Upload UltraSinger.py

UltraSinger.py

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+"""UltraSinger uses AI to automatically create UltraStar song files"""
+
+import copy
+import getopt
+import os
+import sys
+import re
+
+import Levenshtein
+import librosa
+
+from tqdm import tqdm
+from packaging import version
+
+import soundfile as sf
+
+from modules import os_helper
+from modules.Audio.denoise import ffmpeg_reduce_noise
+from modules.Audio.separation import separate_audio
+from modules.Audio.vocal_chunks import (
+    export_chunks_from_transcribed_data,
+    export_chunks_from_ultrastar_data,
+)
+from modules.Audio.silence_processing import remove_silence_from_transcription_data, get_silence_sections
+from modules.csv_handler import export_transcribed_data_to_csv
+from modules.Audio.convert_audio import convert_audio_to_mono_wav, convert_wav_to_mp3
+from modules.Audio.youtube import (
+    download_youtube_audio,
+    download_youtube_thumbnail,
+    download_youtube_video,
+    get_youtube_title,
+)
+from modules.DeviceDetection.device_detection import check_gpu_support
+from modules.console_colors import (
+    ULTRASINGER_HEAD,
+    blue_highlighted,
+    gold_highlighted,
+    light_blue_highlighted,
+    red_highlighted,
+)
+from modules.Midi import midi_creator
+from modules.Midi.midi_creator import (
+    convert_frequencies_to_notes,
+    create_midi_notes_from_pitched_data,
+    most_frequent,
+)
+from modules.Pitcher.pitcher import (
+    get_frequencies_with_high_confidence,
+    get_pitch_with_crepe_file,
+)
+from modules.Pitcher.pitched_data import PitchedData
+from modules.Speech_Recognition.hyphenation import hyphenation, language_check, create_hyphenator
+from modules.Speech_Recognition.Whisper import transcribe_with_whisper
+from modules.Ultrastar import ultrastar_score_calculator, ultrastar_writer, ultrastar_converter, ultrastar_parser
+from modules.Ultrastar.ultrastar_txt import UltrastarTxtValue
+from Settings import Settings
+from modules.Speech_Recognition.TranscribedData import TranscribedData
+from modules.plot import plot, plot_spectrogram
+from modules.musicbrainz_client import get_music_infos
+
+settings = Settings()
+
+
+def convert_midi_notes_to_ultrastar_notes(midi_notes: list[str]) -> list[int]:
+    """Convert midi notes to ultrastar notes"""
+    print(f"{ULTRASINGER_HEAD} Creating Ultrastar notes from midi data")
+
+    ultrastar_note_numbers = []
+    for i in enumerate(midi_notes):
+        pos = i[0]
+        note_number_librosa = librosa.note_to_midi(midi_notes[pos])
+        pitch = ultrastar_converter.midi_note_to_ultrastar_note(
+            note_number_librosa
+        )
+        ultrastar_note_numbers.append(pitch)
+        # todo: Progress?
+        # print(
+        #    f"Note: {midi_notes[i]} midi_note: {str(note_number_librosa)} pitch: {str(pitch)}"
+        # )
+    return ultrastar_note_numbers
+
+
+def pitch_each_chunk_with_crepe(directory: str) -> list[str]:
+    """Pitch each chunk with crepe and return midi notes"""
+    print(
+        f"{ULTRASINGER_HEAD} Pitching each chunk with {blue_highlighted('crepe')}"
+    )
+
+    midi_notes = []
+    for filename in sorted(
+        [f for f in os.listdir(directory) if f.endswith(".wav")],
+        key=lambda x: int(x.split("_")[1]),
+    ):
+        filepath = os.path.join(directory, filename)
+        # todo: stepsize = duration? then when shorter than "it" it should take the duration. Otherwise there a more notes
+        pitched_data = get_pitch_with_crepe_file(
+            filepath,
+            settings.crepe_model_capacity,
+            settings.crepe_step_size,
+            settings.tensorflow_device,
+        )
+        conf_f = get_frequencies_with_high_confidence(
+            pitched_data.frequencies, pitched_data.confidence
+        )
+
+        notes = convert_frequencies_to_notes(conf_f)
+        note = most_frequent(notes)[0][0]
+
+        midi_notes.append(note)
+        # todo: Progress?
