import os
import sys
import gradio as gr

os.system('git clone https://github.com/Rudrabha/Wav2Lip.git')
os.system('curl -o ./Wav2Lip/face_detection/detection/sfd/s3fd.pth https://www.adrianbulat.com/downloads/python-fan/s3fd-619a316812.pth')
os.system('pip3 install moviepy')
os.system('pip3 install speechRecognition')
os.system('pip3 install gtts')
os.system('pip3 install googletrans==3.1.0a0')
os.system('pip install numba==0.53')

title = "Automatic translation and dubbing for Indic Languages"
description = "A demo application to dub and translate videos spoken in Tamil, Hindi, Bengali and Telugu"
article = "Official Repo: https://github.com/Rudrabha/Wav2Lip"

def inference(language,speed,voice,video ):
    import moviepy.editor as mp
    clip = mp.VideoFileClip(video)
    clip.audio.write_audiofile(r"audio.wav")
    os.system('pip3 install pydub')
    os.system('pip3 install transformers==4.11.3 soundfile sentencepiece torchaudio librosa')
    speechlist = []
    from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
    import torch
    import torchaudio
    import librosa
    processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h-lv60-self")
    model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h-lv60-self")
    def get_transcription(audio_path):
      speech, sr = librosa.load(audio_path, sr=16000)
      resampler = torchaudio.transforms.Resample(sr, 16000)
      speech = resampler(speech)
      input_values = processor(speech, return_tensors="pt", sampling_rate=16000)["input_values"]
      logits = model(input_values)["logits"]
      predicted_ids = torch.argmax(logits, dim=-1)
      transcription = processor.decode(predicted_ids[0])
      return transcription.lower()
    speechtext = get_transcription("audio.wav")
    speechlist.append(speechtext)
    text = " ".join(speechlist)
    from googletrans import Translator
    from gtts import gTTS
    translator= Translator()
    if speed == "Slow":
        con = True
    elif speed == "Fast":
        con = False
    if language == "Hindi":
        translation = translator.translate(text, src = 'en', dest='hi', slow=con)
        tts = gTTS(translation.text, lang= "hi")
        tts.save('input_audio.wav')
    elif language == "Tamil":
        translation = translator.translate(text, src = 'en', dest='ta', slow=con)
        tts = gTTS(translation.text, lang= "ta")
        tts.save('input_audio.wav')
    elif language == "Bengali":
        translation = translator.translate(text, src = 'en', dest='bn', slow=con)
        tts = gTTS(translation.text, lang= "hi")
        tts.save('input_audio.wav')
    elif language == "Telugu":
        translation = translator.translate(text, src = 'en', dest='te', slow=con)
        tts = gTTS(translation.text, lang= "hi")
        tts.save('input_audio.wav')
    os.system('mv ./Wav2Lip/* .')
    os.system('pip install -r requirements.txt')
    os.system("python inference.py --checkpoint_path ./wav2lip_gan.pth --video {} --input_audio.wav {}".format(video))
    return "./results/result_voice.mp4"

iface = gr.Interface(inference, inputs=[gr.inputs.Radio(["Tamil", "Hindi", "Bengali", "Telugu"], label = "Enter language to translate to"), gr.inputs.Radio(["Slow", "Fast"], label = "Enter speaking speed"), gr.inputs.Radio(["Male", "Female"], label = "Enter preferred voice"), gr.inputs.Video(type="mp4", source="upload", label="Video to be Translated", optional=False)], outputs=["video"], title=title, description=description, article=article, enable_queue=True)
iface.launch()