app.py

import gradio as gr
import os
from pathlib import Path
import time

import pandas as pd
import re
import time
import os

import whisper
from pytube import YouTube

import psutil
num_cores = psutil.cpu_count()
os.environ["OMP_NUM_THREADS"] = f"{num_cores}"


import torch


# is cuda available?


num_cores = psutil.cpu_count()
os.environ["OMP_NUM_THREADS"] = f"{num_cores}"
headers = {'Authorization': os.environ['DeepL_API_KEY']}

device = "cpu"#torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("DEVICE IS: ")
print(device)

asr_model_base = whisper.load_model("base", device=device)
asr_model_small = whisper.load_model("small", device=device)
whisper_models_dict = {
    'base': asr_model_base,
    'small': asr_model_small
}

whisper_models = ["base", "small"]

transcribe_options = dict(beam_size=3, best_of=3, without_timestamps=False)

source_languages  = {
"Afrikaans":"af",
"Amharic":"am",
"Arabic":"ar",
"Asturian ":"st",
"Azerbaijani":"az",
"Bashkir":"ba",
"Belarusian":"be",
"Bulgarian":"bg",
"Bengali":"bn",
"Breton":"br",
"Bosnian":"bs",
"Catalan; Valencian":"ca",
"Cebuano":"eb",
"Czech":"cs",
"Welsh":"cy",
"Danish":"da",
"German":"de",
"Greeek":"el",
"English":"en",
"Spanish":"es",
"Estonian":"et",
"Persian":"fa",
"Fulah":"ff",
"Finnish":"fi",
"French":"fr",
"Western Frisian":"fy",
"Irish":"ga",
"Gaelic; Scottish Gaelic":"gd",
"Galician":"gl",
"Gujarati":"gu",
"Hausa":"ha",
"Hebrew":"he",
"Hindi":"hi",
"Croatian":"hr",
"Haitian; Haitian Creole":"ht",
"Hungarian":"hu",
"Armenian":"hy",
"Indonesian":"id",
"Igbo":"ig",
"Iloko":"lo",
"Icelandic":"is",
"Italian":"it",
"Japanese":"ja",
"Javanese":"jv",
"Georgian":"ka",
"Kazakh":"kk",
"Central Khmer":"km",
"Kannada":"kn",
"Korean":"ko",
"Luxembourgish; Letzeburgesch":"lb",
"Ganda":"lg",
"Lingala":"ln",
"Lao":"lo",
"Lithuanian":"lt",
"Latvian":"lv",
"Malagasy":"mg",
"Macedonian":"mk",
"Malayalam":"ml",
"Mongolian":"mn",
"Marathi":"mr",
"Malay":"ms",
"Burmese":"my",
"Nepali":"ne",
"Dutch; Flemish":"nl",
"Norwegian":"no",
"Northern Sotho":"ns",
"Occitan (post 1500)":"oc",
"Oriya":"or",
"Panjabi; Punjabi":"pa",
"Polish":"pl",
"Pushto; Pashto":"ps",
"Portuguese":"pt",
"Romanian; Moldavian; Moldovan":"ro",
"Russian":"ru",
"Sindhi":"sd",
"Sinhala; Sinhalese":"si",
"Slovak":"sk",
"Slovenian":"sl",
"Somali":"so",
"Albanian":"sq",
"Serbian":"sr",
"Swati":"ss",
"Sundanese":"su",
"Swedish":"sv",
"Swahili":"sw",
"Tamil":"ta",
"Thai":"th",
"Tagalog":"tl",
"Tswana":"tn",
"Turkish":"tr",
"Ukrainian":"uk",
"Urdu":"ur",
"Uzbek":"uz",
"Vietnamese":"vi",
"Wolof":"wo",
"Xhosa":"xh",
"Yiddish":"yi",
"Yoruba":"yo",
"Chinese":"zh",
"Zulu":"zu",
"Let the model analyze": "Let the model analyze"
}

DeepL_language_codes_for_translation = {
"Bulgarian": "BG",
"Czech": "CS",
"Danish": "DA",
"German": "DE",
"Greek": "EL",
"English": "EN",
"Spanish": "ES",
"Estonian": "ET",
"Finnish": "FI",
"French": "FR",
"Hungarian": "HU",
"Indonesian": "ID",
"Italian": "IT",
"Japanese": "JA",
"Lithuanian": "LT",
"Latvian": "LV",
"Dutch": "NL",
"Polish": "PL",
"Portuguese": "PT",
"Romanian": "RO",
"Russian": "RU",
"Slovak": "SK",
"Slovenian": "SL",
"Swedish": "SV",
"Turkish": "TR",
"Ukrainian": "UK",
"Chinese": "ZH"
}


