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process.py
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import os
import yaml
from tabulate import tabulate
from datasets import Dataset
from dataset_loader import load_dataset_standard
from model_loader import load_sentence_transformer, get_comet_model, get_fasttext_model, get_afrolid_model
from pipelines import rule_filter, semantic_filter, lang_detect_filter, quality_estimation_filter
from validators import quality_estimation
from merge import merge_and_deduplicate_filtered
import logging
from datetime import datetime
import sys
from itertools import chain
from tqdm import tqdm
import argparse
import json
import csv
def load_config(config_path):
with open(config_path, "r") as f:
return yaml.safe_load(f)
def save_to_file(lines, file_path):
with open(file_path, "w", encoding="utf-8") as f:
f.write("\n".join(lines))
def load_custom_metadata(output_dir):
meta_path = os.path.join(output_dir, "metadata.json")
if os.path.exists(meta_path):
with open(meta_path, encoding="utf-8") as f:
return json.load(f)
return None
def setup_logging(debug, log_dir, log_file):
os.makedirs(log_dir, exist_ok=True)
logger = logging.getLogger("my_logger")
logger.setLevel(logging.DEBUG if debug else logging.INFO)
# Prevent duplicate logs if this function is called multiple times
if logger.hasHandlers():
logger.handlers.clear()
for handler in logging.root.handlers[:]:
logging.root.removeHandler(handler)
# Formatter for both console and file
# formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
formatter = logging.Formatter('%(message)s')
# Console handler
console_handler = logging.StreamHandler()
console_handler.setLevel(logging.DEBUG if debug else logging.INFO)
console_handler.setFormatter(formatter)
logger.addHandler(console_handler)
# File handler
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
full_log_path = os.path.join(log_dir, f"{timestamp}_{log_file}")
file_handler = logging.FileHandler(full_log_path)
file_handler.setLevel(logging.DEBUG)
file_handler.setFormatter(formatter)
logger.addHandler(file_handler)
logger.info(f"Logging initialized. Log file: {full_log_path}")
return logger
def setup_logger(config):
log_cfg = config["logging"]
logger = setup_logging(
debug=log_cfg.get("debug", True),
log_dir=log_cfg.get("log_dir", "logs/"),
log_file=log_cfg.get("log_file", "run.log")
)
return logger
def load_models(config):
srclang, tgtlang = config["dataset"]["lang_pair"]
sentence_model = None
model_pool = None
comet_model = None
src_detect_model = None
tgt_detect_model = None
# Load sentence transformer only if semantic filter is enabled (default: True)
if config["preprocessing"].get("apply_semantic_filter", True):
sentence_model = load_sentence_transformer(srclang, tgtlang)
model_pool = sentence_model.start_multi_process_pool()
# Load comet model only if quality estimation filter or validation is enabled (default: True)
if config["preprocessing"].get("apply_quality_estimation_filter", True) or config.get("validation", {}).get("quality_estimation", True):
comet_model = get_comet_model(model_name="masakhane/africomet-qe-stl")
# Load language detection models only if lang detect filter is enabled (default: True)
if config["preprocessing"].get("apply_lang_detect_filter", True):
src_lang_model = config["filters"]["lang_detect_filter"]["source"].get("model", "fasttext")
tgt_lang_model = config["filters"]["lang_detect_filter"]["target"].get("model", "afrolid")
if src_lang_model == "afrolid":
src_detect_model = get_afrolid_model(model_name="UBC-NLP/afrolid_1.5")
else:
src_detect_model = get_fasttext_model(model_name="lid.176.bin")
if tgt_lang_model == "afrolid":
tgt_detect_model = get_afrolid_model(model_name="UBC-NLP/afrolid_1.5")
else:
tgt_detect_model = get_fasttext_model(model_name="lid.176.bin")
return sentence_model, model_pool, comet_model, src_detect_model, tgt_detect_model
def collect_datasets(config):
selected_sources = config["dataset"]["selected_sources"]
all_datasets = list(chain.from_iterable(
[dict(ds, source=source) for ds in config["dataset"].get(source, [])]
for source in selected_sources
))
raw_dir = config["download"]["output_dir"]
os.makedirs(raw_dir, exist_ok=True)
return all_datasets
def apply_rule_filter_if_enabled(source_list, target_list, config, logger):
if config["preprocessing"].get("apply_rule_filter", True):
rule_cfg = config["filters"]["rule_filter"]
logger.info("🧹 Applying rule-based filtering...")
