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alltalk_tts/script.py
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erew123 ddbe382e67 scipy & plotly added
2024-11-22 19:24:56 +00:00

3018 lines
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import os
import sys
import json
import time
import atexit
import signal as system_signal
import inspect
import requests
import platform
import subprocess
import threading
import soundfile as sf
from pathlib import Path
from datetime import datetime, timedelta
from requests.exceptions import RequestException, ConnectionError
import whisper
import torch
import zipfile
from tqdm import tqdm
import gc
import shutil
import mimetypes
import numpy as np
from scipy import signal as scipy_signal
import plotly
"""
Note: The following function names are reserved for TGWUI integration.
When running under text-generation-webui, these functions will be imported from
system/TGWUI Extension/script.py when in TGWUI's Python environment/extensions dir.
Reserved names:
- output_modifier
- input_modifier
- state_modifier
- ui
- history_modifier
- remove_tts_from_history
- toggle_text_in_history
"""
TGWUI_AVAILABLE = False
def output_modifier(string, state):
"""Modify chat output (required for TGWUI)"""
try:
from .system.TGWUI_Extension.script import output_modifier as tgwui_output_modifier
except ImportError:
from system.TGWUI_Extension.script import output_modifier as tgwui_output_modifier
return tgwui_output_modifier(string, state)
def input_modifier(string, state):
"""Modify chat input (required for TGWUI)"""
try:
from .system.TGWUI_Extension.script import input_modifier as tgwui_input_modifier
except ImportError:
from system.TGWUI_Extension.script import input_modifier as tgwui_input_modifier
return tgwui_input_modifier(string, state)
def state_modifier(state):
"""Modify chat state (required for TGWUI)"""
try:
from .system.TGWUI_Extension.script import state_modifier as tgwui_state_modifier
except ImportError:
from system.TGWUI_Extension.script import state_modifier as tgwui_state_modifier
return tgwui_state_modifier(state)
def ui():
"""Create extension UI (required for TGWUI)"""
try:
from system.TGWUI_Extension.script import ui as tgwui_ui
return tgwui_ui()
except ImportError:
import gradio as gr
# Return empty interface if not in TGWUI
return gr.Blocks()
def history_modifier(history):
"""Modify chat history (required for TGWUI)"""
try:
from .system.TGWUI_Extension.script import history_modifier as tgwui_history_modifier
except ImportError:
from system.TGWUI_Extension.script import history_modifier as tgwui_history_modifier
return tgwui_history_modifier(history)
def remove_tts_from_history(history):
"""Remove TTS from history (required for TGWUI)"""
try:
from .system.TGWUI_Extension.script import remove_tts_from_history as tgwui_remove_tts
except ImportError:
from system.TGWUI_Extension.script import remove_tts_from_history as tgwui_remove_tts
return tgwui_remove_tts(history)
def toggle_text_in_history(history):
"""Toggle text in history (required for TGWUI)"""
try:
from .system.TGWUI_Extension.script import toggle_text_in_history as tgwui_toggle_text
except ImportError:
from system.TGWUI_Extension.script import toggle_text_in_history as tgwui_toggle_text
return tgwui_toggle_text(history)
try:
from modules import chat, shared, ui_chat
TGWUI_AVAILABLE = True
except ImportError:
class DummyShared:
processing_message = ""
class DummyState:
def __init__(self):
self.mode = 'chat' # Add default mode
shared = DummyShared()
# pylint: disable=all
#########################
# Central config loader #
#########################
# Confguration file management for confignew.json
try:
from .config import AlltalkConfig, AlltalkTTSEnginesConfig, AlltalkNewEnginesConfig # TGWUI import
from .system.gradio_pages.help_content import AllTalkHelpContent
except ImportError:
from config import AlltalkConfig, AlltalkTTSEnginesConfig, AlltalkNewEnginesConfig # Standalone import
from system.gradio_pages.help_content import AllTalkHelpContent
def initialize_configs():
"""Initialize all configuration instances"""
config = AlltalkConfig.get_instance()
tts_engines_config = AlltalkTTSEnginesConfig.get_instance()
new_engines_config = AlltalkNewEnginesConfig.get_instance()
return config, tts_engines_config, new_engines_config
# Load in configs
config, tts_engines_config, new_engines_config = initialize_configs()
config.save() # Force the config file to save in case it was missing new any settings
##########################
# Central print function #
##########################
def print_message(message, message_type="standard", component="TTS"):
"""Centralized print function for AllTalk messages
Args:
message (str): The message to print
message_type (str): Type of message (standard/warning/error/debug_*/debug)
component (str): Component identifier (TTS/ENG/GEN/API/etc.)
"""
# ANSI color codes
BLUE = "\033[94m"
MAGENTA = "\033[95m"
YELLOW = "\033[93m"
RED = "\033[91m"
GREEN = "\033[92m"
RESET = "\033[0m"
prefix = f"[{config.branding}{component}] "
if message_type.startswith("debug_"):
debug_flag = getattr(config.debugging, message_type, False)
if not debug_flag:
return
if message_type == "debug_func" and "Function entry:" in message:
message_parts = message.split("Function entry:", 1)
print(f"{prefix}{BLUE}Debug{RESET} {YELLOW}{message_type}{RESET} Function entry:{GREEN}{message_parts[1]}{RESET} script.py")
else:
print(f"{prefix}{BLUE}Debug{RESET} {YELLOW}{message_type}{RESET} {message}")
elif message_type == "debug":
print(f"{prefix}{BLUE}Debug{RESET} {message}")
elif message_type == "warning":
print(f"{prefix}{YELLOW}Warning{RESET} {message}")
elif message_type == "error":
print(f"{prefix}{RED}Error{RESET} {message}")
else:
print(f"{prefix}{message}")
def debug_func_entry():
"""Print debug message for function entry if debug_func is enabled"""
if config.debugging.debug_func:
current_func = inspect.currentframe().f_back.f_code.co_name
print_message(f"Function entry: {current_func}", "debug_func")
###########################
# Central config updaters #
###########################
def update_settings_at(delete_output_wavs, gradio_interface, gradio_port_number, output_folder,
api_port_number, gr_debug_tts, transcode_audio_format, generate_help_page,
voice2rvc_page, tts_generator_page, tts_engines_settings_page,
alltalk_documentation_page, api_documentation_page):
"""Update AllTalk main settings using the centralized config system"""
debug_func_entry()
try:
# Get the current config instance
config = AlltalkConfig.get_instance()
# Update main settings
config.delete_output_wavs = delete_output_wavs
config.gradio_interface = gradio_interface == "Enabled"
config.output_folder = output_folder
config.api_def.api_port_number = api_port_number
config.gradio_port_number = gradio_port_number
config.transcode_audio_format = transcode_audio_format
# Update debugging options
for key in vars(config.debugging):
if not key.startswith('_'): # Skip private attributes
setattr(config.debugging, key, key in gr_debug_tts)
# Update gradio pages settings
config.gradio_pages.Generate_Help_page = generate_help_page
config.gradio_pages.Voice2RVC_page = voice2rvc_page
config.gradio_pages.TTS_Generator_page = tts_generator_page
config.gradio_pages.TTS_Engines_Settings_page = tts_engines_settings_page
config.gradio_pages.alltalk_documentation_page = alltalk_documentation_page
config.gradio_pages.api_documentation_page = api_documentation_page
# Save the updated configuration
config.save()
# Tell tts_server.py to update
get_alltalk_settings()
print_message("Default Settings Saved")
return "Settings updated successfully!"
except Exception as e:
print_message(f"Failed to save configuration data: {str(e)}", message_type="error")
return f"Error updating settings: {str(e)}"
def update_settings_api(api_length_stripping, api_legacy_ip_address, api_allowed_filter,
api_max_characters, api_use_legacy_api, api_text_filtering,
api_narrator_enabled, api_text_not_inside, api_language,
api_output_file_name, api_output_file_timestamp, api_autoplay,
api_autoplay_volume):
"""Update API settings using the centralized config system"""
debug_func_entry()
try:
# Get the current config instance
config = AlltalkConfig.get_instance()
# Update API settings
config.api_def.api_length_stripping = api_length_stripping
config.api_def.api_legacy_ip_address = api_legacy_ip_address
config.api_def.api_allowed_filter = api_allowed_filter
config.api_def.api_max_characters = api_max_characters
config.api_def.api_use_legacy_api = api_use_legacy_api == "AllTalk v1 API (Legacy)"
config.api_def.api_text_filtering = api_text_filtering
config.api_def.api_narrator_enabled = api_narrator_enabled
config.api_def.api_text_not_inside = api_text_not_inside
config.api_def.api_language = api_language
config.api_def.api_output_file_name = api_output_file_name
config.api_def.api_output_file_timestamp = api_output_file_timestamp == "Timestamp files"
config.api_def.api_autoplay = api_autoplay == "Play remotely"
config.api_def.api_autoplay_volume = api_autoplay_volume
# Save the updated configuration
config.save()
# Tell tts_server.py to update
get_alltalk_settings()
print_message("API Settings Saved")
return "Default API settings updated successfully!"
except Exception as e:
print_message(f"Failed to save configuration data: {str(e)}", message_type="error")
return f"Error updating settings: {str(e)}"
def update_settings_tgwui(activate, autoplay, show_text, language, narrator_enabled):
"""Update Text-gen-webui settings using the centralized config system"""
debug_func_entry()
try:
# Get the current config instance
config = AlltalkConfig.get_instance()
# Update TGWUI settings
config.tgwui.tgwui_activate_tts = activate == "Enabled"
config.tgwui.tgwui_autoplay_tts = autoplay == "Enabled"
config.tgwui.tgwui_show_text = show_text == "Enabled"
config.tgwui.tgwui_language = language
config.tgwui.tgwui_narrator_enabled = narrator_enabled
# Save the updated configuration
config.save()
print_message("Default Text-gen-webui Settings Saved")
return "Settings updated successfully!"
except Exception as e:
print_message(f"Failed to save configuration data: {str(e)}", message_type="error")
return f"Error updating settings: {str(e)}"
def update_rvc_settings(rvc_enabled, rvc_char_model_file, rvc_narr_model_file, split_audio,
autotune, pitch, filter_radius, index_rate, rms_mix_rate, protect,
hop_length, f0method, embedder_model, training_data_size, progress=None):
"""Update RVC settings using the centralized config system"""
debug_func_entry()
try:
# Get the current config instance
config = AlltalkConfig.get_instance()
# Update RVC settings
config.rvc_settings.rvc_enabled = rvc_enabled
config.rvc_settings.rvc_char_model_file = rvc_char_model_file
config.rvc_settings.rvc_narr_model_file = rvc_narr_model_file
config.rvc_settings.split_audio = split_audio
config.rvc_settings.autotune = autotune
config.rvc_settings.pitch = pitch
config.rvc_settings.filter_radius = filter_radius
config.rvc_settings.index_rate = index_rate
config.rvc_settings.rms_mix_rate = rms_mix_rate
config.rvc_settings.protect = protect
config.rvc_settings.hop_length = hop_length
config.rvc_settings.f0method = f0method
config.rvc_settings.embedder_model = embedder_model
config.rvc_settings.training_data_size = training_data_size
def ensure_directory_exists(directory):
if not os.path.exists(directory):
os.makedirs(directory)
def load_file_urls(json_path):
with open(json_path, 'r') as f:
return json.load(f)
def download_file(url, dest_path):
response = requests.get(url, stream=True)
response.raise_for_status()
with open(dest_path, 'wb') as f:
for chunk in response.iter_content(chunk_size=8192):
f.write(chunk)
if rvc_enabled:
base_dir = os.path.join(this_dir, "models", "rvc_base")
rvc_voices_dir = os.path.join(this_dir, "models", "rvc_voices")
ensure_directory_exists(base_dir)
ensure_directory_exists(rvc_voices_dir)
json_path = os.path.join(this_dir, "system", "tts_engines", "rvc_files.json")
file_urls = load_file_urls(json_path)
for idx, file in enumerate(file_urls):
if not os.path.exists(os.path.join(base_dir, file)):
progress(idx / len(file_urls), desc=f"Downloading Required RVC Files: {file}...")
print(f"[{branding}TTS] Downloading {file}...") # Print statement for terminal
download_file(file_urls[file], os.path.join(base_dir, file))
result = "RVC Base Files downloaded successfully." if len(file_urls) > 0 else "All files are present."
# Save the updated configuration
config.save()
# Tell tts_server.py to update
get_alltalk_settings()
return "RVC settings updated successfully!"
except Exception as e:
print_message(f"Failed to save configuration data: {str(e)}", message_type="error")
return f"Error updating settings: {str(e)}"
###########################################################################
# START-UP # Silence Character Normaliser when it checks the Ready Status #
###########################################################################
import warnings
warnings.filterwarnings('ignore', message='Trying to detect encoding from a tiny portion')
