Merge branch 'main' into fixing_sound_mixer

CHANGELOG.md

@@ -5,10 +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]
+## [0.0.66] - 2025-05-02
 
 ### Added
 
+- Added two new input parameters to `RimeTTSService`: `pause_between_brackets`
+  and `phonemize_between_brackets`.
+
 - Added support for cross-platform local smart turn detection. You can use
   `LocalSmartTurnAnalyzer` for on-device inference using Torch.
 
@@ -116,6 +119,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   case there's no need to push audio to the rest of the pipeline, but this is
   not a very common case.
 
+- Added `RivaSegmentedSTTService`, which allows Riva offline/batch models, such
+  as  to be "canary-1b-asr" used in Pipecat.
+
 ### Deprecated
 
 - Function calls with parameters
@@ -131,8 +137,17 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - `TransportParams.vad_audio_passthrough` parameter is now deprecated, use
   `TransportParams.audio_in_passthrough` instead.
 
+- `ParakeetSTTService` is now deprecated, use `RivaSTTService` instead, which uses
+  the model "parakeet-ctc-1.1b-asr" by default.
+
+- `FastPitchTTSService` is now deprecated, use `RivaTTSService` instead, which uses
+  the model "magpie-tts-multilingual" by default.
+
 ### Fixed
 
+- Fixed an issue with `SimliVideoService` where the bot was continuously outputting
+  audio, which prevents the `BotStoppedSpeakingFrame` from being emitted.
+
 - Fixed an issue where `OpenAIRealtimeBetaLLMService` would add two assistant
   messages to the context.
 

examples/foundational/07r-interruptible-riva-nim.py

@@ -16,8 +16,12 @@ from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.nim.llm import NimLLMService
-from pipecat.services.riva.stt import ParakeetSTTService
+from pipecat.services.riva.stt import (
-from pipecat.services.riva.tts import FastPitchTTSService
+    ParakeetSTTService,
+    RivaSegmentedSTTService,
+    RivaSTTService,
+)
+from pipecat.services.riva.tts import FastPitchTTSService, RivaTTSService
 from pipecat.transports.base_transport import TransportParams
 from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
 from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
@@ -37,11 +41,11 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
         ),
     )
 
-    stt = ParakeetSTTService(api_key=os.getenv("NVIDIA_API_KEY"))
+    stt = RivaSTTService(api_key=os.getenv("NVIDIA_API_KEY"))
 
     llm = NimLLMService(api_key=os.getenv("NVIDIA_API_KEY"), model="meta/llama-3.1-405b-instruct")
 
-    tts = FastPitchTTSService(api_key=os.getenv("NVIDIA_API_KEY"))
+    tts = RivaTTSService(api_key=os.getenv("NVIDIA_API_KEY"))
 
     messages = [
         {

examples/foundational/27-simli-layer.py

@@ -36,6 +36,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
             audio_in_enabled=True,
             audio_out_enabled=True,
             video_out_enabled=True,
+            video_out_is_live=True,
             video_out_width=512,
             video_out_height=512,
             vad_analyzer=SileroVADAnalyzer(),

examples/storytelling-chatbot/.dockerignore

@@ -1,2 +0,0 @@
-frontend/node_modules
-frontend/out

examples/storytelling-chatbot/README.md

@@ -1,4 +1,4 @@
-[![Try](https://img.shields.io/badge/try_it-here-blue)](https://storytelling-chatbot.fly.dev)
+[![Try](https://img.shields.io/badge/try_it-here-blue)](https://gemini-storybot.vercel.app/)
 
 # Storytelling Chatbot
 
@@ -9,7 +9,6 @@ It periodically prompts the user for input for a 'choose your own adventure' sty
 
 We use Gemini 2.0 for creating the story and image prompts, and we add visual elements to the story by generating images using Google's Imagen.
 
