913 lines
39 KiB
Python
913 lines
39 KiB
Python
import html
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import json
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import random
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import subprocess
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import time
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import os
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import requests
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import threading
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import signal
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import sys
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import atexit
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from pathlib import Path
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from datetime import datetime, timedelta
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import re
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import numpy as np
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import soundfile as sf
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import uuid
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######################################
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#### ALLTALK ALLOWED STARTUP TIME ####
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######################################
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startup_wait_time = 120
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# You can change the above setting to a larger number to allow AllTAlk more time to start up. The default setting is 120 seconds (2 minutes).
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# On some older systems you may need to allow AllTalk more time. So you could set it to 240 (4 minutes) which will give AllTalk more to load.
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#################################################################
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#### LOAD PARAMS FROM confignew.json - REQUIRED FOR BRANDING ####
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#################################################################
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# STARTUP VARIABLE - Create "this_dir" variable as the current script directory
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this_dir = Path(__file__).parent.resolve()
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# load config file in and get settings
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def load_config(file_path):
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with open(file_path, "r") as config_file:
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config = json.load(config_file)
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return config
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config_file_path = this_dir / "confignew.json"
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# Load the params dictionary from the confignew.json file
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params = load_config(config_file_path)
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# Required for sentence splitting
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try:
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from TTS.api import TTS
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from TTS.utils.synthesizer import Synthesizer
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except ModuleNotFoundError:
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logger.error(
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f"[{params['branding']}]\033[91mWarning\033[0m Could not find the TTS module. Make sure to install the requirements for the {params['branding']} extension."
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f"[{params['branding']}]\033[91mWarning\033[0m Linux / Mac:\npip install -r /alltalk_tts/requirements.txt\n"
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f"[{params['branding']}]\033[91mWarning\033[0m Windows:\npip install -r \\alltalk_tts\\requirements.txt\n"
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f"[{params['branding']}]\033[91mWarning\033[0m If you used the one-click installer, paste the command above in the terminal window launched after running the cmd_ script. On Windows, that's cmd_windows.bat."
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)
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raise
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# IMPORT - Attempt Importing DeepSpeed (required for displaying Deepspeed checkbox in gradio)
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try:
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import deepspeed
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deepspeed_installed = True
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except ImportError:
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deepspeed_installed = False
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# Import gradio if being used within text generation webUI
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try:
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import gradio as gr
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from modules import chat, shared, ui_chat
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from modules.logging_colors import logger
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from modules.ui import create_refresh_button
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from modules.utils import gradio
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# This is set to check if the script is being run within text generation webui or as a standalone script. False is running as part of text gen web ui or a gradio interface
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running_in_standalone = False
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output_folder_wav = params["output_folder_wav"]
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except ModuleNotFoundError:
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output_folder_wav = params["output_folder_wav_standalone"]
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print(f"[{params['branding']}Startup] Running script.py in standalone mode")
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# This is set to check if the script is being run within text generation webui or as a standalone script. true means standalone
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running_in_standalone = True
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###########################
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#### STARTUP VARIABLES ####
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###########################
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# STARTUP VARIABLE - Import languges file for Gradio to be able to display them in the interface
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with open(this_dir / "languages.json", encoding="utf8") as f:
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languages = json.load(f)
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# Create a global lock
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process_lock = threading.Lock()
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# Base setting for a possible FineTuned model existing and the loader being available
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tts_method_xtts_ft = False
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# Gather the voice files
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def get_available_voices():
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return sorted([voice.name for voice in Path(f"{this_dir}/voices").glob("*.wav")])
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#########################
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#### LICENSE DISPLAY ####
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#########################
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# STARTUP Display Licence Information
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print(f"[{params['branding']}Startup] \033[94mCoqui Public Model License\033[0m")
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print(f"[{params['branding']}Startup] \033[94mhttps://coqui.ai/cpml.txt\033[0m")
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############################################
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#### DELETE OLD OUTPUT WAV FILES IF SET ####
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############################################
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def delete_old_files(folder_path, days_to_keep):
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current_time = datetime.now()
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print(
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f"[{params['branding']}Startup] Deletion of old output folder WAV files is currently enabled and set at",
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delete_output_wavs_setting,
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)
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for file_name in os.listdir(folder_path):
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file_path = os.path.join(folder_path, file_name)
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if os.path.isfile(file_path):
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file_creation_time = datetime.fromtimestamp(os.path.getctime(file_path))
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age = current_time - file_creation_time
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if age > timedelta(days=days_to_keep):
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os.remove(file_path)
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# Extract settings using params dictionary
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delete_output_wavs_setting = params["delete_output_wavs"]
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output_folder_wav = os.path.normpath(output_folder_wav)
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# Check and perform file deletion
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if delete_output_wavs_setting.lower() == "disabled":
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print(
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"["
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+ params["branding"]
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+ "Startup] Old output wav file deletion is set to disabled."
