diff --git "a/app_rvc.py" "b/app_rvc.py" --- "a/app_rvc.py" +++ "b/app_rvc.py" @@ -1,2924 +1,2864 @@ -import gradio as gr -import os -os.system("pip install -q piper-tts==1.2.0") -os.system("pip install -q -r requirements_xtts.txt") -os.system("pip install -q TTS==0.21.1 --no-deps") -import spaces -import torch -if os.environ.get("ZERO_GPU") != "TRUE" and torch.cuda.is_available(): - # onnxruntime GPU - os.system("pip install ort-nightly-gpu --index-url=https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ort-cuda-12-nightly/pypi/simple/") -import librosa -from soni_translate.logging_setup import ( - logger, - set_logging_level, - configure_logging_libs, -); configure_logging_libs() # noqa -import whisperx -import os -from soni_translate.audio_segments import create_translated_audio -from soni_translate.text_to_speech import ( - audio_segmentation_to_voice, - edge_tts_voices_list, - coqui_xtts_voices_list, - piper_tts_voices_list, - create_wav_file_vc, - accelerate_segments, -) -from soni_translate.translate_segments import ( - translate_text, - TRANSLATION_PROCESS_OPTIONS, - DOCS_TRANSLATION_PROCESS_OPTIONS -) -from soni_translate.preprocessor import ( - audio_video_preprocessor, - audio_preprocessor, -) -from soni_translate.postprocessor import ( - OUTPUT_TYPE_OPTIONS, - DOCS_OUTPUT_TYPE_OPTIONS, - sound_separate, - get_no_ext_filename, - media_out, - get_subtitle_speaker, -) -from soni_translate.language_configuration import ( - LANGUAGES, - UNIDIRECTIONAL_L_LIST, - LANGUAGES_LIST, - BARK_VOICES_LIST, - VITS_VOICES_LIST, - OPENAI_TTS_MODELS, -) -from soni_translate.utils import ( - remove_files, - download_list, - upload_model_list, - download_manager, - run_command, - is_audio_file, - is_subtitle_file, - copy_files, - get_valid_files, - get_link_list, - remove_directory_contents, -) -from soni_translate.mdx_net import ( - UVR_MODELS, - MDX_DOWNLOAD_LINK, - mdxnet_models_dir, -) -from soni_translate.speech_segmentation import ( - ASR_MODEL_OPTIONS, - COMPUTE_TYPE_GPU, - COMPUTE_TYPE_CPU, - find_whisper_models, - transcribe_speech, - align_speech, - diarize_speech, - diarization_models, -) -from soni_translate.text_multiformat_processor import ( - BORDER_COLORS, - srt_file_to_segments, - document_preprocessor, - determine_chunk_size, - plain_text_to_segments, - segments_to_plain_text, - process_subtitles, - linguistic_level_segments, - break_aling_segments, - doc_to_txtximg_pages, - page_data_to_segments, - update_page_data, - fix_timestamps_docs, - create_video_from_images, - merge_video_and_audio, -) -from soni_translate.languages_gui import language_data, news -import copy -import logging -import json -from pydub import AudioSegment -from voice_main import ClassVoices -import argparse -import time -import hashlib -import sys - -directories = [ - "downloads", - "logs", - "weights", - "clean_song_output", - "_XTTS_", - f"audio2{os.sep}audio", - "audio", - "outputs", -] -[ - os.makedirs(directory) - for directory in directories - if not os.path.exists(directory) -] - - -class TTS_Info: - def __init__(self, piper_enabled, xtts_enabled): - self.list_edge = edge_tts_voices_list() - self.list_bark = list(BARK_VOICES_LIST.keys()) - self.list_vits = list(VITS_VOICES_LIST.keys()) - self.list_openai_tts = OPENAI_TTS_MODELS - self.piper_enabled = piper_enabled - self.list_vits_onnx = ( - piper_tts_voices_list() if self.piper_enabled else [] - ) - self.xtts_enabled = xtts_enabled - - def tts_list(self): - self.list_coqui_xtts = ( - coqui_xtts_voices_list() if self.xtts_enabled else [] - ) - list_tts = self.list_coqui_xtts + sorted( - self.list_edge - + (self.list_bark if os.environ.get("ZERO_GPU") != "TRUE" else []) - + self.list_vits - + self.list_openai_tts - + self.list_vits_onnx - ) - return list_tts - - -def prog_disp(msg, percent, is_gui, progress=None): - logger.info(msg) - if is_gui: - progress(percent, desc=msg) - - -def warn_disp(wrn_lang, is_gui): - logger.warning(wrn_lang) - if is_gui: - gr.Warning(wrn_lang) - - -class SoniTrCache: - def __init__(self): - self.cache = { - 'media': [[]], - 'refine_vocals': [], - 'transcript_align': [], - 'break_align': [], - 'diarize': [], - 'translate': [], - 'subs_and_edit': [], - 'tts': [], - 'acc_and_vc': [], - 'mix_aud': [], - 'output': [] - } - - self.cache_data = { - 'media': [], - 'refine_vocals': [], - 'transcript_align': [], - 'break_align': [], - 'diarize': [], - 'translate': [], - 'subs_and_edit': [], - 'tts': [], - 'acc_and_vc': [], - 'mix_aud': [], - 'output': [] - } - - self.cache_keys = list(self.cache.keys()) - self.first_task = self.cache_keys[0] - self.last_task = self.cache_keys[-1] - - self.pre_step = None - self.pre_params = [] - - def set_variable(self, variable_name, value): - setattr(self, variable_name, value) - - def task_in_cache(self, step: str, params: list, previous_step_data: dict): - - self.pre_step_cache = None - - if step == self.first_task: - self.pre_step = None - - if self.pre_step: - self.cache[self.pre_step] = self.pre_params - - # Fill data in cache - self.cache_data[self.pre_step] = copy.deepcopy(previous_step_data) - - self.pre_params = params - # logger.debug(f"Step: {str(step)}, Cache params: {str(self.cache)}") - if params == self.cache[step]: - logger.debug(f"In cache: {str(step)}") - - # Set the var needed for next step - # Recovery from cache_data the current step - for key, value in self.cache_data[step].items(): - self.set_variable(key, copy.deepcopy(value)) - logger.debug( - f"Chache load: {str(key)}" - ) - - self.pre_step = step - return True - - else: - logger.debug(f"Flush next and caching {str(step)}") - selected_index = self.cache_keys.index(step) - - for idx, key in enumerate(self.cache.keys()): - if idx >= selected_index: - self.cache[key] = [] - self.cache_data[key] = {} - - # The last is now previous - self.pre_step = step - return False - - def clear_cache(self, media, force=False): - - self.cache["media"] = ( - self.cache["media"] if len(self.cache["media"]) else [[]] - ) - - if media != self.cache["media"][0] or force: - - # Clear cache - self.cache = {key: [] for key in self.cache} - self.cache["media"] = [[]] - - logger.info("Cache flushed") - - -def get_hash(filepath): - with open(filepath, 'rb') as f: - file_hash = hashlib.blake2b() - while chunk := f.read(8192): - file_hash.update(chunk) - - return file_hash.hexdigest()[:18] - - -def check_openai_api_key(): - if not os.environ.get("OPENAI_API_KEY"): - raise ValueError( - "To use GPT for translation, please set up your OpenAI API key " - "as an environment variable in Linux as follows: " - "export OPENAI_API_KEY='your-api-key-here'. Or change the " - "translation process in Advanced settings." - ) - - -class SoniTranslate(SoniTrCache): - def __init__(self, cpu_mode=False): - super().__init__() - if cpu_mode: - os.environ["SONITR_DEVICE"] = "cpu" - else: - os.environ["SONITR_DEVICE"] = ( - "cuda" if torch.cuda.is_available() else "cpu" - ) - - self.device = os.environ.get("SONITR_DEVICE") - self.device = self.device if os.environ.get("ZERO_GPU") != "TRUE" else "cuda" - self.result_diarize = None - self.align_language = None - self.result_source_lang = None - self.edit_subs_complete = False - self.voiceless_id = None - self.burn_subs_id = None - - self.vci = ClassVoices(only_cpu=cpu_mode) - - self.tts_voices = self.get_tts_voice_list() - - logger.info(f"Working in: {self.device}") - - def get_tts_voice_list(self): - try: - from piper import PiperVoice # noqa - - piper_enabled = True - logger.info("PIPER TTS enabled") - except Exception as error: - logger.debug(str(error)) - piper_enabled = False - logger.info("PIPER TTS disabled") - try: - from TTS.api import TTS # noqa - - xtts_enabled = True - logger.info("Coqui XTTS enabled") - logger.info( - "In this app, by using Coqui TTS (text-to-speech), you " - "acknowledge and agree to the license.\n" - "You confirm that you have read, understood, and agreed " - "to the Terms and Conditions specified at the following " - "link:\nhttps://coqui.ai/cpml.txt." - ) - os.environ["COQUI_TOS_AGREED"] = "1" - except Exception as error: - logger.debug(str(error)) - xtts_enabled = False - logger.info("Coqui XTTS disabled") - - self.tts_info = TTS_Info(piper_enabled, xtts_enabled) - - return self.tts_info.tts_list() - - def batch_multilingual_media_conversion(self, *kwargs): - # logger.debug(str(kwargs)) - - media_file_arg = kwargs[0] if kwargs[0] is not None else [] - - link_media_arg = kwargs[1] - link_media_arg = [x.strip() for x in link_media_arg.split(',')] - link_media_arg = get_link_list(link_media_arg) - - path_arg = kwargs[2] - path_arg = [x.strip() for x in path_arg.split(',')] - path_arg = get_valid_files(path_arg) - - edit_text_arg = kwargs[31] - get_text_arg = kwargs[32] - - is_gui_arg = kwargs[-1] - - kwargs = kwargs[3:] - - media_batch = media_file_arg + link_media_arg + path_arg - media_batch = list(filter(lambda x: x != "", media_batch)) - media_batch = media_batch if media_batch else [None] - logger.debug(str(media_batch)) - - remove_directory_contents("outputs") - - if edit_text_arg or get_text_arg: - return self.multilingual_media_conversion( - media_batch[0], "", "", *kwargs - ) - - if "SET_LIMIT" == os.getenv("DEMO") or "TRUE" == os.getenv("ZERO_GPU"): - media_batch = [media_batch[0]] - - result = [] - for media in media_batch: - # Call the nested function with the parameters - output_file = self.multilingual_media_conversion( - media, "", "", *kwargs - ) - - if isinstance(output_file, str): - output_file = [output_file] - result.extend(output_file) - - if is_gui_arg and len(media_batch) > 1: - gr.Info(f"Done: {os.path.basename(output_file[0])}") - - return result - - def multilingual_media_conversion( - self, - media_file=None, - link_media="", - directory_input="", - YOUR_HF_TOKEN="", - preview=False, - transcriber_model="large-v3", - batch_size=4, - compute_type="auto", - origin_language="Automatic detection", - target_language="English (en)", - min_speakers=1, - max_speakers=1, - tts_voice00="en-US-EmmaMultilingualNeural-Female", - tts_voice01="en-US-AndrewMultilingualNeural-Male", - tts_voice02="en-US-AvaMultilingualNeural-Female", - tts_voice03="en-US-BrianMultilingualNeural-Male", - tts_voice04="de-DE-SeraphinaMultilingualNeural-Female", - tts_voice05="de-DE-FlorianMultilingualNeural-Male", - tts_voice06="fr-FR-VivienneMultilingualNeural-Female", - tts_voice07="fr-FR-RemyMultilingualNeural-Male", - tts_voice08="en-US-EmmaMultilingualNeural-Female", - tts_voice09="en-US-AndrewMultilingualNeural-Male", - tts_voice10="en-US-EmmaMultilingualNeural-Female", - tts_voice11="en-US-AndrewMultilingualNeural-Male", - video_output_name="", - mix_method_audio="Adjusting volumes and mixing audio", - max_accelerate_audio=2.1, - acceleration_rate_regulation=False, - volume_original_audio=0.25, - volume_translated_audio=1.80, - output_format_subtitle="srt", - get_translated_text=False, - get_video_from_text_json=False, - text_json="{}", - avoid_overlap=False, - vocal_refinement=False, - literalize_numbers=True, - segment_duration_limit=15, - diarization_model="pyannote_2.1", - translate_process="google_translator_batch", - subtitle_file=None, - output_type="video (mp4)", - voiceless_track=False, - voice_imitation=False, - voice_imitation_max_segments=3, - voice_imitation_vocals_dereverb=False, - voice_imitation_remove_previous=True, - voice_imitation_method="freevc", - dereverb_automatic_xtts=True, - text_segmentation_scale="sentence", - divide_text_segments_by="", - soft_subtitles_to_video=True, - burn_subtitles_to_video=False, - enable_cache=True, - custom_voices=False, - custom_voices_workers=1, - is_gui=False, - progress=gr.Progress(), - ): - if not YOUR_HF_TOKEN: - YOUR_HF_TOKEN = os.getenv("YOUR_HF_TOKEN") - if diarization_model == "disable" or max_speakers == 1: - if YOUR_HF_TOKEN is None: - YOUR_HF_TOKEN = "" - elif not YOUR_HF_TOKEN: - raise ValueError("No valid Hugging Face token") - else: - os.environ["YOUR_HF_TOKEN"] = YOUR_HF_TOKEN - - if ( - "gpt" in translate_process - or transcriber_model == "OpenAI_API_Whisper" - or "OpenAI-TTS" in tts_voice00 - ): - check_openai_api_key() - - if media_file is None: - media_file = ( - directory_input - if os.path.exists(directory_input) - else link_media - ) - media_file = ( - media_file if isinstance(media_file, str) else media_file.name - ) - - if is_subtitle_file(media_file): - subtitle_file = media_file - media_file = "" - - if media_file is None: - media_file = "" - - if not origin_language: - origin_language = "Automatic detection" - - if origin_language in UNIDIRECTIONAL_L_LIST and not subtitle_file: - raise ValueError( - f"The language '{origin_language}' " - "is not supported for transcription (ASR)." - ) - - if get_translated_text: - self.edit_subs_complete = False - if get_video_from_text_json: - if not self.edit_subs_complete: - raise ValueError("Generate the transcription first.") - - if ( - ("sound" in output_type or output_type == "raw media") - and (get_translated_text or get_video_from_text_json) - ): - raise ValueError( - "Please disable 'edit generate subtitles' " - f"first to acquire the {output_type}." - ) - - TRANSLATE_AUDIO_TO = LANGUAGES[target_language] - SOURCE_LANGUAGE = LANGUAGES[origin_language] - - if ( - transcriber_model == "OpenAI_API_Whisper" - and SOURCE_LANGUAGE == "zh-TW" - ): - logger.warning( - "OpenAI API Whisper only supports Chinese (Simplified)." - ) - SOURCE_LANGUAGE = "zh" - - if ( - text_segmentation_scale in ["word", "character"] - and "subtitle" not in output_type - ): - wrn_lang = ( - "Text segmentation by words or characters is typically" - " used for generating subtitles. If subtitles are not the" - " intended output, consider selecting 'sentence' " - "segmentation method to ensure optimal results." - - ) - warn_disp(wrn_lang, is_gui) - - if tts_voice00[:2].lower() != TRANSLATE_AUDIO_TO[:2].lower(): - wrn_lang = ( - "Make sure to select a 'TTS Speaker' suitable for" - " the translation language to avoid errors with the TTS." - ) - warn_disp(wrn_lang, is_gui) - - if "_XTTS_" in tts_voice00 and voice_imitation: - wrn_lang = ( - "When you select XTTS, it is advisable " - "to disable Voice Imitation." - ) - warn_disp(wrn_lang, is_gui) - - if custom_voices and voice_imitation: - wrn_lang = ( - "When you use R.V.C. models, it is advisable" - " to disable Voice Imitation." - ) - warn_disp(wrn_lang, is_gui) - - if not media_file and not subtitle_file: - raise ValueError( - "Specifify a media or SRT file in advanced settings" - ) - - if subtitle_file: - subtitle_file = ( - subtitle_file - if isinstance(subtitle_file, str) - else subtitle_file.name - ) - - if subtitle_file and SOURCE_LANGUAGE == "Automatic detection": - raise Exception( - "To use an SRT file, you need to specify its " - "original language (Source language)" - ) - - if not media_file and subtitle_file: - diarization_model = "disable" - media_file = "audio_support.wav" - if not get_video_from_text_json: - remove_files(media_file) - srt_data = srt_file_to_segments(subtitle_file) - total_duration = srt_data["segments"][-1]["end"] + 30. - support_audio = AudioSegment.silent( - duration=int(total_duration * 1000) - ) - support_audio.export( - media_file, format="wav" - ) - logger.info("Supporting audio for the SRT file, created.") - - if "SET_LIMIT" == os.getenv("DEMO"): - preview = True - mix_method_audio = "Adjusting volumes and mixing audio" - transcriber_model = "medium" - logger.info( - "DEMO; set preview=True; Generation is limited to " - "10 seconds to prevent CPU errors. No limitations with GPU.