add speechmatics tts
This commit is contained in:
Aaron Ng
@@ -20,10 +20,10 @@ from pipecat.processors.aggregators.llm_response import (
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from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
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from pipecat.runner.types import RunnerArguments
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from pipecat.runner.utils import create_transport
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from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
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from pipecat.services.openai.base_llm import BaseOpenAILLMService
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from pipecat.services.openai.llm import OpenAILLMService
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from pipecat.services.speechmatics.stt import SpeechmaticsSTTService
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from pipecat.services.speechmatics.tts import SpeechmaticsTTSService
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from pipecat.transcriptions.language import Language
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from pipecat.transports.base_transport import BaseTransport, TransportParams
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from pipecat.transports.daily.transport import DailyParams
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@@ -51,35 +51,41 @@ transport_params = {
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async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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"""Speechmatics STT Service Example
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"""Speechmatics STT and TTS Service Example
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This example demonstrates using Speechmatics Speech-to-Text service with speaker diarization and intelligent speaker management. Key features:
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This example demonstrates using Speechmatics Speech-to-Text and Text-to-Speech services
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with speaker diarization and intelligent speaker management. Key features:
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1. Speaker Diarization
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1. Speaker Diarization (STT)
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- Automatically identifies and distinguishes between different speakers
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- First speaker is identified as 'S1', others get subsequent IDs
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- Uses `enable_diarization` parameter to manage speaker detection
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2. Smart Speaker Control
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2. Smart Speaker Control (STT)
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- `focus_speakers` parameter lets you target specific speakers (e.g. ["S1"])
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- Other speakers will be wrapped in PASSIVE tags
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- Only processes speech from focused speakers
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- Words from all speakers are wrapped with XML tags for clear speaker identification
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- Other speakers' speech only sent when focused speaker is active
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3. Voice Activity Detection
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3. Voice Activity Detection (STT)
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- Built-in VAD using `enable_vad` parameter
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- Remove `vad_analyzer` from `transport` config to use module's VAD
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- Emits speaker started/stopped events
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4. Configuration Options
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4. Text-to-Speech (TTS)
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- Low latency streaming audio synthesis
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- Multiple voice options available including `sarah`, `theo`, and `megan`
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5. Configuration Options
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- `operating_point` parameter defaults to `ENHANCED` for optimal accuracy
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- Configurable `end_of_utterance_silence_trigger` (default 0.5s)
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- Customizable speaker formatting
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- Additional diarization settings available
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For detailed information about operating points and configuration:
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https://docs.speechmatics.com/rt-api-ref
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For detailed information:
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- STT: https://docs.speechmatics.com/rt-api-ref
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- TTS: https://docs.speechmatics.com/text-to-speech/quickstart
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"""
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logger.info(f"Starting bot")
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@@ -97,10 +103,11 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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),
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)
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tts = ElevenLabsTTSService(
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api_key=os.getenv("ELEVENLABS_API_KEY"),
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voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
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model="eleven_turbo_v2_5",
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tts = SpeechmaticsTTSService(
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api_key=os.getenv("SPEECHMATICS_API_KEY"),
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params=SpeechmaticsTTSService.InputParams(
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voice="sarah",
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),
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)
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llm = OpenAILLMService(
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@@ -112,7 +119,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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{
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"role": "system",
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"content": (
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"You are a helpful British assistant called Alfred. "
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"You are a helpful British assistant called Sarah. "
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"Your goal is to demonstrate your capabilities in a succinct way. "
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"Your output will be converted to audio so don't include special characters in your answers. "
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"Always include punctuation in your responses. "
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@@ -24,10 +24,10 @@ from pipecat.processors.aggregators.llm_response import (
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from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
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from pipecat.runner.types import RunnerArguments
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from pipecat.runner.utils import create_transport
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from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
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from pipecat.services.openai.base_llm import BaseOpenAILLMService
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from pipecat.services.openai.llm import OpenAILLMService
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from pipecat.services.speechmatics.stt import SpeechmaticsSTTService
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from pipecat.services.speechmatics.tts import SpeechmaticsTTSService
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from pipecat.transcriptions.language import Language
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from pipecat.transports.base_transport import BaseTransport, TransportParams
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from pipecat.transports.daily.transport import DailyParams
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@@ -61,19 +61,24 @@ transport_params = {
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async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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"""Run example using Speechmatics STT.
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"""Run example using Speechmatics STT and TTS.
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This example will use diarization within our STT service and output the words spoken by
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each individual speaker and wrap them with XML tags for the LLM to process. Note the
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instructions in the system context for the LLM. This greatly improves the conversation
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experience by allowing the LLM to understand who is speaking in a multi-party call.
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This example demonstrates a complete Speechmatics integration with both Speech-to-Text
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and Text-to-Speech services:
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By default, this example will use our ENHANCED operating point, which is optimized for
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high accuracy. You can change this by setting the `operating_point` parameter to a different
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value.
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STT Features:
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- Diarization to identify and distinguish between different speakers
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- Words spoken by each speaker are wrapped with XML tags for LLM processing
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- System context instructions help the LLM understand multi-party conversations
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- ENHANCED operating point by default for optimal accuracy
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For more information on operating points, see the Speechmatics documentation:
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https://docs.speechmatics.com/rt-api-ref
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TTS Features:
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- Low latency streaming audio synthesis
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- Multiple voice options available including `sarah`, `theo`, and `megan`
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For more information:
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- STT: https://docs.speechmatics.com/rt-api-ref
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- TTS: https://docs.speechmatics.com/text-to-speech/quickstart
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"""
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logger.info(f"Starting bot")
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@@ -87,10 +92,11 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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),
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)
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tts = ElevenLabsTTSService(
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api_key=os.getenv("ELEVENLABS_API_KEY"),
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voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
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model="eleven_turbo_v2_5",
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tts = SpeechmaticsTTSService(
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api_key=os.getenv("SPEECHMATICS_API_KEY"),
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params=SpeechmaticsTTSService.InputParams(
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voice="sarah",
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),
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)
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llm = OpenAILLMService(
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@@ -102,7 +108,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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{
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"role": "system",
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"content": (
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"You are a helpful British assistant called Alfred. "
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"You are a helpful British assistant called Sarah. "
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"Your goal is to demonstrate your capabilities in a succinct way. "
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"Your output will be converted to audio so don't include special characters in your answers. "
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"Always include punctuation in your responses. "
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