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)
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else:
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try:
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days_to_keep = int(delete_output_wavs_setting.split()[0])
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delete_old_files(output_folder_wav, days_to_keep)
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except ValueError:
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print(
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f"[{params['branding']}Startup] Invalid setting for deleting old wav files. Please use 'Disabled' or 'X Days' format."
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)
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########################
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#### STARTUP CHECKS ####
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########################
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# STARTUP Checks routine
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def check_required_files():
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this_dir = Path(__file__).parent.resolve()
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download_script_path = this_dir / "modeldownload.py"
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subprocess.run(["python", str(download_script_path)])
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print(f"[{params['branding']}Startup] All required files are present.")
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# STARTUP Call Check routine
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check_required_files()
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##################################################
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#### Check to see if a finetuned model exists ####
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##################################################
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# Set the path to the directory
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trained_model_directory = this_dir / "models" / "trainedmodel"
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# Check if the directory "trainedmodel" exists
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finetuned_model = trained_model_directory.exists()
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# If the directory exists, check for the existence of the required files
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# If true, this will add a extra option in the Gradio interface for loading Xttsv2 FT
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if finetuned_model:
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required_files = ["model.pth", "config.json", "vocab.json"]
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finetuned_model = all((trained_model_directory / file).exists() for file in required_files)
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if finetuned_model:
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print(f"[{params['branding']}Startup] Finetuned model \033[93mDetected\033[0m")
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####################################################
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#### SET GRADIO BUTTONS BASED ON confignew.json ####
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####################################################
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if params["tts_method_api_tts"] == True:
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gr_modelchoice = "API TTS"
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elif params["tts_method_api_local"] == True:
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gr_modelchoice = "API Local"
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elif params["tts_method_xtts_local"] == True:
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gr_modelchoice = "XTTSv2 Local"
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# Set the default for Narrated text without asterisk or quotes to be Narrator
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non_quoted_text_is = True
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######################
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#### GRADIO STUFF ####
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######################
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def remove_tts_from_history(history):
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for i, entry in enumerate(history["internal"]):
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history["visible"][i] = [history["visible"][i][0], entry[1]]
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return history
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def toggle_text_in_history(history):
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for i, entry in enumerate(history["visible"]):
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visible_reply = entry[1]
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if visible_reply.startswith("<audio"):
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if params["show_text"]:
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reply = history["internal"][i][1]
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history["visible"][i] = [
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history["visible"][i][0],
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f"{visible_reply.split('</audio>')[0]}</audio>\n\n{reply}",
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]
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else:
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history["visible"][i] = [
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history["visible"][i][0],
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f"{visible_reply.split('</audio>')[0]}</audio>",
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]
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return history
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def history_modifier(history):
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# Remove autoplay from the last reply
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if len(history["internal"]) > 0:
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history["visible"][-1] = [
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history["visible"][-1][0],
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history["visible"][-1][1].replace("controls autoplay>", "controls>"),
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]
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return history
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######################################
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#### SUBPROCESS/WEBSERVER STARTUP ####
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######################################
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base_url = f"http://{params['ip_address']}:{params['port_number']}"
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script_path = this_dir / "tts_server.py"
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def signal_handler(sig, frame):
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print(
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f"[{params['branding']}Shutdown] \033[94mReceived Ctrl+C, terminating subprocess\033[92m"
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)
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if process.poll() is None:
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process.terminate()
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process.wait() # Wait for the subprocess to finish
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sys.exit(0)
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# Attach the signal handler to the SIGINT signal (Ctrl+C)
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signal.signal(signal.SIGINT, signal_handler)
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# Start the subprocess
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process = subprocess.Popen(["python", script_path])
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# Check if the subprocess has started successfully
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if process.poll() is None:
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print(f"[{params['branding']}Startup] TTS Subprocess starting")
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print(f"[{params['branding']}Startup]")
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print(
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f"[{params['branding']}Startup] \033[94m {params['branding']}Settings & Documentation:\033[00m",
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f"\033[92mhttp://{params['ip_address']}:{params['port_number']}\033[00m",
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)
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print(f"[{params['branding']}Startup]")
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else:
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print(
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f"[{params['branding']}Startup] \033[91mWarning\033[0m TTS Subprocess Webserver failing to start process"
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)
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print(
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f"[{params['branding']}Startup] \033[91mWarning\033[0m It could be that you have something on port:",
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params["port_number"],
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)
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print(
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f"[{params['branding']}Startup] \033[91mWarning\033[0m Or you have not started in a Python environement with all the necesssary bits installed"
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)
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print(
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f"[{params['branding']}Startup] \033[91mWarning\033[0m Check you are starting Text-generation-webui with either the start_xxxxx file or the Python environment with cmd_xxxxx file."
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)
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print(
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f"[{params['branding']}Startup] \033[91mWarning\033[0m xxxxx is the type of OS you are on e.g. windows, linux or mac."
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)
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print(
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f"[{params['branding']}Startup] \033[91mWarning\033[0m Alternatively, you could check no other Python processes are running that shouldnt be e.g. Restart your computer is the simple way."
