feat: add dial-tone detection and generation

examples/foundational/tones.py

@@ -0,0 +1,162 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import os
+import sys
+
+import aiohttp
+import numpy as np
+from dotenv import load_dotenv
+from dtmf import detect, generate, model, parse
+from loguru import logger
+from runner import configure
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import (
+    BotSpeakingFrame,
+    Frame,
+    InputAudioRawFrame,
+    LLMMessagesFrame,
+    OutputAudioRawFrame,
+    TextFrame,
+    TTSAudioRawFrame,
+    UserStoppedSpeakingFrame,
+)
+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.frame_processor import FrameDirection, FrameProcessor
+from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.openai import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+class DebugProcessor(FrameProcessor):
+    def __init__(self, name, **kwargs):
+        self._name = name
+        super().__init__(**kwargs)
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+        if not (
+            isinstance(frame, InputAudioRawFrame)
+            or isinstance(frame, BotSpeakingFrame)
+            or isinstance(frame, UserStoppedSpeakingFrame)
+            or isinstance(frame, TTSAudioRawFrame)
+            or isinstance(frame, TextFrame)
+        ):
+            logger.debug(f"--- DebugProcessor {self._name}: {frame} {direction}")
+        await self.push_frame(frame, direction)
+
+
+class DTMFProcessor(FrameProcessor):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+        tones = model.String(
+            [
+                model.Tone("1"),
+                model.Tone("2"),
+                model.Tone("3"),
+                model.Tone("4"),
+                model.Pause(),
+                model.Tone("5"),
+                model.Tone("6"),
+                model.Tone("7"),
+                model.Tone("8"),
+                model.Tone("9"),
+            ]
+        )
+
+        tone_audio = generate(tones)
+
+        # Convert the generated audio to a numpy array (assuming the generate function returns an iterable of floats)
+        audio_data = np.array(list(tone_audio), dtype=np.float32)
+
+        # Create an AudioRawFrame with the audio data
+        audio_frame = OutputAudioRawFrame(audio_data, sample_rate=8000, num_channels=1)
+
+        await self.push_frame(audio_frame)
+
+
+async def main():
+    print(detect, generate, parse)
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
+        )
+
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
+
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+
+        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)
+
+        dtmf = DTMFProcessor()
+        dp = DebugProcessor("dp")
+
+        pipeline = Pipeline(
+            [
+                transport.input(),
+                context_aggregator.user(),
+                dtmf,
+                dp,
+                # llm,
+                # tts,
+                transport.output(),
+                context_aggregator.assistant(),
+            ]
+        )
+
+        task = PipelineTask(
+            pipeline,
+            PipelineParams(enable_metrics=True, enable_usage_metrics=True),
+        )
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            await transport.capture_participant_transcription(participant["id"])
+            # Kick off the conversation.
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([LLMMessagesFrame(messages)])
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

pyproject.toml

@@ -29,6 +29,7 @@ dependencies = [
     "pydantic~=2.8.2",
     "pyloudnorm~=0.1.1",
     "resampy~=0.4.3",
+    "dtmf @ git+https://github.com/jamsea/dtmf.git@hush/updateDeps",
 ]
 
 [project.urls]