Update eduport_tts_mal.py

eduport_tts_mal.py

@@ -10,7 +10,6 @@ from torchaudio.transforms import Resample
 from torch.amp import GradScaler, autocast
 from tqdm import tqdm
 from jiwer import wer
-from torchaudio.transforms import TimeStretch, PitchShift
 from safetensors.torch import save_file
 
 # Compute max audio length from the training dataset
@@ -69,22 +68,6 @@ class SpeechDataset(Dataset):
         input_ids = self.tokenizer.encode(transcript, truncation=True, padding='longest', max_length=self.max_length, return_tensors="pt").squeeze(0)
 
         return input_values, input_ids
-
-class AugmentedSpeechDataset(SpeechDataset):
-    def __getitem__(self, idx):
-        input_values, input_ids = super().__getitem__(idx)
-        
-        # Random time stretching
-        if torch.rand(1).item() < 0.3:
-            time_stretch = TimeStretch(factor=torch.uniform(0.8, 1.2))
-            input_values = time_stretch(input_values)
-        
-        # Random pitch shift
-        if torch.rand(1).item() < 0.3:
-            pitch_shift = PitchShift(sample_rate=self.target_sampling_rate, n_steps=torch.randint(-2, 2, (1,)))
-            input_values = pitch_shift(input_values)
-        
-        return input_values, input_ids
         
 def collate_fn(batch):
     audio_inputs, text_inputs = zip(*batch)
@@ -122,8 +105,8 @@ print(max_audio_length)
 train_audios, val_audios, train_transcripts, val_transcripts = train_test_split(audio_files, transcript_files, test_size=0.05, random_state=42)
 
 # Define your dataset and dataloaders
-train_dataset = AugmentedSpeechDataset(train_audios, train_transcripts, tokenizer, processor, max_audio_length=max_audio_length)
-val_dataset = AugmentedSpeechDataset(val_audios, val_transcripts, tokenizer, processor, max_audio_length=max_audio_length)
+train_dataset = SpeechDataset(train_audios, train_transcripts, tokenizer, processor, max_audio_length=max_audio_length)
+val_dataset = SpeechDataset(val_audios, val_transcripts, tokenizer, processor, max_audio_length=max_audio_length)
 
 # Update your DataLoader to use the custom collate_fn
 train_loader = DataLoader(train_dataset, batch_size=1, shuffle=True, collate_fn=collate_fn)