Repair of ML Lib -> Transformations back to np from torch

audio_toolset/audio_to_mel_dataset.py

@@ -93,71 +93,3 @@ class LibrosaAudioToMelDataset(_AudioToMelDataset):
             pass
 
         return self.mel_file_path.exists()
-
-
-import torchaudio
-if sys.platform =='windows':
-    torchaudio.set_audio_backend('soundfile')
-else:
-    torchaudio.set_audio_backend('sox_io')
-
-
-class PyTorchAudioToMelDataset(_AudioToMelDataset):
-
-    @property
-    def audio_file_duration(self):
-        info_obj = torchaudio.info(self.audio_path)
-        return info_obj.num_frames / info_obj.sample_rate
-
-    @property
-    def sampling_rate(self):
-        return self.mel_kwargs['sample_rate']
-
-    def __init__(self, audio_file_path, *args, **kwargs):
-        super(PyTorchAudioToMelDataset, self).__init__(audio_file_path, *args, **kwargs)
-
-        audio_file_path = Path(audio_file_path)
-        # audio_file, sampling_rate = librosa.load(self.audio_path, sr=sampling_rate)
-
-        from torchaudio.transforms import MelSpectrogram
-        self._mel_transform = Compose([MelSpectrogram(**self.mel_kwargs),
-                                       MelToImage()
-                                       ])
-
-    def _build_mel(self):
-        if self.reset:
-            self.mel_file_path.unlink(missing_ok=True)
-        if not self.mel_file_path.exists():
-            self.mel_file_path.parent.mkdir(parents=True, exist_ok=True)
-            lock_file = Path(str(self.mel_file_path).replace(self.mel_file_path.suffix, '.lock'))
-            lock_file.touch(exist_ok=False)
-
-            try:
-                audio_sample, sample_rate = torchaudio.load(self.audio_path)
-            except RuntimeError:
-                import soundfile
-
-                data, samplerate = soundfile.read(self.audio_path)
-                # sf.available_formats()
-                # sf.available_subtypes()
-                soundfile.write(self.audio_path, data, samplerate, subtype='PCM_32')
-
-                audio_sample, sample_rate = torchaudio.load(self.audio_path)
-            if sample_rate != self.sampling_rate:
-                resample = torchaudio.transforms.Resample(orig_freq=int(sample_rate), new_freq=int(self.sampling_rate))
-                audio_sample = resample(audio_sample)
-            if audio_sample.shape[0] > 1:
-                # Transform Stereo to Mono
-                audio_sample = audio_sample.mean(dim=0, keepdim=True)
-            mel_sample = self._mel_transform(audio_sample)
-            with self.mel_file_path.open('wb') as mel_file:
-                pickle.dump(mel_sample, mel_file, protocol=pickle.HIGHEST_PROTOCOL)
-            lock_file.unlink()
-        else:
-            # print(f"Already existed.. Skipping {filename}")
-            # mel_file = mel_file
-            pass
-
-        # with mel_file.open(mode='rb') as f:
-        #     mel_sample = pickle.load(f, fix_imports=True)
-        return self.mel_file_path.exists()