+        # print(filename + " f: " + str(mean))
+
+    return midi_notes
+
+
+def add_hyphen_to_data(transcribed_data: list[TranscribedData], hyphen_words: list[list[str]]):
+    """Add hyphen to transcribed data return new data list"""
+    new_data = []
+
+    for i, data in enumerate(transcribed_data):
+        if not hyphen_words[i]:
+            new_data.append(data)
+        else:
+            chunk_duration = data.end - data.start
+            chunk_duration = chunk_duration / (len(hyphen_words[i]))
+
+            next_start = data.start
+            for j in enumerate(hyphen_words[i]):
+                hyphenated_word_index = j[0]
+                dup = copy.copy(data)
+                dup.start = next_start
+                next_start = data.end - chunk_duration * (
+                    len(hyphen_words[i]) - 1 - hyphenated_word_index
+                )
+                dup.end = next_start
+                dup.word = hyphen_words[i][hyphenated_word_index]
+                dup.is_hyphen = True
+                if hyphenated_word_index == len(hyphen_words[i]) - 1:
+                    dup.is_word_end = True
+                else:
+                    dup.is_word_end = False
+                new_data.append(dup)
+
+    return new_data
+
+
+def get_bpm_from_data(data, sampling_rate):
+    """Get real bpm from audio data"""
+    onset_env = librosa.onset.onset_strength(y=data, sr=sampling_rate)
+    wav_tempo = librosa.beat.tempo(onset_envelope=onset_env, sr=sampling_rate)
+
+    print(
+        f"{ULTRASINGER_HEAD} BPM is {blue_highlighted(str(round(wav_tempo[0], 2)))}"
+    )
+    return wav_tempo[0]
+
+
+def get_bpm_from_file(wav_file: str) -> float:
+    """Get real bpm from audio file"""
+    data, sampling_rate = librosa.load(wav_file, sr=None)
+    return get_bpm_from_data(data, sampling_rate)
+
+
+def correct_words(recognized_words, word_list_file):
+    """Docstring"""
+    with open(word_list_file, "r", encoding="utf-8") as file:
+        text = file.read()
+    word_list = text.split()
+
+    for i, rec_word in enumerate(recognized_words):
+        if rec_word.word in word_list:
+            continue
+
+        closest_word = min(
+            word_list, key=lambda x: Levenshtein.distance(rec_word.word, x)
+        )
+        print(recognized_words[i].word + " - " + closest_word)
+        recognized_words[i].word = closest_word
+    return recognized_words
+
+
+def print_help() -> None:
+    """Print help text"""
+    help_string = """
+    UltraSinger.py [opt] [mode] [transcription] [pitcher] [extra]
+    
+    [opt]
+    -h      This help text.
+    -i      Ultrastar.txt
+            audio like .mp3, .wav, youtube link
+    -o      Output folder
+    
+    [mode]
+    ## INPUT is audio ##
+    default  Creates all
+    
+    # Single file creation selection is in progress, you currently getting all!
+    (-u      Create ultrastar txt file) # In Progress
+    (-m      Create midi file) # In Progress
+    (-s      Create sheet file) # In Progress
+    
+    ## INPUT is ultrastar.txt ##
+    default  Creates all
+
+    # Single selection is in progress, you currently getting all!
+    (-r      repitch Ultrastar.txt (input has to be audio)) # In Progress
+    (-p      Check pitch of Ultrastar.txt input) # In Progress
+    (-m      Create midi file) # In Progress
+
+    [transcription]
+    # Default is whisper
+    --whisper               Multilingual model > tiny|base|small|medium|large-v1|large-v2  >> ((default) is large-v2
+                            English-only model > tiny.en|base.en|small.en|medium.en
+    --whisper_align_model   Use other languages model for Whisper provided from huggingface.co
+    --language              Override the language detected by whisper, does not affect transcription but steps after transcription
+    --whisper_batch_size    Reduce if low on GPU mem >> ((default) is 16)
+    --whisper_compute_type  Change to "int8" if low on GPU mem (may reduce accuracy) >> ((default) is "float16" for cuda devices, "int8" for cpu)
+    
+    [pitcher]
+    # Default is crepe
+    --crepe            tiny|full >> ((default) is full)
+    --crepe_step_size  unit is miliseconds >> ((default) is 10)
+    
+    [extra]
+    --hyphenation           True|False >> ((default) is True)
+    --disable_separation    True|False >> ((default) is False)
+    --disable_karaoke       True|False >> ((default) is False)
+    --create_audio_chunks   True|False >> ((default) is False)
+    --keep_cache            True|False >> ((default) is False)
+    --plot                  True|False >> ((default) is False)
+    --format_version        0.3.0|1.0.0|1.1.0 >> ((default) is 1.0.0)
+    
+    [device]
+    --force_cpu             True|False >> ((default) is False)  All steps will be forced to cpu
+    --force_whisper_cpu     True|False >> ((default) is False)  Only whisper will be forced to cpu
+    --force_crepe_cpu       True|False >> ((default) is False)  Only crepe will be forced to cpu
+    """
+    print(help_string)
+
+
+def remove_unecessary_punctuations(transcribed_data: list[TranscribedData]) -> None:
+    """Remove unecessary punctuations from transcribed data"""
+    punctuation = ".,"
+    for i, data in enumerate(transcribed_data):
+        data.word = data.word.translate(
+            {ord(i): None for i in punctuation}
+        )
+
+
+def hyphenate_each_word(language: str, transcribed_data: list[TranscribedData]) -> list[list[str]] | None:
+    """Hyphenate each word in the transcribed data."""