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)
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# Cleanly kill off this script, but allow text-generation-webui to keep running, albeit without this alltalk_tts
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sys.exit(1)
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timeout = startup_wait_time # Gather timeout setting from startup_wait_time
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# Introduce a delay before starting the check loop
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time.sleep(26) # Wait 26 secs before checking if the tts_server.py has started up.
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start_time = time.time()
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while time.time() - start_time < timeout:
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try:
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response = requests.get(f"{base_url}/ready")
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if response.status_code == 200:
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break
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except requests.RequestException as e:
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# Print the exception for debugging purposes
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print(
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f"[{params['branding']}Startup] \033[91mWarning\033[0m TTS Subprocess has NOT started up yet, Will keep trying for {timeout} seconds maximum. Please wait."
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)
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time.sleep(5)
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else:
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print(f"\n[{params['branding']}Startup] Startup timed out. Full help available here \033[92mhttps://github.com/erew123/alltalk_tts#-help-with-problems\033[0m")
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print(f"[{params['branding']}Startup] On older system you may wish to open and edit \033[94mscript.py\033[0m with a text editor and changing the")
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print(f"[{params['branding']}Startup] \033[94mstartup_wait_time = 120\033[0m setting to something like \033[94mstartup_wait_time = 240\033[0m as this will allow")
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print(f"[{params['branding']}Startup] AllTalk more time to try load the model into your VRAM. Otherise please visit the Github for")
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print(f"[{params['branding']}Startup] a list of other possible troubleshooting options.")
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# Cleanly kill off this script, but allow text-generation-webui to keep running, albeit without this alltalk_tts
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sys.exit(1)
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#####################################
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#### MODEL LOADING AND UNLOADING ####
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#####################################
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# MODEL - Swap model based on Gradio selection API TTS, API Local, XTTSv2 Local
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def send_reload_request(tts_method):
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global tts_method_xtts_ft
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try:
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params["tts_model_loaded"] = False
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url = f"{base_url}/api/reload"
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payload = {"tts_method": tts_method}
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response = requests.post(url, params=payload)
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response.raise_for_status() # Raises an HTTPError for bad responses
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json_response = response.json()
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# Check if the reload operation was successful
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if json_response.get("status") == "model-success":
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# Update tts_tts_model_loaded to True if the reload was successful
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params["tts_model_loaded"] = True
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# Update local script parameters based on the tts_method
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if tts_method == "API TTS":
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params["tts_method_api_local"] = False
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params["tts_method_xtts_local"] = False
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params["tts_method_api_tts"] = True
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params["deepspeed_activate"] = False
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audio_path = this_dir / "templates" / "apitts.wav"
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tts_method_xtts_ft = False
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elif tts_method == "API Local":
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params["tts_method_api_tts"] = False
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params["tts_method_xtts_local"] = False
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params["tts_method_api_local"] = True
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params["deepspeed_activate"] = False
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audio_path = this_dir / "templates" / "apilocal.wav"
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tts_method_xtts_ft = False
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elif tts_method == "XTTSv2 Local":
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params["tts_method_api_tts"] = False
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params["tts_method_api_local"] = False
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params["tts_method_xtts_local"] = True
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audio_path = this_dir / "templates" / "xttslocal.wav"
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tts_method_xtts_ft = False
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elif tts_method == "XTTSv2 FT":
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params["tts_method_api_tts"] = False
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params["tts_method_api_local"] = False
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params["tts_method_xtts_local"] = False
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audio_path = this_dir / "templates" / "xttsfinetuned.wav"
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tts_method_xtts_ft = True
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return f'<audio src="file/{audio_path}" controls autoplay></audio>'
|
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except requests.exceptions.RequestException as e:
|
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# Handle the HTTP request error
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print(
