Initial commit for gain logic

Author: CamiWilliams

test/assets/sinewave_soxgain5.wav

@@ -7,6 +7,7 @@
 import pytest
 import unittest
 import common_utils
+import os
 
 from torchaudio.common_utils import IMPORT_LIBROSA
 
@@ -247,6 +248,19 @@ def test_create_fb(self):
         self._test_create_fb(n_mels=56, fmin=1900.0, fmax=900.0)
         self._test_create_fb(n_mels=10, fmin=1900.0, fmax=900.0)
 
+    def test_gain(self):
+        test_dirpath, test_dir = common_utils.create_temp_assets_dir()
+        test_filepath = os.path.join(test_dirpath, "assets", "sinewave.wav")
+
+        waveform, sample_rate = torchaudio.load(test_filepath)
+        waveform_gain = F.gain(waveform, 5)
+        self.assertTrue(waveform_gain.abs().max().item(), 1.)
+
+        test_filepath_sox_gain = os.path.join(test_dirpath, "assets", "sinewave_soxgain5.wav")
+        sox_gain_waveform, sox_sr = torchaudio.load(test_filepath_sox_gain)
+
+        self.assertTrue(torch.allclose(waveform_gain, sox_gain_waveform))
+
 
 def _num_stft_bins(signal_len, fft_len, hop_length, pad):
     return (signal_len + 2 * pad - fft_len + hop_length) // hop_length

test/test_functional.py

@@ -801,3 +801,21 @@ def compute_deltas(specgram, win_length=5, mode="replicate"):
     return torch.nn.functional.conv1d(
         specgram, kernel, groups=specgram.shape[1] // specgram.shape[0]
     ) / denom
+
+def gain(waveform, gain_db):
+    r"""Apply amplification or attenuation to the audio signal.
+
+    Args:
+       waveform (torch.Tensor): Tensor of audio of dimension (channel, time)
+       gain_db(float, default=0.0) Gain adjustment in decibels (dB).
+
+    Returns:
+       torch.Tensor: waveform amplified by gain_db
+    """
+    assert waveform.dim() == 2
+    
+    #gain_db = 20log(Tensor_output/tensor_input)    
+    ratio = 10 ** (gain_db/20)
+    
+    return waveform * ratio
+