668 lines
20 KiB
Python
668 lines
20 KiB
Python
import json
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from audita.config import AuditaConfig
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from audita.pipeline import process_transcript
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from audita.schemas import CorrectionCandidate, CorrectionSet, parse_glossary_yaml, parse_source_transcript_json
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class FakeLLMClient:
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def __init__(self, responses):
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self.responses = list(responses)
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self.calls = 0
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self.messages = []
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def create_corrections(self, messages, config):
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self.calls += 1
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self.messages.append(messages)
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return self.responses.pop(0)
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def _config(tmp_path, glossary_max_llm_passes=3, grammar_max_llm_passes=3):
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return AuditaConfig(
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api_key="key",
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max_section_tokens=16000,
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glossary_confidence_threshold=0.8,
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grammar_confidence_threshold=0.8,
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max_retries=3,
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glossary_max_llm_passes=glossary_max_llm_passes,
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grammar_max_llm_passes=grammar_max_llm_passes,
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work_dir=tmp_path / "work",
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)
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def _glossary():
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return parse_glossary_yaml(
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"""
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glossary:
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- name: "Chauntea"
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category: deity
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summary: "Chauntea is a deity."
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"""
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)
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def _transcript():
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return parse_source_transcript_json(
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"""
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[
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{"speaker": "Eric", "start": 0.0, "end": 1.0, "text": "I ask Chontia."},
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{"speaker": "Mike", "start": 10.0, "end": 11.0, "text": "Then Lyra."}
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]
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"""
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)
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def test_pipeline_processes_with_fake_llm_and_cleans_work_dir(tmp_path):
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correction = CorrectionCandidate(
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id=1,
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original_text="Chontia",
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corrected_text="Chauntea",
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confidence=0.95,
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)
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fake_client = FakeLLMClient(
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[
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CorrectionSet(corrections=[correction]),
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CorrectionSet(corrections=[]),
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]
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)
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revised = process_transcript(
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_transcript(),
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_glossary(),
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_config(tmp_path),
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llm_client=fake_client,
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)
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assert [segment.speaker for segment in revised] == ["Eric", "Mike"]
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assert revised[0].text == "I ask Chauntea."
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assert fake_client.calls == 2
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assert list((tmp_path / "work").iterdir()) == []
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def test_glossary_stage_can_correct_toward_protected_term(tmp_path):
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transcript = parse_source_transcript_json(
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"""
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[
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{"speaker": "Eric", "start": 0.0, "end": 1.0, "text": "Frank moves."}
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]
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"""
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)
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glossary = parse_glossary_yaml(
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"""
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glossary:
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- name: "Hrank"
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category: pc
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summary: "Hrank is a player character."
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"""
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)
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glossary_correction = CorrectionCandidate(
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id=1,
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original_text="Frank",
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corrected_text="Hrank",
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confidence=0.95,
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)
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fake_client = FakeLLMClient(
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[
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CorrectionSet(corrections=[glossary_correction]),
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CorrectionSet(corrections=[]),
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]
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)
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revised = process_transcript(
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transcript,
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glossary,
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_config(tmp_path),
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llm_client=fake_client,
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)
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assert revised[0].text == "Hrank moves."
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assert list((tmp_path / "work").iterdir()) == []
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def test_glossary_stage_cannot_change_away_from_protected_term(tmp_path):
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transcript = parse_source_transcript_json(
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"""
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[
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{"speaker": "Eric", "start": 0.0, "end": 1.0, "text": "Hrank moves."}
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]
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"""
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)
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glossary = parse_glossary_yaml(
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"""
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glossary:
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- name: "Hrank"
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category: pc
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summary: "Hrank is a player character."
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"""
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)
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glossary_reversal = CorrectionCandidate(
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id=1,
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original_text="Hrank",
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corrected_text="Frank",
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confidence=0.95,
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)
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fake_client = FakeLLMClient(
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[
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CorrectionSet(corrections=[glossary_reversal]),
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CorrectionSet(corrections=[]),
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]
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)
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revised = process_transcript(
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transcript,
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glossary,
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_config(tmp_path, glossary_max_llm_passes=1),
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llm_client=fake_client,
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)
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assert revised[0].text == "Hrank moves."