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f"[{params['branding']}Server] \033[91mWarning\033[0m Error during request to webserver process: {e}"
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)
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return {"status": "error", "message": str(e)}
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|
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##################
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#### LOW VRAM ####
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##################
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# LOW VRAM - Gradio Checkbox handling
|
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def send_lowvram_request(low_vram):
|
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try:
|
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params["tts_model_loaded"] = False
|
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if low_vram:
|
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audio_path = this_dir / "templates" / "lowvramenabled.wav"
|
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else:
|
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audio_path = this_dir / "templates" / "lowvramdisabled.wav"
|
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url = f"{base_url}/api/lowvramsetting?new_low_vram_value={low_vram}"
|
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headers = {"Content-Type": "application/json"}
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response = requests.post(url, headers=headers)
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response.raise_for_status() # Raises an HTTPError for bad responses
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json_response = response.json()
|
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# Check if the low VRAM request was successful
|
|
if json_response.get("status") == "lowvram-success":
|
|
# Update any relevant variables or perform other actions on success
|
|
params["tts_model_loaded"] = True
|
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return f'<audio src="file/{audio_path}" controls autoplay></audio>'
|
|
except requests.exceptions.RequestException as e:
|
|
# Handle the HTTP request error
|
|
print(
|
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f"[{params['branding']}Server] \033[91mWarning\033[0m Error during request to webserver process: {e}"
|
|
)
|
|
return {"status": "error", "message": str(e)}
|
|
|
|
|
|
###################
|
|
#### DeepSpeed ####
|
|
###################
|
|
# DEEPSPEED - Reload the model when DeepSpeed checkbox is enabled/disabled
|
|
def send_deepspeed_request(deepspeed_param):
|
|
try:
|
|
params["tts_model_loaded"] = False
|
|
if deepspeed_param:
|
|
audio_path = this_dir / "templates" / "deepspeedenabled.wav"
|
|
else:
|
|
audio_path = this_dir / "templates" / "deepspeeddisabled.wav"
|
|
|
|
url = f"{base_url}/api/deepspeed?new_deepspeed_value={deepspeed_param}"
|
|
headers = {"Content-Type": "application/json"}
|
|
response = requests.post(url, headers=headers)
|
|
response.raise_for_status() # Raises an HTTPError for bad responses
|
|
json_response = response.json()
|
|
# Check if the deepspeed request was successful
|
|
if json_response.get("status") == "deepspeed-success":
|
|
# Update any relevant variables or perform other actions on success
|
|
params["tts_model_loaded"] = True
|
|
return f'<audio src="file/{audio_path}" controls autoplay></audio>'
|
|
except requests.exceptions.RequestException as e:
|
|
# Handle the HTTP request error
|
|
print(
|
|
f"[{params['branding']}Server] \033[91mWarning\033[0m Error during request to webserver process: {e}"
|
|
)
|
|
return {"status": "error", "message": str(e)}
|
|
|
|
|
|
# DEEPSPEED - Display DeepSpeed Checkbox Yes or No
|
|
deepspeed_condition = params["tts_method_xtts_local"] == "True" and deepspeed_installed
|
|
|
|
|
|
#############################################################
|
|
#### TTS STRING CLEANING & PROCESSING PRE SENDING TO TTS ####
|
|
#############################################################
|
|
def new_split_into_sentences(self, text):
|
|
sentences = self.seg.segment(text)
|
|
if params["remove_trailing_dots"]:
|
|
sentences_without_dots = []
|
|
for sentence in sentences:
|
|
if sentence.endswith(".") and not sentence.endswith("..."):
|
|
sentence = sentence[:-1]
|
|
|
|
sentences_without_dots.append(sentence)
|
|
|
|
return sentences_without_dots
|
|
else:
|
|
return sentences
|
|
|
|
|
|
Synthesizer.split_into_sentences = new_split_into_sentences
|
|
|
|
|
|
# Check model is loaded and string isnt empty, before sending a TTS request.
|
|
def before_audio_generation(string, params):
|
|
# Check Model is loaded into cuda or cpu and error if not
|
|
if not params["tts_model_loaded"]:
|
|
print(
|
|
f"[{params['branding']}Model] \033[91mWarning\033[0m Model is still loading, please wait before trying to generate TTS"
|
|
)
|
|
return
|
|
string = html.unescape(string) or random_sentence()
|
|
if string == "":
|
|
return "*Empty string*"
|
|
return string
|
|
|
|
|
|
##################
|
|
#### Narrator ####
|
|
##################
|
|
def combine(audio_files, output_folder, state):
|
|
audio = np.array([])
|
|
|
|
for audio_file in audio_files:
|
|
audio_data, sample_rate = sf.read(audio_file)
|
|
# Ensure all audio files have the same sample rate
|
|
if audio.size == 0:
|
|
audio = audio_data
|
|
else:
|
|
audio = np.concatenate((audio, audio_data))
|
|
|
|
# Save the combined audio to a file with a specified sample rate
|
|
if "character_menu" in state:
|
|
output_file_path = os.path.join(output_folder, f'{state["character_menu"]}_{int(time.time())}_combined.wav')
|
|
else:
|
|
output_file_path = os.path.join(output_folder, f'TTSOUT_{int(time.time())}_combined.wav')
|
|
sf.write(output_file_path, audio, samplerate=sample_rate)
|
|
# Clean up unnecessary files
|
|
for audio_file in audio_files:
|
|
os.remove(audio_file)
|
|
|
|
return output_file_path
|
|
|
|
|
|
################################
|
|
#### TTS PREVIEW GENERATION ####
|
|
################################
|
|
# PREVIEW VOICE - Generate Random Sentence if Voice Preview box is empty
|
|
def random_sentence():
|
|
with open(this_dir / "harvard_sentences.txt") as f:
|
|
return random.choice(list(f))
|
|
|
|
|
|
# PREVIEW VOICE- Generate TTS Function
|
|
def voice_preview(string):
|
|
if not params["activate"]:
|
|
return string
|
|
# Clean the string, capture model not loaded, and move model to cuda if needed
|
|
cleaned_string = before_audio_generation(string, params)
|
|
if cleaned_string is None:
|
|
return
|
|
string = cleaned_string
|
|
# Setup the output file
|
|
output_file = Path(params["output_folder_wav"]) / "voice_preview.wav"
|
|
# Generate the audio
|
|
language_code = languages.get(params["language"])
|
|
temperature = params["local_temperature"]
|
|
repetition_penalty = params["local_repetition_penalty"]
|
|
# Convert the WindowsPath object to a string before using it in JSON payload
|
|
output_file_str = output_file.as_posix()
|
|
# Lock before making the generate request
|
|
with process_lock:
|
|
generate_response = send_generate_request(
|
|
string, params["voice"], language_code, temperature, repetition_penalty, output_file_str
|
|
)
|
|
# Check if lock is already acquired
|
|
if process_lock.locked():
|
|
print(
|
|
f"[{params['branding']}Model] \033[91mWarning\033[0m Audio generation is already in progress. Please wait."