-
 ---
 
 ### It uses the following AI services:
@@ -20,7 +19,7 @@ Transcribes inbound participant voice media to text.
 
 **Google Gemini 2.0 - LLM**
 
-Our creative writer LLM. You can see the context used to prompt it [here](src/prompts.py)
+Our creative writer LLM. You can see the context used to prompt it [here](server/prompts.py)
 
 **ElevenLabs - Text-to-Speech**
 
@@ -34,47 +33,76 @@ Adds pictures to our story. Prompting is quite key for style consistency, so we
 
 ## Setup
 
-**Install requirements**
+### Client
 
-```shell
+1. Navigate to the client directory:
-python3 -m venv venv
-source venv/bin/activate
-pip install -r requirements.txt
-```
 
-**Create environment file and set variables:**
+   ```shell
+   cd client
+   ```
 
-```shell
+2. Install dependencies:
-mv env.example .env
-```
 
-When deploying to production, to ensure only this app can spawn a new bot, set your `ENV` to `production`
+   ```shell
+   npm install
+   ```
 
-**Build the frontend:**
+3. Build the client:
 
-This project uses a custom frontend, which needs to built. Note: this is done automatically as part of the Docker deployment.
+   ```shell
+   npm run build
+   ```
 
-```shell
+### Server
-cd frontend/
-npm install
-npm run build
-```
 
-The build UI files can be found in `frontend/out`
+1. Navigate to the server directory
 
-## Running it locally
+   ```shell
+   cd ../server
+   ```
 
-Start the API / bot manager:
+2. Set up your virtual environment and install requirements
 
-`python src/bot_runner.py --host localhost`
+   ```shell
+   python3 -m venv venv
+   source venv/bin/activate
+   pip install -r requirements.txt
+   ```
 
-If you'd like to run a custom domain or port:
+3. Create environment file and set variables
 
-`python src/bot_runner.py --host somehost --p someport`
+   ```shell
+   mv env.example .env
+   ```
 
-➡️ Open the host URL in your browser `http://localhost:7860`
+   You'll need API keys for:
 
-If you've run previous versions of the demo, make sure to set `ENV=dev`, and remove the `RUN_AS_VM` line from the .env file.
+   - DAILY_API_KEY
+   - ELEVENLABS_API_KEY
+   - ELEVENLABS_VOICE_ID
+   - GOOGLE_API_KEY
+
+4. (Optional) Deployment:
+
+   When deploying to production, to ensure only this app can spawn new bot processes, set your `ENV` to `production`
+
+## Run it locally
+
+1. Navigate back to the demo's root directory:
+
+   ```shell
+   cd ..
+   ```
+
+2. Run the application:
+
+   ```shell
+   python server/bot_runner.py --host localhost
+   ```
+
+   You can run with a custom domain or port using: `python server/bot_runner.py --host somehost --p someport`
+
+3. ➡️ Open the host URL in your browser: http://localhost:7860
 
 ---
 

examples/storytelling-chatbot/client/package-lock.json

@@ -1,11 +1,11 @@
 {
-  "name": "frontend",
+  "name": "client",
   "version": "0.1.0",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
-      "name": "frontend",
+      "name": "client",
       "version": "0.1.0",
       "dependencies": {
         "@daily-co/daily-js": "^0.62.0",

examples/storytelling-chatbot/client/package.json

@@ -1,5 +1,5 @@
 {
-  "name": "frontend",
+  "name": "client",
   "version": "0.1.0",
   "private": true,
   "scripts": {

examples/storytelling-chatbot/server/.dockerignore

@@ -0,0 +1,2 @@
+client/node_modules
+client/out

examples/storytelling-chatbot/server/Dockerfile

@@ -44,11 +44,11 @@ COPY ./requirements.txt requirements.txt
 RUN pip3 install --no-cache-dir --upgrade -r requirements.txt
 
 # Copy everything else
-COPY --chown=user ./src/ src/
+COPY --chown=user ./server/ server/
 
-# Copy frontend app and build
+# Copy client app and build
-COPY --chown=user ./frontend/ frontend/
+COPY --chown=user ./client/ client/
-RUN cd frontend && npm install && npm run build
+RUN cd client && npm install && npm run build
 
 # Start the FastAPI server
-CMD python3 src/bot_runner.py --port ${FAST_API_PORT}
+CMD python3 server/bot_runner.py --port ${FAST_API_PORT}

examples/storytelling-chatbot/server/bot_runner.py

@@ -57,7 +57,7 @@ app.add_middleware(
 )
 
