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Overview

The PipelineParams class provides a structured way to configure various aspects of pipeline execution. These parameters control behaviors like audio settings, metrics collection, heartbeat monitoring, and interruption handling.

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Basic Usage

from pipecat.pipeline.task import PipelineParams, PipelineTask

# Create with default parameters
params = PipelineParams()

# Or customize specific parameters
params = PipelineParams(
    allow_interruptions=True,
    audio_in_sample_rate=16000,
    enable_metrics=True
)

# Pass to PipelineTask
pipeline = Pipeline([...])
task = PipelineTask(pipeline, params=params)

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Available Parameters

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allow_interruptions
bool
default:"False"

Whether to allow pipeline interruptions. When enabled, a user’s speech will immediately interrupt the bot’s response.

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audio_in_sample_rate
int
default:"16000"

Input audio sample rate in Hz.

Setting the audio_in_sample_rate as a PipelineParam sets the input sample rate for all corresponding services in the pipeline.

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audio_out_sample_rate
int
default:"24000"

Output audio sample rate in Hz.

Setting the audio_out_sample_rate as a PipelineParam sets the output sample rate for all corresponding services in the pipeline.

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enable_heartbeats
bool
default:"False"

Whether to enable heartbeat monitoring to detect pipeline stalls. See Heartbeats for details.

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heartbeats_period_secs
float
default:"1.0"

Period between heartbeats in seconds (when heartbeats are enabled).

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enable_metrics
bool
default:"False"

Whether to enable metrics collection for pipeline performance.

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enable_usage_metrics
bool
default:"False"

Whether to enable usage metrics tracking.

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report_only_initial_ttfb
bool
default:"False"

Whether to report only initial time to first byte metric.

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send_initial_empty_metrics
bool
default:"True"

Whether to send initial empty metrics frame at pipeline start.

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start_metadata
Dict[str, Any]
default:"{}"

Additional metadata to include in the StartFrame.

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Common Configurations

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Audio Processing Configuration

You can set the audio input and output sample rates in the PipelineParams to set the sample rate for all input and output services in the pipeline. This acts as a convenience to avoid setting the sample rate for each service individually. Note, if services are set individually, they will supersede the values set in PipelineParams.

params = PipelineParams(
    audio_in_sample_rate=8000,   # Lower quality input audio
    audio_out_sample_rate=8000  # High quality output audio
)

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Performance Monitoring Configuration

Pipeline heartbeats provide a way to monitor the health of your pipeline by sending periodic heartbeat frames through the system. When enabled, the pipeline will send heartbeat frames every second and monitor their progress through the pipeline.

params = PipelineParams(
    enable_heartbeats=True,
    heartbeats_period_secs=2.0,  # Send heartbeats every 2 seconds
    enable_metrics=True
)

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How Parameters Are Used

The parameters you set in PipelineParams are passed to various components of the pipeline:

  1. StartFrame: Many parameters are included in the StartFrame that initializes the pipeline
  2. Metrics Collection: Metrics settings configure what performance data is gathered
  3. Heartbeat Monitoring: Controls the pipeline’s health monitoring system
  4. Audio Processing: Sample rates affect how audio is processed throughout the pipeline

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Complete Example

from pipecat.frames.frames import TTSSpeakFrame
from pipecat.observers.file_observer import FileObserver
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.pipeline.runner import PipelineRunner

# Create comprehensive parameters
params = PipelineParams(
    allow_interruptions=True,
    audio_in_sample_rate=8000,
    audio_out_sample_rate=8000,
    enable_heartbeats=True,
    enable_metrics=True,
    enable_usage_metrics=True,
    heartbeats_period_secs=1.0,
    report_only_initial_ttfb=False,
    start_metadata={
        "conversation_id": "conv-123",
        "session_data": {
            "user_id": "user-456",
            "start_time": "2023-10-25T14:30:00Z"
        }
    }
)

# Create pipeline and task
pipeline = Pipeline([...])
task = PipelineTask(
    pipeline,
    params=params,
    observers=[FileObserver("pipeline_logs.jsonl")]
)

# Run the pipeline
runner = PipelineRunner()
await runner.run(task)

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Additional Information

  • Parameters are immutable once the pipeline starts
  • The start_metadata dictionary can contain any serializable data
  • For metrics collection to work properly, enable_metrics must be set to True