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processing.py

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+import os
+from datasets import Dataset, DatasetDict, load_dataset
+from datasets.features import Audio
+import pandas as pd
+
+# 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]
+        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(text).input_ids for text in batch["text"]]
+
+    return batch
+
+# Use Dataset.map to apply the function
+dataset_dict = dataset_dict.map(
+    prepare_dataset,
+    remove_columns=dataset_dict.column_names["train"],
+    batch_size=8,  # Process smaller batches
+    batched=True,   # Enable batched processing
+)
+
+dataset_dict.save_to_disk("processed_dataset")