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app_rvc.py
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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
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
)