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run_dirs = list((tmp_path / "work").iterdir())
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assert len(run_dirs) == 1
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diagnostics = json.loads((run_dirs[0] / "skipped-corrections.json").read_text(encoding="utf-8"))
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skipped = diagnostics["skipped_corrections"][0]
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assert skipped["stage"] == "glossary"
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assert skipped["reason"] == "correction changes protected glossary term usage"
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def test_glossary_stage_cannot_decanonicalize_protected_term(tmp_path):
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transcript = parse_source_transcript_json(
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"""
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[
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{"speaker": "Eric", "start": 0.0, "end": 1.0, "text": "Hrank moves."}
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]
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"""
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)
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glossary = parse_glossary_yaml(
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"""
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glossary:
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- name: "Hrank"
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category: pc
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summary: "Hrank is a player character."
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"""
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)
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decapitalization = CorrectionCandidate(
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id=1,
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original_text="Hrank",
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corrected_text="hrank",
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confidence=0.95,
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)
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fake_client = FakeLLMClient(
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[
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CorrectionSet(corrections=[decapitalization]),
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CorrectionSet(corrections=[]),
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]
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)
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revised = process_transcript(
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transcript,
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glossary,
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_config(tmp_path, glossary_max_llm_passes=1),
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llm_client=fake_client,
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)
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assert revised[0].text == "Hrank moves."
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run_dirs = list((tmp_path / "work").iterdir())
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assert len(run_dirs) == 1
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diagnostics = json.loads((run_dirs[0] / "skipped-corrections.json").read_text(encoding="utf-8"))
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skipped = diagnostics["skipped_corrections"][0]
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assert skipped["stage"] == "glossary"
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assert skipped["reason"] == "correction changes protected glossary term capitalization"
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def test_pipeline_normalizes_before_llm_prompts(tmp_path):
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transcript = parse_source_transcript_json(
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"""
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[
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{"speaker": "Eric", "start": 0.0, "end": 1.0, "text": "I ask"},
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{"speaker": "Eric", "start": 2.0, "end": 3.0, "text": "Chontia."},
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{"speaker": "Mike", "start": 10.0, "end": 11.0, "text": "Then Lyra."}
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]
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"""
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)
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fake_client = FakeLLMClient(
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[
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CorrectionSet(corrections=[]),
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CorrectionSet(corrections=[]),
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]
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)
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progress = []
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process_transcript(
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transcript,
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_glossary(),
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_config(tmp_path),
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llm_client=fake_client,
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progress=progress.append,
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)
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glossary_prompt = fake_client.messages[0][1]["content"]
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glossary_payload = json.loads(glossary_prompt.split("Transcript section:\n", maxsplit=1)[1])
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assert glossary_payload[0] == {"id": 1, "original_text": "I ask Chontia."}
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assert any("Normalized transcript from 3 to 2 segments" in message for message in progress)
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def test_pipeline_skips_bad_glossary_correction_and_preserves_diagnostics(tmp_path):
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correction = CorrectionCandidate(
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id=1,
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original_text="Different text.",
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corrected_text="Chauntea",
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confidence=0.95,
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)
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fake_client = FakeLLMClient(
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[
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CorrectionSet(corrections=[correction]),
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CorrectionSet(corrections=[]),
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]
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)
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progress = []
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revised = process_transcript(
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_transcript(),
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_glossary(),
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_config(tmp_path, glossary_max_llm_passes=1),
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llm_client=fake_client,
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progress=progress.append,
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)
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assert revised[0].text == "I ask Chontia."
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assert any("Skipping glossary correction for id 1" in message for message in progress)
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preserved = list((tmp_path / "work").iterdir())
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assert len(preserved) == 1
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skipped_path = preserved[0] / "skipped-corrections.json"
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assert skipped_path.exists()
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diagnostics = json.loads(skipped_path.read_text(encoding="utf-8"))
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assert diagnostics["skipped_corrections"][0]["stage"] == "glossary"
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assert diagnostics["skipped_corrections"][0]["id"] == 1
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assert "does not match any substring" in diagnostics["skipped_corrections"][0]["reason"]
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def test_pipeline_retries_skipped_segment_and_cleans_work_dir_when_fixed(tmp_path):
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first_pass = CorrectionCandidate(
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id=1,
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original_text="Contia",
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corrected_text="Chauntea",
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confidence=0.95,
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)
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second_pass = CorrectionCandidate(
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id=1,
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original_text="Chontia",
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corrected_text="Chauntea",
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confidence=0.95,
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)
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fake_client = FakeLLMClient(
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[
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CorrectionSet(corrections=[first_pass]),
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CorrectionSet(corrections=[second_pass]),
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CorrectionSet(corrections=[]),
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]
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)
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revised = process_transcript(
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_transcript(),
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_glossary(),
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_config(tmp_path, glossary_max_llm_passes=3),
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llm_client=fake_client,
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)
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assert fake_client.calls == 3
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assert [segment.speaker for segment in revised] == ["Eric", "Mike"]
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assert revised[0].text == "I ask Chauntea."
