{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting torchaudio\n",
      "  Downloading torchaudio-2.5.1-cp312-cp312-win_amd64.whl.metadata (6.5 kB)\n",
      "Requirement already satisfied: torch==2.5.1 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from torchaudio) (2.5.1)\n",
      "Requirement already satisfied: filelock in c:\\users\\asus\\anaconda3\\lib\\site-packages (from torch==2.5.1->torchaudio) (3.13.1)\n",
      "Requirement already satisfied: typing-extensions>=4.8.0 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from torch==2.5.1->torchaudio) (4.11.0)\n",
      "Requirement already satisfied: networkx in c:\\users\\asus\\anaconda3\\lib\\site-packages (from torch==2.5.1->torchaudio) (3.3)\n",
      "Requirement already satisfied: jinja2 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from torch==2.5.1->torchaudio) (3.1.4)\n",
      "Requirement already satisfied: fsspec in c:\\users\\asus\\anaconda3\\lib\\site-packages (from torch==2.5.1->torchaudio) (2024.6.1)\n",
      "Requirement already satisfied: setuptools in c:\\users\\asus\\anaconda3\\lib\\site-packages (from torch==2.5.1->torchaudio) (75.1.0)\n",
      "Requirement already satisfied: sympy==1.13.1 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from torch==2.5.1->torchaudio) (1.13.1)\n",
      "Requirement already satisfied: mpmath<1.4,>=1.1.0 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from sympy==1.13.1->torch==2.5.1->torchaudio) (1.3.0)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from jinja2->torch==2.5.1->torchaudio) (2.1.3)\n",
      "Downloading torchaudio-2.5.1-cp312-cp312-win_amd64.whl (2.4 MB)\n",
      "   ---------------------------------------- 0.0/2.4 MB ? eta -:--:--\n",
      "   ---------------------------------------- 2.4/2.4 MB 11.6 MB/s eta 0:00:00\n",
      "Installing collected packages: torchaudio\n",
      "Successfully installed torchaudio-2.5.1\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install torchaudio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'datasets'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[3], line 5\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mos\u001b[39;00m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorchaudio\u001b[39;00m\n\u001b[1;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mdatasets\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m DatasetDict, load_dataset\n\u001b[0;32m      7\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mprepare_dataset\u001b[39m(directory):\n\u001b[0;32m      8\u001b[0m     data \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpath\u001b[39m\u001b[38;5;124m\"\u001b[39m: [], \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabel\u001b[39m\u001b[38;5;124m\"\u001b[39m: []}\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'datasets'"
     ]
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n",
      "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n",
      "\u001b[1;31mClick <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "## The data is not pushed to repo, only model and training logs etc are uploaded\n",
    "\n",
    "import os\n",
    "import torchaudio\n",
    "from datasets import DatasetDict, load_dataset\n",
    "\n",
    "def prepare_dataset(directory):\n",
    "    data = {\"path\": [], \"label\": []}\n",
    "    labels = {\"fake\": 0, \"real\": 1}  # Map fake to 0 and real to 1\n",
    "\n",
    "    for label, label_id in labels.items():\n",
    "        folder_path = os.path.join(directory, label)\n",
    "        for file in os.listdir(folder_path):\n",
    "            if file.endswith(\".wav\"):\n",
    "                data[\"path\"].append(os.path.join(folder_path, file))\n",
    "                data[\"label\"].append(label_id)\n",
    "    return data\n",
    "\n",
    "# Prepare train, validation, and test datasets\n",
    "train_data = prepare_dataset(r\"dataset\\for-norm\\for-norm\\testing\")\n",
    "val_data = prepare_dataset(r\"dataset\\for-norm\\for-norm\\testing\")\n",
    "test_data = prepare_dataset(r\"dataset\\for-norm\\for-norm\\testing\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import Dataset\n",
    "\n",
    "train_dataset = Dataset.from_dict(train_data)\n",
    "val_dataset = Dataset.from_dict(val_data)\n",
