aic-sdk 2.4.0

Python bindings for ai-coustics SDK

aic-sdk - Python Bindings for ai-coustics SDK

Python wrapper for the ai-coustics Speech Enhancement SDK.

For comprehensive documentation, visit docs.ai-coustics.com.

[!NOTE] This SDK requires a license key. Generate your key at developers.ai-coustics.com.

Installation

pip install aic-sdk

Quick Start

import aic_sdk as aic
import numpy as np
import os

# Get your license key from the environment variable
license_key = os.environ["AIC_SDK_LICENSE"]

# Download and load a model (or download manually at https://artifacts.ai-coustics.io/)
model_path = aic.Model.download("quail-vf-2.1-l-16khz", "./models")
model = aic.Model.from_file(model_path)

# Get optimal configuration
config = aic.ProcessorConfig.optimal(model, num_channels=2)

# Create and initialize processor in one step
processor = aic.Processor(model, license_key, config)

# Process audio (2D NumPy array: channels × frames)
audio_buffer = np.zeros((config.num_channels, config.num_frames), dtype=np.float32)
processed = processor.process(audio_buffer)

Usage

SDK Information

# Get SDK version
print(f"SDK version: {aic.get_sdk_version()}")

# Get compatible model version
print(f"Compatible model version: {aic.get_compatible_model_version()}")

Loading Models

Download models and find available IDs at artifacts.ai-coustics.io.

From File

model = aic.Model.from_file("path/to/model.aicmodel")

Download from CDN (Sync)

model_path = aic.Model.download("quail-vf-2.1-l-16khz", "./models")
model = aic.Model.from_file(model_path)

Download from CDN (Async)

model_path = await aic.Model.download_async("quail-vf-2.1-l-16khz", "./models")
model = aic.Model.from_file(model_path)

Model Information

# Get model ID
model_id = model.get_id()

# Get optimal sample rate for the model
optimal_rate = model.get_optimal_sample_rate()

# Get optimal frame count for a specific sample rate
optimal_frames = model.get_optimal_num_frames(48000)

Configuring the Processor

# Get optimal configuration for the model
config = aic.ProcessorConfig.optimal(model, num_channels=1, allow_variable_frames=False)
print(config)  # ProcessorConfig(sample_rate=48000, num_channels=1, num_frames=480, allow_variable_frames=False)

# Or create from scratch
config = aic.ProcessorConfig(
    sample_rate=48000,
    num_channels=2,
    num_frames=480,
    allow_variable_frames=False # up to num_frames
)

# Option 1: Create and initialize in one step
processor = aic.Processor(model, license_key, config)

# Option 2: Create first, then initialize separately
processor = aic.Processor(model, license_key)
processor.initialize(config)

OpenTelemetry Configuration

Pass an OtelConfig to override telemetry settings for a single processor instance, independently of the AIC_SDK_OTEL_ENABLE environment variable:

# Disable telemetry for this processor
processor = aic.Processor(model, license_key, otel_config=aic.OtelConfig(enable=False))

# Enable with a session ID and custom export interval
processor = aic.Processor(
    model, license_key,
    otel_config=aic.OtelConfig(enable=True, session_id="my-session", export_interval_ms=5_000),
)

The same otel_config parameter is available on ProcessorAsync.

Processing Audio

# Synchronous processing
import numpy as np

# Create audio buffer (channels × frames)
audio = np.zeros((config.num_channels, config.num_frames), dtype=np.float32)

# Process
processed = processor.process(audio)

Processor Context

# Get processor context
proc_ctx = processor.get_processor_context()

# Get output delay in samples
delay = proc_ctx.get_output_delay()

# Reset processor state (clears internal buffers)
proc_ctx.reset()

# Set enhancement parameters
proc_ctx.set_parameter(aic.ProcessorParameter.EnhancementLevel, 0.8)
proc_ctx.set_parameter(aic.ProcessorParameter.Bypass, 0.0)

# Get parameter values
level = proc_ctx.get_parameter(aic.ProcessorParameter.EnhancementLevel)
print(f"Enhancement level: {level}")

Async API

import asyncio
import numpy as np
import aic_sdk as aic

async def process_audio():
    # Download and load model (or download manually at https://artifacts.ai-coustics.io/)
    model_path = await aic.Model.download_async("quail-vf-2.1-l-16khz", "./models")
    model = aic.Model.from_file(model_path)

    # Get optimal config
    config = aic.ProcessorConfig.optimal(model, num_channels=2)

    # Create and initialize async processor in one step
    processor = aic.ProcessorAsync(model, "your-license-key", config)

    # Get processor and VAD contexts
    proc_ctx = processor.get_processor_context()
    vad_ctx = processor.get_vad_context()