+    lang_region = language_check(language)
+    if lang_region is None:
+        print(
+            f"{ULTRASINGER_HEAD} {red_highlighted('Error in hyphenation for language ')} {blue_highlighted(language)}{red_highlighted(', maybe you want to disable it?')}"
+        )
+        return None
+
+    hyphenated_word = []
+    try:
+        hyphenator = create_hyphenator(lang_region)
+        for i in tqdm(enumerate(transcribed_data)):
+            pos = i[0]
+            hyphenated_word.append(
+                hyphenation(transcribed_data[pos].word, hyphenator)
+            )
+    except:
+        print(f"{ULTRASINGER_HEAD} {red_highlighted('Error in hyphenation for language ')} {blue_highlighted(language)}{red_highlighted(', maybe you want to disable it?')}")
+        return None
+
+    return hyphenated_word
+
+
+def print_support() -> None:
+    """Print support text"""
+    print()
+    print(
+        f"{ULTRASINGER_HEAD} {gold_highlighted('Do you like UltraSinger? Want it to be even better? Then help with your')} {light_blue_highlighted('support')}{gold_highlighted('!')}"
+    )
+    print(
+        f"{ULTRASINGER_HEAD} See project page -> https://github.com/rakuri255/UltraSinger"
+    )
+    print(
+        f"{ULTRASINGER_HEAD} {gold_highlighted('This will help a lot to keep this project alive and improved.')}"
+    )
+
+def print_version() -> None:
+    """Print version text"""
+    print()
+    print(
+        f"{ULTRASINGER_HEAD} {gold_highlighted('*****************************')}"
+    )
+    print(
+        f"{ULTRASINGER_HEAD} {gold_highlighted('UltraSinger Version:')} {light_blue_highlighted(settings.APP_VERSION)}"
+    )
+    print(
+        f"{ULTRASINGER_HEAD} {gold_highlighted('*****************************')}"
+    )
+
+def run() -> None:
+    """The processing function of this program"""
+    is_audio = ".txt" not in settings.input_file_path
+    ultrastar_class = None
+    real_bpm = None
+    (title, artist, year, genre) = (None, None, None, None)
+
+    if not is_audio:  # Parse Ultrastar txt
+        print(
+            f"{ULTRASINGER_HEAD} {gold_highlighted('re-pitch mode')}"
+        )
+        (
+            basename_without_ext,
+            real_bpm,
+            song_output,
+            ultrastar_audio_input_path,
+            ultrastar_class,
+        ) = parse_ultrastar_txt()
+    elif settings.input_file_path.startswith("https:"):  # Youtube
+        print(
+            f"{ULTRASINGER_HEAD} {gold_highlighted('full automatic mode')}"
+        )
+        (
+            basename_without_ext,
+            song_output,
+            ultrastar_audio_input_path,
+            (title, artist, year, genre)
+        ) = download_from_youtube()
+    else:  # Audio File
+        print(
+            f"{ULTRASINGER_HEAD} {gold_highlighted('full automatic mode')}"
+        )
+        (
+            basename_without_ext,
+            song_output,
+            ultrastar_audio_input_path,
+            (title, artist, year, genre)
+        ) = infos_from_audio_input_file()
+
+    cache_path = os.path.join(song_output, "cache")
+    settings.processing_audio_path = os.path.join(
+        cache_path, basename_without_ext + ".wav"
+    )
+    os_helper.create_folder(cache_path)
+
+    # Separate vocal from audio
+    audio_separation_path = separate_vocal_from_audio(
+        basename_without_ext, cache_path, ultrastar_audio_input_path
+    )
+    vocals_path = os.path.join(audio_separation_path, "vocals.wav")
+    instrumental_path = os.path.join(audio_separation_path, "no_vocals.wav")
+
+    # Move instrumental and vocals
+    if settings.create_karaoke and version.parse(settings.format_version) < version.parse("1.1.0"):
+        karaoke_output_path = os.path.join(song_output, basename_without_ext + " [Karaoke].mp3")
+        convert_wav_to_mp3(instrumental_path, karaoke_output_path)
+
+    if version.parse(settings.format_version) >= version.parse("1.1.0"):
+        instrumental_output_path = os.path.join(song_output, basename_without_ext + " [Instrumental].mp3")
+        convert_wav_to_mp3(instrumental_path, instrumental_output_path)
+        vocals_output_path = os.path.join(song_output, basename_without_ext + " [Vocals].mp3")
