Far-field speech recognition is a more challenging task compared to near-field recognition. Multi-channel methods such as beamforming help reduce the noises and enhance the target speech. 
TorchAudio now adds support for differentiable Minimum Variance Distortionless Response (MVDR) beamforming on multi-channel audio using Time-Frequency masks. Researchers can easily assemble it with any multi-channel ASR pipeline. There are three solutions (ref_channel, stv_evd, stv_power) and it supports single-channel and multi-channel (perform average in the method) masks. It provides an online option that recursively updates the parameters for streaming audio. We also provide a tutorial on how to apply MVDR beamforming to the multi-channel audio in the example directory.
```python



from torchaudio.transforms import MVDR, Spectrogram, InverseSpectrogram
Load the multi-channel noisy audio
waveform_mix, sr = torchaudio.load('mix.wav')
Initialize the stft and istft modules


