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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "f1e90f25",
"metadata": {},
"outputs": [],
"source": [
"# Step 1: Install dependency\n",
"!pip install ffmpeg-python\n",
"\n",
"# Step 2: Clone the Wav2Lip repository\n",
"!git clone https://github.com/justinjohn0306/Wav2Lip\n",
"\n",
"# Step 3: Download pretrained model\n",
"import requests\n",
"url = \"https://iiitaphyd-my.sharepoint.com/personal/radrabha_m_research_iiit_ac_in/_layouts/15/download.aspx?share=EdjI7bZlgApMqsVoEUUXpLsBxqXbn5z8VTmoxp55YNDcIA\"\n",
"response = requests.get(url)\n",
"\n",
"with open(\"Wav2Lip/checkpoints/wav2lip_gan.pth\", \"wb\") as f:\n",
" f.write(response.content)\n",
" \n",
"# Step 4: Install the required dependencies for Wav2Lip\n",
"!cd Wav2Lip && pip install -r requirements.txt\n",
"!pip install pyaudio\n",
"\n",
"\n",
"# Step 5: Download pretrained model for face detection\n",
"url = \"https://www.adrianbulat.com/downloads/python-fan/s3fd-619a316812.pth\"\n",
"response = requests.get(url)\n",
"\n",
"with open(\"Wav2Lip/face_detection/detection/sfd/s3fd.pth\", \"wb\") as f:\n",
" f.write(response.content)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8e86c988",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import subprocess\n",
"from urllib import parse as urlparse\n",
"\n",
"# Step 1: Install yt-dlp\n",
"subprocess.run(['pip', 'install', 'yt-dlp'])\n",
"\n",
"# Step 2: Define YouTube URL and Video ID\n",
"YOUTUBE_URL = 'https://www.youtube.com/watch?v=vAnWYLTdvfY'\n",
"url_data = urlparse.urlparse(YOUTUBE_URL)\n",
"query = urlparse.parse_qs(url_data.query)\n",
"YOUTUBE_ID = query[\"v\"][0]\n",
"\n",
"# Remove previous input video\n",
"if os.path.isfile('input_vid.mp4'):\n",
" os.remove('input_vid.mp4')\n",
"\n",
"# Trim video (start, end) seconds\n",
"start = 35\n",
"end = 62\n",
"interval = end - start\n",
"\n",
"# Step 3: Download and trim the YouTube video\n",
"subprocess.run(['yt-dlp', '-f', 'bestvideo[ext=mp4]', '--output', \"youtube.%(ext)s\", f'https://www.youtube.com/watch?v={YOUTUBE_ID}'])\n",
"\n",
"# Cut the video using FFmpeg\n",
"subprocess.run(['ffmpeg', '-y', '-i', 'youtube.mp4', '-ss', str(start), '-t', str(interval), '-async', '1', 'input_vid.mp4'])\n",
"\n",
"# Display video.\n",
"from IPython.display import HTML\n",
"from base64 import b64encode\n",
"\n",
"def show_video(path):\n",
" mp4 = open(path, 'rb').read()\n",
" data_url = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n",
" return HTML(f\"\"\"<video width=600 controls><source src=\"{data_url}\"></video>\"\"\")\n",
"\n",
"# Preview the trimmed video\n",
"show_video('input_vid.mp4')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7da8e818",
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"import os\n",
"from IPython.display import Audio\n",
"from IPython.core.display import display\n",
"\n",
"upload_method = 'Path' # Change this to 'Record' or 'Path'\n",
"\n",
"# Remove previous input audio\n",
"if os.path.isfile('input_audio.wav'):\n",
" os.remove('input_audio.wav')\n",
"\n",
"def display_audio():\n",
" display(Audio('input_audio.wav'))\n",
"\n",
"if upload_method == 'Record':\n",
" import pyaudio\n",
" import wave\n",
"\n",
" CHUNK = 1024\n",
" FORMAT = pyaudio.paInt16\n",
" CHANNELS = 1\n",
" RATE = 16000\n",
" RECORD_SECONDS = 5\n",
" WAVE_OUTPUT_FILENAME = \"input_audio.wav\"\n",
"\n",
" p = pyaudio.PyAudio()\n",
"\n",
" stream = p.open(format=FORMAT,\n",
" channels=CHANNELS,\n",
" rate=RATE,\n",
" input=True,\n",
" frames_per_buffer=CHUNK)\n",
"\n",
" print(\"Recording...\")\n",
"\n",
" frames = []\n",
"\n",
" for i in range(0, int(RATE / CHUNK * RECORD_SECONDS)):\n",
" data = stream.read(CHUNK)\n",
" frames.append(data)\n",
"\n",
" print(\"Finished recording.\")\n",
"\n",
" stream.stop_stream()\n",
" stream.close()\n",
" p.terminate()\n",
"\n",
" wf = wave.open(WAVE_OUTPUT_FILENAME, 'wb')\n",
" wf.setnchannels(CHANNELS)\n",
" wf.setsampwidth(p.get_sample_size(FORMAT))\n",
" wf.setframerate(RATE)\n",
" wf.writeframes(b''.join(frames))\n",
" wf.close()\n",
"\n",
" display_audio()\n",
"\n",
"elif upload_method == 'Path':\n",
" # Add the full path to your audio\n",
" PATH_TO_YOUR_AUDIO = 'C:/Users/justi/OneDrive/Desktop/wav2lip/Wav2Lip/input_audio.wav'\n",
"\n",
" # Load audio with specified sampling rate\n",
" import librosa\n",
" audio, sr = librosa.load(PATH_TO_YOUR_AUDIO, sr=None)\n",
"\n",
" # Save audio with specified sampling rate\n",
" import soundfile as sf\n",
" sf.write('input_audio.wav', audio, sr, format='wav')\n",
"\n",
" display_audio()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "63289945",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# Define the parameters for the Wav2Lip model\n",
"pad_top = 0\n",
"pad_bottom = 10\n",
"pad_left = 0\n",
"pad_right = 0\n",
"rescaleFactor = 1\n",
"nosmooth = False\n",
"\n",
"# Set the path to the Wav2Lip model and input files\n",
"checkpoint_path = \"checkpoints/wav2lip_gan.pth\"\n",
"input_face = \"input_vid.mp4\"\n",
"input_audio = \"input_audio.wav\"\n",
"\n",
"# Run the Wav2Lip model\n",
"!cd Wav2Lip && python inference.py --checkpoint_path {checkpoint_path} --face {input_face} --audio {input_audio} --pads {pad_top} {pad_bottom} {pad_left} {pad_right} --resize_factor {rescaleFactor} {\"--nosmooth\" if nosmooth else \"\"}\n",
"\n",
"# Preview the output video\n",
"print(\"Final Video Preview\")\n",
"print(\"Find the output video at\", 'Wav2Lip/results/result_voice.mp4')\n",
"show_video('Wav2Lip/results/result_voice.mp4')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3fbafa56",
"metadata": {},
"outputs": [],
"source": []
}
],
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"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
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"language_info": {
"codemirror_mode": {
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},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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