Add files using upload-large-folder tool

optimized_processing.py

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+import os
+from datasets import Dataset, DatasetDict, load_dataset
+from datasets.features import Audio
+import pandas as pd
+import numpy as np
+from tqdm import tqdm
+
+# Function to load your custom dataset
+def load_custom_dataset(data_dir):
+    data = {
+        "audio": [],
+        "text": []
+    }
+
+    wav_dir = os.path.join(data_dir, 'wav')
+    txt_dir = os.path.join(data_dir, 'transcription')
+
+        # Assuming filenames in 'wav' and 'txt' match
+    for wav_file in os.listdir(wav_dir):
+            if wav_file.endswith('.wav'):
+                txt_file = wav_file.replace('.wav', '.txt')
+                wav_path = os.path.join(wav_dir, wav_file)
+                txt_path = os.path.join(txt_dir, txt_file)
+
+                # Read the transcription text
+                with open(txt_path, 'r', encoding='utf-8') as f:
+                    transcription = f.read().strip()
+
+                # Append to the dataset
+                data["audio"].append(wav_path)
+                data["text"].append(transcription)
+
+    # Create a pandas dataframe
+    df = pd.DataFrame(data)
+
+    # Convert to a Hugging Face dataset
+    dataset = Dataset.from_pandas(df)
+
+    # Define the audio feature (for .wav files)
+    dataset = dataset.cast_column("audio", Audio(sampling_rate=16_000))  # Adjust the sampling rate if needed
+
+    return dataset
+
+# Load your custom dataset
+custom_train_dataset = load_custom_dataset("./")
+
+# Load Common Voice test set (Malayalam)
+common_voice_test = load_dataset("mozilla-foundation/common_voice_11_0", "ml", split="test", trust_remote_code=True)
+
+common_voice_test = common_voice_test.select_columns(["audio", "sentence"])
+
+# Combine them into a DatasetDict
+dataset_dict = DatasetDict({
+    "train": custom_train_dataset,
+    "test": common_voice_test
+})
+
+# Now you have the `dataset_dict` with your custom train set and the Common Voice test set
+print(dataset_dict)
+
+from transformers import WhisperFeatureExtractor
+
+feature_extractor = WhisperFeatureExtractor.from_pretrained("openai/whisper-small")
+
+from transformers import WhisperTokenizer
+
+tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-small", language="Malayalam", task="transcribe")
+
+from transformers import WhisperProcessor
+
+processor = WhisperProcessor.from_pretrained("openai/whisper-small", language="Malayalam", task="transcribe")
+
+print(dataset_dict['train'][0])
+
+import gc  # for garbage collection
+
+def prepare_dataset(batch):
+    # Prepare input features for each audio file in the batch
+    audio_arrays = [item["array"] for item in batch["audio"]]
+    sampling_rates = [item["sampling_rate"] for item in batch["audio"]]
+
+    # Extract features for each audio sample
+    features = []
+    for audio, sr in zip(audio_arrays, sampling_rates):
+        feature = feature_extractor(audio, sampling_rate=sr).input_features[0]
+        feature = np.array(feature, dtype=np.float16)
+        features.append(feature)
+
+        # Free memory after each feature extraction
+        del audio  # Remove reference to the audio array
+        del sr
+        gc.collect()  # Trigger garbage collection to free memory
+
+    # Store features in batch
+    batch["input_features"] = features
+
+    # Encode target text to label ids
+    # Consider using a tokenizer with padding strategy (e.g., `padding="max_length"` or `padding="longest"`)
+    batch["labels"] = tokenizer(batch["text"], padding="longest", truncation=True).input_ids
+
+    return batch
+
+# Function to process and save dataset in batches
+def process_and_save_in_batches(dataset, batch_size=1000, save_path="processed_dataset"):
+    # Create an empty list to store the processed batches
+    all_processed = []
+
+    # Loop through the dataset in chunks
+    for start_idx in range(0, len(dataset), batch_size):
+        # Get the batch slice
+        batch = dataset[start_idx:start_idx+batch_size]
+        batch = Dataset.from_dict(batch)
+        # Apply the processing function to the batch
+        processed_batch = batch.map(
+            prepare_dataset,
+            remove_columns=dataset.column_names,
+            batched=True,
+            batch_size=batch_size,
+            num_proc = None,
+        )
+        print(f"Batch {start_idx} done")
+        # Append the processed batch to the list
+        all_processed.append(processed_batch)
+        
+        # Clear memory after processing each batch
+        del batch  # Remove reference to the batch
+        gc.collect()  # Trigger garbage collection
+
+        # Save each processed batch to disk
+        processed_batch.save_to_disk(os.path.join(save_path, f"batch_{start_idx // batch_size}"))
+        del processed_batch  # Free memory after saving the batch
+        gc.collect()
+
+    # Optionally, if you want to save the whole dataset in one file at the end
+    # You can merge all processed batches (not recommended for large datasets)
+    final_dataset = concatenate_datasets(all_processed)
+    final_dataset.save_to_disk(save_path)
+
+# Process and save the dataset in batches
+process_and_save_in_batches(dataset_dict['train'], batch_size=1000, save_path="processed_dataset")

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