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data_generator.py

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+import os
+import torch
+import torchaudio
+from tqdm import tqdm
+from chatterbox.tts_turbo import ChatterboxTurboTTS
+
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+BASE_DIR = "/home/cloud/StyleTTS2-fine-tuning"
+OUTPUT_DIR = os.path.join(BASE_DIR, "Data")
+
+REFERENCE_AUDIO_PATH = os.path.join(OUTPUT_DIR, "reference_wavs/british_accent_audio.wav")
+INPUT_TEXT_FILE = os.path.join(OUTPUT_DIR, "source_text_final.txt")
+TRAIN_LIST_PATH = os.path.join(OUTPUT_DIR, "train_list_new.txt")
+WAVS_DIR = os.path.join(OUTPUT_DIR, "wavs")
+TARGET_SAMPLE_RATE = 24000
+
+os.makedirs(WAVS_DIR, exist_ok=True)
+
+model = ChatterboxTurboTTS.from_pretrained(device=DEVICE)
+
+def get_sentences(text_path):
+    if not os.path.exists(text_path):
+        return []
+    
+    with open(text_path, 'r', encoding='utf-8') as f:
+        lines = f.readlines()
+    
+    valid_sentences = []
+    for line in lines:
+        cleaned = line.strip() 
+        if cleaned:  
+            valid_sentences.append(cleaned)
+    
+    return valid_sentences
+
+def get_completed_indices():
+    if not os.path.exists(TRAIN_LIST_PATH):
+        return set()
+    
+    completed = set()
+    with open(TRAIN_LIST_PATH, "r", encoding="utf-8") as f:
+        for line in f:
+            parts = line.strip().split("|")
+            if parts and len(parts) >= 1:
+                filename = parts[0]
+                try:
+                    number_part = filename.split("_")[1].split(".")[0]
+                    completed.add(int(number_part))
+                except:
+                    continue
+    return completed
+
+def generate_dataset():
+    sentences = get_sentences(INPUT_TEXT_FILE)
+    completed_indices = get_completed_indices()
+    
+    print(f"Total sentences: {len(sentences)}")
+    print(f"Already done:    {len(completed_indices)}")
+    
+    resampler = None
+    if model.sr != TARGET_SAMPLE_RATE:
+        resampler = torchaudio.transforms.Resample(orig_freq=model.sr, new_freq=TARGET_SAMPLE_RATE).to(DEVICE)
+
+    for i, sentence in enumerate(tqdm(sentences)):
+        if (i + 1) in completed_indices:
+            continue
+
+        filename = f"file_{i+1:04d}.wav"
+        filepath = os.path.join(WAVS_DIR, filename)
+        print(f"Generating {filename}...")
+        try:
+            with torch.inference_mode():
+                wav_tensor = model.generate(
+                    sentence,
+                    audio_prompt_path=REFERENCE_AUDIO_PATH
+                )
+
+            if wav_tensor.dim() == 1:
+                wav_tensor = wav_tensor.unsqueeze(0)
+
+            if wav_tensor.shape[0] > 1:
+                wav_tensor = wav_tensor.mean(dim=0, keepdim=True)
+
+            wav_tensor = wav_tensor.cpu()
+
+            if resampler:
+                wav_tensor = resampler(wav_tensor)
+
+            torchaudio.save(filepath, wav_tensor, TARGET_SAMPLE_RATE)
+
+            with open(TRAIN_LIST_PATH, "a", encoding="utf-8") as f:
+                f.write(f"{filename}|{sentence}|0\n")
+
+            del wav_tensor
+            torch.cuda.empty_cache()
+
+            if (i + 1) % 50 == 0:
+                torch.cuda.synchronize()
+
+        except Exception as e:
+            print(f"Error at sample {i+1}: {e}")
+            continue
+
+
+if __name__ == "__main__":
+    generate_dataset()