fix simulstreaming vram leak: cap cross-attn accumulation + token budget

fixes #283, fixes #275

- accumulated_cross_attns was growing unboundedly during decoding loop,
  using up to ~5GB for repetition loops. now capped to rolling window of 16
- max_tokens_per_chunk was using TOKENS_PER_SECOND (mel frame rate = 50)
  instead of actual text token rate (~15/s), allowing 10-40x too many
  decoding steps
- removed unused torch.cat on early return path
- removed dead self.committed/last_result_tokens lists (never read)
- same fixes applied to mlx variant

whisperlivekit/simul_whisper/backend.py

@@ -46,8 +46,6 @@ class SimulStreamingOnlineProcessor:
         self.logfile = logfile
         self.end = 0.0
         self.buffer = []
-        self.committed: List[ASRToken] = []
-        self.last_result_tokens: List[ASRToken] = []        
         self.model = self._create_alignatt()
         
         if asr.tokenizer:
@@ -122,7 +120,6 @@ class SimulStreamingOnlineProcessor:
                 self.buffer.extend(timestamped_words)
                 return [], self.end
             
-            self.committed.extend(timestamped_words)
             self.buffer = []
             return timestamped_words, self.end
         except Exception as e:

whisperlivekit/simul_whisper/mlx/simul_whisper.py

@@ -532,7 +532,9 @@ class MLXAlignAtt:
         accumulated_cross_attns = []
 
         audio_duration_s = self.segments_len()
-        max_tokens_per_chunk = max(50, int(audio_duration_s * TOKENS_PER_SECOND * 2.0))
+        # ~15 text tokens/s is a generous upper bound for speech; TOKENS_PER_SECOND (50)
+        # is the mel-frame rate and was causing 10-40x over-allocation on repetition loops.
+        max_tokens_per_chunk = max(50, int(audio_duration_s * 15 * 1.5))
         tokens_produced_this_chunk = 0
 
         while not completed and current_tokens.shape[1] < self.max_text_len:
@@ -558,6 +560,8 @@ class MLXAlignAtt:
             mx.eval(logits)
             
             accumulated_cross_attns.append(cross_qk)
+            if len(accumulated_cross_attns) > 16:
+                accumulated_cross_attns = accumulated_cross_attns[-16:]
 
             if new_segment and self.tokenizer.no_speech is not None:
                 probs_at_sot = mx.softmax(logits[:, self.state.sot_index, :], axis=-1)

whisperlivekit/simul_whisper/simul_whisper.py

@@ -390,7 +390,6 @@ class AlignAtt:
             return []
         if not self._apply_minseglen():
             logger.debug(f"applied minseglen {self.cfg.audio_min_len} > {self.segments_len()}.")
-            input_segments = torch.cat(self.state.segments, dim=0)
             return []
 
         # input_segments is concatenation of audio, it's one array
@@ -485,7 +484,9 @@ class AlignAtt:
         accumulated_cross_attns = []
         
         audio_duration_s = self.segments_len()
-        max_tokens_per_chunk = max(50, int(audio_duration_s * TOKENS_PER_SECOND * 2.0))  # 2x margin, min 50
+        # ~15 text tokens/s is a generous upper bound for speech; TOKENS_PER_SECOND (50)
+        # is the mel-frame rate and was causing 10-40x over-allocation on repetition loops.
+        max_tokens_per_chunk = max(50, int(audio_duration_s * 15 * 1.5))
         tokens_produced_this_chunk = 0
         
         while not completed and current_tokens.shape[1] < self.max_text_len:  # bos is 3 tokens
@@ -506,8 +507,12 @@ class AlignAtt:
             result = self.logits(tokens_for_logits, encoder_feature, return_cross_attn=True)
             logits, cross_attns = result
             
-            # Accumulate cross-attention from this forward pass
+            # Accumulate cross-attention from this forward pass (rolling window to
+            # bound VRAM — only the last entry matters for alignment, and the
+            # median_filter kernel is 7, so 16 entries is more than enough).
             accumulated_cross_attns.append(cross_attns)
+            if len(accumulated_cross_attns) > 16:
+                accumulated_cross_attns = accumulated_cross_attns[-16:]
 
             if new_segment and self.tokenizer.no_speech is not None:
                 probs_at_sot = logits[:, self.state.sot_index, :].float().softmax(dim=-1)