wehub-resource-sync
146 lines
5.4 KiB
Python
146 lines
5.4 KiB
Python
"""Unit tests for ``collapse_repetitive_artifacts``.
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The eval harness (``test_refinement_samples.py``) is interactive and
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LLM-dependent; these are the fast, deterministic tests for the
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deterministic pre-processor that runs before the LLM ever sees a
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transcript. They pin the behaviour for both the single-word loops the
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original algorithm handled and the multi-word / CJK / emoji loops the
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character-level pass added.
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"""
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from backend.services.refinement import collapse_repetitive_artifacts
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# ── single-word loops (word-level pass) ─────────────────────────────────
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def test_single_word_loop_stripped():
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raw = "Hello " + ("URL " * 8).strip() + " goodbye"
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assert collapse_repetitive_artifacts(raw) == "Hello goodbye"
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def test_single_word_loop_with_punctuation_normalized():
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# URL, URL, URL, URL, URL, URL. — six repeats if you normalize
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# trailing punctuation; word-level pass strips them all.
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raw = "Hello URL, URL, URL, URL, URL, URL. goodbye"
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assert collapse_repetitive_artifacts(raw) == "Hello goodbye"
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def test_single_word_loop_case_insensitive():
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raw = "hi " + " ".join(["Url", "URL", "url", "Url", "URL", "url"]) + " bye"
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assert collapse_repetitive_artifacts(raw) == "hi bye"
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def test_short_single_word_run_preserved():
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# Five repeats — below threshold.
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raw = "no no no no no"
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assert collapse_repetitive_artifacts(raw) == raw
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def test_rhetorical_repetition_preserved():
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raw = "I said no, no, no, no, no and she left"
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# Five repeats of "no" — below threshold.
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assert collapse_repetitive_artifacts(raw) == raw
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# ── multi-word loops (character-level pass) ─────────────────────────────
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def test_multi_word_english_loop_stripped():
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# Classic Whisper tail hallucination. Word-level pass sees no
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# consecutive identical tokens, so it's the character-level pass's
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# job to catch this.
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loop = "thanks for watching " * 6
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raw = f"Okay so the meeting is at three. {loop}"
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result = collapse_repetitive_artifacts(raw)
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assert "thanks for watching" not in result
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assert "Okay so the meeting is at three" in result
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def test_three_word_loop_stripped():
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loop = "please like and " * 7
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raw = f"The point is clear. {loop}right"
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result = collapse_repetitive_artifacts(raw)
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assert "please like and" not in result
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assert "The point is clear" in result
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def test_long_phrase_loop_within_60_char_cap():
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unit = "Please like and subscribe to my channel. " # 41 chars, within cap
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raw = "End of video. " + unit * 6
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result = collapse_repetitive_artifacts(raw)
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assert unit.strip() not in result
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assert "End of video" in result
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def test_multi_word_short_run_preserved():
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# Five repeats of a multi-word unit — below threshold.
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raw = "thanks for watching thanks for watching thanks for watching thanks for watching thanks for watching"
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assert collapse_repetitive_artifacts(raw) == raw
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# ── CJK loops (character-level pass, no whitespace) ──────────────────────
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def test_cjk_loop_stripped():
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# Common Chinese Whisper hallucination: "thanks for watching".
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# text.split() yields one token for the whole loop; only the
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# character-level pass can catch this.
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prefix = "會議在三點開始"
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loop = "謝謝觀看" * 7
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raw = prefix + loop
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result = collapse_repetitive_artifacts(raw)
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assert "謝謝觀看" not in result
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assert prefix in result
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def test_japanese_loop_stripped():
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# Same pattern, kana/kanji mix. "ご視聴ありがとうございました" is a
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# frequent Japanese Whisper tail hallucination.
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loop = "ご視聴ありがとうございました" * 6
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raw = f"明日の会議は午後三時です。{loop}"
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result = collapse_repetitive_artifacts(raw)
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assert "ご視聴ありがとうございました" not in result
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assert "明日の会議は午後三時です" in result
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def test_cjk_short_run_preserved():
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# Five repeats — below threshold, stays in.
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raw = "好好好好好"
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assert collapse_repetitive_artifacts(raw) == raw
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# ── whitespace / edge cases ──────────────────────────────────────────────
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def test_empty_string_passes_through():
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assert collapse_repetitive_artifacts("") == ""
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def test_below_word_threshold_passes_through_unmodified():
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raw = "just three words"
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assert collapse_repetitive_artifacts(raw) == raw
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def test_emphasis_vowel_run_preserved():
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# "wooooooow" is 1 char (plus 8 o's). Character-level min unit is 2,
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# so "oo…" doesn't get stripped and this legitimate emphasis stays.
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raw = "that's wooooooow amazing"
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assert collapse_repetitive_artifacts(raw) == raw
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def test_custom_threshold_honored():
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# With min_run=3, even short rhetorical repetition should now strip.
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raw = "ha ha ha ha context"
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result = collapse_repetitive_artifacts(raw, min_run=3)
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assert "ha ha" not in result
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assert "context" in result
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def test_leading_and_trailing_whitespace_stripped_after_collapse():
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# When character pass fires, the normalised result is stripped so
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# downstream prompts don't carry edge whitespace from the removal.
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loop = "loop-phrase " * 7
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raw = loop
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assert collapse_repetitive_artifacts(raw) == ""
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