Initial dataset upload with LFS tracking for audio files

generate_hf_meta.py

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+import os
+import soundfile as sf
+from tqdm import tqdm
+
+def create_hf_metafile(dataset_type, base_data_path, output_meta_path):
+    subset_path = os.path.join(base_data_path, dataset_type)  # Path to the train or test directory
+    prompts_file = os.path.join(subset_path, "prompts.txt")
+    waves_base_dir = os.path.join(subset_path, "waves")
+
+    if not os.path.exists(prompts_file):
+        print(f"Error: Cannot find file {prompts_file}")
+        return
+    if not os.path.exists(waves_base_dir):
+        print(f"Error: Cannot find directory {waves_base_dir}")
+        return
+
+    print(f"Processing {dataset_type}...")
+
+    # Read prompts.txt
+    prompt_data = {}
+    with open(prompts_file, "r", encoding="utf-8") as pf:
+        for line in pf:
+            try:
+                parts = line.strip().split(" ", 1)
+                if len(parts) == 2:
+                    file_id, transcription = parts
+                    prompt_data[file_id] = transcription.upper()  # Convert to uppercase for consistency
+            except ValueError:
+                print(f"Ignoring line with incorrect format in {prompts_file}: {line.strip()}")
+                continue
+
+    with open(output_meta_path, "w", encoding="utf-8") as meta_f:
+        # Iterate through speaker directories in waves_base_dir
+        for speaker_dir in tqdm(os.listdir(waves_base_dir)):
+            speaker_path = os.path.join(waves_base_dir, speaker_dir)
+            if os.path.isdir(speaker_path):
+                for wav_filename in os.listdir(speaker_path):
+                    if wav_filename.endswith(".wav"):
+                        file_id_without_ext = os.path.splitext(wav_filename)[0]
+
+                        if file_id_without_ext in prompt_data:
+                            transcription = prompt_data[file_id_without_ext]
+                            full_wav_path = os.path.join(speaker_path, wav_filename)
+
+                            try:
+                                frames = sf.SoundFile(full_wav_path).frames
+                                samplerate = sf.SoundFile(full_wav_path).samplerate
+                                duration = frames / samplerate
+                            except Exception as e:
+                                print(f"Error reading file {full_wav_path}: {e}. Skipping.")
+                                continue
+
+                            # Create relative path for Hugging Face Hub
+                            # Example: train/waves/SPEAKER01/SPEAKER01_001.wav
+                            relative_path = os.path.join(dataset_type, "waves", speaker_dir, wav_filename).replace(os.sep, '/')
+
+                            meta_f.write(f"{relative_path}|{transcription}|{duration:.4f}\n")
+                        # else:
+                            # print(f"No transcription found for {file_id_without_ext} in {dataset_type}")
+    print(f"Meta file created: {output_meta_path}")
+
+if __name__ == "__main__":
+    current_script_dir = os.path.dirname(os.path.abspath(__file__))  # Directory containing this script
+
+    # Generate meta file for training set
+    create_hf_metafile(
+        dataset_type="train",
+        base_data_path=current_script_dir,  # Root directory of the dataset is the current directory
+        output_meta_path=os.path.join(current_script_dir, "train_meta.txt")
+    )
+
+    # Generate meta file for test set
+    create_hf_metafile(
+        dataset_type="test",
+        base_data_path=current_script_dir,  # Root directory of the dataset is the current directory
+        output_meta_path=os.path.join(current_script_dir, "test_meta.txt")
+    )
+    print("Meta file creation completed.")