###########################################
# START-UP # AllTalk allowed startup time #
###########################################
startup_wait_time = 240
# You can change the above setting to a larger number to allow AllTAlk more time to start up.
# The default setting is 240 seconds (4 minutes). If its taking longer though, you may have a
# Very old system or system issue.
##############################################
# START-UP # Load confignew.json into params #
##############################################
this_dir = Path(__file__).parent.resolve()
config = AlltalkConfig.get_instance()
branding = config.branding
github_site = "erew123"
github_repository = "alltalk_tts"
github_branch = "alltalkbeta"
current_folder = os.path.basename(os.getcwd())
output_folder = config.get_output_directory()
delete_output_wavs_setting = config.delete_output_wavs
gradio_enabled = config.gradio_interface
script_path = this_dir / "tts_server.py"
tunnel_url = None
running_on_google_colab = False
running_in_docker = False
docker_url = f"http://localhost:{config.api_def.api_port_number}"
############################################
# START-UP # Display initial splash screen #
############################################
print_message("\033[94m _ _ _ \033[1;35m_____ _ _ \033[0m _____ _____ ____ ") # pylint: disable=line-too-long
print_message("\033[94m / \ | | |\033[1;35m_ _|_ _| | | __ \033[0m |_ _|_ _/ ___| ") # pylint: disable=line-too-long
print_message("\033[94m / _ \ | | |\033[1;35m | |/ _` | | |/ / \033[0m | | | | \___ \ ") # pylint: disable=line-too-long
print_message("\033[94m / ___ \| | |\033[1;35m | | (_| | | < \033[0m | | | | ___) |") # pylint: disable=line-too-long
print_message("\033[94m/_/ \_\_|_|\033[1;35m |_|\__,_|_|_|\_\ \033[0m |_| |_| |____/ ") # pylint: disable=line-too-long
print_message("")
#############################################################################
# START-UP # Check current folder name has dashes '-' in it and error if so #
#############################################################################
# Get the directory of the current script
this_script_path = Path(__file__).resolve()
this_script_dir = this_script_path.parent
# Get the current folder name
this_current_folder = this_script_dir.name
if "-" in this_current_folder:
print_message("")
print_message("The current folder name contains a dash ('\033[93m-\033[0m') and this causes errors/issues. Please ensure", message_type="warning")
print_message("the folder name does not have a dash e.g. rename ('\033[93malltalk_tts-main\033[0m') to ('\033[93malltalk_tts\033[0m').", message_type="warning")
print_message("")
print_message("\033[92mCurrent folder:\033[0m {this_current_folder}", message_type="warning")
sys.exit(1)
##############################################
# START-UP # Check if we are on Google Colab #
##############################################
def check_google_colab():
debug_func_entry()
try:
import google.colab
return True
except ImportError:
return False
running_on_google_colab = check_google_colab()
###############################################################################
# START-UP # Test if we are running within Text-gen-webui or as a Standalone #
###############################################################################
try:
import gradio as gr
from modules import chat, shared, ui_chat
from modules.logging_colors import logger
from modules.ui import create_refresh_button
from modules.utils import gradio
print_message("\033[92mStart-up Mode : \033[93mText-gen-webui mode\033[0m")
running_in_standalone = False
running_in_tgwui = True
except ModuleNotFoundError:
running_in_standalone = True
running_in_tgwui = False
print_message("\033[92mStart-up Mode : \033[93mStandalone mode\033[0m")
######################################################
# START-UP # Check if this is a first time start-up #
######################################################
def run_firsttime_script():
debug_func_entry()
try:
if running_on_google_colab:
script_path = '/content/alltalk_tts/system/config/firstrun.py'
elif running_in_standalone:
script_path = os.path.join(this_dir, 'system', 'config', 'firstrun.py')
elif running_in_tgwui:
script_path = os.path.join(this_dir, 'system', 'config', 'firstrun_tgwui.py')
else:
script_path = os.path.join(this_dir, 'system', 'config', 'firstrun.py')
subprocess.run([sys.executable, script_path], check=True)
except subprocess.CalledProcessError as e:
print_message("Error occurred while running the script: " + str(e), message_type="error")
except Exception as e:
print_message("An unexpected error occurred: " + str(e), message_type="error")
# Call the function to run the startup script
run_firsttime_script()
###########################################################
# START-UP # Delete files in outputs folder if configured #
###########################################################
def delete_old_files(folder_path, days_to_keep):
debug_func_entry()
current_time = datetime.now()
print_message("\033[92mWAV file deletion :\033[93m", delete_output_wavs_setting,"\033[0m")
for file_name in os.listdir(folder_path):
file_path = os.path.join(folder_path, file_name)
if os.path.isfile(file_path):
file_creation_time = datetime.fromtimestamp(os.path.getctime(file_path))
age = current_time - file_creation_time
if age > timedelta(days=days_to_keep):
os.remove(file_path)
# Check and perform file deletion
if delete_output_wavs_setting.lower() == "disabled":
print_message("\033[92mWAV file deletion :\033[93m Disabled\033[0m")
else:
try:
days_to_keep = int(delete_output_wavs_setting.split()[0])
delete_old_files(output_folder, days_to_keep)
except ValueError:
print_message("\033[92mWAV file deletion :\033[93m Invalid setting for deleting old wav files. Please use 'Disabled' or 'X Days' format\033[0m")
#####################################################################
# START-UP # Check Githubs last update and output into splashscreen #
#####################################################################
def format_datetime(iso_str):
"""Formats an ISO datetime string into a human-readable format with ordinal day numbers.
Example: Converts "2024-03-19T10:30:00Z" to "19th March 2024 at 10:30"
"""
debug_func_entry()
def _ordinal(n):
"""Helper function to convert numbers to ordinal form (1st, 2nd, 3rd, etc)"""
debug_func_entry()
return "%d%s" % (n, "th" if 4 <= n % 100 <= 20 else {1: "st", 2: "nd", 3: "rd"}.get(n % 10, "th"))
dt = datetime.strptime(iso_str, "%Y-%m-%dT%H:%M:%SZ")
return dt.strftime(f"{_ordinal(dt.day)} %B %Y at %H:%M")
def fetch_latest_commit_sha_and_date(owner, repo, branch):
debug_func_entry()
# Modified URL to include the branch
url = f"https://api.github.com/repos/{owner}/{repo}/commits/{branch}"
try:
response = requests.get(url)
if response.status_code == 200:
commit_data = response.json()
latest_commit_sha = commit_data['sha']
latest_commit_date = commit_data['commit']['committer']['date']
return latest_commit_sha, latest_commit_date
else:
print_message(f"\033[92mGithub updated :\033[91m Failed to fetch the latest commit from branch {branch} due to an unexpected response from GitHub")
return None, None
except ConnectionError:
print_message("\033[92mGithub updated :\033[91m Could not reach GitHub to check for updates\033[0m")
return None, None
def read_or_initialize_sha(file_path, owner, repo, branch):
debug_func_entry()
if os.path.exists(file_path):
with open(file_path, 'r') as file:
data = json.load(file)
return data.get("last_known_commit_sha")
else:
# File doesn't exist, fetch the latest SHA and create the file
latest_commit_sha, _ = fetch_latest_commit_sha_and_date(owner, repo, branch)
if latest_commit_sha:
with open(file_path, 'w') as file:
json.dump({"last_known_commit_sha": latest_commit_sha}, file)
return latest_commit_sha
return None
def update_sha_file(file_path, new_sha):
debug_func_entry()
with open(file_path, 'w') as file:
json.dump({"last_known_commit_sha": new_sha}, file)
# Define the file path based on your directory structure
sha_file_path = this_dir / "system" / "config" / "at_github_sha.json"
# Read or initialize the SHA
last_known_commit_sha = read_or_initialize_sha(sha_file_path, github_site, github_repository, github_branch)
# Fetch the latest commit SHA and date from the specific branch
latest_commit_sha, latest_commit_date = fetch_latest_commit_sha_and_date(github_site, github_repository, github_branch)
formatted_date = format_datetime(latest_commit_date) if latest_commit_date else "an unknown date"
if latest_commit_sha and latest_commit_sha != last_known_commit_sha:
print_message(f"\033[92mGithub updated :\033[93m {formatted_date} \033[92mBranch:\033[93m {github_branch}\033[0m")
# Update the file with the new SHA
update_sha_file(sha_file_path, latest_commit_sha)
elif latest_commit_sha == last_known_commit_sha:
print_message(f"\033[92mGithub updated :\033[93m {formatted_date} \033[92mBranch:\033[93m {github_branch}\033[0m")
##################################################
# START-UP # Configure the subprocess handler ####
##################################################
def signal_handler(sig, frame):
debug_func_entry()
config.save()
print_message("\033[94mReceived Ctrl+C, terminating subprocess. Kill your Python processes if this fails to exit.\033[92m")
if process.poll() is None:
process.terminate()
process.wait() # Wait for the subprocess to finish
sys.exit(0)
#####################################################################
# START-UP # Start the Subprocess and Check for Google Colab/Docker #
#####################################################################
try:
import google.colab
running_on_google_colab = True
with open('/content/alltalk_tts/googlecolab.json', 'r') as f:
data = json.load(f)
tunnel_url_1, tunnel_url_2 = data.get('google_ip_address', [None, None])
except FileNotFoundError:
print_message("Could not find IP address")
tunnel_url_1, tunnel_url_2 = None, None
except ImportError:
running_on_google_colab = False
# Attach the signal handler to the SIGINT signal (Ctrl+C)
system_signal.signal(system_signal.SIGINT, signal_handler)
# Check if we're running in docker
if os.path.isfile("/.dockerenv") and 'google.colab' not in sys.modules:
print_message("")
print_message("\033[94mRunning in Docker environment. Please note:\033[0m")
print_message("Docker environments have various considerations:")
print_message("AllTalk v2 is running 2x web servers. One webserver is the API address that exernal TTS")
print_message("generation requests are sent to. The other webserver is the Gradio management interface.")
print_message("If you want to access and use both API calls and the Gradio interface, in a docker style")
print_message("environment, you will need to somehow make these accessable, depending on your scenario:")
print_message(" 1. A Local Area Network (LAN) scenario:")
print_message(" - Ensure you've exposed the Gradio and API ports correctly.")
print_message(" - The application should work as expected.")
print_message("2. Internet/Remotely accessed scenario:")
print_message(" - You'll need to set up a secure tunnel or VPN to the host server.")
print_message(" - If you want just API requests, you can map just the API and control AllTalk via API calls.")
print_message(" - If you want both API and Gradio interfaces you need to make both accessible.")
print_message(" 3. Default Addresses:")
print_message(f" - Internal API address: http://localhost:{config.api_def.api_port_number}")
print_message(f" - Internal Gradio address: http://localhost:{config.gradio_port_number}")
print_message(" Tunnel Setup:")
print_message(" - If using a tunnel, you'll need to provide the API external URL/Address in Gradio.")
print_message(" - Look for 'API URL/Address' option in the Gradio interface `Generate` page.")
running_in_docker = True
else:
running_in_docker = False
# Start the subprocess (now unified for both Docker and non-Docker environments)
process = subprocess.Popen([sys.executable, script_path])
# Check if the subprocess has started successfully
if process.poll() is None:
if running_on_google_colab:
print_message("")
print_message("\033[94mGoogle Colab Detected\033[00m")
print_message("")
else:
print_message("TTS Subprocess Webserver failing to start process", message_type="warning")
print_message(f"It could be that you have something on port: {config.port_number}", message_type="warning")
print_message("Or you have not started in a Python environment with all the necessary bits installed", message_type="warning")
print_message("Check you are starting Text-generation-webui with either the start_xxxxx file or the Python environment with cmd_xxxxx file.", message_type="warning")
print_message("xxxxx is the type of OS you are on e.g. windows, linux or mac.", message_type="warning")
print_message("Alternatively, you could check no other Python processes are running that shouldn't be e.g. Restart your computer is the simple way.", message_type="warning")
# Cleanly kill off this script, but allow text-generation-webui to keep running, albeit without this alltalk_tts
sys.exit(1)
timeout = startup_wait_time # Gather timeout setting from startup_wait_time
initial_delay = 5 # Initial delay before starting the check loop
warning_delay = 60 # Delay before displaying warnings
# Introduce a delay before starting the check loop
time.sleep(initial_delay)
start_time = time.time()
warning_displayed = False
url = f"http://localhost:{config.api_def.api_port_number}/api/ready"
while time.time() - start_time < timeout:
try:
response = requests.get(url)
if response.status_code == 200 and response.text == "Ready":
break
except requests.RequestException as e:
# Log the exception if needed
pass
if not warning_displayed and time.time() - start_time >= warning_delay:
print_message("TTS Engine has NOT started up yet. Will keep trying for " + str(timeout) + " seconds maximum. Please wait.", message_type="warning")
print_message("Mechanical hard drives and a slow PCI BUS are examples of things that can affect load times.", message_type="warning")
print_message("Some TTS engines index their AI TTS models on loading, which can be slow on CPU or old systems.", message_type="warning")
print_message("Using one of the other TTS engines on slower systems can help ease this issue.", message_type="warning")
warning_displayed = True
time.sleep(1)
else:
print_message("")
print_message("Startup timed out. Full help available here https://github.com/erew123/alltalk_tts#-help-with-problems")
print_message("On older systems, you may wish to open and edit script.py with a text editor and change the")
print_message("startup_wait_time = 240 setting to something like startup_wait_time = 460 as this will allow")
print_message("AllTalk more time (6 mins) to try load the model into your VRAM. Otherwise, please visit the GitHub for")
print_message("a list of other possible troubleshooting options.")
# Cleanly kill off this script, but allow text-generation-webui to keep running, albeit without this alltalk_tts
sys.exit(1)
if running_on_google_colab:
print_message("")
print_message("\033[94mAPI Address :\033[00m \033[92m" + tunnel_url_1 + "\033[00m")
print_message("\033[94mGradio Light:\033[00m \033[92m" + tunnel_url_2 + "\033[00m")
print_message("\033[94mGradio Dark :\033[00m \033[92m" + tunnel_url_2 + "?__theme=dark\033[00m")
print_message("")
else:
print_message("")
print_message("\033[94mAPI Address :\033[00m \033[92m127.0.0.1:" + str(config.api_def.api_port_number) + "\033[00m")
print_message("\033[94mGradio Light:\033[00m \033[92mhttp://127.0.0.1:" + str(config.gradio_port_number) + "\033[00m")
print_message("\033[94mGradio Dark :\033[00m \033[92mhttp://127.0.0.1:" + str(config.gradio_port_number) + "?__theme=dark\033[00m")
print_message("")
print_message("\033[94mAllTalk WIKI:\033[00m \033[92mhttps://github.com/erew123/alltalk_tts/wiki\033[00m")
print_message("\033[94mErrors Help :\033[00m \033[92mhttps://github.com/erew123/alltalk_tts/wiki/Error-Messages-List\033[00m")
print_message("")
#########################################
# START-UP # Espeak-ng check on Windows #
#########################################
def check_espeak_ng():
debug_func_entry()
if platform.system() == "Windows":
try:
result = subprocess.run(['espeak-ng', '--version'], capture_output=True, text=True, check=True)
return
except (subprocess.CalledProcessError, FileNotFoundError) as e:
# If the command returns an error, print the error message
print_message("")
print_message("Espeak-ng for Windows\033[91m WAS NOT FOUND. \033[0mYou can install\033[0m", message_type="warning")
print_message("\033[0mit from this location \033[93m\\alltalk_tts\\system\espeak-ng\\\033[0m", message_type="warning")
print_message("Then close this command prompt window and open a new", message_type="warning")
print_message("command prompt, before re-starting.", message_type="warning")
elif platform.system() == "Darwin": # macOS
try:
result = subprocess.run(['espeak-ng', '--version'], capture_output=True, text=True, check=True)
return
except FileNotFoundError:
print_message("")
print_message("Espeak-ng for macOS\033[91m WAS NOT FOUND. \033[0mYou can install\033[0m", message_type="warning")
print_message("\033[0mit using Homebrew: \033[93mbrew install espeak-ng\033[0m", message_type="warning")
else: # Linux
try:
result = subprocess.run(['espeak-ng', '--version'], capture_output=True, text=True, check=True)
return
except FileNotFoundError:
print_message("")
print_message("Espeak-ng for Linux\033[91m WAS NOT FOUND. \033[0mYou can install\033[0m", message_type="warning")
print_message("\033[0mit using your package manager, e.g., \033[93mapt-get install espeak-ng\033[0m", message_type="warning")
print_message("")
print_message("")
check_espeak_ng()
####################################
# START-UP # Subprecess management #
####################################
def start_subprocess():
debug_func_entry()
global process
if process is None or process.poll() is not None:
process = subprocess.Popen([sys.executable, script_path])
return "Subprocess started."
else:
return "Subprocess is already running."
def stop_subprocess():
debug_func_entry()
global process
if process is not None:
process.terminate()
process.wait()
process = None
return "Subprocess stopped."
else:
return "Subprocess is not running."
def restart_subprocess():
debug_func_entry()
stop_subprocess()
print_message("")
print_message("\033[94mSwapping TTS Engine. Please wait.\033[00m")
print_message("")
return start_subprocess()
def check_subprocess_status():
debug_func_entry()
global process
if process is None or process.poll() is not None:
return "Subprocess is not running."
else:
return "Subprocess is running."