+        convert_wav_to_mp3(vocals_path, vocals_output_path)
+
+    if settings.use_separated_vocal:
+        input_path = vocals_path
+    else:
+        input_path = ultrastar_audio_input_path
+
+    # Denoise vocal audio
+    denoised_output_path = os.path.join(
+        cache_path, basename_without_ext + "_denoised.wav"
+    )
+    denoise_vocal_audio(input_path, denoised_output_path)
+
+    # Convert to mono audio
+    mono_output_path = os.path.join(
+        cache_path, basename_without_ext + "_mono.wav"
+    )
+    convert_audio_to_mono_wav(denoised_output_path, mono_output_path)
+
+    # Mute silence sections
+    mute_output_path = os.path.join(
+        cache_path, basename_without_ext + "_mute.wav"
+    )
+    mute_no_singing_parts(mono_output_path, mute_output_path)
+
+    # Define the audio file to process
+    settings.processing_audio_path = mute_output_path
+
+    # Audio transcription
+    transcribed_data = None
+    language = settings.language
+    if is_audio:
+        detected_language, transcribed_data = transcribe_audio()
+        if language is None:
+            language = detected_language
+
+        remove_unecessary_punctuations(transcribed_data)
+
+        if settings.hyphenation:
+            hyphen_words = hyphenate_each_word(language, transcribed_data)
+            if hyphen_words is not None:
+                transcribed_data = add_hyphen_to_data(transcribed_data, hyphen_words)
+
+        transcribed_data = remove_silence_from_transcription_data(
+            settings.processing_audio_path, transcribed_data
+        )
+
+        # todo: do we need to correct words?
+        # lyric = 'input/faber_lyric.txt'
+        # --corrected_words = correct_words(vosk_speech, lyric)
+
+    # Create audio chunks
+    if settings.create_audio_chunks:
+        create_audio_chunks(
+            cache_path,
+            is_audio,
+            transcribed_data,
+            ultrastar_audio_input_path,
+            ultrastar_class,
+        )
+
+    # Pitch the audio
+    midi_notes, pitched_data, ultrastar_note_numbers = pitch_audio(
+        is_audio, transcribed_data, ultrastar_class
+    )
+
+    # Create plot
+    if settings.create_plot:
+        vocals_path = os.path.join(audio_separation_path, "vocals.wav")
+        plot_spectrogram(vocals_path, song_output, "vocals.wav")
+        plot_spectrogram(settings.processing_audio_path, song_output, "processing audio")
+        plot(pitched_data, song_output, transcribed_data, ultrastar_class, midi_notes)
+
+    # Write Ultrastar txt
+    if is_audio:
+        real_bpm, ultrastar_file_output = create_ultrastar_txt_from_automation(
+            basename_without_ext,
+            song_output,
+            transcribed_data,
+            ultrastar_audio_input_path,
+            ultrastar_note_numbers,
+            language,
+            title,
+            artist,
+            year,
+            genre
+        )
+    else:
+        ultrastar_file_output = create_ultrastar_txt_from_ultrastar_data(
+            song_output, ultrastar_class, ultrastar_note_numbers
+        )
+
+    # Calc Points
+    ultrastar_class, simple_score, accurate_score = calculate_score_points(
+        is_audio, pitched_data, ultrastar_class, ultrastar_file_output
+    )
+
+    # Add calculated score to Ultrastar txt #Todo: Missing Karaoke
+    ultrastar_writer.add_score_to_ultrastar_txt(
+        ultrastar_file_output, simple_score
+    )
+
+    # Midi
+    if settings.create_midi:
+        create_midi_file(real_bpm, song_output, ultrastar_class, basename_without_ext)
+
+    # Cleanup
+    if not settings.keep_cache:
+        remove_cache_folder(cache_path)
+
+    # Print Support
+    print_support()
+
+
+def mute_no_singing_parts(mono_output_path, mute_output_path):
+    print(
+        f"{ULTRASINGER_HEAD} Mute audio parts with no singing"
+    )
+    silence_sections = get_silence_sections(mono_output_path)
+    y, sr = librosa.load(mono_output_path, sr=None)
+    # Mute the parts of the audio with no singing
+    for i in silence_sections:
+        # Define the time range to mute
+
+        start_time = i[0]  # Start time in seconds
+        end_time = i[1]  # End time in seconds