|
|
)
|
|
return
|
|
if generate_response.get("status") == "generate-success":
|
|
# Handle Gradio and playback
|
|
autoplay = "autoplay" if params["autoplay"] else ""
|
|
return f'<audio src="file/{output_file_str}?{int(time.time())}" controls {autoplay}></audio>'
|
|
else:
|
|
# Handle the case where audio generation was not successful
|
|
return f"[{params['branding']}Server] Audio generation failed. Status: {generate_response.get('status')}"
|
|
|
|
|
|
def process_text(text):
|
|
# Normalize HTML encoded quotes
|
|
text = html.unescape(text)
|
|
|
|
# Replace ellipsis with a single dot
|
|
text = re.sub(r'\.{3,}', '.', text)
|
|
|
|
# Pattern to identify combined narrator and character speech
|
|
combined_pattern = r'(\*[^*"]+\*|"[^"*]+")'
|
|
|
|
# List to hold parts of speech along with their type
|
|
ordered_parts = []
|
|
|
|
# Track the start of the next segment
|
|
start = 0
|
|
|
|
# Find all matches
|
|
for match in re.finditer(combined_pattern, text):
|
|
# Add the text before the match, if any, as ambiguous
|
|
if start < match.start():
|
|
ambiguous_text = text[start:match.start()].strip()
|
|
if ambiguous_text:
|
|
ordered_parts.append(('ambiguous', ambiguous_text))
|
|
|
|
# Add the matched part as either narrator or character
|
|
matched_text = match.group(0)
|
|
if matched_text.startswith('*') and matched_text.endswith('*'):
|
|
ordered_parts.append(('narrator', matched_text.strip('*').strip()))
|
|
elif matched_text.startswith('"') and matched_text.endswith('"'):
|
|
ordered_parts.append(('character', matched_text.strip('"').strip()))
|
|
else:
|
|
# In case of mixed or improperly formatted parts
|
|
if '*' in matched_text:
|
|
ordered_parts.append(('narrator', matched_text.strip('*').strip('"')))
|
|
else:
|
|
ordered_parts.append(('character', matched_text.strip('"').strip('*')))
|
|
|
|
# Update the start of the next segment
|
|
start = match.end()
|
|
|
|
# Add any remaining text after the last match as ambiguous
|
|
if start < len(text):
|
|
ambiguous_text = text[start:].strip()
|
|
if ambiguous_text:
|
|
ordered_parts.append(('ambiguous', ambiguous_text))
|
|
|
|
return ordered_parts
|
|
|
|
#################################
|
|
#### TTS STANDARD GENERATION ####
|
|
#################################
|
|
# STANDARD VOICE - Generate TTS Function
|
|
def output_modifier(string, state):
|
|
if not params["activate"]:
|
|
return string
|
|
original_string = string
|
|
#print("ORIGINAL STRING IS:", original_string)
|
|
cleaned_string = before_audio_generation(string, params)
|
|
if cleaned_string is None:
|
|
return
|
|
string = cleaned_string
|
|
language_code = languages.get(params["language"])
|
|
temperature = params["local_temperature"]
|
|
repetition_penalty = params["local_repetition_penalty"]
|
|
# Create a list to store generated audio paths
|
|
audio_files = []
|
|
if process_lock.acquire(blocking=False):
|
|
try:
|
|
if params["narrator_enabled"]:
|
|
processed_parts = process_text(original_string)
|
|
|
|
audio_files_all_paragraphs = []
|
|
for part_type, part in processed_parts:
|
|
# Skip parts that are too short
|
|
if len(part.strip()) <= 3:
|
|
continue
|
|
|
|
# Determine the voice to use based on the part type
|
|
if part_type == 'narrator':
|
|
voice_to_use = params["narrator_voice"]
|
|
print(f"[{params['branding']}TTSGen] \033[92mNarrator\033[0m") # Green
|
|
elif part_type == 'character':
|
|
voice_to_use = params["voice"]
|
|