###################################################################
# START-UP # Register the termination code to be executed at exit #
###################################################################
atexit.register(lambda: process.terminate() if process.poll() is None else None)
##########################
# Setup global variables #
##########################
# Load the language options from languages
with open(os.path.join(this_dir, "system", "config", "languages.json"), "r") as f:
languages_list = json.load(f)
current_model_loaded = None
#deepspeed_installed = True
tgwui_lovram = False
tgwui_deepspeed = False
# Create a global lock for tracking TTS generation occuring
process_lock = threading.Lock()
# Pull the values for IP address, port and protocol for communication with the AllTalk renote server
alltalk_protocol = "http://"
alltalk_ip_port = f"127.0.0.1:{config.api_def.api_port_number}"
# Pull the connection timeout value for communication requests with the AllTalk remote server
connection_timeout = 10
# Create a few base global variables that are required
models_available = None # Gets populated with the list of all models/engines available on the AllTalk Server
at_settings = None # Gets populated with the list of all settings currently set on the AllTalk Server
current_model_loaded = None # Gets populated with the name of the current loaded TTS engine/model on the AllTalk Server
# Used to detect if a model is loaded in to AllTalk server to block TTS genereation if needed.
tts_model_loaded = None
#########################
# Endpoint API Builders #
#########################
def build_url(endpoint, include_api=True):
"""
Build URL for standard API endpoints using static predefined URLs.
Used for endpoints that don't need dynamic domain handling.
Examples:
- voice2rvc
- reload
- stop-generation
Args:
endpoint (str): The API endpoint to append to the base URL
include_api (bool): Whether to include /api/ in the URL
True: Uses '/api/'
False: Uses ''
Returns:
str: Complete URL in format: {base_url}/api/{endpoint}
where base_url is determined by runtime environment:
- Colab: tunnel_url_1
- Docker: docker_url
- Local: alltalk_protocol + alltalk_ip_port
"""
debug_func_entry()
global running_on_google_colab, running_in_docker, tunnel_url_1, docker_url, alltalk_protocol, alltalk_ip_port
print_message(f"\033[94mRunning on Docker is: \033[0m{running_in_docker}", message_type="debug_gradio_IP")
print_message(f"\033[94mRunning on Google is: \033[0m{running_on_google_colab}", message_type="debug_gradio_IP")
if running_on_google_colab:
base_url = tunnel_url_1
elif running_in_docker:
base_url = docker_url
else:
base_url = f"{alltalk_protocol}{alltalk_ip_port}"
prefix = "/api/" if include_api else "/"
final_url = f"{base_url}{prefix}{endpoint.lstrip('/')}"
print_message(f"\033[94mBase URL is set as : \033[0m{final_url}", message_type="debug_gradio_IP")
return final_url
def build_dynamic_url(endpoint, include_protocol=True, include_api=True):
"""
Build URL for endpoints that may need the current domain.
Used when the URL needs to reference the actual server domain
rather than predefined static URLs.
Examples:
- tts-generate
- tts-generate-streaming
- File URLs (with include_protocol=True)
Args:
endpoint (str): The API endpoint or path to append
include_protocol (bool): Whether to include protocol prefix
True: Uses 'protocol' global variable
False: Uses 'http://'
include_api (bool): Whether to include /api/ in the URL
True: Uses '/api/'
False: Uses ''
Returns:
str: Complete URL with format depending on environment:
- Colab: tunnel_url_1/api/{endpoint}
- Docker: docker_url/api/{endpoint}
- Local: {protocol_prefix}{my_current_url}/api/{endpoint}
"""
debug_func_entry()
global running_on_google_colab, running_in_docker, tunnel_url_1, docker_url, my_current_url, protocol
print_message(f"\033[94mRunning on Docker is: \033[0m{running_in_docker}", message_type="debug_gradio_IP")
print_message(f"\033[94mRunning on Google is: \033[0m{running_on_google_colab}", message_type="debug_gradio_IP")
prefix = "/api/" if include_api else "/"
if running_on_google_colab:
base_url = tunnel_url_1
elif running_in_docker:
base_url = docker_url
else:
protocol_prefix = f"{protocol}" if include_protocol else "http://"
base_url = f"{protocol_prefix}{my_current_url}"
final_url = f"{base_url}{prefix}{endpoint.lstrip('/')}"
print_message(f"\033[94mBase URL is set as : \033[0m{final_url}", message_type="debug_gradio_IP")
return final_url
def update_docker_address(new_url):
"""
Used within the gradio interface to update the IP/URL
when running inside a docker environment.
"""
global docker_url
docker_url = new_url
print_message(f"\033[94mDocker IP/URL for API set to: \033[0m{new_url}")
return f"Docker IP/URL for API set to: {new_url}"
#################################################
# Pull all the settings from the AllTalk Server #
#################################################
def get_alltalk_settings():
"""Retrieves current settings from the AllTalk server including voices, models, and configuration.
Returns a dictionary of settings with default values if server is unreachable.
"""
debug_func_entry()
# Define endpoints
endpoints = {name: build_url(name) for name in ['voices', 'rvcvoices', 'currentsettings']}
# Default settings in case of failure
default_settings = {
"voices": ["Please Refresh Settings"],
"rvcvoices": ["Please Refresh Settings"],
"engines_available": ["Please Refresh Settings"],
"current_engine_loaded": ["Please Refresh Settings"],
"models_available": ["Please Refresh Settings"],
"current_model_loaded": "Please Refresh Settings",
"manufacturer_name": "",
"deepspeed_capable": False,
"deepspeed_available": False,
"deepspeed_enabled": False,
"generationspeed_capable": False,
"generationspeed_set": 1.0,
"lowvram_capable": False,
"lowvram_enabled": False,
"pitch_capable": False,
"pitch_set": 0,
"repetitionpenalty_capable": False,
"repetitionpenalty_set": 10.0,
"streaming_capable": False,
"temperature_capable": False,
"temperature_set": 0.75,
"ttsengines_installed": False,
"languages_capable": False,
"multivoice_capable": False,
"multimodel_capable": False,
}
try:
# Make all requests with a single session for efficiency
with requests.Session() as session:
responses = {
name: session.get(url, timeout=connection_timeout)
for name, url in endpoints.items()
}
# Check if all responses are successful
if not all(r.status_code == 200 for r in responses.values()):
failed_endpoints = [name for name, r in responses.items()
if r.status_code != 200]
print_message(f"Failed to retrieve data from: {', '.join(failed_endpoints)}",
message_type="warning",
component="TTS")
print_message(f"Please confirm you have the correct IP/URL & that the system isnt currenly restarting",
message_type="warning",
component="TTS")
return default_settings
# Parse responses
voices_data = responses["voices"].json()
rvcvoices_data = responses["rvcvoices"].json()
settings_data = responses["currentsettings"].json()
# Update global variables
global engines_available, current_engine_loaded, models_available, current_model_loaded
engines_available = sorted(settings_data["engines_available"])
current_engine_loaded = settings_data["current_engine_loaded"]
models_available = sorted([model["name"] for model in settings_data["models_available"]])
current_model_loaded = settings_data["current_model_loaded"]
# Construct and return settings dictionary
return {
"voices": sorted(voices_data["voices"], key=lambda s: s.lower()),
"rvcvoices": rvcvoices_data["rvcvoices"],
"engines_available": engines_available,
"current_engine_loaded": current_engine_loaded,
"models_available": models_available,
"current_model_loaded": current_model_loaded,
**{k: settings_data.get(k, default_settings[k])
for k in default_settings if k not in ["voices", "rvcvoices", "engines_available",
"current_engine_loaded", "models_available",
"current_model_loaded"]}
}
except (RequestException, ConnectionError) as e:
print_message(f"Unable to connect to the {branding} server: {str(e)}",
message_type="warning",
component="TTS")
print_message(f"Please confirm you have the correct IP/URL & that the system isnt currenly restarting",
message_type="warning",
component="TTS")
return default_settings
# Pull all the current settings from the AllTalk server, if its online.
at_settings = get_alltalk_settings()
#############################
#### TTS STOP GENERATION ####
#############################
def stop_generate_tts():
debug_func_entry()
api_url = build_url('stop-generation')
try:
response = requests.put(api_url, timeout=connection_timeout)
if response.status_code == 200:
return response.json()["message"]
else:
print_message(f"Failed to stop generation. Status code:\n{response.status_code}", message_type="warning")
return {"message": "Failed to stop generation"}
except (RequestException, ConnectionError) as e:
print_message(f"Unable to connect to the {branding} server. Status code:\n{str(e)}", message_type="warning")
return {"message": "Failed to stop generation"}
def send_api_request(endpoint, payload=None, headers=None, params=None):
"""Base function for making API requests to the AllTalk server.
Args:
endpoint (str): API endpoint (e.g., '/api/reload')
payload (dict, optional): POST data
headers (dict, optional): Request headers
params (dict, optional): URL parameters
Returns:
dict: Response data or error status
"""
debug_func_entry()
try:
# Construct base URL based on environment
base_url = build_url(endpoint.lstrip('/'))
response = requests.post(
base_url,
json=payload,
headers=headers or {"Content-Type": "application/json"},
params=params
)
response.raise_for_status()
return {
"status": "success",
"response": response.json() if response.content else None,
"raw_response": response
}
except requests.exceptions.RequestException as e:
print_message(f"Error during request to webserver process: Status code:\n{e}",
message_type="warning")
return {"status": "error", "message": str(e)}
def send_reload_request(value_sent):
"""Sends a request to reload the TTS model.
Args:
value_sent: The TTS method/model to load
"""
debug_func_entry()
params = {"tts_method": value_sent}
return send_api_request("/api/reload", params=params)
def send_lowvram_request(value_sent):
"""Sends a request to change the low VRAM setting and returns audio feedback.
Args:
value_sent (bool): Whether to enable low VRAM mode
Returns:
str: HTML audio element or error message
"""
debug_func_entry()
config.tgwui.tts_model_loaded = False
response = send_api_request(
"/api/lowvramsetting",
params={"new_low_vram_value": value_sent}
)
if response["status"] == "success":
json_response = response["response"]
if json_response.get("status") == "lowvram-success":
config.tgwui.tts_model_loaded = True
audio_path = this_dir / ("lowvramenabled.wav" if value_sent else "lowvramdisabled.wav")
return f'<audio src="file/{audio_path}" controls autoplay></audio>'
return response
########################################
#### Whisper Transcription Handling ####
########################################
class TranscriptionProgress:
def __init__(self):
self.current_file = ""
self.total_files = 0
self.completed_files = 0
self.current_progress = 0
whisper_progress = TranscriptionProgress()
# File size constants (in bytes)
WARNING_SIZE = 25 * 1024 * 1024 # 25MB
MAX_SIZE = 100 * 1024 * 1024 # 100MB
# Allowed audio formats
ALLOWED_AUDIO_FORMATS = {
'.mp3', '.wav', '.m4a', '.ogg', '.flac', '.aac',
'.wma', '.aiff', '.alac', '.opus'
}
def setup_directories(script_dir):
"""Setup required directories"""
debug_func_entry()
base_dir = os.path.join(script_dir, "transcriptions")
uploads_dir = os.path.join(base_dir, "uploads")
output_dir = os.path.join(base_dir, "output")
for directory in [base_dir, uploads_dir, output_dir]:
os.makedirs(directory, exist_ok=True)
print_message(f"Created/verified directory: {directory}", "debug_transcribe")
return base_dir, uploads_dir, output_dir
def validate_audio_file(file_path):
"""Validate file type and size"""
debug_func_entry()
file_size = os.path.getsize(file_path)
file_ext = os.path.splitext(file_path)[1].lower()
mime_type = mimetypes.guess_type(file_path)[0]
print_message(f"Validating file: {file_path}", "debug_transcribe")
print_message(f"File size: {file_size/1024/1024:.1f}MB", "debug_transcribe")
print_message(f"File extension: {file_ext}", "debug_transcribe")
print_message(f"MIME type: {mime_type}", "debug_transcribe")
warnings = []
errors = []
if file_ext not in ALLOWED_AUDIO_FORMATS:
msg = f"Invalid file format: {file_ext}"
print_message(msg, "error")
errors.append(msg)
if mime_type and not mime_type.startswith('audio/'):
msg = f"File does not appear to be audio: {mime_type}"
print_message(msg, "error")
errors.append(msg)
if file_size > MAX_SIZE:
msg = f"File too large: {file_size/1024/1024:.1f}MB (max {MAX_SIZE/1024/1024}MB)"
print_message(msg, "error")
errors.append(msg)
elif file_size > WARNING_SIZE:
msg = f"File is large ({file_size/1024/1024:.1f}MB) and may take longer to process"
print_message(msg, "warning")
warnings.append(msg)
return warnings, errors
def cleanup_gpu_memory():
"""Thorough GPU memory cleanup"""
debug_func_entry()
if torch.cuda.is_available():
# Clear PyTorch's CUDA cache
torch.cuda.empty_cache()
# Force garbage collection
gc.collect()
# Clear any remaining CUDA memory
with torch.cuda.device('cuda'):
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
print_message("GPU memory cleaned", "debug_transcribe")
def create_output_directory(base_dir, prefix=""):
"""Create a uniquely named output directory for this batch of transcriptions"""
debug_func_entry()
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
dir_name = f"{prefix}_{timestamp}" if prefix else timestamp
output_dir = os.path.join(base_dir, "output", dir_name)
os.makedirs(output_dir, exist_ok=True)
print_message(f"Created output directory: {output_dir}", "debug_transcribe")
return output_dir
def create_output_filename(input_path, output_dir, prefix=""):
"""Create an output filename based on the input path"""
debug_func_entry()
stem = Path(input_path).stem
if prefix:
return os.path.join(output_dir, f"{prefix}_{stem}")
return os.path.join(output_dir, stem)
def delete_uploaded_files():
"""Delete all files in the uploads directory"""
debug_func_entry()
script_dir = os.path.dirname(os.path.abspath(__file__))
uploads_dir = os.path.join(script_dir, "transcriptions", "uploads")
deleted_count = 0
print_message(f"Attempting to delete files in: {uploads_dir}", "debug_transcribe")
if os.path.exists(uploads_dir):
for file in os.listdir(uploads_dir):
file_path = os.path.join(uploads_dir, file)
try:
if os.path.isfile(file_path):
os.unlink(file_path)
deleted_count += 1
print_message(f"Deleted: {file}", "debug_transcribe")
