-
Notifications
You must be signed in to change notification settings - Fork 1
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
feat(): add training and inference code
- Loading branch information
Showing
9 changed files
with
630 additions
and
8 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,161 @@ | ||
| import argparse | ||
| import csv | ||
| from itertools import chain | ||
| from pathlib import Path | ||
|
|
||
| from signwriting.formats.fsw_to_sign import fsw_to_sign | ||
| from signwriting.tokenizer import SignWritingTokenizer, normalize_signwriting | ||
| from tqdm import tqdm | ||
|
|
||
| from spoken_language_tokenizer import tokenize_spoken_text | ||
|
|
||
| csv.field_size_limit(int(1e6)) | ||
|
|
||
|
|
||
| def load_csv(data_path: Path): | ||
| with open(data_path, 'r', encoding="utf-8") as f: | ||
| reader = csv.DictReader(f) | ||
| return list(reader) | ||
|
|
||
|
|
||
| DIRECTIONS = { | ||
| "spoken-to-signed": { | ||
| "expanded": 1, | ||
| "more": 2, | ||
| "cleaned": 2, | ||
| }, | ||
| "signed-to-spoken": { | ||
| "expanded": 1, | ||
| "more": 3, | ||
| "cleaned": 4, | ||
| } | ||
| } | ||
|
|
||
| CLEAN_DIRECTIONS = { | ||
| "spoken-to-signed": { | ||
| "more": 1, | ||
| "cleaned": 1, | ||
| }, | ||
| "signed-to-spoken": { | ||
| "more": 1, | ||
| "cleaned": 1, | ||
| } | ||
| } | ||
|
|
||
| sw_tokenizer = SignWritingTokenizer() | ||
|
|
||
|
|
||
| def process_row(row, files, spoken_direction, repeats=1): | ||
| lang_token_1, lang_token_2, *signs = row["source"].split(" ") | ||
| # if not (lang_token_1 == "<en>" and lang_token_2 == "<ase>"): | ||
| # return | ||
|
|
||
| signs = normalize_signwriting(" ".join(signs)).split(" ") | ||
|
|
||
| tokenized_signs = [sw_tokenizer.text_to_tokens(sign) for sign in signs] | ||
| signed_tokens = chain.from_iterable(tokenized_signs) | ||
| signed = " ".join(signed_tokens) | ||
|
|
||
| # sign language factors | ||
| signs = [fsw_to_sign(sign) for sign in signs] | ||
| for sign in signs: # override box position same as the tokenizer does | ||
| sign["box"]["position"] = (500, 500) | ||
| units = list(chain.from_iterable([[sign["box"]] + sign["symbols"] for sign in signs])) | ||
|
|
||
| spoken = tokenize_spoken_text(row["target"]) | ||
|
|
||
| if spoken_direction == "source": | ||
| spoken = f"{lang_token_1} {lang_token_2} {spoken}" | ||
| else: | ||
| signed = f"{lang_token_1} {lang_token_2} {signed}" | ||
| units.insert(0, {"symbol": lang_token_1, "position": [0, 0]}) | ||
| units.insert(0, {"symbol": lang_token_2, "position": [0, 0]}) | ||
|
|
||
| factors = [ | ||
| [s["symbol"][:4] for s in units], | ||
| ["c" + (s["symbol"][4] if len(s["symbol"]) > 4 else '0') for s in units], | ||
| ["r" + (s["symbol"][5] if len(s["symbol"]) > 5 else '0') for s in units], | ||
| ["p" + str(s["position"][0]) for s in units], | ||
| ["p" + str(s["position"][1]) for s in units], | ||
| ] | ||
|
|
||
| for _ in range(repeats): | ||
| files["spoken"].write(spoken + "\n") | ||
| files["signed"].write(signed + "\n") | ||
|
|
||
| for i, factor_file in files["signed_factors"].items(): | ||
| factor_file.write(" ".join(factors[i]) + "\n") | ||
|
|
||
|
|
||
| def create_files(split_dir, spoken_d, signed_d): | ||
| return { | ||
| "spoken": open(split_dir / f"{spoken_d}.txt", "w", encoding="utf-8"), | ||
| "signed": open(split_dir / f"{signed_d}.txt", "w", encoding="utf-8"), | ||
| "signed_factors": { | ||
| i: open(split_dir / f"{signed_d}_{i}.txt", "w", encoding="utf-8") | ||
| for i in range(5) | ||
| } | ||
| } | ||
|
|
||
|
|
||
| # pylint: disable=too-many-locals | ||
| def create_parallel_data(data_dir: Path, output_dir: Path, clean_only=False): | ||
| directions_obj = CLEAN_DIRECTIONS if clean_only else DIRECTIONS | ||
|
|
||
| for direction, partitions in directions_obj.items(): | ||
| direction_output_dir = output_dir / direction | ||
| direction_output_dir.mkdir(parents=True, exist_ok=True) | ||
|
|
||
| train_dir = direction_output_dir / "train" | ||
| train_dir.mkdir(parents=True, exist_ok=True) | ||
| dev_dir = direction_output_dir / "dev" | ||
| dev_dir.mkdir(parents=True, exist_ok=True) | ||
| test_dir = direction_output_dir / "test" | ||
| test_dir.mkdir(parents=True, exist_ok=True) | ||
|
|
||
| spoken_d = "source" if direction == "spoken-to-signed" else "target" | ||
| signed_d = "target" if direction == "spoken-to-signed" else "source" | ||
|
|
||
| split_files = { | ||
| "train": create_files(train_dir, spoken_d, signed_d), | ||
| "dev": create_files(dev_dir, spoken_d, signed_d), | ||
| } | ||
| for partition, repeats in partitions.items(): | ||
| for split, files in split_files.items(): | ||
| split_path = data_dir / partition / f"{split}.csv" | ||
| if not split_path.exists(): | ||
| if split == "train": | ||
| raise FileNotFoundError(f"File {split_path} does not exist") | ||
| continue | ||
| with open(split_path, 'r', encoding="utf-8") as f: | ||
| reader = csv.DictReader(f) | ||
| for row in tqdm(reader): | ||
| process_row(row, files, spoken_d, repeats) | ||
|
|
||
| test_files = create_files(test_dir, spoken_d, signed_d) | ||
| test_path = data_dir / "test" / "all.csv" | ||
| with open(test_path, 'r', encoding="utf-8") as f: | ||
| reader = csv.DictReader(f) | ||
| for row in tqdm(reader): | ||
| process_row(row, test_files, spoken_d, repeats=1) | ||
|
|
||
|
|
||
| def main(): | ||
| parser = argparse.ArgumentParser() | ||
| parser.add_argument('--data-dir', type=str, help='Path to data directory') | ||
| parser.add_argument('--output-dir', type=str, help='Path to output directory', | ||
| default="parallel") | ||
| parser.add_argument('--clean-only', action='store_true', help='Use only cleaned data') | ||
| args = parser.parse_args() | ||
|
|
||
| data_dir = Path(args.data_dir) | ||
|
|
||
| # create output directory | ||
| output_dir = Path(args.output_dir) | ||
| output_dir.mkdir(parents=True, exist_ok=True) | ||
|
|
||
| create_parallel_data(data_dir, output_dir, clean_only=args.clean_only) | ||
|
|
||
|
|
||
| if __name__ == "__main__": | ||
| main() |
Oops, something went wrong.