File size: 2,705 Bytes
a5bbc9e | 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 | import csv
from pathlib import Path
ROOT = Path(__file__).resolve().parents[1]
GOLD_PATH = ROOT / "data" / "gold.tsv"
PREDICTIONS_PATH = ROOT / "data" / "predictions.tsv"
PHONEME_CHARS = set("abdefhijklmnopstuvwzɡʁʃʒʔˈχ")
PHONEME_TRANSLATION = str.maketrans({"x": "χ", "r": "ʁ", "g": "ɡ"})
def normalize_phonemes(text):
text = text.translate(PHONEME_TRANSLATION)
return "".join(char for char in text if char in PHONEME_CHARS)
def read_tsv(path):
if not path.exists() or path.stat().st_size == 0:
return []
with path.open("r", encoding="utf-8-sig", newline="") as f:
return list(csv.DictReader(f, delimiter="\t"))
def read_predictions(path):
if not path.exists() or path.stat().st_size == 0:
return []
with path.open("r", encoding="utf-8-sig", newline="") as f:
return list(csv.DictReader(f, delimiter="\t"))
def write_predictions(path, rows, fieldnames):
path.parent.mkdir(parents=True, exist_ok=True)
with path.open("w", encoding="utf-8", newline="") as f:
writer = csv.DictWriter(f, delimiter="\t", fieldnames=fieldnames)
writer.writeheader()
writer.writerows(rows)
def parse_label(label):
targets = {}
for part in label.split():
if "=" not in part:
continue
index, ipa = part.split("=", 1)
targets[int(index)] = normalize_phonemes(ipa)
return targets
def indexed_prediction(gold_row, phonemes):
phoneme_tokens = phonemes.split()
parts = []
for index in parse_label(gold_row["Label"]):
pred = normalize_phonemes(phoneme_tokens[index]) if index < len(phoneme_tokens) else ""
parts.append(f"{index}={pred}")
return " ".join(parts)
def load_prediction_rows():
gold_rows = read_tsv(GOLD_PATH)
pred_rows = read_predictions(PREDICTIONS_PATH)
if not pred_rows:
pred_rows = [{} for _ in gold_rows]
if len(pred_rows) != len(gold_rows):
raise ValueError(
"Predictions must have the same number of rows as gold: "
f"got {len(pred_rows)}, expected {len(gold_rows)}."
)
return gold_rows, pred_rows
def update_prediction_column(column_name, predict):
gold_rows, pred_rows = load_prediction_rows()
for gold_row, pred_row in zip(gold_rows, pred_rows):
phonemes = predict(gold_row["Text"])
pred_row[column_name] = indexed_prediction(gold_row, phonemes)
fieldnames = []
for row in pred_rows:
for key in row:
if key not in fieldnames:
fieldnames.append(key)
write_predictions(PREDICTIONS_PATH, pred_rows, fieldnames)
print(f"Updated {PREDICTIONS_PATH} column: {column_name}")
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