From c26ec9e4d00a02ce33d028bfd507527091d097d9 Mon Sep 17 00:00:00 2001
From: dujing <dujing@xmov.ai>
Date: Tue, 27 Jun 2023 18:07:37 +0800
Subject: [PATCH] fix repeat activation, add a interval restrict.

---
 wekws/bin/stream_kws_ctc.py | 17 ++++++++++++++---
 1 file changed, 14 insertions(+), 3 deletions(-)

diff --git a/wekws/bin/stream_kws_ctc.py b/wekws/bin/stream_kws_ctc.py
index e9fe32a..f609b72 100644
--- a/wekws/bin/stream_kws_ctc.py
+++ b/wekws/bin/stream_kws_ctc.py
@@ -252,6 +252,7 @@ class KeyWordSpotter(torch.nn.Module):
         self.activated = False
 
         self.total_frames = 0   # frame offset, for absolute time
+        self.last_active_pos = -1  # the last frame of being activated
         self.result = {}
 
     def set_keywords(self, keywords):
@@ -378,16 +379,25 @@ class KeyWordSpotter(torch.nn.Module):
 
         duration = end - start
         if hit_keyword is not None:
-            if self.hit_score >= self.threshold and self.min_frames <= duration <= self.max_frames:
+            if self.hit_score >= self.threshold and \
+                    self.min_frames <= duration <= self.max_frames \
+                    and (self.last_active_pos==-1 or end-self.last_active_pos >= self.interval_frames):
                 self.activated = True
+                self.last_active_pos = end
                 logging.info(
                     f"Frame {absolute_time} detect {hit_keyword} from {start} to {end} frame. "
-                    f"duration {duration}, score {self.hit_score} Activated.")
+                    f"duration {duration}, score {self.hit_score}, Activated.")
+
+            elif self.last_active_pos>0 and end-self.last_active_pos < self.interval_frames:
+                logging.info(
+                    f"Frame {absolute_time} detect {hit_keyword} from {start} to {end} frame. "
+                    f"but interval {end-self.last_active_pos} is lower than {self.interval_frames}, Deactivated. ")
 
             elif self.hit_score < self.threshold:
                 logging.info(
                     f"Frame {absolute_time} detect {hit_keyword} from {start} to {end} frame. "
                     f"but {self.hit_score} is lower than {self.threshold}, Deactivated. ")
+
             elif self.min_frames > duration or duration > self.max_frames:
                 logging.info(
                     f"Frame {absolute_time} detect {hit_keyword} from {start} to {end} frame. "
@@ -404,7 +414,7 @@ class KeyWordSpotter(torch.nn.Module):
     def forward(self, wave_chunk):
         feature = self.accept_wave(wave_chunk)
         if feature is None or feature.size(0) < 1:
-            return self.result
+            return {}  # # the feature is not enough to get result.
         feature = feature.unsqueeze(0)   # add a batch dimension
         logits, self.in_cache = self.model(feature, self.in_cache)
         probs = logits.softmax(2)  # (batch_size, maxlen, vocab_size)
@@ -434,6 +444,7 @@ class KeyWordSpotter(torch.nn.Module):
         self.feats_ctx_offset = 0  # after downsample, offset exist.
         self.in_cache = torch.zeros(0, 0, 0, dtype=torch.float)
         self.total_frames = 0   # frame offset, for absolute time
+        self.last_active_pos = -1  # the last frame of being activated
         self.result = {}
 
 def demo():