DeepgramTTSService: re-add base_url to constructor

2025-04-16 14:48:31 -07:00
parent 31f7082d12
commit e9af585edd
4 changed files with 108 additions and 89 deletions
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -9,6 +9,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0

 ### Added

+- `DeepgramTTSService` accepts `base_url` argument again, allowing you to
+  connect to an on-prem service.
+
 - It is now possible to disable `SoundfileMixer` when created. You can then use
  `MixerEnableFrame` to dynamically enable it when necessary.

@@ -25,6 +28,11 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0

 - `SoundfileMixer` constructor arguments need to be keywords.

+### Deprecated
+
+- `DeepgramSTTService` parameter `url` is now deprecated, use `base_url`
+  instead.
+
 ### Fixed

 - Fixed a `TavusVideoService` issue that was causing audio choppiness.
--- a/examples/foundational/16-gpu-container-local-bot.py
+++ b/examples/foundational/16-gpu-container-local-bot.py
@@ -6,7 +6,6 @@

 import os

-import aiohttp
 from dotenv import load_dotenv
 from loguru import logger

@@ -40,104 +39,101 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection):
        ),
    )

-    # Create an HTTP session
-    async with aiohttp.ClientSession() as session:
-        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))

-        tts = DeepgramTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("DEEPGRAM_API_KEY"),
-            voice="aura-asteria-en",
-            base_url="http://0.0.0.0:8080/v1/speak",
-        )
+    tts = DeepgramTTSService(
+        api_key=os.getenv("DEEPGRAM_API_KEY"),
+        voice="aura-asteria-en",
+        base_url="http://0.0.0.0:8080",
+    )

-        llm = OpenAILLMService(
-            # To use OpenAI
-            # api_key=os.getenv("OPENAI_API_KEY"),
-            # Or, to use a local vLLM (or similar) api server
-            model="meta-llama/Meta-Llama-3-8B-Instruct",
-            base_url="http://0.0.0.0:8000/v1",
-        )
+    llm = OpenAILLMService(
+        # To use OpenAI
+        # api_key=os.getenv("OPENAI_API_KEY"),
+        # Or, to use a local vLLM (or similar) api server
+        model="meta-llama/Meta-Llama-3-8B-Instruct",
+        base_url="http://0.0.0.0:8000/v1",
+    )

-        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.",
-            },
+    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)
+
+    pipeline = Pipeline(
+        [
+            transport.input(),  # Transport user input
+            stt,  # STT
+            context_aggregator.user(),
+            llm,  # LLM
+            tts,  # TTS
+            transport.output(),  # Transport bot output
+            context_aggregator.assistant(),
        ]
+    )

-        context = OpenAILLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            allow_interruptions=True,
+            enable_metrics=True,
+        ),
+    )

-        pipeline = Pipeline(
-            [
-                transport.input(),  # Transport user input
-                stt,  # STT
-                context_aggregator.user(),
-                llm,  # LLM
-                tts,  # TTS
-                transport.output(),  # Transport bot output
-                context_aggregator.assistant(),
-            ]
-        )
+    # When the first participant joins, the bot should introduce itself.
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info(f"Client connected")
+        # Kick off the conversation.
+        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+        await task.queue_frames([context_aggregator.user().get_context_frame()])

-        task = PipelineTask(
-            pipeline,
-            params=PipelineParams(
-                allow_interruptions=True,
-                enable_metrics=True,
-            ),
-        )
-
-        # When the first participant joins, the bot should introduce itself.
-        @transport.event_handler("on_client_connected")
-        async def on_client_connected(transport, client):
-            logger.info(f"Client connected")
-            # Kick off the conversation.
-            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        # Handle "latency-ping" messages. The client will send app messages that look like
-        # this:
-        #   { "latency-ping": { ts: <client-side timestamp> }}
-        #
-        # We want to send an immediate pong back to the client from this handler function.
-        # Also, we will push a frame into the top of the pipeline and send it after the
-        #
-        @transport.event_handler("on_app_message")
-        async def on_app_message(transport, message, sender):
-            try:
-                if "latency-ping" in message:
-                    logger.debug(f"Received latency ping app message: {message}")
-                    ts = message["latency-ping"]["ts"]
-                    # Send immediately
-                    transport.output().send_message(
-                        DailyTransportMessageFrame(
-                            message={"latency-pong-msg-handler": {"ts": ts}}, participant_id=sender
-                        )
+    # Handle "latency-ping" messages. The client will send app messages that look like
+    # this:
+    #   { "latency-ping": { ts: <client-side timestamp> }}
+    #
+    # We want to send an immediate pong back to the client from this handler function.
+    # Also, we will push a frame into the top of the pipeline and send it after the
+    #
+    @transport.event_handler("on_app_message")
+    async def on_app_message(transport, message, sender):
+        try:
+            if "latency-ping" in message:
+                logger.debug(f"Received latency ping app message: {message}")
+                ts = message["latency-ping"]["ts"]
+                # Send immediately
+                transport.output().send_message(
+                    DailyTransportMessageFrame(
+                        message={"latency-pong-msg-handler": {"ts": ts}}, participant_id=sender
                    )
-                    # And push to the pipeline for the Daily transport.output to send
-                    await task.queue_frame(
-                        DailyTransportMessageFrame(
-                            message={"latency-pong-pipeline-delivery": {"ts": ts}},
-                            participant_id=sender,
-                        )
+                )
+                # And push to the pipeline for the Daily transport.output to send
+                await task.queue_frame(
+                    DailyTransportMessageFrame(
+                        message={"latency-pong-pipeline-delivery": {"ts": ts}},
+                        participant_id=sender,
                    )
-            except Exception as e:
-                logger.debug(f"message handling error: {e} - {message}")
+                )
+        except Exception as e:
+            logger.debug(f"message handling error: {e} - {message}")