    "test_dataset = Dataset.from_dict(test_data)\n",
    "\n",
    "dataset = DatasetDict({\"train\": train_dataset, \"validation\": val_dataset, \"test\": test_dataset})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\60165\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\transformers\\configuration_utils.py:302: UserWarning: Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 Transformers. Using `model.gradient_checkpointing_enable()` instead, or if you are using the `Trainer` API, pass `gradient_checkpointing=True` in your `TrainingArguments`.\n",
      "  warnings.warn(\n",
      "Some weights of Wav2Vec2ForSequenceClassification were not initialized from the model checkpoint at facebook/wav2vec2-base and are newly initialized: ['classifier.bias', 'classifier.weight', 'projector.bias', 'projector.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoProcessor\n",
    "from transformers import AutoModelForAudioClassification\n",
    "\n",
    "# Initialize processor\n",
    "model_name = \"facebook/wav2vec2-base\"  # Replace with your model if different\n",
    "model = AutoModelForAudioClassification.from_pretrained(model_name, num_labels=2)  # Adjust `num_labels` based on your dataset\n",
    "processor = AutoProcessor.from_pretrained(model_name)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocess Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "af2c5f31f0db43ee9975023b28e2c57c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/4634 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "051c3226b30145f3adbc708cf8afd6a5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/4634 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "86de10f37b7441e3a9c9f0ee88bf5149",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/4634 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0) <class 'torch.Tensor'>\n",
      "torch.int64\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "\n",
    "def preprocess_function(batch):\n",
    "    audio = torchaudio.load(batch[\"path\"])[0].squeeze().numpy()\n",
    "    inputs = processor(\n",
    "        audio,\n",
    "        sampling_rate=16000,\n",
    "        padding=True,\n",
    "        truncation=True,\n",
    "        max_length=32000,  \n",
    "        return_tensors=\"pt\"\n",
    "    )\n",
    "    batch[\"input_values\"] = inputs.input_values[0]\n",
    "    # Ensure labels are converted to LongTensor\n",
    "    batch[\"label\"] = torch.tensor(batch[\"label\"], dtype=torch.long)  # Convert label to LongTensor\n",
    "    return batch\n",
    "\n",
    "processed_dataset = dataset.map(preprocess_function, remove_columns=[\"path\"], batched=False)\n",
    "# Set format to torch tensors for compatibility with PyTorch\n",
    "processed_dataset.set_format(type=\"torch\", columns=[\"input_values\", \"label\"])\n",
    "\n",
    "# Double-check the label type again\n",
    "print(processed_dataset[\"train\"][0][\"label\"], type(processed_dataset[\"train\"][0][\"label\"]))\n",
    "print(processed_dataset[\"train\"][0][\"label\"].dtype)  # Should print torch.int64\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Map Training Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Labels: {0: 'Fake', 1: 'Real'}\n",
      "Labels: {0: 'Fake', 1: 'Real'}\n"
     ]
    }
   ],
   "source": [
    "# Ensure labels are in numerical format (e.g., 0, 1)\n",
    "id2label = {0: \"Fake\", 1: \"Real\"}  # Define the mapping based on your dataset\n",
    "label2id = {v: k for k, v in id2label.items()}  # Reverse mapping\n",
    "\n",
    "\n",
    "print(\"Labels:\", id2label)\n",
    "\n",
    "# Update the model's configuration with labels\n",
    "model.config.id2label = id2label\n",
    "model.config.label2id = label2id\n",
    "\n",
    "print(\"Labels:\", model.config.id2label)  # Verify\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import DataCollatorWithPadding\n",
    "\n",
    "# Use the processor's tokenizer for padding\n",
    "data_collator = DataCollatorWithPadding(tokenizer=processor, padding=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize Training Arguments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TrainingArguments initialized successfully!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\60165\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\transformers\\training_args.py:1545: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from transformers import TrainingArguments\n",