\n" - "DEMO; set Adjusting volumes and mixing audio\n" - "DEMO; set whisper model to medium" - ) - - # Check GPU - if self.device == "cpu" and compute_type not in COMPUTE_TYPE_CPU: - logger.info("Compute type changed to float32") - compute_type = "float32" - - base_video_file = "Video.mp4" - base_audio_wav = "audio.wav" - dub_audio_file = "audio_dub_solo.ogg" - vocals_audio_file = "audio_Vocals_DeReverb.wav" - voiceless_audio_file = "audio_Voiceless.wav" - mix_audio_file = "audio_mix.mp3" - vid_subs = "video_subs_file.mp4" - video_output_file = "video_dub.mp4" - - if os.path.exists(media_file): - media_base_hash = get_hash(media_file) - else: - media_base_hash = media_file - self.clear_cache(media_base_hash, force=(not enable_cache)) - - if not get_video_from_text_json: - self.result_diarize = ( - self.align_language - ) = self.result_source_lang = None - if not self.task_in_cache("media", [media_base_hash, preview], {}): - if is_audio_file(media_file): - prog_disp( - "Processing audio...", 0.15, is_gui, progress=progress - ) - audio_preprocessor(preview, media_file, base_audio_wav) - else: - prog_disp( - "Processing video...", 0.15, is_gui, progress=progress - ) - audio_video_preprocessor( - preview, media_file, base_video_file, base_audio_wav - ) - logger.debug("Set file complete.") - - if "sound" in output_type: - prog_disp( - "Separating sounds in the file...", - 0.50, - is_gui, - progress=progress - ) - separate_out = sound_separate(base_audio_wav, output_type) - final_outputs = [] - for out in separate_out: - final_name = media_out( - media_file, - f"{get_no_ext_filename(out)}", - video_output_name, - "wav", - file_obj=out, - ) - final_outputs.append(final_name) - logger.info(f"Done: {str(final_outputs)}") - return final_outputs - - if output_type == "raw media": - output = media_out( - media_file, - "raw_media", - video_output_name, - "wav" if is_audio_file(media_file) else "mp4", - file_obj=base_audio_wav if is_audio_file(media_file) else base_video_file, - ) - logger.info(f"Done: {output}") - return output - - if os.environ.get("IS_DEMO") == "TRUE": - duration_verify = librosa.get_duration(filename=base_audio_wav) - logger.info(f"Duration: {duration_verify} seconds") - if duration_verify > 1500: - raise RuntimeError( - "The audio is too long to process in this demo. Alternatively, you" - " can install the app locally or use the Colab notebook available " - "in the SoniTranslate repository." - ) - elif duration_verify > 300: - tts_voices_list = [ - tts_voice00, tts_voice01, tts_voice02, tts_voice03, tts_voice04, - tts_voice05, tts_voice06, tts_voice07, tts_voice08, tts_voice09, - tts_voice10, tts_voice11 - ] - - for tts_voice_ in tts_voices_list: - if "_XTTS_" in tts_voice_: - raise RuntimeError( - "XTTS is too slow to be used for audio longer than 5 " - "minutes in this demo. Alternatively, you can install " - "the app locally or use the Colab notebook available in" - " the SoniTranslate repository." - ) - - if not self.task_in_cache("refine_vocals", [vocal_refinement], {}): - self.vocals = None - if vocal_refinement: - try: - from soni_translate.mdx_net import process_uvr_task - _, _, _, _, file_vocals = process_uvr_task( - orig_song_path=base_audio_wav, - main_vocals=False, - dereverb=True, - remove_files_output_dir=True, - ) - remove_files(vocals_audio_file) - copy_files(file_vocals, ".") - self.vocals = vocals_audio_file - except Exception as error: - logger.error(str(error)) - - if not self.task_in_cache("transcript_align", [ - subtitle_file, - SOURCE_LANGUAGE, - transcriber_model, - compute_type, - batch_size, - literalize_numbers, - segment_duration_limit, - ( - "l_unit" - if text_segmentation_scale in ["word", "character"] - and subtitle_file - else "sentence" - ) - ], {"vocals": self.vocals}): - if subtitle_file: - prog_disp( - "From SRT file...", 0.30, is_gui, progress=progress - ) - audio = whisperx.load_audio( - base_audio_wav if not self.vocals else self.vocals - ) - self.result = srt_file_to_segments(subtitle_file) - self.result["language"] = SOURCE_LANGUAGE - else: - prog_disp( - "Transcribing...", 0.30, is_gui, progress=progress - ) - SOURCE_LANGUAGE = ( - None - if SOURCE_LANGUAGE == "Automatic detection" - else SOURCE_LANGUAGE - ) - audio, self.result = transcribe_speech( - base_audio_wav if not self.vocals else self.vocals, - transcriber_model, - compute_type, - batch_size, - SOURCE_LANGUAGE, - literalize_numbers, - segment_duration_limit, - ) - logger.debug( - "Transcript complete, " - f"segments count {len(self.result['segments'])}" - ) - - self.align_language = self.result["language"] - if ( - not subtitle_file - or text_segmentation_scale in ["word", "character"] - ): - prog_disp("Aligning...", 0.45, is_gui, progress=progress) - try: - if self.align_language in ["vi"]: - logger.info( - "Deficient alignment for the " - f"{self.align_language} language, skipping the" - " process. It is suggested to reduce the " - "duration of the segments as an alternative." - ) - else: - self.result = align_speech(audio, self.result) - logger.debug( - "Align complete, " - f"segments count {len(self.result['segments'])}" - ) - except Exception as error: - logger.error(str(error)) - - if self.result["segments"] == []: - raise ValueError("No active speech found in audio") - - if not self.task_in_cache("break_align", [ - divide_text_segments_by, - text_segmentation_scale, - self.align_language - ], { - "result": self.result, - "align_language": self.align_language - }): - if self.align_language in ["ja", "zh", "zh-TW"]: - divide_text_segments_by += "|!|?|...|。" - if text_segmentation_scale in ["word", "character"]: - self.result = linguistic_level_segments( - self.result, - text_segmentation_scale, - ) - elif divide_text_segments_by: - try: - self.result = break_aling_segments( - self.result, - break_characters=divide_text_segments_by, - ) - except Exception as error: - logger.error(str(error)) - - if not self.task_in_cache("diarize", [ - min_speakers, - max_speakers, - YOUR_HF_TOKEN[:len(YOUR_HF_TOKEN)//2], - diarization_model - ], { - "result": self.result - }): - prog_disp("Diarizing...", 0.60, is_gui, progress=progress) - diarize_model_select = diarization_models[diarization_model] - self.result_diarize = diarize_speech( - base_audio_wav if not self.vocals else self.vocals, - self.result, - min_speakers, - max_speakers, - YOUR_HF_TOKEN, - diarize_model_select, - ) - logger.debug("Diarize complete") - self.result_source_lang = copy.deepcopy(self.result_diarize) - - if not self.task_in_cache("translate", [ - TRANSLATE_AUDIO_TO, - translate_process - ], { - "result_diarize": self.result_diarize - }): - prog_disp("Translating...", 0.70, is_gui, progress=progress) - lang_source = ( - self.align_language - if self.align_language - else SOURCE_LANGUAGE - ) - self.result_diarize["segments"] = translate_text( - self.result_diarize["segments"], - TRANSLATE_AUDIO_TO, - translate_process, - chunk_size=1800, - source=lang_source, - ) - logger.debug("Translation complete") - logger.debug(self.result_diarize) - - if get_translated_text: - - json_data = [] - for segment in self.result_diarize["segments"]: - start = segment["start"] - text = segment["text"] - speaker = int(segment.get("speaker", "SPEAKER_00")[-2:]) + 1 - json_data.append( - {"start": start, "text": text, "speaker": speaker} - ) - - # Convert list of dictionaries to a JSON string with indentation - json_string = json.dumps(json_data, indent=2) - logger.info("Done") - self.edit_subs_complete = True - return json_string.encode().decode("unicode_escape") - - if get_video_from_text_json: - - if self.result_diarize is None: - raise ValueError("Generate the transcription first.") - # with open('text_json.json', 'r') as file: - text_json_loaded = json.loads(text_json) - for i, segment in enumerate(self.result_diarize["segments"]): - segment["text"] = text_json_loaded[i]["text"] - segment["speaker"] = "SPEAKER_{:02d}".format( - int(text_json_loaded[i]["speaker"]) - 1 - ) - - # Write subtitle - if not self.task_in_cache("subs_and_edit", [ - copy.deepcopy(self.result_diarize), - output_format_subtitle, - TRANSLATE_AUDIO_TO - ], { - "result_diarize": self.result_diarize - }): - if output_format_subtitle == "disable": - self.sub_file = "sub_tra.srt" - elif output_format_subtitle != "ass": - self.sub_file = process_subtitles( - self.result_source_lang, - self.align_language, - self.result_diarize, - output_format_subtitle, - TRANSLATE_AUDIO_TO, - ) - - # Need task - if output_format_subtitle != "srt": - _ = process_subtitles( - self.result_source_lang, - self.align_language, - self.result_diarize, - "srt", - TRANSLATE_AUDIO_TO, - ) - - if output_format_subtitle == "ass": - convert_ori = "ffmpeg -i sub_ori.srt sub_ori.ass -y" - convert_tra = "ffmpeg -i sub_tra.srt sub_tra.ass -y" - self.sub_file = "sub_tra.ass" - run_command(convert_ori) - run_command(convert_tra) - - format_sub = ( - output_format_subtitle - if output_format_subtitle != "disable" - else "srt" - ) - - if output_type == "subtitle": - - out_subs = [] - tra_subs = media_out( - media_file, - TRANSLATE_AUDIO_TO, - video_output_name, - format_sub, - file_obj=self.sub_file, - ) - out_subs.append(tra_subs) - - ori_subs = media_out( - media_file, - self.align_language, - video_output_name, - format_sub, - file_obj=f"sub_ori.{format_sub}", - ) - out_subs.append(ori_subs) - logger.info(f"Done: {out_subs}") - return out_subs - - if output_type == "subtitle [by speaker]": - output = get_subtitle_speaker( - media_file, - result=self.result_diarize, - language=TRANSLATE_AUDIO_TO, - extension=format_sub, - base_name=video_output_name, - ) - logger.info(f"Done: {str(output)}") - return output - - if "video [subtitled]" in output_type: - output = media_out( - media_file, - TRANSLATE_AUDIO_TO + "_subtitled", - video_output_name, - "wav" if is_audio_file(media_file) else ( - "mkv" if "mkv" in output_type else "mp4" - ), - file_obj=base_audio_wav if is_audio_file(media_file) else base_video_file, - soft_subtitles=False if is_audio_file(media_file) else True, - subtitle_files=output_format_subtitle, - ) - msg_out = output[0] if isinstance(output, list) else output - logger.info(f"Done: {msg_out}") - return output - - if not self.task_in_cache("tts", [ - TRANSLATE_AUDIO_TO, - tts_voice00, - tts_voice01, - tts_voice02, - tts_voice03, - tts_voice04, - tts_voice05, - tts_voice06, - tts_voice07, - tts_voice08, - tts_voice09, - tts_voice10, - tts_voice11, - dereverb_automatic_xtts - ], { - "sub_file": self.sub_file - }): - prog_disp("Text to speech...", 0.80, is_gui, progress=progress) - self.valid_speakers = audio_segmentation_to_voice( - self.result_diarize, - TRANSLATE_AUDIO_TO, - is_gui, - tts_voice00, - tts_voice01, - tts_voice02, - tts_voice03, - tts_voice04, - tts_voice05, - tts_voice06, - tts_voice07, - tts_voice08, - tts_voice09, - tts_voice10, - tts_voice11, - dereverb_automatic_xtts, - ) - - if not self.task_in_cache("acc_and_vc", [ - max_accelerate_audio, - acceleration_rate_regulation, - voice_imitation, - voice_imitation_max_segments, - voice_imitation_remove_previous, - voice_imitation_vocals_dereverb, - voice_imitation_method, - custom_voices, - custom_voices_workers, - copy.deepcopy(self.vci.model_config), - avoid_overlap - ], { - "valid_speakers": self.valid_speakers - }): - audio_files, speakers_list = accelerate_segments( - self.result_diarize, - max_accelerate_audio, - self.valid_speakers, - acceleration_rate_regulation, - ) - - # Voice Imitation (Tone color converter) - if voice_imitation: - prog_disp( - "Voice Imitation...", 0.85, is_gui, progress=progress - ) - from soni_translate.text_to_speech import toneconverter - - try: - toneconverter( - copy.deepcopy(self.result_diarize), - voice_imitation_max_segments, - voice_imitation_remove_previous, - voice_imitation_vocals_dereverb, - voice_imitation_method, - ) - except Exception as error: - logger.error(str(error)) - - # custom voice - if custom_voices: - prog_disp( - "Applying customized voices...", - 0.90, - is_gui, - progress=progress, - ) - - try: - self.vci( - audio_files, - speakers_list, - overwrite=True, - parallel_workers=custom_voices_workers, - ) - self.vci.unload_models() - except Exception as error: - logger.error(str(error)) - - prog_disp( - "Creating final translated video...", - 0.95, - is_gui, - progress=progress, - ) - remove_files(dub_audio_file) - create_translated_audio( - self.result_diarize, - audio_files, - dub_audio_file, - False, - avoid_overlap, - ) - - # Voiceless track, change with file - hash_base_audio_wav = get_hash(base_audio_wav) - if voiceless_track: - if self.voiceless_id != hash_base_audio_wav: - from soni_translate.mdx_net import process_uvr_task - - try: - # voiceless_audio_file_dir = "clean_song_output/voiceless" - remove_files(voiceless_audio_file) - uvr_voiceless_audio_wav, _ = process_uvr_task( - orig_song_path=base_audio_wav, - song_id="voiceless", - only_voiceless=True, - remove_files_output_dir=False, - ) - copy_files(uvr_voiceless_audio_wav, ".") - base_audio_wav = voiceless_audio_file - self.voiceless_id = hash_base_audio_wav - - except Exception as error: - logger.error(str(error)) - else: - base_audio_wav = voiceless_audio_file - - if not self.task_in_cache("mix_aud", [ - mix_method_audio, - volume_original_audio, - volume_translated_audio, - voiceless_track - ], {}): - # TYPE MIX AUDIO - remove_files(mix_audio_file) - command_volume_mix = f'ffmpeg -y -i {base_audio_wav} -i {dub_audio_file} -filter_complex "[0:0]volume={volume_original_audio}[a];[1:0]volume={volume_translated_audio}[b];[a][b]amix=inputs=2:duration=longest" -c:a libmp3lame {mix_audio_file}' - command_background_mix = f'ffmpeg -i {base_audio_wav} -i {dub_audio_file} -filter_complex "[1:a]asplit=2[sc][mix];[0:a][sc]sidechaincompress=threshold=0.003:ratio=20[bg]; [bg][mix]amerge[final]" -map [final] {mix_audio_file}' - if mix_method_audio == "Adjusting volumes and mixing audio": - # volume mix - run_command(command_volume_mix) - else: - try: - # background mix - run_command(command_background_mix) - except Exception as error_mix: - # volume mix except - logger.error(str(error_mix)) - run_command(command_volume_mix) - - if "audio" in output_type or is_audio_file(media_file): - output = media_out( - media_file, - TRANSLATE_AUDIO_TO, - video_output_name, - "wav" if "wav" in output_type else ( - "ogg" if "ogg" in output_type else "mp3" - ), - file_obj=mix_audio_file, - subtitle_files=output_format_subtitle, - ) - msg_out = output[0] if isinstance(output, list) else output - logger.info(f"Done: {msg_out}") - return output - - hash_base_video_file = get_hash(base_video_file) - - if burn_subtitles_to_video: - hashvideo_text = [ - hash_base_video_file, - [seg["text"] for seg in self.result_diarize["segments"]] - ] - if self.burn_subs_id != hashvideo_text: - try: - logger.info("Burn subtitles") - remove_files(vid_subs) - command = f"ffmpeg -i {base_video_file} -y -vf subtitles=sub_tra.srt -max_muxing_queue_size 9999 {vid_subs}" - run_command(command) - base_video_file = vid_subs - self.burn_subs_id = hashvideo_text - except Exception as error: - logger.error(str(error)) - else: - base_video_file = vid_subs - - if not self.task_in_cache("output", [ - hash_base_video_file, - hash_base_audio_wav, - burn_subtitles_to_video - ], {}): - # Merge new audio + video - remove_files(video_output_file) - run_command( - f"ffmpeg -i {base_video_file} -i {mix_audio_file} -c:v copy -c:a copy -map 0:v -map 1:a -shortest {video_output_file}" - ) - - output = media_out( - media_file, - TRANSLATE_AUDIO_TO, - video_output_name, - "mkv" if "mkv" in output_type else "mp4", - file_obj=video_output_file, - soft_subtitles=soft_subtitles_to_video, - subtitle_files=output_format_subtitle, - ) - msg_out = output[0] if isinstance(output, list) else output - logger.info(f"Done: {msg_out}") - - return output - - def hook_beta_processor( - self, - document, - tgt_lang, - translate_process, - ori_lang, - tts, - name_final_file, - custom_voices, - custom_voices_workers, - output_type, - chunk_size, - width, - height, - start_page, - end_page, - bcolor, - is_gui, - progress - ): - prog_disp("Processing pages...", 0.10, is_gui, progress=progress) - doc_data = doc_to_txtximg_pages(document, width, height, start_page, end_page, bcolor) - result_diarize = page_data_to_segments(doc_data, 1700) - - prog_disp("Translating...", 0.20, is_gui, progress=progress) - result_diarize["segments"] = translate_text( - result_diarize["segments"], - tgt_lang, - translate_process, - chunk_size=0, - source=ori_lang, - ) - chunk_size = ( - chunk_size if chunk_size else determine_chunk_size(tts) - ) - doc_data = update_page_data(result_diarize, doc_data) - - prog_disp("Text to speech...", 0.30, is_gui, progress=progress) - result_diarize = page_data_to_segments(doc_data, chunk_size) - valid_speakers = audio_segmentation_to_voice( - result_diarize, - tgt_lang, - is_gui, - tts, - ) - - # fix format and set folder output - audio_files, speakers_list = accelerate_segments( - result_diarize, - 1.0, - valid_speakers, - ) - - # custom voice - if custom_voices: - prog_disp( - "Applying customized voices...", - 0.60, - is_gui, - progress=progress, - ) - self.vci( - audio_files, - speakers_list, - overwrite=True, - parallel_workers=custom_voices_workers, - ) - self.vci.unload_models() - - # Update time segments and not concat - result_diarize = fix_timestamps_docs(result_diarize, audio_files) - final_wav_file = "audio_book.wav" - remove_files(final_wav_file) - - prog_disp("Creating audio file...", 0.70, is_gui, progress=progress) - create_translated_audio( - result_diarize, audio_files, final_wav_file, False - ) - - prog_disp("Creating video file...", 0.80, is_gui, progress=progress) - video_doc = create_video_from_images( - doc_data, - result_diarize - ) - - # Merge video and audio - prog_disp("Merging...", 0.90, is_gui, progress=progress) - vid_out = merge_video_and_audio(video_doc, final_wav_file) - - # End - output = media_out( - document, - tgt_lang, - name_final_file, - "mkv" if "mkv" in output_type else "mp4", - file_obj=vid_out, - ) - logger.info(f"Done: {output}") - return output - - def multilingual_docs_conversion( - self, - string_text="", # string - document=None, # doc path gui - directory_input="", # doc path - origin_language="English (en)", - target_language="English (en)", - tts_voice00="en-US-EmmaMultilingualNeural-Female", - name_final_file="", - translate_process="google_translator", - output_type="audio", - chunk_size=None, - custom_voices=False, - custom_voices_workers=1, - start_page=1, - end_page=99999, - width=1280, - height=720, - bcolor="dynamic", - is_gui=False, - progress=gr.Progress(), - ): - if "gpt" in translate_process: - check_openai_api_key() - - SOURCE_LANGUAGE = LANGUAGES[origin_language] - if translate_process != "disable_translation": - TRANSLATE_AUDIO_TO = LANGUAGES[target_language] - else: - TRANSLATE_AUDIO_TO = SOURCE_LANGUAGE - logger.info("No translation") - if tts_voice00[:2].lower() != TRANSLATE_AUDIO_TO[:2].lower(): - logger.debug( - "Make sure to select a 'TTS Speaker' suitable for the " - "translation language to avoid errors with the TTS." - ) - - self.clear_cache(string_text, force=True) - - is_string = False - if document is None: - if os.path.exists(directory_input): - document = directory_input - else: - document = string_text - is_string = True - document = document if isinstance(document, str) else document.name - if not document: - raise Exception("No data found") - - if os.environ.get("IS_DEMO") == "TRUE" and not is_string: - raise RuntimeError( - "This option is disabled in this demo. " - "Alternatively, you can install " - "the app locally or use the Colab notebook available in" - " the SoniTranslate repository." - ) - - if "videobook" in output_type: - if not document.lower().endswith(".pdf"): - raise ValueError( - "Videobooks are only compatible with PDF files." - ) - - return self.hook_beta_processor( - document, - TRANSLATE_AUDIO_TO, - translate_process, - SOURCE_LANGUAGE, - tts_voice00, - name_final_file, - custom_voices, - custom_voices_workers, - output_type, - chunk_size, - width, - height, - start_page, - end_page, - bcolor, - is_gui, - progress - ) - - # audio_wav = "audio.wav" - final_wav_file = "audio_book.wav" - - prog_disp("Processing text...", 0.15, is_gui, progress=progress) - result_file_path, result_text = document_preprocessor( - document, is_string, start_page, end_page - ) - - if ( - output_type == "book (txt)" - and translate_process == "disable_translation" - ): - return result_file_path - - if "SET_LIMIT" == os.getenv("DEMO"): - result_text = result_text[:50] - logger.info( - "DEMO; Generation is limited to 50 characters to prevent " - "CPU errors. No limitations with GPU.\n" - ) - - if translate_process != "disable_translation": - # chunks text for translation - result_diarize = plain_text_to_segments(result_text, 1700) - prog_disp("Translating...", 0.30, is_gui, progress=progress) - # not or iterative with 1700 chars - result_diarize["segments"] = translate_text( - result_diarize["segments"], - TRANSLATE_AUDIO_TO, - translate_process, - chunk_size=0, - source=SOURCE_LANGUAGE, - ) - - txt_file_path, result_text = segments_to_plain_text(result_diarize) - - if output_type == "book (txt)": - return media_out( - result_file_path if is_string else document, - TRANSLATE_AUDIO_TO, - name_final_file, - "txt", - file_obj=txt_file_path, - ) - - # (TTS limits) plain text to result_diarize - chunk_size = ( - chunk_size if chunk_size else determine_chunk_size(tts_voice00) - ) - result_diarize = plain_text_to_segments(result_text, chunk_size) - logger.debug(result_diarize) - - prog_disp("Text to speech...", 0.45, is_gui, progress=progress) - valid_speakers = audio_segmentation_to_voice( - result_diarize, - TRANSLATE_AUDIO_TO, - is_gui, - tts_voice00, - ) - - # fix format and set folder output - audio_files, speakers_list = accelerate_segments( - result_diarize, - 1.0, - valid_speakers, - ) - - # custom voice - if custom_voices: - prog_disp( - "Applying customized voices...", - 0.80, - is_gui, - progress=progress, - ) - self.vci( - audio_files, - speakers_list, - overwrite=True, - parallel_workers=custom_voices_workers, - ) - self.vci.unload_models() - - prog_disp( - "Creating final audio file...", 0.90, is_gui, progress=progress - ) - remove_files(final_wav_file) - create_translated_audio( - result_diarize, audio_files, final_wav_file, True - ) - - output = media_out( - result_file_path if is_string else document, - TRANSLATE_AUDIO_TO, - name_final_file, - "mp3" if "mp3" in output_type else ( - "ogg" if "ogg" in output_type else "wav" - ), - file_obj=final_wav_file, - ) - - logger.info(f"Done: {output}") - - return output - - -title = "
📽️ SoniTranslate 🈷️
" - - -def create_gui(theme, logs_in_gui=False): - with gr.Blocks(theme=theme) as app: - gr.Markdown(title) - gr.Markdown(lg_conf["description"]) - - if os.environ.get("ZERO_GPU") == "TRUE": - gr.Markdown( - """ - -
- ⚠️ Important ⚠️ - -
- """ - ) - - with gr.Tab(lg_conf["tab_translate"]): - with gr.Row(): - with gr.Column(): - input_data_type = gr.Dropdown( - ["SUBMIT VIDEO", "URL", "Find Video Path"], - value="SUBMIT VIDEO", - label=lg_conf["video_source"], - ) - - def swap_visibility(data_type): - if data_type == "URL": - return ( - gr.update(visible=False, value=None), - gr.update(visible=True, value=""), - gr.update(visible=False, value=""), - ) - elif data_type == "SUBMIT VIDEO": - return ( - gr.update(visible=True, value=None), - gr.update(visible=False, value=""), - gr.update(visible=False, value=""), - ) - elif data_type == "Find Video Path": - return ( - gr.update(visible=False, value=None), - gr.update(visible=False, value=""), - gr.update(visible=True, value=""), - ) - - video_input = gr.File( - label="VIDEO", - file_count="multiple", - type="filepath", - ) - blink_input = gr.Textbox( - visible=False, - label=lg_conf["link_label"], - info=lg_conf["link_info"], - placeholder=lg_conf["link_ph"], - ) - directory_input = gr.Textbox( - visible=False, - label=lg_conf["dir_label"], - info=lg_conf["dir_info"], - placeholder=lg_conf["dir_ph"], - ) - input_data_type.change( - fn=swap_visibility, - inputs=input_data_type, - outputs=[video_input, blink_input, directory_input], - ) - - gr.HTML() - - SOURCE_LANGUAGE = gr.Dropdown( - LANGUAGES_LIST, - value=LANGUAGES_LIST[0], - label=lg_conf["sl_label"], - info=lg_conf["sl_info"], - ) - TRANSLATE_AUDIO_TO = gr.Dropdown( - LANGUAGES_LIST[1:], - value="English (en)", - label=lg_conf["tat_label"], - info=lg_conf["tat_info"], - ) - - gr.HTML("
") - - gr.Markdown(lg_conf["num_speakers"]) - MAX_TTS = 12 - min_speakers = gr.Slider( - 1, - MAX_TTS, - value=1, - label=lg_conf["min_sk"], - step=1, - visible=False, - ) - max_speakers = gr.Slider( - 1, - MAX_TTS, - value=1, - step=1, - label=lg_conf["max_sk"], - ) - gr.Markdown(lg_conf["tts_select"]) - - def submit(value): - visibility_dict = { - f"tts_voice{i:02d}": gr.update(visible=i < value) - for i in range(MAX_TTS) - } - return [value for value in visibility_dict.values()] - - tts_voice00 = gr.Dropdown( - SoniTr.tts_info.tts_list(), - value="en-US-EmmaMultilingualNeural-Female", - label=lg_conf["sk1"], - visible=True, - interactive=True, - ) - tts_voice01 = gr.Dropdown( - SoniTr.tts_info.tts_list(), - value="en-US-AndrewMultilingualNeural-Male", - label=lg_conf["sk2"], - visible=False, - interactive=True, - ) - tts_voice02 = gr.Dropdown( - SoniTr.tts_info.tts_list(), - value="en-US-AvaMultilingualNeural-Female", - label=lg_conf["sk3"], - visible=False, - interactive=True, - ) - tts_voice03 = gr.Dropdown( - SoniTr.tts_info.tts_list(), - value="en-US-BrianMultilingualNeural-Male", - label=lg_conf["sk4"], - visible=False, - interactive=True, - ) - tts_voice04 = gr.Dropdown( - SoniTr.tts_info.tts_list(), - value="de-DE-SeraphinaMultilingualNeural-Female", - label=lg_conf["sk4"], - visible=False, - interactive=True, - ) - tts_voice05 = gr.Dropdown( - SoniTr.tts_info.tts_list(), - value="de-DE-FlorianMultilingualNeural-Male", - label=lg_conf["sk6"], - visible=False, - interactive=True, - ) - tts_voice06 = gr.Dropdown( - SoniTr.tts_info.tts_list(), - value="fr-FR-VivienneMultilingualNeural-Female", - label=lg_conf["sk7"], - visible=False, - interactive=True, - ) - tts_voice07 = gr.Dropdown( - SoniTr.tts_info.tts_list(), - value="fr-FR-RemyMultilingualNeural-Male", - label=lg_conf["sk8"], - visible=False, - interactive=True, - ) - tts_voice08 = gr.Dropdown( - SoniTr.tts_info.tts_list(), - value="en-US-EmmaMultilingualNeural-Female", - label=lg_conf["sk9"], - visible=False, - interactive=True, - ) - tts_voice09 = gr.Dropdown( - SoniTr.tts_info.tts_list(), - value="en-US-AndrewMultilingualNeural-Male", - label=lg_conf["sk10"], - visible=False, - interactive=True, - ) - tts_voice10 = gr.Dropdown( - SoniTr.tts_info.tts_list(), - value="en-US-EmmaMultilingualNeural-Female", - label=lg_conf["sk11"], - visible=False, - interactive=True, - ) - tts_voice11 = gr.Dropdown( - SoniTr.tts_info.tts_list(), - value="en-US-AndrewMultilingualNeural-Male", - label=lg_conf["sk12"], - visible=False, - interactive=True, - ) - max_speakers.change( - submit, - max_speakers, - [ - tts_voice00, - tts_voice01, - tts_voice02, - tts_voice03, - tts_voice04, - tts_voice05, - tts_voice06, - tts_voice07, - tts_voice08, - tts_voice09, - tts_voice10, - tts_voice11, - ], - ) - - with gr.Column(): - with gr.Accordion( - lg_conf["vc_title"], - open=False, - ): - gr.Markdown(lg_conf["vc_subtitle"]) - voice_imitation_gui = gr.Checkbox( - False, - label=lg_conf["vc_active_label"], - info=lg_conf["vc_active_info"], - ) - openvoice_models = ["openvoice", "openvoice_v2"] - voice_imitation_method_options = ( - ["freevc"] + openvoice_models - if SoniTr.tts_info.xtts_enabled - else openvoice_models - ) - voice_imitation_method_gui = gr.Dropdown( - voice_imitation_method_options, - value=voice_imitation_method_options[-1], - label=lg_conf["vc_method_label"], - info=lg_conf["vc_method_info"], - ) - voice_imitation_max_segments_gui = gr.Slider( - label=lg_conf["vc_segments_label"], - info=lg_conf["vc_segments_info"], - value=3, - step=1, - minimum=1, - maximum=10, - visible=True, - interactive=True, - ) - voice_imitation_vocals_dereverb_gui = gr.Checkbox( - False, - label=lg_conf["vc_dereverb_label"], - info=lg_conf["vc_dereverb_info"], - ) - voice_imitation_remove_previous_gui = gr.Checkbox( - True, - label=lg_conf["vc_remove_label"], - info=lg_conf["vc_remove_info"], - ) - - if SoniTr.tts_info.xtts_enabled: - with gr.Column(): - with gr.Accordion( - lg_conf["xtts_title"], - open=False, - ): - gr.Markdown(lg_conf["xtts_subtitle"]) - wav_speaker_file = gr.File( - label=lg_conf["xtts_file_label"] - ) - wav_speaker_name = gr.Textbox( - label=lg_conf["xtts_name_label"], - value="", - info=lg_conf["xtts_name_info"], - placeholder="default_name", - lines=1, - ) - wav_speaker_start = gr.Number( - label="Time audio start", - value=0, - visible=False, - ) - wav_speaker_end = gr.Number( - label="Time audio end", - value=0, - visible=False, - ) - wav_speaker_dir = gr.Textbox( - label="Directory save", - value="_XTTS_", - visible=False, - ) - wav_speaker_dereverb = gr.Checkbox( - True, - label=lg_conf["xtts_dereverb_label"], - info=lg_conf["xtts_dereverb_info"] - ) - wav_speaker_output = gr.HTML() - create_xtts_wav = gr.Button( - lg_conf["xtts_button"] - ) - gr.Markdown(lg_conf["xtts_footer"]) - else: - wav_speaker_dereverb = gr.Checkbox( - False, - label=lg_conf["xtts_dereverb_label"], - info=lg_conf["xtts_dereverb_info"], - visible=False - ) - - with gr.Column(): - with gr.Accordion( - lg_conf["extra_setting"], open=False - ): - audio_accelerate = gr.Slider( - label=lg_conf["acc_max_label"], - value=1.9, - step=0.1, - minimum=1.0, - maximum=2.5, - visible=True, - interactive=True, - info=lg_conf["acc_max_info"], - ) - acceleration_rate_regulation_gui = gr.Checkbox( - False, - label=lg_conf["acc_rate_label"], - info=lg_conf["acc_rate_info"], - ) - avoid_overlap_gui = gr.Checkbox( - False, - label=lg_conf["or_label"], - info=lg_conf["or_info"], - ) - - gr.HTML("
") - - audio_mix_options = [ - "Mixing audio with sidechain compression", - "Adjusting volumes and mixing audio", - ] - AUDIO_MIX = gr.Dropdown( - audio_mix_options, - value=audio_mix_options[1], - label=lg_conf["aud_mix_label"], - info=lg_conf["aud_mix_info"], - ) - volume_original_mix = gr.Slider( - label=lg_conf["vol_ori"], - info="for Adjusting volumes and mixing audio", - value=0.25, - step=0.05, - minimum=0.0, - maximum=2.50, - visible=True, - interactive=True, - ) - volume_translated_mix = gr.Slider( - label=lg_conf["vol_tra"], - info="for Adjusting volumes and mixing audio", - value=1.80, - step=0.05, - minimum=0.0, - maximum=2.50, - visible=True, - interactive=True, - ) - main_voiceless_track = gr.Checkbox( - label=lg_conf["voiceless_tk_label"], - info=lg_conf["voiceless_tk_info"], - ) - - gr.HTML("
") - sub_type_options = [ - "disable", - "srt", - "vtt", - "ass", - "txt", - "tsv", - "json", - "aud", - ] - - sub_type_output = gr.Dropdown( - sub_type_options, - value=sub_type_options[1], - label=lg_conf["sub_type"], - ) - soft_subtitles_to_video_gui = gr.Checkbox( - label=lg_conf["soft_subs_label"], - info=lg_conf["soft_subs_info"], - ) - burn_subtitles_to_video_gui = gr.Checkbox( - label=lg_conf["burn_subs_label"], - info=lg_conf["burn_subs_info"], - ) - - gr.HTML("
") - gr.Markdown(lg_conf["whisper_title"]) - literalize_numbers_gui = gr.Checkbox( - True, - label=lg_conf["lnum_label"], - info=lg_conf["lnum_info"], - ) - vocal_refinement_gui = gr.Checkbox( - False, - label=lg_conf["scle_label"], - info=lg_conf["scle_info"], - ) - segment_duration_limit_gui = gr.Slider( - label=lg_conf["sd_limit_label"], - info=lg_conf["sd_limit_info"], - value=15, - step=1, - minimum=1, - maximum=30, - ) - whisper_model_default = ( - "large-v3" - if SoniTr.device == "cuda" - else "medium" - ) - - WHISPER_MODEL_SIZE = gr.Dropdown( - ASR_MODEL_OPTIONS + find_whisper_models(), - value=whisper_model_default, - label="Whisper ASR model", - info=lg_conf["asr_model_info"], - allow_custom_value=True, - ) - com_t_opt, com_t_default = ( - [COMPUTE_TYPE_GPU, "float16"] - if SoniTr.device == "cuda" - else [COMPUTE_TYPE_CPU, "float32"] - ) - compute_type = gr.Dropdown( - com_t_opt, - value=com_t_default, - label=lg_conf["ctype_label"], - info=lg_conf["ctype_info"], - ) - batch_size_value = 8 if os.environ.get("ZERO_GPU") != "TRUE" else 32 - batch_size = gr.Slider( - minimum=1, - maximum=32, - value=batch_size_value, - label=lg_conf["batchz_label"], - info=lg_conf["batchz_info"], - step=1, - ) - input_srt = gr.File( - label=lg_conf["srt_file_label"], - file_types=[".srt", ".ass", ".vtt"], - height=130, - ) - - gr.HTML("
") - text_segmentation_options = [ - "sentence", - "word", - "character" - ] - text_segmentation_scale_gui = gr.Dropdown( - text_segmentation_options, - value=text_segmentation_options[0], - label=lg_conf["tsscale_label"], - info=lg_conf["tsscale_info"], - ) - divide_text_segments_by_gui = gr.Textbox( - label=lg_conf["divide_text_label"], - value="", - info=lg_conf["divide_text_info"], - ) - - gr.HTML("
") - pyannote_models_list = list( - diarization_models.keys() - ) - diarization_process_dropdown = gr.Dropdown( - pyannote_models_list, - value=pyannote_models_list[1], - label=lg_conf["diarization_label"], - ) - translate_process_dropdown = gr.Dropdown( - TRANSLATION_PROCESS_OPTIONS, - value=TRANSLATION_PROCESS_OPTIONS[0], - label=lg_conf["tr_process_label"], - ) - - gr.HTML("
") - main_output_type = gr.Dropdown( - OUTPUT_TYPE_OPTIONS, - value=OUTPUT_TYPE_OPTIONS[0], - label=lg_conf["out_type_label"], - ) - VIDEO_OUTPUT_NAME = gr.Textbox( - label=lg_conf["out_name_label"], - value="", - info=lg_conf["out_name_info"], - ) - play_sound_gui = gr.Checkbox( - True, - label=lg_conf["task_sound_label"], - info=lg_conf["task_sound_info"], - ) - enable_cache_gui = gr.Checkbox( - True, - label=lg_conf["cache_label"], - info=lg_conf["cache_info"], - ) - PREVIEW = gr.Checkbox( - label="Preview", info=lg_conf["preview_info"] - ) - is_gui_dummy_check = gr.Checkbox( - True, visible=False - ) - - with gr.Column(variant="compact"): - edit_sub_check = gr.Checkbox( - label=lg_conf["edit_sub_label"], - info=lg_conf["edit_sub_info"], - interactive=(False if os.environ.get("IS_DEMO") == "TRUE" else True), - ) - dummy_false_check = gr.Checkbox( - False, - visible=False, - ) - - def visible_component_subs(input_bool): - if input_bool: - return gr.update(visible=True), gr.update( - visible=True - ) - else: - return gr.update(visible=False), gr.update( - visible=False - ) - - subs_button = gr.Button( - lg_conf["button_subs"], - variant="primary", - visible=False, - ) - subs_edit_space = gr.Textbox( - visible=False, - lines=10, - label=lg_conf["editor_sub_label"], - info=lg_conf["editor_sub_info"], - placeholder=lg_conf["editor_sub_ph"], - ) - edit_sub_check.change( - visible_component_subs, - [edit_sub_check], - [subs_button, subs_edit_space], - ) - - with gr.Row(): - video_button = gr.Button( - lg_conf["button_translate"], - variant="primary", - ) - with gr.Row(): - video_output = gr.File( - label=lg_conf["output_result_label"], - file_count="multiple", - interactive=False, - - ) # gr.Video() - - gr.HTML("