except Exception as e:
error_msg = f"Error while deleting {file}: {str(e)}"
print_message(error_msg, "error")
return error_msg
msg = f"Deleted {deleted_count} uploaded audio files"
print_message(msg)
return msg
msg = "No files to delete - uploads directory not found"
print_message(msg, "warning")
return msg
def create_srt_content(segments):
"""Convert whisper segments to SRT format"""
debug_func_entry()
srt_content = []
for i, segment in enumerate(segments, start=1):
# Convert start/end times to SRT format (HH:MM:SS,mmm)
start_time = timedelta(seconds=segment['start'])
end_time = timedelta(seconds=segment['end'])
# Format with milliseconds
start_str = str(start_time).replace('.', ',')[:11]
end_str = str(end_time).replace('.', ',')[:11]
# Ensure proper formatting for times less than 1 hour
if len(start_str) < 11:
start_str = "0" + start_str
if len(end_str) < 11:
end_str = "0" + end_str
# Build SRT segment
srt_content.append(f"{i}\n{start_str} --> {end_str}\n{segment['text'].strip()}\n")
return "\n".join(srt_content)
def process_audio_files(audio_files, model_size, output_format, delete_after, prefix="", gradio_progress=gr.Progress(track_tqdm=True)):
"""Process multiple audio files and return paths to transcription files"""
debug_func_entry()
try:
print_message(f"Starting transcription process with model: {model_size}", "debug_transcribe")
# Setup directories
script_dir = os.path.dirname(os.path.abspath(__file__))
base_dir, uploads_dir, _ = setup_directories(script_dir)
# Create specific output directory for this batch
output_dir = create_output_directory(base_dir, prefix)
print_message(f"Using prefix: {prefix if prefix else 'none'}", "debug_transcribe")
# Validate and copy files to uploads directory
processed_files = []
validation_messages = []
for audio_file in audio_files:
print_message(f"Processing file: {audio_file}", "debug_transcribe")
warnings, errors = validate_audio_file(audio_file)
file_name = Path(audio_file).name
if errors:
for error in errors:
validation_messages.append(f"Skipping {file_name}: {error}")
continue
validation_messages.extend([f"Warning for {file_name}: {warning}" for warning in warnings])
# Copy file to uploads directory
upload_path = os.path.join(uploads_dir, file_name)
shutil.copy2(audio_file, upload_path)
print_message(f"Copied to uploads: {upload_path}", "debug_transcribe")
processed_files.append(upload_path)
if not processed_files:
msg = "No valid files to process\n" + "\n".join(validation_messages)
print_message(msg, "error")
return None, msg
# Load model
device = "cuda" if torch.cuda.is_available() else "cpu"
print_message(f"Loading Whisper model {model_size} on {device}", "debug_transcribe")
model = whisper.load_model(model_size, device=device)
output_files = []
metadata = []
whisper_progress.total_files = len(processed_files)
for idx, audio_file in enumerate(tqdm(processed_files, desc="Processing files")):
try:
whisper_progress.current_file = Path(audio_file).name
whisper_progress.completed_files = idx
print_message(f"Transcribing: {audio_file}", "debug_transcribe")
# Transcribe audio
result = model.transcribe(audio_file)
# Create output file
base_output_path = create_output_filename(audio_file, output_dir, prefix)
# Save based on format
if output_format == "txt":
output_path = f"{base_output_path}.txt"
with open(output_path, 'w', encoding='utf-8') as f:
f.write(result["text"])
elif output_format == "srt":
output_path = f"{base_output_path}.srt"
with open(output_path, 'w', encoding='utf-8') as f:
f.write(create_srt_content(result["segments"]))
else: # json
output_path = f"{base_output_path}.json"
with open(output_path, 'w', encoding='utf-8') as f:
json.dump(result, f, indent=4, ensure_ascii=False)
print_message(f"Saved transcription to: {output_path}", "debug_transcribe")
output_files.append(output_path)
# Collect metadata
metadata.append({
"original_file": Path(audio_file).name,
"duration": result["segments"][-1]["end"] if result["segments"] else 0,
"output_file": Path(output_path).name,
"model_used": model_size,
"format": output_format,
"timestamp": datetime.now().isoformat()
})
# Delete audio file if requested
if delete_after:
os.unlink(audio_file)
print_message(f"Deleted audio file: {audio_file}", "debug_transcribe")
except Exception as e:
error_msg = f"Error processing {Path(audio_file).name}: {str(e)}"
print_message(error_msg, "error")
validation_messages.append(error_msg)
continue
# Create summary file
summary_path = os.path.join(output_dir, f"summary_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json")
with open(summary_path, 'w', encoding='utf-8') as f:
json.dump({
"processed_files": metadata,
"total_files": len(processed_files),
"successful_transcriptions": len(output_files),
"processing_date": datetime.now().isoformat(),
"validation_messages": validation_messages
}, f, indent=4)
print_message(f"Created summary file: {summary_path}", "debug_transcribe")
# Create ZIP file containing all outputs
zip_path = os.path.join(base_dir, "output", f"{prefix}_transcriptions_{datetime.now().strftime('%Y%m%d_%H%M%S')}.zip" if prefix else f"transcriptions_{datetime.now().strftime('%Y%m%d_%H%M%S')}.zip")
with zipfile.ZipFile(zip_path, 'w') as zipf:
for file in output_files + [summary_path]:
# Preserve the directory structure in the ZIP
arcname = os.path.relpath(file, os.path.dirname(output_dir))
zipf.write(file, arcname)
print_message(f"Created ZIP file: {zip_path}", "debug_transcribe")
status_message = "\n".join(validation_messages + [
f"Processed {m['original_file']}: {m['duration']:.2f} seconds"
for m in metadata
])
status_message += f"\nFiles saved in: {output_dir}"
return zip_path, status_message
finally:
# Cleanup GPU memory
if 'model' in locals():
del model
cleanup_gpu_memory()
########################
#### Live Dictation ####
########################
def setup_transcription_directory():
"""Setup directory for saving transcriptions"""
debug_func_entry()
script_dir = os.path.dirname(os.path.abspath(__file__))
transcripts_dir = os.path.join(script_dir, "transcriptions", "live_dictation")
os.makedirs(transcripts_dir, exist_ok=True)
return transcripts_dir
def create_transcript_file(directory, prefix=""):
"""Create a new transcript file with timestamp"""
debug_func_entry()
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
filename = f"{prefix}_dictation_{timestamp}.txt" if prefix else f"dictation_{timestamp}.txt"
return os.path.join(directory, filename)
def load_whisper_model(model_name):
"""Load the Whisper model"""
debug_func_entry()
print_message(f"Loading Whisper model: {model_name}", "debug_transcribe")
device = "cuda" if torch.cuda.is_available() else "cpu"
model = whisper.load_model(model_name, device=device)
print_message(f"Model loaded on {device}", "debug_transcribe")
return model
def butter_bandpass(lowcut, highcut, fs, order=5):
"""Design a butterworth bandpass filter"""
debug_func_entry()
nyq = 0.5 * fs
low = lowcut / nyq
high = highcut / nyq
b, a = scipy_signal.butter(order, [low, high], btype='band')
return b, a
def apply_audio_processing(audio_data, sample_rate, settings):
"""Apply audio processing based on settings"""
debug_func_entry()
if not settings.get("enable_audio_processing"):
return audio_data
# Convert to float32 and normalize
audio_data = audio_data.astype(np.float32)
if settings.get("bandpass_filter"):
# Apply bandpass filter
b, a = butter_bandpass(
settings.get("bandpass_low", 85),
settings.get("bandpass_high", 3800),
sample_rate
)
audio_data = scipy_signal.filtfilt(b, a, audio_data)
if settings.get("noise_reduction"):
# Simple noise reduction
noise_floor = np.mean(np.abs(audio_data)) * 2
audio_data[np.abs(audio_data) < noise_floor] = 0
if settings.get("compression"):
# Apply compression
threshold = 0.1
ratio = 0.5
makeup_gain = 1.5
mask = np.abs(audio_data) > threshold
audio_data[mask] = np.sign(audio_data[mask]) * (
threshold + (np.abs(audio_data[mask]) - threshold) * ratio
)
audio_data *= makeup_gain
# Final normalization
max_val = np.max(np.abs(audio_data))
if max_val > 0:
audio_data = audio_data / max_val
return audio_data
def identify_speaker(segments):
"""Simple speaker identification based on segment characteristics"""
debug_func_entry()
# Very basic approach - could be enhanced with proper speaker diarization
current_speaker = 1
last_end_time = 0
for segment in segments:
# If there's a significant gap, might be a different speaker
if segment['start'] - last_end_time > 1.0:
current_speaker = 2 if current_speaker == 1 else 1
last_end_time = segment['end']
return current_speaker
def visualize_audio_levels(audio_data):
"""Create audio level visualization"""
debug_func_entry()
import plotly.graph_objects as go
# Calculate RMS in windows
window_size = 1024
windows = range(0, len(audio_data), window_size)
rms_values = [np.sqrt(np.mean(audio_data[i:i+window_size]**2)) for i in windows]
# Create time points
time_points = [i/len(windows) for i in range(len(windows))]
# Create plot
fig = go.Figure()
fig.add_trace(go.Scatter(
x=time_points,
y=rms_values,
mode='lines',
name='Audio Level'
))
fig.update_layout(
title="Real-time Audio Levels",
xaxis_title="Time",
yaxis_title="Level"
)
return fig
def process_audio(audio, state):
"""Process audio chunk and update transcript"""
debug_func_entry()
# Default return values for error cases
empty_plot = visualize_audio_levels(np.zeros(1024))
default_return = (
state if state else None,
state["display_text"] if state and "display_text" in state else "",
empty_plot
)
if audio is None or state is None or not state.get("is_active"):
return default_return
try:
# Get audio data and sample rate
sample_rate, audio_data = audio[0], audio[1]
# Convert to float32 and normalize
audio_data = audio_data.astype(np.float32) / 32768.0
# Calculate RMS energy
energy = np.sqrt(np.mean(audio_data**2))
print_message(f"Audio energy level: {energy}", "debug_transcribe")
# Skip if too quiet
if energy < 0.005:
print_message("Audio too quiet, skipping", "debug_transcribe")
plot_data = visualize_audio_levels(audio_data)
return state, state["display_text"], plot_data
# Resample to 16kHz if needed
if sample_rate != 16000:
print_message(f"Resampling from {sample_rate} to 16000", "debug_transcribe")
number_of_samples = round(len(audio_data) * 16000 / sample_rate)
audio_data = scipy_signal.resample(audio_data, number_of_samples).astype(np.float32)
# Simple normalization
max_val = np.max(np.abs(audio_data))
if max_val > 0:
audio_data = audio_data / max_val
print_message(f"Processing audio chunk - shape: {audio_data.shape}, range: [{audio_data.min():.3f}, {audio_data.max():.3f}]", "debug_transcribe")
# Process with Whisper
result = model.transcribe(
audio_data,
language=state.get("source_language", "auto"),
task="translate" if state.get("translate_to_english") else "transcribe",
temperature=0.0,
condition_on_previous_text=True
)
transcribed_text = result["text"].strip()
if transcribed_text:
# Format text based on timestamps setting
if state.get("add_timestamps"):
timestamp = datetime.now().strftime("%H:%M:%S")
formatted_text = f"[{timestamp}] {transcribed_text}"
state["text"] += formatted_text + "\n"
else:
# For non-timestamped text, append with space and handle sentence endings
if state["text"] and not state["text"].endswith(('.', '!', '?', '\n')):
state["text"] += " "
state["text"] += transcribed_text
# Update display text
state["display_text"] = state["text"]
# Get confidence scores (if available)
try:
confidences = [segment.get('confidence', 1.0) for segment in result['segments']]
avg_confidence = sum(confidences) / len(confidences) if confidences else 1.0
state["display_text"] += f"\n\nConfidence: {avg_confidence:.2%}"
except:
pass
# Save transcript
save_transcript(state, state.get("export_format", "txt"))
# Create audio plot
try:
plot_data = visualize_audio_levels(audio_data)
except Exception as plot_error:
print_message(f"Plot error (non-critical): {plot_error}", "debug_transcribe")
plot_data = None
return state, state["display_text"], plot_data
else:
return default_return
except Exception as e:
error_msg = f"Error processing audio: {str(e)}"
print_message(error_msg, "error")
return default_return
def create_srt_file(segments, filename):
"""Create an SRT file from segments"""
debug_func_entry()
srt_content = create_srt_content(segments)
with open(filename, "w", encoding="utf-8") as f:
f.write(srt_content)
def save_transcript(state, format_type):
"""Save transcript in specified format"""
debug_func_entry()
if not state or not state.get("text"):
return
# Format text based on timestamps setting
if state.get("add_timestamps"):
save_text = state["text"] # Already formatted with timestamps
else:
# Format as flowing text with proper sentence spacing
sentences = state["text"].replace("\n", " ").split(". ")
save_text = ". ".join(s.strip() for s in sentences if s.strip())
if not save_text.endswith(('.', '!', '?')):
save_text += "."
if format_type == "txt":
with open(state["output_file"], "w", encoding="utf-8") as f:
f.write(save_text)
elif format_type == "srt":
srt_filename = state["output_file"].replace(".txt", ".srt")
create_srt_file(state.get("segments", []), srt_filename)
elif format_type == "json":
json_filename = state["output_file"].replace(".txt", ".json")
metadata = {
"text": save_text,
"segments": state.get("segments", []),
"word_count": len(save_text.split()),
"model": state["model_name"],
"settings": state["settings"]
}
with open(json_filename, "w", encoding="utf-8") as f:
json.dump(metadata, f, indent=2)
print_message(f"Saved transcript to: {state['output_file']}", "debug_transcribe")
def start_new_dictation(model_name, prefix, language, source_lang, translate, export_fmt, timestamps,
diarization, audio_proc, bandpass, noise_red, compress,
silence_thresh, bp_low, bp_high):
"""Load model and prepare for dictation"""
debug_func_entry()
global model
try:
model = load_whisper_model(model_name)
transcripts_dir = setup_transcription_directory()
output_file = create_transcript_file(transcripts_dir, prefix)
state = {
"text": "",
"display_text": "",
"output_file": output_file,
"chunks": 0,
"is_active": True,
"model_name": model_name,
"word_count": 0,
# Settings
"language": language,
"source_language": source_lang,
"translate_to_english": translate,
"export_format": export_fmt,
"add_timestamps": timestamps,
"enable_diarization": diarization,
"settings": {
"enable_audio_processing": audio_proc,
"bandpass_filter": bandpass,
"noise_reduction": noise_red,
"compression": compress,
"silence_threshold": silence_thresh,
"bandpass_low": bp_low,
"bandpass_high": bp_high
}
}
print_message(f"Started new transcription: {output_file}", "debug_transcribe")
message = "Model loaded! Click the microphone icon to start/stop recording."
