57 lines
2.0 KiB
Python
57 lines
2.0 KiB
Python
import numpy as np
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from sentence_api.audio_metrics import AudioMetrics, analyze_samples
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from sentence_api.scoring import align_text, duration_similarity_score, score_attempt, tokenize
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def test_tokenize_supports_mixed_chinese_and_english():
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assert tokenize("你好，World! Don't stop.") == ["你", "好", "world", "don't", "stop"]
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def test_identical_reading_with_matching_duration_scores_100():
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result = score_attempt(
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reference_text="The meeting starts at nine.",
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recognized_text="The meeting starts at nine",
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reference_speech_duration_ms=2000,
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student_metrics=AudioMetrics(
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recording_duration_ms=2400,
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speech_duration_ms=2000,
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internal_silence_ms=0,
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internal_pause_ratio=0.0,
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),
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)
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assert result.overall_score == 100
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assert result.duration_score == 100
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assert result.missing_tokens == []
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def test_alignment_reports_missing_extra_and_substituted_tokens():
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result = align_text(
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"The meeting starts at nine",
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"The lesson start at nine today",
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)
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assert result.content_score < 70
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assert result.extra_tokens == ["today"]
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assert ("meeting", "lesson") in result.substitutions
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assert ("starts", "start") in result.substitutions
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def test_duration_score_allows_students_to_read_more_slowly():
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assert duration_similarity_score(0.8) == 100
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assert duration_similarity_score(1.3) == 100
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assert duration_similarity_score(1.5) == 60
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assert duration_similarity_score(1.8) == 20
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def test_vad_excludes_leading_and_trailing_silence():
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sample_rate = 16_000
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silence = np.zeros(sample_rate // 2, dtype=np.float32)
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time = np.arange(sample_rate, dtype=np.float32) / sample_rate
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speech = (0.25 * np.sin(2 * np.pi * 220 * time)).astype(np.float32)
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metrics = analyze_samples(np.concatenate([silence, speech, silence]), sample_rate)
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assert metrics.recording_duration_ms == 2000
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assert 900 <= metrics.speech_duration_ms <= 1050
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