") - - if ( - os.getenv("YOUR_HF_TOKEN") is None - or os.getenv("YOUR_HF_TOKEN") == "" - ): - HFKEY = gr.Textbox( - visible=True, - label="HF Token", - info=lg_conf["ht_token_info"], - placeholder=lg_conf["ht_token_ph"], - ) - else: - HFKEY = gr.Textbox( - visible=False, - label="HF Token", - info=lg_conf["ht_token_info"], - placeholder=lg_conf["ht_token_ph"], - ) - - gr.Examples( - examples=[ - [ - ["./assets/Video_main.mp4"], - "", - "", - "", - False, - whisper_model_default, - batch_size_value, - com_t_default, - "Spanish (es)", - "English (en)", - 1, - 2, - "en-US-EmmaMultilingualNeural-Female", - "en-US-AndrewMultilingualNeural-Male", - ], - ], # no update - fn=SoniTr.batch_multilingual_media_conversion, - inputs=[ - video_input, - blink_input, - directory_input, - HFKEY, - PREVIEW, - WHISPER_MODEL_SIZE, - batch_size, - compute_type, - SOURCE_LANGUAGE, - TRANSLATE_AUDIO_TO, - min_speakers, - max_speakers, - tts_voice00, - tts_voice01, - ], - outputs=[video_output], - cache_examples=False, - ) - - with gr.Tab(lg_conf["tab_docs"]): - with gr.Column(): - with gr.Accordion("Docs", open=True): - with gr.Column(variant="compact"): - with gr.Column(): - input_doc_type = gr.Dropdown( - [ - "WRITE TEXT", - "SUBMIT DOCUMENT", - "Find Document Path", - ], - value="SUBMIT DOCUMENT", - label=lg_conf["docs_input_label"], - info=lg_conf["docs_input_info"], - ) - - def swap_visibility(data_type): - if data_type == "WRITE TEXT": - return ( - gr.update(visible=True, value=""), - gr.update(visible=False, value=None), - gr.update(visible=False, value=""), - ) - elif data_type == "SUBMIT DOCUMENT": - return ( - gr.update(visible=False, value=""), - gr.update(visible=True, value=None), - gr.update(visible=False, value=""), - ) - elif data_type == "Find Document Path": - return ( - gr.update(visible=False, value=""), - gr.update(visible=False, value=None), - gr.update(visible=True, value=""), - ) - - text_docs = gr.Textbox( - label="Text", - value="This is an example", - info="Write a text", - placeholder="...", - lines=5, - visible=False, - ) - input_docs = gr.File( - label="Document", visible=True - ) - directory_input_docs = gr.Textbox( - visible=False, - label="Document Path", - info="Example: /home/my_doc.pdf", - placeholder="Path goes here...", - ) - input_doc_type.change( - fn=swap_visibility, - inputs=input_doc_type, - outputs=[ - text_docs, - input_docs, - directory_input_docs, - ], - ) - - gr.HTML() - - tts_documents = gr.Dropdown( - list( - filter( - lambda x: x != "_XTTS_/AUTOMATIC.wav", - SoniTr.tts_info.tts_list(), - ) - ), - value="en-US-EmmaMultilingualNeural-Female", - label="TTS", - visible=True, - interactive=True, - ) - - gr.HTML() - - docs_SOURCE_LANGUAGE = gr.Dropdown( - LANGUAGES_LIST[1:], - value="English (en)", - label=lg_conf["sl_label"], - info=lg_conf["docs_source_info"], - ) - docs_TRANSLATE_TO = gr.Dropdown( - LANGUAGES_LIST[1:], - value="English (en)", - label=lg_conf["tat_label"], - info=lg_conf["tat_info"], - ) - - with gr.Column(): - with gr.Accordion( - lg_conf["extra_setting"], open=False - ): - docs_translate_process_dropdown = gr.Dropdown( - DOCS_TRANSLATION_PROCESS_OPTIONS, - value=DOCS_TRANSLATION_PROCESS_OPTIONS[ - 0 - ], - label="Translation process", - ) - - gr.HTML("
") - - docs_output_type = gr.Dropdown( - DOCS_OUTPUT_TYPE_OPTIONS, - value=DOCS_OUTPUT_TYPE_OPTIONS[2], - label="Output type", - ) - docs_OUTPUT_NAME = gr.Textbox( - label="Final file name", - value="", - info=lg_conf["out_name_info"], - ) - docs_chunk_size = gr.Number( - label=lg_conf["chunk_size_label"], - value=0, - visible=True, - interactive=True, - info=lg_conf["chunk_size_info"], - ) - gr.HTML("
") - start_page_gui = gr.Number( - step=1, - value=1, - minimum=1, - maximum=99999, - label="Start page", - ) - end_page_gui = gr.Number( - step=1, - value=99999, - minimum=1, - maximum=99999, - label="End page", - ) - gr.HTML("
Videobook config") - videobook_width_gui = gr.Number( - step=1, - value=1280, - minimum=100, - maximum=4096, - label="Width", - ) - videobook_height_gui = gr.Number( - step=1, - value=720, - minimum=100, - maximum=4096, - label="Height", - ) - videobook_bcolor_gui = gr.Dropdown( - BORDER_COLORS, - value=BORDER_COLORS[0], - label="Border color", - ) - docs_dummy_check = gr.Checkbox( - True, visible=False - ) - - with gr.Row(): - docs_button = gr.Button( - lg_conf["docs_button"], - variant="primary", - ) - with gr.Row(): - docs_output = gr.File( - label="Result", - interactive=False, - ) - - with gr.Tab("Custom voice R.V.C. (Optional)"): - - with gr.Column(): - with gr.Accordion("Get the R.V.C. Models", open=True): - url_links = gr.Textbox( - label="URLs", - value="", - info=lg_conf["cv_url_info"], - placeholder="urls here...", - lines=1, - ) - download_finish = gr.HTML() - download_button = gr.Button("DOWNLOAD MODELS") - - def update_models(): - models_path, index_path = upload_model_list() - - dict_models = { - f"fmodel{i:02d}": gr.update( - choices=models_path - ) - for i in range(MAX_TTS+1) - } - dict_index = { - f"findex{i:02d}": gr.update( - choices=index_path, value=None - ) - for i in range(MAX_TTS+1) - } - dict_changes = {**dict_models, **dict_index} - return [value for value in dict_changes.values()] - - with gr.Column(): - with gr.Accordion(lg_conf["replace_title"], open=False): - with gr.Column(variant="compact"): - with gr.Column(): - gr.Markdown(lg_conf["sec1_title"]) - enable_custom_voice = gr.Checkbox( - False, - label="ENABLE", - info=lg_conf["enable_replace"] - ) - workers_custom_voice = gr.Number( - step=1, - value=1, - minimum=1, - maximum=50, - label="workers", - visible=False, - ) - - gr.Markdown(lg_conf["sec2_title"]) - gr.Markdown(lg_conf["sec2_subtitle"]) - - PITCH_ALGO_OPT = [ - "pm", - "harvest", - "crepe", - "rmvpe", - "rmvpe+", - ] - - def model_conf(): - return gr.Dropdown( - models_path, - # value="", - label="Model", - visible=True, - interactive=True, - ) - - def pitch_algo_conf(): - return gr.Dropdown( - PITCH_ALGO_OPT, - value=PITCH_ALGO_OPT[3], - label="Pitch algorithm", - visible=True, - interactive=True, - ) - - def pitch_lvl_conf(): - return gr.Slider( - label="Pitch level", - minimum=-24, - maximum=24, - step=1, - value=0, - visible=True, - interactive=True, - ) - - def index_conf(): - return gr.Dropdown( - index_path, - value=None, - label="Index", - visible=True, - interactive=True, - ) - - def index_inf_conf(): - return gr.Slider( - minimum=0, - maximum=1, - label="Index influence", - value=0.75, - ) - - def respiration_filter_conf(): - return gr.Slider( - minimum=0, - maximum=7, - label="Respiration median filtering", - value=3, - step=1, - interactive=True, - ) - - def envelope_ratio_conf(): - return gr.Slider( - minimum=0, - maximum=1, - label="Envelope ratio", - value=0.25, - interactive=True, - ) - - def consonant_protec_conf(): - return gr.Slider( - minimum=0, - maximum=0.5, - label="Consonant breath protection", - value=0.5, - interactive=True, - ) - - def button_conf(tts_name): - return gr.Button( - lg_conf["cv_button_apply"]+" "+tts_name, - variant="primary", - ) - - TTS_TABS = [ - 'TTS Speaker {:02d}'.format(i) for i in range(1, MAX_TTS+1) - ] - - CV_SUBTITLES = [ - lg_conf["cv_tts1"], - lg_conf["cv_tts2"], - lg_conf["cv_tts3"], - lg_conf["cv_tts4"], - lg_conf["cv_tts5"], - lg_conf["cv_tts6"], - lg_conf["cv_tts7"], - lg_conf["cv_tts8"], - lg_conf["cv_tts9"], - lg_conf["cv_tts10"], - lg_conf["cv_tts11"], - lg_conf["cv_tts12"], - ] - - configs_storage = [] - - for i in range(MAX_TTS): # Loop from 00 to 11 - with gr.Accordion(CV_SUBTITLES[i], open=False): - gr.Markdown(TTS_TABS[i]) - with gr.Column(): - tag_gui = gr.Textbox( - value=TTS_TABS[i], visible=False - ) - model_gui = model_conf() - pitch_algo_gui = pitch_algo_conf() - pitch_lvl_gui = pitch_lvl_conf() - index_gui = index_conf() - index_inf_gui = index_inf_conf() - rmf_gui = respiration_filter_conf() - er_gui = envelope_ratio_conf() - cbp_gui = consonant_protec_conf() - - with gr.Row(variant="compact"): - button_config = button_conf( - TTS_TABS[i] - ) - - confirm_conf = gr.HTML() - - button_config.click( - SoniTr.vci.apply_conf, - inputs=[ - tag_gui, - model_gui, - pitch_algo_gui, - pitch_lvl_gui, - index_gui, - index_inf_gui, - rmf_gui, - er_gui, - cbp_gui, - ], - outputs=[confirm_conf], - ) - - configs_storage.append({ - "tag": tag_gui, - "model": model_gui, - "index": index_gui, - }) - - with gr.Column(): - with gr.Accordion("Test R.V.C.", open=False): - with gr.Row(variant="compact"): - text_test = gr.Textbox( - label="Text", - value="This is an example", - info="write a text", - placeholder="...", - lines=5, - ) - with gr.Column(): - tts_test = gr.Dropdown( - sorted(SoniTr.tts_info.list_edge), - value="en-GB-ThomasNeural-Male", - label="TTS", - visible=True, - interactive=True, - ) - model_test = model_conf() - index_test = index_conf() - pitch_test = pitch_lvl_conf() - pitch_alg_test = pitch_algo_conf() - with gr.Row(variant="compact"): - button_test = gr.Button("Test audio") - - with gr.Column(): - with gr.Row(): - original_ttsvoice = gr.Audio() - ttsvoice = gr.Audio() - - button_test.click( - SoniTr.vci.make_test, - inputs=[ - text_test, - tts_test, - model_test, - index_test, - pitch_test, - pitch_alg_test, - ], - outputs=[ttsvoice, original_ttsvoice], - ) - - download_button.click( - download_list, - [url_links], - [download_finish], - queue=False - ).then( - update_models, - [], - [ - elem["model"] for elem in configs_storage - ] + [model_test] + [ - elem["index"] for elem in configs_storage - ] + [index_test], - ) - - with gr.Tab(lg_conf["tab_help"]): - gr.Markdown(lg_conf["tutorial"]) - gr.Markdown(news) - - def play_sound_alert(play_sound): - - if not play_sound: - return None - - # silent_sound = "assets/empty_audio.mp3" - sound_alert = "assets/sound_alert.mp3" - - time.sleep(0.25) - # yield silent_sound - yield None - - time.sleep(0.25) - yield sound_alert - - sound_alert_notification = gr.Audio( - value=None, - type="filepath", - format="mp3", - autoplay=True, - visible=False, - ) - - if logs_in_gui: - logger.info("Logs in gui need public url") - - class Logger: - def __init__(self, filename): - self.terminal = sys.stdout - self.log = open(filename, "w") - - def write(self, message): - self.terminal.write(message) - self.log.write(message) - - def flush(self): - self.terminal.flush() - self.log.flush() - - def isatty(self): - return False - - sys.stdout = Logger("output.log") - - def read_logs(): - sys.stdout.flush() - with open("output.log", "r") as f: - return f.read() - - with gr.Accordion("Logs", open=False): - logs = gr.Textbox(label=">>>") - app.load(read_logs, None, logs, every=1) - - if SoniTr.tts_info.xtts_enabled: - # Update tts list - def update_tts_list(): - update_dict = { - f"tts_voice{i:02d}": gr.update(choices=SoniTr.tts_info.tts_list()) - for i in range(MAX_TTS) - } - update_dict["tts_documents"] = gr.update( - choices=list( - filter( - lambda x: x != "_XTTS_/AUTOMATIC.wav", - SoniTr.tts_info.tts_list(), - ) - ) - ) - return [value for value in update_dict.values()] - - create_xtts_wav.click( - create_wav_file_vc, - inputs=[ - wav_speaker_name, - wav_speaker_file, - wav_speaker_start, - wav_speaker_end, - wav_speaker_dir, - wav_speaker_dereverb, - ], - outputs=[wav_speaker_output], - ).then( - update_tts_list, - None, - [ - tts_voice00, - tts_voice01, - tts_voice02, - tts_voice03, - tts_voice04, - tts_voice05, - tts_voice06, - tts_voice07, - tts_voice08, - tts_voice09, - tts_voice10, - tts_voice11, - tts_documents, - ], - ) - - # Run translate text - subs_button.click( - SoniTr.batch_multilingual_media_conversion, - inputs=[ - video_input, - blink_input, - directory_input, - HFKEY, - PREVIEW, - WHISPER_MODEL_SIZE, - batch_size, - compute_type, - SOURCE_LANGUAGE, - TRANSLATE_AUDIO_TO, - min_speakers, - max_speakers, - tts_voice00, - tts_voice01, - tts_voice02, - tts_voice03, - tts_voice04, - tts_voice05, - tts_voice06, - tts_voice07, - tts_voice08, - tts_voice09, - tts_voice10, - tts_voice11, - VIDEO_OUTPUT_NAME, - AUDIO_MIX, - audio_accelerate, - acceleration_rate_regulation_gui, - volume_original_mix, - volume_translated_mix, - sub_type_output, - edit_sub_check, # TRUE BY DEFAULT - dummy_false_check, # dummy false - subs_edit_space, - avoid_overlap_gui, - vocal_refinement_gui, - literalize_numbers_gui, - segment_duration_limit_gui, - diarization_process_dropdown, - translate_process_dropdown, - input_srt, - main_output_type, - main_voiceless_track, - voice_imitation_gui, - voice_imitation_max_segments_gui, - voice_imitation_vocals_dereverb_gui, - voice_imitation_remove_previous_gui, - voice_imitation_method_gui, - wav_speaker_dereverb, - text_segmentation_scale_gui, - divide_text_segments_by_gui, - soft_subtitles_to_video_gui, - burn_subtitles_to_video_gui, - enable_cache_gui, - enable_custom_voice, - workers_custom_voice, - is_gui_dummy_check, - ], - outputs=subs_edit_space, - ).then( - play_sound_alert, [play_sound_gui], [sound_alert_notification] - ) - - # Run translate tts and complete - video_button.click( - SoniTr.batch_multilingual_media_conversion, - inputs=[ - video_input, - blink_input, - directory_input, - HFKEY, - PREVIEW, - WHISPER_MODEL_SIZE, - batch_size, - compute_type, - SOURCE_LANGUAGE, - TRANSLATE_AUDIO_TO, - min_speakers, - max_speakers, - tts_voice00, - tts_voice01, - tts_voice02, - tts_voice03, - tts_voice04, - tts_voice05, - tts_voice06, - tts_voice07, - tts_voice08, - tts_voice09, - tts_voice10, - tts_voice11, - VIDEO_OUTPUT_NAME, - AUDIO_MIX, - audio_accelerate, - acceleration_rate_regulation_gui, - volume_original_mix, - volume_translated_mix, - sub_type_output, - dummy_false_check, - edit_sub_check, - subs_edit_space, - avoid_overlap_gui, - vocal_refinement_gui, - literalize_numbers_gui, - segment_duration_limit_gui, - diarization_process_dropdown, - translate_process_dropdown, - input_srt, - main_output_type, - main_voiceless_track, - voice_imitation_gui, - voice_imitation_max_segments_gui, - voice_imitation_vocals_dereverb_gui, - voice_imitation_remove_previous_gui, - voice_imitation_method_gui, - wav_speaker_dereverb, - text_segmentation_scale_gui, - divide_text_segments_by_gui, - soft_subtitles_to_video_gui, - burn_subtitles_to_video_gui, - enable_cache_gui, - enable_custom_voice, - workers_custom_voice, - is_gui_dummy_check, - ], - outputs=video_output, - trigger_mode="multiple", - ).then( - play_sound_alert, [play_sound_gui], [sound_alert_notification] - ) - - # Run docs process - docs_button.click( - SoniTr.multilingual_docs_conversion, - inputs=[ - text_docs, - input_docs, - directory_input_docs, - docs_SOURCE_LANGUAGE, - docs_TRANSLATE_TO, - tts_documents, - docs_OUTPUT_NAME, - docs_translate_process_dropdown, - docs_output_type, - docs_chunk_size, - enable_custom_voice, - workers_custom_voice, - start_page_gui, - end_page_gui, - videobook_width_gui, - videobook_height_gui, - videobook_bcolor_gui, - docs_dummy_check, - ], - outputs=docs_output, - trigger_mode="multiple", - ).then( - play_sound_alert, [play_sound_gui], [sound_alert_notification] - ) - - return app - - -def get_language_config(language_data, language=None, base_key="english"): - base_lang = language_data.get(base_key) - - if language not in language_data: - logger.error( - f"Language {language} not found, defaulting to {base_key}" - ) - return base_lang - - lg_conf = language_data.get(language, {}) - lg_conf.update((k, v) for k, v in base_lang.items() if k not in lg_conf) - - return lg_conf - - -def create_parser(): - parser = argparse.ArgumentParser( - formatter_class=argparse.ArgumentDefaultsHelpFormatter - ) - parser.add_argument( - "--theme", - type=str, - default="Taithrah/Minimal", - help=( - "Specify the theme; find themes in " - "https://huggingface.co/spaces/gradio/theme-gallery;" - " Example: --theme aliabid94/new-theme" - ), - ) - parser.add_argument( - "--public_url", - action="store_true", - default=False, - help="Enable public link", - ) - parser.add_argument( - "--logs_in_gui", - action="store_true", - default=False, - help="Displays the operations performed in Logs", - ) - parser.add_argument( - "--verbosity_level", - type=str, - default="info", - help=( - "Set logger verbosity level: " - "debug, info, warning, error, or critical" - ), - ) - parser.add_argument( - "--language", - type=str, - default="english", - help=" Select