Add voice_settings and optimize_streaming_latency to ElevenLabs

examples/foundational/07d-interruptible-elevenlabs.py

@@ -5,26 +5,27 @@
 #
 
 import asyncio
-import aiohttp
 import os
 import sys
 
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
 from pipecat.frames.frames import LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.llm_response import (
-    LLMAssistantResponseAggregator, LLMUserResponseAggregator)
+    LLMAssistantResponseAggregator,
+    LLMUserResponseAggregator,
+)
 from pipecat.services.elevenlabs import ElevenLabsTTSService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 from pipecat.vad.silero import SileroVADAnalyzer
 
-from runner import configure
-
-from loguru import logger
-
-from dotenv import load_dotenv
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -43,18 +44,16 @@ async def main():
                 audio_out_enabled=True,
                 transcription_enabled=True,
                 vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer()
-            )
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
         )
 
         tts = ElevenLabsTTSService(
             api_key=os.getenv("ELEVENLABS_API_KEY", ""),
             voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
         )
 
-        llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_API_KEY"),
-            model="gpt-4o")
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
         messages = [
             {
@@ -66,28 +65,32 @@ async def main():
         tma_in = LLMUserResponseAggregator(messages)
         tma_out = LLMAssistantResponseAggregator(messages)
 
-        pipeline = Pipeline([
-            transport.input(),   # Transport user input
-            tma_in,              # User responses
-            llm,                 # LLM
-            tts,                 # TTS
-            transport.output(),  # Transport bot output
-            tma_out              # Assistant spoken responses
-        ])
-
-        task = PipelineTask(pipeline, PipelineParams(
-            allow_interruptions=True,
-            enable_metrics=True,
-            enable_usage_metrics=True,
-            report_only_initial_ttfb=True,
-        ))
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                tma_in,  # User responses
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                tma_out,  # Assistant spoken responses
+            ]
+        )
+
+        task = PipelineTask(
+            pipeline,
+            PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+                report_only_initial_ttfb=True,
+            ),
+        )
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
             transport.capture_participant_transcription(participant["id"])
             # Kick off the conversation.
-            messages.append(
-                {"role": "system", "content": "Please introduce yourself to the user."})
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
             await task.queue_frames([LLMMessagesFrame(messages)])
 
         runner = PipelineRunner()

src/pipecat/services/elevenlabs.py

@@ -7,9 +7,10 @@
 import asyncio
 import base64
 import json
+from typing import Any, AsyncGenerator, Dict, List, Literal, Mapping, Optional, Tuple
 
-from typing import Any, AsyncGenerator, List, Literal, Mapping, Tuple
-from pydantic import BaseModel
+from loguru import logger
+from pydantic import BaseModel, model_validator
 
 from pipecat.frames.frames import (
     CancelFrame,
@@ -19,19 +20,19 @@
     StartInterruptionFrame,
     TTSAudioRawFrame,
     TTSStartedFrame,
-    TTSStoppedFrame)
+    TTSStoppedFrame,
+)
 from pipecat.processors.frame_processor import FrameDirection
 from pipecat.services.ai_services import AsyncWordTTSService
 
-from loguru import logger
-
 # See .env.example for ElevenLabs configuration needed
 try:
     import websockets
 except ModuleNotFoundError as e:
     logger.error(f"Exception: {e}")
     logger.error(
-        "In order to use ElevenLabs, you need to `pip install pipecat-ai[elevenlabs]`. Also, set `ELEVENLABS_API_KEY` environment variable.")
+        "In order to use ElevenLabs, you need to `pip install pipecat-ai[elevenlabs]`. Also, set `ELEVENLABS_API_KEY` environment variable."
+    )
     raise Exception(f"Missing module: {e}")
 
 
@@ -49,7 +50,7 @@ def sample_rate_from_output_format(output_format: str) -> int:
 
 
 def calculate_word_times(
-        alignment_info: Mapping[str, Any], cumulative_time: float
+    alignment_info: Mapping[str, Any], cumulative_time: float
 ) -> List[Tuple[str, float]]:
     zipped_times = list(zip(alignment_info["chars"], alignment_info["charStartTimesMs"]))
 
@@ -59,7 +60,7 @@ def calculate_word_times(
     # and using the previous word time, also taking into account there might not
     # be a space at the end.
     times = []
-    for (i, (a, b)) in enumerate(zipped_times):
+    for i, (a, b) in enumerate(zipped_times):
         if a == " " or i == len(zipped_times) - 1:
             t = cumulative_time + (zipped_times[i - 1][1] / 1000.0)
             times.append(t)
@@ -72,16 +73,32 @@ def calculate_word_times(
 class ElevenLabsTTSService(AsyncWordTTSService):
     class InputParams(BaseModel):
         output_format: Literal["pcm_16000", "pcm_22050", "pcm_24000", "pcm_44100"] = "pcm_16000"
+        optimize_streaming_latency: Optional[str] = None
+        stability: Optional[float] = None
+        similarity_boost: Optional[float] = None
+        style: Optional[float] = None
+        use_speaker_boost: Optional[bool] = None
+
+        @model_validator(mode="after")
+        def validate_voice_settings(self):
+            stability = self.stability
+            similarity_boost = self.similarity_boost
+            if (stability is None) != (similarity_boost is None):
+                raise ValueError(
+                    "Both 'stability' and 'similarity_boost' must be provided when using voice settings"
+                )
+            return self
 
