Use torchaudio melscale 'slaney' instead of librosa in WaveRNN pipeline preprocessing (#1444)

* Use torchaudio melscale instead of librosa

examples/pipeline_wavernn/main.py

@@ -17,7 +17,7 @@ from torchaudio.models.wavernn import WaveRNN
 
 from datasets import collate_factory, split_process_dataset
 from losses import LongCrossEntropyLoss, MoLLoss
-from processing import LinearToMel, NormalizeDB
+from processing import NormalizeDB
 from utils import MetricLogger, count_parameters, save_checkpoint
 
 
@@ -269,12 +269,12 @@ def main(args):
     }
 
     transforms = torch.nn.Sequential(
-        torchaudio.transforms.Spectrogram(**melkwargs),
-        LinearToMel(
+        torchaudio.transforms.MelSpectrogram(
             sample_rate=args.sample_rate,
-            n_fft=args.n_fft,
             n_mels=args.n_freq,
-            fmin=args.f_min,
+            f_min=args.f_min,
+            mel_scale='slaney',
+            **melkwargs,
         ),
         NormalizeDB(min_level_db=args.min_level_db, normalization=args.normalization),
     )

examples/pipeline_wavernn/processing.py

-import librosa
 import torch
 import torch.nn as nn
 
 
-# TODO Replace by torchaudio, once https://github.com/pytorch/audio/pull/593 is resolved
-class LinearToMel(nn.Module):
-    def __init__(self, sample_rate, n_fft, n_mels, fmin, htk=False, norm="slaney"):
-        super().__init__()
-        self.sample_rate = sample_rate
-        self.n_fft = n_fft
-        self.n_mels = n_mels
-        self.fmin = fmin
-        self.htk = htk
-        self.norm = norm
-
-    def forward(self, specgram):
-        specgram = librosa.feature.melspectrogram(
-            S=specgram.squeeze(0).numpy(),
-            sr=self.sample_rate,
-            n_fft=self.n_fft,
-            n_mels=self.n_mels,
-            fmin=self.fmin,
-            htk=self.htk,
-            norm=self.norm,
-        )
-        return torch.from_numpy(specgram)
-
-
 class NormalizeDB(nn.Module):
     r"""Normalize the spectrogram with a minimum db value
     """
@@ -37,7 +12,7 @@ class NormalizeDB(nn.Module):
         self.normalization = normalization
 
     def forward(self, specgram):
-        specgram = torch.log10(torch.clamp(specgram, min=1e-5))
+        specgram = torch.log10(torch.clamp(specgram.squeeze(0), min=1e-5))
         if self.normalization:
             return torch.clamp(
                 (self.min_level_db - 20 * specgram) / self.min_level_db, min=0, max=1