address changes

examples/foundational/07a-interruptible-speechmatics-vad.py

@@ -6,6 +6,7 @@
 
 import os
 
+import aiohttp
 from dotenv import load_dotenv
 from loguru import logger
 
@@ -89,90 +90,89 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     """
 
     logger.info(f"Starting bot")
-
-    stt = SpeechmaticsSTTService(
-        api_key=os.getenv("SPEECHMATICS_API_KEY"),
-        params=SpeechmaticsSTTService.InputParams(
-            language=Language.EN,
-            enable_vad=True,
-            enable_diarization=True,
-            focus_speakers=["S1"],
-            end_of_utterance_silence_trigger=0.5,
-            speaker_active_format="<{speaker_id}>{text}</{speaker_id}>",
-            speaker_passive_format="<PASSIVE><{speaker_id}>{text}</{speaker_id}></PASSIVE>",
-        ),
-    )
-
-    tts = SpeechmaticsTTSService(
-        api_key=os.getenv("SPEECHMATICS_API_KEY"),
-        params=SpeechmaticsTTSService.InputParams(
-            voice="sarah",
-        ),
-    )
-
-    llm = OpenAILLMService(
-        api_key=os.getenv("OPENAI_API_KEY"),
-        params=BaseOpenAILLMService.InputParams(temperature=0.75),
-    )
-
-    messages = [
-        {
-            "role": "system",
-            "content": (
-                "You are a helpful British assistant called Sarah. "
-                "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. "
-                "Always include punctuation in your responses. "
-                "Give very short replies - do not give longer replies unless strictly necessary. "
-                "Respond to what the user said in a concise, funny, creative and helpful way. "
-                "Use `<Sn/>` tags to identify different speakers - do not use tags in your replies. "
-                "Do not respond to speakers within `<PASSIVE/>` tags unless explicitly asked to. "
+    async with aiohttp.ClientSession() as session:
+        stt = SpeechmaticsSTTService(
+            api_key=os.getenv("SPEECHMATICS_API_KEY"),
+            params=SpeechmaticsSTTService.InputParams(
+                language=Language.EN,
+                enable_vad=True,
+                enable_diarization=True,
+                focus_speakers=["S1"],
+                end_of_utterance_silence_trigger=0.5,
+                speaker_active_format="<{speaker_id}>{text}</{speaker_id}>",
+                speaker_passive_format="<PASSIVE><{speaker_id}>{text}</{speaker_id}></PASSIVE>",
             ),
-        },
-    ]
+        )
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(
-        context,
-        user_params=LLMUserAggregatorParams(aggregation_timeout=0.005),
-    )
+        tts = SpeechmaticsTTSService(
+            api_key=os.getenv("SPEECHMATICS_API_KEY"),
+            voice_id="sarah",
+            aiohttp_session=session,
+        )
 
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
+        llm = OpenAILLMService(
+            api_key=os.getenv("OPENAI_API_KEY"),
+            params=BaseOpenAILLMService.InputParams(temperature=0.75),
+        )
+
+        messages = [
+            {
+                "role": "system",
+                "content": (
+                    "You are a helpful British assistant called Sarah. "
+                    "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. "
+                    "Always include punctuation in your responses. "
+                    "Give very short replies - do not give longer replies unless strictly necessary. "
+                    "Respond to what the user said in a concise, funny, creative and helpful way. "
+                    "Use `<Sn/>` tags to identify different speakers - do not use tags in your replies. "
+                    "Do not respond to speakers within `<PASSIVE/>` tags unless explicitly asked to. "
+                ),
+            },
         ]
-    )
 
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
+        context = LLMContext(messages)
+        context_aggregator = LLMContextAggregatorPair(
+            context,
+            user_params=LLMUserAggregatorParams(aggregation_timeout=0.005),
+        )
 
-    @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": "Say a short hello to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                stt,
+                context_aggregator.user(),  # User responses
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
 
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
+        task = PipelineTask(
+            pipeline,
+            params=PipelineParams(
+                enable_metrics=True,
+                enable_usage_metrics=True,
+            ),
+            idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
+        )
 
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+        @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": "Say a short hello to the user."})
+            await task.queue_frames([LLMRunFrame()])
 
-    await runner.run(task)
+        @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):

examples/foundational/07a-interruptible-speechmatics.py

@@ -6,6 +6,7 @@
 
 import os
 
+import aiohttp
 from dotenv import load_dotenv
 from loguru import logger
 
@@ -82,85 +83,85 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     """
     logger.info(f"Starting bot")
 
