_file, "--tries", wget_max_retry, "--timeout", wget_timeout])
if not os.path.exists(output_file):
return False
if output_file.endswith('.tar.gz') or output_file.endswith('.tgz') or output_file.endswith('.tar'):
if output_file.endswith('.tar'):
tar = tarfile.open(output_file)
else:
tar = tarfile.open(output_file, "r:gz")
tar.extractall(os.path.dirname(output_file))
tar.close()
os.remove(output_file)
elif output_file.endswith('.gz'):
with gzip.open(output_file, 'rb') as f:
with open(output_file.replace('.gz', ''), 'wb') as f_write:
f_write.write(f.read())
os.remove(output_file)
elif output_file.endswith('.zip'):
with zipfile.ZipFile(output_file, 'r') as zip_ref:
zip_ref.extractall()
os.remove(output_file)
return True
def get_metadata():
url_metadata = url_metadata_dict[direction]
meta_data_filename = os.path.basename(url_metadata)
meta_data_path = p_join("download", "meta", meta_data_filename)
if not os.path.exists(meta_data_path.replace(".gz", "")):
assert wget(url_metadata, output_file=meta_data_path)
df = pd.read_csv(meta_data_path.replace(".gz", ""), sep=r'[\t\s]', header=None)
df = df[[0, 2, 3, 4, 9, 10, 11, 12]]
df.columns = ["id", "url", "duration_start", "duration_end", "laser_score", "direction", "side", "line_no"]
if direction == "enA-jpn":
df = df[df["side"] == "enA"]
assert len(df["direction"].unique()) == 1
df.pop("direction")
return df.sort_values(by=["line_no", "side"])
def to_json_serializable(val):
if "float" in str(type(val)):
return float(val)
if "int" in str(type(val)):
return int(val)
return str(val)
def cleanup(features, feature_file):
if os.path.exists(feature_file):
os.remove(feature_file)
for _side in sides:
for _unrelated_audio_file in glob(p_join(cache_dir_audio, _side, f"{features['line_no']}.*")):
os.remove(_unrelated_audio_file)
# create a dummy so that we can skip from next run
with open(feature_file, "w") as f:
json.dump({"dummy": "dummy"}, f)
def get_audio(dataframe: pd.DataFrame):
features = {"line_no": int(dataframe.pop('line_no').values[0])}
feature_file = p_join(cache_dir_feature, f'{features["line_no"]}.json')
for side, df in dataframe.groupby("side"):
df.pop("side")
features.update({f"{side}.{k}": to_json_serializable(v) for k, v in df.iloc[0].to_dict().items()})
identifier = os.path.basename(features[f"{side}.url"]).split(".")[-1]
features[f"{side}.path"] = str(p_join(cache_dir_audio, side, f"{features['line_no']}.{identifier}"))
start, end = features[f"{side}.duration_start"], features[f"{side}.duration_end"]
if not os.path.exists(features[f"{side}.path"]):
print(f"WGET {features[f'{side}.url']}")
flag = wget(features[f"{side}.url"], output_file=features[f"{side}.path"])
if not flag:
print("\n#### ERROR: wget failure ####\n")
cleanup(features, feature_file)
return None
else:
try:
print(f"LOAD AUDIO FROM {features[f'{side}.path']}")
wav, sr = sf.read(features[f"{side}.path"])
print(f"wav shape:{wav.shape}")
if wav.ndim > 1:
wav = wav[:, 0]
wav = wav[floor(start / sampling_rate * sr):ceil(end / sampling_rate * sr)]
print(f"wav shape (after truncate):{wav.shape}")
wav = wav[:int(end/sampling_rate * sr) + sr]
print(f"SAVING: {features[f'{side}.path']}")
sf.write(features[f"{side}.path"], wav, sr)
# if sr != sampling_rate:
# print(f"RESAMPLING: {wav.shape} length audio")
# wav = librosa.resample(wav, orig_sr=sr, target_sr=sampling_rate)
# sf.write(features[f"{side}.path"], wav[start:end], sampling_rate)
except Exception as e:
print(f"\n#### ERROR ####\n {e}")
cleanup(features, feature_file)
return None
print(f"\n### SUCCESS! ###\n:{features['line_no']}")
with open(feature_file, "w") as f:
json.dump(features, f)
return features["line_no"]
def loader(feature: str) -> Dict:
with open(feature) as f_reader:
return json.load(f_reader)
if __name__ == '__main__':
if not skip_download:
df_metadata = get_metadata()
print(f"metadata: {len(df_metadata)}, {line_no_start} --> {line_no_end}")
inputs = [
g for line_no, g in df_metadata.groupby("line_no")
if line_no_start <= line_no < line_no_end and not os.path.exists(
p_join(cache_dir_feature, f'{int(line_no)}.json')
)
]
print(f"filtered unique lines: {len(inputs)}")
inputs = [g for g in inputs if len(g["side"].unique()) == 2 and set(g["side"].unique()) == sides]
print(f"removed side != 2: {len(inputs)}")
if n_pool == 1:
for g in tqdm(inputs, total=len(inputs)):
line_no = get_audio(g)
else:
with Pool(n_pool) as pool:
for line_no in pool.imap_unordered(get_audio, inputs):
if line_no:
print(line_no)
print("UPLOADING TO HF!!!")
features = [p_join(cache_dir_feature, f'{i}.json') for i in range(line_no_start, line_no_end)]
print(f"- raw feature: {len(features)}")
features = [i for i in features if os.path.exists(i)]
print(f"- path exists: {len(features)}")
features = [loader(i) for i in features]
features = [i for i in features if "dummy" not in i]
print(f"- dummy removed: {len(features)}")
print(f"push {len(features)} records to hub")
data_dict = {}
for side in sides:
data_dict.update({f"{side}.audio": [i.pop(f"{side}.path") for i in features]})
data_dict.update({k: [i[k] for i in features] for k in features[0].keys()})
audio_dataset = Dataset.from_dict(data_dict)
for side in sides:
audio_dataset = audio_dataset.cast_column(f"{side}.audio", Audio())
DatasetDict({"train": audio_dataset}).push_to_hub(
f"{hf_org}/{hf_dataset}",
config_name=f"subset_{dataset_id}"
)
print("clear the workspace")
for i in tqdm(range(line_no_start, line_no_end), total=line_no_end - line_no_start):
for audio_file in glob(p_join(cache_dir_audio, "*", f"{i}.*")):
os.remove(audio_file)
if os.path.exists(p_join(cache_dir_feature, f"{i}.json")):
os.remove(p_join(cache_dir_feature, f"{i}.json"))
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