+
+        # Convert time to sample indices
+        start_sample = int(start_time * sr)
+        end_sample = int(end_time * sr)
+
+        y[start_sample:end_sample] = 0
+    sf.write(mute_output_path, y, sr)
+
+
+def get_unused_song_output_dir(path: str) -> str:
+    """Get an unused song output dir"""
+    # check if dir exists and add (i) if it does
+    i = 1
+    if os_helper.check_if_folder_exists(path):
+        path = f"{path} ({i})"
+    else:
+        return path
+
+    while os_helper.check_if_folder_exists(path):
+        path = path.replace(f"({i - 1})", f"({i})")
+        i += 1
+        if i > 999:
+            print(
+                f"{ULTRASINGER_HEAD} {red_highlighted('Error: Could not create output folder! (999) is the maximum number of tries.')}"
+            )
+            sys.exit(1)
+    return path
+
+
+def transcribe_audio() -> (str, list[TranscribedData]):
+    """Transcribe audio with AI"""
+    if settings.transcriber == "whisper":
+        device = "cpu" if settings.force_whisper_cpu else settings.pytorch_device
+        transcribed_data, detected_language = transcribe_with_whisper(
+            settings.processing_audio_path,
+            settings.whisper_model,
+            device,
+            settings.whisper_align_model,
+            settings.whisper_batch_size,
+            settings.whisper_compute_type,
+            settings.language,
+        )
+    else:
+        raise NotImplementedError
+    return detected_language, transcribed_data
+
+
+def separate_vocal_from_audio(
+        basename_without_ext: str, cache_path: str, ultrastar_audio_input_path: str
+) -> str:
+    """Separate vocal from audio"""
+    audio_separation_path = os.path.join(
+        cache_path, "separated", "htdemucs", basename_without_ext
+    )
+
+    if settings.use_separated_vocal or settings.create_karaoke:
+        separate_audio(ultrastar_audio_input_path, cache_path, settings.pytorch_device)
+
+    return audio_separation_path
+
+def calculate_score_points(
+    is_audio: bool, pitched_data: PitchedData, ultrastar_class: UltrastarTxtValue, ultrastar_file_output: str
+):
+    """Calculate score points"""
+    if is_audio:
+        ultrastar_class = ultrastar_parser.parse_ultrastar_txt(
+            ultrastar_file_output
+        )
+        (
+            simple_score,
+            accurate_score,
+        ) = ultrastar_score_calculator.calculate_score(
+            pitched_data, ultrastar_class
+        )
+        ultrastar_score_calculator.print_score_calculation(
+            simple_score, accurate_score
+        )
+    else:
+        print(
+            f"{ULTRASINGER_HEAD} {blue_highlighted('Score of original Ultrastar txt')}"
+        )
+        (
+            simple_score,
+            accurate_score,
+        ) = ultrastar_score_calculator.calculate_score(
+            pitched_data, ultrastar_class
+        )
+        ultrastar_score_calculator.print_score_calculation(
+            simple_score, accurate_score
+        )
+        print(
+            f"{ULTRASINGER_HEAD} {blue_highlighted('Score of re-pitched Ultrastar txt')}"
+        )
+        ultrastar_class = ultrastar_parser.parse_ultrastar_txt(
+            ultrastar_file_output
+        )
+        (
+            simple_score,
+            accurate_score,
+        ) = ultrastar_score_calculator.calculate_score(
+            pitched_data, ultrastar_class
+        )
+        ultrastar_score_calculator.print_score_calculation(
+            simple_score, accurate_score
+        )
+    return ultrastar_class, simple_score, accurate_score
+
+
+def create_ultrastar_txt_from_ultrastar_data(
+    song_output: str, ultrastar_class: UltrastarTxtValue, ultrastar_note_numbers: list[int]
+) -> str:
+    """Create Ultrastar txt from Ultrastar data"""
+    output_repitched_ultrastar = os.path.join(
+        song_output, ultrastar_class.title + ".txt"
+    )
+    ultrastar_writer.create_repitched_txt_from_ultrastar_data(
+        settings.input_file_path,
+        ultrastar_note_numbers,
+        output_repitched_ultrastar,
+    )
+    return output_repitched_ultrastar
+
+
+def create_ultrastar_txt_from_automation(
+    basename_without_ext: str,
