DPDFNet Python API

This page describes how to use the Python API for DPDFNet with sherpa-onnx.

Please refer to Install the Python Package for how to install the Python package of sherpa-onnx.

The following is a quick way to do that:

pip install sherpa-onnx soundfile

Offline speech enhancement

Download a DPDFNet model and a test wave file:

wget https://github.com/k2-fsa/sherpa-onnx/releases/download/speech-enhancement-models/dpdfnet2.onnx
wget https://github.com/k2-fsa/sherpa-onnx/releases/download/speech-enhancement-models/inp_16k.wav

The following example uses OfflineSpeechDenoiserDpdfNetModelConfig and OfflineSpeechDenoiser:

import numpy as np
import sherpa_onnx
import soundfile as sf


def load_audio(filename: str):
    samples, sample_rate = sf.read(
        filename,
        always_2d=True,
        dtype="float32",
    )
    samples = np.ascontiguousarray(samples[:, 0])
    return samples, sample_rate


config = sherpa_onnx.OfflineSpeechDenoiserConfig(
    model=sherpa_onnx.OfflineSpeechDenoiserModelConfig(
        dpdfnet=sherpa_onnx.OfflineSpeechDenoiserDpdfNetModelConfig(
            model="./dpdfnet2.onnx",
        ),
        num_threads=1,
        debug=False,
        provider="cpu",
    )
)

assert config.validate(), config

denoiser = sherpa_onnx.OfflineSpeechDenoiser(config)
samples, sample_rate = load_audio("./inp_16k.wav")
denoised = denoiser.run(samples, sample_rate)

sf.write("enhanced.wav", denoised.samples, denoised.sample_rate)
print(f"Saved to enhanced.wav at {denoised.sample_rate} Hz")

You can also run the upstream example directly:

git clone https://github.com/k2-fsa/sherpa-onnx
cd sherpa-onnx
python3 ./python-api-examples/offline-speech-enhancement-dpdfnet.py

The example script is available at

https://github.com/k2-fsa/sherpa-onnx/blob/master/python-api-examples/offline-speech-enhancement-dpdfnet.py

Streaming status

The current sherpa-onnx Python API contains the offline DPDFNet bindings and the offline example shown above.

Note

The online streaming DPDFNet denoiser is available in the core runtime and C API, but a corresponding Python binding/example is not present in the current Python API yet. Please refer to DPDFNet C API for streaming usage.

Hints

You can try DPDFNet in your browser at Ceva-IP/DPDFNetDemo and download the model files from the speech-enhancement-models release.