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assert list((tmp_path / "work").iterdir()) == []
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def test_pipeline_retry_prompt_contains_only_valid_deduped_ids(tmp_path):
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first_bad = CorrectionCandidate(
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id=1,
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original_text="Contia",
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corrected_text="Chauntea",
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confidence=0.95,
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)
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second_bad_same_segment = CorrectionCandidate(
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id=1,
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original_text="Still wrong",
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corrected_text="Chauntea",
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confidence=0.95,
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)
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invalid_segment = CorrectionCandidate(
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id=99,
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original_text="Missing",
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corrected_text="Chauntea",
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confidence=0.95,
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)
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fake_client = FakeLLMClient(
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[
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CorrectionSet(corrections=[first_bad, second_bad_same_segment, invalid_segment]),
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CorrectionSet(corrections=[]),
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CorrectionSet(corrections=[]),
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]
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)
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process_transcript(
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_transcript(),
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_glossary(),
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_config(tmp_path, glossary_max_llm_passes=2),
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llm_client=fake_client,
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)
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assert fake_client.calls == 3
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retry_prompt = fake_client.messages[1][1]["content"]
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retry_payload = json.loads(retry_prompt.split("Transcript section:\n", maxsplit=1)[1])
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assert retry_payload == [{"id": 1, "original_text": "I ask Chontia."}]
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assert "Retry guidance" in retry_prompt
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def test_pipeline_writes_stage_metadata_for_unresolved_retries(tmp_path):
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first_pass = CorrectionCandidate(
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id=1,
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original_text="Contia",
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corrected_text="Chauntea",
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confidence=0.95,
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)
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fake_client = FakeLLMClient(
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[
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CorrectionSet(corrections=[first_pass]),
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CorrectionSet(corrections=[]),
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CorrectionSet(corrections=[]),
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]
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)
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process_transcript(
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_transcript(),
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_glossary(),
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_config(tmp_path, glossary_max_llm_passes=2),
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llm_client=fake_client,
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)
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run_dirs = list((tmp_path / "work").iterdir())
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assert len(run_dirs) == 1
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assert (run_dirs[0] / "glossary" / "pass-0001").exists()
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assert (run_dirs[0] / "grammar" / "pass-0001").exists()
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assert (run_dirs[0] / "normalization" / "source-transcript.json").exists()
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assert (run_dirs[0] / "normalization" / "normalized-transcript.json").exists()
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assert (run_dirs[0] / "normalization" / "summary.json").exists()
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metadata = json.loads((run_dirs[0] / "metadata.json").read_text(encoding="utf-8"))
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assert metadata["normalization"]["source_segment_count"] == 2
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assert metadata["normalization"]["normalized_segment_count"] == 2
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assert metadata["normalization"]["merge_count"] == 0
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assert metadata["glossary_max_llm_passes"] == 2
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assert metadata["grammar_max_llm_passes"] == 3
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assert metadata["glossary_confidence_threshold"] == 0.8
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assert metadata["grammar_confidence_threshold"] == 0.8
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assert [item["stage"] for item in metadata["stages"]] == ["glossary", "grammar"]
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assert [item["pass_number"] for item in metadata["stages"][0]["passes"]] == [1, 2]
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assert metadata["stages"][0]["passes"][0]["retry_segment_count"] == 1
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assert metadata["stages"][0]["passes"][1]["retry_pass"] is True
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def test_grammar_stage_runs_after_glossary_and_sees_corrected_text(tmp_path):
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transcript = parse_source_transcript_json(
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"""
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[
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{"speaker": "Eric", "start": 0.0, "end": 1.0, "text": "i ask Chontia."},
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{"speaker": "Mike", "start": 10.0, "end": 11.0, "text": "Then Lyra."}
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]
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"""
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)
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glossary_correction = CorrectionCandidate(
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id=1,
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original_text="Chontia",
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corrected_text="Chauntea",
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confidence=0.95,
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)
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grammar_correction = CorrectionCandidate(
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id=1,
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original_text="i",
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corrected_text="I",
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confidence=0.95,
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)
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fake_client = FakeLLMClient(
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[
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CorrectionSet(corrections=[glossary_correction]),
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CorrectionSet(corrections=[grammar_correction]),
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]
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)
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revised = process_transcript(
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transcript,
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_glossary(),
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_config(tmp_path),
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llm_client=fake_client,
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)
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grammar_prompt = fake_client.messages[1][1]["content"]
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grammar_payload = json.loads(grammar_prompt.split("Transcript section:\n", maxsplit=1)[1])
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assert grammar_payload[0]["original_text"] == "i ask Chauntea."