return (
state,
message,
gr.update(interactive=True),
gr.update(interactive=False),
gr.update(interactive=True)
)
except Exception as e:
error_msg = f"Error loading model: {str(e)}"
print_message(error_msg, "error")
return (
None,
f"Error: {error_msg}",
gr.update(interactive=False),
gr.update(interactive=True),
gr.update(interactive=False)
)
def finish_dictation(state):
"""End dictation and cleanup"""
debug_func_entry()
global model
if state is not None:
print_message(f"Finished dictation: {state['output_file']}", "debug_transcribe")
save_transcript(state, state.get("export_format", "txt"))
if 'model' in globals():
# Clear model references
model.to('cpu') # Move model to CPU first
del model
model = None
# Thorough cleanup
cleanup_gpu_memory()
# Clear CUDA memory pool
torch.cuda.synchronize()
return (
None,
"Dictation finished. Please refresh the page before starting a new session.",
gr.update(interactive=False, value=None),
gr.update(interactive=True),
gr.update(interactive=False)
)
##################################################################
# _ _ _ _____ _ _ ____ _ _ #
# / \ | | |_ _|_ _| | | __ / ___|_ __ __ _ __| (_) ___ #
# / _ \ | | | | |/ _` | | |/ / | | _| '__/ _` |/ _` | |/ _ \ #
# / ___ \| | | | | (_| | | < | |_| | | | (_| | (_| | | (_) | #
# /_/ \_\_|_| |_|\__,_|_|_|\_\ \____|_| \__,_|\__,_|_|\___/ #
# #
##################################################################
if gradio_enabled == True:
import importlib
import gradio as gr
# Get the directory of the current script
script_dir = Path(__file__).resolve().parent
my_current_url = "null"
at_default_voice_gr = config.tgwui.tgwui_character_voice
####################################################
# Dynamically import the Themes builder for Gradio #
####################################################
themesmodule_path = this_dir / 'system' / 'gradio_pages' / 'themes' / 'loadThemes.py'
# Add the directory containing the module to the system path
sys.path.insert(0, str(this_dir / 'system' / 'gradio_pages' / 'themes'))
# Import the module dynamically
spec = importlib.util.spec_from_file_location("loadThemes", themesmodule_path)
loadThemes = importlib.util.module_from_spec(spec)
spec.loader.exec_module(loadThemes)
# Load the theme list from JSON file
theme_list = loadThemes.get_list()
# Load the selected theme from configuration
selected_theme = loadThemes.load_json()
###########################################################
# Finish Dynamically import the Themes builder for Gradio #
###########################################################
# Check if the script is running in standalone mode
if script_dir.name == "alltalk_tts":
# Running in standalone mode, add the script's directory to the Python module search path
sys.path.insert(0, str(script_dir))
else:
# Running as part of text-generation-webui, add the parent directory of "alltalk_tts" to the Python module search path
sys.path.insert(0, str(script_dir.parent))
########################################################################################
# Dynamically import modules and set path based on being a standalone or part of TGWUI #
########################################################################################
# Function to get TTS engines data
def get_tts_engines_data(): #TODO: we should be pulling this from our central function now?
debug_func_entry()
global engines_available, engine_loaded, selected_model
tts_engines_file = os.path.join(this_dir, "system", "tts_engines", "tts_engines.json")
with open(tts_engines_file, "r") as f:
tts_engines_data = json.load(f)
engines_available = [engine["name"] for engine in tts_engines_data["engines_available"]]
engine_loaded = tts_engines_data["engine_loaded"]
selected_model = tts_engines_data["selected_model"]
return engines_available, engine_loaded, selected_model
# Determine if running as standalone or within another project
if __name__ == "__main__" or "text-generation-webui" not in this_dir.parts:
# Standalone execution
base_package = None # No base package needed for absolute imports
from system.gradio_pages.alltalk_diskspace import get_disk_interface
else:
# Running within text-generation-webui
# Dynamically build the base package using the current folder name
from .system.gradio_pages.alltalk_diskspace import get_disk_interface
current_folder_name = this_dir.name
base_package = f"extensions.{current_folder_name}"
# Function to dynamically import a module from a given path
def dynamic_import(module_name, package=None):
debug_func_entry()
try:
if package:
module = importlib.import_module(module_name, package=package)
else:
module = importlib.import_module(module_name)
return module
except ModuleNotFoundError as e:
print_message(f"Module not found: {module_name} - {e}", message_type="error")
return None
except Exception as e:
print_message(f"Error importing {module_name}: {e}", message_type="error")
return None
# Call the function to populate engines_available
get_tts_engines_data() #TODO: we should be pulling this from our central function now?
# Dynamically import modules and load JSON data for each available engine
for engine_name in engines_available:
if base_package:
module_name = f"{base_package}.system.tts_engines.{engine_name}.{engine_name}_settings_page"
else:
module_name = f"system.tts_engines.{engine_name}.{engine_name}_settings_page"
module = dynamic_import(module_name, base_package)
if module:
globals()[f"{engine_name}_at_gradio_settings_page"] = getattr(module, f"{engine_name}_at_gradio_settings_page")
globals()[f"{engine_name}_model_update_settings"] = getattr(module, f"{engine_name}_model_update_settings")
json_file_path = os.path.join(this_dir, "system", "tts_engines", engine_name, "model_settings.json")
with open(json_file_path, "r") as f:
globals()[f"{engine_name}_model_config_data"] = json.load(f)
###########################################
# Finishing Dynamically importing Modules #
###########################################
def confirm(message):
debug_func_entry()
'''Need to test if this is a dead function'''
return gr.Interface.fn("confirmation", f"""<script>
var confirmation = confirm("{message}");
gr.Interface.send(confirmation);
</script>""")
##########################################################################################
# Pulls the current AllTalk Server settings & updates gradio when Refresh button pressed #
##########################################################################################
def at_update_dropdowns():
debug_func_entry()
global at_settings
at_settings = get_alltalk_settings() # Pull all the current settings from the AllTalk server, if its online.
engines_available = at_settings["engines_available"]
current_engine_loaded = at_settings["current_engine_loaded"]
current_voices = at_settings["voices"]
current_character_voice = config.tgwui.tgwui_character_voice
current_narrator_voice = config.tgwui.tgwui_narrator_voice
rvccurrent_voices = at_settings["rvcvoices"]
rvccurrent_character_voice = config.rvc_settings.rvc_char_model_file
rvccurrent_narrator_voice = config.rvc_settings.rvc_narr_model_file
current_temperature_capable = at_settings["temperature_capable"]
current_repetitionpenalty_capable = at_settings["repetitionpenalty_capable"]
current_languages_capable = at_settings["languages_capable"]
current_generationspeed_capable = at_settings["generationspeed_capable"]
current_pitch_capable = at_settings["pitch_capable"]
current_languages_capable = at_settings["languages_capable"]
if at_settings["languages_capable"]:
language_label = "Languages"
else:
language_label = "Model not multi language"
if at_settings["streaming_capable"]:
gen_choices = [("Standard", "false"), ("Streaming (Disable Narrator)", "true")]
else:
gen_choices = [("Standard", "false")]
if current_character_voice not in current_voices:
current_character_voice = current_voices[0] if current_voices else ""
if current_narrator_voice not in current_voices:
current_narrator_voice = current_voices[0] if current_voices else ""
if rvccurrent_character_voice not in rvccurrent_voices:
rvccurrent_character_voice = rvccurrent_voices[0] if rvccurrent_voices else ""
if rvccurrent_narrator_voice not in rvccurrent_voices:
rvccurrent_narrator_voice = rvccurrent_voices[0] if rvccurrent_voices else ""
return (
gr.Dropdown(choices=gen_choices, interactive=True),
gr.Dropdown(choices=current_voices, value=current_character_voice, interactive=True),
gr.Dropdown(choices=rvccurrent_voices, value=rvccurrent_character_voice, interactive=True),
gr.Dropdown(choices=current_voices, value=current_narrator_voice, interactive=True),
gr.Dropdown(choices=rvccurrent_voices, value=rvccurrent_narrator_voice, interactive=True),
gr.Slider(interactive=current_generationspeed_capable),
gr.Slider(interactive=current_pitch_capable),
gr.Slider(interactive=current_temperature_capable),
gr.Slider(interactive=current_repetitionpenalty_capable),
gr.Dropdown(interactive=current_languages_capable, label=language_label),
gr.Dropdown(choices=models_available, value=current_model_loaded),
gr.Dropdown(choices=engines_available, value=current_engine_loaded),
)
######################################################################################
# Sends request to reload the current TTS engine & set the default TTS engine loaded #
######################################################################################
def set_engine_loaded(engine_name):
debug_func_entry()
global models_available, at_update_dropdowns
# Use the config system to update the engine
tts_engines_config = AlltalkTTSEnginesConfig.get_instance()
# Update the engine and selected model
for engine in tts_engines_config.engines_available:
if engine.name == engine_name:
tts_engines_config.engine_loaded = engine_name
tts_engines_config.selected_model = engine.selected_model
break
# Save changes
tts_engines_config.save()
# Restart the subprocess
restart_subprocess()
# Wait for the engine to be ready with error handling and retries
max_retries = 80
retry_delay = 1 # seconds
retries = 0
while retries < max_retries:
try:
response = requests.get(url)
if response.status_code == 200 and response.text == "Ready":
break
except requests.exceptions.RequestException:
pass
retries += 1
if retries == max_retries:
raise Exception("Failed to connect to the TTS engine after multiple retries.")
time.sleep(retry_delay)
# Get updated available models
models_available = at_settings["models_available"]
# Update the dropdowns directly
print_message("")
print_message("Server Ready")
# Update UI elements
return_values = at_update_dropdowns()
return (
"TTS Engine changed successfully!", # Output message
*return_values # Unpack all the UI update values
)
###############################
# Sends voice2rvc request off #
###############################
def voice2rvc(audio, rvc_voice, rvc_pitch, rvc_f0method):
debug_func_entry()
# Save the uploaded or recorded audio to a file
input_tts_path = this_dir / "outputs" / "voice2rvcInput.wav"
if rvc_voice == "Disabled":
print_message("Voice2RVC Convert: No RVC voice was selected")
return
if audio == None:
print_message("Voice2RVC Convert: No recorded audio was provided")
return
if isinstance(audio, tuple):
sample_rate, audio_data = audio
# Save the numpy array as a wav file
sf.write(input_tts_path, audio_data, sample_rate)
else:
# It's a file path
os.rename(audio, input_tts_path)
# Define the output path for the processed audio
output_rvc_path = this_dir / "outputs" / "voice2rvcOutput.wav"
url = build_url('voice2rvc')
# Submit the paths to the API endpoint
response = requests.post(url, data={
"input_tts_path": str(input_tts_path),
"output_rvc_path": str(output_rvc_path),
"pth_name": rvc_voice,
"pitch": rvc_pitch,
"method": str(rvc_f0method),
})
if response.status_code == 200:
result = response.json()
if result["status"] == "success":
result_path = result["output_path"]
else:
result_path = None
else:
result_path = None
return result_path
##################################################################################################
# Sends request to reload the current TTS engine model & set the default TTS engine model loaded #
##################################################################################################
def change_model_loaded(engine_name, selected_model):
debug_func_entry()
global at_update_dropdowns
try:
print_message("")
print_message("\033[94mChanging model loaded. Please wait.\033[00m")
print_message("")
url = build_url('reload')
payload = {"tts_method": selected_model}
response = requests.post(url, params=payload)
response.raise_for_status() # Raises an HTTPError for bad responses
# Update the tts_engines.json file
tts_engines_file = os.path.join(this_dir, "system", "tts_engines", "tts_engines.json")
with open(tts_engines_file, "r") as f:
tts_engines_data = json.load(f)
tts_engines_data["selected_model"] = f"{selected_model}"
for engine in tts_engines_data["engines_available"]:
if engine["name"] == engine_name:
engine["selected_model"] = f"{selected_model}"
break
with open(tts_engines_file, "w") as f:
json.dump(tts_engines_data, f)
print_message("")
print_message("Server Ready")
gen_stream_value, gen_char_choices, rvcgen_char_choices, gen_narr_choices, rvcgen_narr_choices, gen_speed_interactive, gen_pitch_interactive, gen_temperature_interactive, gen_repetition_interactive, gen_lang_interactive, model_choices_gr_interactive, engine_choices_interactive = at_update_dropdowns()
return ("TTS Model changed successfully!", # This is your output message
gen_stream_value, gen_char_choices, rvcgen_char_choices, gen_narr_choices, rvcgen_narr_choices, gen_speed_interactive, gen_pitch_interactive, gen_temperature_interactive, gen_repetition_interactive, gen_lang_interactive, model_choices_gr_interactive, engine_choices_interactive
)
except requests.exceptions.RequestException as e:
# Handle the HTTP request error
print_message(f"Error during request to webserver process: Status code:\n{e}", message_type="warning")
return {"status": "error", "message": str(e)}
debugging_options = config.debugging
debugging_choices = list(vars(debugging_options).keys())
default_values = [key for key, value in vars(debugging_options).items() if value]
def generate_tts(gen_text, gen_char, rvcgen_char, rvcgen_char_pitch, gen_narr, rvcgen_narr, rvcgen_narr_pitch, gen_narren, gen_textni, gen_repetition, gen_lang, gen_filter, gen_speed, gen_pitch, gen_autopl, gen_autoplvol, gen_filen, gen_temperature, gen_filetime, gen_stream, gen_stopcurrentgen):
debug_func_entry()
api_url = build_dynamic_url('tts-generate', include_protocol=False)
if api_url == "http://null/api/tts-generate": # Check if api_url is null
print_message("The URL in the API request was not set. This is usually because of a TTS request from", message_type="error")
print_message("the Gradio web interface when the AllTalk server has been resarted but the Gradio page", message_type="error")
print_message("was not re-loaded. Please refresh your Gradio page and try again.", message_type="error")
return None, str("Error: Did you restart the AllTalk Server and not refresh your Gradio page? Please see the console/terminal message.")
if gen_text == "":
print_message("No Text was sent to generate as TTS")
return None, str("No Text was sent to generate as TTS")
if gen_stopcurrentgen:
stop_generate_tts()
if gen_stream == "true":
api_url = build_dynamic_url('tts-generate-streaming', include_protocol=False)
encoded_text = requests.utils.quote(gen_text)
streaming_url = f"{api_url}?text={encoded_text}&voice={gen_char}&language={gen_lang}&output_file={gen_filen}"
return streaming_url, str("TTS Streaming Audio Generated")
else:
data = {
"text_input": gen_text,
"text_filtering": gen_filter,
"character_voice_gen": gen_char,
"rvccharacter_voice_gen": rvcgen_char,
"rvccharacter_pitch": rvcgen_char_pitch,
"narrator_enabled": str(gen_narren).lower(),
"narrator_voice_gen": gen_narr,
"rvcnarrator_voice_gen": rvcgen_narr,
"rvcnarrator_pitch": rvcgen_narr_pitch,
"text_not_inside": gen_textni,
"language": gen_lang,
"output_file_name": gen_filen,
"output_file_timestamp": str(gen_filetime).lower(),
"autoplay": str(gen_autopl).lower(),
"autoplay_volume": str(gen_autoplvol),
"speed": str(gen_speed),
"pitch": str(gen_pitch),
"temperature": str(gen_temperature),
"repetition_penalty": str(gen_repetition),
}
print_message("\033[94mDebug of data > def generate_tts > script.py\033[0m", message_type="debug_tts_variables")
for key, value in data.items():
print_message(f"{key}: {value}", message_type="debug_tts_variables")
print_message(f"API Url being used: {api_url}", message_type="debug_tts_variables")
max_retries = 180 #3 minutes
retry_delay = 1 # seconds
retries = 0
while retries < max_retries:
try:
response = requests.post(api_url, data=data, timeout=60)
response.raise_for_status()
result = response.json()
if gen_autopl == "true":
return None, str("TTS Audio Generated (Played remotely)")
else:
if config.api_def.api_use_legacy_api:
return result['output_file_url'], str("TTS Audio Generated")
else:
# Prepend the URL and PORT to the output_file_url
output_file_url = build_dynamic_url(result['output_file_url'].lstrip('/'), include_protocol=False, include_api=False)
return output_file_url, str("TTS Audio Generated")
except requests.exceptions.Timeout:
retries += 1
if retries == max_retries:
error_message = "Request timed out after maximum retries"
print_message(f"Error: {error_message}", message_type="error")
return None, str(error_message)
except json.JSONDecodeError as e:
retries += 1
if retries == max_retries:
error_message = "Failed to parse API response"
print_message(f"Error: {error_message}", message_type="error")
print_message(f"Error Details: {str(e)}", message_type="error")
print_message(f"Raw response: {response.content}", message_type="error")
return None, str(error_message)
except requests.exceptions.RequestException as e:
retries += 1
if retries == max_retries:
error_message = "An error occurred while communicating with the API"
print_message(f"Error: {error_message}", message_type="error")
print_message(f"Error Details: {str(e)}", message_type="error")
return None, str(error_message)
time.sleep(retry_delay)
def alltalk_gradio():
debug_func_entry()
global languages_list, at_settings
# Get the URL IP or domain name
def get_domain_name(request: gr.Request):
global my_current_url
if running_on_google_colab:
my_current_url = tunnel_url_1
return None
elif running_in_docker:
my_current_url = docker_url
return None
if request:
host = request.headers.get("host", "Unknown")
my_current_url = host.split(":")[0] # Split the host by ":" and take the first part
my_current_url = my_current_url + ":" + str(config.api_def.api_port_number)
return None
else:
return "Unable to retrieve the domain name."
