app.py

import gradio as gr
import os
os.system("python -m unidic download")
from pytubefix import YouTube
import torch
from openvoice import se_extractor
from openvoice.api import ToneColorConverter
import whisper
from moviepy.editor import VideoFileClip
from pydub import AudioSegment
from df.enhance import enhance, init_df, load_audio, save_audio
import translators as ts
from melo.api import TTS
from concurrent.futures import ThreadPoolExecutor
import ffmpeg
import nltk
nltk.download('averaged_perceptron_tagger_eng')

def process_video(video_file, youtube_url, language_choice):
    if language_choice == None:
        return None, "Language not selected."
    elif video_file == None and youtube_url == "":
        return None, "Upload either a video or a valid youtube URL."        
    elif video_file != None and youtube_url != "":
        return None, "Videos contradict. Delete either the uploaded video or youtube URL."
    elif video_file == None and youtube_url != "":
        yt = YouTube(youtube_url)
        yt.streams.filter(progressive=True, file_extension='mp4').first().download(filename="original.mp4")
        video_file = "original.mp4"
    
    # Initialize paths and devices
    ckpt_converter = 'checkpoints_v2/converter'
    device = "cuda:0" if torch.cuda.is_available() else "cpu"
    output_dir = 'outputs_v2'
    os.makedirs(output_dir, exist_ok=True)

    tone_color_converter = ToneColorConverter(f'{ckpt_converter}/config.json', device=device)
    tone_color_converter.load_ckpt(f'{ckpt_converter}/checkpoint.pth')

    # Process the reference video
    reference_video = VideoFileClip(video_file)
    reference_audio = os.path.join(output_dir, "reference_audio.wav")
    reference_video.audio.write_audiofile(reference_audio)
    audio = AudioSegment.from_file(reference_audio)
    resampled_audio = audio.set_frame_rate(48000)
    resampled_audio.export(reference_audio, format="wav")

    # Enhance the audio
    model, df_state, _ = init_df()
    audio, _ = load_audio(reference_audio, sr=df_state.sr())
    enhanced = enhance(model, df_state, audio)
    save_audio(reference_audio, enhanced, df_state.sr())
    reference_speaker = reference_audio  # This is the voice you want to clone

    src_path = os.path.join(output_dir, "tmp.wav")

    # Speed is adjustable
    speed = 1.0

    # Transcribe the original audio with timestamps
    sttmodel = whisper.load_model("base")
    sttresult = sttmodel.transcribe(reference_speaker, verbose=True)

    # Print the original transcription
    print(sttresult["text"])
    print(sttresult["language"])

    # Get the segments with start and end times
    segments = sttresult['segments']

    if sttresult["language"] == language_choice[0:2]:
        print("Chosen language is the same as the video's original language. Only adding subtitles.")
        segments = sttresult['segments']

        # Generate subtitles file in SRT format
        srt_path = os.path.join(output_dir, 'subtitles.srt')
        with open(srt_path, 'w', encoding='utf-8') as srt_file:
            for i, segment in enumerate(segments):
                start = segment['start']
                end = segment['end']
                text = segment['text']

                start_hours, start_minutes = divmod(int(start), 3600)
                start_minutes, start_seconds = divmod(start_minutes, 60)
                start_milliseconds = int((start * 1000) % 1000)

                end_hours, end_minutes = divmod(int(end), 3600)
                end_minutes, end_seconds = divmod(end_minutes, 60)
                end_milliseconds = int((end * 1000) % 1000)

                srt_file.write(f"{i+1}\n")
                srt_file.write(f"{start_hours:02}:{start_minutes:02}:{start_seconds:02},{start_milliseconds:03} --> "
                               f"{end_hours:02}:{end_minutes:02}:{end_seconds:02},{end_milliseconds:03}\n")
                srt_file.write(f"{text}\n\n")

        # Add subtitles to the video
        final_video_with_subs_path = os.path.join(output_dir, f'final_video_with_subs.mp4')
        try:
            (
                ffmpeg
                .input(video_file)
                .output(final_video_with_subs_path, vf=f"subtitles={srt_path}")
                .run(overwrite_output=True)
            )
        except ffmpeg.Error as e:
            print('ffmpeg error:', e)
            print(e.stderr.decode('utf-8'))

        print(f"Final video with subtitles saved to: {final_video_with_subs_path}")
        return final_video_with_subs_path, "Video language and language selection are the same, audio not changed."
    else:
        target_se, audio_name = se_extractor.get_se(reference_speaker, tone_color_converter, vad=False)
        
        # Choose the target language for translation
        language = 'EN_NEWEST'
        match language_choice[0:2]:
            case 'en':
                language = 'EN_NEWEST'
            case 'es':
                language = 'ES'
            case 'fr':
                language = 'FR'
            case 'zh':
                language = 'ZH'
            case 'ja':
                language = 'JP'
            case 'ko':
                language = 'KR'
            case _:
                language = 'EN_NEWEST'
    
        # Translate the transcription segment by segment
        def translate_segment(segment):
            return segment["start"], segment["end"], ts.translate_text(query_text=segment["text"], translator="google", to_language=language_choice)
    
        # Batch translation to reduce memory load
        batch_size = 2
        translation_segments = []
        for i in range(0, len(segments), batch_size):
            batch = segments[i:i + batch_size]
            with ThreadPoolExecutor(max_workers=5) as executor:
                batch_translations = list(executor.map(translate_segment, batch))
            translation_segments.extend(batch_translations)
    