the language of the interface: english, spanish", - ) - parser.add_argument( - "--cpu_mode", - action="store_true", - default=False, - help="Enable CPU mode to run the program without utilizing GPU acceleration.", - ) - return parser - - -if __name__ == "__main__": - - parser = create_parser() - - args = parser.parse_args() - # Simulating command-line arguments - # args_list = "--theme aliabid94/new-theme --public_url".split() - # args = parser.parse_args(args_list) - - set_logging_level(args.verbosity_level) - - for id_model in UVR_MODELS: - download_manager( - os.path.join(MDX_DOWNLOAD_LINK, id_model), mdxnet_models_dir - ) - - models_path, index_path = upload_model_list() - - SoniTr = SoniTranslate(cpu_mode=args.cpu_mode if os.environ.get("ZERO_GPU") != "TRUE" else "cpu") - - lg_conf = get_language_config(language_data, language=args.language) - - app = create_gui(args.theme, logs_in_gui=args.logs_in_gui) - - app.queue() - - app.launch( - max_threads=1, - share=args.public_url, - show_error=True, - quiet=False, - debug=(True if logger.isEnabledFor(logging.DEBUG) else False), - ) +import gradio as gr +from soni_translate.logging_setup import ( + logger, + set_logging_level, + configure_logging_libs, +); configure_logging_libs() # noqa +import whisperx +import torch +import os +from soni_translate.audio_segments import create_translated_audio +from soni_translate.text_to_speech import ( + audio_segmentation_to_voice, + edge_tts_voices_list, + coqui_xtts_voices_list, + piper_tts_voices_list, + create_wav_file_vc, + accelerate_segments, +) +from soni_translate.translate_segments import ( + translate_text, + TRANSLATION_PROCESS_OPTIONS, + DOCS_TRANSLATION_PROCESS_OPTIONS +) +from soni_translate.preprocessor import ( + audio_video_preprocessor, + audio_preprocessor, +) +from soni_translate.postprocessor import ( + OUTPUT_TYPE_OPTIONS, + DOCS_OUTPUT_TYPE_OPTIONS, + sound_separate, + get_no_ext_filename, + media_out, + get_subtitle_speaker, +) +from soni_translate.language_configuration import ( + LANGUAGES, + UNIDIRECTIONAL_L_LIST, + LANGUAGES_LIST, + BARK_VOICES_LIST, + VITS_VOICES_LIST, + OPENAI_TTS_MODELS, +) +from soni_translate.utils import ( + remove_files, + download_list, + upload_model_list, + download_manager, + run_command, + is_audio_file, + is_subtitle_file, + copy_files, + get_valid_files, + get_link_list, + remove_directory_contents, +) +from soni_translate.mdx_net import ( + UVR_MODELS, + MDX_DOWNLOAD_LINK, + mdxnet_models_dir, +) +from soni_translate.speech_segmentation import ( + ASR_MODEL_OPTIONS, + COMPUTE_TYPE_GPU, + COMPUTE_TYPE_CPU, + find_whisper_models, + transcribe_speech, + align_speech, + diarize_speech, + diarization_models, +) +from soni_translate.text_multiformat_processor import ( + BORDER_COLORS, + srt_file_to_segments, + document_preprocessor, + determine_chunk_size, + plain_text_to_segments, + segments_to_plain_text, + process_subtitles, + linguistic_level_segments, + break_aling_segments, + doc_to_txtximg_pages, + page_data_to_segments, + update_page_data, + fix_timestamps_docs, + create_video_from_images, + merge_video_and_audio, +) +from soni_translate.languages_gui import language_data, news +import copy +import logging +import json +from pydub import AudioSegment +from voice_main import ClassVoices +import argparse +import time +import hashlib +import sys + +directories = [ + "downloads", + "logs", + "weights", + "clean_song_output", + "_XTTS_", + f"audio2{os.sep}audio", + "audio", + "outputs", +] +[ + os.makedirs(directory) + for directory in directories + if not os.path.exists(directory) +] + + +class TTS_Info: + def __init__(self, piper_enabled, xtts_enabled): + self.list_edge = edge_tts_voices_list() + self.list_bark = list(BARK_VOICES_LIST.keys()) + self.list_vits = list(VITS_VOICES_LIST.keys()) + self.list_openai_tts = OPENAI_TTS_MODELS + self.piper_enabled = piper_enabled + self.list_vits_onnx = ( + piper_tts_voices_list() if self.piper_enabled else [] + ) + self.xtts_enabled = xtts_enabled + + def tts_list(self): + self.list_coqui_xtts = ( + coqui_xtts_voices_list() if self.xtts_enabled else [] + ) + list_tts = self.list_coqui_xtts + sorted( + self.list_edge + + self.list_bark + + self.list_vits + + self.list_openai_tts + + self.list_vits_onnx + ) + return list_tts + + +def prog_disp(msg, percent, is_gui, progress=None): + logger.info(msg) + if is_gui: + progress(percent, desc=msg) + + +def warn_disp(wrn_lang, is_gui): + logger.warning(wrn_lang) + if is_gui: + gr.Warning(wrn_lang) + + +class SoniTrCache: + def __init__(self): + self.cache = { + 'media': [[]], + 'refine_vocals': [], + 'transcript_align': [], + 'break_align': [], + 'diarize': [], + 'translate': [], + 'subs_and_edit': [], + 'tts': [], + 'acc_and_vc': [], + 'mix_aud': [], + 'output': [] + } + + self.cache_data = { + 'media': [], + 'refine_vocals': [], + 'transcript_align': [], + 'break_align': [], + 'diarize': [], + 'translate': [], + 'subs_and_edit': [], + 'tts': [], + 'acc_and_vc': [], + 'mix_aud': [], + 'output': [] + } + + self.cache_keys = list(self.cache.keys()) + self.first_task = self.cache_keys[0] + self.last_task = self.cache_keys[-1] + + self.pre_step = None + self.pre_params = [] + + def set_variable(self, variable_name, value): + setattr(self, variable_name, value) + + def task_in_cache(self, step: str, params: list, previous_step_data: dict): + + self.pre_step_cache = None + + if step == self.first_task: + self.pre_step = None + + if self.pre_step: + self.cache[self.pre_step] = self.pre_params + + # Fill data in cache + self.cache_data[self.pre_step] = copy.deepcopy(previous_step_data) + + self.pre_params = params + # logger.debug(f"Step: {str(step)}, Cache params: {str(self.cache)}") + if params == self.cache[step]: + logger.debug(f"In cache: {str(step)}") + + # Set the var needed for next step + # Recovery from cache_data the current step + for key, value in self.cache_data[step].items(): + self.set_variable(key, copy.deepcopy(value)) + logger.debug( + f"Chache load: {str(key)}" + ) + + self.pre_step = step + return True + + else: + logger.debug(f"Flush next and caching {str(step)}") + selected_index = self.cache_keys.index(step) + + for idx, key in enumerate(self.cache.keys()): + if idx >= selected_index: + self.cache[key] = [] + self.cache_data[key] = {} + + # The last is now previous + self.pre_step = step + return False + + def clear_cache(self, media, force=False): + + self.cache["media"] = ( + self.cache["media"] if len(self.cache["media"]) else [[]] + ) + + if media != self.cache["media"][0] or force: + + # Clear cache + self.cache = {key: [] for key in self.cache} + self.cache["media"] = [[]] + + logger.info("Cache flushed") + + +def get_hash(filepath): + with open(filepath, 'rb') as f: + file_hash = hashlib.blake2b() + while chunk := f.read(8192): + file_hash.update(chunk) + + return file_hash.hexdigest()[:18] + + +def check_openai_api_key(): + if not os.environ.get("OPENAI_API_KEY"): + raise ValueError( + "To use GPT for translation, please set up your OpenAI API key " + "as an environment variable in Linux as follows: " + "export OPENAI_API_KEY='your-api-key-here'. Or change the " + "translation process in Advanced settings." + ) + + +class SoniTranslate(SoniTrCache): + def __init__(self, cpu_mode=False): + super().__init__() + if cpu_mode: + os.environ["SONITR_DEVICE"] = "cpu" + else: + os.environ["SONITR_DEVICE"] = ( + "cuda" if torch.cuda.is_available() else "cpu" + ) + + self.device = os.environ.get("SONITR_DEVICE") + self.result_diarize = None + self.align_language = None + self.result_source_lang = None + self.edit_subs_complete = False + self.voiceless_id = None + self.burn_subs_id = None + + self.vci = ClassVoices(only_cpu=cpu_mode) + + self.tts_voices = self.get_tts_voice_list() + + logger.info(f"Working in: {self.device}") + + def get_tts_voice_list(self): + try: + from piper import PiperVoice # noqa + + piper_enabled = True + logger.info("PIPER TTS enabled") + except Exception as error: + logger.debug(str(error)) + piper_enabled = False + logger.info("PIPER TTS disabled") + try: + from TTS.api import TTS # noqa + + xtts_enabled = True + logger.info("Coqui XTTS enabled") + logger.info( + "In this app, by using Coqui TTS (text-to-speech), you " + "acknowledge and agree to the license.\n" + "You confirm that you have read, understood, and agreed " + "to the Terms and Conditions specified at the following " + "link:\nhttps://coqui.ai/cpml.txt." + ) + os.environ["COQUI_TOS_AGREED"] = "1" + except Exception as error: + logger.debug(str(error)) + xtts_enabled = False + logger.info("Coqui XTTS disabled") + + self.tts_info = TTS_Info(piper_enabled, xtts_enabled) + + return self.tts_info.tts_list() + + def batch_multilingual_media_conversion(self, *kwargs): + # logger.debug(str(kwargs)) + + media_file_arg = kwargs[0] if kwargs[0] is not None else [] + + link_media_arg = kwargs[1] + link_media_arg = [x.strip() for x in link_media_arg.split(',')] + link_media_arg = get_link_list(link_media_arg) + + path_arg = kwargs[2] + path_arg = [x.strip() for x in path_arg.split(',')] + path_arg = get_valid_files(path_arg) + + edit_text_arg = kwargs[31] + get_text_arg = kwargs[32] + + is_gui_arg = kwargs[-1] + + kwargs = kwargs[3:] + + media_batch = media_file_arg + link_media_arg + path_arg + media_batch = list(filter(lambda x: x != "", media_batch)) + media_batch = media_batch if media_batch else [None] + logger.debug(str(media_batch)) + + remove_directory_contents("outputs") + + if edit_text_arg or get_text_arg: + return self.multilingual_media_conversion( + media_batch[0], "", "", *kwargs + ) + + if "SET_LIMIT" == os.getenv("DEMO"): + media_batch = [media_batch[0]] + + result = [] + for media in media_batch: + # Call the nested function with the parameters + output_file = self.multilingual_media_conversion( + media, "", "", *kwargs + ) + + if isinstance(output_file, str): + output_file = [output_file] + result.extend(output_file) + + if is_gui_arg and len(media_batch) > 1: + gr.Info(f"Done: {os.path.basename(output_file[0])}") + + return result + + def multilingual_media_conversion( + self, + media_file=None, + link_media="", + directory_input="", + YOUR_HF_TOKEN="", + preview=False, + transcriber_model="large-v3", + batch_size=4, + compute_type="auto", + origin_language="Automatic detection", + target_language="English (en)", + min_speakers=1, + max_speakers=1, + tts_voice00="en-US-EmmaMultilingualNeural-Female", + tts_voice01="en-US-AndrewMultilingualNeural-Male", + tts_voice02="en-US-AvaMultilingualNeural-Female", + tts_voice03="en-US-BrianMultilingualNeural-Male", + tts_voice04="de-DE-SeraphinaMultilingualNeural-Female", + tts_voice05="de-DE-FlorianMultilingualNeural-Male", + tts_voice06="fr-FR-VivienneMultilingualNeural-Female", + tts_voice07="fr-FR-RemyMultilingualNeural-Male", + tts_voice08="en-US-EmmaMultilingualNeural-Female", + tts_voice09="en-US-AndrewMultilingualNeural-Male", + tts_voice10="en-US-EmmaMultilingualNeural-Female", + tts_voice11="en-US-AndrewMultilingualNeural-Male", + video_output_name="", + mix_method_audio="Adjusting volumes and mixing audio", + max_accelerate_audio=2.1, + acceleration_rate_regulation=False, + volume_original_audio=0.25, + volume_translated_audio=1.80, + output_format_subtitle="srt", + get_translated_text=False, + get_video_from_text_json=False, + text_json="{}", + avoid_overlap=False, + vocal_refinement=False, + literalize_numbers=True, + segment_duration_limit=15, + diarization_model="pyannote_2.1", + translate_process="google_translator_batch", + subtitle_file=None, + output_type="video (mp4)", + voiceless_track=False, + voice_imitation=False, + voice_imitation_max_segments=3, + voice_imitation_vocals_dereverb=False, + voice_imitation_remove_previous=True, + voice_imitation_method="freevc", + dereverb_automatic_xtts=True, + text_segmentation_scale="sentence", + divide_text_segments_by="", + soft_subtitles_to_video=True, + burn_subtitles_to_video=False, + enable_cache=True, + custom_voices=False, + custom_voices_workers=1, + is_gui=False, + progress=gr.Progress(), + ): + if not YOUR_HF_TOKEN: + YOUR_HF_TOKEN = os.getenv("YOUR_HF_TOKEN") + if diarization_model == "disable" or max_speakers == 1: + if YOUR_HF_TOKEN is None: + YOUR_HF_TOKEN = "" + elif not YOUR_HF_TOKEN: + raise ValueError("No valid Hugging Face token") + else: + os.environ["YOUR_HF_TOKEN"] = YOUR_HF_TOKEN + + if ( + "gpt" in translate_process + or transcriber_model == "OpenAI_API_Whisper" + or "OpenAI-TTS" in tts_voice00 + ): + check_openai_api_key() + + if media_file is None: + media_file = ( + directory_input + if os.path.exists(directory_input) + else link_media + ) + media_file = ( + media_file if isinstance(media_file, str) else media_file.name + ) + + if is_subtitle_file(media_file): + subtitle_file = media_file + media_file = "" + + if media_file is None: + media_file = "" + + if not origin_language: + origin_language = "Automatic detection" + + if origin_language in UNIDIRECTIONAL_L_LIST and not subtitle_file: + raise ValueError( + f"The language '{origin_language}' " + "is not supported for transcription (ASR)." + ) + + if get_translated_text: + self.edit_subs_complete = False + if get_video_from_text_json: + if not self.edit_subs_complete: + raise ValueError("Generate the transcription first.") + + if ( + ("sound" in output_type or output_type == "raw media") + and (get_translated_text or get_video_from_text_json) + ): + raise ValueError( + "Please disable 'edit generate subtitles' " + f"first to acquire the {output_type}." + ) + + TRANSLATE_AUDIO_TO = LANGUAGES[target_language] + SOURCE_LANGUAGE = LANGUAGES[origin_language] + + if ( + transcriber_model == "OpenAI_API_Whisper" + and SOURCE_LANGUAGE == "zh-TW" + ): + logger.warning( + "OpenAI API Whisper only supports Chinese (Simplified)." + ) + SOURCE_LANGUAGE = "zh" + + if ( + text_segmentation_scale in ["word", "character"] + and "subtitle" not in output_type + ): + wrn_lang = ( + "Text segmentation by words or characters is typically" + " used for generating subtitles. If subtitles are not the" + " intended output, consider selecting 'sentence' " + "segmentation method to ensure optimal results." + + ) + warn_disp(wrn_lang, is_gui) + + if tts_voice00[:2].lower() != TRANSLATE_AUDIO_TO[:2].lower(): + wrn_lang = ( + "Make sure to select a 'TTS Speaker' suitable for" + " the translation language to avoid errors with the TTS." + ) + warn_disp(wrn_lang, is_gui) + + if "_XTTS_" in tts_voice00 and voice_imitation: + wrn_lang = ( + "When you select XTTS, it is advisable " + "to disable Voice Imitation." + ) + warn_disp(wrn_lang, is_gui) + + if custom_voices and voice_imitation: + wrn_lang = ( + "When you use R.V.C. models, it is advisable" + " to disable Voice Imitation." + ) + warn_disp(wrn_lang, is_gui) + + if not media_file and not subtitle_file: + raise ValueError( + "Specifify a media or SRT file in advanced settings" + ) + + if subtitle_file: + subtitle_file = ( + subtitle_file + if isinstance(subtitle_file, str) + else subtitle_file.name + ) + + if subtitle_file and SOURCE_LANGUAGE == "Automatic detection": + raise Exception( + "To use an SRT file, you need to specify its " + "original language (Source language)" + ) + + if not media_file and subtitle_file: + diarization_model = "disable" + media_file = "audio_support.wav" + if not get_video_from_text_json: + remove_files(media_file) + srt_data = srt_file_to_segments(subtitle_file) + total_duration = srt_data["segments"][-1]["end"] + 30. + support_audio = AudioSegment.silent( + duration=int(total_duration * 1000) + ) + support_audio.export( + media_file, format="wav" + ) + logger.info("Supporting audio for the SRT file, created.") + + if "SET_LIMIT" == os.getenv("DEMO"): + preview = True + mix_method_audio = "Adjusting volumes and mixing audio" + transcriber_model = "medium" + logger.info( + "DEMO; set preview=True; Generation is limited to " + "10 seconds to prevent CPU errors. No limitations with GPU.