with gr.Blocks(css=AllTalkHelpContent.custom_css, theme=selected_theme, title="AllTalk", analytics_enabled=False) as app:
with gr.Row():
gr.Markdown("## AllTalk TTS V2")
gr.Markdown("")
gr.Markdown("")
dark_mode_btn = gr.Button("Light/Dark Mode", variant="primary", size="sm")
dark_mode_btn.click(None, None, None,
js="""() => {
if (document.querySelectorAll('.dark').length) {
document.querySelectorAll('.dark').forEach(el => el.classList.remove('dark'));
// localStorage.setItem('darkMode', 'disabled');
} else {
document.querySelector('body').classList.add('dark');
// localStorage.setItem('darkMode', 'enabled');
}
}""", show_api=False)
if config.firstrun_splash:
with gr.Tab("AllTalk v2 Welcome page"):
def modify_config():
config.firstrun_splash = False
config.save()
return f"Welcome screen is disabled."
gr.Markdown(AllTalkHelpContent.WELCOME, elem_classes="custom-markdown")
with gr.Row():
gr.Markdown(AllTalkHelpContent.WELCOME1, elem_classes="custom-markdown")
gr.Markdown(AllTalkHelpContent.WELCOME2, elem_classes="custom-markdown")
update_btn = gr.Button("Click here to hide welcome screen on the next startup")
update_btn.click(fn=modify_config, inputs=None, outputs=None)
with gr.Tab("Generate TTS"):
with gr.Row():
gen_text = gr.Textbox(label="Text Input", lines=6)
if running_in_docker:
with gr.Row():
with gr.Accordion("Docker IP/URL for API Address updater", open=False):
with gr.Row():
docker_url = f"http://localhost:{config.api_def.api_port_number}"
docker_upd = gr.Textbox(label="Docker IP/URL for API Address", value=docker_url, show_label=False, scale=2)
update_docker_btn = gr.Button("Update Docker IP/URL API Address", scale=1)
with gr.Accordion("HELP - 🐳 Docker IP/URL for API Addresss", open=False):
with gr.Row():
gr.Markdown(AllTalkHelpContent.DOCKER_EXPLAINER, elem_classes="custom-markdown")
with gr.Row():
with gr.Group():
with gr.Row():
engine_choices = gr.Dropdown(choices=engines_available, label="TTS Engine", value=engine_loaded, show_label=False)
engine_btn = gr.Button("Swap TTS Engine")
with gr.Group():
with gr.Row():
model_choices_gr = gr.Dropdown(choices=models_available, label="TTS Models", value=current_model_loaded, interactive=True, show_label=False, allow_custom_value=True)
model_btn_gr = gr.Button("Load Different Model")
with gr.Group():
at_available_voices_gr = at_settings["voices"]
rvcat_available_voices_gr = at_settings["rvcvoices"]
with gr.Row():
at_default_voice_gr = config.tgwui.tgwui_character_voice
if at_default_voice_gr not in at_available_voices_gr:
at_default_voice_gr = at_available_voices_gr[0] if at_available_voices_gr else ""
gen_char = gr.Dropdown(choices=at_available_voices_gr, label="Character Voice", value=at_default_voice_gr, allow_custom_value=True,)
rvcat_default_voice_gr = config.rvc_settings.rvc_char_model_file
if rvcat_default_voice_gr not in rvcat_available_voices_gr:
rvcat_default_voice_gr = rvcat_available_voices_gr[0] if rvcat_available_voices_gr else ""
rvcgen_char = gr.Dropdown(choices=rvcat_available_voices_gr, label="RVC Character Voice", value=rvcat_default_voice_gr, allow_custom_value=True,)
at_narrator_voice_gr = config.tgwui.tgwui_narrator_voice
if at_narrator_voice_gr not in at_available_voices_gr:
at_narrator_voice_gr = at_available_voices_gr[0] if at_available_voices_gr else ""
gen_narr = gr.Dropdown(choices=at_available_voices_gr, label="Narrator Voice", value=at_narrator_voice_gr, allow_custom_value=True,)
rvcat_narrator_voice_gr = config.rvc_settings.rvc_narr_model_file
if rvcat_narrator_voice_gr not in rvcat_available_voices_gr:
rvcat_narrator_voice_gr = rvcat_available_voices_gr[0] if rvcat_available_voices_gr else ""
rvcgen_narr = gr.Dropdown(choices=rvcat_available_voices_gr, label="RVC Narrator Voice", value=rvcat_narrator_voice_gr, allow_custom_value=True,)
with gr.Group():
with gr.Row():
rvcat_default_pitch_gr = gr.Slider(
minimum=-24,
maximum=24,
step=1,
label="RVC Character Pitch",
info="Corrects the Character input TTS pitch to match the desired RVC voice output pitch.",
value=config.rvc_settings.pitch,
interactive=True,
visible=False
)
rvcat_narrator_pitch_gr = gr.Slider(
minimum=-24,
maximum=24,
step=1,
label="RVC Narrator Pitch",
info="Corrects the Narrator input TTS pitch to match the desired RVC voice output pitch.",
value=config.rvc_settings.pitch,
interactive=True,
visible=False
)
def update_visibility(char_voice, narr_voice):
is_visible = char_voice != "Disabled" or narr_voice != "Disabled"
return gr.update(visible=is_visible), gr.update(visible=is_visible)
rvcgen_char.change(fn=update_visibility, inputs=[rvcgen_char, rvcgen_narr], outputs=[rvcat_default_pitch_gr, rvcat_narrator_pitch_gr])
rvcgen_narr.change(fn=update_visibility, inputs=[rvcgen_char, rvcgen_narr], outputs=[rvcat_default_pitch_gr, rvcat_narrator_pitch_gr])
with gr.Accordion("Advanced Engine/Model Settings", open=False):
with gr.Row():
#Get the current URL from the page
domain_name_output = gr.Textbox(label="Domain Name", visible=False)
def on_load(request: gr.Request):
domain_name = get_domain_name(request)
domain_name_output.value = domain_name
app.load(on_load, inputs=None, outputs=None)
if at_settings["streaming_capable"]:
gen_choices = [("Standard", "false"), ("Streaming (Disable Narrator)", "true")]
else:
gen_choices = [("Standard", "false")]
#Continue on with the Gradio interface
gen_stream = gr.Dropdown(choices=gen_choices, label="Generation Mode", value="false")
gen_lang = gr.Dropdown(value=config.api_def.api_language, choices=["ar", "zh", "cs", "nl", "en", "fr", "de", "hi", "hu", "it", "ja", "ko", "pl", "pt", "ru", "es", "tr"], label="Languages" if at_settings["languages_capable"] else "Model not multi language", interactive=at_settings["languages_capable"], allow_custom_value=True)
gen_narren = gr.Dropdown(choices=[("Enabled", "true"), ("Disabled", "false"), ("Enabled (Silent)", "silent")], label="Narrator Enabled/Disabled", value="true" if config.api_def.api_narrator_enabled == "true" else ("silent" if config.api_def.api_narrator_enabled == "silent" else "false"), allow_custom_value=True)
gen_textni = gr.Dropdown(choices=[("Character", "character"), ("Narrator", "narrator"), ("Silent", "silent")], label="Narrator Text-not-inside", value=config.api_def.api_text_not_inside)
gen_stopcurrentgen = gr.Dropdown(choices={("Stop", "true"), ("Dont stop", "false")}, label="Auto-Stop current generation", value="true")
with gr.Row():
gen_filter = gr.Dropdown(value=config.api_def.api_text_filtering, label="Text filtering", choices=["none", "standard", "html"])
gen_filetime = gr.Dropdown(
choices=[("Timestamp files", "true"), ("Dont Timestamp (Over-write)", "false")],
label="Include Timestamp",
value="true" if config.api_def.api_output_file_timestamp else "false",
allow_custom_value=True
)
gen_autopl = gr.Dropdown(choices={("Play locally", "false"), ("Play remotely", "true")}, label="Play Locally or Remotely", value="true" if config.api_def.api_autoplay else "false", allow_custom_value=True)
gen_autoplvol = gr.Dropdown(choices=[str(i / 10) for i in range(11)], value=str(config.api_def.api_autoplay_volume), label="Remote play volume", allow_custom_value=True)
gen_filen = gr.Textbox(value=config.api_def.api_output_file_name, label="Output File Name")
with gr.Row():
gen_speed = gr.Slider(minimum=0.25, maximum=2.00, step=0.25, label="Speed", value="1.00", interactive=at_settings["generationspeed_capable"])
gen_pitch = gr.Slider(minimum=-10, maximum=10, step=1, label="Pitch", value="1", interactive=at_settings["pitch_capable"])
gen_temperature = gr.Slider(minimum=0.05, maximum=1.0, step=0.05, label="Temperature", value=0.75, interactive=at_settings["temperature_capable"])
gen_repetition = gr.Slider(minimum=1.0, maximum=20.0, step=1.0, label="Repetition Penalty", value=10, interactive=at_settings["repetitionpenalty_capable"])
#Toggle narrator selection on Streaming select
def update_narren_and_autopl(gen_stream):
debug_func_entry()
if gen_stream == "true":
return "false", "false"
else:
return gen_narren.value, gen_autopl.value
gen_stream.change(update_narren_and_autopl, inputs=[gen_stream], outputs=[gen_narren, gen_autopl])
with gr.Row():
output_audio = gr.Audio(show_label=False, label="Generated Audio", autoplay=True, scale=3)
output_message = gr.Textbox( label="Status/Result", lines=5, scale=1)
with gr.Row():
dark_mode_btn = gr.Button("Light/Dark Mode", variant="primary")
refresh_button = gr.Button("Refresh Server Settings", elem_id="refresh_button")
stop_button = gr.Button("Interupt TTS Generation")
submit_button = gr.Button("Generate TTS")
model_btn_gr.click(fn=change_model_loaded, inputs=[engine_choices, model_choices_gr], outputs=[output_message, gen_stream, gen_char, rvcgen_char, gen_narr, rvcgen_narr, gen_speed, gen_pitch, gen_temperature, gen_repetition, gen_lang, model_choices_gr, engine_choices])
engine_btn.click(fn=set_engine_loaded, inputs=[engine_choices], outputs=[output_message, gen_stream, gen_char, rvcgen_char, gen_narr, rvcgen_narr, gen_speed, gen_pitch, gen_temperature, gen_repetition, gen_lang, model_choices_gr, engine_choices])
dark_mode_btn.click(None, None, None,
js="""() => {
if (document.querySelectorAll('.dark').length) {
document.querySelectorAll('.dark').forEach(el => el.classList.remove('dark'));
// localStorage.setItem('darkMode', 'disabled');
} else {
document.querySelector('body').classList.add('dark');
// localStorage.setItem('darkMode', 'enabled');
}
}""", show_api=False)
refresh_button.click(at_update_dropdowns, None, [gen_stream, gen_char, rvcgen_char, gen_narr, rvcgen_narr, gen_speed, gen_pitch, gen_temperature, gen_repetition, gen_lang, model_choices_gr, engine_choices])
stop_button.click(stop_generate_tts, inputs=[], outputs=[output_message])
submit_button.click(generate_tts, inputs=[gen_text, gen_char, rvcgen_char, rvcat_default_pitch_gr, gen_narr, rvcgen_narr, rvcat_narrator_pitch_gr, gen_narren, gen_textni, gen_repetition, gen_lang, gen_filter, gen_speed, gen_pitch, gen_autopl, gen_autoplvol, gen_filen, gen_temperature, gen_filetime, gen_stream, gen_stopcurrentgen], outputs=[output_audio, output_message])
if running_in_docker:
update_docker_btn.click(
fn=update_docker_address,
inputs=[docker_upd],
outputs=[output_message]
)
if config.gradio_pages.Generate_Help_page:
with gr.Accordion("HELP - 🎯 TTS Generation Basics", open=False):
with gr.Row():
gr.Markdown(AllTalkHelpContent.GENERATE_SCREEN1, elem_classes="custom-markdown")
gr.Markdown(AllTalkHelpContent.GENERATE_SCREEN2, elem_classes="custom-markdown")
with gr.Accordion("HELP - ⚙️ Advanced TTS Features", open=False):
with gr.Row():
gr.Markdown(AllTalkHelpContent.GENERATE_SCREEN3, elem_classes="custom-markdown")
gr.Markdown(AllTalkHelpContent.GENERATE_SCREEN4, elem_classes="custom-markdown")
if config.gradio_pages.Voice2RVC_page:
with gr.Tab("Voice2RVC"):
with gr.Row():
audio_input = gr.Audio(sources=["microphone", "upload"], type="numpy", label="Record audio or Upload a spoken audio file")
if running_in_docker:
with gr.Row():
with gr.Accordion("Docker IP/URL for API Address updater", open=False):
with gr.Row():
docker_url = f"http://localhost:{config.api_def.api_port_number}"
docker_upd = gr.Textbox(label="Docker IP/URL for API Address", value=docker_url, show_label=False)
update_docker_btn = gr.Button("Update Docker IP/URL API Address")