    "\n",
    "training_args = TrainingArguments(\n",
    "    output_dir=\"./results\",\n",
    "    evaluation_strategy=\"epoch\",\n",
    "    save_strategy=\"epoch\",\n",
    "    learning_rate=5e-5,\n",
    "    per_device_train_batch_size=8,\n",
    "    per_device_eval_batch_size=8,\n",
    "    num_train_epochs=3,\n",
    "    weight_decay=0.01,\n",
    "    logging_dir=\"./logs\",\n",
    "    logging_steps=10,\n",
    "    save_total_limit=2,\n",
    "    fp16=True,  \n",
    "    push_to_hub=False,\n",
    ")\n",
    "print(\"TrainingArguments initialized successfully!\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import Trainer\n",
    "\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    args=training_args,\n",
    "    train_dataset=processed_dataset[\"train\"],\n",
    "    eval_dataset=processed_dataset[\"validation\"],\n",
    "    tokenizer=processor,  # Required for the data collator\n",
    "    data_collator=data_collator,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Start Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "  0%|          | 0/1740 [00:00<?, ?it/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "text/plain": [
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      ]
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     "text": [
      "{'eval_loss': 4.472154250834137e-05, 'eval_runtime': 541.083, 'eval_samples_per_second': 8.564, 'eval_steps_per_second': 1.072, 'epoch': 3.0}\n",
      "{'train_runtime': 10729.5411, 'train_samples_per_second': 1.296, 'train_steps_per_second': 0.162, 'train_loss': 0.01232232698998762, 'epoch': 3.0}\n"
     ]
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     "data": {
      "text/plain": [
       "TrainOutput(global_step=1740, training_loss=0.01232232698998762, metrics={'train_runtime': 10729.5411, 'train_samples_per_second': 1.296, 'train_steps_per_second': 0.162, 'total_flos': 2.5228134820702045e+17, 'train_loss': 0.01232232698998762, 'epoch': 3.0})"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.train()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save The Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
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     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Save the trained model and processor\n",
    "trainer.save_model(\"./trained_model\")  # Saves the model to the specified directory\n",
    "processor.save_pretrained(\"./trained_model\")  # Saves the processor as well\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
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       "model_id": "ae19dee506db46f4b39d402e43d16276",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation Metrics:\n",
      "eval_loss: 4.472154250834137e-05\n",
      "eval_runtime: 527.4045\n",
      "eval_samples_per_second: 8.786\n",
      "eval_steps_per_second: 1.1\n",
      "epoch: 3.0\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model on the validation dataset\n",
    "evaluation_metrics = trainer.evaluate()\n",
    "\n",
    "# Print the evaluation metrics\n",
    "print(\"Evaluation Metrics:\")\n",
    "for metric, value in evaluation_metrics.items():\n",
    "    print(f\"{metric}: {value}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import AutoProcessor, AutoModelForAudioClassification\n",
    "\n",
    "# Load the trained model and processor\n",
    "model_path = \"./trained_model\"  # Path to your saved model\n",
    "model = AutoModelForAudioClassification.from_pretrained(model_path)\n",
    "processor = AutoProcessor.from_pretrained(model_path)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Single Audio Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_audio(file_path, sampling_rate=16000, duration=10):\n",
    "    \"\"\"\n",
    "    Prepares audio by loading, resampling, and returning it in manageable chunks.\n",
    "    \n",
    "    Parameters:\n",
    "    - file_path: Path to the audio file.\n",
    "    - sampling_rate: Target sampling rate for the audio.\n",
    "    - duration: Duration in seconds for each chunk.\n",
    "    \n",
    "    Returns:\n",
