fix: 3 bug fixes + remove dead code from deep review

app/audio_cleaning.py

@@ -315,8 +315,8 @@ def clean_wav_files(
     denoise_applied = snr_db < 20.0
     if denoise_applied:
         samples = _spectral_denoise(samples, voice_start, voice_end)
-    # <8 dB il rumore è troppo alto - riprova
-        warnings.append("low_snr_retry_recommended")
+        if snr_db < 8.0:
+            warnings.append("low_snr_retry_recommended")
 
     # 7. rimozione pezzi senza voce, lasciando 120 ms di padding per testa e coda
     padding = round(PADDING_MS * TARGET_SAMPLE_RATE / 1000)

app/redis_bus.py

@@ -64,33 +64,6 @@ def probe_capabilities(
         )
 
 
-def get_json(key: str, *, redis_client: Redis | None = None) -> Any | None:
-    """Read a disposable JSON cache value."""
-    cache = redis_client or client
-    cached = cache.get(key)
-    if cached is None:
-        return None
-    if isinstance(cached, bytes):
-        cached = cached.decode("utf-8")
-    return json.loads(cached)
-
-
-def set_json(
-    key: str,
-    value: Any,
-    *,
-    ttl_seconds: int,
-    redis_client: Redis | None = None,
-) -> None:
-    """Write a JSON cache value with a mandatory finite lifetime."""
-    if ttl_seconds <= 0:
-        raise ValueError("ttl_seconds must be positive")
-    cache = redis_client or client
-    cache.setex(
-        key,
-        ttl_seconds,
-        json.dumps(value, ensure_ascii=False),
-    )
 
 
 def publish_audio_frame(

app/streaming_inference.py

@@ -170,6 +170,14 @@ def __init__(
             else None
         )
         self._emitted_sentence_words: set[int] = set()
+    def _shutdown_executors(self) -> None:
+        if self._executor is not None:
+            self._executor.shutdown(wait=False, cancel_futures=True)
+        if self._sentence_executor is not None:
+            self._sentence_executor.shutdown(wait=False, cancel_futures=True)
+
+    def __del__(self) -> None:
+        self._shutdown_executors()
 
     @property
     def pcm(self) -> bytes:
@@ -324,6 +332,11 @@ def push_frame(self, frame: bytes) -> dict:
                 }
                 for index, token in enumerate(tokens)
             ]
+
+            if self._emitted_sentence_words:
+                current_word_index = max(self._emitted_sentence_words) + 1
+                if current_word_index >= len(tokens):
+                    current_word_index = len(tokens) - 1
         return {
             "type": "stream.inference.partial",
             "sequence": len(self._frames),