source_language_list = [key[0] for key in source_languages.items()]
translation_models_list = [key[0] for key in DeepL_language_codes_for_translation.items()]

  
videos_out_path = Path("./videos_out")
videos_out_path.mkdir(parents=True, exist_ok=True)

def get_youtube(video_url):
    yt = YouTube(video_url)
    abs_video_path = yt.streams.filter(progressive=True, file_extension='mp4').order_by('resolution').desc().first().download()
    print("LADATATTU POLKUUN")
    print(abs_video_path)
    
    return abs_video_path

def speech_to_text(video_file_path, selected_translation_lang, whisper_model):
    """
    # Youtube with translated subtitles using OpenAI Whisper and Opus-MT models.
    # Currently supports only English audio
    This space allows you to:
    1. Download youtube video with a given url
    2. Watch it in the first video component
    3. Run automatic speech recognition on the video using fast Whisper models
    4. Translate the recognized transcriptions to 26 languages supported by deepL (If source language not supported this will return original transciption)
    5. Burn the translations to the original video and watch the video in the 2nd video component
    
    Speech Recognition is based on models from OpenAI Whisper https://github.com/openai/whisper
    This space is using c++ implementation by https://github.com/ggerganov/whisper.cpp
    """
    
    if(video_file_path == None):
        raise ValueError("Error no video input")
    print(video_file_path)
    try:
        audio = whisper.load_audio(video_file_path)
    except Exception as e:
        raise RuntimeError("Error converting video to audio")

    last_time = time.time()

    try:
        print(f'Transcribing via local model')
        
        transcribe_options = dict(beam_size=5, best_of=5, without_timestamps=False)
        transcription = whisper_models_dict.get(whisper_model).transcribe(audio, **transcribe_options)
        
        df = pd.DataFrame(columns=['start','end','text'])

        

        for i,segment in enumerate(transcription['segments']):
            new_row = {'start': segment['start'],
            'end': segment['end'],
            'text': segment['text']
                            }
            df = df.append(new_row, ignore_index=True)
        
        return (df)
    except Exception as e:
        raise RuntimeError("Error Running inference with local model", e)



def translate_transcriptions(df, selected_translation_lang_2):
    if selected_translation_lang_2 is None:
            selected_translation_lang_2 = 'English'
    df.reset_index(inplace=True)
    
    print("start_translation")
    translations = []
    
    

    text_combined = ""
    for i, sentence in enumerate(df['text']):
        if i == 0:
            text_combined = sentence
        else:
            text_combined = text_combined + '\n' + sentence

    data = {'text': text_combined,
    'tag_spitting': 'xml',
    'target_lang': DeepL_language_codes_for_translation.get(selected_translation_lang_2)
           }
    try:
        response = requests.post('https://api-free.deepl.com/v2/translate', headers=headers, data=data)
    
        # Print the response from the server
        translated_sentences = json.loads(response.text)
        translated_sentences = translated_sentences['translations'][0]['text'].split('\n')
        df['translation'] = translated_sentences
    except Exception as e:
        print(e)
        df['translation'] = df['text']
        
    print("translations done")

    return df

def create_srt_and_burn(df, video_in):
    
    print("Starting creation of video wit srt")
    
       
    with open('testi.srt','w', encoding="utf-8") as file:
        for i in range(len(df)):
            file.write(str(i+1))
            file.write('\n')
            start = df.iloc[i]['start']
           
    
            milliseconds = round(start * 1000.0)

            hours = milliseconds // 3_600_000
            milliseconds -= hours * 3_600_000

            minutes = milliseconds // 60_000
            milliseconds -= minutes * 60_000

            seconds = milliseconds // 1_000
            milliseconds -= seconds * 1_000

            file.write(f"{hours}:{minutes:02d}:{seconds:02d}.{milliseconds:03d}")
            
            stop = df.iloc[i]['end']
            
            
            milliseconds = round(stop * 1000.0)

            hours = milliseconds // 3_600_000
            milliseconds -= hours * 3_600_000

            minutes = milliseconds // 60_000
            milliseconds -= minutes * 60_000

            seconds = milliseconds // 1_000
            milliseconds -= seconds * 1_000

            
            file.write(' --> ')
            file.write(f"{hours}:{minutes:02d}:{seconds:02d}.{milliseconds:03d}")
            file.write('\n')
            file.writelines(df.iloc[i]['translation'])
            if int(i) != len(df)-1:
                file.write('\n\n')
        
    print("SRT DONE")        
    try:
        file1 = open('./testi.srt', 'r', encoding="utf-8")
        Lines = file1.readlines()
        
        count = 0
        # Strips the newline character
        for line in Lines:
            count += 1
            print("{}".format(line))
                
        print(type(video_in))
        print(video_in)
        
        video_out = video_in.replace('.mp4', '_out.mp4')
        print(video_out)
        command = 'ffmpeg -i "{}" -y -vf subtitles=./testi.srt "{}"'.format(video_in, video_out)
        print(command)
        os.system(command)
        return video_out
    except Exception as e:
        print(e)
        return video_out