 # Mount the static directory
-STATIC_DIR = "frontend/out"
+STATIC_DIR = "client/out"
 
 
 # ------------ Fast API Routes ------------ #
@@ -175,7 +175,7 @@ async def virtualize_bot(room_url: str, token: str):
             image = data[0]["config"]["image"]
 
         # Machine configuration
-        cmd = f"python src/bot.py -u {room_url} -t {token}"
+        cmd = f"python server/bot.py -u {room_url} -t {token}"
         cmd = cmd.split()
         worker_props = {
             "config": {

pyproject.toml

@@ -47,7 +47,7 @@ canonical = [ "aiofiles~=24.1.0" ]
 cartesia = [ "cartesia~=1.4.0", "websockets~=13.1" ]
 cerebras = []
 deepseek = []
-daily = [ "daily-python~=0.18.0" ]
+daily = [ "daily-python~=0.18.1" ]
 deepgram = [ "deepgram-sdk~=3.8.0" ]
 elevenlabs = [ "websockets~=13.1" ]
 fal = [ "fal-client~=0.5.9" ]
@@ -78,7 +78,7 @@ perplexity = []
 playht = [ "pyht~=0.1.12", "websockets~=13.1" ]
 qwen = []
 rime = [ "websockets~=13.1" ]
-riva = [ "nvidia-riva-client~=2.19.0" ]
+riva = [ "nvidia-riva-client~=2.19.1" ]
 sentry = [ "sentry-sdk~=2.23.1" ]
 local-smart-turn = [ "coremltools>=8.0", "transformers", "torch==2.5.0", "torchaudio==2.5.0" ]
 remote-smart-turn = []

src/pipecat/services/rime/tts.py

@@ -68,6 +68,8 @@ class RimeTTSService(AudioContextWordTTSService):
         language: Optional[Language] = Language.EN
         speed_alpha: Optional[float] = 1.0
         reduce_latency: Optional[bool] = False
+        pause_between_brackets: Optional[bool] = False
+        phonemize_between_brackets: Optional[bool] = False
 
     def __init__(
         self,
@@ -117,6 +119,8 @@ class RimeTTSService(AudioContextWordTTSService):
             else "eng",
             "speedAlpha": params.speed_alpha,
             "reduceLatency": params.reduce_latency,
+            "pauseBetweenBrackets": json.dumps(params.pause_between_brackets),
+            "phonemizeBetweenBrackets": json.dumps(params.phonemize_between_brackets),
         }
 
         # State tracking

src/pipecat/services/riva/stt.py

@@ -5,7 +5,7 @@
 #
 
 import asyncio
-from typing import AsyncGenerator, Optional
+from typing import AsyncGenerator, List, Mapping, Optional
 
 from loguru import logger
 from pydantic import BaseModel
@@ -13,12 +13,13 @@ from pydantic import BaseModel
 from pipecat.frames.frames import (
     CancelFrame,
     EndFrame,
+    ErrorFrame,
     Frame,
     InterimTranscriptionFrame,
     StartFrame,
     TranscriptionFrame,
 )
-from pipecat.services.stt_service import STTService
+from pipecat.services.stt_service import SegmentedSTTService, STTService
 from pipecat.transcriptions.language import Language
 from pipecat.utils.time import time_now_iso8601
 
@@ -31,7 +32,59 @@ except ModuleNotFoundError as e:
     raise Exception(f"Missing module: {e}")
 
