Python

The Python binding wraps the C++ engine with a Pythonic API. Install with pip, integrate with NumPy arrays, process files in one call, or drive real-time streaming. The full engine runs natively; the Python layer is a thin idiomatic wrapper. See Overview for what's inside the engine.

Install

python -m pip install rapidly

Wheels ship for Windows, Linux, and macOS. Python 3 and later.

Try it

The examples.py script in the Rapidly SDK on GitHub is the fastest starting point. It provides a command-line interface for trying models on audio files without writing code.

1. Clone or download examples.py from the Rapidly SDK repository.
2. Open a terminal and cd to the directory.
3. Run python examples.py and follow the on-screen prompts to pick a model and process a file.

Listing and updating models

Import the module and list the available models:

import rapidly

models = rapidly.list_models()
print(models)

Download and update to the latest models:

rapidly.update_models()

See Models for the catalog and picking guidance.

Licensing

Call add_license on the engine before constructing any processor. See Pricing for licensing options.

import rapidly

engine = rapidly.RapidlyEngine()
if not engine.add_license("lk_..."):
    raise RuntimeError("Rapidly license could not be verified.")

Process a file

Use process_file for one-shot file conversions:

import rapidly

# Initialize the engine
engine = rapidly.RapidlyEngine()

# List the available models in the models folder
models = rapidly.list_models()

# Create a processor to inspect the available output busses for the chosen model
processor = engine.create_processor(models[0], 2, 44100)

# Read the available output bus names
num_buses = processor.get_number_of_output_buses()
bus_names = [processor.get_output_bus_name(i) for i in range(num_buses)]

Then run the file through the model:

rapidly.process_file(
    model_file_path=models[0],
    input_file_path=input_file_path,
    output_file_path=output_file_path,
    selected_output_bus=0,  # The processed result is on bus 0 in most models
)

This applies models[0] to input_file_path and writes the result to output_file_path.

Stream processing

For real-time or streaming workloads, drive a Processor directly with process():

import numpy as np
import rapidly

engine = rapidly.RapidlyEngine()
processor = engine.create_processor(
    "models/speech-denoise-32ms.v1.0.rapidly", 2, 44100
)

# audio_block: np.ndarray of shape (num_samples, num_channels), dtype=float32
processed = processor.process(audio_block)

# Reset between unrelated streams to clear internal delay lines
processor.reset()

# Inspect the model configuration (sample rate, latency, block size, ...)
info = processor.get_info()

Parameters

Processor exposes typed getters and setters for the most common controls, plus generic per-bus controls.

Threading

Drive each processor from a single thread. For short offline jobs use a with block to release the native resources automatically; for long-running real-time work call the processor's release method explicitly. For backends running many streams in parallel, see Parallel processing and One vs multiple engines.