print(f"[{params['branding']}TTSGen] \033[36mCharacter\033[0m") # Yellow
|
|
else:
|
|
# Handle ambiguous parts based on user preference
|
|
voice_to_use = params["voice"] if non_quoted_text_is else params["narrator_voice"]
|
|
voice_description = "\033[36mCharacter (Text-not-inside)\033[0m" if non_quoted_text_is else "\033[92mNarrator (Text-not-inside)\033[0m"
|
|
print(f"[{params['branding']}TTSGen] {voice_description}")
|
|
|
|
# Replace multiple exclamation marks, question marks, or other punctuation with a single instance
|
|
cleaned_part = re.sub(r'([!?.])\1+', r'\1', part)
|
|
# Further clean to remove any other unwanted characters
|
|
cleaned_part = re.sub(r'[^a-zA-Z0-9\s\.,;:!?\-\'"\u0400-\u04FFÀ-ÿ\u0150\u0151\u0170\u0171\$]', '', cleaned_part)
|
|
# Remove all newline characters (single or multiple)
|
|
cleaned_part = re.sub(r'\n+', ' ', cleaned_part)
|
|
|
|
# Generate TTS and output to a file
|
|
output_filename = get_output_filename(state)
|
|
generate_response = send_generate_request(
|
|
cleaned_part, voice_to_use, language_code, temperature, repetition_penalty, output_filename
|
|
)
|
|
audio_path = generate_response.get("data", {}).get("audio_path")
|
|
audio_files_all_paragraphs.append(audio_path)
|
|
|
|
# Combine audio files across paragraphs
|
|
final_output_file = combine(
|
|
audio_files_all_paragraphs, params["output_folder_wav"], state
|
|
)
|
|
else:
|
|
# Decode HTML entities first
|
|
cleaned_part = html.unescape(original_string)
|
|
# Replace multiple instances of certain punctuation marks with a single instance
|
|
cleaned_part = re.sub(r'([!?.])\1+', r'\1', cleaned_part)
|
|
# Further clean to remove any other unwanted characters
|
|
cleaned_part = re.sub(r'[^a-zA-Z0-9\s\.,;:!?\-\'"\u0400-\u04FFÀ-ÿ\u0150\u0151\u0170\u0171\$]', '', cleaned_part)
|
|
# Remove all newline characters (single or multiple)
|
|
cleaned_part = re.sub(r'\n+', ' ', cleaned_part)
|
|
# Process the part and give it a non-character name if being used vai API or standalone.
|
|
if "character_menu" in state:
|
|
output_file = Path(f'{params["output_folder_wav"]}/{state["character_menu"]}_{int(time.time())}.wav')
|
|
else:
|
|
output_file = Path(f'{params["output_folder_wav"]}/TTSOUT_{int(time.time())}.wav')
|
|
output_file_str = output_file.as_posix()
|
|
output_file = get_output_filename(state)
|
|
generate_response = send_generate_request(
|
|
cleaned_part, params["voice"], language_code, temperature, repetition_penalty, output_file_str
|
|
)
|
|
audio_path = generate_response.get("data", {}).get("audio_path")
|
|
final_output_file = audio_path
|
|
finally:
|
|
# Always release the lock, whether an exception occurs or not
|
|
process_lock.release()
|
|
else:
|
|
# The lock is already acquired
|
|
print(
|
|
f"[{params['branding']}Model] \033[91mWarning\033[0m Audio generation is already in progress. Please wait."
|
|
)
|
|
return
|
|
|
|
if generate_response.get("status") == "generate-success":
|
|
audio_path = generate_response.get("data", {}).get("audio_path")
|
|
if audio_path:
|
|
# Handle Gradio and playback
|
|
autoplay = "autoplay" if params["autoplay"] else ""
|
|
string = (
|
|
f'<audio src="file/{final_output_file}" controls {autoplay}></audio>'
|
|
)
|
|
|
|
if params["show_text"]:
|
|
string += f"\n\n{original_string}"
|
|
shared.processing_message = "*Is typing...*"
|
|
|
|
return string
|
|
else:
|
|
print(
|
|
f"[{params['branding']}Server] \033[91mWarning\033[0m No audio path in the response."
