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Python bindings for RNNoise, a recurrent neural network for audio noise reduction.
pip install pyrnnoise
# Basic usage
denoise input.wav output.wav
# With voice activity detection plot
denoise input.wav output.wav --plot
from pyrnnoise import RNNoise
# Create denoiser instance
denoiser = RNNoise(sample_rate=48000)
# Process audio file
for speech_prob in denoiser.denoise_wav("input.wav", "output.wav"):
print(f"Processing frame with speech probability: {speech_prob}")
The RNNoise
class provides several methods for processing audio at different levels:
denoise_frame(frame, partial=False)
: Process a single audio frame (480 samples at 48kHz)
denoise_chunk(chunk, partial=False)
: Process a chunk of audio data
Example using denoise_chunk:
import numpy as np
from pyrnnoise import RNNoise
# Create denoiser instance
denoiser = RNNoise(sample_rate=48000)
# Generate or load some audio data (stereo in this example)
audio_data = np.random.randint(-32768, 32767, (2, 48000), dtype=np.int16)
# Process audio chunk
for speech_prob, denoised_audio in denoiser.denoise_chunk(audio_data):
print(f"Speech probability: {speech_prob}")
# Process denoised_audio as needed
# Clone with submodules
git submodule update --init
# Build RNNoise library
cmake -B pyrnnoise/build -DCMAKE_BUILD_TYPE=Release
cmake --build pyrnnoise/build --target install
# Install Python package in development mode
pip install -e .
FAQs
PyRnNoise
We found that pyrnnoise demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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