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Running
on
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Running
on
Zero
| import tempfile | |
| import gradio as gr | |
| import subprocess | |
| import os, stat | |
| from googletrans import Translator | |
| from TTS.api import TTS | |
| import ffmpeg | |
| import whisper | |
| from scipy.signal import wiener | |
| import soundfile as sf | |
| from pydub import AudioSegment | |
| import numpy as np | |
| import librosa | |
| from zipfile import ZipFile | |
| os.environ["COQUI_TOS_AGREED"] = "1" | |
| ZipFile("ffmpeg.zip").extractall() | |
| st = os.stat('ffmpeg') | |
| os.chmod('ffmpeg', st.st_mode | stat.S_IEXEC) | |
| def process_video(video, high_quality, target_language): | |
| output_filename = "resized_video.mp4" | |
| if high_quality: | |
| ffmpeg.input(video).output(output_filename, vf='scale=-1:720').run() | |
| video_path = output_filename | |
| else: | |
| video_path = video | |
| # Debugging Step 1: Check if video_path exists | |
| if not os.path.exists(video_path): | |
| return f"Error: {video_path} does not exist." | |
| ffmpeg.input(video_path).output('output_audio.wav', acodec='pcm_s24le', ar=48000, map='a').run() | |
| y, sr = sf.read("output_audio.wav") | |
| y = y.astype(np.float32) | |
| y_denoised = wiener(y) | |
| sf.write("output_audio_denoised.wav", y_denoised, sr) | |
| sound = AudioSegment.from_file("output_audio_denoised.wav", format="wav") | |
| sound = sound.apply_gain(0) # Reduce gain by 5 dB | |
| sound = sound.low_pass_filter(3000).high_pass_filter(100) | |
| sound.export("output_audio_processed.wav", format="wav") | |
| shell_command = f"ffmpeg -y -i output_audio_processed.wav -af lowpass=3000,highpass=100 output_audio_final.wav".split(" ") | |
| subprocess.run([item for item in shell_command], capture_output=False, text=True, check=True) | |
| model = whisper.load_model("base") | |
| result = model.transcribe("output_audio_final.wav") | |
| whisper_text = result["text"] | |
| whisper_language = result['language'] | |
| print(whisper_text) | |
| language_mapping = {'English': 'en', 'Spanish': 'es', 'French': 'fr', 'German': 'de', 'Italian': 'it', 'Portuguese': 'pt', 'Polish': 'pl', 'Turkish': 'tr', 'Russian': 'ru', 'Dutch': 'nl', 'Czech': 'cs', 'Arabic': 'ar', 'Chinese (Simplified)': 'zh-cn'} | |
| target_language_code = language_mapping[target_language] | |
| translator = Translator() | |
| try: | |
| translated_text = translator.translate(whisper_text, src=whisper_language, dest=target_language_code).text | |
| except AttributeError as e: | |
| print("Failed to translate text. Likely an issue with token extraction in the Google Translate API.") | |
| translated_text = "Translation failed due to API issue." | |
| tts = TTS("tts_models/multilingual/multi-dataset/xtts_v1") | |
| tts.to('cuda') # Replacing deprecated gpu=True | |
| tts.tts_to_file(translated_text, speaker_wav='output_audio_final.wav', file_path="output_synth.wav", language=target_language_code) | |
| pad_top = 0 | |
| pad_bottom = 15 | |
| pad_left = 0 | |
| pad_right = 0 | |
| rescaleFactor = 1 | |
| # Debugging Step 2: Remove quotes around the video path | |
| video_path_fix = video_path | |
| cmd = f"python Wav2Lip/inference.py --checkpoint_path '/Wav2Lip/checkpoints/wav2lip_gan.pth' --face {shlex.quote(video_path_fix)} --audio 'output_synth.wav' --pads {pad_top} {pad_bottom} {pad_left} {pad_right} --resize_factor {rescaleFactor} --nosmooth --outfile 'output_video.mp4'" | |
| subprocess.run(cmd, shell=True) | |
| # Debugging Step 3: Check if output video exists | |
| if not os.path.exists("output_video.mp4"): | |
| return "Error: output_video.mp4 was not generated." | |
| return "output_video.mp4" | |
| iface = gr.Interface( | |
| fn=process_video, | |
| inputs=[ | |
| gr.Video(), | |
| gr.inputs.Checkbox(label="High Quality"), | |
| gr.inputs.Dropdown(choices=["English", "Spanish", "French", "German", "Italian", "Portuguese", "Polish", "Turkish", "Russian", "Dutch", "Czech", "Arabic", "Chinese (Simplified)"], label="Target Language for Dubbing") | |
| ], | |
| outputs=gr.outputs.File(), | |
| live=False | |
| ) | |
| iface.launch() |