+    song_output: str,
+    transcribed_data: list[TranscribedData],
+    ultrastar_audio_input_path: str,
+    ultrastar_note_numbers: list[int],
+    language: str,
+    title: str,
+    artist: str,
+    year: str,
+    genre: str
+):
+    """Create Ultrastar txt from automation"""
+    ultrastar_header = UltrastarTxtValue()
+    ultrastar_header.version = settings.format_version
+    ultrastar_header.title = basename_without_ext
+    ultrastar_header.artist = basename_without_ext
+    ultrastar_header.mp3 = basename_without_ext + ".mp3"
+    ultrastar_header.audio = basename_without_ext + ".mp3"
+    ultrastar_header.vocals = basename_without_ext + " [Vocals].mp3"
+    ultrastar_header.instrumental = basename_without_ext + " [Instrumental].mp3"
+    ultrastar_header.video = basename_without_ext + ".mp4"
+    ultrastar_header.language = language
+    cover = basename_without_ext + " [CO].jpg"
+    ultrastar_header.cover = (
+        cover
+        if os_helper.check_file_exists(os.path.join(song_output, cover))
+        else None
+    )
+    ultrastar_header.creator = f"{ultrastar_header.creator} {Settings.APP_VERSION}"
+    ultrastar_header.comment = f"{ultrastar_header.comment} {Settings.APP_VERSION}"
+
+    # Additional data
+    if title is not None:
+        ultrastar_header.title = title
+    if artist is not None:
+        ultrastar_header.artist = artist
+    if year is not None:
+        ultrastar_header.year = extract_year(year)
+    if genre is not None:
+        ultrastar_header.genre = format_separated_string(genre)
+
+    real_bpm = get_bpm_from_file(ultrastar_audio_input_path)
+    ultrastar_file_output = os.path.join(
+        song_output, basename_without_ext + ".txt"
+    )
+    ultrastar_writer.create_ultrastar_txt_from_automation(
+        transcribed_data,
+        ultrastar_note_numbers,
+        ultrastar_file_output,
+        ultrastar_header,
+        real_bpm,
+    )
+    if settings.create_karaoke and version.parse(settings.format_version) < version.parse("1.1.0"):
+        title = basename_without_ext + " [Karaoke]"
+        ultrastar_header.title = title
+        ultrastar_header.mp3 = title + ".mp3"
+        karaoke_output_path = os.path.join(song_output, title)
+        karaoke_txt_output_path = karaoke_output_path + ".txt"
+        ultrastar_writer.create_ultrastar_txt_from_automation(
+            transcribed_data,
+            ultrastar_note_numbers,
+            karaoke_txt_output_path,
+            ultrastar_header,
+            real_bpm,
+        )
+    return real_bpm, ultrastar_file_output
+
+def extract_year(date: str) -> str:
+    match = re.search(r'\b\d{4}\b', date)
+    if match:
+        return match.group(0)
+    else:
+        return date
+
+def format_separated_string(data: str) -> str:
+    temp = re.sub(r'[;/]', ',', data)
+    words = temp.split(',')
+    words = [s for s in words if s.strip()]
+
+    for i, word in enumerate(words):
+        if "-" not in word:
+            words[i] = word.strip().capitalize() + ', '
+        else:
+            dash_words = word.split('-')
+            capitalized_dash_words = [dash_word.strip().capitalize() for dash_word in dash_words]
+            formatted_dash_word = '-'.join(capitalized_dash_words) + ', '
+            words[i] = formatted_dash_word
+
+    formatted_string = ''.join(words)
+
+    if formatted_string.endswith(', '):
+        formatted_string = formatted_string[:-2]
+
+    return formatted_string
+
+def infos_from_audio_input_file() -> tuple[str, str, str, tuple[str, str, str, str]]:
+    """Infos from audio input file"""
+    basename = os.path.basename(settings.input_file_path)
+    basename_without_ext = os.path.splitext(basename)[0]
+
+    artist, title = None, None
+    if " - " in basename_without_ext:
+        artist, title = basename_without_ext.split(" - ", 1)
+        search_string = f"{artist} - {title}"
+    else:
+        search_string = basename_without_ext
+
+    # Get additional data for song
+    (title_info, artist_info, year_info, genre_info) = get_music_infos(search_string)
+
+    if title_info is not None:
+        title = title_info
+        artist = artist_info
+
+    if artist is not None and title is not None:
