torchaudio.functional.resample

torchaudio.functional.resample(waveform: Tensor, orig_freq: int, new_freq: int, lowpass_filter_width: int = 6, rolloff: float = 0.99, resampling_method: str = 'sinc_interpolation', beta: Optional[float] = None) → Tensor

Resamples the waveform at the new frequency using bandlimited interpolation. [Smith, 2020].

Note

transforms.Resample precomputes and reuses the resampling kernel, so using it will result in more efficient computation if resampling multiple waveforms with the same resampling parameters.

Parameters:

• waveform (Tensor) – The input signal of dimension (…, time)

• orig_freq (int) – The original frequency of the signal

• new_freq (int) – The desired frequency

• lowpass_filter_width (int, optional) – Controls the sharpness of the filter, more == sharper but less efficient. (Default: 6)

• rolloff (float, optional) – The roll-off frequency of the filter, as a fraction of the Nyquist. Lower values reduce anti-aliasing, but also reduce some of the highest frequencies. (Default: 0.99)

• resampling_method (str, optional) – The resampling method to use. Options: ["sinc_interpolation", "kaiser_window"] (Default: "sinc_interpolation")

• beta (float or None, optional) – The shape parameter used for kaiser window.

Returns:

The waveform at the new frequency of dimension (…, time).

Return type:

Tensor

Tutorials using resample:

Speech Recognition with Wav2Vec2

Speech Recognition with Wav2Vec2

ASR Inference with CTC Decoder

ASR Inference with CTC Decoder

Audio Resampling

Audio Resampling