best-man-speech-practice / tests /test_extract_transcript_stats.py
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from __future__ import annotations
import unittest
from evals.extract_transcript_stats import flatten_stats, metadata_for_output
class ExtractTranscriptStatsTests(unittest.TestCase):
def test_metadata_omits_text_by_default(self) -> None:
row = {"id": "1", "type": "toast", "text": "private speech", "transcript": "private"}
self.assertEqual(metadata_for_output(row, include_text=False), {"id": "1", "type": "toast"})
def test_flatten_stats_formats_notable_fillers(self) -> None:
flattened = flatten_stats(
{
"word_count": 12,
"filler_count": 2,
"filler_counts": {"um": 2},
"notable_fillers": [{"filler": "um", "count": 2}],
}
)
self.assertEqual(flattened["computed_word_count"], 12)
self.assertEqual(flattened["computed_filler_count"], 2)
self.assertEqual(flattened["computed_notable_fillers"], "um:2")
self.assertEqual(flattened["computed_filler_counts_json"], '{"um": 2}')
if __name__ == "__main__":
unittest.main()