        # Generate the translated audio for each segment
        model = TTS(language=language, device=device)
        speaker_ids = model.hps.data.spk2id
    
        def generate_segment_audio(segment, speaker_id):
            start, end, translated_text = segment
            segment_path = os.path.join(output_dir, f'segment_{start}_{end}.wav')
            model.tts_to_file(translated_text, speaker_id, segment_path, speed=speed)
            return segment_path, start, end, translated_text
    
        for speaker_key in speaker_ids.keys():
            speaker_id = speaker_ids[speaker_key]
            speaker_key = speaker_key.lower().replace('_', '-')
    
            source_se = torch.load(f'checkpoints_v2/base_speakers/ses/{speaker_key}.pth', map_location=device)
    
            segment_files = []
            subtitle_entries = []
            for segment in translation_segments:
                segment_file, start, end, translated_text = generate_segment_audio(segment, speaker_id)
    
                # Run the tone color converter
                encode_message = "@MyShell"
                tone_color_converter.convert(
                audio_src_path=segment_file,
                src_se=source_se,
                tgt_se=target_se,
                output_path=segment_file,
                message=encode_message)
                
                segment_files.append((segment_file, start, end, translated_text))
    
            # Combine the audio segments
            combined_audio = AudioSegment.empty()
            video_segments = []
            previous_end = 0
            subtitle_counter = 1
            for segment_file, start, end, translated_text in segment_files:
                segment_audio = AudioSegment.from_file(segment_file)
                combined_audio += segment_audio
                
                # Calculate the duration of the audio segment
                audio_duration = len(segment_audio) / 1000.0
    
                # Add the subtitle entry for this segment
                subtitle_entries.append((subtitle_counter, previous_end, previous_end + audio_duration, translated_text))
                subtitle_counter += 1
    
                # Get the corresponding video segment and adjust its speed to match the audio duration
                video_segment = (
                    ffmpeg
                    .input(reference_video.filename, ss=start, to=end)
                    .filter('setpts', f'PTS / {(end - start) / audio_duration}')
                )
                video_segments.append((video_segment, ffmpeg.input(segment_file)))
                previous_end += audio_duration
    
            save_path = os.path.join(output_dir, f'output_v2_{speaker_key}.wav')
            combined_audio.export(save_path, format="wav")
    
            # Combine video and audio segments using ffmpeg
            video_and_audio_files = [item for sublist in video_segments for item in sublist]
            joined = (
                ffmpeg
                .concat(*video_and_audio_files, v=1, a=1)
                .node
            )
    
            final_video_path = os.path.join(output_dir, f'final_video_{speaker_key}.mp4')
            try:
                (
                    ffmpeg
                    .output(joined[0], joined[1], final_video_path, vcodec='libx264', acodec='aac')
                    .run(overwrite_output=True)
                )
            except ffmpeg.Error as e:
                print('ffmpeg error:', e)
                print(e.stderr.decode('utf-8'))
    
            print(f"Final video without subtitles saved to: {final_video_path}")
    
            # Generate subtitles file in SRT format
            srt_path = os.path.join(output_dir, 'subtitles.srt')
            with open(srt_path, 'w', encoding='utf-8') as srt_file:
                for entry in subtitle_entries:
                    index, start, end, text = entry
                    start_hours, start_minutes = divmod(int(start), 3600)
                    start_minutes, start_seconds = divmod(start_minutes, 60)
                    start_milliseconds = int((start * 1000) % 1000)
    
                    end_hours, end_minutes = divmod(int(end), 3600)
                    end_minutes, end_seconds = divmod(end_minutes, 60)
                    end_milliseconds = int((end * 1000) % 1000)
    
                    srt_file.write(f"{index}\n")
                    srt_file.write(f"{start_hours:02}:{start_minutes:02}:{start_seconds:02},{start_milliseconds:03} --> "
                                   f"{end_hours:02}:{end_minutes:02}:{end_seconds:02},{end_milliseconds:03}\n")
                    srt_file.write(f"{text}\n\n")
    
            # Add subtitles to the video
            final_video_with_subs_path = os.path.join(output_dir, f'final_video_with_subs_{speaker_key}.mp4')
            try:
                (
                    ffmpeg
                    .input(final_video_path)
                    .output(final_video_with_subs_path, vf=f"subtitles={srt_path}")
                    .run(overwrite_output=True)
                )
            except ffmpeg.Error as e:
                print('ffmpeg error:', e)
                print(e.stderr.decode('utf-8'))
    
            print(f"Final video with subtitles saved to: {final_video_with_subs_path}")
    
            return final_video_with_subs_path, "Video successfully translated."

#Gradio Interface
language_choices = ts.get_languages("google")["en"]
language_choices.remove("auto")

gr.Interface(
    fn=process_video,
    inputs=[
        gr.Video(label="Upload a video from your device storage", sources=['upload']),
        gr.Textbox(label="OR enter a YouTube video URL"),
        gr.Dropdown(choices=language_choices, label="Choose Language for Translation (Expressed in ISO 639-1 code)")
    ],
    outputs=[
        gr.Video(label="Translated Video", format='mp4'),
        gr.Textbox(show_label=False)
    ],
    title="Video Translation and Voice Cloning",
    description="Upload a video, choose a language to translate the audio, and download the processed video with translated audio."
).launch()