source_list, target_list = rule_filter(
source_texts=source_list,
target_texts=target_list,
min_length=rule_cfg.get("min_length", 3),
max_length=rule_cfg.get("max_length", 200),
max_length_ratio=rule_cfg.get("max_length_ratio", 2.0),
lower=rule_cfg.get("lowercase", False),
)
logger.info(f"✅ Rule filter output: {len(source_list)} sentence pairs")
return source_list, target_list
def apply_semantic_filter_if_enabled(source_list, target_list, config, srclang, tgtlang, logger, sentence_model, model_pool):
if config["preprocessing"].get("apply_semantic_filter", True):
sem_cfg = config["filters"]["semantic_filter"]
logger.info("🧠 Applying semantic filtering...")
source_list, target_list = semantic_filter(
source_list,
target_list,
srclang=srclang,
tgtlang=tgtlang,
threshold=sem_cfg.get("threshold", 0.7),
chunk_size=sem_cfg.get("chunk_size", 1000),
batch_size=sem_cfg.get("batch_size", 2048),
model=sentence_model,
pool=model_pool
)
logger.info(f"✅ Semantic filter output: {len(source_list)} sentence pairs")
return source_list, target_list
def apply_lang_detect_filter_if_enabled(source_list, target_list, src_detect_model, tgt_detect_model, config, logger):
if config["preprocessing"].get("apply_lang_detect_filter", True):
lang_cfg = config["filters"]["lang_detect_filter"]
logger.info("🌐 Applying language detection filter...")
source_list, target_list = lang_detect_filter(
source_list,
target_list,
src_detect_model,
tgt_detect_model,
lang_cfg
)
logger.info(f"✅ Language detection output: {len(source_list)} sentence pairs")
return source_list, target_list
def apply_quality_estimation_filter_if_enabled(source_list, target_list, config, logger, comet_model):
if config["preprocessing"].get("apply_quality_estimation_filter", False):
qe_cfg = config["filters"]["quality_estimation_filter"]
logger.info("🧪 Applying quality estimation filter...")
source_list, target_list = quality_estimation_filter(
source_list,
target_list,
comet_model,
threshold=qe_cfg.get("min_score", 0.7),
batch_size=qe_cfg.get("batch_size", 32)
)
logger.info(f"✅ Quality estimation filter output: {len(source_list)} sentence pairs")
return source_list, target_list
def run_validation(source_list, target_list, config, comet_model):
quality_score = None
if config["validation"].get("quality_estimation", True):
quality_score = quality_estimation(source_list, target_list, comet_model=comet_model)
return quality_score
def save_dataset(source_list, target_list, srclang, tgtlang, ds_cfg, config, file_path, dataset_name, lang_pair, original_len, after_rule_len, after_semantic_len, after_lang_detect_len, after_qe, quality_score, logger):
save_format = config["output"].get("save_format", "txt")
if save_format == "hf":
dataset_dict = {srclang: source_list, tgtlang: target_list}
Dataset.from_dict(dataset_dict).save_to_disk(file_path)
logger.info(f"✅ Hugging Face dataset saved to:\n {file_path}")
custom_metadata = {
"source": ds_cfg["source"],
"dataset_name": dataset_name,
"lang_pair": lang_pair,
"original_rows": original_len,
"after_rule": after_rule_len or original_len,
"after_semantic": after_semantic_len or after_rule_len or original_len,
"after_lang_detect": after_lang_detect_len or after_semantic_len or after_rule_len or original_len,
"after_qe": after_qe or after_lang_detect_len or after_semantic_len or after_rule_len or original_len,