|
|
)
|
|
else:
|
|
print(
|
|
f"[{params['branding']}Server] \033[91mWarning\033[0m Audio generation failed. Status:",
|
|
generate_response.get("message"),
|
|
)
|
|
|
|
|
|
def get_output_filename(state):
|
|
if "character_menu" in state:
|
|
return Path(f'{params["output_folder_wav"]}/{state["character_menu"]}_{str(uuid.uuid4())[:8]}.wav').as_posix()
|
|
else:
|
|
return Path(f'{params["output_folder_wav"]}/TTSOUT_{str(uuid.uuid4())[:8]}.wav').as_posix()
|
|
|
|
|
|
###############################################
|
|
#### SEND GENERATION REQUEST TO TTS ENGINE ####
|
|
###############################################
|
|
def send_generate_request(text, voice, language, temperature, repetition_penalty, output_file):
|
|
url = f"{base_url}/api/generate"
|
|
payload = {
|
|
"text": text,
|
|
"voice": voice,
|
|
"language": language,
|
|
"temperature": temperature,
|
|
"repetition_penalty": repetition_penalty,
|
|
"output_file": output_file,
|
|
}
|
|
headers = {"Content-Type": "application/json"}
|
|
response = requests.post(url, json=payload, headers=headers)
|
|
return response.json()
|
|
|
|
|
|
################################
|
|
#### SUBPORCESS TERMINATION ####
|
|
################################
|
|
# Register the termination code to be executed at exit
|
|
atexit.register(lambda: process.terminate() if process.poll() is None else None)
|
|
|
|
|
|
######################
|
|
#### GRADIO STUFF ####
|
|
######################
|
|
def state_modifier(state):
|
|
if not params["activate"]:
|
|
return state
|
|
|
|
state["stream"] = False
|
|
return state
|
|
|
|
def update_narrator_enabled(value):
|
|
if value == "Enabled":
|
|
params["narrator_enabled"] = True
|
|
elif value == "Disabled":
|
|
params["narrator_enabled"] = False
|
|
|
|
def update_non_quoted_text_is(value):
|
|
global non_quoted_text_is
|
|
if value == "Narrator":
|
|
non_quoted_text_is = False
|
|
elif value == "Char":
|
|
non_quoted_text_is = True
|
|
|
|
def input_modifier(string, state):
|
|
if not params["activate"]:
|
|
return string
|
|
|
|
shared.processing_message = "*Is recording a voice message...*"
|
|
return string
|
|
|
|
|
|
def ui():
|
|
with gr.Accordion(params["branding"] + " TTS (XTTSv2)"):
|
|
# Activate alltalk_tts, Enable autoplay, Show text
|
|
with gr.Row():
|
|
activate = gr.Checkbox(value=params['activate'], label='Enable TTS')
|
|
autoplay = gr.Checkbox(value=params['autoplay'], label='Autoplay TTS')
|
|
show_text = gr.Checkbox(value=params['show_text'], label='Show Text')
|
|
|
|
# Low vram enable, Deepspeed enable, Remove trailing dots
|
|
with gr.Row():
|
|
low_vram = gr.Checkbox(value=params['low_vram'], label='Enable Low VRAM Mode')
|
|
low_vram_play = gr.HTML(visible=False)
|
|
deepspeed_checkbox = gr.Checkbox(value=params['deepspeed_activate'], label='Enable DeepSpeed', visible=deepspeed_installed)
|
|
deepspeed_checkbox_play = gr.HTML(visible=False)
|
|
remove_trailing_dots = gr.Checkbox(value=params['remove_trailing_dots'], label='Remove trailing "."')
|
|
|
|
# TTS method, Character voice selection
|
|
with gr.Row():
|
|
model_loader_choices=["API TTS", "API Local", "XTTSv2 Local"]
|
|
if finetuned_model:
|
|
model_loader_choices.append("XTTSv2 FT")
|
|
tts_radio_buttons = gr.Radio(
|
|
choices=model_loader_choices,
|
|
label="TTS Method (Each method sounds slightly different)",
|
|
value=gr_modelchoice, # Set the default value
|
|
)
|
|
tts_radio_buttons_play = gr.HTML(visible=False)
|
|
with gr.Row():
|
|
available_voices = get_available_voices()
|
|
default_voice = params["voice"] # Check if the default voice is in the list of available voices
|
|
|
|
if default_voice not in available_voices:
|
|
default_voice = available_voices[ 0 ] # Choose the first available voice as the default
|
|
# Add allow_custom_value=True to the Dropdown
|
|
voice = gr.Dropdown(available_voices, label="Character Voice", value=default_voice, allow_custom_value=True, )
|
|
create_refresh_button(voice, lambda: None, lambda: {"choices": get_available_voices(), "value": params["voice"],}, "refresh-button",)
|
|
|
|
# Language, Narrator voice
|
|
with gr.Row():
|
|
language = gr.Dropdown(languages.keys(), label="Language", value=params["language"])
|
|
with gr.Row():
|
|
narrator_voice_gr = gr.Dropdown(get_available_voices(), label="Narrator Voice", allow_custom_value=True, value=params["narrator_voice"])
|
|
create_refresh_button(narrator_voice_gr, lambda: None, lambda: {"choices": get_available_voices(), "value": params["narrator_voice"],}, "refresh-button")
|
|
|
|
# Temperature, Repetition Penalty
|
|
with gr.Row():
|
|
local_temperature_gr = gr.Slider(minimum=0.05, maximum=1, step=0.05, label="Temperature", value=params["local_temperature"])
|
|