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assert revised[0].text == "I ask Chauntea."
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def test_grammar_stage_cannot_reverse_glossary_protected_term(tmp_path):
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transcript = parse_source_transcript_json(
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"""
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[
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{"speaker": "Eric", "start": 0.0, "end": 1.0, "text": "Frank moves."}
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]
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"""
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)
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glossary = parse_glossary_yaml(
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"""
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glossary:
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- name: "Hrank"
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category: pc
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summary: "Hrank is a player character."
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"""
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)
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glossary_correction = CorrectionCandidate(
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id=1,
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original_text="Frank",
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corrected_text="Hrank",
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confidence=0.95,
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)
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grammar_reversal = CorrectionCandidate(
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id=1,
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original_text="Hrank",
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corrected_text="Frank",
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confidence=0.95,
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)
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fake_client = FakeLLMClient(
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[
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CorrectionSet(corrections=[glossary_correction]),
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CorrectionSet(corrections=[grammar_reversal]),
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]
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)
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progress = []
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revised = process_transcript(
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transcript,
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glossary,
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_config(tmp_path, grammar_max_llm_passes=1),
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llm_client=fake_client,
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progress=progress.append,
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)
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assert revised[0].text == "Hrank moves."
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assert any("Skipping grammar correction for id 1" in message for message in progress)
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run_dirs = list((tmp_path / "work").iterdir())
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assert len(run_dirs) == 1
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diagnostics = json.loads((run_dirs[0] / "skipped-corrections.json").read_text(encoding="utf-8"))
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assert diagnostics["skipped_corrections"][0]["stage"] == "grammar"
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assert diagnostics["skipped_corrections"][0]["reason"] == "correction changes protected glossary term usage"
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def test_grammar_stage_can_correct_toward_protected_term(tmp_path):
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transcript = parse_source_transcript_json(
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"""
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[
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{"speaker": "Eric", "start": 0.0, "end": 1.0, "text": "Pawpaw's just worn out."}
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]
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"""
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)
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glossary = parse_glossary_yaml(
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"""
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glossary:
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- name: "Popov"
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category: npc
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summary: "Popov is an allied NPC."
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"""
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)
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grammar_correction = CorrectionCandidate(
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id=1,
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original_text="Pawpaw's",
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corrected_text="Popov's",
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confidence=0.95,
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)
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fake_client = FakeLLMClient(
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[
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CorrectionSet(corrections=[]),
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CorrectionSet(corrections=[grammar_correction]),
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]
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)
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revised = process_transcript(
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transcript,
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glossary,
|
|
_config(tmp_path),
|
|
llm_client=fake_client,
|
|
)
|
|
|
|
assert revised[0].text == "Popov's just worn out."
|
|
assert list((tmp_path / "work").iterdir()) == []
|
|
|
|
|
|
def test_grammar_stage_cannot_change_away_from_protected_term(tmp_path):
|
|
transcript = parse_source_transcript_json(
|
|
"""
|
|
[
|
|
{"speaker": "Eric", "start": 0.0, "end": 1.0, "text": "Popov's just worn out."}
|
|
]
|
|
"""
|
|
)
|
|
glossary = parse_glossary_yaml(
|
|
"""
|
|
glossary:
|
|
- name: "Popov"
|
|
category: npc
|
|
summary: "Popov is an allied NPC."