\n" + "DEMO; set Adjusting volumes and mixing audio\n" + "DEMO; set whisper model to medium" + ) + + # Check GPU + if self.device == "cpu" and compute_type not in COMPUTE_TYPE_CPU: + logger.info("Compute type changed to float32") + compute_type = "float32" + + base_video_file = "Video.mp4" + base_audio_wav = "audio.wav" + dub_audio_file = "audio_dub_solo.ogg" + vocals_audio_file = "audio_Vocals_DeReverb.wav" + voiceless_audio_file = "audio_Voiceless.wav" + mix_audio_file = "audio_mix.mp3" + vid_subs = "video_subs_file.mp4" + video_output_file = "video_dub.mp4" + + if os.path.exists(media_file): + media_base_hash = get_hash(media_file) + else: + media_base_hash = media_file + self.clear_cache(media_base_hash, force=(not enable_cache)) + + if not get_video_from_text_json: + self.result_diarize = ( + self.align_language + ) = self.result_source_lang = None + if not self.task_in_cache("media", [media_base_hash, preview], {}): + if is_audio_file(media_file): + prog_disp( + "Processing audio...", 0.15, is_gui, progress=progress + ) + audio_preprocessor(preview, media_file, base_audio_wav) + else: + prog_disp( + "Processing video...", 0.15, is_gui, progress=progress + ) + audio_video_preprocessor( + preview, media_file, base_video_file, base_audio_wav + ) + logger.debug("Set file complete.") + + if "sound" in output_type: + prog_disp( + "Separating sounds in the file...", + 0.50, + is_gui, + progress=progress + ) + separate_out = sound_separate(base_audio_wav, output_type) + final_outputs = [] + for out in separate_out: + final_name = media_out( + media_file, + f"{get_no_ext_filename(out)}", + video_output_name, + "wav", + file_obj=out, + ) + final_outputs.append(final_name) + logger.info(f"Done: {str(final_outputs)}") + return final_outputs + + if output_type == "raw media": + output = media_out( + media_file, + "raw_media", + video_output_name, + "wav" if is_audio_file(media_file) else "mp4", + file_obj=base_audio_wav if is_audio_file(media_file) else base_video_file, + ) + logger.info(f"Done: {output}") + return output + + if not self.task_in_cache("refine_vocals", [vocal_refinement], {}): + self.vocals = None + if vocal_refinement: + try: + from soni_translate.mdx_net import process_uvr_task + _, _, _, _, file_vocals = process_uvr_task( + orig_song_path=base_audio_wav, + main_vocals=False, + dereverb=True, + remove_files_output_dir=True, + ) + remove_files(vocals_audio_file) + copy_files(file_vocals, ".") + self.vocals = vocals_audio_file + except Exception as error: + logger.error(str(error)) + + if not self.task_in_cache("transcript_align", [ + subtitle_file, + SOURCE_LANGUAGE, + transcriber_model, + compute_type, + batch_size, + literalize_numbers, + segment_duration_limit, + ( + "l_unit" + if text_segmentation_scale in ["word", "character"] + and subtitle_file + else "sentence" + ) + ], {"vocals": self.vocals}): + if subtitle_file: + prog_disp( + "From SRT file...", 0.30, is_gui, progress=progress + ) + audio = whisperx.load_audio( + base_audio_wav if not self.vocals else self.vocals + ) + self.result = srt_file_to_segments(subtitle_file) + self.result["language"] = SOURCE_LANGUAGE + else: + prog_disp( + "Transcribing...", 0.30, is_gui, progress=progress + ) + SOURCE_LANGUAGE = ( + None + if SOURCE_LANGUAGE == "Automatic detection" + else SOURCE_LANGUAGE + ) + audio, self.result = transcribe_speech( + base_audio_wav if not self.vocals else self.vocals, + transcriber_model, + compute_type, + batch_size, + SOURCE_LANGUAGE, + literalize_numbers, + segment_duration_limit, + ) + logger.debug( + "Transcript complete, " + f"segments count {len(self.result['segments'])}" + ) + + self.align_language = self.result["language"] + if ( + not subtitle_file + or text_segmentation_scale in ["word", "character"] + ): + prog_disp("Aligning...", 0.45, is_gui, progress=progress) + try: + if self.align_language in ["vi"]: + logger.info( + "Deficient alignment for the " + f"{self.align_language} language, skipping the" + " process. It is suggested to reduce the " + "duration of the segments as an alternative." + ) + else: + self.result = align_speech(audio, self.result) + logger.debug( + "Align complete, " + f"segments count {len(self.result['segments'])}" + ) + except Exception as error: + logger.error(str(error)) + + if self.result["segments"] == []: + raise ValueError("No active speech found in audio") + + if not self.task_in_cache("break_align", [ + divide_text_segments_by, + text_segmentation_scale, + self.align_language + ], { + "result": self.result, + "align_language": self.align_language + }): + if self.align_language in ["ja", "zh", "zh-TW"]: + divide_text_segments_by += "|!|?|...|。" + if text_segmentation_scale in ["word", "character"]: + self.result = linguistic_level_segments( + self.result, + text_segmentation_scale, + ) + elif divide_text_segments_by: + try: + self.result = break_aling_segments( + self.result, + break_characters=divide_text_segments_by, + ) + except Exception as error: + logger.error(str(error)) + + if not self.task_in_cache("diarize", [ + min_speakers, + max_speakers, + YOUR_HF_TOKEN[:len(YOUR_HF_TOKEN)//2], + diarization_model + ], { + "result": self.result + }): + prog_disp("Diarizing...", 0.60, is_gui, progress=progress) + diarize_model_select = diarization_models[diarization_model] + self.result_diarize = diarize_speech( + base_audio_wav if not self.vocals else self.vocals, + self.result, + min_speakers, + max_speakers, + YOUR_HF_TOKEN, + diarize_model_select, + ) + logger.debug("Diarize complete") + self.result_source_lang = copy.deepcopy(self.result_diarize) + + if not self.task_in_cache("translate", [ + TRANSLATE_AUDIO_TO, + translate_process + ], { + "result_diarize": self.result_diarize + }): + prog_disp("Translating...", 0.70, is_gui, progress=progress) + lang_source = ( + self.align_language + if self.align_language + else SOURCE_LANGUAGE + ) + self.result_diarize["segments"] = translate_text( + self.result_diarize["segments"], + TRANSLATE_AUDIO_TO, + translate_process, + chunk_size=1800, + source=lang_source, + ) + logger.debug("Translation complete") + logger.debug(self.result_diarize) + + if get_translated_text: + + json_data = [] + for segment in self.result_diarize["segments"]: + start = segment["start"] + text = segment["text"] + speaker = int(segment.get("speaker", "SPEAKER_00")[-2:]) + 1 + json_data.append( + {"start": start, "text": text, "speaker": speaker} + ) + + # Convert list of dictionaries to a JSON string with indentation + json_string = json.dumps(json_data, indent=2) + logger.info("Done") + self.edit_subs_complete = True + return json_string.encode().decode("unicode_escape") + + if get_video_from_text_json: + + if self.result_diarize is None: + raise ValueError("Generate the transcription first.") + # with open('text_json.json', 'r') as file: + text_json_loaded = json.loads(text_json) + for i, segment in enumerate(self.result_diarize["segments"]): + segment["text"] = text_json_loaded[i]["text"] + segment["speaker"] = "SPEAKER_{:02d}".format( + int(text_json_loaded[i]["speaker"]) - 1 + ) + + # Write subtitle + if not self.task_in_cache("subs_and_edit", [ + copy.deepcopy(self.result_diarize), + output_format_subtitle, + TRANSLATE_AUDIO_TO + ], { + "result_diarize": self.result_diarize + }): + if output_format_subtitle == "disable": + self.sub_file = "sub_tra.srt" + elif output_format_subtitle != "ass": + self.sub_file = process_subtitles( + self.result_source_lang, + self.align_language, + self.result_diarize, + output_format_subtitle, + TRANSLATE_AUDIO_TO, + ) + + # Need task + if output_format_subtitle != "srt": + _ = process_subtitles( + self.result_source_lang, + self.align_language, + self.result_diarize, + "srt", + TRANSLATE_AUDIO_TO, + ) + + if output_format_subtitle == "ass": + convert_ori = "ffmpeg -i sub_ori.srt sub_ori.ass -y" + convert_tra = "ffmpeg -i sub_tra.srt sub_tra.ass -y" + self.sub_file = "sub_tra.ass" + run_command(convert_ori) + run_command(convert_tra) + + format_sub = ( + output_format_subtitle + if output_format_subtitle != "disable" + else "srt" + ) + + if output_type == "subtitle": + + out_subs = [] + tra_subs = media_out( + media_file, + TRANSLATE_AUDIO_TO, + video_output_name, + format_sub, + file_obj=self.sub_file, + ) + out_subs.append(tra_subs) + + ori_subs = media_out( + media_file, + self.align_language, + video_output_name, + format_sub, + file_obj=f"sub_ori.{format_sub}", + ) + out_subs.append(ori_subs) + logger.info(f"Done: {out_subs}") + return out_subs + + if output_type == "subtitle [by speaker]": + output = get_subtitle_speaker( + media_file, + result=self.result_diarize, + language=TRANSLATE_AUDIO_TO, + extension=format_sub, + base_name=video_output_name, + ) + logger.info(f"Done: {str(output)}") + return output + + if "video [subtitled]" in output_type: + output = media_out( + media_file, + TRANSLATE_AUDIO_TO + "_subtitled", + video_output_name, + "wav" if is_audio_file(media_file) else ( + "mkv" if "mkv" in output_type else "mp4" + ), + file_obj=base_audio_wav if is_audio_file(media_file) else base_video_file, + soft_subtitles=False if is_audio_file(media_file) else True, + subtitle_files=output_format_subtitle, + ) + msg_out = output[0] if isinstance(output, list) else output + logger.info(f"Done: {msg_out}") + return output + + if not self.task_in_cache("tts", [ + TRANSLATE_AUDIO_TO, + tts_voice00, + tts_voice01, + tts_voice02, + tts_voice03, + tts_voice04, + tts_voice05, + tts_voice06, + tts_voice07, + tts_voice08, + tts_voice09, + tts_voice10, + tts_voice11, + dereverb_automatic_xtts + ], { + "sub_file": self.sub_file + }): + prog_disp("Text to speech...", 0.80, is_gui, progress=progress) + self.valid_speakers = audio_segmentation_to_voice( + self.result_diarize, + TRANSLATE_AUDIO_TO, + is_gui, + tts_voice00, + tts_voice01, + tts_voice02, + tts_voice03, + tts_voice04, + tts_voice05, + tts_voice06, + tts_voice07, + tts_voice08, + tts_voice09, + tts_voice10, + tts_voice11, + dereverb_automatic_xtts, + ) + + if not self.task_in_cache("acc_and_vc", [ + max_accelerate_audio, + acceleration_rate_regulation, + voice_imitation, + voice_imitation_max_segments, + voice_imitation_remove_previous, + voice_imitation_vocals_dereverb, + voice_imitation_method, + custom_voices, + custom_voices_workers, + copy.deepcopy(self.vci.model_config), + avoid_overlap + ], { + "valid_speakers": self.valid_speakers + }): + audio_files, speakers_list = accelerate_segments( + self.result_diarize, + max_accelerate_audio, + self.valid_speakers, + acceleration_rate_regulation, + ) + + # Voice Imitation (Tone color converter) + if voice_imitation: + prog_disp( + "Voice Imitation...", 0.85, is_gui, progress=progress + ) + from soni_translate.text_to_speech import toneconverter + + try: + toneconverter( + copy.deepcopy(self.result_diarize), + voice_imitation_max_segments, + voice_imitation_remove_previous, + voice_imitation_vocals_dereverb, + voice_imitation_method, + ) + except Exception as error: + logger.error(str(error)) + + # custom voice + if custom_voices: + prog_disp( + "Applying customized voices...", + 0.90, + is_gui, + progress=progress, + ) + + try: + self.vci( + audio_files, + speakers_list, + overwrite=True, + parallel_workers=custom_voices_workers, + ) + self.vci.unload_models() + except Exception as error: + logger.error(str(error)) + + prog_disp( + "Creating final translated video...", + 0.95, + is_gui, + progress=progress, + ) + remove_files(dub_audio_file) + create_translated_audio( + self.result_diarize, + audio_files, + dub_audio_file, + False, + avoid_overlap, + ) + + # Voiceless track, change with file + hash_base_audio_wav = get_hash(base_audio_wav) + if voiceless_track: + if self.voiceless_id != hash_base_audio_wav: + from soni_translate.mdx_net import process_uvr_task + + try: + # voiceless_audio_file_dir = "clean_song_output/voiceless" + remove_files(voiceless_audio_file) + uvr_voiceless_audio_wav, _ = process_uvr_task( + orig_song_path=base_audio_wav, + song_id="voiceless", + only_voiceless=True, + remove_files_output_dir=False, + ) + copy_files(uvr_voiceless_audio_wav, ".") + base_audio_wav = voiceless_audio_file + self.voiceless_id = hash_base_audio_wav + + except Exception as error: + logger.error(str(error)) + else: + base_audio_wav = voiceless_audio_file + + if not self.task_in_cache("mix_aud", [ + mix_method_audio, + volume_original_audio, + volume_translated_audio, + voiceless_track + ], {}): + # TYPE MIX AUDIO + remove_files(mix_audio_file) + command_volume_mix = f'ffmpeg -y -i {base_audio_wav} -i {dub_audio_file} -filter_complex "[0:0]volume={volume_original_audio}[a];[1:0]volume={volume_translated_audio}[b];[a][b]amix=inputs=2:duration=longest" -c:a libmp3lame {mix_audio_file}' + command_background_mix = f'ffmpeg -i {base_audio_wav} -i {dub_audio_file} -filter_complex "[1:a]asplit=2[sc][mix];[0:a][sc]sidechaincompress=threshold=0.003:ratio=20[bg]; [bg][mix]amerge[final]" -map [final] {mix_audio_file}' + if mix_method_audio == "Adjusting volumes and mixing audio": + # volume mix + run_command(command_volume_mix) + else: + try: + # background mix + run_command(command_background_mix) + except Exception as error_mix: + # volume mix except + logger.error(str(error_mix)) + run_command(command_volume_mix) + + if "audio" in output_type or is_audio_file(media_file): + output = media_out( + media_file, + TRANSLATE_AUDIO_TO, + video_output_name, + "wav" if "wav" in output_type else ( + "ogg" if "ogg" in output_type else "mp3" + ), + file_obj=mix_audio_file, + subtitle_files=output_format_subtitle, + ) + msg_out = output[0] if isinstance(output, list) else output + logger.info(f"Done: {msg_out}") + return output + + hash_base_video_file = get_hash(base_video_file) + + if burn_subtitles_to_video: + hashvideo_text = [ + hash_base_video_file, + [seg["text"] for seg in self.result_diarize["segments"]] + ] + if self.burn_subs_id != hashvideo_text: + try: + logger.info("Burn subtitles") + remove_files(vid_subs) + command = f"ffmpeg -i {base_video_file} -y -vf subtitles=sub_tra.srt -max_muxing_queue_size 9999 {vid_subs}" + run_command(command) + base_video_file = vid_subs + self.burn_subs_id = hashvideo_text + except Exception as error: + logger.error(str(error)) + else: + base_video_file = vid_subs + + if not self.task_in_cache("output", [ + hash_base_video_file, + hash_base_audio_wav, + burn_subtitles_to_video + ], {}): + # Merge new audio + video + remove_files(video_output_file) + run_command( + f"ffmpeg -i {base_video_file} -i {mix_audio_file} -c:v copy -c:a copy -map 0:v -map 1:a -shortest {video_output_file}" + ) + + output = media_out( + media_file, + TRANSLATE_AUDIO_TO, + video_output_name, + "mkv" if "mkv" in output_type else "mp4", + file_obj=video_output_file, + soft_subtitles=soft_subtitles_to_video, + subtitle_files=output_format_subtitle, + ) + msg_out = output[0] if isinstance(output, list) else output + logger.info(f"Done: {msg_out}") + + return output + + def hook_beta_processor( + self, + document, + tgt_lang, + translate_process, + ori_lang, + tts, + name_final_file, + custom_voices, + custom_voices_workers, + output_type, + chunk_size, + width, + height, + start_page, + end_page, + bcolor, + is_gui, + progress + ): + prog_disp("Processing pages...", 0.10, is_gui, progress=progress) + doc_data = doc_to_txtximg_pages(document, width, height, start_page, end_page, bcolor) + result_diarize = page_data_to_segments(doc_data, 1700) + + prog_disp("Translating...", 0.20, is_gui, progress=progress) + result_diarize["segments"] = translate_text( + result_diarize["segments"], + tgt_lang, + translate_process, + chunk_size=0, + source=ori_lang, + ) + chunk_size = ( + chunk_size if chunk_size else determine_chunk_size(tts) + ) + doc_data = update_page_data(result_diarize, doc_data) + + prog_disp("Text to speech...", 0.30, is_gui, progress=progress) + result_diarize = page_data_to_segments(doc_data, chunk_size) + valid_speakers = audio_segmentation_to_voice( + result_diarize, + tgt_lang, + is_gui, + tts, + ) + + # fix format and set folder output + audio_files, speakers_list = accelerate_segments( + result_diarize, + 1.0, + valid_speakers, + ) + + # custom voice + if custom_voices: + prog_disp( + "Applying customized voices...", + 0.60, + is_gui, + progress=progress, + ) + self.vci( + audio_files, + speakers_list, + overwrite=True, + parallel_workers=custom_voices_workers, + ) + self.vci.unload_models() + + # Update time segments and not concat + result_diarize = fix_timestamps_docs(result_diarize, audio_files) + final_wav_file = "audio_book.wav" + remove_files(final_wav_file) + + prog_disp("Creating audio file...", 0.70, is_gui, progress=progress) + create_translated_audio( + result_diarize, audio_files, final_wav_file, False + ) + + prog_disp("Creating video file...", 0.80, is_gui, progress=progress) + video_doc = create_video_from_images( + doc_data, + result_diarize + ) + + # Merge video and audio + prog_disp("Merging...", 0.90, is_gui, progress=progress) + vid_out = merge_video_and_audio(video_doc, final_wav_file) + + # End + output = media_out( + document, + tgt_lang, + name_final_file, + "mkv" if "mkv" in output_type else "mp4", + file_obj=vid_out, + ) + logger.info(f"Done: {output}") + return output + + def multilingual_docs_conversion( + self, + string_text="", # string + document=None, # doc path gui + directory_input="", # doc path + origin_language="English (en)", + target_language="English (en)", + tts_voice00="en-US-EmmaMultilingualNeural-Female", + name_final_file="", + translate_process="google_translator", + output_type="audio", + chunk_size=None, + custom_voices=False, + custom_voices_workers=1, + start_page=1, + end_page=99999, + width=1280, + height=720, + bcolor="dynamic", + is_gui=False, + progress=gr.Progress(), + ): + if "gpt" in translate_process: + check_openai_api_key() + + SOURCE_LANGUAGE = LANGUAGES[origin_language] + if translate_process != "disable_translation": + TRANSLATE_AUDIO_TO = LANGUAGES[target_language] + else: + TRANSLATE_AUDIO_TO = SOURCE_LANGUAGE + logger.info("No translation") + if tts_voice00[:2].lower() != TRANSLATE_AUDIO_TO[:2].lower(): + logger.debug( + "Make sure to select a 'TTS Speaker' suitable for the " + "translation language to avoid errors with the TTS." + ) + + self.clear_cache(string_text, force=True) + + is_string = False + if document is None: + if os.path.exists(directory_input): + document = directory_input + else: + document = string_text + is_string = True + document = document if isinstance(document, str) else document.name + if not document: + raise Exception("No data found") + + if "videobook" in output_type: + if not document.lower().endswith(".pdf"): + raise ValueError( + "Videobooks are only compatible with PDF files." + ) + + return self.hook_beta_processor( + document, + TRANSLATE_AUDIO_TO, + translate_process, + SOURCE_LANGUAGE, + tts_voice00, + name_final_file, + custom_voices, + custom_voices_workers, + output_type, + chunk_size, + width, + height, + start_page, + end_page, + bcolor, + is_gui, + progress + ) + + # audio_wav = "audio.wav" + final_wav_file = "audio_book.wav" + + prog_disp("Processing text...", 0.15, is_gui, progress=progress) + result_file_path, result_text = document_preprocessor( + document, is_string, start_page, end_page + ) + + if ( + output_type == "book (txt)" + and translate_process == "disable_translation" + ): + return result_file_path + + if "SET_LIMIT" == os.getenv("DEMO"): + result_text = result_text[:50] + logger.info( + "DEMO; Generation is limited to 50 characters to prevent " + "CPU errors. No limitations with GPU.