支持24k音频超分48k采样率

支持24k音频超分48k采样率

tools/AP_BWE_main/datasets1/dataset.py

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+import os
+import random
+import torch
+import torchaudio
+import torch.utils.data
+import torchaudio.functional as aF
+
+def amp_pha_stft(audio, n_fft, hop_size, win_size, center=True):
+
+    hann_window = torch.hann_window(win_size).to(audio.device)
+    stft_spec = torch.stft(audio, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window,
+                           center=center, pad_mode='reflect', normalized=False, return_complex=True)
+    log_amp = torch.log(torch.abs(stft_spec)+1e-4)
+    pha = torch.angle(stft_spec)
+
+    com = torch.stack((torch.exp(log_amp)*torch.cos(pha), 
+                       torch.exp(log_amp)*torch.sin(pha)), dim=-1)
+
+    return log_amp, pha, com
+
+
+def amp_pha_istft(log_amp, pha, n_fft, hop_size, win_size, center=True):
+    
+    amp = torch.exp(log_amp)
+    com = torch.complex(amp*torch.cos(pha), amp*torch.sin(pha))
+    hann_window = torch.hann_window(win_size).to(com.device)
+    audio = torch.istft(com, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window, center=center)
+
+    return audio
+
+
+def get_dataset_filelist(a):
+    with open(a.input_training_file, 'r', encoding='utf-8') as fi:
+        training_indexes = [x.split('|')[0] for x in fi.read().split('\n') if len(x) > 0]
+
+    with open(a.input_validation_file, 'r', encoding='utf-8') as fi:
+        validation_indexes = [x.split('|')[0] for x in fi.read().split('\n') if len(x) > 0]
+
+    return training_indexes, validation_indexes
+
+
+class Dataset(torch.utils.data.Dataset):
+    def __init__(self, training_indexes, wavs_dir, segment_size, hr_sampling_rate, lr_sampling_rate,
+                 split=True, shuffle=True, n_cache_reuse=1, device=None):
+        self.audio_indexes = training_indexes
+        random.seed(1234)
+        if shuffle:
+            random.shuffle(self.audio_indexes)
+        self.wavs_dir = wavs_dir
+        self.segment_size = segment_size
+        self.hr_sampling_rate = hr_sampling_rate
+        self.lr_sampling_rate = lr_sampling_rate
+        self.split = split
+        self.cached_wav = None
+        self.n_cache_reuse = n_cache_reuse
+        self._cache_ref_count = 0
+        self.device = device
+
+    def __getitem__(self, index):
+        filename = self.audio_indexes[index]
+        if self._cache_ref_count == 0:
+            audio, orig_sampling_rate = torchaudio.load(os.path.join(self.wavs_dir, filename + '.wav'))
+            self.cached_wav = audio
+            self._cache_ref_count = self.n_cache_reuse
+        else:
+            audio = self.cached_wav
+            self._cache_ref_count -= 1
+
+        if orig_sampling_rate == self.hr_sampling_rate:
+            audio_hr = audio
+        else:
+            audio_hr = aF.resample(audio, orig_freq=orig_sampling_rate, new_freq=self.hr_sampling_rate)
+
+        audio_lr = aF.resample(audio, orig_freq=orig_sampling_rate, new_freq=self.lr_sampling_rate)
+        audio_lr = aF.resample(audio_lr, orig_freq=self.lr_sampling_rate, new_freq=self.hr_sampling_rate)
+        audio_lr = audio_lr[:, : audio_hr.size(1)]
+
+        if self.split:
+            if audio_hr.size(1) >= self.segment_size:
+                max_audio_start = audio_hr.size(1) - self.segment_size
+                audio_start = random.randint(0, max_audio_start)
+                audio_hr = audio_hr[:, audio_start: audio_start+self.segment_size]
+                audio_lr = audio_lr[:, audio_start: audio_start+self.segment_size]
+            else:
+                audio_hr = torch.nn.functional.pad(audio_hr, (0, self.segment_size - audio_hr.size(1)), 'constant')
+                audio_lr = torch.nn.functional.pad(audio_lr, (0, self.segment_size - audio_lr.size(1)), 'constant')
+
+        return (audio_hr.squeeze(), audio_lr.squeeze())
+
+    def __len__(self):
+        
+        return len(self.audio_indexes)