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File size: 20,219 Bytes
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"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting fastapi (from -r requirements.txt (line 1))\n",
" Using cached fastapi-0.115.6-py3-none-any.whl.metadata (27 kB)\n",
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" Using cached PySoundFile-0.9.0.post1-py2.py3.cp26.cp27.cp32.cp33.cp34.cp35.cp36.pp27.pp32.pp33-none-win_amd64.whl.metadata (9.4 kB)\n",
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"Installing collected packages: mpmath, urllib3, tqdm, sympy, sniffio, safetensors, regex, pyyaml, python-multipart, pydantic-core, pycparser, pillow, numpy, networkx, MarkupSafe, idna, h11, future, fsspec, filelock, click, charset-normalizer, certifi, annotated-types, uvicorn, requests, pydantic, jinja2, ffmpeg-python, cffi, anyio, torch, starlette, PySoundFile, huggingface-hub, torchvision, torchaudio, tokenizers, fastapi, transformers\n",
"Successfully installed MarkupSafe-3.0.2 PySoundFile-0.9.0.post1 annotated-types-0.7.0 anyio-4.7.0 certifi-2024.12.14 cffi-1.17.1 charset-normalizer-3.4.0 click-8.1.7 fastapi-0.115.6 ffmpeg-python-0.2.0 filelock-3.16.1 fsspec-2024.10.0 future-1.0.0 h11-0.14.0 huggingface-hub-0.27.0 idna-3.10 jinja2-3.1.4 mpmath-1.3.0 networkx-3.4.2 numpy-2.2.0 pillow-11.0.0 pycparser-2.22 pydantic-2.10.3 pydantic-core-2.27.1 python-multipart-0.0.19 pyyaml-6.0.2 regex-2024.11.6 requests-2.32.3 safetensors-0.4.5 sniffio-1.3.1 starlette-0.41.3 sympy-1.13.1 tokenizers-0.21.0 torch-2.5.1 torchaudio-2.5.1 torchvision-0.20.1 tqdm-4.67.1 transformers-4.47.0 urllib3-2.2.3 uvicorn-0.34.0\n",
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"pip install -r requirements.txt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['soundfile']\n"
]
}
],
"source": [
"import torchaudio\n",
"print(str(torchaudio.list_audio_backends()))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"pip list --format=freeze > requirements.txt"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"<>:13: SyntaxWarning: invalid escape sequence '\\m'\n",
"<>:17: SyntaxWarning: invalid escape sequence '\\H'\n",
"<>:13: SyntaxWarning: invalid escape sequence '\\m'\n",
"<>:17: SyntaxWarning: invalid escape sequence '\\H'\n",
"C:\\Users\\Asus\\AppData\\Local\\Temp\\ipykernel_18220\\208613059.py:13: SyntaxWarning: invalid escape sequence '\\m'\n",
" model_path = \"Deepfake\\model\"\n",
"C:\\Users\\Asus\\AppData\\Local\\Temp\\ipykernel_18220\\208613059.py:17: SyntaxWarning: invalid escape sequence '\\H'\n",
" cache_dir=\"D:\\HuggingFace\",\n"
]
}
],
"source": [
"from transformers import pipeline\n",
"from transformers import AutoProcessor, AutoModelForAudioClassification\n",
"from fastapi import FastAPI\n",
"from pydantic import BaseModel\n",
"import uvicorn\n",
"import torchaudio\n",
"import torch\n",
"\n",
"# Define the input schema\n",
"class InputData(BaseModel):\n",
" input: str\n",
"\n",
"model_path = \"Deepfake\\model\"\n",
"processor = AutoProcessor.from_pretrained(model_path)\n",
"# Instantiate the model\n",
"model = AutoModelForAudioClassification.from_pretrained(pretrained_model_name_or_path=model_path,\n",
" cache_dir=\"D:\\HuggingFace\",\n",
" local_files_only=True,\n",
" )\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Functions"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 6,
"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": 14,
"metadata": {},
"outputs": [],
"source": [
"import torch.nn.functional as F\n",
"\n",
"def predict_audio(file_path):\n",
" \"\"\"\n",
" Predicts the class of an audio file by aggregating predictions from chunks and calculates confidence.\n",
" \n",
" Args:\n",
" file_path (str): Path to the audio file.\n",
"\n",
" Returns:\n",
" dict: Contains the predicted class label and average confidence score.\n",
" \"\"\"\n",
" # Prepare audio chunks\n",
" audio_chunks = prepare_audio(file_path)\n",
" predictions = []\n",
" confidences = []\n",
"\n",
" for i, chunk in enumerate(audio_chunks):\n",
" # Prepare input for the model\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",
" \n",
" # Apply softmax to calculate probabilities\n",
" probabilities = F.softmax(logits, dim=1)\n",
" \n",
" # Get the predicted class and its confidence\n",
" confidence, predicted_class = torch.max(probabilities, dim=1)\n",
" predictions.append(predicted_class.item())\n",
" confidences.append(confidence.item())\n",
" \n",
" # Aggregate predictions (majority voting)\n",
" aggregated_prediction_id = max(set(predictions), key=predictions.count)\n",
" predicted_label = model.config.id2label[aggregated_prediction_id]\n",
" \n",
" # Calculate average confidence across chunks\n",
" average_confidence = sum(confidences) / len(confidences)\n",
"\n",
" return {\n",
" \"predicted_label\": predicted_label,\n",
" \"average_confidence\": average_confidence\n",
" }\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Chunk shape: (160000,)\n",
"Chunk shape: (160000,)\n",
"Chunk shape: (160000,)\n",
"Chunk shape: (160000,)\n",
"Chunk shape: (160000,)\n",
"Chunk shape: (160000,)\n",
"Chunk shape: (160000,)\n",
"Chunk shape: (160000,)\n",
"Chunk shape: (160000,)\n",
"Chunk shape: (160000,)\n",
"Predicted Class: {'predicted_label': 'Real', 'average_confidence': 0.9984144032001495}\n"
]
},
{
"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": [
"# Example: Test a single audio file\n",
"file_path = r\"D:\\repos\\GODAM\\audioFiles\\test.wav\" # Replace with your audio file path\n",
"predicted_class = predict_audio(file_path)\n",
"print(f\"Predicted Class: {predicted_class}\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "modelEnv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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