update_docker_btn.click(
fn=update_docker_address,
inputs=[docker_upd],
outputs=[gr.Text(label="Status")]
)
with gr.Row():
rvc_voices_dropdown = gr.Dropdown(choices=at_settings["rvcvoices"], label="Select RVC Voice to generate as", value=at_settings["rvcvoices"][0], scale=1)
rvc_pitch_slider = gr.Slider(minimum=-24, maximum=24, step=1, label="RVC Pitch", info="Depending on the pitch of your input audio, you will need to adjust this accordingly to change the pitch for the output voice. The higher the value, the higher the pitch.", value=0, interactive=True, scale=2)
with gr.Row():
rvc_f0method = gr.Radio(label="Pitch Extraction Algorithm", info="Select the algorithm to be used for extracting the pitch (F0) during audio conversion. The default algorithm is rmvpe, which is generally recommended for most cases due to its balance of accuracy and performance.", choices=["crepe", "crepe-tiny", "dio", "fcpe", "harvest", "hybrid[rmvpe+fcpe]", "pm", "rmvpe"], value=config.rvc_settings.f0method, interactive=True, scale=1)
submit_button = gr.Button("Submit to RVC", scale=0)
audio_output = gr.Audio(label="Converted Audio")
submit_button.click(fn=voice2rvc, inputs=[audio_input, rvc_voices_dropdown, rvc_pitch_slider, rvc_f0method], outputs=audio_output)
with gr.Accordion("HELP - 🎯 Voice2RVC Basics", open=False):
with gr.Row():
gr.Markdown(AllTalkHelpContent.VOICE2RVC, elem_classes="custom-markdown")
with gr.Row():
gr.Markdown(AllTalkHelpContent.VOICE2RVC1, elem_classes="custom-markdown")
gr.Markdown(AllTalkHelpContent.VOICE2RVC2, elem_classes="custom-markdown")
with gr.Tab("Transcribe"):
with gr.Row():
with gr.Group():
audio_files = gr.File(
file_count="multiple",
label="Upload Audio Files",
elem_classes="small-file-upload"
)
delete_after_process = gr.Checkbox(
label="Clean Up Temporary Audio Files After Processing",
value=False
)
with gr.Row():
with gr.Group():
with gr.Row():
prefix_input = gr.Textbox(
label="Output Prefix (optional)",
placeholder="e.g., project_name, meeting_date",
value="",
scale=3
)
model_choices = gr.Dropdown(
choices=["tiny", "base", "small", "medium", "large-v3"],
value="base",
label="Whisper Model Size",
scale=1
)
format_choices = gr.Dropdown(
choices=["txt", "json", "srt"],
value="txt",
label="Output Format",
scale=1
)
with gr.Row():
delete_btn = gr.Button("Delete Uploaded Audio")
process_btn = gr.Button("Transcribe", variant="primary")
with gr.Row():
status_output = gr.Textbox(label="Processing Status", lines=2)
with gr.Row():
output_zip = gr.File(label="Download Transcriptions (ZIP)", elem_classes="small-file-upload2")
with gr.Accordion("HELP - 🎯 Transcribe Basics", open=False):
with gr.Row():
gr.Markdown(AllTalkHelpContent.TRANSCRIBE, elem_classes="custom-markdown")
with gr.Row():
gr.Markdown(AllTalkHelpContent.TRANSCRIBE1, elem_classes="custom-markdown")
gr.Markdown(AllTalkHelpContent.TRANSCRIBE2, elem_classes="custom-markdown")
process_btn.click(
fn=process_audio_files,
inputs=[audio_files, model_choices, format_choices, delete_after_process, prefix_input],
outputs=[output_zip, status_output]
)
delete_btn.click(
fn=delete_uploaded_files,
inputs=[],
outputs=[status_output]
)
with gr.Tab("Dictate"):
state = gr.State(None)
# Sort the dictionary by full names alphabetically
sorted_languages = dict(sorted(AllTalkHelpContent.WHISPER_LANGUAGES.items(), key=lambda item: item[1]))
# Reverse the dictionary for lookups (Full Name -> Code)
name_to_code = {name: code for code, name in sorted_languages.items()}
def process_language(language_name):
# Convert selected language name back to its 2-digit code
return name_to_code[language_name]
with gr.Row():
model_choices = gr.Dropdown(
choices=["tiny", "base", "small", "medium", "turbo", "large-v3", "large-v3-turbo"],
value="large-v3",
label="Whisper Model",
scale=1
)
language_select = gr.Dropdown(
choices=list(sorted_languages.values()),
value="English",
label="Language",
allow_custom_value=True
)
language_select.change(
fn=process_language,
inputs=language_select,
)
export_format = gr.Dropdown(
choices=["txt", "srt", "json"],
value="txt",
label="Output Format",
scale=1
)
prefix_input = gr.Textbox(
label="Output File Prefix (optional)",
placeholder="e.g., meeting_notes, todo_list",
scale=2
)
start_btn = gr.Button("Load Model", variant="primary")
finish_btn = gr.Button("Finish & Unload", interactive=False)
with gr.Accordion("Advanced Settings (Change Before Loading The Model)", open=False):
with gr.Row():
# Update the dropdowns
translate_to_english = gr.Dropdown(
label="Translate to English",
choices={False, True},
value=False,
allow_custom_value=True
)
source_language = gr.Dropdown(
choices=list(sorted_languages.values()),
value="English",
label="Source Language",
allow_custom_value=True
)
source_language.change(
fn=process_language,
inputs=source_language,
)
add_timestamps = gr.Dropdown(
label="Add Timestamps",
choices={False, True},
value=False
)
enable_diarization = gr.Dropdown(
label="Enable Speaker Diarization",
choices={False, True},
value=False
)
with gr.Row():
enable_audio_processing = gr.Checkbox(
label="Enable ALL Audio Enhancements",
value=True
)
bandpass_filter = gr.Checkbox(
label="Apply Bandpass Filter",
value=True
)
noise_reduction = gr.Checkbox(
label="Apply Noise Reduction",
value=True
)
compression = gr.Checkbox(
label="Apply Audio Compression",
value=True
)
# At the start of interface setup
checkbox_state = gr.State({
"processing": False
})
def update_audio_settings(main_enable, state_dict):
"""Enable all individual settings when main is checked"""
if not state_dict["processing"]:
state_dict["processing"] = True
state_dict["processing"] = False
# Return individual updates and state separately
return (
gr.update(value=main_enable), # bandpass
gr.update(value=main_enable), # noise
gr.update(value=main_enable), # compression
state_dict
)
return (
gr.update(), # bandpass
gr.update(), # noise
gr.update(), # compression
state_dict
)
def update_main_setting(bandpass, noise, compress, state_dict):
"""Main should only be checked if all individuals are checked"""
if not state_dict["processing"]:
state_dict["processing"] = True
ret = gr.update(value=all([bandpass, noise, compress]))
state_dict["processing"] = False
return ret, state_dict
return gr.update(), state_dict
# In interface:
enable_audio_processing.change(
fn=update_audio_settings,
inputs=[enable_audio_processing, checkbox_state],
outputs=[bandpass_filter, noise_reduction, compression, checkbox_state]
)
for checkbox in [bandpass_filter, noise_reduction, compression]:
checkbox.change(
fn=update_main_setting,
inputs=[bandpass_filter, noise_reduction, compression, checkbox_state],
outputs=[enable_audio_processing, checkbox_state]
)
with gr.Row():
silence_threshold = gr.Slider(
minimum=0.001,
maximum=0.02,
value=0.008,
label="Silence Threshold"
)
bandpass_low = gr.Slider(
minimum=50,
maximum=200,
value=85,
label="Bandpass Low Freq (Hz)"
)
bandpass_high = gr.Slider(
minimum=2000,
maximum=8000,
value=3800,
label="Bandpass High Freq (Hz)"
)
with gr.Row():
# Audio input with waveform visualization
waveform_opts = gr.WaveformOptions(
sample_rate=16000, # Set to Whisper's expected rate
show_recording_waveform=True,
show_controls=False,
waveform_color="#1f77b4",
waveform_progress_color="#2ecc71"
)
# Audio input with configured waveform
audio_input = gr.Audio(
sources=["microphone"],
type="numpy",
streaming=True,
label="Click `Record` to start dictation and `Stop` to pause or stop dictation",
show_label=True,
interactive=False,
waveform_options=waveform_opts
)
with gr.Accordion("Show Audio Levels Graph", open=False):
with gr.Row():
audio_plot = gr.Plot(
label="Audio Levels",
show_label=True,
container=True,
)
with gr.Row():
text_output = gr.Textbox(
label="Live Transcription",
lines=10,
placeholder="Transcription will appear here as you speak...",
show_copy_button=True
)
# Dictatation Button click handlers
start_btn.click(
fn=start_new_dictation,
inputs=[
model_choices, prefix_input,
language_select, source_language, translate_to_english, export_format,
add_timestamps, enable_diarization,
enable_audio_processing, bandpass_filter, noise_reduction, compression,
silence_threshold, bandpass_low, bandpass_high
],
outputs=[state, text_output, audio_input, start_btn, finish_btn]
)
finish_btn.click(
fn=finish_dictation,
inputs=[state],
outputs=[state, text_output, audio_input, start_btn, finish_btn]
)
def on_start_recording(state):
if state and state.get("is_active"):
print_message("Recording started", "debug_transcribe")
state["is_recording"] = True
return state
def on_stop_recording(state):
if state and state.get("is_active"):
print_message("Recording stopped", "debug_transcribe")
state["is_recording"] = False
return state
# Add the handlers to the audio input
audio_input.start_recording(
fn=on_start_recording,
inputs=[state],
outputs=[state]
).success(
fn=None,
js="() => {console.log('Recording started');}"
)
audio_input.stop_recording(
fn=on_stop_recording,
inputs=[state],
outputs=[state]
).success(
fn=None,
js="() => {console.log('Recording stopped');}"
)
audio_input.stream(
fn=process_audio,
inputs=[audio_input, state],
outputs=[state, text_output, audio_plot],
show_progress=False,
concurrency_limit=1
)
with gr.Accordion("HELP - 🎯 Dictate Basics", open=False):
with gr.Row():
gr.Markdown(AllTalkHelpContent.WHISPER_HELP, elem_classes="custom-markdown")
with gr.Row():
gr.Markdown(AllTalkHelpContent.WHISPER_HELP1, elem_classes="custom-markdown")
gr.Markdown(AllTalkHelpContent.WHISPER_HELP2, elem_classes="custom-markdown")
if config.gradio_pages.TTS_Generator_page:
with gr.Tab("TTS Generator"):
gr.Markdown("""### TTS Generator for long audio generation tasks. [Click here for access](http://127.0.0.1:7851/static/tts_generator/tts_generator.html)""")
with gr.Accordion("HELP - 🎯 TTS Generator Basics", open=False):
with gr.Row():
gr.Markdown(AllTalkHelpContent.TTS_GENERATOR, elem_classes="custom-markdown")
with gr.Row():
gr.Markdown(AllTalkHelpContent.TTS_GENERATOR1, elem_classes="custom-markdown")
gr.Markdown(AllTalkHelpContent.TTS_GENERATOR2, elem_classes="custom-markdown")
with gr.Tab("Global Settings"):
with gr.Tab("AllTalk Settings"):
with gr.Row():
delete_output_wavs = gr.Dropdown(value=config.delete_output_wavs, label="Del WAV's older than", choices=["Disabled", "1 Day", "2 Days", "3 Days", "4 Days", "5 Days", "6 Days", "7 Days", "14 Days", "21 Days", "28 Days"])
api_port_number = gr.Number(
value=config.api_def.api_port_number,
label="API Port Number",
precision=0
)
gradio_port_number = gr.Number(
value=config.gradio_port_number,
label="Gradio Port Number",
precision=0
)
output_folder = gr.Textbox(
value=config.output_folder,
label=f"Output Folder name (sub {config.branding})"
)
with gr.Row():
transcode_audio_format = gr.Dropdown(choices={"Disabled": "disabled", "aac": "aac", "flac": "flac", "mp3": "mp3", "opus": "opus", "wav": "wav"}, label="Audio Transcoding", value=config.transcode_audio_format)
with gr.Row():
themes_select = gr.Dropdown(loadThemes.get_list(), value=loadThemes.read_json(), label="Gradio Theme Selection", visible=True)
def update_theme_selection(theme_name):
config = AlltalkConfig.get_instance()
config.theme.clazz = theme_name
config.save() # Save the updated configuration
return f"Theme '{theme_name}' has been selected and saved."