 
-class ParakeetSTTService(STTService):
+def language_to_riva_language(language: Language) -> Optional[str]:
+    """Maps Language enum to Riva ASR language codes.
+
+    Source:
+    https://docs.nvidia.com/deeplearning/riva/user-guide/docs/asr/asr-riva-build-table.html?highlight=fr%20fr
+
+    Args:
+        language: Language enum value.
+
+    Returns:
+        Optional[str]: Riva language code or None if not supported.
+    """
+    language_map = {
+        # Arabic
+        Language.AR: "ar-AR",
+        # English
+        Language.EN: "en-US",  # Default to US
+        Language.EN_US: "en-US",
+        Language.EN_GB: "en-GB",
+        # French
+        Language.FR: "fr-FR",
+        Language.FR_FR: "fr-FR",
+        # German
+        Language.DE: "de-DE",
+        Language.DE_DE: "de-DE",
+        # Hindi
+        Language.HI: "hi-IN",
+        Language.HI_IN: "hi-IN",
+        # Italian
+        Language.IT: "it-IT",
+        Language.IT_IT: "it-IT",
+        # Japanese
+        Language.JA: "ja-JP",
+        Language.JA_JP: "ja-JP",
+        # Korean
+        Language.KO: "ko-KR",
+        Language.KO_KR: "ko-KR",
+        # Portuguese
+        Language.PT: "pt-BR",  # Default to Brazilian
+        Language.PT_BR: "pt-BR",
+        # Russian
+        Language.RU: "ru-RU",
+        Language.RU_RU: "ru-RU",
+        # Spanish
+        Language.ES: "es-ES",  # Default to Spain
+        Language.ES_ES: "es-ES",
+        Language.ES_US: "es-US",  # US Spanish
+    }
+
+    return language_map.get(language)
+
+
+class RivaSTTService(STTService):
     class InputParams(BaseModel):
         language: Optional[Language] = Language.EN_US
 
@@ -40,7 +93,10 @@ class ParakeetSTTService(STTService):
         *,
         api_key: str,
         server: str = "grpc.nvcf.nvidia.com:443",
-        function_id: str = "1598d209-5e27-4d3c-8079-4751568b1081",
+        model_function_map: Mapping[str, str] = {
+            "function_id": "1598d209-5e27-4d3c-8079-4751568b1081",
+            "model_name": "parakeet-ctc-1.1b-asr",
+        },
         sample_rate: Optional[int] = None,
         params: InputParams = InputParams(),
         **kwargs,
@@ -48,7 +104,7 @@ class ParakeetSTTService(STTService):
         super().__init__(sample_rate=sample_rate, **kwargs)
         self._api_key = api_key
         self._profanity_filter = False
-        self._automatic_punctuation = False
+        self._automatic_punctuation = True
         self._no_verbatim_transcripts = False
         self._language_code = params.language
         self._boosted_lm_words = None
@@ -60,11 +116,12 @@ class ParakeetSTTService(STTService):
         self._stop_history_eou = -1
         self._stop_threshold_eou = -1.0
         self._custom_configuration = ""
+        self._function_id = model_function_map.get("function_id")
 
-        self.set_model_name("parakeet-ctc-1.1b-asr")
+        self.set_model_name(model_function_map.get("model_name"))
 
         metadata = [
-            ["function-id", function_id],
+            ["function-id", self._function_id],
             ["authorization", f"Bearer {api_key}"],
         ]
         auth = riva.client.Auth(None, True, server, metadata)
@@ -79,6 +136,13 @@ class ParakeetSTTService(STTService):
     def can_generate_metrics(self) -> bool:
         return False
 
+    async def set_model(self, model: str):
+        logger.warning(f"Cannot set model after initialization. Set model and function id like so:")
+        example = {"function_id": "<UUID>", "model_name": "<model_name>"}
+        logger.warning(
+            f"{self.__class__.__name__}(api_key=<api_key>, model_function_map={example})"
+        )
+
     async def start(self, frame: StartFrame):
         await super().start(frame)
 
@@ -196,3 +260,262 @@ class ParakeetSTTService(STTService):
 