\n" + ) + + if translate_process != "disable_translation": + # chunks text for translation + result_diarize = plain_text_to_segments(result_text, 1700) + prog_disp("Translating...", 0.30, is_gui, progress=progress) + # not or iterative with 1700 chars + result_diarize["segments"] = translate_text( + result_diarize["segments"], + TRANSLATE_AUDIO_TO, + translate_process, + chunk_size=0, + source=SOURCE_LANGUAGE, + ) + + txt_file_path, result_text = segments_to_plain_text(result_diarize) + + if output_type == "book (txt)": + return media_out( + result_file_path if is_string else document, + TRANSLATE_AUDIO_TO, + name_final_file, + "txt", + file_obj=txt_file_path, + ) + + # (TTS limits) plain text to result_diarize + chunk_size = ( + chunk_size if chunk_size else determine_chunk_size(tts_voice00) + ) + result_diarize = plain_text_to_segments(result_text, chunk_size) + logger.debug(result_diarize) + + prog_disp("Text to speech...", 0.45, is_gui, progress=progress) + valid_speakers = audio_segmentation_to_voice( + result_diarize, + TRANSLATE_AUDIO_TO, + is_gui, + tts_voice00, + ) + + # fix format and set folder output + audio_files, speakers_list = accelerate_segments( + result_diarize, + 1.0, + valid_speakers, + ) + + # custom voice + if custom_voices: + prog_disp( + "Applying customized voices...", + 0.80, + is_gui, + progress=progress, + ) + self.vci( + audio_files, + speakers_list, + overwrite=True, + parallel_workers=custom_voices_workers, + ) + self.vci.unload_models() + + prog_disp( + "Creating final audio file...", 0.90, is_gui, progress=progress + ) + remove_files(final_wav_file) + create_translated_audio( + result_diarize, audio_files, final_wav_file, True + ) + + output = media_out( + result_file_path if is_string else document, + TRANSLATE_AUDIO_TO, + name_final_file, + "mp3" if "mp3" in output_type else ( + "ogg" if "ogg" in output_type else "wav" + ), + file_obj=final_wav_file, + ) + + logger.info(f"Done: {output}") + + return output + + +title = "
📽️ SoniTranslate 🈷️
" + + +def create_gui(theme, logs_in_gui=False): + with gr.Blocks(theme=theme) as app: + gr.Markdown(title) + gr.Markdown(lg_conf["description"]) + + with gr.Tab(lg_conf["tab_translate"]): + with gr.Row(): + with gr.Column(): + input_data_type = gr.Dropdown( + ["SUBMIT VIDEO", "URL", "Find Video Path"], + value="SUBMIT VIDEO", + label=lg_conf["video_source"], + ) + + def swap_visibility(data_type): + if data_type == "URL": + return ( + gr.update(visible=False, value=None), + gr.update(visible=True, value=""), + gr.update(visible=False, value=""), + ) + elif data_type == "SUBMIT VIDEO": + return ( + gr.update(visible=True, value=None), + gr.update(visible=False, value=""), + gr.update(visible=False, value=""), + ) + elif data_type == "Find Video Path": + return ( + gr.update(visible=False, value=None), + gr.update(visible=False, value=""), + gr.update(visible=True, value=""), + ) + + video_input = gr.File( + label="VIDEO", + file_count="multiple", + type="filepath", + ) + blink_input = gr.Textbox( + visible=False, + label=lg_conf["link_label"], + info=lg_conf["link_info"], + placeholder=lg_conf["link_ph"], + ) + directory_input = gr.Textbox( + visible=False, + label=lg_conf["dir_label"], + info=lg_conf["dir_info"], + placeholder=lg_conf["dir_ph"], + ) + input_data_type.change( + fn=swap_visibility, + inputs=input_data_type, + outputs=[video_input, blink_input, directory_input], + ) + + gr.HTML() + + SOURCE_LANGUAGE = gr.Dropdown( + LANGUAGES_LIST, + value=LANGUAGES_LIST[0], + label=lg_conf["sl_label"], + info=lg_conf["sl_info"], + ) + TRANSLATE_AUDIO_TO = gr.Dropdown( + LANGUAGES_LIST[1:], + value="English (en)", + label=lg_conf["tat_label"], + info=lg_conf["tat_info"], + ) + + gr.HTML("
") + + gr.Markdown(lg_conf["num_speakers"]) + MAX_TTS = 12 + min_speakers = gr.Slider( + 1, + MAX_TTS, + value=1, + label=lg_conf["min_sk"], + step=1, + visible=False, + ) + max_speakers = gr.Slider( + 1, + MAX_TTS, + value=2, + step=1, + label=lg_conf["max_sk"], + ) + gr.Markdown(lg_conf["tts_select"]) + + def submit(value): + visibility_dict = { + f"tts_voice{i:02d}": gr.update(visible=i < value) + for i in range(MAX_TTS) + } + return [value for value in visibility_dict.values()] + + tts_voice00 = gr.Dropdown( + SoniTr.tts_info.tts_list(), + value="en-US-EmmaMultilingualNeural-Female", + label=lg_conf["sk1"], + visible=True, + interactive=True, + ) + tts_voice01 = gr.Dropdown( + SoniTr.tts_info.tts_list(), + value="en-US-AndrewMultilingualNeural-Male", + label=lg_conf["sk2"], + visible=True, + interactive=True, + ) + tts_voice02 = gr.Dropdown( + SoniTr.tts_info.tts_list(), + value="en-US-AvaMultilingualNeural-Female", + label=lg_conf["sk3"], + visible=False, + interactive=True, + ) + tts_voice03 = gr.Dropdown( + SoniTr.tts_info.tts_list(), + value="en-US-BrianMultilingualNeural-Male", + label=lg_conf["sk4"], + visible=False, + interactive=True, + ) + tts_voice04 = gr.Dropdown( + SoniTr.tts_info.tts_list(), + value="de-DE-SeraphinaMultilingualNeural-Female", + label=lg_conf["sk4"], + visible=False, + interactive=True, + ) + tts_voice05 = gr.Dropdown( + SoniTr.tts_info.tts_list(), + value="de-DE-FlorianMultilingualNeural-Male", + label=lg_conf["sk6"], + visible=False, + interactive=True, + ) + tts_voice06 = gr.Dropdown( + SoniTr.tts_info.tts_list(), + value="fr-FR-VivienneMultilingualNeural-Female", + label=lg_conf["sk7"], + visible=False, + interactive=True, + ) + tts_voice07 = gr.Dropdown( + SoniTr.tts_info.tts_list(), + value="fr-FR-RemyMultilingualNeural-Male", + label=lg_conf["sk8"], + visible=False, + interactive=True, + ) + tts_voice08 = gr.Dropdown( + SoniTr.tts_info.tts_list(), + value="en-US-EmmaMultilingualNeural-Female", + label=lg_conf["sk9"], + visible=False, + interactive=True, + ) + tts_voice09 = gr.Dropdown( + SoniTr.tts_info.tts_list(), + value="en-US-AndrewMultilingualNeural-Male", + label=lg_conf["sk10"], + visible=False, + interactive=True, + ) + tts_voice10 = gr.Dropdown( + SoniTr.tts_info.tts_list(), + value="en-US-EmmaMultilingualNeural-Female", + label=lg_conf["sk11"], + visible=False, + interactive=True, + ) + tts_voice11 = gr.Dropdown( + SoniTr.tts_info.tts_list(), + value="en-US-AndrewMultilingualNeural-Male", + label=lg_conf["sk12"], + visible=False, + interactive=True, + ) + max_speakers.change( + submit, + max_speakers, + [ + tts_voice00, + tts_voice01, + tts_voice02, + tts_voice03, + tts_voice04, + tts_voice05, + tts_voice06, + tts_voice07, + tts_voice08, + tts_voice09, + tts_voice10, + tts_voice11, + ], + ) + + with gr.Column(): + with gr.Accordion( + lg_conf["vc_title"], + open=False, + ): + gr.Markdown(lg_conf["vc_subtitle"]) + voice_imitation_gui = gr.Checkbox( + False, + label=lg_conf["vc_active_label"], + info=lg_conf["vc_active_info"], + ) + openvoice_models = ["openvoice", "openvoice_v2"] + voice_imitation_method_options = ( + ["freevc"] + openvoice_models + if SoniTr.tts_info.xtts_enabled + else openvoice_models + ) + voice_imitation_method_gui = gr.Dropdown( + voice_imitation_method_options, + value=voice_imitation_method_options[0], + label=lg_conf["vc_method_label"], + info=lg_conf["vc_method_info"], + ) + voice_imitation_max_segments_gui = gr.Slider( + label=lg_conf["vc_segments_label"], + info=lg_conf["vc_segments_info"], + value=3, + step=1, + minimum=1, + maximum=10, + visible=True, + interactive=True, + ) + voice_imitation_vocals_dereverb_gui = gr.Checkbox( + False, + label=lg_conf["vc_dereverb_label"], + info=lg_conf["vc_dereverb_info"], + ) + voice_imitation_remove_previous_gui = gr.Checkbox( + True, + label=lg_conf["vc_remove_label"], + info=lg_conf["vc_remove_info"], + ) + + if SoniTr.tts_info.xtts_enabled: + with gr.Column(): + with gr.Accordion( + lg_conf["xtts_title"], + open=False, + ): + gr.Markdown(lg_conf["xtts_subtitle"]) + wav_speaker_file = gr.File( + label=lg_conf["xtts_file_label"] + ) + wav_speaker_name = gr.Textbox( + label=lg_conf["xtts_name_label"], + value="", + info=lg_conf["xtts_name_info"], + placeholder="default_name", + lines=1, + ) + wav_speaker_start = gr.Number( + label="Time audio start", + value=0, + visible=False, + ) + wav_speaker_end = gr.Number( + label="Time audio end", + value=0, + visible=False, + ) + wav_speaker_dir = gr.Textbox( + label="Directory save", + value="_XTTS_", + visible=False, + ) + wav_speaker_dereverb = gr.Checkbox( + True, + label=lg_conf["xtts_dereverb_label"], + info=lg_conf["xtts_dereverb_info"] + ) + wav_speaker_output = gr.HTML() + create_xtts_wav = gr.Button( + lg_conf["xtts_button"] + ) + gr.Markdown(lg_conf["xtts_footer"]) + else: + wav_speaker_dereverb = gr.Checkbox( + False, + label=lg_conf["xtts_dereverb_label"], + info=lg_conf["xtts_dereverb_info"], + visible=False + ) + + with gr.Column(): + with gr.Accordion( + lg_conf["extra_setting"], open=False + ): + audio_accelerate = gr.Slider( + label=lg_conf["acc_max_label"], + value=1.9, + step=0.1, + minimum=1.0, + maximum=2.5, + visible=True, + interactive=True, + info=lg_conf["acc_max_info"], + ) + acceleration_rate_regulation_gui = gr.Checkbox( + False, + label=lg_conf["acc_rate_label"], + info=lg_conf["acc_rate_info"], + ) + avoid_overlap_gui = gr.Checkbox( + False, + label=lg_conf["or_label"], + info=lg_conf["or_info"], + ) + + gr.HTML("
") + + audio_mix_options = [ + "Mixing audio with sidechain compression", + "Adjusting volumes and mixing audio", + ] + AUDIO_MIX = gr.Dropdown( + audio_mix_options, + value=audio_mix_options[1], + label=lg_conf["aud_mix_label"], + info=lg_conf["aud_mix_info"], + ) + volume_original_mix = gr.Slider( + label=lg_conf["vol_ori"], + info="for Adjusting volumes and mixing audio", + value=0.25, + step=0.05, + minimum=0.0, + maximum=2.50, + visible=True, + interactive=True, + ) + volume_translated_mix = gr.Slider( + label=lg_conf["vol_tra"], + info="for Adjusting volumes and mixing audio", + value=1.80, + step=0.05, + minimum=0.0, + maximum=2.50, + visible=True, + interactive=True, + ) + main_voiceless_track = gr.Checkbox( + label=lg_conf["voiceless_tk_label"], + info=lg_conf["voiceless_tk_info"], + ) + + gr.HTML("
") + sub_type_options = [ + "disable", + "srt", + "vtt", + "ass", + "txt", + "tsv", + "json", + "aud", + ] + + sub_type_output = gr.Dropdown( + sub_type_options, + value=sub_type_options[1], + label=lg_conf["sub_type"], + ) + soft_subtitles_to_video_gui = gr.Checkbox( + label=lg_conf["soft_subs_label"], + info=lg_conf["soft_subs_info"], + ) + burn_subtitles_to_video_gui = gr.Checkbox( + label=lg_conf["burn_subs_label"], + info=lg_conf["burn_subs_info"], + ) + + gr.HTML("
") + gr.Markdown(lg_conf["whisper_title"]) + literalize_numbers_gui = gr.Checkbox( + True, + label=lg_conf["lnum_label"], + info=lg_conf["lnum_info"], + ) + vocal_refinement_gui = gr.Checkbox( + False, + label=lg_conf["scle_label"], + info=lg_conf["scle_info"], + ) + segment_duration_limit_gui = gr.Slider( + label=lg_conf["sd_limit_label"], + info=lg_conf["sd_limit_info"], + value=15, + step=1, + minimum=1, + maximum=30, + ) + whisper_model_default = ( + "large-v3" + if SoniTr.device == "cuda" + else "medium" + ) + + WHISPER_MODEL_SIZE = gr.Dropdown( + ASR_MODEL_OPTIONS + find_whisper_models(), + value=whisper_model_default, + label="Whisper ASR model", + info=lg_conf["asr_model_info"], + allow_custom_value=True, + ) + com_t_opt, com_t_default = ( + [COMPUTE_TYPE_GPU, "float16"] + if SoniTr.device == "cuda" + else [COMPUTE_TYPE_CPU, "float32"] + ) + compute_type = gr.Dropdown( + com_t_opt, + value=com_t_default, + label=lg_conf["ctype_label"], + info=lg_conf["ctype_info"], + ) + batch_size = gr.Slider( + minimum=1, + maximum=32, + value=8, + label=lg_conf["batchz_label"], + info=lg_conf["batchz_info"], + step=1, + ) + input_srt = gr.File( + label=lg_conf["srt_file_label"], + file_types=[".srt", ".ass", ".vtt"], + height=130, + ) + + gr.HTML("
") + text_segmentation_options = [ + "sentence", + "word", + "character" + ] + text_segmentation_scale_gui = gr.Dropdown( + text_segmentation_options, + value=text_segmentation_options[0], + label=lg_conf["tsscale_label"], + info=lg_conf["tsscale_info"], + ) + divide_text_segments_by_gui = gr.Textbox( + label=lg_conf["divide_text_label"], + value="", + info=lg_conf["divide_text_info"], + ) + + gr.HTML("
") + pyannote_models_list = list( + diarization_models.keys() + ) + diarization_process_dropdown = gr.Dropdown( + pyannote_models_list, + value=pyannote_models_list[1], + label=lg_conf["diarization_label"], + ) + translate_process_dropdown = gr.Dropdown( + TRANSLATION_PROCESS_OPTIONS, + value=TRANSLATION_PROCESS_OPTIONS[0], + label=lg_conf["tr_process_label"], + ) + + gr.HTML("
") + main_output_type = gr.Dropdown( + OUTPUT_TYPE_OPTIONS, + value=OUTPUT_TYPE_OPTIONS[0], + label=lg_conf["out_type_label"], + ) + VIDEO_OUTPUT_NAME = gr.Textbox( + label=lg_conf["out_name_label"], + value="", + info=lg_conf["out_name_info"], + ) + play_sound_gui = gr.Checkbox( + True, + label=lg_conf["task_sound_label"], + info=lg_conf["task_sound_info"], + ) + enable_cache_gui = gr.Checkbox( + True, + label=lg_conf["cache_label"], + info=lg_conf["cache_info"], + ) + PREVIEW = gr.Checkbox( + label="Preview", info=lg_conf["preview_info"] + ) + is_gui_dummy_check = gr.Checkbox( + True, visible=False + ) + + with gr.Column(variant="compact"): + edit_sub_check = gr.Checkbox( + label=lg_conf["edit_sub_label"], + info=lg_conf["edit_sub_info"], + ) + dummy_false_check = gr.Checkbox( + False, + visible=False, + ) + + def visible_component_subs(input_bool): + if input_bool: + return gr.update(visible=True), gr.update( + visible=True + ) + else: + return gr.update(visible=False), gr.update( + visible=False + ) + + subs_button = gr.Button( + lg_conf["button_subs"], + variant="primary", + visible=False, + ) + subs_edit_space = gr.Textbox( + visible=False, + lines=10, + label=lg_conf["editor_sub_label"], + info=lg_conf["editor_sub_info"], + placeholder=lg_conf["editor_sub_ph"], + ) + edit_sub_check.change( + visible_component_subs, + [edit_sub_check], + [subs_button, subs_edit_space], + ) + + with gr.Row(): + video_button = gr.Button( + lg_conf["button_translate"], + variant="primary", + ) + with gr.Row(): + video_output = gr.File( + label=lg_conf["output_result_label"], + file_count="multiple", + interactive=False, + + ) # gr.Video() + + gr.HTML("