"quality_score": quality_score,
"processed_at": datetime.utcnow().isoformat()
}
with open(os.path.join(file_path, "metadata.json"), "w", encoding="utf-8") as f:
json.dump(custom_metadata, f, ensure_ascii=False, indent=2)
elif save_format == "txt":
out_src = os.path.join(config["download"]["output_dir"], f"{config['output'].get('filtered_prefix', 'filtered')}.{srclang}")
out_tgt = os.path.join(config["download"]["output_dir"], f"{config['output'].get('filtered_prefix', 'filtered')}.{tgtlang}")
save_to_file(source_list, out_src)
save_to_file(target_list, out_tgt)
logger.info(f"✅ Saved filtered files:\n - {out_src}\n - {out_tgt}")
else:
raise ValueError(f"❌ Unknown output format: {save_format}")
def process_dataset(ds_cfg, config, logger, sentence_model, model_pool, comet_model, src_detect_model, tgt_detect_model):
source = ds_cfg["source"]
srclang, tgtlang = config["dataset"]["lang_pair"]
output_prefix = config["output"].get("filtered_prefix", "filtered")
raw_dir = config["download"]["output_dir"]
name = ds_cfg['name']
dataset_name = f"{output_prefix}-{name}"
lang_pair = f"{srclang}-{tgtlang}"
output_dir = config["output"].get("save_dir", os.path.join(raw_dir, "filtered_dataset"))
file_path = os.path.join(output_dir, lang_pair, dataset_name)
exclude_datasets_path = os.path.join(output_dir, lang_pair, "exclude/", dataset_name)
print(exclude_datasets_path)
# Cache check
if config["preprocessing"].get("from_cache", True) and (os.path.exists(file_path) or os.path.exists(exclude_datasets_path)):
meta = load_custom_metadata(file_path) or load_custom_metadata(exclude_datasets_path)
if meta:
logger.info(f"✅ Cached: {meta['dataset_name']} — {meta['after_semantic']} pairs | QE: {meta['quality_score']}")
return {
"source": meta['source'],
"name": meta['dataset_name'],
"original": meta["original_rows"],
"after_rule": meta["after_rule"],
"after_semantic": meta['after_semantic'],
"after_lang_detect": meta['after_lang_detect'],
"after_qe": meta['after_qe'],
"translation_quality": meta['quality_score']
}
logger.info(f"⚠ Found dataset at {output_dir} but no custom metadata — processing anyway")
BLUE = "\033[1;34m"
RESET = "\033[0m"
logger.info(f"{BLUE}\n" + "=" * 80)
logger.info(f"📦 STARTING DATASET: {source.upper()} - {name}")
logger.info(f"🔤 Language Pair: {srclang}-{tgtlang}")
logger.info(f"{"=" * 80}{RESET}")
# Load data
source_list, target_list = load_dataset_standard(ds_cfg, srclang, tgtlang, raw_dir=raw_dir, dataset_cache=config["download"].get("dataset_cache", "dataset_cache/"))
if len(source_list) != len(target_list):
logger.error(f"❌ Length mismatch. Source:{len(source_list)} Target:{len(target_list)}")
sys.exit(1)
original_len = len(source_list)
# Apply rule filter
source_list, target_list = apply_rule_filter_if_enabled(source_list, target_list, config, logger)
after_rule_len = len(source_list)
if after_rule_len == 0:
logger.warning("⚠️ All segments removed after rule filter. Skipping further filtering.")
after_semantic_len = 0
after_lang_detect_len = 0
after_qe_len = 0
else:
# Apply semantic filter
source_list, target_list = apply_semantic_filter_if_enabled(
source_list, target_list, config, srclang, tgtlang, logger, sentence_model, model_pool
)
after_semantic_len = len(source_list)
if after_semantic_len == 0:
logger.warning("⚠️ All segments removed after semantic filter. Skipping further filtering.")