     def __iter__(self):
         return self
+
+
+class RivaSegmentedSTTService(SegmentedSTTService):
+    """Speech-to-text service using NVIDIA Riva's offline/batch models.
+
+    By default, his service uses NVIDIA's Riva Canary ASR API to perform speech-to-text
+    transcription on audio segments. It inherits from SegmentedSTTService to handle
+    audio buffering and speech detection.
+
+    Args:
+        api_key: NVIDIA API key for authentication
+        server: Riva server address (defaults to NVIDIA Cloud Function endpoint)
+        model_function_map: Mapping of model name and its corresponding NVIDIA Cloud Function ID
+        sample_rate: Audio sample rate in Hz. If not provided, uses the pipeline's rate
+        params: Additional configuration parameters for Riva
+        **kwargs: Additional arguments passed to SegmentedSTTService
+    """
+
+    class InputParams(BaseModel):
+        language: Optional[Language] = Language.EN_US
+        profanity_filter: bool = False
+        automatic_punctuation: bool = True
+        verbatim_transcripts: bool = False
+        boosted_lm_words: Optional[List[str]] = None
+        boosted_lm_score: float = 4.0
+
+    def __init__(
+        self,
+        *,
+        api_key: str,
+        server: str = "grpc.nvcf.nvidia.com:443",
+        model_function_map: Mapping[str, str] = {
+            "function_id": "ee8dc628-76de-4acc-8595-1836e7e857bd",
+            "model_name": "canary-1b-asr",
+        },
+        sample_rate: Optional[int] = None,
+        params: InputParams = InputParams(),
+        **kwargs,
+    ):
+        super().__init__(sample_rate=sample_rate, **kwargs)
+
+        # Set model name
+        self.set_model_name(model_function_map.get("model_name"))
+
+        # Initialize Riva settings
+        self._api_key = api_key
+        self._server = server
+        self._function_id = model_function_map.get("function_id")
+        self._model_name = model_function_map.get("model_name")
+
+        # Store the language as a Language enum and as a string
+        self._language_enum = params.language or Language.EN_US
+        self._language = self.language_to_service_language(self._language_enum) or "en-US"
+
+        # Configure transcription parameters
+        self._profanity_filter = params.profanity_filter
+        self._automatic_punctuation = params.automatic_punctuation
+        self._verbatim_transcripts = params.verbatim_transcripts
+        self._boosted_lm_words = params.boosted_lm_words
+        self._boosted_lm_score = params.boosted_lm_score
+
+        # Voice activity detection thresholds (use Riva defaults)
+        self._start_history = -1
+        self._start_threshold = -1.0
+        self._stop_history = -1
+        self._stop_threshold = -1.0
+        self._stop_history_eou = -1
+        self._stop_threshold_eou = -1.0
+        self._custom_configuration = ""
+
+        # Create Riva client
+        self._config = None
+        self._asr_service = None
+        self._settings = {"language": self._language_enum}
+
+    def language_to_service_language(self, language: Language) -> Optional[str]:
+        """Convert pipecat Language enum to Riva's language code."""
+        return language_to_riva_language(language)
+
+    def _initialize_client(self):
+        """Initialize the Riva ASR client with authentication metadata."""
+        if self._asr_service is not None:
+            return
+
+        # Set up authentication metadata for NVIDIA Cloud Functions
+        metadata = [
+            ["function-id", self._function_id],
+            ["authorization", f"Bearer {self._api_key}"],
+        ]
+
+        # Create authenticated client
+        auth = riva.client.Auth(None, True, self._server, metadata)
+        self._asr_service = riva.client.ASRService(auth)
+
+        logger.info(f"Initialized RivaSegmentedSTTService with model: {self.model_name}")
+
+    def _create_recognition_config(self):
+        """Create the Riva ASR recognition configuration."""
+        # Create base configuration
+        config = riva.client.RecognitionConfig(
+            language_code=self._language,  # Now using the string, not a tuple
+            max_alternatives=1,
+            profanity_filter=self._profanity_filter,
+            enable_automatic_punctuation=self._automatic_punctuation,
+            verbatim_transcripts=self._verbatim_transcripts,
+        )
+
+        # Add word boosting if specified
+        if self._boosted_lm_words:
+            riva.client.add_word_boosting_to_config(
+                config, self._boosted_lm_words, self._boosted_lm_score
+            )
+
+        # Add voice activity detection parameters
+        riva.client.add_endpoint_parameters_to_config(
+            config,
+            self._start_history,
+            self._start_threshold,
+            self._stop_history,
+            self._stop_history_eou,
+            self._stop_threshold,
+            self._stop_threshold_eou,
+        )
+
+        # Add any custom configuration
+        if self._custom_configuration:
+            riva.client.add_custom_configuration_to_config(config, self._custom_configuration)
+