    "    - A list of audio chunks, each as a numpy array.\n",
    "    \"\"\"\n",
    "    # Load and resample the audio file\n",
    "    waveform, original_sampling_rate = torchaudio.load(file_path)\n",
    "    \n",
    "    # Convert stereo to mono if necessary\n",
    "    if waveform.shape[0] > 1:  # More than 1 channel\n",
    "        waveform = torch.mean(waveform, dim=0, keepdim=True)\n",
    "    \n",
    "    # Resample if needed\n",
    "    if original_sampling_rate != sampling_rate:\n",
    "        resampler = torchaudio.transforms.Resample(orig_freq=original_sampling_rate, new_freq=sampling_rate)\n",
    "        waveform = resampler(waveform)\n",
    "    \n",
    "    # Calculate chunk size in samples\n",
    "    chunk_size = sampling_rate * duration\n",
    "    audio_chunks = []\n",
    "\n",
    "    # Split the audio into chunks\n",
    "    for start in range(0, waveform.shape[1], chunk_size):\n",
    "        chunk = waveform[:, start:start + chunk_size]\n",
    "        \n",
    "        # Pad the last chunk if it's shorter than the chunk size\n",
    "        if chunk.shape[1] < chunk_size:\n",
    "            padding = chunk_size - chunk.shape[1]\n",
    "            chunk = torch.nn.functional.pad(chunk, (0, padding))\n",
    "        \n",
    "        audio_chunks.append(chunk.squeeze().numpy())\n",
    "    \n",
    "    return audio_chunks\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Logits for chunk 44: tensor([[-3.8786,  4.3105]])\n",
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      "Logits for chunk 46: tensor([[ 4.7127, -5.2213]])\n",
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      "Logits for chunk 49: tensor([[ 3.4856, -3.8528]])\n",
      "Chunk shape: (160000,)\n",
      "Logits for chunk 50: tensor([[ 4.6485, -5.1538]])\n",
      "Chunk shape: (160000,)\n",
      "Logits for chunk 51: tensor([[ 4.6274, -5.1355]])\n",
      "Chunk shape: (160000,)\n",
      "Logits for chunk 52: tensor([[ 4.6852, -5.1872]])\n",
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      "Logits for chunk 53: tensor([[ 4.7341, -5.2452]])\n",
      "Chunk shape: (160000,)\n",
      "Logits for chunk 54: tensor([[-4.5378,  5.0152]])\n",
      "Chunk shape: (160000,)\n",
      "Logits for chunk 55: tensor([[ 4.6822, -5.1887]])\n",
      "Chunk shape: (160000,)\n",
      "Logits for chunk 56: tensor([[ 4.7186, -5.2252]])\n",
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      "Logits for chunk 60: tensor([[ 4.5292, -5.0253]])\n",
      "Predicted Class: Fake\n"
     ]
    }
   ],
   "source": [
    "def predict_audio(file_path):\n",
    "    \"\"\"\n",
    "    Predicts the class of an audio file by aggregating predictions from chunks.\n",
    "    \n",
    "    Args:\n",
    "        file_path (str): Path to the audio file.\n",
    "\n",
    "    Returns:\n",
    "        str: Predicted class label.\n",
    "    \"\"\"\n",
    "    # Prepare audio chunks\n",
    "    audio_chunks = prepare_audio(file_path)\n",
    "    predictions = []\n",
    "\n",
    "    for i, chunk in enumerate(audio_chunks):\n",
    "        # Prepare input for the model\n",
    "        print(f\"Chunk shape: {chunk.shape}\")\n",
    "        inputs = processor(\n",
    "            chunk, sampling_rate=16000, return_tensors=\"pt\", padding=True\n",
    "        )\n",
    "        \n",
    "        # Perform inference\n",
    "        with torch.no_grad():\n",
    "            outputs = model(**inputs)\n",
    "            logits = outputs.logits\n",
    "            print(f\"Logits for chunk {i + 1}: {logits}\")  # Print the logits\n",
    "            predicted_class = torch.argmax(logits, dim=1).item()\n",
    "            predictions.append(predicted_class)\n",
    "    \n",
    "    # Aggregate predictions (e.g., majority voting)\n",
    "    aggregated_prediction = max(set(predictions), key=predictions.count)\n",
    "    \n",
    "    # Convert class ID to label\n",
    "    return model.config.id2label[aggregated_prediction]\n",
    "\n",
    "# Example: Test a single audio file\n",
    "file_path = r\"D:\\Year 3 Sem 2\\Godamlah\\Deepfake\\deepfake model ver3\\data\\KAGGLE\\AUDIO\\FAKE\\biden-to-linus.wav\"  # Replace with your audio file path\n",
    "predicted_class = predict_audio(file_path)\n",