# ---- Gradio Layout -----
video_in = gr.Video(label="Video file", mirror_webcam=False)
youtube_url_in = gr.Textbox(label="Youtube url", lines=1, interactive=True)
video_out = gr.Video(label="Video Out", mirror_webcam=False)


df_init = pd.DataFrame(columns=['start','end','text','translation'])
selected_source_lang = gr.Dropdown(choices=source_language_list, type="value", value="Let the model analyze", label="Spoken language in video", interactive=True)
selected_translation_lang_2 = gr.Dropdown(choices=translation_models_list, type="value", value="English", label="In which language you want the transcriptions?", interactive=True)
selected_whisper_model = gr.Dropdown(choices=whisper_models, type="value", value="base", label="Selected Whisper model", interactive=True)

transcription_df = gr.DataFrame(value=df_init,label="Transcription dataframe", row_count=(0, "dynamic"), max_rows = 10, wrap=True, overflow_row_behaviour='paginate')
transcription_and_translation_df = gr.DataFrame(value=df_init,label="Transcription and translation dataframe", max_rows = 10, wrap=True, overflow_row_behaviour='paginate')

demo = gr.Blocks(css='''
#cut_btn, #reset_btn { align-self:stretch; }
#\\31 3 { max-width: 540px; }
.output-markdown {max-width: 65ch !important;}
''')
demo.encrypt = False
with demo:
    transcription_var = gr.Variable()
    
    with gr.Row():
        with gr.Column():
            gr.Markdown('''
            ### This space allows you to: 
            ##### 1. Download youtube video with a given URL
            ##### 2. Watch it in the first video component
            ##### 3. Run automatic speech recognition on the video using Whisper
            ##### 4. Translate the recognized transcriptions to 26 languages supported by deepL
            ##### 5. Burn the translations to the original video and watch the video in the 2nd video component
            ''')
            
        with gr.Column():
            gr.Markdown('''
            ### 1. Insert Youtube URL below. Some test videos below:
            ##### 1. https://www.youtube.com/watch?v=nlMuHtV82q8&ab_channel=NothingforSale24
            ##### 2. https://www.youtube.com/watch?v=JzPfMbG1vrE&ab_channel=ExplainerVideosByLauren
            ##### 3. https://www.youtube.com/watch?v=S68vvV0kod8&ab_channel=Pearl-CohnTelevision
            ''')
            
    with gr.Row():
        with gr.Column():
            youtube_url_in.render()
            download_youtube_btn = gr.Button("Step 1. Download Youtube video")
            download_youtube_btn.click(get_youtube, [youtube_url_in], [
                video_in])
            print(video_in)
            

    with gr.Row():
        with gr.Column():
            video_in.render()
            with gr.Column():
                gr.Markdown('''
                ##### Here you can start the transcription and translation process.
                ##### Be aware that processing will last some time. With base model it is around 3x speed 
                ''')
            selected_source_lang.render()
            selected_whisper_model.render()
            transcribe_btn = gr.Button("Step 2. Transcribe audio")
            transcribe_btn.click(speech_to_text, [video_in, selected_source_lang, selected_whisper_model], transcription_df)

            
    with gr.Row():
        gr.Markdown('''
        ##### Here you will get transcription  output
        ##### ''')

    with gr.Row():
        with gr.Column():
            transcription_df.render()
            
    with gr.Row():
        with gr.Column():
            gr.Markdown('''
            ##### Here you will get translated transcriptions. 
            ##### Please remember to select target language
            ##### ''')
            selected_translation_lang_2.render()
            translate_transcriptions_button = gr.Button("Step 3. Translate transcription")
            translate_transcriptions_button.click(translate_transcriptions, [transcription_df, selected_translation_lang_2], transcription_and_translation_df)
            transcription_and_translation_df.render()
            
    with gr.Row():
        with gr.Column():
            gr.Markdown('''
            ##### Now press the Step 4. Button to create output video with translated transcriptions
            ##### ''')
            translate_and_make_srt_btn = gr.Button("Step 4. Create and burn srt to video")
            print(video_in)
            translate_and_make_srt_btn.click(create_srt_and_burn, [transcription_and_translation_df,video_in], [
                video_out])
            video_out.render()

                
demo.launch()