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dataset.py

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+import os
+import pandas as pd
+
+import torch
+import torchaudio
+import torch.nn.utils.rnn as rnn_utils
+
+import whisper
+
+def collate_fn(batch):
+    (seq, label) = zip(*batch)
+    seql = [x.reshape(-1,) for x in seq]
+    data = rnn_utils.pad_sequence(seql, batch_first=True, padding_value=0)
+    label = torch.tensor(list(label))
+    return data, label
+
+def collate_mel_fn(batch):
+    (seq, label) = zip(*batch)
+    data = torch.stack([x.reshape(80, -1) for x in seq])
+    label = torch.tensor(list(label))
+    return data, label
+
+class S2IDataset(torch.utils.data.Dataset):
+    def __init__(self, csv_path=None, wav_dir_path=None):
+        self.df = pd.read_csv(csv_path)
+        self.wav_dir = wav_dir_path
+        self.resmaple = torchaudio.transforms.Resample(8000, 16000)
+
+    def __len__(self):
+        return len(self.df)
+    
+    def __getitem__(self, idx):
+        if torch.is_tensor(idx):
+            idx = idx.tolist()
+        
+        row = self.df.iloc[idx]
+        intent_class = row["intent_class"]
+        wav_path = os.path.join(self.wav_dir, row["audio_path"])
+        speaker_id = row["speaker_id"]
+        template = row["template"]
+
+        wav_tensor, _= torchaudio.load(wav_path)
+        wav_tensor = self.resmaple(wav_tensor)
+        intent_class = int(intent_class)
+        return wav_tensor, intent_class
+
+class S2IMELDataset(torch.utils.data.Dataset):
+    def __init__(self, csv_path=None, wav_dir_path=None):
+        self.df = pd.read_csv(csv_path)
+        self.wav_dir = wav_dir_path
+        self.resmaple = torchaudio.transforms.Resample(8000, 16000)
+
+    def __len__(self):
+        return len(self.df)
+    
+    def __getitem__(self, idx):
+        if torch.is_tensor(idx):
+            idx = idx.tolist()
+        
+        row = self.df.iloc[idx]
+        intent_class = row["intent_class"]
+        wav_path = os.path.join(self.wav_dir, row["audio_path"])
+        speaker_id = row["speaker_id"]
+        template = row["template"]
+
+        wav_tensor, _= torchaudio.load(wav_path)
+        wav_tensor = self.resmaple(wav_tensor)
+            
+        wav_tensor = whisper.pad_or_trim(wav_tensor.flatten())
+        mel = whisper.log_mel_spectrogram(wav_tensor)
+
+        intent_class = int(intent_class)
+        return mel, intent_class
+
+if __name__ == "__main__":
+    dataset = S2IMELDataset(
+        csv_path="/root/Speech2Intent/dataset/speech-to-intent/train.csv",
+        wav_dir_path="/root/Speech2Intent/dataset/speech-to-intent/",
+        sr=16000)
+    wav_tensor, intent_class = dataset[0] 
+    print(wav_tensor.shape, intent_class)
+
+    trainloader = torch.utils.data.DataLoader(
+            dataset, 
+            batch_size=3, 
+            shuffle=True, 
+            num_workers=4,
+            collate_fn = collate_mel_fn,
+        )
+    x, y = next(iter(trainloader))
+    print(x.shape)
+    print(y.shape)