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Observability framework for Pipecat voice and multimodal conversational AI

Project description

Voiceground

Observability framework for Pipecat voice and multimodal conversational AI.

Features

  • VoicegroundObserver: Track conversation events following Pipecat's Observer pattern
  • StaticReporter: Generate interactive HTML reports with timeline visualization

Installation

pip install voiceground

Or with UV:

uv add voiceground

Quick Start

import uuid
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.task import PipelineTask
from voiceground import VoicegroundObserver, StaticReporter

# Create observer with static reporter
conversation_id = str(uuid.uuid4())
reporter = StaticReporter(output_dir="./reports")
observer = VoicegroundObserver(
    reporters=[reporter],
    conversation_id=conversation_id
)

# Create pipeline task with observer
task = PipelineTask(
    pipeline=Pipeline([...]),
    observers=[observer]
)

# Run your pipeline

Tested With

Voiceground has been tested with the following Pipecat providers:

LLM Providers

  • OpenAI (GPT)

STT Providers

  • ElevenLabs

TTS Providers

  • ElevenLabs

Event Categories

Voiceground tracks the following event categories:

Category Types Description
user_speak start, end User speech events
bot_speak start, end Bot speech events
stt start, end Speech-to-text processing (includes transcription text)
llm start, first_byte, end LLM response generation (includes generated text)
tts start, first_byte, end Text-to-speech synthesis
tool_call start, end LLM function/tool calling
system start, end System events (e.g., context aggregation)

Report Features

The generated HTML reports include:

  • Timeline Visualization: Interactive timeline showing all events and their relationships
  • Events Table: Detailed view of all tracked events with timestamps, sources, and data
  • Turns Table: Conversation turns with performance metrics:
    • Response time
    • LLM Time to First Byte (TTFB)
    • TTS latency
    • STT processing time
  • Metrics Summary: Average metrics across the conversation
  • Event Highlighting: Hover over events or turns to see related events highlighted

Examples

See the examples/ directory for complete working examples:

  • basic_pipeline.py: Basic voice conversation with STT, LLM, and TTS
  • tool_calling_pipeline.py: Example with LLM function calling

To run an example:

# Install example dependencies
uv sync --all-extras

# Set required environment variables
export OPENAI_API_KEY=your_key
export ELEVENLABS_API_KEY=your_key
export VOICE_ID=your_voice_id

# Run the example
python examples/basic_pipeline.py

Note: On macOS, you'll need to install portaudio for audio support:

brew install portaudio

Development

# Clone the repository
git clone https://github.com/poseneror/voiceground.git
cd voiceground

# Install all dependencies (including dev and examples)
uv sync --all-extras

# Run tests
uv run pytest

# Run linting
uv run ruff check .

# Run type checking
uv run mypy src

# Build the client
python scripts/develop.py build

# Run example (requires portaudio on macOS: brew install portaudio)
python scripts/develop.py example

License

BSD-2-Clause License - see LICENSE for details.

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