added local test

examples/foundational/07zb-interruptible-camb-local.py

@@ -0,0 +1,144 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+"""Camb.ai MARS-8 TTS example with local audio (microphone/speakers).
+
+This example demonstrates:
+- Basic TTS synthesis with Camb.ai MARS-8
+- Local audio input/output (no WebRTC or Daily needed)
+- Handling interruptions
+
+Requirements:
+- CAMB_API_KEY environment variable
+- OPENAI_API_KEY environment variable (for LLM)
+- DEEPGRAM_API_KEY environment variable (for STT)
+
+Usage:
+    export CAMB_API_KEY=your_camb_api_key
+    export OPENAI_API_KEY=your_openai_api_key
+    export DEEPGRAM_API_KEY=your_deepgram_api_key
+    python 07zb-interruptible-camb-local.py
+"""
+
+import asyncio
+import os
+import sys
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.audio.vad.vad_analyzer import VADParams
+from pipecat.frames.frames import LLMRunFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import (
+    LLMContextAggregatorPair,
+)
+from pipecat.services.camb.tts import CambTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transports.local.audio import LocalAudioTransport, LocalAudioTransportParams
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def main():
+    # Local audio transport - uses your microphone and speakers
+    transport = LocalAudioTransport(
+        LocalAudioTransportParams(
+            audio_in_enabled=True,
+            audio_out_enabled=True,
+            vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
+        )
+    )
+
+    # Deepgram STT for speech recognition
+    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+    # Create HTTP session for Camb.ai TTS
+    async with aiohttp.ClientSession() as session:
+        # Camb.ai TTS with MARS-8-flash model
+        tts = CambTTSService(
+            api_key=os.getenv("CAMB_API_KEY"),
+            aiohttp_session=session,
+            voice_id=2681,  # Attic voice
+            model="mars-8-flash",
+            params=CambTTSService.InputParams(
+                speed=1.0,
+            ),
+        )
+
+        # OpenAI LLM
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+
+        # System prompt
+        messages = [
+            {
+                "role": "system",
+                "content": """You are a helpful voice assistant powered by Camb.ai's MARS-8
+text-to-speech technology. Keep your responses concise and conversational since
+they will be spoken aloud. Avoid special characters, emojis, or bullet points.""",
+            },
+        ]
+
+        # Context management
+        context = LLMContext(messages)
+        context_aggregator = LLMContextAggregatorPair(context)
+
+        # Build the pipeline
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Microphone input
+                stt,  # Speech-to-text
+                context_aggregator.user(),  # User context
+                llm,  # Language model
+                tts,  # Camb.ai TTS
+                transport.output(),  # Speaker output
+                context_aggregator.assistant(),  # Assistant context
+            ]
+        )
+
+        # Create pipeline task
+        task = PipelineTask(
+            pipeline,
+            params=PipelineParams(
+                enable_metrics=True,
+                enable_usage_metrics=True,
+            ),
+        )
+
+        # Run the pipeline
+        runner = PipelineRunner()
+        logger.info("Starting Camb.ai TTS bot with local audio...")
+        logger.info("Speak into your microphone to interact with the bot.")
+
+        # Start the conversation with a greeting after a short delay
+        async def start_greeting():
+            await asyncio.sleep(1)  # Wait for pipeline to start
+            messages.append(
+                {
+                    "role": "system",
+                    "content": "Please introduce yourself briefly and ask how you can help.",
+                }
+            )
+            await task.queue_frames([LLMRunFrame()])
+
+        # Run greeting and pipeline concurrently
+        await asyncio.gather(
+            runner.run(task),
+            start_greeting(),
+        )
+
+
+if __name__ == "__main__":
+    asyncio.run(main())