local_repetition_penalty_gr = gr.Slider(minimum=0.5, maximum=20, step=0.5, label="Repetition Penalty", value=params["local_repetition_penalty"])
|
|
|
|
# Narrator enable, Non quoted text, Explanation text
|
|
with gr.Row():
|
|
with gr.Row():
|
|
narrator_enabled_gr = gr.Radio(choices={"Enabled": "true", "Disabled": "false"}, label="Narrator", value="Enabled" if params.get("narrator_enabled") else "Disabled")
|
|
non_quoted_text_is_gr = gr.Radio(choices={"Character": "true", "Narrator": "false"}, label='Unmarked text NOT inside of * or " is', value="Character" if non_quoted_text_is else "Narrator")
|
|
explanation_text = gr.HTML(
|
|
f"<p>⚙️ <a href='http://{params['ip_address']}:{params['port_number']}'>Settings and Documentation Page</a><a href='http://{params['ip_address']}:{params['port_number']}'></a>⚙️<br>- Low VRAM Mode and Deepspeed take 15 seconds to be enabled or disabled.<br>- The DeepSpeed checkbox is only visible if DeepSpeed is present.</p>")
|
|
|
|
# Preview speech
|
|
with gr.Row():
|
|
preview_text = gr.Text(
|
|
show_label=False,
|
|
placeholder="Preview text",
|
|
elem_id="silero_preview_text",
|
|
)
|
|
preview_play = gr.Button("Preview")
|
|
preview_audio = gr.HTML(visible=False)
|
|
|
|
with gr.Row():
|
|
convert = gr.Button("Permanently replace audios with the message texts")
|
|
convert_cancel = gr.Button("Cancel", visible=False)
|
|
convert_confirm = gr.Button(
|
|
"Confirm (cannot be undone)", variant="stop", visible=False
|
|
)
|
|
|
|
# Convert history with confirmation
|
|
convert_arr = [convert_confirm, convert, convert_cancel]
|
|
convert.click(
|
|
lambda: [
|
|
gr.update(visible=True),
|
|
gr.update(visible=False),
|
|
gr.update(visible=True),
|
|
],
|
|
None,
|
|
convert_arr,
|
|
)
|
|
convert_confirm.click(
|
|
lambda: [
|
|
gr.update(visible=False),
|
|
gr.update(visible=True),
|
|
gr.update(visible=False),
|
|
],
|
|
None,
|
|
convert_arr,
|
|
).then(remove_tts_from_history, gradio("history"), gradio("history")).then(
|
|
chat.save_history,
|
|
gradio("history", "unique_id", "character_menu", "mode"),
|
|
None,
|
|
).then(
|
|
chat.redraw_html, gradio(ui_chat.reload_arr), gradio("display")
|
|
)
|
|
|
|
convert_cancel.click(
|
|
lambda: [
|
|
gr.update(visible=False),
|
|
gr.update(visible=True),
|
|
gr.update(visible=False),
|
|
],
|
|
None,
|
|
convert_arr,
|
|
)
|
|
|
|
# Toggle message text in history
|
|
show_text.change(lambda x: params.update({"show_text": x}), show_text, None).then(
|
|
toggle_text_in_history, gradio("history"), gradio("history")
|
|
).then(
|
|
chat.save_history,
|
|
gradio("history", "unique_id", "character_menu", "mode"),
|
|
None,
|
|
).then(
|
|
chat.redraw_html, gradio(ui_chat.reload_arr), gradio("display")
|
|
)
|
|
|
|
# Event functions to update the parameters in the backend
|
|
activate.change(lambda x: params.update({"activate": x}), activate, None)
|
|
autoplay.change(lambda x: params.update({"autoplay": x}), autoplay, None)
|
|
low_vram.change(lambda x: params.update({"low_vram": x}), low_vram, None)
|
|
low_vram.change(lambda x: send_lowvram_request(x), low_vram, low_vram_play, None)
|
|
tts_radio_buttons.change(send_reload_request, tts_radio_buttons, tts_radio_buttons_play, None)
|
|
deepspeed_checkbox.change(send_deepspeed_request, deepspeed_checkbox, deepspeed_checkbox_play, None)
|
|
remove_trailing_dots.change(
|
|
lambda x: params.update({"remove_trailing_dots": x}), remove_trailing_dots, None
|
|
)
|
|
voice.change(lambda x: params.update({"voice": x}), voice, None)
|
|
language.change(lambda x: params.update({"language": x}), language, None)
|
|
|
|
# TSS Settings
|
|
local_temperature_gr.change(lambda x: params.update({"local_temperature": x}), local_temperature_gr, None)
|
|
local_repetition_penalty_gr.change(lambda x: params.update({"local_repetition_penalty": x}), local_repetition_penalty_gr, None)
|
|
|
|
# Narrator selection actions
|
|
narrator_enabled_gr.change(update_narrator_enabled, narrator_enabled_gr, None)
|
|
non_quoted_text_is_gr.change(update_non_quoted_text_is, non_quoted_text_is_gr, None)
|
|
narrator_voice_gr.change(lambda x: params.update({"narrator_voice": x}), narrator_voice_gr, None)
|
|
|
|
# Play preview
|
|
preview_text.submit(voice_preview, preview_text, preview_audio)
|
|
preview_play.click(voice_preview, preview_text, preview_audio)
|
|
|
|
|
|
##########################################
|
|
#### STANDALONE MODE LOOP TERMINATION ####
|
|
##########################################
|
|
## Loop to keep the script from exiting out if its being run as a standalone script and not part of text-generation-webui
|
|
if running_in_standalone:
|
|
while True:
|
|
try:
|
|
time.sleep(1) # Add a small delay to avoid high CPU usage
|
|
except KeyboardInterrupt:
|
|
break # Allow graceful exit on Ctrl+C
|