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4 Commits
mb/voicema
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hush/prere
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a39f8b4882 | ||
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76fc36f621 | ||
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c0878c5e09 | ||
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c6a1013051 |
@@ -5,6 +5,7 @@
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#
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import os
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import wave
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from dotenv import load_dotenv
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from loguru import logger
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@@ -13,7 +14,14 @@ from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
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from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.audio.vad.vad_analyzer import VADParams
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from pipecat.frames.frames import LLMRunFrame
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from pipecat.frames.frames import (
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LLMFullResponseEndFrame,
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LLMFullResponseStartFrame,
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LLMRunFrame,
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LLMTextFrame,
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OutputAudioRawFrame,
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TextFrame,
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)
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from pipecat.pipeline.pipeline import Pipeline
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from pipecat.pipeline.runner import PipelineRunner
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from pipecat.pipeline.task import PipelineParams, PipelineTask
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@@ -103,7 +111,27 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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logger.info(f"Client connected")
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# Kick off the conversation.
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messages.append({"role": "system", "content": "Please introduce yourself to the user."})
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await task.queue_frames([LLMRunFrame()])
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audio_file_path = os.path.join(os.path.dirname(__file__), "assets", "pre-recorded.wav")
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with wave.open(audio_file_path, "rb") as wav_file:
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llm_text_frame = TextFrame(text="This is a pre-recorded message.")
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llm_text_frame.skip_tts = True
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audio_data = wav_file.readframes(wav_file.getnframes())
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output_audio_raw_frame = OutputAudioRawFrame(
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audio=audio_data, sample_rate=44100, num_channels=1
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)
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await task.queue_frames(
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[
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LLMRunFrame(),
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LLMFullResponseStartFrame(),
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llm_text_frame,
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output_audio_raw_frame,
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LLMFullResponseEndFrame(),
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]
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)
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@transport.event_handler("on_client_disconnected")
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async def on_client_disconnected(transport, client):
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BIN
examples/foundational/assets/pre-recorded.wav
Normal file
BIN
examples/foundational/assets/pre-recorded.wav
Normal file
Binary file not shown.
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