    # Process audio
    audio = np.zeros((2, config.num_frames), dtype=np.float32)
    result = await processor.process_async(audio)

    # Process multiple buffers concurrently
    buffers = [np.random.randn(2, config.num_frames).astype(np.float32) for _ in range(4)]
    results = await asyncio.gather(*[
        processor.process_async(buf) for buf in buffers
    ])

asyncio.run(process_audio())

Voice Activity Detection (VAD)

# Get VAD context from processor
vad_ctx = processor.get_vad_context()

# Configure VAD parameters
vad_ctx.set_parameter(aic.VadParameter.Sensitivity, 6.0)
vad_ctx.set_parameter(aic.VadParameter.SpeechHoldDuration, 0.05)
vad_ctx.set_parameter(aic.VadParameter.MinimumSpeechDuration, 0.0)

# Get parameter values
sensitivity = vad_ctx.get_parameter(aic.VadParameter.Sensitivity)
print(f"VAD sensitivity: {sensitivity}")

# Check for speech (after processing audio through the processor)
if vad_ctx.is_speech_detected():
    print("Speech detected!")

Audio Analysis

The analysis API runs the Tyto analysis model to score audio quality, predicting the likelihood of failure of downstream models (speech-to-text, VAD, turn-taking, speech-to-speech). Each AnalysisResult exposes seven scores in the 0.0–1.0 range (lower is less problematic, except speaker_loudness): risk_score, speaker_reverb, speaker_loudness, interfering_speech, media_speech, noise, and packet_loss.

Whole-file analysis

FileAnalyzer analyzes a mono buffer that is already loaded in memory, returning one result per five-second window:

import numpy as np
import aic_sdk as aic

# Use an analysis model (Tyto), not an enhancement model.
model = aic.Model.from_file(aic.Model.download("tyto-l-16khz", "./models"))
analyzer = aic.FileAnalyzer(model, license_key)

# audio: 1D mono float32 NumPy array
sample_rate = 16000
results = analyzer.analyze(audio, sample_rate)  # optional: step_samples=sample_rate * 5
for result in results:
    print(f"Risk score: {result.risk_score}")

Streaming analysis

For streaming use, analyzer_pair() returns a Collector (buffers audio, safe to call from the audio thread) and an Analyzer (runs the model off the audio thread):

collector, analyzer = aic.analyzer_pair(model, license_key)

config = aic.ProcessorConfig.optimal(model)
collector.initialize(config)

# Buffer audio (2D NumPy array: channels × frames) as it arrives.
collector.buffer(np.zeros((config.num_channels, config.num_frames), dtype=np.float32))

# Run the analysis off the audio thread.
result = analyzer.analyze_buffered()
print(f"Risk score: {result.risk_score}")

When to Use Sync vs Async

• Processor (sync): Simple scripts, command-line tools, batch processing
• ProcessorAsync (async): Web servers, real-time applications, concurrent stream processing

ProcessorAsync runs CPU-bound work on a dedicated Rayon thread pool. By default the pool is sized to the number of logical cores reported by the OS. Set the AIC_NUM_THREADS environment variable to override the worker count, for example AIC_NUM_THREADS=2 caps concurrent processing at two threads.

Error Handling

The SDK provides specific exception types for different error conditions. All exceptions include a message attribute with details about the error.

Catching Specific Errors

import aic_sdk as aic

try:
    processor = aic.Processor(model, license_key, config)
except aic.LicenseFormatInvalidError as e:
    print(f"Invalid license format: {e.message}")
except aic.LicenseExpiredError as e:
    print(f"License expired: {e.message}")
except aic.ModelInvalidError as e:
    print(f"Invalid model: {e.message}")

Catching Multiple Error Types

try:
    processor = aic.Processor(model, license_key, config)
except (aic.LicenseFormatInvalidError, aic.LicenseExpiredError) as e:
    print(f"License error: {e.message}")
except (aic.ModelInvalidError, aic.ModelVersionUnsupportedError) as e:
    print(f"Model error: {e.message}")

For a complete list of all available exception types and their descriptions, see the type stubs file.

Examples

See the basic.py or basic_async.py file for a complete working example.

For a complete file enhancement example with parallel processing, see enhance_files.py.

For an audio-analysis example that scores an audio file with the Tyto model, see analyze_file.py.

For a benchmarking example that tests how many concurrent processing sessions your CPU can support, see benchmark.py.

Documentation

• Full Documentation: docs.ai-coustics.com
• Python API Reference: See the type stubs for detailed type information
• Available Models: artifacts.ai-coustics.io

License

This Python wrapper is distributed under the Apache 2.0 license. The core C SDK is distributed under the proprietary AIC-SDK license.