+        basename_without_ext = f"{artist} - {title}"
+        extension = os.path.splitext(basename)[1]
+        basename = f"{basename_without_ext}{extension}"
+
+    song_output = os.path.join(settings.output_file_path, basename_without_ext)
+    song_output = get_unused_song_output_dir(song_output)
+    os_helper.create_folder(song_output)
+    os_helper.copy(settings.input_file_path, song_output)
+    os_helper.rename(os.path.join(song_output, os.path.basename(settings.input_file_path)), os.path.join(song_output, basename))
+    ultrastar_audio_input_path = os.path.join(song_output, basename)
+    return basename_without_ext, song_output, ultrastar_audio_input_path, (title, artist, year_info, genre_info)
+
+
+FILENAME_REPLACEMENTS = (('?:"', ""), ("<", "("), (">", ")"), ("/\\|*", "-"))
+
+
+def sanitize_filename(fname: str) -> str:
+    """Sanitize filename"""
+    for old, new in FILENAME_REPLACEMENTS:
+        for char in old:
+            fname = fname.replace(char, new)
+    if fname.endswith("."):
+        fname = fname.rstrip(" .")  # Windows does not like trailing periods
+    return fname
+
+
+def download_from_youtube() -> tuple[str, str, str, tuple[str, str, str, str]]:
+    """Download from YouTube"""
+    (artist, title) = get_youtube_title(settings.input_file_path)
+
+    # Get additional data for song
+    (title_info, artist_info, year_info, genre_info) = get_music_infos(f"{artist} - {title}")
+
+    if title_info is not None:
+        title = title_info
+        artist = artist_info
+
+    basename_without_ext = sanitize_filename(f"{artist} - {title}")
+    basename = basename_without_ext + ".mp3"
+    song_output = os.path.join(settings.output_file_path, basename_without_ext)
+    song_output = get_unused_song_output_dir(song_output)
+    os_helper.create_folder(song_output)
+    download_youtube_audio(
+        settings.input_file_path, basename_without_ext, song_output
+    )
+    download_youtube_video(
+        settings.input_file_path, basename_without_ext, song_output
+    )
+    download_youtube_thumbnail(
+        settings.input_file_path, basename_without_ext, song_output
+    )
+    ultrastar_audio_input_path = os.path.join(song_output, basename)
+    return basename_without_ext, song_output, ultrastar_audio_input_path, (title, artist, year_info, genre_info)
+
+
+def parse_ultrastar_txt() -> tuple[str, float, str, str, UltrastarTxtValue]:
+    """Parse Ultrastar txt"""
+    ultrastar_class = ultrastar_parser.parse_ultrastar_txt(
+        settings.input_file_path
+    )
+    real_bpm = ultrastar_converter.ultrastar_bpm_to_real_bpm(
+        float(ultrastar_class.bpm.replace(",", "."))
+    )
+    ultrastar_mp3_name = ultrastar_class.mp3
+    basename_without_ext = os.path.splitext(ultrastar_mp3_name)[0]
+    dirname = os.path.dirname(settings.input_file_path)
+    ultrastar_audio_input_path = os.path.join(dirname, ultrastar_mp3_name)
+    song_output = os.path.join(
+        settings.output_file_path,
+        ultrastar_class.artist.strip() + " - " + ultrastar_class.title.strip(),
+    )
+    song_output = get_unused_song_output_dir(str(song_output))
+    os_helper.create_folder(song_output)
+
+    return (
+        str(basename_without_ext),
+        real_bpm,
+        song_output,
+        str(ultrastar_audio_input_path),
+        ultrastar_class,
+    )
+
+
+def create_midi_file(real_bpm: float,
+                     song_output: str,
+                     ultrastar_class: UltrastarTxtValue,
+                     basename_without_ext: str) -> None:
+    """Create midi file"""
+    print(
+        f"{ULTRASINGER_HEAD} Creating Midi with {blue_highlighted('pretty_midi')}"
+    )
+
+    voice_instrument = [
+        midi_creator.convert_ultrastar_to_midi_instrument(ultrastar_class)
+    ]
+    midi_output = os.path.join(song_output, f"{basename_without_ext}.mid")
+    midi_creator.instruments_to_midi(
+        voice_instrument, real_bpm, midi_output
+    )
+
+
+def pitch_audio(is_audio: bool, transcribed_data: list[TranscribedData], ultrastar_class: UltrastarTxtValue) -> tuple[
+    list[str], PitchedData, list[int]]:
+    """Pitch audio"""
+    # todo: chunk pitching as option?