+        return config
+
+    def can_generate_metrics(self) -> bool:
+        """Indicates whether this service can generate processing metrics."""
+        return True
+
+    async def set_model(self, model: str):
+        logger.warning(f"Cannot set model after initialization. Set model and function id like so:")
+        example = {"function_id": "<UUID>", "model_name": "<model_name>"}
+        logger.warning(
+            f"{self.__class__.__name__}(api_key=<api_key>, model_function_map={example})"
+        )
+
+    async def start(self, frame: StartFrame):
+        """Initialize the service when the pipeline starts."""
+        await super().start(frame)
+        self._initialize_client()
+        self._config = self._create_recognition_config()
+
+    async def set_language(self, language: Language):
+        """Set the language for the STT service."""
+        logger.info(f"Switching STT language to: [{language}]")
+        self._language_enum = language
+        self._language = self.language_to_service_language(language) or "en-US"
+        self._settings["language"] = language
+
+        # Update configuration with new language
+        if self._config:
+            self._config.language_code = self._language
+
+    async def run_stt(self, audio: bytes) -> AsyncGenerator[Frame, None]:
+        """Transcribe an audio segment.
+
+        Args:
+            audio: Raw audio bytes in WAV format (already converted by base class).
+
+        Yields:
+            Frame: TranscriptionFrame containing the transcribed text.
+        """
+        try:
+            await self.start_processing_metrics()
+            await self.start_ttfb_metrics()
+
+            # Make sure the client is initialized
+            if self._asr_service is None:
+                self._initialize_client()
+
+            # Make sure the config is created
+            if self._config is None:
+                self._config = self._create_recognition_config()
+
+            # Type assertion to satisfy the IDE
+            assert self._asr_service is not None, "ASR service not initialized"
+            assert self._config is not None, "Recognition config not created"
+
+            # Process audio with Riva ASR - explicitly request non-future response
+            raw_response = self._asr_service.offline_recognize(audio, self._config, future=False)
+
+            await self.stop_ttfb_metrics()
+            await self.stop_processing_metrics()
+
+            # Process the response - handle different possible return types
+            try:
+                # If it's a future-like object, get the result
+                if hasattr(raw_response, "result"):
+                    response = raw_response.result()
+                else:
+                    response = raw_response
+
+                # Process transcription results
+                transcription_found = False
+
+                # Now we can safely check results
+                # Type hint for the IDE
+                results = getattr(response, "results", [])
+
+                for result in results:
+                    alternatives = getattr(result, "alternatives", [])
+                    if alternatives:
+                        text = alternatives[0].transcript.strip()
+                        if text:
+                            logger.debug(f"Transcription: [{text}]")
+                            yield TranscriptionFrame(
+                                text, "", time_now_iso8601(), self._language_enum
+                            )
+                            transcription_found = True
+
+                if not transcription_found:
+                    logger.debug("No transcription results found in Riva response")
+
+            except AttributeError as ae:
+                logger.error(f"Unexpected response structure from Riva: {ae}")
+                yield ErrorFrame(f"Unexpected Riva response format: {str(ae)}")
+
+        except Exception as e:
+            logger.exception(f"Riva Canary ASR error: {e}")
+            yield ErrorFrame(f"Riva Canary ASR error: {str(e)}")
+
+
+class ParakeetSTTService(RivaSTTService):
+    """Deprecated: Use RivaSTTService instead."""
+
+    def __init__(
+        self,
+        *,
+        api_key: str,
+        server: str = "grpc.nvcf.nvidia.com:443",
+        model_function_map: Mapping[str, str] = {
+            "function_id": "1598d209-5e27-4d3c-8079-4751568b1081",
+            "model_name": "parakeet-ctc-1.1b-asr",
+        },
+        sample_rate: Optional[int] = None,
+        params: RivaSTTService.InputParams = RivaSTTService.InputParams(),  # Use parent class's type
+        **kwargs,
+    ):
+        super().__init__(
+            api_key=api_key,
+            server=server,
+            model_function_map=model_function_map,
+            sample_rate=sample_rate,
+            params=params,
+            **kwargs,
+        )
+        import warnings
+
+        with warnings.catch_warnings():
+            warnings.simplefilter("always")
+            warnings.warn(
+                "`ParakeetSTTService` is deprecated, use `RivaSTTService` instead.",
+                DeprecationWarning,
+            )

