File size: 9,556 Bytes
eac7224 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 |
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-jpn": "https://dl.fbaipublicfiles.com/seamless/data/seamless.dataset.metadata.public.enA-jpn.withduration.tsv.gz"
}
direction_speech = os.getenv("DIRECTION_SPEECH", "enA")
direction_text = os.getenv("DIRECTION_TEXT", "jpn")
direction = os.getenv("DIRECTION", "enA-jpn")
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 = 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):
resampler = {}
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)}")
if direction == "enA-jaA":
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}"
)
# DatasetDict({"train": audio_dataset.select(list(range(1000)))}).push_to_hub(
# f"{hf_org}/{hf_dataset}",
# config_name=f"subset_{dataset_id}"
# )
# # 2 panel
# dataset_id = 75
# DatasetDict({"train": audio_dataset.select(list(range(3000, len(audio_dataset))))}).push_to_hub(
# f"{hf_org}/{hf_dataset}",
# config_name=f"subset_{dataset_id}"
# )
#
#
# audio_dataset = audio_dataset.select(list(range(2500)))
# dataset_to_push = DatasetDict({"train": audio_dataset})
# repo_name = f"{hf_org}/{hf_dataset}"
# dataset_to_push.push_to_hub(repo_name, config_name=f"subset_{dataset_id}")
# dataset_to_push.push_to_hub(repo_name, config_name=f"subset_{dataset_id}", max_shard_size="2GiB")
# dataset_to_push.push_to_hub(repo_name, config_name=f"subset_{dataset_id}", num_shards={"train": 1})
# 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)
|