app.py

import torch
import psutil
from pytube import YouTube
import time
import re
import pandas as pd
import pysrt
from pathlib import Path
import gradio as gr
import os
import requests
import json
import base64

os.system('git clone https://github.com/ggerganov/whisper.cpp.git')
os.system('make -C ./whisper.cpp')
os.system('wget https://huggingface.co/datasets/tensorops/ggml-whisper-medium-th-combined/resolve/main/ggml-whisper-medium-th-combined.bin')


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


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

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

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()
    return abs_video_path


def speech_to_text(video_file_path):
    """
    # Youtube with translated subtitles using OpenAI Whisper models.
    # Currently supports only Thai 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. Burn the transcriptions 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:
        _, file_ending = os.path.splitext(f'{video_file_path}')
        print(f'file enging is {file_ending}')
        print("starting conversion to wav")
        os.system(
            f'ffmpeg -i "{video_file_path}" -ar 16000 -ac 1 -c:a pcm_s16le "{video_file_path.replace(file_ending, ".wav")}"')
        print("conversion to wav ready")

        print("starting whisper c++")
        srt_path = str(video_file_path.replace(file_ending, ".wav")) + ".srt"
        os.system(f'rm -f {srt_path}')
        os.system(
            f'./whisper.cpp/main "{video_file_path.replace(file_ending, ".wav")}" -t 4 -l "th" -m ./ggml-whisper-medium-th-combined.bin -osrt')
        print("starting whisper done with whisper")
    except Exception as e:
        raise RuntimeError("Error converting video to audio")

    try:

        df = pd.DataFrame(columns=['start', 'end', 'text'])
        srt_path = str(video_file_path.replace(file_ending, ".wav")) + ".srt"
        subs = pysrt.open(srt_path)

        objects = []
        for sub in subs:

            start_hours = str(str(sub.start.hours) + "00")[0:2] if len(
                str(sub.start.hours)) == 2 else str("0" + str(sub.start.hours) + "00")[0:2]
            end_hours = str(str(sub.end.hours) + "00")[0:2] if len(
                str(sub.end.hours)) == 2 else str("0" + str(sub.end.hours) + "00")[0:2]

            start_minutes = str(str(sub.start.minutes) + "00")[0:2] if len(
                str(sub.start.minutes)) == 2 else str("0" + str(sub.start.minutes) + "00")[0:2]
            end_minutes = str(str(sub.end.minutes) + "00")[0:2] if len(
                str(sub.end.minutes)) == 2 else str("0" + str(sub.end.minutes) + "00")[0:2]

            start_seconds = str(str(sub.start.seconds) + "00")[0:2] if len(
                str(sub.start.seconds)) == 2 else str("0" + str(sub.start.seconds) + "00")[0:2]
            end_seconds = str(str(sub.end.seconds) + "00")[0:2] if len(
                str(sub.end.seconds)) == 2 else str("0" + str(sub.end.seconds) + "00")[0:2]

            start_millis = str(str(sub.start.milliseconds) + "000")[0:3]
            end_millis = str(str(sub.end.milliseconds) + "000")[0:3]
            objects.append([sub.text, f'{start_hours}:{start_minutes}:{start_seconds}.{start_millis}',
                           f'{end_hours}:{end_minutes}:{end_seconds}.{end_millis}'])

        for object in objects:
            srt_to_df = {
                'start': [object[1]],
                'end': [object[2]],
                'text': [object[0]]
            }

            df = pd.concat([df, pd.DataFrame(srt_to_df)])

        df.to_csv('subtitles.csv', index=False)

        print("Starting SRT-file creation")
        df.reset_index(inplace=True)
        with open('subtitles.vtt', 'w', encoding="utf-8") as file:
            print("Starting WEBVTT-file creation")

            for i in range(len(df)):
                if i == 0:
                    file.write('WEBVTT')
                    file.write('\n')

                else:
                    file.write(str(i+1))
                    file.write('\n')
                    start = df.iloc[i]['start']

                    file.write(f"{start.strip()}")

                    stop = df.iloc[i]['end']

                    file.write(' --> ')
                    file.write(f"{stop}")
                    file.write('\n')
                    file.writelines(df.iloc[i]['text'])
                    if int(i) != len(df)-1:
                        file.write('\n\n')

        print("WEBVTT DONE")

        with open('subtitles.srt', 'w', encoding="utf-8") as file:
            print("Starting SRT-file creation")

            for i in range(len(df)):
                file.write(str(i+1))
                file.write('\n')
                start = df.iloc[i]['start']

                file.write(f"{start.strip()}")

                stop = df.iloc[i]['end']

                file.write(' --> ')
                file.write(f"{stop}")
                file.write('\n')
                file.writelines(df.iloc[i]['text'])
                if int(i) != len(df)-1:
                    file.write('\n\n')

        print("SRT DONE")
        subtitle_files = ['subtitles.vtt', 'subtitles.srt', 'subtitles.csv']

        return df, subtitle_files

    except Exception as e:
        raise RuntimeError("Error Running inference with local model", e)


def burn_srt_to_video(srt_file, video_in):

    print("Starting creation of video wit srt")

    try:
        video_out = video_in.replace('.mp4', '_out.mp4')
        print(os.system('ls -lrth'))
        print(video_in)
        print(video_out)
        command = 'ffmpeg -i "{}" -y -vf subtitles=./subtitles.srt "{}"'.format(
            video_in, video_out)
        os.system(command)

        return video_out

    except Exception as e:
        print(e)
        return video_out


def create_video_player(subtitle_files, video_in):

    with open(video_in, "rb") as file:
        video_base64 = base64.b64encode(file.read())
    with open('./subtitles.vtt', "rb") as file:
        subtitle_base64 = base64.b64encode(file.read())

    video_player = f'''<video id="video" controls preload="metadata">
      <source src="data:video/mp4;base64,{str(video_base64)[2:-1]}" type="video/mp4" />
      <track
        label="Thai"
        kind="subtitles"
        srclang="th"
        src="data:text/vtt;base64,{str(subtitle_base64)[2:-1]}"
        default />
    </video>
    '''
    return video_player


# ---- 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'])

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')

subtitle_files = gr.File(
    label="Download srt-file",
    file_count="multiple",
    type="file",
    interactive=False,
)

video_player = gr.HTML(
    '<p>video will be played here after you press the button at step 3')

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 Thai speech recognition on the video using Whisper
            ##### 4. 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=UIHPIESyIXM
            ##### 2. https://www.youtube.com/watch?v=YlfaFK7OFUo
            ''')

    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 process.
                ##### Be aware that processing will take some time.
                ''')
            transcribe_btn = gr.Button("Step 2. Transcribe audio")
            transcribe_btn.click(speech_to_text, [
                                 video_in], [transcription_df, subtitle_files])

    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(
                '''##### From here, you can download the transcription output in different formats. ''')
            subtitle_files.render()

    with gr.Row():
        with gr.Column():
            gr.Markdown('''
            ##### Now press the Step 3. Button to create output video with translated transcriptions
            ##### ''')
            create_video_button = gr.Button(
                "Step 3. Create and add subtitles to video")
            print(video_in)
            create_video_button.click(create_video_player, [subtitle_files, video_in], [
                video_player])
            video_player.render()

demo.launch()