themes_select.change(
fn=update_theme_selection,
inputs=[themes_select],
outputs=[gr.Textbox(label="Gradio Selection Result")]
)
with gr.Row():
with gr.Column():
gr_debug_tts = gr.CheckboxGroup(choices=debugging_choices, label="Debugging Options list", value=default_values)
with gr.Column():
gradio_interface = gr.Dropdown(choices={"Enabled": "true", "Disabled": "false"}, label="Gradio Interface", value="Enabled" if config.gradio_interface else "Disabled", info="**WARNING**: This will disable the AllTalk Gradio interface from loading. To re-enable the interface, go to the API address in a web browser and enable it there. http://127.0.0.1:7851/", allow_custom_value=True)
with gr.Group():
gr.Markdown("### Disable Gradio Interface Tabs")
with gr.Row():
generate_help_page = gr.Checkbox(label="Generate Help", value=config.gradio_pages.Generate_Help_page)
voice2rvc_page = gr.Checkbox(label="Voice2RVC", value=config.gradio_pages.Voice2RVC_page)
tts_generator_page = gr.Checkbox(label="TTS Generator", value=config.gradio_pages.TTS_Generator_page)
tts_engines_settings_page = gr.Checkbox(label="TTS Engines Settings", value=config.gradio_pages.TTS_Engines_Settings_page)
alltalk_documentation_page = gr.Checkbox(label="AllTalk Documentation", value=config.gradio_pages.alltalk_documentation_page)
api_documentation_page = gr.Checkbox(label="API Documentation", value=config.gradio_pages.api_documentation_page)
with gr.Row():
submit_button = gr.Button("Update Settings")
output_message = gr.Textbox(label="Output Message", interactive=False, show_label=False)
with gr.Accordion("⚙️ HELP - AllTalk Settings Page", open=False):
with gr.Row():
gr.Markdown(AllTalkHelpContent.ALLTALK_SETTINGS_PAGE1, elem_classes="custom-markdown")
gr.Markdown(AllTalkHelpContent.ALLTALK_SETTINGS_PAGE2, elem_classes="custom-markdown")
with gr.Accordion("🔍 HELP - AllTalk Debug Settings", open=False):
with gr.Row():
gr.Markdown(AllTalkHelpContent.DEBUG_HELP1, elem_classes="custom-markdown")
gr.Markdown(AllTalkHelpContent.DEBUG_HELP2, elem_classes="custom-markdown")
# Update the function to include these new settings
submit_button.click(
update_settings_at,
inputs=[delete_output_wavs, gradio_interface, gradio_port_number, output_folder, api_port_number, gr_debug_tts, transcode_audio_format, generate_help_page, voice2rvc_page, tts_generator_page, tts_engines_settings_page, alltalk_documentation_page, api_documentation_page],
outputs=output_message
)
with gr.Tab("AllTalk API Defaults"):
with gr.Row():
# API Version Settings
with gr.Column(scale=1):
with gr.Group():
with gr.Row():
api_use_legacy_api = gr.Dropdown(
choices=["AllTalk v2 API", "AllTalk v1 API (Legacy)"],
label="API Version",
scale=1,
allow_custom_value=True,
value=config.api_def.api_use_legacy_api
)
api_legacy_ip_address = gr.Textbox(
value=config.api_def.api_legacy_ip_address,
scale=1,
label="Legacy API IP Address"
)
# Request Processing
with gr.Column(scale=2):
with gr.Group():
with gr.Row():
api_length_stripping = gr.Slider(
minimum=1, maximum=20, step=1,
value=int(config.api_def.api_length_stripping),
scale=2,
label="Minimum Sentence Length"
)
api_max_characters = gr.Slider(
minimum=50, maximum=10000, step=50,
value=int(config.api_def.api_max_characters),
scale=2,
label="Maximum Request Characters"
)
api_text_filtering = gr.Dropdown(
value=config.api_def.api_text_filtering,
label="Text Filtering Mode",
scale=2,
choices=["none", "standard", "html"]
)
api_language = gr.Dropdown(
value=config.api_def.api_language,
label="Default Language",
scale=2,
choices=["ar", "zh", "cs", "nl", "en", "fr", "de", "hi", "hu", "it", "ja", "ko", "pl", "pt", "ru", "es", "tr"]
)
with gr.Row():
with gr.Column(scale=1):
with gr.Group():
with gr.Row():
api_narrator_enabled = gr.Dropdown(
choices=[("Enabled", "true"), ("Disabled", "false"), ("Enabled (Silent)", "silent")],
label="Narrator Mode",
allow_custom_value=True,
value="true" if config.api_def.api_narrator_enabled == "true" else ("silent" if config.api_def.api_narrator_enabled == "silent" else "false")
)
api_text_not_inside = gr.Dropdown(
choices=["character", "narrator", "silent"],
label="Text-Not-Inside Handling",
allow_custom_value=True,
value=config.api_def.api_text_not_inside
)
with gr.Column(scale=2):
with gr.Group():
with gr.Row():
api_output_file_name = gr.Textbox(
value=config.api_def.api_output_file_name,
label="Default Filename"
)
api_output_file_timestamp = gr.Dropdown(
choices=["Timestamp files", "Dont Timestamp (Over-write)"],
label="File Timestamping",
allow_custom_value=True,
value="Timestamp files" if config.api_def.api_output_file_timestamp else "Dont Timestamp (Over-write)"
)
api_autoplay = gr.Dropdown(
choices=["Play locally", "Play remotely"],
label="Playback Location",
allow_custom_value=True,
value="Play remotely" if config.api_def.api_autoplay else "Play locally"
)
api_autoplay_volume = gr.Slider(
minimum=0.1, maximum=0.9, step=0.1,
label="Remote Playback Volume",
value=float(config.api_def.api_autoplay_volume)
)
with gr.Row():
with gr.Column(scale=2):
with gr.Group():
with gr.Row():
api_allowed_filter = gr.Textbox(
value=config.api_def.api_allowed_filter,
label="Allowed Characters & Unicode Ranges",
lines=3
)
with gr.Column(scale=1):
with gr.Group():
with gr.Row():
with gr.Column(scale=2):
output_message = gr.Textbox(
label="Status",
interactive=False
)
submit_button = gr.Button("Update Settings")
# Help Accordions
with gr.Accordion("HELP - 🎯 Quick Start Guide", open=False):
gr.Markdown(AllTalkHelpContent.API_DEFAULTS1, elem_classes="custom-markdown")
with gr.Accordion("HELP - 🔄 Version & Compatibility Settings (API v1/v2, Legacy support)", open=False):
gr.Markdown(AllTalkHelpContent.API_DEFAULTS2, elem_classes="custom-markdown")
with gr.Accordion("HELP - 🎛️ Request Configuration (Character limits, language, file names)", open=False):
gr.Markdown(AllTalkHelpContent.API_DEFAULTS3, elem_classes="custom-markdown")
with gr.Accordion("HELP - 📝 Text Processing & Filtering (Filtering modes, character sets, language)", open=False):
gr.Markdown(AllTalkHelpContent.API_DEFAULTS4, elem_classes="custom-markdown")
with gr.Accordion("HELP - 🗣️ Narrator & Playback (Narrator modes, text handling, playback options", open=False):
gr.Markdown(AllTalkHelpContent.API_DEFAULTS5, elem_classes="custom-markdown")
with gr.Accordion("HELP - 📚 API Allowed Text Filtering/Passthrough", open=False):
gr.Markdown(AllTalkHelpContent.API_DEFAULTS6, elem_classes="custom-markdown")
with gr.Accordion("HELP - ❗ Troubleshooting & Best Practices", open=False):
gr.Markdown(AllTalkHelpContent.API_DEFAULTS7, elem_classes="custom-markdown")
submit_button.click(update_settings_api, inputs=[api_length_stripping, api_legacy_ip_address, api_allowed_filter, api_max_characters, api_use_legacy_api, api_text_filtering, api_narrator_enabled, api_text_not_inside, api_language, api_output_file_name, api_output_file_timestamp, api_autoplay, api_autoplay_volume], outputs=output_message)
def rvc_update_dropdowns():
debug_func_entry()
global at_settings
at_settings = get_alltalk_settings() # Pull all the current settings from the AllTalk server, if its online.
current_voices = at_settings["rvcvoices"]
current_char = config.rvc_settings.rvc_char_model_file
current_narr = config.rvc_settings.rvc_narr_model_file
if current_char not in current_voices:
current_char = current_voices[0] if current_voices else ""
if current_narr not in current_voices:
current_narr = current_voices[0] if current_voices else ""
return (
gr.Dropdown(choices=current_voices, value=current_char, interactive=True),
gr.Dropdown(choices=current_voices, value=current_narr, interactive=True),
)
def gr_update_rvc_settings(rvc_enabled, rvc_char_model_file, rvc_narr_model_file, split_audio, autotune, pitch,
filter_radius, index_rate, rms_mix_rate, protect, hop_length, f0method, embedder_model, training_data_size):
debug_func_entry()
progress = gr.Progress(track_tqdm=True)
return update_rvc_settings(rvc_enabled, rvc_char_model_file, rvc_narr_model_file, split_audio, autotune, pitch,
filter_radius, index_rate, rms_mix_rate, protect, hop_length, f0method, embedder_model, training_data_size, progress)
with gr.Tab("RVC Settings"):
with gr.Row():
with gr.Column(scale=3):
with gr.Row():
rvc_model_file_available = at_settings["rvcvoices"]
rvc_char_model_file_default = config.rvc_settings.rvc_char_model_file
if rvc_char_model_file_default not in rvc_model_file_available:
rvc_char_model_file_default = rvc_model_file_available[0] if rvc_model_file_available else ""
rvc_char_model_file_gr = gr.Dropdown(choices=rvc_model_file_available, label="Default Character Voice Model", info="Select the Character voice model used for conversion.", value=rvc_char_model_file_default, allow_custom_value=True,)
rvc_narr_model_file_default = config.rvc_settings.rvc_narr_model_file
if rvc_narr_model_file_default not in rvc_model_file_available:
rvc_narr_model_file_default = rvc_model_file_available[0] if rvc_model_file_available else ""
rvc_narr_model_file_gr = gr.Dropdown(choices=rvc_model_file_available, label="Default Narrator Voice Model", info="Select the Narrator voice model used for conversion.", value=rvc_narr_model_file_default, allow_custom_value=True,)
rvc_refresh_button = gr.Button("Refresh Model Choices")
with gr.Column(scale=0):
rvc_enabled = gr.Checkbox(label="Enable RVC", info="RVC (Real-Time Voice Cloning) enhances TTS by replicating voice characteristics for characters or narrators, adding depth to synthesized speech.", value=config.rvc_settings.rvc_enabled, interactive=True)
with gr.Row(equal_height=True):
with gr.Column():
pitch = gr.Slider(minimum=-24, maximum=24, step=1, label="Pitch", info="Set the pitch of the audio, the higher the value, the higher the pitch.", value=config.rvc_settings.pitch, interactive=True)
with gr.Column():
hop_length = gr.Slider(minimum=1, maximum=512, step=1, label="Hop Length", info="Smaller hop lengths require more time for inference but tend to yield higher pitch accuracy.", value=config.rvc_settings.hop_length, interactive=True,)
with gr.Row(equal_height=True):
with gr.Column():
training_data_size = gr.Slider(minimum=10000, maximum=100000, step=5000, label="Training Data Size", info="Determines the number of training data points used to train the FAISS index. Increasing the size may improve the quality but can also increase computation time.", value=config.rvc_settings.training_data_size, interactive=True)
with gr.Column():
index_rate = gr.Slider(minimum=0, maximum=1, label="Index Influence Ratio", info="Sets the influence exerted by the index file on the final output. A higher value increases the impact of the index, potentially enhancing detail but also increasing the risk of artifacts.", value=config.rvc_settings.index_rate, interactive=True)
with gr.Row(equal_height=True):
with gr.Column():
rms_mix_rate = gr.Slider(minimum=0, maximum=1, label="Volume Envelope", info="Substitute or blend with the volume envelope of the output. The closer the ratio is to 1, the more the output envelope is employed.", value=config.rvc_settings.rms_mix_rate, interactive=True,)
with gr.Column():
protect = gr.Slider(minimum=0, maximum=0.5, label="Protect Voiceless Consonants/Breath sounds", info="Prevents sound artifacts. Higher values (up to 0.5) provide stronger protection but may affect indexing.", value=config.rvc_settings.protect, interactive=True,)
with gr.Column():
filter_radius = gr.Slider(minimum=0, maximum=7, label="Filter Radius", info="If the number is greater than or equal to three, employing median filtering on the collected tone results has the potential to decrease respiration.", value=config.rvc_settings.filter_radius, step=1, interactive=True,)
with gr.Row():
with gr.Column():
with gr.Row():
with gr.Row():
embedder_model = gr.Radio(label="Embedder Model", info="Model used for learning speaker embedding.", choices=["hubert", "contentvec"], value=config.rvc_settings.embedder_model, interactive=True,)
with gr.Row():
split_audio = gr.Checkbox(label="Split Audio", info="Split the audio into chunks for inference to obtain better results in some cases.", value=config.rvc_settings.split_audio, interactive=True,)
autotune = gr.Checkbox(label="Autotune", info="Apply a soft autotune to your inferences, recommended for singing conversions.", value=config.rvc_settings.autotune, interactive=True,)
with gr.Column():
f0method = gr.Radio(label="Pitch Extraction Algorithm", info="Select the algorithm to be used for extracting the pitch (F0) during audio conversion. The default algorithm is rmvpe, which is generally recommended for most cases due to its balance of accuracy and performance.", choices=["crepe", "crepe-tiny", "dio", "fcpe", "harvest", "hybrid[rmvpe+fcpe]", "pm", "rmvpe"], value=config.rvc_settings.f0method, interactive=True,)
with gr.Row():
update_button = gr.Button("Update RVC Settings")
update_output = gr.Textbox(label="Update Status", show_label=False)
rvc_refresh_button.click(rvc_update_dropdowns, None, [rvc_char_model_file_gr, rvc_narr_model_file_gr])
update_button.click(fn=gr_update_rvc_settings, inputs=[rvc_enabled, rvc_char_model_file_gr, rvc_narr_model_file_gr, split_audio, autotune, pitch, filter_radius, index_rate, rms_mix_rate, protect, hop_length, f0method, embedder_model, training_data_size], outputs=[update_output])
with gr.Accordion("🗣️ HELP - RVC Settings Page", open=False, elem_classes=["gr-accordion"]):
with gr.Row():
gr.Markdown(AllTalkHelpContent.RVC_PAGE1, elem_classes="custom-markdown")
gr.Markdown(AllTalkHelpContent.RVC_PAGE2, elem_classes="custom-markdown")
with gr.Tab("Text-generation-webui Settings"):
with gr.Row():
activate = gr.Dropdown(choices={"Enabled": "true", "Disabled": "false"}, label="Activate TTS", value="Enabled" if config.tgwui.tgwui_activate_tts else "Disabled", allow_custom_value=True)
autoplay = gr.Dropdown(choices={"Enabled": "true", "Disabled": "false"}, label="Autoplay TTS", value="Enabled" if config.tgwui.tgwui_autoplay_tts else "Disabled", allow_custom_value=True)
show_text = gr.Dropdown(choices={"Enabled": "true", "Disabled": "false"}, label="Show Text", value="Enabled" if config.tgwui.tgwui_show_text else "Disabled", allow_custom_value=True)
narrator_enabled = gr.Dropdown(choices=[("Enabled", "true"), ("Disabled", "false"), ("Enabled (Silent)", "silent")], label="Narrator enabled", value="true" if config.tgwui.tgwui_narrator_enabled == "true" else ("silent" if config.tgwui.tgwui_narrator_enabled == "silent" else "false"), allow_custom_value=True)
language = gr.Dropdown(value=config.tgwui.tgwui_language, label="Default Language", choices=languages_list, allow_custom_value=True)
with gr.Row():
submit_button = gr.Button("Update Settings")
output_message = gr.Textbox(label="Output Message", interactive=False, show_label=False)
submit_button.click(update_settings_tgwui, inputs=[activate, autoplay, show_text, language, narrator_enabled], outputs=output_message)
disk_space_page = get_disk_interface()
disk_space_page()
if config.gradio_pages.TTS_Engines_Settings_page:
with gr.Tab("TTS Engines Settings"):
with gr.Tabs():
for engine_name in engines_available:
with gr.Tab(f"{engine_name.capitalize()} TTS"):
gr.Markdown(f"### &nbsp;&nbsp;{engine_name.capitalize()} TTS")
globals()[f"{engine_name}_at_gradio_settings_page"](globals()[f"{engine_name}_model_config_data"])
if config.gradio_pages.alltalk_documentation_page:
with gr.Tab("Documentation"):
with gr.Accordion("📖 HELP - Narrator Function", open=False):
gr.Markdown(AllTalkHelpContent.NARRATOR, elem_classes="custom-markdown")
with gr.Row():
gr.Markdown(AllTalkHelpContent.NARRATOR1, elem_classes="custom-markdown")
gr.Markdown(AllTalkHelpContent.NARRATOR2, elem_classes="custom-markdown")
with gr.Accordion("🔌 HELP - API Standard TTS Generation Endpoints", open=False):
gr.Markdown(AllTalkHelpContent.API_STANDARD, elem_classes="custom-markdown")
with gr.Accordion("🔌 HELP - API Streaming TTS Generation Endpoints", open=False):
gr.Markdown(AllTalkHelpContent.API_STREAMING, elem_classes="custom-markdown")
with gr.Accordion("🔌 HELP - API Server Control Endpoints", open=False):
gr.Markdown(AllTalkHelpContent.API_CONTROL, elem_classes="custom-markdown")
with gr.Accordion("🔌 HELP - API Server Status Endpoints", open=False):
gr.Markdown(AllTalkHelpContent.API_STATUS, elem_classes="custom-markdown")
with gr.Accordion("🔌 HELP - API OpenAI V1 Speech Endpoints", open=False):
gr.Markdown(AllTalkHelpContent.API_OPENAPI, elem_classes="custom-markdown")
with gr.Tab("About this project"):
with gr.Row():
gr.Markdown(AllTalkHelpContent.WELCOME1, elem_classes="custom-markdown")
gr.Markdown(AllTalkHelpContent.WELCOME2, elem_classes="custom-markdown")
return app
if __name__ == "__main__":
app = alltalk_gradio().queue()
app.launch(server_name="0.0.0.0", server_port=config.gradio_port_number, prevent_thread_lock=True, quiet=True)
if not running_in_standalone:
app = alltalk_gradio().queue()
app.launch(server_name="0.0.0.0", server_port=config.gradio_port_number, prevent_thread_lock=True, quiet=True)
#########################################
# START-UP # Final Splash before Gradio #
#########################################
print_message("Please use \033[91mCtrl+C\033[0m when exiting AllTalk otherwise a")
print_message("subprocess may continue running in the background.")
print_message("")
print_message("Server Ready")
###############################################################################################
# START-UP # Loop to keep the script from exiting out if its being run as a standalone script #
###############################################################################################
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
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