-    stt = SpeechmaticsSTTService(
-        api_key=os.getenv("SPEECHMATICS_API_KEY"),
-        params=SpeechmaticsSTTService.InputParams(
-            language=Language.EN,
-            enable_diarization=True,
-            end_of_utterance_silence_trigger=0.5,
-            speaker_active_format="<{speaker_id}>{text}</{speaker_id}>",
-        ),
-    )
-
-    tts = SpeechmaticsTTSService(
-        api_key=os.getenv("SPEECHMATICS_API_KEY"),
-        params=SpeechmaticsTTSService.InputParams(
-            voice="sarah",
-        ),
-    )
-
-    llm = OpenAILLMService(
-        api_key=os.getenv("OPENAI_API_KEY"),
-        params=BaseOpenAILLMService.InputParams(temperature=0.75),
-    )
-
-    messages = [
-        {
-            "role": "system",
-            "content": (
-                "You are a helpful British assistant called Sarah. "
-                "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. "
-                "Always include punctuation in your responses. "
-                "Give very short replies - do not give longer replies unless strictly necessary. "
-                "Respond to what the user said in a concise, funny, creative and helpful way. "
-                "Use `<Sn/>` tags to identify different speakers - do not use tags in your replies."
+    async with aiohttp.ClientSession() as session:
+        stt = SpeechmaticsSTTService(
+            api_key=os.getenv("SPEECHMATICS_API_KEY"),
+            params=SpeechmaticsSTTService.InputParams(
+                language=Language.EN,
+                enable_diarization=True,
+                end_of_utterance_silence_trigger=0.5,
+                speaker_active_format="<{speaker_id}>{text}</{speaker_id}>",
             ),
-        },
-    ]
+        )
 
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(
-        context,
-        user_params=LLMUserAggregatorParams(aggregation_timeout=0.005),
-    )
+        tts = SpeechmaticsTTSService(
+            api_key=os.getenv("SPEECHMATICS_API_KEY"),
+            voice_id="sarah",
+            aiohttp_session=session,
+        )
 
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,  # STT
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
+        llm = OpenAILLMService(
+            api_key=os.getenv("OPENAI_API_KEY"),
+            params=BaseOpenAILLMService.InputParams(temperature=0.75),
+        )
+
+        messages = [
+            {
+                "role": "system",
+                "content": (
+                    "You are a helpful British assistant called Sarah. "
+                    "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. "
+                    "Always include punctuation in your responses. "
+                    "Give very short replies - do not give longer replies unless strictly necessary. "
+                    "Respond to what the user said in a concise, funny, creative and helpful way. "
+                    "Use `<Sn/>` tags to identify different speakers - do not use tags in your replies."
+                ),
+            },
         ]
-    )
 
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
+        context = LLMContext(messages)
+        context_aggregator = LLMContextAggregatorPair(
+            context,
+            user_params=LLMUserAggregatorParams(aggregation_timeout=0.005),
+        )
 
-    @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": "Say a short hello to the user."})
-        await task.queue_frames([LLMRunFrame()])
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                stt,  # STT
+                context_aggregator.user(),  # User responses
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
 
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
+        task = PipelineTask(
+            pipeline,
+            params=PipelineParams(
+                enable_metrics=True,
+                enable_usage_metrics=True,
+            ),
+            idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
+        )
 
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+        @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": "Say a short hello to the user."})
+            await task.queue_frames([LLMRunFrame()])
 
-    await runner.run(task)
+        @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):

src/pipecat/services/speechmatics/tts.py

@@ -6,7 +6,6 @@
 
 """Speechmatics TTS service integration."""
 
-import os
 from typing import AsyncGenerator, Optional
 from urllib.parse import urlencode
 
@@ -41,55 +40,56 @@ class SpeechmaticsTTSService(TTSService):
     It converts text to speech and returns raw PCM audio data for real-time playback.
     """
 
+    SPEECHMATICS_SAMPLE_RATE = 16000
+
     class InputParams(BaseModel):
-        """Configuration parameters for Speechmatics TTS service.
+        """Optional input parameters for Speechmatics TTS configuration."""
 
