Merge pull request #219 from pipecat-ai/aleix/switch-voices

switch voices and languages

CHANGELOG.md

@@ -5,11 +5,13 @@ All notable changes to **pipecat** will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
-
+## [Unreleased]
-## Unreleased
 
 ### Added
 
+- Added a new `FunctionFilter`. This filter will let you filter frames based on
+  a given function, except system messages which should never be filtered.
+
 - Added `enable_metrics` to `PipelineParams`.
 
 - Added `MetricsFrame`. The `MetricsFrame` will report different metrics in the

examples/foundational/14-function-calling.py

@@ -41,7 +41,7 @@ async def start_fetch_weather(llm):
 
 
 async def fetch_weather_from_api(llm, args):
-    return ({"conditions": "nice", "temperature": "75"})
+    return {"conditions": "nice", "temperature": "75"}
 
 
 async def main(room_url: str, token):

examples/foundational/15-switch-voices.py

@@ -0,0 +1,159 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import aiohttp
+import os
+import sys
+
+from pipecat.frames.frames import LLMMessagesFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.parallel_pipeline import ParallelPipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.llm_response import (
+    LLMAssistantContextAggregator,
+    LLMUserContextAggregator
+)
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.filters.function_filter import FunctionFilter
+from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.openai import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.vad.silero import SileroVADAnalyzer
+
+from openai.types.chat import ChatCompletionToolParam
+
+from runner import configure
+
+from loguru import logger
+
+from dotenv import load_dotenv
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+current_voice = "News Lady"
+
+
+async def switch_voice(llm, args):
+    global current_voice
+    current_voice = args["voice"]
+    return {"voice": f"You are now using your {current_voice} voice. Your responses should now be as if you were a {current_voice}."}
+
+
+async def news_lady_filter(frame) -> bool:
+    return current_voice == "News Lady"
+
+
+async def british_lady_filter(frame) -> bool:
+    return current_voice == "British Lady"
+
+
+async def barbershop_man_filter(frame) -> bool:
+    return current_voice == "Barbershop Man"
+
+
+async def main(room_url: str, token):
+    async with aiohttp.ClientSession() as session:
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Pipecat",
+            DailyParams(
+                audio_out_enabled=True,
+                audio_out_sample_rate=44100,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer()
+            )
+        )
+
+        news_lady = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_name="Newslady",
+            output_format="pcm_44100"
+        )
+
+        british_lady = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_name="British Lady",
+            output_format="pcm_44100"
+        )
+
+        barbershop_man = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_name="Barbershop Man",
+            output_format="pcm_44100"
+        )
+
+        llm = OpenAILLMService(
+            api_key=os.getenv("OPENAI_API_KEY"),
+            model="gpt-4o")
+        llm.register_function("switch_voice", switch_voice)
+
+        tools = [
+            ChatCompletionToolParam(
+                type="function",
+                function={
+                    "name": "switch_voice",
+                    "description": "Switch your voice only when the user asks you to",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "voice": {
+                                "type": "string",
+                                "description": "The voice the user wants you to use",
+                            },
+                        },
+                        "required": ["voice"],
+                    },
+                })]
+        messages = [
+            {
+                "role": "system",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities. Respond to what the user said in a creative and helpful way. Your output should not include non-alphanumeric characters. You can do the following voices: 'News Lady', 'British Lady' and 'Barbershop Man'.",
+            },
+        ]
+
+        context = OpenAILLMContext(messages, tools)
+        tma_in = LLMUserContextAggregator(context)
+        tma_out = LLMAssistantContextAggregator(context)
+
+        pipeline = Pipeline([
+            transport.input(),   # Transport user input
+            tma_in,              # User responses
+            llm,                 # LLM
+            ParallelPipeline(    # TTS (one of the following vocies)
+                [FunctionFilter(news_lady_filter), news_lady],            # News Lady voice
+                [FunctionFilter(british_lady_filter), british_lady],      # British Lady voice
+                [FunctionFilter(barbershop_man_filter), barbershop_man],  # Barbershop Man voice
+            ),
+            transport.output(),  # Transport bot output
+            tma_out              # Assistant spoken responses
+        ])
+
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            transport.capture_participant_transcription(participant["id"])
+            # Kick off the conversation.
+            messages.append(
+                {
+                    "role": "system",
+                    "content": f"Please introduce yourself to the user and let them know the voices you can do. Your initial responses should be as if you were a {current_voice}."})
+            await task.queue_frames([LLMMessagesFrame(messages)])
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    (url, token) = configure()
+    asyncio.run(main(url, token))

