audio glitch test

khk/audio-glitch.py

@@ -0,0 +1,185 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import os
+import time
+import statistics
+
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+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.network.fastapi_websocket import FastAPIWebsocketParams
+from pipecat.transports.services.daily import DailyParams
+from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIObserver, RTVIProcessor
+from pipecat.processors.frame_processor import FrameProcessor, FrameDirection
+from pipecat.frames.frames import Frame, TTSStartedFrame, TTSStoppedFrame, TTSAudioRawFrame
+from pipecat.pipeline.parallel_pipeline import ParallelPipeline
+
+
+load_dotenv(override=True)
+
+# We store functions so objects (e.g. SileroVADAnalyzer) don't get
+# instantiated. The function will be called when the desired transport gets
+# selected.
+transport_params = {
+    "daily": lambda: DailyParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(),
+    ),
+    "twilio": lambda: FastAPIWebsocketParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(),
+    ),
+    "webrtc": lambda: TransportParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(),
+    ),
+}
+
+
+class AudioTimingProcessor(FrameProcessor):
+    def __init__(self, print_interval=False):
+        super().__init__()
+        self.print_interval = print_interval
+        self.tts_started_time = None
+        self.tts_stopped_time = None
+        self.tts_last_frame_time = None
+        self.tts_audio_frame_intervals = []
+        self.tts_audio_frame_count = 0
+        self.dummy_sum_of_intervals = 0
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        if isinstance(frame, TTSStartedFrame):
+            self.tts_started_time = time.time()
+        elif isinstance(frame, TTSAudioRawFrame):
+            self.tts_audio_frame_count += 1
+            if self.tts_last_frame_time is not None:
+                self.tts_audio_frame_intervals.append(time.time() - self.tts_last_frame_time)
+                # tiny but pointless amount of computation
+                self.dummy_sum_of_intervals += time.time() - self.tts_audio_frame_intervals[-1] + sum(i * i for i in range(10000))
+
+            self.tts_last_frame_time = time.time()
+        elif isinstance(frame, TTSStoppedFrame):
+            self.print_intervals()
+            self.tts_stopped_time = time.time()
+            self.tts_audio_frame_count = 0
+            self.tts_audio_frame_intervals = []
+
+        await self.push_frame(frame, direction)
+
+    def print_intervals(self):
+        if not self.print_interval:
+            return
+        
+        # print max, min, median, audio frame count. 
+        if self.tts_audio_frame_intervals:
+            logger.info(f"TTS audio frame intervals: max={max(self.tts_audio_frame_intervals):.2f}, min={min(self.tts_audio_frame_intervals):.2f}, median={statistics.median(self.tts_audio_frame_intervals):.2f}, audio frame count={self.tts_audio_frame_count}")
+        else:
+            logger.info(f"TTS audio frame intervals: no data available, audio frame count={self.tts_audio_frame_count}")
+
+
+async def run_bot(transport: BaseTransport):
+    logger.info(f"Starting bot")
+
+    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+    tts = CartesiaTTSService(
+        api_key=os.getenv("CARTESIA_API_KEY"),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+    )
+
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+
+    rtvi = RTVIProcessor(config=RTVIConfig(config=[]))
+
+ 
+    # Create a bunch of the above simple processors to test audio frame delay glitching.
+    # On my machine, 200 processors causes a big problem. 100 shows just occasional very small glitches.
+    # Commit 061f2086b278f8df11cef73a6170d8413ef6334a is worse than current main (which makes sense).
+    NUM_PROCESSORS_IN_PARALLEL_PIPELINE = 200
+    silent_timing_processors = [AudioTimingProcessor() for _ in range(NUM_PROCESSORS_IN_PARALLEL_PIPELINE-1)]
+    extra_processors = ParallelPipeline(
+        [AudioTimingProcessor(print_interval=True)], 
+        [*silent_timing_processors, AudioTimingProcessor(print_interval=True)]
+    )
+
+
+    messages = [
+        {
+            "role": "system",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+        },
+    ]
+
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
+
+    pipeline = Pipeline(
+        [
+            transport.input(),  # Transport user input
+            rtvi,  # RTVI processor
+            stt,
+            context_aggregator.user(),  # User responses
+            llm,  # LLM
+            tts,  # TTS
+            extra_processors,
+            transport.output(),  # Transport bot output
+            context_aggregator.assistant(),  # Assistant spoken responses
+        ]
+    )
+
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            enable_metrics=True,
+            enable_usage_metrics=True,
+        ),
+        observers=[RTVIObserver(rtvi)],
+    )
+
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info(f"Client connected")
+        # Kick off the conversation.
+        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info(f"Client disconnected")
+        await task.cancel()
+
+    runner = PipelineRunner(handle_sigint=False)
+
+    await runner.run(task)
+
+
+async def bot(runner_args: RunnerArguments):
+    """Main bot entry point compatible with Pipecat Cloud."""
+    transport = await create_transport(runner_args, transport_params)
+    await run_bot(transport)
+
+
+if __name__ == "__main__":
+    from pipecat.runner.run import main
+
+    main()