Align Together STT/TTS services with Pipecat patterns

STT:
- Add Settings class alias and 4-step init pattern
- Add resampler to convert pipeline audio to 16kHz for Together API
- Add keepalive support and _update_settings with reconnect
- Pass language to transcription frames
- Remove unnecessary OpenAI-Beta header

TTS:
- Add Settings class alias and 4-step init pattern
- Use push_start_frame=True for base class audio context management
- Route audio through append_to_audio_context instead of push_frame
- Track pending commits for proper audio context lifecycle
- Replace _handle_interruption with on_audio_context_interrupted
- Add _update_settings with reconnect
- Guard against stale audio after interruption

examples/foundational/07e-interruptible-together.py

@@ -21,7 +21,6 @@ from pipecat.processors.aggregators.llm_response_universal import (
 )
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.services.together.llm import TogetherLLMService
 from pipecat.services.together.stt import TogetherSTTService
 from pipecat.services.together.tts import TogetherTTSService
@@ -55,12 +54,17 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     stt = TogetherSTTService(api_key=os.getenv("TOGETHER_API_KEY"))
 
-    tts = TogetherTTSService(api_key=os.getenv("TOGETHER_API_KEY"))
+    tts = TogetherTTSService(
+        api_key=os.getenv("TOGETHER_API_KEY"),
+        settings=TogetherTTSService.Settings(
+            voice="tara",
+        ),
+    )
 
     llm = TogetherLLMService(
         api_key=os.getenv("TOGETHER_API_KEY"),
         settings=TogetherLLMService.Settings(
-            model="Qwen/Qwen3.5-9B",
+            model="openai/gpt-oss-120b",
             system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
         ),
     )

examples/foundational/13n-together-transcription.py

@@ -0,0 +1,83 @@
+#
+# Copyright (c) 2024-2026, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import os
+
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import Frame, TranscriptionFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineTask
+from pipecat.processors.audio.vad_processor import VADProcessor
+from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
+from pipecat.runner.types import RunnerArguments
+from pipecat.runner.utils import create_transport
+from pipecat.services.together.stt import TogetherSTTService
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.daily.transport import DailyParams
+from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+
+load_dotenv(override=True)
+
+
+class TranscriptionLogger(FrameProcessor):
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        if isinstance(frame, TranscriptionFrame):
+            print(f"Transcription: {frame.text}")
+
+        # Push all frames through
+        await self.push_frame(frame, direction)
+
+
+# We use lambdas to defer transport parameter creation until the transport
+# type is selected at runtime.
+transport_params = {
+    "daily": lambda: DailyParams(audio_in_enabled=True),
+    "twilio": lambda: FastAPIWebsocketParams(audio_in_enabled=True),
+    "webrtc": lambda: TransportParams(audio_in_enabled=True),
+}
+
+
+async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+    logger.info(f"Starting bot")
+
+    stt = TogetherSTTService(api_key=os.getenv("TOGETHER_API_KEY"))
+
+    tl = TranscriptionLogger()
+    vad_processor = VADProcessor(vad_analyzer=SileroVADAnalyzer())
+
+    pipeline = Pipeline([transport.input(), vad_processor, stt, tl])
+
+    task = PipelineTask(
+        pipeline,
+        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
+    )
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info(f"Client disconnected")
+        await task.cancel()
+
+    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+
+    await runner.run(task)
+
+
+async def bot(runner_args: RunnerArguments):
+    """Main bot entry point compatible with Pipecat Cloud."""
+    transport = await create_transport(runner_args, transport_params)
+    await run_bot(transport, runner_args)
+
+
+if __name__ == "__main__":
+    from pipecat.runner.run import main
+
+    main()