Update app.py

app.py

@@ -5,7 +5,9 @@ import moviepy.editor as mp
 from TTS.api import TTS
 import torch
 import assemblyai as aai
+
 os.environ["COQUI_TOS_AGREED"] = "1"
+
 # Download necessary models if not already present
 model_files = {
     "wav2lip.pth": "https://github.com/justinjohn0306/Wav2Lip/releases/download/models/wav2lip.pth",
@@ -15,14 +17,12 @@ model_files = {
     "s3fd.pth": "https://www.adrianbulat.com/downloads/python-fan/s3fd-619a316812.pth"
 }
 
-
-
 device = "cpu"
 
+# Initialize TTS model
 tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2").to(device)
 
-
-
+# Download models
 for filename, url in model_files.items():
     file_path = os.path.join("checkpoints" if "pth" in filename else "face_detection", filename)
     if not os.path.exists(file_path):
@@ -31,8 +31,6 @@ for filename, url in model_files.items():
         with open(file_path, 'wb') as f:
             f.write(r.content)
 
-
-
 # Translation class
 class translation:
     def __init__(self, video_path, original_language, target_language):
@@ -87,11 +85,11 @@ class translation:
         translated_text = self.translate_text(transcript_text)
         translated_audio_path = self.generate_audio(translated_text)
 
-        # Run Wav2Lip inference
-        os.system(f"python inference.py --checkpoint_path 'checkpoints/wav2lip_gan.pth' --face {self.video_path} --audio {translated_audio_path} --outfile 'output_video.mp4'")
+        # Run Wav2Lip inference (update the path to inference.py)
+        inference_script_path = "inference.py"  # Update this to the actual location of inference.py
+        os.system(f"python {inference_script_path} --checkpoint_path 'checkpoints/wav2lip_gan.pth' --face {self.video_path} --audio {translated_audio_path} --outfile 'output_video.mp4'")
         return 'output_video.mp4'
 
-
 # Gradio Interface
 def app(video_path, original_language, target_language):
     translator = translation(video_path, original_language, target_language)
@@ -108,4 +106,119 @@ interface = gr.Interface(
     outputs=gr.Video(label="Translated Video")
 )
 
-interface.launch()
+interface.launch()
+
+
+
+
+# import os
+# import requests
+# import gradio as gr
+# import moviepy.editor as mp
+# from TTS.api import TTS
+# import torch
+# import assemblyai as aai
+# os.environ["COQUI_TOS_AGREED"] = "1"
+# # Download necessary models if not already present
+# model_files = {
+#     "wav2lip.pth": "https://github.com/justinjohn0306/Wav2Lip/releases/download/models/wav2lip.pth",
+#     "wav2lip_gan.pth": "https://github.com/justinjohn0306/Wav2Lip/releases/download/models/wav2lip_gan.pth",
+#     "resnet50.pth": "https://github.com/justinjohn0306/Wav2Lip/releases/download/models/resnet50.pth",
+#     "mobilenet.pth": "https://github.com/justinjohn0306/Wav2Lip/releases/download/models/mobilenet.pth",
+#     "s3fd.pth": "https://www.adrianbulat.com/downloads/python-fan/s3fd-619a316812.pth"
+# }
+
+
+
+# device = "cpu"
+
+# tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2").to(device)
+
+
+
+# for filename, url in model_files.items():
+#     file_path = os.path.join("checkpoints" if "pth" in filename else "face_detection", filename)
+#     if not os.path.exists(file_path):
+#         print(f"Downloading {filename}...")
+#         r = requests.get(url)
+#         with open(file_path, 'wb') as f:
+#             f.write(r.content)
+
+
+
+# # Translation class
+# class translation:
+#     def __init__(self, video_path, original_language, target_language):
+#         self.video_path = video_path
+#         self.original_language = original_language
+#         self.target_language = target_language
+
+#     def org_language_parameters(self, original_language):
+#         language_codes = {'English': 'en', 'German': 'de', 'Italian': 'it', 'Spanish': 'es'}
+#         self.lan_code = language_codes.get(original_language, '')
+
+#     def target_language_parameters(self, target_language):
+#         language_codes = {'English': 'en', 'German': 'de', 'Italian': 'it', 'Spanish': 'es'}
+#         self.tran_code = language_codes.get(target_language, '')
+
+#     def extract_audio(self):
+#         video = mp.VideoFileClip(self.video_path)
+#         audio = video.audio
+#         audio_path = "output_audio.wav"
+#         audio.write_audiofile(audio_path)
+#         return audio_path
+
+#     def transcribe_audio(self, audio_path):
+#         aai.settings.api_key = os.getenv("ASSEMBLYAI_API_KEY")
+#         config = aai.TranscriptionConfig(language_code=self.lan_code)
+#         transcriber = aai.Transcriber(config=config)
+#         transcript = transcriber.transcribe(audio_path)
+#         return transcript.text
+
+#     def translate_text(self, transcript_text):
+#         base_url = "https://api.cognitive.microsofttranslator.com/translate"
+#         headers = {
+#             "Ocp-Apim-Subscription-Key": os.getenv("MICROSOFT_TRANSLATOR_API_KEY"),
+#             "Content-Type": "application/json",
+#             "Ocp-Apim-Subscription-Region": "southeastasia"
+#         }
+#         params = {"api-version": "3.0", "from": self.lan_code, "to": self.tran_code}
+#         body = [{"text": transcript_text}]
+#         response = requests.post(base_url, headers=headers, params=params, json=body)
+#         translation = response.json()[0]["translations"][0]["text"]
+#         return translation
+
+#     def generate_audio(self, translated_text):
+#         tts.tts_to_file(text=translated_text, speaker_wav='output_audio.wav', file_path="output_synth.wav", language=self.tran_code)
+#         return "output_synth.wav"
+
+#     def translate_video(self):
+#         audio_path = self.extract_audio()
+#         self.org_language_parameters(self.original_language)
+#         self.target_language_parameters(self.target_language)
+#         transcript_text = self.transcribe_audio(audio_path)
+#         translated_text = self.translate_text(transcript_text)
+#         translated_audio_path = self.generate_audio(translated_text)
+
+#         # Run Wav2Lip inference
+#         os.system(f"python inference.py --checkpoint_path 'checkpoints/wav2lip_gan.pth' --face {self.video_path} --audio {translated_audio_path} --outfile 'output_video.mp4'")
+#         return 'output_video.mp4'
+
+
+# # Gradio Interface
+# def app(video_path, original_language, target_language):
+#     translator = translation(video_path, original_language, target_language)
+#     video_file = translator.translate_video()
+#     return video_file
+
+# interface = gr.Interface(
+#     fn=app,
+#     inputs=[
+#         gr.Video(label="Video Path"),
+#         gr.Dropdown(["English", "German", "Italian", "Spanish"], label="Original Language"),
+#         gr.Dropdown(["English", "German", "Italian", "Spanish"], label="Targeted Language"),
+#     ],
+#     outputs=gr.Video(label="Translated Video")
+# )
+
+# interface.launch()