Add benchmark-driven model promotion workflow and pipeline stages

tests/test_asr_whisper.py

@@ -0,0 +1,80 @@
+import sys
+import unittest
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parents[1]
+SRC = ROOT / "src"
+if str(SRC) not in sys.path:
+    sys.path.insert(0, str(SRC))
+
+from stages.asr_whisper import WhisperAsrStage
+
+
+class _Word:
+    def __init__(self, word: str, start: float, end: float, probability: float = 0.9):
+        self.word = word
+        self.start = start
+        self.end = end
+        self.probability = probability
+
+
+class _Segment:
+    def __init__(self, text: str, start: float, end: float, words=None):
+        self.text = text
+        self.start = start
+        self.end = end
+        self.words = words or []
+
+
+class _ModelWithWordTimestamps:
+    def __init__(self):
+        self.kwargs = {}
+
+    def transcribe(self, _audio, language=None, vad_filter=None, word_timestamps=False):
+        self.kwargs = {
+            "language": language,
+            "vad_filter": vad_filter,
+            "word_timestamps": word_timestamps,
+        }
+        words = [_Word("hello", 0.0, 0.3), _Word("world", 0.31, 0.6)]
+        return [_Segment("hello world", 0.0, 0.6, words=words)], {}
+
+
+class _ModelWithoutWordTimestamps:
+    def __init__(self):
+        self.kwargs = {}
+
+    def transcribe(self, _audio, language=None, vad_filter=None):
+        self.kwargs = {
+            "language": language,
+            "vad_filter": vad_filter,
+        }
+        return [_Segment("hello", 0.0, 0.2, words=[])], {}
+
+
+class WhisperAsrStageTests(unittest.TestCase):
+    def test_transcribe_requests_word_timestamps_when_supported(self):
+        model = _ModelWithWordTimestamps()
+        stage = WhisperAsrStage(model, configured_language="auto")
+
+        result = stage.transcribe(object())
+
+        self.assertTrue(model.kwargs["word_timestamps"])
+        self.assertEqual(result.raw_text, "hello world")
+        self.assertEqual(len(result.words), 2)
+        self.assertEqual(result.words[0].text, "hello")
+        self.assertGreaterEqual(result.words[0].start_s, 0.0)
+
+    def test_transcribe_skips_word_timestamps_when_not_supported(self):
+        model = _ModelWithoutWordTimestamps()
+        stage = WhisperAsrStage(model, configured_language="auto")
+
+        result = stage.transcribe(object())
+
+        self.assertNotIn("word_timestamps", model.kwargs)
+        self.assertEqual(result.raw_text, "hello")
+        self.assertEqual(result.words, [])
+
+
+if __name__ == "__main__":
+    unittest.main()