-        Parameters:
-            voice: Voice model to use for synthesis. Defaults to "sarah".
-        """
-
-        voice: str = "sarah"
+        pass
 
     def __init__(
         self,
         *,
-        api_key: str | None = None,
-        base_url: str | None = None,
-        aiohttp_session: aiohttp.ClientSession | None = None,
-        sample_rate: Optional[int] = 16000,
-        params: InputParams | None = None,
+        api_key: str,
+        base_url: str = "https://preview.tts.speechmatics.com",
+        voice_id: str = "sarah",
+        aiohttp_session: aiohttp.ClientSession,
+        sample_rate: Optional[int] = SPEECHMATICS_SAMPLE_RATE,
+        params: Optional[InputParams] = None,
         **kwargs,
     ):
         """Initialize the Speechmatics TTS service.
 
         Args:
-            api_key: Speechmatics API key for authentication. Uses environment variable
-                `SPEECHMATICS_API_KEY` if not provided.
-            base_url: Base URL for Speechmatics TTS API. Defaults to
-                `https://preview.tts.speechmatics.com`.
+            api_key: Speechmatics API key for authentication.
+            base_url: Base URL for Speechmatics TTS API.
+            voice_id: Voice model to use for synthesis.
             aiohttp_session: Shared aiohttp session for HTTP requests.
-            sample_rate: Audio sample rate in Hz. Defaults to 16000.
+            sample_rate: Audio sample rate in Hz.
             params: Optional[InputParams]: Input parameters for the service.
             **kwargs: Additional arguments passed to TTSService.
         """
+        if sample_rate and sample_rate != self.SPEECHMATICS_SAMPLE_RATE:
+            logger.warning(
+                f"Speechmatics TTS only supports {self.SPEECHMATICS_SAMPLE_RATE}Hz sample rate. "
+                f"Current rate of {sample_rate}Hz may cause issues."
+            )
         super().__init__(sample_rate=sample_rate, **kwargs)
 
         # Service parameters
-        self._api_key: str = api_key or os.getenv("SPEECHMATICS_API_KEY")
-        self._base_url: str = base_url or "https://preview.tts.speechmatics.com"
-        self._session = aiohttp_session or aiohttp.ClientSession()
+        self._api_key: str = api_key
+        self._base_url: str = base_url
+        self._session = aiohttp_session
 
         # Check we have required attributes
         if not self._api_key:
             raise ValueError("Missing Speechmatics API key")
-        if not self._base_url:
-            raise ValueError("Missing Speechmatics base URL")
 
         # Default parameters
         self._params = params or SpeechmaticsTTSService.InputParams()
 
-        # Set voice from parameters
-        self.set_voice(self._params.voice)
+        # Set voice from constructor parameter
+        self.set_voice(voice_id)
 
     def can_generate_metrics(self) -> bool:
         """Check if this service can generate processing metrics.
@@ -140,23 +140,6 @@ class SpeechmaticsTTSService(TTSService):
                 first_chunk = True
                 buffer = b""
 
-                # Helper to move all complete 2-byte int16 samples from buffer into a frame
-                def _emit_complete_samples():
-                    nonlocal buffer
-                    if len(buffer) < 2:
-                        return None
-                    complete_samples = len(buffer) // 2
-                    complete_bytes = complete_samples * 2
-
-                    audio_data = buffer[:complete_bytes]
-                    buffer = buffer[complete_bytes:]  # Keep remaining bytes for next iteration
-
-                    return TTSAudioRawFrame(
-                        audio=audio_data,
-                        sample_rate=self.sample_rate,
-                        num_channels=1,
-                    )
-
                 async for chunk in response.content.iter_any():
                     if not chunk:
                         continue
@@ -166,15 +149,19 @@ class SpeechmaticsTTSService(TTSService):
 
                     buffer += chunk
 
-                    # Emit a frame for all complete samples currently in buffer
-                    frame = _emit_complete_samples()
-                    if frame:
-                        yield frame
+                    # Emit all complete 2-byte int16 samples from buffer
+                    if len(buffer) >= 2:
+                        complete_samples = len(buffer) // 2
+                        complete_bytes = complete_samples * 2
 
-                # Process any remaining bytes in buffer after streaming ends
-                frame = _emit_complete_samples()
-                if frame:
-                    yield frame
+                        audio_data = buffer[:complete_bytes]
+                        buffer = buffer[complete_bytes:]  # Keep remaining bytes for next iteration
+
+                        yield TTSAudioRawFrame(
+                            audio=audio_data,
+                            sample_rate=self.sample_rate,
+                            num_channels=1,
+                        )
 
         except Exception as e:
             logger.exception(f"Error generating TTS: {e}")