") + + if ( + os.getenv("YOUR_HF_TOKEN") is None + or os.getenv("YOUR_HF_TOKEN") == "" + ): + HFKEY = gr.Textbox( + visible=True, + label="HF Token", + info=lg_conf["ht_token_info"], + placeholder=lg_conf["ht_token_ph"], + ) + else: + HFKEY = gr.Textbox( + visible=False, + label="HF Token", + info=lg_conf["ht_token_info"], + placeholder=lg_conf["ht_token_ph"], + ) + + gr.Examples( + examples=[ + [ + ["./assets/Video_main.mp4"], + "", + "", + "", + False, + whisper_model_default, + 4, + com_t_default, + "Spanish (es)", + "English (en)", + 1, + 2, + "en-CA-ClaraNeural-Female", + "en-AU-WilliamNeural-Male", + ], + ], # no update + fn=SoniTr.batch_multilingual_media_conversion, + inputs=[ + video_input, + blink_input, + directory_input, + HFKEY, + PREVIEW, + WHISPER_MODEL_SIZE, + batch_size, + compute_type, + SOURCE_LANGUAGE, + TRANSLATE_AUDIO_TO, + min_speakers, + max_speakers, + tts_voice00, + tts_voice01, + ], + outputs=[video_output], + cache_examples=False, + ) + + with gr.Tab(lg_conf["tab_docs"]): + with gr.Column(): + with gr.Accordion("Docs", open=True): + with gr.Column(variant="compact"): + with gr.Column(): + input_doc_type = gr.Dropdown( + [ + "WRITE TEXT", + "SUBMIT DOCUMENT", + "Find Document Path", + ], + value="SUBMIT DOCUMENT", + label=lg_conf["docs_input_label"], + info=lg_conf["docs_input_info"], + ) + + def swap_visibility(data_type): + if data_type == "WRITE TEXT": + return ( + gr.update(visible=True, value=""), + gr.update(visible=False, value=None), + gr.update(visible=False, value=""), + ) + elif data_type == "SUBMIT DOCUMENT": + return ( + gr.update(visible=False, value=""), + gr.update(visible=True, value=None), + gr.update(visible=False, value=""), + ) + elif data_type == "Find Document Path": + return ( + gr.update(visible=False, value=""), + gr.update(visible=False, value=None), + gr.update(visible=True, value=""), + ) + + text_docs = gr.Textbox( + label="Text", + value="This is an example", + info="Write a text", + placeholder="...", + lines=5, + visible=False, + ) + input_docs = gr.File( + label="Document", visible=True + ) + directory_input_docs = gr.Textbox( + visible=False, + label="Document Path", + info="Example: /home/my_doc.pdf", + placeholder="Path goes here...", + ) + input_doc_type.change( + fn=swap_visibility, + inputs=input_doc_type, + outputs=[ + text_docs, + input_docs, + directory_input_docs, + ], + ) + + gr.HTML() + + tts_documents = gr.Dropdown( + list( + filter( + lambda x: x != "_XTTS_/AUTOMATIC.wav", + SoniTr.tts_info.tts_list(), + ) + ), + value="en-US-EmmaMultilingualNeural-Female", + label="TTS", + visible=True, + interactive=True, + ) + + gr.HTML() + + docs_SOURCE_LANGUAGE = gr.Dropdown( + LANGUAGES_LIST[1:], + value="English (en)", + label=lg_conf["sl_label"], + info=lg_conf["docs_source_info"], + ) + docs_TRANSLATE_TO = gr.Dropdown( + LANGUAGES_LIST[1:], + value="English (en)", + label=lg_conf["tat_label"], + info=lg_conf["tat_info"], + ) + + with gr.Column(): + with gr.Accordion( + lg_conf["extra_setting"], open=False + ): + docs_translate_process_dropdown = gr.Dropdown( + DOCS_TRANSLATION_PROCESS_OPTIONS, + value=DOCS_TRANSLATION_PROCESS_OPTIONS[ + 0 + ], + label="Translation process", + ) + + gr.HTML("
") + + docs_output_type = gr.Dropdown( + DOCS_OUTPUT_TYPE_OPTIONS, + value=DOCS_OUTPUT_TYPE_OPTIONS[2], + label="Output type", + ) + docs_OUTPUT_NAME = gr.Textbox( + label="Final file name", + value="", + info=lg_conf["out_name_info"], + ) + docs_chunk_size = gr.Number( + label=lg_conf["chunk_size_label"], + value=0, + visible=True, + interactive=True, + info=lg_conf["chunk_size_info"], + ) + gr.HTML("
") + start_page_gui = gr.Number( + step=1, + value=1, + minimum=1, + maximum=99999, + label="Start page", + ) + end_page_gui = gr.Number( + step=1, + value=99999, + minimum=1, + maximum=99999, + label="End page", + ) + gr.HTML("
Videobook config") + videobook_width_gui = gr.Number( + step=1, + value=1280, + minimum=100, + maximum=4096, + label="Width", + ) + videobook_height_gui = gr.Number( + step=1, + value=720, + minimum=100, + maximum=4096, + label="Height", + ) + videobook_bcolor_gui = gr.Dropdown( + BORDER_COLORS, + value=BORDER_COLORS[0], + label="Border color", + ) + docs_dummy_check = gr.Checkbox( + True, visible=False + ) + + with gr.Row(): + docs_button = gr.Button( + lg_conf["docs_button"], + variant="primary", + ) + with gr.Row(): + docs_output = gr.File( + label="Result", + interactive=False, + ) + + with gr.Tab("Custom voice R.V.C. (Optional)"): + + with gr.Column(): + with gr.Accordion("Get the R.V.C. Models", open=True): + url_links = gr.Textbox( + label="URLs", + value="", + info=lg_conf["cv_url_info"], + placeholder="urls here...", + lines=1, + ) + download_finish = gr.HTML() + download_button = gr.Button("DOWNLOAD MODELS") + + def update_models(): + models_path, index_path = upload_model_list() + + dict_models = { + f"fmodel{i:02d}": gr.update( + choices=models_path + ) + for i in range(MAX_TTS+1) + } + dict_index = { + f"findex{i:02d}": gr.update( + choices=index_path, value=None + ) + for i in range(MAX_TTS+1) + } + dict_changes = {**dict_models, **dict_index} + return [value for value in dict_changes.values()] + + with gr.Column(): + with gr.Accordion(lg_conf["replace_title"], open=False): + with gr.Column(variant="compact"): + with gr.Column(): + gr.Markdown(lg_conf["sec1_title"]) + enable_custom_voice = gr.Checkbox( + False, + label="ENABLE", + info=lg_conf["enable_replace"] + ) + workers_custom_voice = gr.Number( + step=1, + value=1, + minimum=1, + maximum=50, + label="workers", + visible=False, + ) + + gr.Markdown(lg_conf["sec2_title"]) + gr.Markdown(lg_conf["sec2_subtitle"]) + + PITCH_ALGO_OPT = [ + "pm", + "harvest", + "crepe", + "rmvpe", + "rmvpe+", + ] + + def model_conf(): + return gr.Dropdown( + models_path, + # value="", + label="Model", + visible=True, + interactive=True, + ) + + def pitch_algo_conf(): + return gr.Dropdown( + PITCH_ALGO_OPT, + value=PITCH_ALGO_OPT[3], + label="Pitch algorithm", + visible=True, + interactive=True, + ) + + def pitch_lvl_conf(): + return gr.Slider( + label="Pitch level", + minimum=-24, + maximum=24, + step=1, + value=0, + visible=True, + interactive=True, + ) + + def index_conf(): + return gr.Dropdown( + index_path, + value=None, + label="Index", + visible=True, + interactive=True, + ) + + def index_inf_conf(): + return gr.Slider( + minimum=0, + maximum=1, + label="Index influence", + value=0.75, + ) + + def respiration_filter_conf(): + return gr.Slider( + minimum=0, + maximum=7, + label="Respiration median filtering", + value=3, + step=1, + interactive=True, + ) + + def envelope_ratio_conf(): + return gr.Slider( + minimum=0, + maximum=1, + label="Envelope ratio", + value=0.25, + interactive=True, + ) + + def consonant_protec_conf(): + return gr.Slider( + minimum=0, + maximum=0.5, + label="Consonant breath protection", + value=0.5, + interactive=True, + ) + + def button_conf(tts_name): + return gr.Button( + lg_conf["cv_button_apply"]+" "+tts_name, + variant="primary", + ) + + TTS_TABS = [ + 'TTS Speaker {:02d}'.format(i) for i in range(1, MAX_TTS+1) + ] + + CV_SUBTITLES = [ + lg_conf["cv_tts1"], + lg_conf["cv_tts2"], + lg_conf["cv_tts3"], + lg_conf["cv_tts4"], + lg_conf["cv_tts5"], + lg_conf["cv_tts6"], + lg_conf["cv_tts7"], + lg_conf["cv_tts8"], + lg_conf["cv_tts9"], + lg_conf["cv_tts10"], + lg_conf["cv_tts11"], + lg_conf["cv_tts12"], + ] + + configs_storage = [] + + for i in range(MAX_TTS): # Loop from 00 to 11 + with gr.Accordion(CV_SUBTITLES[i], open=False): + gr.Markdown(TTS_TABS[i]) + with gr.Column(): + tag_gui = gr.Textbox( + value=TTS_TABS[i], visible=False + ) + model_gui = model_conf() + pitch_algo_gui = pitch_algo_conf() + pitch_lvl_gui = pitch_lvl_conf() + index_gui = index_conf() + index_inf_gui = index_inf_conf() + rmf_gui = respiration_filter_conf() + er_gui = envelope_ratio_conf() + cbp_gui = consonant_protec_conf() + + with gr.Row(variant="compact"): + button_config = button_conf( + TTS_TABS[i] + ) + + confirm_conf = gr.HTML() + + button_config.click( + SoniTr.vci.apply_conf, + inputs=[ + tag_gui, + model_gui, + pitch_algo_gui, + pitch_lvl_gui, + index_gui, + index_inf_gui, + rmf_gui, + er_gui, + cbp_gui, + ], + outputs=[confirm_conf], + ) + + configs_storage.append({ + "tag": tag_gui, + "model": model_gui, + "index": index_gui, + }) + + with gr.Column(): + with gr.Accordion("Test R.V.C.", open=False): + with gr.Row(variant="compact"): + text_test = gr.Textbox( + label="Text", + value="This is an example", + info="write a text", + placeholder="...", + lines=5, + ) + with gr.Column(): + tts_test = gr.Dropdown( + sorted(SoniTr.tts_info.list_edge), + value="en-GB-ThomasNeural-Male", + label="TTS", + visible=True, + interactive=True, + ) + model_test = model_conf() + index_test = index_conf() + pitch_test = pitch_lvl_conf() + pitch_alg_test = pitch_algo_conf() + with gr.Row(variant="compact"): + button_test = gr.Button("Test audio") + + with gr.Column(): + with gr.Row(): + original_ttsvoice = gr.Audio() + ttsvoice = gr.Audio() + + button_test.click( + SoniTr.vci.make_test, + inputs=[ + text_test, + tts_test, + model_test, + index_test, + pitch_test, + pitch_alg_test, + ], + outputs=[ttsvoice, original_ttsvoice], + ) + + download_button.click( + download_list, + [url_links], + [download_finish], + queue=False + ).then( + update_models, + [], + [ + elem["model"] for elem in configs_storage + ] + [model_test] + [ + elem["index"] for elem in configs_storage + ] + [index_test], + ) + + with gr.Tab(lg_conf["tab_help"]): + gr.Markdown(lg_conf["tutorial"]) + gr.Markdown(news) + + def play_sound_alert(play_sound): + + if not play_sound: + return None + + # silent_sound = "assets/empty_audio.mp3" + sound_alert = "assets/sound_alert.mp3" + + time.sleep(0.25) + # yield silent_sound + yield None + + time.sleep(0.25) + yield sound_alert + + sound_alert_notification = gr.Audio( + value=None, + type="filepath", + format="mp3", + autoplay=True, + visible=False, + ) + + if logs_in_gui: + logger.info("Logs in gui need public url") + + class Logger: + def __init__(self, filename): + self.terminal = sys.stdout + self.log = open(filename, "w") + + def write(self, message): + self.terminal.write(message) + self.log.write(message) + + def flush(self): + self.terminal.flush() + self.log.flush() + + def isatty(self): + return False + + sys.stdout = Logger("output.log") + + def read_logs(): + sys.stdout.flush() + with open("output.log", "r") as f: + return f.read() + + with gr.Accordion("Logs", open=False): + logs = gr.Textbox(label=">>>") + app.load(read_logs, None, logs, every=1) + + if SoniTr.tts_info.xtts_enabled: + # Update tts list + def update_tts_list(): + update_dict = { + f"tts_voice{i:02d}": gr.update(choices=SoniTr.tts_info.tts_list()) + for i in range(MAX_TTS) + } + update_dict["tts_documents"] = gr.update( + choices=list( + filter( + lambda x: x != "_XTTS_/AUTOMATIC.wav", + SoniTr.tts_info.tts_list(), + ) + ) + ) + return [value for value in update_dict.values()] + + create_xtts_wav.click( + create_wav_file_vc, + inputs=[ + wav_speaker_name, + wav_speaker_file, + wav_speaker_start, + wav_speaker_end, + wav_speaker_dir, + wav_speaker_dereverb, + ], + outputs=[wav_speaker_output], + ).then( + update_tts_list, + None, + [ + tts_voice00, + tts_voice01, + tts_voice02, + tts_voice03, + tts_voice04, + tts_voice05, + tts_voice06, + tts_voice07, + tts_voice08, + tts_voice09, + tts_voice10, + tts_voice11, + tts_documents, + ], + ) + + # Run translate text + subs_button.click( + SoniTr.batch_multilingual_media_conversion, + inputs=[ + video_input, + blink_input, + directory_input, + HFKEY, + PREVIEW, + WHISPER_MODEL_SIZE, + batch_size, + compute_type, + SOURCE_LANGUAGE, + TRANSLATE_AUDIO_TO, + min_speakers, + max_speakers, + tts_voice00, + tts_voice01, + tts_voice02, + tts_voice03, + tts_voice04, + tts_voice05, + tts_voice06, + tts_voice07, + tts_voice08, + tts_voice09, + tts_voice10, + tts_voice11, + VIDEO_OUTPUT_NAME, + AUDIO_MIX, + audio_accelerate, + acceleration_rate_regulation_gui, + volume_original_mix, + volume_translated_mix, + sub_type_output, + edit_sub_check, # TRUE BY DEFAULT + dummy_false_check, # dummy false + subs_edit_space, + avoid_overlap_gui, + vocal_refinement_gui, + literalize_numbers_gui, + segment_duration_limit_gui, + diarization_process_dropdown, + translate_process_dropdown, + input_srt, + main_output_type, + main_voiceless_track, + voice_imitation_gui, + voice_imitation_max_segments_gui, + voice_imitation_vocals_dereverb_gui, + voice_imitation_remove_previous_gui, + voice_imitation_method_gui, + wav_speaker_dereverb, + text_segmentation_scale_gui, + divide_text_segments_by_gui, + soft_subtitles_to_video_gui, + burn_subtitles_to_video_gui, + enable_cache_gui, + enable_custom_voice, + workers_custom_voice, + is_gui_dummy_check, + ], + outputs=subs_edit_space, + ).then( + play_sound_alert, [play_sound_gui], [sound_alert_notification] + ) + + # Run translate tts and complete + video_button.click( + SoniTr.batch_multilingual_media_conversion, + inputs=[ + video_input, + blink_input, + directory_input, + HFKEY, + PREVIEW, + WHISPER_MODEL_SIZE, + batch_size, + compute_type, + SOURCE_LANGUAGE, + TRANSLATE_AUDIO_TO, + min_speakers, + max_speakers, + tts_voice00, + tts_voice01, + tts_voice02, + tts_voice03, + tts_voice04, + tts_voice05, + tts_voice06, + tts_voice07, + tts_voice08, + tts_voice09, + tts_voice10, + tts_voice11, + VIDEO_OUTPUT_NAME, + AUDIO_MIX, + audio_accelerate, + acceleration_rate_regulation_gui, + volume_original_mix, + volume_translated_mix, + sub_type_output, + dummy_false_check, + edit_sub_check, + subs_edit_space, + avoid_overlap_gui, + vocal_refinement_gui, + literalize_numbers_gui, + segment_duration_limit_gui, + diarization_process_dropdown, + translate_process_dropdown, + input_srt, + main_output_type, + main_voiceless_track, + voice_imitation_gui, + voice_imitation_max_segments_gui, + voice_imitation_vocals_dereverb_gui, + voice_imitation_remove_previous_gui, + voice_imitation_method_gui, + wav_speaker_dereverb, + text_segmentation_scale_gui, + divide_text_segments_by_gui, + soft_subtitles_to_video_gui, + burn_subtitles_to_video_gui, + enable_cache_gui, + enable_custom_voice, + workers_custom_voice, + is_gui_dummy_check, + ], + outputs=video_output, + trigger_mode="multiple", + ).then( + play_sound_alert, [play_sound_gui], [sound_alert_notification] + ) + + # Run docs process + docs_button.click( + SoniTr.multilingual_docs_conversion, + inputs=[ + text_docs, + input_docs, + directory_input_docs, + docs_SOURCE_LANGUAGE, + docs_TRANSLATE_TO, + tts_documents, + docs_OUTPUT_NAME, + docs_translate_process_dropdown, + docs_output_type, + docs_chunk_size, + enable_custom_voice, + workers_custom_voice, + start_page_gui, + end_page_gui, + videobook_width_gui, + videobook_height_gui, + videobook_bcolor_gui, + docs_dummy_check, + ], + outputs=docs_output, + trigger_mode="multiple", + ).then( + play_sound_alert, [play_sound_gui], [sound_alert_notification] + ) + + return app + + +def get_language_config(language_data, language=None, base_key="english"): + base_lang = language_data.get(base_key) + + if language not in language_data: + logger.error( + f"Language {language} not found, defaulting to {base_key}" + ) + return base_lang + + lg_conf = language_data.get(language, {}) + lg_conf.update((k, v) for k, v in base_lang.items() if k not in lg_conf) + + return lg_conf + + +def create_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + parser.add_argument( + "--theme", + type=str, + default="Taithrah/Minimal", + help=( + "Specify the theme; find themes in " + "https://huggingface.co/spaces/gradio/theme-gallery;" + " Example: --theme aliabid94/new-theme" + ), + ) + parser.add_argument( + "--public_url", + action="store_true", + default=False, + help="Enable public link", + ) + parser.add_argument( + "--logs_in_gui", + action="store_true", + default=False, + help="Displays the operations performed in Logs", + ) + parser.add_argument( + "--verbosity_level", + type=str, + default="info", + help=( + "Set logger verbosity level: " + "debug, info, warning, error, or critical" + ), + ) + parser.add_argument( + "--language", + type=str, + default="english", + help=" Select the language of the interface: english, spanish", + ) + parser.add_argument( + "--cpu_mode", + action="store_true", + default=False, + help="Enable CPU mode to run the program without utilizing GPU acceleration.", + ) + return parser + + +if __name__ == "__main__": + + parser = create_parser() + + args = parser.parse_args() + # Simulating command-line arguments + # args_list = "--theme aliabid94/new-theme --public_url".split() + # args = parser.parse_args(args_list) + + set_logging_level(args.verbosity_level) + + for id_model in UVR_MODELS: + download_manager( + os.path.join(MDX_DOWNLOAD_LINK, id_model), mdxnet_models_dir + ) + + models_path, index_path = upload_model_list() + + SoniTr = SoniTranslate(cpu_mode=args.cpu_mode) + + lg_conf = get_language_config(language_data, language=args.language) + + app = create_gui(args.theme, logs_in_gui=args.logs_in_gui) + + app.queue() + + app.launch( + max_threads=1, + share=args.public_url, + show_error=True, + quiet=False, + debug=(True if logger.isEnabledFor(logging.DEBUG) else False), + )