address feedback.

src/pipecat/audio/filters/aic_filter.py

@@ -11,7 +11,7 @@ enhance audio streams in real time. It mirrors the structure of other filters li
 the Koala filter and integrates with Pipecat's input transport pipeline.
 
 Classes:
-    AICFilter: For aic-sdk >= 2.0.0 (uses 'aic_sdk' module)
+    AICFilter: For aic-sdk (uses 'aic_sdk' module)
 """
 
 import os
@@ -30,7 +30,7 @@ class AICFilter(BaseAudioFilter):
     """Audio filter using ai-coustics' AIC SDK for real-time enhancement.
 
     Buffers incoming audio to the model's preferred block size and processes
-    planar frames in-place using float32 samples in the linear -1..+1 range.
+    frames using float32 samples normalized to the -1..+1 range.
 
     .. note::
         This class requires aic-sdk >= 2.0.0 (uses 'aic_sdk' module).
@@ -84,12 +84,21 @@ class AICFilter(BaseAudioFilter):
         self._frames_per_block = 0
         self._audio_buffer = bytearray()
 
+        # Audio format constants
+        self._bytes_per_sample = 2  # int16 = 2 bytes
+        self._dtype = np.int16
+        self._scale = 32768.0  # 2^15, for normalizing int16 (-32768..32767) to float32 (-1.0..1.0)
+
         # AIC SDK objects
         self._model = None
         self._processor = None
         self._processor_ctx = None
         self._vad_ctx = None
 
+        # Pre-allocated buffers (resized in start() once frames_per_block is known)
+        self._in_f32 = None
+        self._out_i16 = None
+
     def get_vad_context(self):
         """Return the VAD context once the processor exists.
 
@@ -156,6 +165,13 @@ class AICFilter(BaseAudioFilter):
                 logger.debug(f"Model downloaded to: {model_path}")
                 self._model = Model.from_file(model_path)
 
+            # Get optimal frames for this sample rate
+            self._frames_per_block = self._model.get_optimal_num_frames(self._sample_rate)
+
+            # Allocate processing buffers now that we know the block size
+            self._in_f32 = np.zeros((1, self._frames_per_block), dtype=np.float32)
+            self._out_i16 = np.zeros(self._frames_per_block, dtype=np.int16)
+
             # Create configuration
             config = ProcessorConfig.optimal(
                 self._model,
@@ -163,7 +179,7 @@ class AICFilter(BaseAudioFilter):
             )
 
             # Create async processor
-            self._processor = ProcessorAsync(self._model, self._license_key or "", config)
+            self._processor = ProcessorAsync(self._model, self._license_key, config)
 
             # Get contexts for parameter control and VAD
             self._processor_ctx = self._processor.get_processor_context()
@@ -171,12 +187,10 @@ class AICFilter(BaseAudioFilter):
 
             # Apply initial parameters
             if self._enhancement_level is not None:
-                level = float(self._enhancement_level if self._enabled else 0.0)
+                level = self._enhancement_level if self._enabled else 0.0
                 self._processor_ctx.set_parameter(ProcessorParameter.EnhancementLevel, level)
             if self._voice_gain is not None:
-                self._processor_ctx.set_parameter(
-                    ProcessorParameter.VoiceGain, float(self._voice_gain)
-                )
+                self._processor_ctx.set_parameter(ProcessorParameter.VoiceGain, self._voice_gain)
 
             self._aic_ready = True
 
@@ -225,7 +239,7 @@ class AICFilter(BaseAudioFilter):
             self._enabled = frame.enable
             if self._processor_ctx is not None:
                 try:
-                    level = float(self._enhancement_level if self._enabled else 0.0)
+                    level = self._enhancement_level if self._enabled else 0.0
                     self._processor_ctx.set_parameter(ProcessorParameter.EnhancementLevel, level)
                 except Exception as e:  # noqa: BLE001
                     logger.error(f"AIC set_parameter failed: {e}")
@@ -237,52 +251,41 @@ class AICFilter(BaseAudioFilter):
         model's required block length. Returns enhanced audio data.
 
         Args:
-            audio: Raw audio data as bytes to be filtered (int16 PCM, planar).
+            audio: Raw audio data as bytes (int16 PCM).
 
         Returns:
-            Enhanced audio data as bytes (int16 PCM, planar).
+            Enhanced audio data as bytes (int16 PCM).
         """
         if not self._aic_ready or self._processor is None:
             return audio
 
         self._audio_buffer.extend(audio)
+        available_frames = len(self._audio_buffer) // self._bytes_per_sample
+        num_blocks = available_frames // self._frames_per_block
+
+        if num_blocks == 0:
+            return b""
 
         filtered_chunks: List[bytes] = []
+        mv = memoryview(self._audio_buffer)
+        block_size = self._frames_per_block * self._bytes_per_sample
 
-        # Number of int16 samples currently buffered
-        available_frames = len(self._audio_buffer) // 2
+        for i in range(num_blocks):
+            start = i * block_size
+            block_i16 = np.frombuffer(mv[start : start + block_size], dtype=self._dtype)
 
-        while available_frames >= self._frames_per_block:
-            # Consume exactly one block worth of frames
-            samples_to_consume = self._frames_per_block * 1
-            bytes_to_consume = samples_to_consume * 2
-            block_bytes = bytes(self._audio_buffer[:bytes_to_consume])
+            # Reuse input buffer, in-place divide
+            np.copyto(self._in_f32[0], block_i16)
+            self._in_f32 /= self._scale
 
-            # Convert to float32 in -1..+1 range and reshape to (channels, frames)
-            block_i16 = np.frombuffer(block_bytes, dtype=np.int16)
-            # Convert to float32 and normalize
-            block_f32 = block_i16.astype(np.float32)
-            
-            block_f32 *= (1.0 / 32768.0)
-            
-            # Reshape to (1, frames) for AIC SDK
-            block_f32 = block_f32.reshape((1, self._frames_per_block))
+            out_f32 = await self._processor.process_async(self._in_f32)
 
-            # Process via async processor; returns ndarray (same shape)
-            out_f32 = await self._processor.process_async(block_f32)
+            # Convert float32 output back to int16
+            np.multiply(out_f32, self._scale, out=self._in_f32)  # reuse in_f32 as temp
+            np.clip(self._in_f32, -self._scale, self._scale - 1, out=self._in_f32)
+            np.copyto(self._out_i16, self._in_f32[0].astype(self._dtype))
 
-            # Convert back to int16 bytes
-            # Denormalize and convert back to int16
-            out_f32 *= 32768.0
-            
-            # In-place clip to valid int16 range (-32768 to 32767)
-            np.clip(out_f32, -32768.0, 32767, out=out_f32)
-            out_i16 = out_f32.astype(dtype)
-            filtered_chunks.append(out_i16.reshape(-1).tobytes())
+            filtered_chunks.append(self._out_i16.tobytes())
 
-            # Slide buffer
-            self._audio_buffer = self._audio_buffer[bytes_to_consume:]
-            available_frames = len(self._audio_buffer) // 2
-
-        # Do not flush incomplete frames; keep them buffered for the next call
+        self._audio_buffer = self._audio_buffer[num_blocks * block_size :]
         return b"".join(filtered_chunks)