src/pipecat/services/riva/tts.py

@@ -5,7 +5,11 @@
 #
 
 import asyncio
-from typing import AsyncGenerator, Optional
+import os
+from typing import AsyncGenerator, Mapping, Optional
+
+# Suppress gRPC fork warnings
+os.environ["GRPC_ENABLE_FORK_SUPPORT"] = "false"
 
 from loguru import logger
 from pydantic import BaseModel
@@ -27,10 +31,10 @@ except ModuleNotFoundError as e:
     logger.error("In order to use NVIDIA Riva TTS, you need to `pip install pipecat-ai[riva]`.")
     raise Exception(f"Missing module: {e}")
 
-FASTPITCH_TIMEOUT_SECS = 5
+RIVA_TTS_TIMEOUT_SECS = 5
 
 
-class FastPitchTTSService(TTSService):
+class RivaTTSService(TTSService):
     class InputParams(BaseModel):
         language: Optional[Language] = Language.EN_US
         quality: Optional[int] = 20
@@ -38,11 +42,14 @@ class FastPitchTTSService(TTSService):
     def __init__(
         self,
         *,
-        api_key: str,
+        api_key: str = None,
         server: str = "grpc.nvcf.nvidia.com:443",
-        voice_id: str = "English-US.Female-1",
+        voice_id: str = "Magpie-Multilingual.EN-US.Ray",
         sample_rate: Optional[int] = None,
-        function_id: str = "0149dedb-2be8-4195-b9a0-e57e0e14f972",
+        model_function_map: Mapping[str, str] = {
+            "function_id": "877104f7-e885-42b9-8de8-f6e4c6303969",
+            "model_name": "magpie-tts-multilingual",
+        },
         params: InputParams = InputParams(),
         **kwargs,
     ):
@@ -51,12 +58,13 @@ class FastPitchTTSService(TTSService):
         self._voice_id = voice_id
         self._language_code = params.language
         self._quality = params.quality
+        self._function_id = model_function_map.get("function_id")
 
-        self.set_model_name("fastpitch-hifigan-tts")
+        self.set_model_name(model_function_map.get("model_name"))
         self.set_voice(voice_id)
 
         metadata = [
-            ["function-id", function_id],
+            ["function-id", self._function_id],
             ["authorization", f"Bearer {api_key}"],
         ]
         auth = riva.client.Auth(None, True, server, metadata)
@@ -68,6 +76,13 @@ class FastPitchTTSService(TTSService):
             riva.client.proto.riva_tts_pb2.RivaSynthesisConfigRequest()
         )
 
+    async def set_model(self, model: str):
+        logger.warning(f"Cannot set model after initialization. Set model and function id like so:")
+        example = {"function_id": "<UUID>", "model_name": "<model_name>"}
+        logger.warning(
+            f"{self.__class__.__name__}(api_key=<api_key>, model_function_map={example})"
+        )
+
     async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
         def read_audio_responses(queue: asyncio.Queue):
             def add_response(r):
@@ -100,7 +115,7 @@ class FastPitchTTSService(TTSService):
             await asyncio.to_thread(read_audio_responses, queue)
 
             # Wait for the thread to start.
-            resp = await asyncio.wait_for(queue.get(), FASTPITCH_TIMEOUT_SECS)
+            resp = await asyncio.wait_for(queue.get(), RIVA_TTS_TIMEOUT_SECS)
             while resp:
                 await self.stop_ttfb_metrics()
                 frame = TTSAudioRawFrame(
@@ -109,9 +124,46 @@ class FastPitchTTSService(TTSService):
                     num_channels=1,
                 )
                 yield frame
-                resp = await asyncio.wait_for(queue.get(), FASTPITCH_TIMEOUT_SECS)
+                resp = await asyncio.wait_for(queue.get(), RIVA_TTS_TIMEOUT_SECS)
         except asyncio.TimeoutError:
             logger.error(f"{self} timeout waiting for audio response")
 
