Recording high quality audio.

examples/foundational/34-audio-recording.py

@@ -50,25 +50,14 @@ import aiofiles
 from dotenv import load_dotenv
 from loguru import logger
 
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.processors.audio.audio_buffer_processor import AudioBufferProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.cartesia.tts import CartesiaTTSService
-from pipecat.services.deepgram.stt import DeepgramSTTService
-from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
 
 load_dotenv(override=True)
 
@@ -94,20 +83,10 @@ transport_params = {
     "daily": lambda: DailyParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
     ),
     "webrtc": lambda: TransportParams(
         audio_in_enabled=True,
         audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
     ),
 }
 
@@ -115,38 +94,13 @@ transport_params = {
 async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
-    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"), audio_passthrough=True)
-
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",
-    )
-
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4")
-
     # Create audio buffer processor
-    audiobuffer = AudioBufferProcessor()
+    audiobuffer = AudioBufferProcessor(sample_rate=48000)
-
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful assistant demonstrating audio recording capabilities. Keep your responses brief and clear.",
-        },
-    ]
-
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [
             transport.input(),
-            stt,
-            context_aggregator.user(),
-            llm,
-            tts,
-            transport.output(),
             audiobuffer,  # Add audio buffer to pipeline
-            context_aggregator.assistant(),
         ]
     )
 
@@ -155,6 +109,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         params=PipelineParams(
             enable_metrics=True,
             enable_usage_metrics=True,
+            audio_in_sample_rate=48000,
+            audio_out_sample_rate= 48000
         ),
         idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
     )
@@ -165,7 +121,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         # Start recording audio
         await audiobuffer.start_recording()
         # Start conversation - empty prompt to let LLM follow system instructions
-        await task.queue_frames([LLMRunFrame()])
+        # await task.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):

src/pipecat/transports/smallwebrtc/transport.py

@@ -235,7 +235,7 @@ class SmallWebRTCClient:
 
         # We are always resampling it for 16000 if the sample_rate that we receive is bigger than that.
         # otherwise we face issues with Silero VAD
-        self._pipecat_resampler = AudioResampler("s16", "mono", 16000)
+        self._pipecat_resampler = AudioResampler("s16", "mono", 48000)
 
         @self._webrtc_connection.event_handler("connected")
         async def on_connected(connection: SmallWebRTCConnection):
@@ -366,31 +366,16 @@ class SmallWebRTCClient:
                 await asyncio.sleep(0.01)
                 continue
 
-            if frame.sample_rate > self._in_sample_rate:
+            resampled_frames = self._pipecat_resampler.resample(frame)
-                resampled_frames = self._pipecat_resampler.resample(frame)
+            for resampled_frame in resampled_frames:
-                for resampled_frame in resampled_frames:
-                    # 16-bit PCM bytes
-                    pcm_array = resampled_frame.to_ndarray().astype(np.int16)
-                    pcm_bytes = pcm_array.tobytes()
-                    del pcm_array  # free NumPy array immediately
-
-                    audio_frame = InputAudioRawFrame(
-                        audio=pcm_bytes,
-                        sample_rate=resampled_frame.sample_rate,
-                        num_channels=self._audio_in_channels,
-                    )
-                    del pcm_bytes  # reference kept in audio_frame
-
-                    yield audio_frame
-            else:
                 # 16-bit PCM bytes
-                pcm_array = frame.to_ndarray().astype(np.int16)
+                pcm_array = resampled_frame.to_ndarray().astype(np.int16)
                 pcm_bytes = pcm_array.tobytes()
                 del pcm_array  # free NumPy array immediately
 
                 audio_frame = InputAudioRawFrame(
                     audio=pcm_bytes,
-                    sample_rate=frame.sample_rate,
+                    sample_rate=resampled_frame.sample_rate,
                     num_channels=self._audio_in_channels,
                 )
                 del pcm_bytes  # reference kept in audio_frame