examples/foundational/15a-switch-languages.py

@@ -0,0 +1,153 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import aiohttp
+import os
+import sys
+
+from pipecat.frames.frames import LLMMessagesFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.parallel_pipeline import ParallelPipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.llm_response import (
+    LLMAssistantContextAggregator,
+    LLMUserContextAggregator
+)
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.filters.function_filter import FunctionFilter
+from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.services.openai import OpenAILLMService
+from pipecat.services.whisper import Model, WhisperSTTService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.vad.silero import SileroVADAnalyzer
+
+from openai.types.chat import ChatCompletionToolParam
+
+from runner import configure
+
+from loguru import logger
+
+from dotenv import load_dotenv
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+current_language = "English"
+
+
+async def switch_language(llm, args):
+    global current_language
+    current_language = args["language"]
+    return {"voice": f"Your answers from now on should be in {current_language}."}
+
+
+async def english_filter(frame) -> bool:
+    return current_language == "English"
+
+
+async def spanish_filter(frame) -> bool:
+    return current_language == "Spanish"
+
+
+async def main(room_url: str, token):
+    async with aiohttp.ClientSession() as session:
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Pipecat",
+            DailyParams(
+                audio_in_enabled=True,
+                audio_out_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+                vad_audio_passthrough=True
+            )
+        )
+
+        stt = WhisperSTTService(model=Model.LARGE)
+
+        english_tts = ElevenLabsTTSService(
+            aiohttp_session=session,
+            api_key=os.getenv("ELEVENLABS_API_KEY"),
+            voice_id="pNInz6obpgDQGcFmaJgB",
+        )
+
+        spanish_tts = ElevenLabsTTSService(
+            aiohttp_session=session,
+            api_key=os.getenv("ELEVENLABS_API_KEY"),
+            model="eleven_multilingual_v2",
+            voice_id="9F4C8ztpNUmXkdDDbz3J",
+        )
+
+        llm = OpenAILLMService(
+            api_key=os.getenv("OPENAI_API_KEY"),
+            model="gpt-4o")
+        llm.register_function("switch_language", switch_language)
+
+        tools = [
+            ChatCompletionToolParam(
+                type="function",
+                function={
+                    "name": "switch_language",
+                    "description": "Switch to another language when the user asks you to",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "language": {
+                                "type": "string",
+                                "description": "The language the user wants you to speak",
+                            },
+                        },
+                        "required": ["language"],
+                    },
+                })]
+        messages = [
+            {
+                "role": "system",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities. Respond to what the user said in a creative and helpful way. Your output should not include non-alphanumeric characters. You can speak the following languages: 'English' and 'Spanish'.",
+            },
+        ]
+
+        context = OpenAILLMContext(messages, tools)
+        tma_in = LLMUserContextAggregator(context)
+        tma_out = LLMAssistantContextAggregator(context)
+
+        pipeline = Pipeline([
+            transport.input(),   # Transport user input
+            stt,                 # STT
+            tma_in,              # User responses
+            llm,                 # LLM
+            ParallelPipeline(    # TTS (bot will speak the chosen language)
+                [FunctionFilter(english_filter), english_tts],  # English
+                [FunctionFilter(spanish_filter), spanish_tts],  # Spanish
+            ),
+            transport.output(),  # Transport bot output
+            tma_out              # Assistant spoken responses
+        ])
+
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            transport.capture_participant_transcription(participant["id"])
+            # Kick off the conversation.
+            messages.append(
+                {
+                    "role": "system",
+                    "content": f"Please introduce yourself to the user and let them know the languages you speak. Your initial responses should be in {current_language}."})
+            await task.queue_frames([LLMMessagesFrame(messages)])
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    (url, token) = configure()
+    asyncio.run(main(url, token))

src/pipecat/processors/filters/function_filter.py

@@ -0,0 +1,30 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+from typing import Awaitable, Callable
+
+from pipecat.frames.frames import Frame, SystemFrame
+from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
+
+
+class FunctionFilter(FrameProcessor):
+
+    def __init__(self, filter: Callable[[Frame], Awaitable[bool]]):
+        super().__init__()
+        self._filter = filter
+
+    #
+    # Frame processor
+    #
+
+    def _should_passthrough_frame(self, frame):
+        return isinstance(frame, SystemFrame)
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        passthrough = self._should_passthrough_frame(frame)
+        allowed = await self._filter(frame)
+        if passthrough or allowed:
+            await self.push_frame(frame, direction)

src/pipecat/services/whisper.py

@@ -45,7 +45,7 @@ class WhisperSTTService(STTService):
                  model: Model = Model.DISTIL_MEDIUM_EN,
                  device: str = "auto",
                  compute_type: str = "default",
-                 no_speech_prob: float = 0.1,
+                 no_speech_prob: float = 0.4,
                  **kwargs):
 
         super().__init__(**kwargs)
@@ -86,4 +86,5 @@ class WhisperSTTService(STTService):
 
         if text:
             await self.stop_ttfb_metrics()
+            logger.debug(f"Transcription: [{text}]")
             yield TranscriptionFrame(text, "", int(time.time_ns() / 1000000))

src/pipecat/transports/base_input.py

@@ -177,7 +177,7 @@ class BaseInputTransport(FrameProcessor):
                     vad_state = self._handle_vad(frame.audio, vad_state)
                     audio_passthrough = self._params.vad_audio_passthrough
 
-                    # Push audio downstream if passthrough.
+                # Push audio downstream if passthrough.
                 if audio_passthrough:
                     future = asyncio.run_coroutine_threadsafe(
                         self._internal_push_frame(frame), self._loop)