[实时转写]：完成录音期间转写显示，包含partial事件、本地streaming STT和终端实时反馈

src/owner_voice_pet/stt.py

@@ -13,7 +13,7 @@ from pathlib import Path
 from typing import Any
 
 from .config import AppConfig
-from .models import AudioSegment, ErrorCode, ProviderError, Transcript
+from .models import AudioFrame, AudioSegment, ErrorCode, ProviderError, Transcript
 from .speech_models import stt_model_paths
 
 _MEANINGFUL_TEXT = re.compile(r"[\w\u4e00-\u9fff]", re.UNICODE)
@@ -58,6 +58,36 @@ class MetadataSttProvider:
             raw_metadata=dict(segment.metadata),
         )
 
+    def start_stream(self) -> "MetadataRealtimeTranscriptSession":
+        return MetadataRealtimeTranscriptSession(self.language)
+
+
+class MetadataRealtimeTranscriptSession:
+    def __init__(self, language: str = "zh") -> None:
+        self.language = language
+        self._last_text = ""
+
+    def accept_frame(self, frame: AudioFrame) -> Transcript | None:
+        text = str(
+            frame.metadata.get("partial_transcript")
+            or frame.metadata.get("transcript")
+            or ""
+        ).strip()
+        if not is_valid_transcript_text(text) or text == self._last_text:
+            return None
+        self._last_text = text
+        return Transcript(
+            text=text,
+            language=str(frame.metadata.get("language", self.language)),
+            confidence=float(frame.metadata.get("stt_confidence", 1.0)),
+            duration_ms=int(frame.metadata.get("duration_ms", 20)),
+            provider="metadata-stt-partial",
+            raw_metadata=dict(frame.metadata),
+        )
+
+    def finish(self) -> Transcript | None:
+        return None
+
 
 class CloudAsrSttProvider:
     def __init__(
@@ -209,6 +239,17 @@ class SherpaOnnxSttProvider:
             ) from exc
         self.loaded = True
 
+    def start_stream(self) -> "SherpaOnnxRealtimeTranscriptSession":
+        if not self.loaded or self._recognizer is None:
+            raise ProviderError(
+                ErrorCode.STT_TRANSCRIBE_FAILED,
+                "sherpa-onnx STT provider is not loaded",
+                False,
+                "sherpa-onnx-stt",
+                "stt",
+            )
+        return SherpaOnnxRealtimeTranscriptSession(self._recognizer, self.language)
+
     def transcribe(self, segment: AudioSegment) -> Transcript:
         if not self.loaded or self._recognizer is None:
             raise ProviderError(
@@ -264,6 +305,64 @@ def _segment_to_float32(segment: AudioSegment, np: Any) -> Any:
     return samples
 
 
+class SherpaOnnxRealtimeTranscriptSession:
+    def __init__(self, recognizer: Any, language: str = "zh") -> None:
+        self.recognizer = recognizer
+        self.language = language
+        self.stream = recognizer.create_stream()
+        self._last_text = ""
+
+    def accept_frame(self, frame: AudioFrame) -> Transcript | None:
+        try:
+            import numpy as np
+
+            samples = _frame_to_float32(frame, np)
+            if samples.size == 0:
+                return None
+            self.stream.accept_waveform(frame.sample_rate, samples)
+            while self.recognizer.is_ready(self.stream):
+                self.recognizer.decode_stream(self.stream)
+            text = _recognizer_result_text(self.recognizer, self.stream)
+        except Exception as exc:
+            raise ProviderError(
+                ErrorCode.STT_TRANSCRIBE_FAILED,
+                f"sherpa-onnx realtime transcription failed: {exc}",
+                True,
+                "sherpa-onnx-stt",
+                "stt",
+            ) from exc
+        if not is_valid_transcript_text(text) or text == self._last_text:
+            return None
+        self._last_text = text
+        return Transcript(
+            text=text,
+            language=self.language,
+            confidence=None,
+            duration_ms=int(frame.metadata.get("duration_ms", 20)),
+            provider="sherpa-onnx-stt-partial",
+        )
+
+    def finish(self) -> Transcript | None:
+        return None
+
+
+def _frame_to_float32(frame: AudioFrame, np: Any) -> Any:
+    samples = np.frombuffer(frame.pcm, dtype=np.int16).astype(np.float32) / 32768.0
+    if frame.channels > 1 and samples.size:
+        samples = samples.reshape(-1, frame.channels).mean(axis=1)
+    return samples
+
+
+def _recognizer_result_text(recognizer: Any, stream: Any) -> str:
+    if hasattr(recognizer, "get_result"):
+        result = recognizer.get_result(stream)
+    else:
+        result = recognizer.get_result_all(stream)
+    if isinstance(result, str):
+        return result.strip()
+    return str(getattr(result, "text", "")).strip()
+
+
 def _segment_to_wav_bytes(segment: AudioSegment) -> bytes:
     buffer = io.BytesIO()
     with wave.open(buffer, "wb") as wav: