fix(qwen-asr): enable timestamp output when forced_aligner is configured

backend/python/qwen-asr/backend.py

@@ -134,6 +134,156 @@ def LoadModel(self, request, context):
 
         return backend_pb2.Result(message="Model loaded successfully", success=True)
 
+    @staticmethod
+    def _is_cjk(ch):
+        """Check if a character is CJK (Chinese/Japanese/Korean)."""
+        cp = ord(ch)
+        return (
+            0x4E00 <= cp <= 0x9FFF      # CJK Unified Ideographs
+            or 0x3400 <= cp <= 0x4DBF   # Extension A
+            or 0x20000 <= cp <= 0x2A6DF # Extension B
+            or 0xF900 <= cp <= 0xFAFF   # Compatibility Ideographs
+            or 0x3040 <= cp <= 0x309F   # Hiragana
+            or 0x30A0 <= cp <= 0x30FF   # Katakana
+            or 0xAC00 <= cp <= 0xD7AF   # Hangul Syllables
+        )
+
+    @staticmethod
+    def _is_punct(ch):
+        """Check if a character is punctuation (no space before it)."""
+        import unicodedata
+        cat = unicodedata.category(ch)
+        return cat.startswith('P')
+
+    @staticmethod
+    def _smart_join(tokens):
+        """Join tokens with spaces for non-CJK text, without spaces for CJK.
+
+        Rules:
+          - Between two CJK chars: no space
+          - Between two non-CJK tokens: space
+          - Before punctuation: no space
+          - CJK adjacent to non-CJK: no space (smooth mixed-text transition)
+        """
+        if not tokens:
+            return ""
+        result = [tokens[0]]
+        for token in tokens[1:]:
+            if not token:
+                continue
+            prev_ch = result[-1][-1] if result[-1] else ''
+            curr_ch = token[0]
+            # Punctuation never gets a space before it
+            if BackendServicer._is_punct(curr_ch):
+                result.append(token)
+            # CJK to CJK: no space
+            elif prev_ch and BackendServicer._is_cjk(prev_ch) and BackendServicer._is_cjk(curr_ch):
+                result.append(token)
+            # CJK adjacent to non-CJK or vice versa: no space
+            elif prev_ch and (BackendServicer._is_cjk(prev_ch) or BackendServicer._is_cjk(curr_ch)):
+                result.append(token)
+            # Both non-CJK (Latin, Cyrillic, etc.): add space
+            else:
+                result.append(' ' + token)
+        return "".join(result)
+
+    @staticmethod
+    def _extract_word_info(ts):
+        """Return (start_sec, end_sec, text) from a ForcedAlignItem or tuple."""
+        if hasattr(ts, 'start_time') and hasattr(ts, 'end_time') and hasattr(ts, 'text'):
+            return (
+                float(ts.start_time) if ts.start_time is not None else 0.0,
+                float(ts.end_time) if ts.end_time is not None else 0.0,
+                str(ts.text) if ts.text else "",
+            )
+        elif isinstance(ts, (list, tuple)) and len(ts) >= 3:
+            return (
+                float(ts[0]) if ts[0] is not None else 0.0,
+                float(ts[1]) if ts[1] is not None else 0.0,
+                ts[2] if len(ts) > 2 and ts[2] is not None else "",
+            )
+        return (0.0, 0.0, "")
+
+    @staticmethod
+    def _compute_gap_threshold(time_stamps):
+        """Compute a gap threshold for sentence boundary detection.
+
+        Uses the median inter-item gap multiplied by a factor, with a
+        minimum floor of 0.3s.  Returns 0 if there are too few items.
+        """
+        if len(time_stamps) < 2:
+            return 0.0
+        gaps = []
+        for i in range(1, len(time_stamps)):
+            prev_s, prev_e, _ = BackendServicer._extract_word_info(time_stamps[i - 1])
+            curr_s, _, _ = BackendServicer._extract_word_info(time_stamps[i])
+            gaps.append(curr_s - prev_e)
+        if not gaps:
+            return 0.0
+        gaps.sort()
+        median = gaps[len(gaps) // 2]
+        # threshold = max(median * 4, 0.3s)
+        return max(median * 4, 0.3)
+
+    def _build_segments(self, time_stamps, granularity):
+        """Build TranscriptSegment list from forced-aligner output.
+
+        granularity:
+          - "word": one segment per aligned item (character / word)
+          - "segment" (default): merge consecutive items, splitting at
+            time gaps that exceed a dynamic threshold (sentence boundaries).
+        """
+        if granularity == "word":
+            result = []
+            for idx, ts in enumerate(time_stamps):
+                s, e, t = self._extract_word_info(ts)
+                result.append(backend_pb2.TranscriptSegment(
+                    id=idx,
+                    start=int(s * 1_000_000_000),
+                    end=int(e * 1_000_000_000),
+                    text=t,
+                ))
+            return result
+
+        # segment mode — merge at time-gap boundaries
+        threshold = self._compute_gap_threshold(time_stamps)
+        result = []
+        buf_text = []
+        buf_start = None
+        buf_end = 0.0
+        prev_end = None
+
+        for ts in time_stamps:
+            s, e, t = self._extract_word_info(ts)
+
+            # Detect sentence boundary via time gap
+            if prev_end is not None and (s - prev_end) >= threshold and buf_text:
+                result.append(backend_pb2.TranscriptSegment(
+                    id=len(result),
+                    start=int(buf_start * 1_000_000_000),
+                    end=int(buf_end * 1_000_000_000),
+                    text=self._smart_join(buf_text),
+                ))
+                buf_text = []
+                buf_start = None
+
+            if buf_start is None:
+                buf_start = s
+            buf_text.append(t)
+            buf_end = e
+            prev_end = e
+
+        # flush remaining
+        if buf_text and buf_start is not None:
+            result.append(backend_pb2.TranscriptSegment(
+                id=len(result),
+                start=int(buf_start * 1_000_000_000),
+                end=int(buf_end * 1_000_000_000),
+                text=self._smart_join(buf_text),
+            ))
+
+        return result
+
     def AudioTranscription(self, request, context):
         result_segments = []
         text = ""
@@ -147,11 +297,22 @@ def AudioTranscription(self, request, context):
             if request.language and request.language.strip():
                 language = request.language.strip()
 