    "print(f\"Predicted Class: {predicted_class}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chunk shape: (160000,)\n",
      "Logits for chunk 1: tensor([[-3.3933,  3.7590]])\n",
      "Chunk shape: (160000,)\n",
      "Logits for chunk 1: tensor([[-3.3933,  3.7590]])\n",
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      "Logits for chunk 1: tensor([[ 4.7569, -5.2631]])\n",
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      "Chunk shape: (160000,)\n",
      "Logits for chunk 1: tensor([[ 4.7639, -5.2653]])\n",
      "{'file': 'human voice 1 to mr beast.mp3', 'predicted_class': 'Real'}\n",
      "{'file': 'human voice 1 to mr beast.wav', 'predicted_class': 'Real'}\n",
      "{'file': 'human voice 1.mp3', 'predicted_class': 'Real'}\n",
      "{'file': 'human voice 1.wav', 'predicted_class': 'Real'}\n",
      "{'file': 'human voice 2 to Jett.mp3', 'predicted_class': 'Fake'}\n",
      "{'file': 'human voice 2 to Jett.wav', 'predicted_class': 'Fake'}\n",
      "{'file': 'human voice 2.mp3', 'predicted_class': 'Real'}\n",
      "{'file': 'human voice 2.wav', 'predicted_class': 'Real'}\n",
      "{'file': 'text to audio jett.mp3', 'predicted_class': 'Fake'}\n",
      "{'file': 'text to audio jett.wav', 'predicted_class': 'Fake'}\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "def batch_predict(test_folder, limit=10):\n",
    "    \"\"\"\n",
    "    Batch processes audio files for predictions.\n",
    "\n",
    "    Args:\n",
    "        test_folder (str): Path to the folder containing audio files.\n",
    "        limit (int): Maximum number of files to process. Set to None for all files.\n",
    "\n",
    "    Returns:\n",
    "        list: A list of dictionaries containing file names and predicted classes.\n",
    "    \"\"\"\n",
    "    results = []\n",
    "    files = os.listdir(test_folder)\n",
    "\n",
    "    # Limit the number of files processed if a limit is provided\n",
    "    if limit is not None:\n",
    "        files = files[:limit]\n",
    "\n",
    "    # Process each file in the folder\n",
    "    for file_name in files:\n",
    "        file_path = os.path.join(test_folder, file_name)\n",
    "        try:\n",
    "            predicted_class = predict_audio(file_path)  # Use the predict_audio function\n",
    "            results.append({\"file\": file_name, \"predicted_class\": predicted_class})\n",
    "        except Exception as e:\n",
    "            print(f\"Error processing {file_name}: {e}\")\n",
    "    \n",
    "    return results\n",
    "\n",
    "# Specify the folder path and limit\n",
    "test_folder = r\"D:\\Year 3 Sem 2\\Godamlah\\Deepfake\\deepfake model ver3\\data\\real life test audio\"  # Replace with your test folder path\n",
    "results = batch_predict(test_folder, limit=10)\n",
    "\n",
    "# Print results\n",
    "for result in results:\n",
    "    print(result)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "model_id": "479899631f95453e9f82355f7511cff3",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_loss': 4.472154250834137e-05, 'eval_model_preparation_time': 0.0032, 'eval_accuracy': 1.0, 'eval_runtime': 1168.3696, 'eval_samples_per_second': 3.966, 'eval_steps_per_second': 0.496}\n"
     ]
    }
   ],
   "source": [
    "import evaluate\n",
    "\n",
    "# Load the accuracy metric\n",
    "accuracy_metric = evaluate.load(\"accuracy\")\n",
    "\n",
    "def compute_metrics(eval_pred):\n",
    "    logits, labels = eval_pred\n",
    "    predictions = logits.argmax(axis=-1)  # Get the predicted class\n",
    "    accuracy = accuracy_metric.compute(predictions=predictions, references=labels)\n",
    "    return {\"accuracy\": accuracy[\"accuracy\"]}\n",
    "\n",
    "from transformers import Trainer\n",
    "\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    args=training_args,\n",
    "    train_dataset=processed_dataset[\"train\"],\n",
    "    eval_dataset=processed_dataset[\"validation\"],\n",
    "    tokenizer=processor,  # Required for padding\n",
    "    data_collator=data_collator,\n",
    "    compute_metrics=compute_metrics,  # Add this line\n",
    ")\n",
    "\n",
    "# Evaluate the model\n",
    "metrics = trainer.evaluate()\n",
    "print(metrics)\n"
   ]
  }
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