Add ReplyOnStopWords (#35)

* add code

* fix dependencies

* add code:

backend/gradio_webrtc/pause_detection/vad.py

@@ -1,10 +1,13 @@
 import logging
 import warnings
 from dataclasses import dataclass
-from typing import List
+from typing import List, Literal, overload
 
 import numpy as np
 from huggingface_hub import hf_hub_download
+from numpy.typing import NDArray
+
+from ..utils import AudioChunk
 
 logger = logging.getLogger(__name__)
 
@@ -76,7 +79,7 @@ class SileroVADModel:
         return h, c
 
     @staticmethod
-    def collect_chunks(audio: np.ndarray, chunks: List[dict]) -> np.ndarray:
+    def collect_chunks(audio: np.ndarray, chunks: List[AudioChunk]) -> np.ndarray:
         """Collects and concatenates audio chunks."""
         if not chunks:
             return np.array([], dtype=np.float32)
@@ -90,7 +93,7 @@ class SileroVADModel:
         audio: np.ndarray,
         vad_options: SileroVadOptions,
         **kwargs,
-    ) -> List[dict]:
+    ) -> List[AudioChunk]:
         """This method is used for splitting long audios into speech chunks using silero VAD.
 
         Args:
@@ -236,15 +239,33 @@ class SileroVADModel:
 
         return speeches
 
+    @overload
     def vad(
         self,
-        audio_tuple: tuple[int, np.ndarray],
+        audio_tuple: tuple[int, NDArray],
         vad_parameters: None | SileroVadOptions,
-    ) -> float:
+        return_chunks: Literal[True],
+    ) -> tuple[float, List[AudioChunk]]: ...
+
+    @overload
+    def vad(
+        self,
+        audio_tuple: tuple[int, NDArray],
+        vad_parameters: None | SileroVadOptions,
+        return_chunks: bool = False,
+    ) -> float: ...
+
+    def vad(
+        self,
+        audio_tuple: tuple[int, NDArray],
+        vad_parameters: None | SileroVadOptions,
+        return_chunks: bool = False,
+    ) -> float | tuple[float, List[AudioChunk]]:
         sampling_rate, audio = audio_tuple
         logger.debug("VAD audio shape input: %s", audio.shape)
         try:
-            audio = audio.astype(np.float32) / 32768.0
+            if audio.dtype != np.float32:
+                audio = audio.astype(np.float32) / 32768.0
             sr = 16000
             if sr != sampling_rate:
                 try:
@@ -262,7 +283,8 @@ class SileroVADModel:
             audio = self.collect_chunks(audio, speech_chunks)
             logger.debug("VAD audio shape: %s", audio.shape)
             duration_after_vad = audio.shape[0] / sr
-
+            if return_chunks:
+                return duration_after_vad, speech_chunks
             return duration_after_vad
         except Exception as e:
             import math
@@ -280,7 +302,7 @@ class SileroVADModel:
             raise ValueError(
                 f"Too many dimensions for input audio chunk {len(x.shape)}"
             )
-        if sr / x.shape[1] > 31.25:
+        if sr / x.shape[1] > 31.25:  # type: ignore
             raise ValueError("Input audio chunk is too short")
 
         h, c = state