-            context = ""
+            ctx = ""
             if request.prompt and request.prompt.strip():
-                context = request.prompt.strip()
+                ctx = request.prompt.strip()
+
+            # Determine requested granularity (default: segment)
+            granularities = list(request.timestamp_granularities) if request.timestamp_granularities else []
+            granularity = "word" if "word" in granularities else "segment"
 
-            results = self.model.transcribe(audio=audio_path, language=language, context=context)
+            has_aligner = getattr(self.model, 'forced_aligner', None) is not None
+            try:
+                results = self.model.transcribe(
+                    audio=audio_path, language=language, context=ctx,
+                    return_time_stamps=has_aligner,
+                )
+            except TypeError:
+                results = self.model.transcribe(audio=audio_path, language=language, context=ctx)
 
             if not results:
                 return backend_pb2.TranscriptResult(segments=[], text="")
@@ -160,17 +321,7 @@ def AudioTranscription(self, request, context):
             text = r.text or ""
 
             if getattr(r, 'time_stamps', None) and len(r.time_stamps) > 0:
-                for idx, ts in enumerate(r.time_stamps):
-                    start_ms = 0
-                    end_ms = 0
-                    seg_text = text
-                    if isinstance(ts, (list, tuple)) and len(ts) >= 3:
-                        start_ms = int(float(ts[0]) * 1000) if ts[0] is not None else 0
-                        end_ms = int(float(ts[1]) * 1000) if ts[1] is not None else 0
-                        seg_text = ts[2] if len(ts) > 2 and ts[2] is not None else ""
-                    result_segments.append(backend_pb2.TranscriptSegment(
-                        id=idx, start=start_ms, end=end_ms, text=seg_text
-                    ))
+                result_segments = self._build_segments(r.time_stamps, granularity)
             else:
                 if text:
                     result_segments.append(backend_pb2.TranscriptSegment(