Update placement of STTMuteFilter in examples to reflect the new recommendation

examples/foundational/24-stt-mute-filter.py

@@ -111,8 +111,8 @@ async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_si
     pipeline = Pipeline(
         [
             transport.input(),  # Transport user input
-            stt_mute_processor,  # Add the mute processor before STT
             stt,  # STT
+            stt_mute_processor,  # Add the mute processor before STT
             context_aggregator.user(),  # User responses
             llm,  # LLM
             tts,  # TTS

examples/translation-chatbot/bot.py

@@ -0,0 +1,216 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import os
+import sys
+from typing import List
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import (
+    Frame,
+    LLMMessagesFrame,
+    TranscriptionFrame,
+    TranscriptionMessage,
+    TranscriptionUpdateFrame,
+)
+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.processors.filters.stt_mute_filter import STTMuteConfig, STTMuteFilter, STTMuteStrategy
+from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
+from pipecat.processors.frameworks.rtvi import RTVIObserver, RTVIProcessor
+from pipecat.processors.transcript_processor import TranscriptProcessor
+from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+"""
+This example looks a bit different than the chatbot example, because it isn't waiting on the user to stop talking to start translating.
+It also isn't saving what the user or bot says into the context object for use in subsequent interactions.
+"""
+
+
+# We need to use a custom service here to yield LLM frames without saving
+# any context
+class TranslationProcessor(FrameProcessor):
+    """A processor that translates text frames from a source language to a target language."""
+
+    def __init__(self, in_language, out_language):
+        """Initialize the TranslationProcessor with source and target languages.
+
+        Args:
+            in_language (str): The language of the input text.
+            out_language (str): The language to translate the text into.
+        """
+        super().__init__()
+        self._out_language = out_language
+        self._in_language = in_language
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        """Process a frame and translate text frames.
+
+        Args:
+            frame (Frame): The frame to process.
+            direction (FrameDirection): The direction of the frame.
+        """
+        await super().process_frame(frame, direction)
+
+        if isinstance(frame, TranscriptionFrame):
+            logger.debug(f"Translating {self._in_language}: {frame.text} to {self._out_language}")
+            context = [
+                {
+                    "role": "system",
+                    "content": f"You will be provided with a sentence in {self._in_language}, and your task is to only translate it into {self._out_language}.",
+                },
+                {"role": "user", "content": frame.text},
+            ]
+            await self.push_frame(LLMMessagesFrame(context))
+        else:
+            await self.push_frame(frame)
+
+
+class TranscriptHandler:
+    """Simple handler to demonstrate transcript processing.
+
+    Maintains a list of conversation messages and logs them with timestamps.
+    """
+
+    def __init__(self, in_language="English", out_language="Spanish"):
+        """Initialize the TranscriptHandler with an empty list of messages."""
+        self.messages: List[TranscriptionMessage] = []
+        self.in_language = in_language
+        self.out_language = out_language
+
+    async def on_transcript_update(
+        self, processor: TranscriptProcessor, frame: TranscriptionUpdateFrame
+    ):
+        """Handle new transcript messages.
+
+        Args:
+            processor: The TranscriptProcessor that emitted the update
+            frame: TranscriptionUpdateFrame containing new messages
+        """
+        self.messages.extend(frame.messages)
+
+        # Log the new messages
+        logger.info("New transcript messages:")
+        for msg in frame.messages:
+            timestamp = f"[{msg.timestamp}] " if msg.timestamp else ""
+            message = {
+                "event": "translation",
+                "timestamp": msg.timestamp,
+                "role": msg.role,
+                "language": self.out_language if msg.role == "assistant" else self.in_language,
+                "text": msg.content,
+            }
+            logger.info(f"{timestamp}{msg.role}: {msg.content}")
+
+
+async def main():
+    """Main function to set up and run the translation chatbot pipeline."""
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Translator",
+            DailyParams(
+                audio_in_enabled=True,
+                audio_out_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
+        )
+
+        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+        stt_mute_processor = STTMuteFilter(
+            config=STTMuteConfig(
+                strategies={
+                    STTMuteStrategy.ALWAYS,
+                }
+            ),
+        )
+
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="34dbb662-8e98-413c-a1ef-1a3407675fe7",  # Spanish Narrator Man
+            model="sonic-2",
+        )
+
+        in_language = "English"
+        out_language = "Spanish"
+
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+        context = OpenAILLMContext()
+        context_aggregator = llm.create_context_aggregator(context)
+
+        tp = TranslationProcessor(in_language=in_language, out_language=out_language)
+
+        transcript = TranscriptProcessor()
+        transcript_handler = TranscriptHandler(in_language=in_language, out_language=out_language)
+
+        # Register event handler for transcript updates
+        @transcript.event_handler("on_transcript_update")
+        async def on_transcript_update(processor, frame):
+            await transcript_handler.on_transcript_update(processor, frame)
+
+        rtvi = RTVIProcessor()
+
+        pipeline = Pipeline(
+            [
+                transport.input(),
+                rtvi,
+                stt,
+                stt_mute_processor,  # We don't want to interrupt the translator bot
+                transcript.user(),  # User transcripts
+                tp,
+                llm,
+                tts,
+                transport.output(),
+                transcript.assistant(),
+                context_aggregator.assistant(),
+            ]
+        )
+
+        task = PipelineTask(
+            pipeline,
+            params=PipelineParams(
+                enable_metrics=True,
+                enable_usage_metrics=True,
+            ),
+            observers=[RTVIObserver(rtvi)],
+        )
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            logger.info("First participant joined")
+
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            await task.cancel()
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())