         await self.start_tts_usage_metrics(text)
         yield TTSStoppedFrame()
+
+
+class FastPitchTTSService(RivaTTSService):
+    class InputParams(BaseModel):
+        language: Optional[Language] = Language.EN_US
+        quality: Optional[int] = 20
+
+    def __init__(
+        self,
+        *,
+        api_key: str = None,
+        server: str = "grpc.nvcf.nvidia.com:443",
+        voice_id: str = "English-US.Female-1",
+        sample_rate: Optional[int] = None,
+        model_function_map: Mapping[str, str] = {
+            "function_id": "0149dedb-2be8-4195-b9a0-e57e0e14f972",
+            "model_name": "fastpitch-hifigan-tts",
+        },
+        params: InputParams = InputParams(),
+        **kwargs,
+    ):
+        super().__init__(
+            api_key=api_key,
+            voice_id=voice_id,
+            sample_rate=sample_rate,
+            model_function_map=model_function_map,
+            params=params,
+            **kwargs,
+        )
+        import warnings
+
+        with warnings.catch_warnings():
+            warnings.simplefilter("always")
+            warnings.warn(
+                "`FastPitchTTSService` is deprecated, use `RivaTTSService` instead.",
+                DeprecationWarning,
+            )

src/pipecat/services/simli/video.py

@@ -64,13 +64,16 @@ class SimliVideoService(FrameProcessor):
         async for audio_frame in self._simli_client.getAudioStreamIterator():
             resampled_frames = self._pipecat_resampler.resample(audio_frame)
             for resampled_frame in resampled_frames:
-                await self.push_frame(
+                audio_array = resampled_frame.to_ndarray()
-                    TTSAudioRawFrame(
+                # Only push frame is there is audio (e.g. not silence)
-                        audio=resampled_frame.to_ndarray().tobytes(),
+                if audio_array.any():
-                        sample_rate=self._pipecat_resampler.rate,
+                    await self.push_frame(
-                        num_channels=1,
+                        TTSAudioRawFrame(
-                    ),
+                            audio=audio_array.tobytes(),
-                )
+                            sample_rate=self._pipecat_resampler.rate,
+                            num_channels=1,
+                        ),
+                    )
 
     async def _consume_and_process_video(self):
         await self._pipecat_resampler_event.wait()

src/pipecat/transports/base_output.py

@@ -369,7 +369,7 @@ class BaseOutputTransport(FrameProcessor):
         #
 
         def _create_audio_task(self):
-            if not self._audio_task and self._params.audio_out_enabled:
+            if not self._audio_task:
                 self._audio_queue = asyncio.Queue()
                 self._audio_task = self._transport.create_task(self._audio_task_handler())
 
@@ -380,7 +380,9 @@ class BaseOutputTransport(FrameProcessor):
 
         async def _bot_started_speaking(self):
             if not self._bot_speaking:
-                logger.debug(f"Bot [{self._destination}] started speaking")
+                logger.debug(
+                    f"Bot{f' [{self._destination}]' if self._destination else ''} started speaking"
+                )
 
                 downstream_frame = BotStartedSpeakingFrame()
                 downstream_frame.transport_destination = self._destination
@@ -393,7 +395,9 @@ class BaseOutputTransport(FrameProcessor):
 
         async def _bot_stopped_speaking(self):
             if self._bot_speaking:
-                logger.debug(f"Bot [{self._destination}] stopped speaking")
+                logger.debug(
+                    f"Bot{f' [{self._destination}]' if self._destination else ''} stopped speaking"
+                )
 
                 downstream_frame = BotStoppedSpeakingFrame()
                 downstream_frame.transport_destination = self._destination

src/pipecat/transports/services/daily.py

@@ -11,14 +11,6 @@ from dataclasses import dataclass
 from typing import Any, Awaitable, Callable, Dict, Mapping, Optional
 
 import aiohttp
-from daily import (
-    AudioData,
-    CustomAudioSource,
-    VideoFrame,
-    VirtualCameraDevice,
-    VirtualMicrophoneDevice,
-    VirtualSpeakerDevice,
-)
 from loguru import logger
 from pydantic import BaseModel
 
@@ -50,7 +42,17 @@ from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.utils.asyncio import BaseTaskManager
 
 try:
-    from daily import CallClient, Daily, EventHandler
+    from daily import (
+        AudioData,
+        CallClient,
+        CustomAudioSource,
+        Daily,
+        EventHandler,
+        VideoFrame,
+        VirtualCameraDevice,
+        VirtualMicrophoneDevice,
+        VirtualSpeakerDevice,
+    )
 except ModuleNotFoundError as e:
     logger.error(f"Exception: {e}")
     logger.error(