after_lang_detect_len = 0
after_qe_len = 0
else:
# Apply lang detect filter
source_list, target_list = apply_lang_detect_filter_if_enabled(
source_list, target_list, src_detect_model, tgt_detect_model, config, logger
)
after_lang_detect_len = len(source_list)
if after_lang_detect_len == 0:
after_qe_len = 0
logger.warning("⚠️ All segments removed after language detection filter.")
else:
# Apply quality estimation filter
source_list, target_list = apply_quality_estimation_filter_if_enabled(
source_list, target_list, config, logger, comet_model
)
after_qe_len = len(source_list)
# Only run validation if something remains
if len(source_list) > 0:
quality_score = run_validation(source_list, target_list, config, comet_model)
logger.info(f"✅ Validation done. QE:{quality_score}")
else:
quality_score = None
logger.info("⚠️ Skipped validation because no segments remain after filtering.")
save_dataset(source_list, target_list, srclang, tgtlang,ds_cfg, config, file_path, dataset_name, lang_pair, original_len, after_rule_len, after_semantic_len, after_lang_detect_len, after_qe_len, quality_score, logger)
return {
"source": ds_cfg["source"],
"name": ds_cfg["name"],
"original": original_len,
"after_rule": after_rule_len,
"after_semantic": after_semantic_len,
"after_lang_detect": after_lang_detect_len,
"after_qe": after_qe_len,
"translation_quality": quality_score
}
def log_final_summary(summary_log, logger):
total_original = sum(entry["original"] for entry in summary_log)
total_after_rule = sum(entry["after_rule"] for entry in summary_log)
total_after_semantic = sum(entry["after_semantic"] for entry in summary_log)
total_after_lang_detect = sum(entry["after_lang_detect"] for entry in summary_log)
total_after_qe = sum(entry["after_qe"] for entry in summary_log)
summary_table = [
[
entry["source"],
entry["name"],
entry["original"],
entry["after_rule"],
entry["after_semantic"],
entry["after_lang_detect"],
entry["after_qe"],
entry["translation_quality"]
]
for entry in summary_log
]
summary_table.append([
"TOTAL", "-", total_original, total_after_rule, total_after_semantic, total_after_lang_detect, total_after_qe, "-"
])
logger.info("\n📊 Final Dataset Summary:\n" + tabulate(
summary_table,
headers=[
"Source", "Dataset", "Original", "After Rule", "After Semantic", "After Lang Detect", "After QE", "Translation Quality"
],
tablefmt="github"
))
with open("summary.csv", "w", newline="") as f:
writer = csv.writer(f)
writer.writerow([
"Source", "Dataset", "Original", "After Rule", "After Semantic",
"After Lang Detect", "After QE", "Translation Quality"
])
writer.writerows(summary_table)
def main(config_path):
config = load_config(config_path)
logger = setup_logger(config)
logger.info("🚀 Starting preprocessing pipeline")
sentence_model, model_pool, comet_model, src_detect_model, tgt_detect_model = load_models(config)
summary_log = []
datasets = collect_datasets(config)
for ds_cfg in tqdm(datasets, desc="\033[1;34mProcessing datasets\033[0m"):
summary = process_dataset(ds_cfg, config, logger, sentence_model, model_pool, comet_model, src_detect_model, tgt_detect_model)
if summary:
summary_log.append(summary)
if model_pool is not None:
sentence_model.stop_multi_process_pool(model_pool)
log_final_summary(summary_log, logger)
merge_cfg = config.get("merge_and_dedup", {})
if merge_cfg.get("merge", False):
data_dir = config["output"].get("save_dir", os.path.join(config["download"]["output_dir"], "filtered_dataset"))
merge_and_deduplicate_filtered(
data_dir,
logger,
config,
src_col=config["dataset"]["lang_pair"][0],
tgt_col=config["dataset"]["lang_pair"][1],
dedup=merge_cfg.get("dedup", True),
dedup_against_test=merge_cfg.get("dedup_against_test", True)
)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Run preprocessing pipeline")
parser.add_argument(
"--config",
type=str,
required=True,
help="Path to the config YAML file(required)"
)
args = parser.parse_args()
main(args.config)