|
|
"""
|
|
)
|
|
grammar_correction = CorrectionCandidate(
|
|
id=1,
|
|
original_text="Popov's",
|
|
corrected_text="Pawpaw's",
|
|
confidence=0.95,
|
|
)
|
|
fake_client = FakeLLMClient(
|
|
[
|
|
CorrectionSet(corrections=[]),
|
|
CorrectionSet(corrections=[grammar_correction]),
|
|
]
|
|
)
|
|
|
|
revised = process_transcript(
|
|
transcript,
|
|
glossary,
|
|
_config(tmp_path, grammar_max_llm_passes=1),
|
|
llm_client=fake_client,
|
|
)
|
|
|
|
assert revised[0].text == "Popov's just worn out."
|
|
run_dirs = list((tmp_path / "work").iterdir())
|
|
assert len(run_dirs) == 1
|
|
diagnostics = json.loads((run_dirs[0] / "skipped-corrections.json").read_text(encoding="utf-8"))
|
|
assert diagnostics["skipped_corrections"][0]["stage"] == "grammar"
|
|
assert diagnostics["skipped_corrections"][0]["reason"] == "correction changes protected glossary term usage"
|
|
|
|
|
|
def test_grammar_stage_retries_repeated_span_and_applies_unique_retry(tmp_path):
|
|
transcript = parse_source_transcript_json(
|
|
"""
|
|
[
|
|
{"speaker": "Eric", "start": 0.0, "end": 1.0, "text": "there and there."}
|
|
]
|
|
"""
|
|
)
|
|
repeated_span = CorrectionCandidate(
|
|
id=1,
|
|
original_text="there",
|
|
corrected_text="their",
|
|
confidence=0.95,
|
|
)
|
|
unique_retry = CorrectionCandidate(
|
|
id=1,
|
|
original_text="there and there",
|
|
corrected_text="their and there",
|
|
confidence=0.95,
|
|
)
|
|
fake_client = FakeLLMClient(
|
|
[
|
|
CorrectionSet(corrections=[]),
|
|
CorrectionSet(corrections=[repeated_span]),
|
|
CorrectionSet(corrections=[unique_retry]),
|
|
]
|
|
)
|
|
|
|
revised = process_transcript(
|
|
transcript,
|
|
_glossary(),
|
|
_config(tmp_path, grammar_max_llm_passes=2),
|
|
llm_client=fake_client,
|
|
)
|
|
|
|
assert fake_client.calls == 3
|
|
assert revised[0].text == "their and there."
|
|
retry_prompt = fake_client.messages[2][1]["content"]
|
|
retry_payload = json.loads(retry_prompt.split("Transcript section:\n", maxsplit=1)[1])
|
|
assert retry_payload == [{"id": 1, "original_text": "there and there."}]
|
|
|
|
|
|
def test_below_threshold_grammar_corrections_are_not_retried(tmp_path):
|
|
correction = CorrectionCandidate(
|
|
id=1,
|
|
original_text="I",
|
|
corrected_text="i",
|
|
confidence=0.7,
|
|
)
|
|
fake_client = FakeLLMClient(
|
|
[
|
|
CorrectionSet(corrections=[]),
|
|
CorrectionSet(corrections=[correction]),
|
|
]
|
|
)
|
|
|
|
revised = process_transcript(
|
|
_transcript(),
|
|
_glossary(),
|
|
_config(tmp_path, grammar_max_llm_passes=3),
|
|
llm_client=fake_client,
|
|
)
|
|
|
|
assert fake_client.calls == 2
|
|
assert revised[0].text == "I ask Chontia."
|
|
|
|
|
|
def test_unresolved_grammar_skip_preserves_diagnostics(tmp_path):
|
|
correction = CorrectionCandidate(
|
|
id=1,
|
|
original_text="a",
|
|
corrected_text="A",
|
|
confidence=0.95,
|
|
)
|
|
fake_client = FakeLLMClient(
|
|
[
|
|
CorrectionSet(corrections=[]),
|
|
CorrectionSet(corrections=[correction]),
|
|
]
|
|
)
|
|
|
|
process_transcript(
|
|
_transcript(),
|
|
_glossary(),
|
|
_config(tmp_path, grammar_max_llm_passes=1),
|
|
llm_client=fake_client,
|
|
)
|
|
|
|
run_dirs = list((tmp_path / "work").iterdir())
|
|
assert len(run_dirs) == 1
|
|
diagnostics = json.loads((run_dirs[0] / "skipped-corrections.json").read_text(encoding="utf-8"))
|
|
assert diagnostics["skipped_corrections"][0]["stage"] == "grammar"
|
|
assert "more than once" in diagnostics["skipped_corrections"][0]["reason"]
|