+    # midi_notes = pitch_each_chunk_with_crepe(chunk_folder_name)
+    device = "cpu" if settings.force_crepe_cpu else settings.tensorflow_device
+    pitched_data = get_pitch_with_crepe_file(
+        settings.processing_audio_path,
+        settings.crepe_model_capacity,
+        settings.crepe_step_size,
+        device,
+    )
+    if is_audio:
+        start_times = []
+        end_times = []
+        for i, data in enumerate(transcribed_data):
+            start_times.append(data.start)
+            end_times.append(data.end)
+        midi_notes = create_midi_notes_from_pitched_data(
+            start_times, end_times, pitched_data
+        )
+
+    else:
+        midi_notes = create_midi_notes_from_pitched_data(
+            ultrastar_class.startTimes, ultrastar_class.endTimes, pitched_data
+        )
+    ultrastar_note_numbers = convert_midi_notes_to_ultrastar_notes(midi_notes)
+    return midi_notes, pitched_data, ultrastar_note_numbers
+
+
+def create_audio_chunks(
+    cache_path: str,
+    is_audio: bool,
+    transcribed_data: list[TranscribedData],
+    ultrastar_audio_input_path: str,
+    ultrastar_class: UltrastarTxtValue
+) -> None:
+    """Create audio chunks"""
+    audio_chunks_path = os.path.join(
+        cache_path, settings.audio_chunk_folder_name
+    )
+    os_helper.create_folder(audio_chunks_path)
+    if is_audio:  # and csv
+        csv_filename = os.path.join(audio_chunks_path, "_chunks.csv")
+        export_chunks_from_transcribed_data(
+            settings.processing_audio_path, transcribed_data, audio_chunks_path
+        )
+        export_transcribed_data_to_csv(transcribed_data, csv_filename)
+    else:
+        export_chunks_from_ultrastar_data(
+            ultrastar_audio_input_path, ultrastar_class, audio_chunks_path
+        )
+
+def denoise_vocal_audio(input_path: str, output_path: str) -> None:
+    """Denoise vocal audio"""
+    ffmpeg_reduce_noise(input_path, output_path)
+
+
+def main(argv: list[str]) -> None:
+    """Main function"""
+    print_version()
+    init_settings(argv)
+    run()
+    sys.exit()
+
+def remove_cache_folder(cache_path: str) -> None:
+    """Remove cache folder"""
+    os_helper.remove_folder(cache_path)
+
+def init_settings(argv: list[str]) -> None:
+    """Init settings"""
+    long, short = arg_options()
+    opts, args = getopt.getopt(argv, short, long)
+    if len(opts) == 0:
+        print_help()
+        sys.exit()
+    for opt, arg in opts:
+        if opt == "-h":
+            print_help()
+            sys.exit()
+        elif opt in ("-i", "--ifile"):
+            settings.input_file_path = arg
+        elif opt in ("-o", "--ofile"):
+            settings.output_file_path = arg
+        elif opt in ("--whisper"):
+            settings.transcriber = "whisper"
+            settings.whisper_model = arg
+        elif opt in ("--whisper_align_model"):
+            settings.whisper_align_model = arg
+        elif opt in ("--whisper_batch_size"):
+            settings.whisper_batch_size = int(arg)
+        elif opt in ("--whisper_compute_type"):
+            settings.whisper_compute_type = arg
+        elif opt in ("--language"):
+            settings.language = arg
+        elif opt in ("--crepe"):
+            settings.crepe_model_capacity = arg
+        elif opt in ("--crepe_step_size"):
+            settings.crepe_step_size = int(arg)
+        elif opt in ("--plot"):
+            settings.create_plot = arg in ["True", "true"]
+        elif opt in ("--midi"):
+            settings.create_midi = arg in ["True", "true"]
+        elif opt in ("--hyphenation"):
+            settings.hyphenation = eval(arg.title())
+        elif opt in ("--disable_separation"):
+            settings.use_separated_vocal = not arg
+        elif opt in ("--disable_karaoke"):
+            settings.create_karaoke = not arg
+        elif opt in ("--create_audio_chunks"):
+            settings.create_audio_chunks = arg
+        elif opt in ("--force_cpu"):
+            settings.force_cpu = arg
+            if settings.force_cpu:
+                os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
+        elif opt in ("--force_whisper_cpu"):
+            settings.force_whisper_cpu = eval(arg.title())
+        elif opt in ("--force_crepe_cpu"):
+            settings.force_crepe_cpu = eval(arg.title())
+        elif opt in ("--format_version"):
+            if arg != '0.3.0' and arg != '1.0.0' and arg != '1.1.0':
+                print(
+                    f"{ULTRASINGER_HEAD} {red_highlighted('Error: Format version')} {blue_highlighted(arg)} {red_highlighted('is not supported.')}"
+                )
+                sys.exit(1)
+            settings.format_version = arg
+        elif opt in ("--keep_cache"):
+            settings.keep_cache = arg
+    if settings.output_file_path == "":
+        if settings.input_file_path.startswith("https:"):
+            dirname = os.getcwd()
+        else:
+            dirname = os.path.dirname(settings.input_file_path)
+        settings.output_file_path = os.path.join(dirname, "output")
+
+    if not settings.force_cpu:
+        settings.tensorflow_device, settings.pytorch_device = check_gpu_support()
+
+
+def arg_options():
+    short = "hi:o:amv:"
+    long = [
+        "ifile=",
+        "ofile=",
+        "crepe=",
+        "crepe_step_size=",
+        "whisper=",
+        "whisper_align_model=",
+        "whisper_batch_size=",
+        "whisper_compute_type=",
+        "language=",
+        "plot=",
+        "midi=",
+        "hyphenation=",
+        "disable_separation=",
+        "disable_karaoke=",
+        "create_audio_chunks=",
+        "force_cpu=",
+        "force_whisper_cpu=",
+        "force_crepe_cpu=",
+        "format_version=",
+        "keep_cache"
+    ]
+    return long, short
+
+
+if __name__ == "__main__":
+    main(sys.argv[1:])