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Running on Zero
File size: 3,396 Bytes
f1ef7e2 | 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 | """
Export AppTek selected-domain metadata while preserving original source identifiers.
This creates a mapping between:
- our exported call_id, for example APPTEK_BANKING_0048
- AppTek/source audio id, for example en_CA_Banking_1586889
No audio is downloaded or copied here.
"""
from pathlib import Path
import pandas as pd
from datasets import load_dataset, Audio
DATASET_NAME = "apptek-com/apptek_callcenter_dialogues"
ML_SERVICES_ROOT = Path(__file__).resolve().parents[2]
EXISTING_METADATA_PATH = (
ML_SERVICES_ROOT
/ "data"
/ "processed"
/ "apptek_selected_domains"
/ "apptek_selected_domain_metadata.csv"
)
OUTPUT_PATH = (
ML_SERVICES_ROOT
/ "data"
/ "processed"
/ "apptek_selected_domains"
/ "apptek_selected_domain_metadata_with_source_ids.csv"
)
DOMAIN_MAPPING = {
"banking": "banking",
"health": "healthcare",
"telecom": "telecommunications",
}
def get_source_id_from_audio(audio_obj):
if not isinstance(audio_obj, dict):
return None, None
audio_path = audio_obj.get("path")
if not audio_path:
return None, None
audio_path = str(audio_path)
source_id = Path(audio_path).stem
return source_id, audio_path
def main():
existing_df = pd.read_csv(EXISTING_METADATA_PATH)
print("Loading AppTek dataset...")
ds = load_dataset(DATASET_NAME, split="test")
ds = ds.cast_column("audio", Audio(decode=False))
selected_rows = []
counters = {
"banking": 0,
"healthcare": 0,
"telecommunications": 0,
}
for row in ds:
raw_domain = row.get("domain")
if raw_domain not in DOMAIN_MAPPING:
continue
selected_domain = DOMAIN_MAPPING[raw_domain]
counters[selected_domain] += 1
call_id = f"APPTEK_{selected_domain.upper()}_{counters[selected_domain]:04d}"
source_apptek_id, source_audio_path = get_source_id_from_audio(row.get("audio"))
selected_rows.append(
{
"call_id": call_id,
"source_apptek_id": source_apptek_id,
"selected_domain": selected_domain,
"raw_domain": raw_domain,
"gender": row.get("gender"),
"accent": row.get("accent"),
"source_audio_path": source_audio_path,
}
)
source_df = pd.DataFrame(selected_rows)
merged = existing_df.merge(
source_df,
on=["call_id", "selected_domain", "raw_domain", "gender", "accent"],
how="left",
)
merged.to_csv(OUTPUT_PATH, index=False)
print("Saved:", OUTPUT_PATH)
print("Rows:", len(merged))
print()
print("Missing source IDs:", merged["source_apptek_id"].isna().sum())
print()
print("Sample:")
print(
merged[
[
"call_id",
"source_apptek_id",
"selected_domain",
"raw_domain",
"gender",
"accent",
"duration_seconds",
"audio_path",
]
].head(20).to_string(index=False)
)
print()
print("Rows containing 1586889:")
mask = merged.astype(str).apply(
lambda col: col.str.contains("1586889", case=False, na=False)
).any(axis=1)
print(merged[mask].to_string(index=False))
if __name__ == "__main__":
main()
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