     def __init__(
-            self,
-            *,
-            api_key: str,
-            voice_id: str,
-            model: str = "eleven_turbo_v2_5",
-            url: str = "wss://api.elevenlabs.io",
-            params: InputParams = InputParams(),
-            **kwargs):
+        self,
+        *,
+        api_key: str,
+        voice_id: str,
+        model: str = "eleven_turbo_v2_5",
+        url: str = "wss://api.elevenlabs.io",
+        params: InputParams = InputParams(),
+        **kwargs,
+    ):
         # Aggregating sentences still gives cleaner-sounding results and fewer
         # artifacts than streaming one word at a time. On average, waiting for a
         # full sentence should only "cost" us 15ms or so with GPT-4o or a Llama
@@ -102,14 +119,15 @@ def __init__(
             push_stop_frames=True,
             stop_frame_timeout_s=2.0,
             sample_rate=sample_rate_from_output_format(params.output_format),
-            **kwargs
+            **kwargs,
         )
 
         self._api_key = api_key
         self._voice_id = voice_id
         self.set_model_name(model)
         self._url = url
         self._params = params
+        self._voice_settings = self._set_voice_settings()
 
         # Websocket connection to ElevenLabs.
         self._websocket = None
@@ -121,6 +139,27 @@ def __init__(
     def can_generate_metrics(self) -> bool:
         return True
 
+    def _set_voice_settings(self):
+        voice_settings = {}
+        if self._params.stability is not None and self._params.similarity_boost is not None:
+            voice_settings["stability"] = self._params.stability
+            voice_settings["similarity_boost"] = self._params.similarity_boost
+            if self._params.style is not None:
+                voice_settings["style"] = self._params.style
+            if self._params.use_speaker_boost is not None:
+                voice_settings["use_speaker_boost"] = self._params.use_speaker_boost
+        else:
+            if self._params.style is not None:
+                logger.warning(
+                    "'style' is set but will not be applied because 'stability' and 'similarity_boost' are not both set."
+                )
+            if self._params.use_speaker_boost is not None:
+                logger.warning(
+                    "'use_speaker_boost' is set but will not be applied because 'stability' and 'similarity_boost' are not both set."
+                )
+
+        return voice_settings or None
+
     async def set_model(self, model: str):
         await super().set_model(model)
         logger.debug(f"Switching TTS model to: [{model}]")
@@ -133,6 +172,28 @@ async def set_voice(self, voice: str):
         await self._disconnect()
         await self._connect()
 
+    async def set_voice_settings(
+        self,
+        stability: Optional[float] = None,
+        similarity_boost: Optional[float] = None,
+        style: Optional[float] = None,
+        use_speaker_boost: Optional[bool] = None,
+    ):
+        self._params.stability = stability if stability is not None else self._params.stability
+        self._params.similarity_boost = (
+            similarity_boost if similarity_boost is not None else self._params.similarity_boost
+        )
+        self._params.style = style if style is not None else self._params.style
+        self._params.use_speaker_boost = (
+            use_speaker_boost if use_speaker_boost is not None else self._params.use_speaker_boost
+        )
+
+        self._set_voice_settings()
+
+        if self._websocket:
+            msg = {"voice_settings": self._voice_settings}
+            await self._websocket.send(json.dumps(msg))
+
     async def start(self, frame: StartFrame):
         await super().start(frame)
         await self._connect()
@@ -163,15 +224,21 @@ async def _connect(self):
             model = self.model_name
             output_format = self._params.output_format
             url = f"{self._url}/v1/text-to-speech/{voice_id}/stream-input?model_id={model}&output_format={output_format}"
+
+            if self._params.optimize_streaming_latency:
+                url += f"&optimize_streaming_latency={self._params.optimize_streaming_latency}"
+
             self._websocket = await websockets.connect(url)
             self._receive_task = self.get_event_loop().create_task(self._receive_task_handler())
             self._keepalive_task = self.get_event_loop().create_task(self._keepalive_task_handler())
 
             # According to ElevenLabs, we should always start with a single space.
-            msg = {
+            msg: Dict[str, Any] = {
                 "text": " ",
                 "xi_api_key": self._api_key,
             }
+            if self._voice_settings:
+                msg["voice_settings"] = self._voice_settings
             await self._websocket.send(json.dumps(msg))
         except Exception as e:
             logger.error(f"{self} initialization error: {e}")