-        @transport.event_handler("on_client_disconnected")
-        async def on_client_disconnected(transport, client):
-            logger.info(f"Client disconnected")
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info(f"Client disconnected")

-        @transport.event_handler("on_client_closed")
-        async def on_client_closed(transport, client):
-            logger.info(f"Client closed connection")
-            await task.cancel()
+    @transport.event_handler("on_client_closed")
+    async def on_client_closed(transport, client):
+        logger.info(f"Client closed connection")
+        await task.cancel()

-        runner = PipelineRunner(handle_sigint=False)
+    runner = PipelineRunner(handle_sigint=False)

-        await runner.run(task)
+    await runner.run(task)


 if __name__ == "__main__":
--- a/src/pipecat/services/deepgram/stt.py
+++ b/src/pipecat/services/deepgram/stt.py
@@ -45,6 +45,7 @@ class DeepgramSTTService(STTService):
        *,
        api_key: str,
        url: str = "",
+        base_url: str = "",
        sample_rate: Optional[int] = None,
        live_options: Optional[LiveOptions] = None,
        addons: Optional[Dict] = None,
@@ -53,6 +54,17 @@ class DeepgramSTTService(STTService):
        sample_rate = sample_rate or (live_options.sample_rate if live_options else None)
        super().__init__(sample_rate=sample_rate, **kwargs)

+        if url:
+            import warnings
+
+            with warnings.catch_warnings():
+                warnings.simplefilter("always")
+                warnings.warn(
+                    "Parameter 'url' is deprecated, use 'base_url' instead.",
+                    DeprecationWarning,
+                )
+            base_url = url
+
        default_options = LiveOptions(
            encoding="linear16",
            language=Language.EN,
@@ -81,7 +93,7 @@ class DeepgramSTTService(STTService):
        self._client = DeepgramClient(
            api_key,
            config=DeepgramClientOptions(
-                url=url,
+                url=base_url,
                options={"keepalive": "true"},  # verbose=logging.DEBUG
            ),
        )
--- a/src/pipecat/services/deepgram/tts.py
+++ b/src/pipecat/services/deepgram/tts.py
@@ -18,7 +18,7 @@ from pipecat.frames.frames import (
 from pipecat.services.tts_service import TTSService

 try:
-    from deepgram import DeepgramClient, SpeakOptions
+    from deepgram import DeepgramClient, DeepgramClientOptions, SpeakOptions
 except ModuleNotFoundError as e:
    logger.error(f"Exception: {e}")
    logger.error("In order to use Deepgram, you need to `pip install pipecat-ai[deepgram]`.")
@@ -31,6 +31,7 @@ class DeepgramTTSService(TTSService):
        *,
        api_key: str,
        voice: str = "aura-helios-en",
+        base_url: str = "",
        sample_rate: Optional[int] = None,
        encoding: str = "linear16",
        **kwargs,
@@ -41,7 +42,9 @@ class DeepgramTTSService(TTSService):
            "encoding": encoding,
        }
        self.set_voice(voice)
-        self._deepgram_client = DeepgramClient(api_key=api_key)
+
+        client_options = DeepgramClientOptions(url=base_url)
+        self._deepgram_client = DeepgramClient(api_key, config=client_options)

    def can_generate_metrics(self) -> bool:
        return True