From 51fa4ce9e91dd8bc7e2dea0d05fe85247eaca606 Mon Sep 17 00:00:00 2001 From: erew123 <35898566+erew123@users.noreply.github.com> Date: Mon, 1 Apr 2024 10:48:36 +0100 Subject: [PATCH 1/9] Cleaner Start-up & mini diags Updated various bits of code to clean up the AllTalk start-up messages. --- modeldownload.py | 145 ------ script.py | 1073 --------------------------------------- tts_server.py | 1247 ---------------------------------------------- 3 files changed, 2465 deletions(-) diff --git a/modeldownload.py b/modeldownload.py index b4edae5..e69de29 100644 --- a/modeldownload.py +++ b/modeldownload.py @@ -1,145 +0,0 @@ -import os -from pathlib import Path -import requests -from tqdm import tqdm -import importlib.metadata as metadata -import json -from packaging import version - -################################################################# -#### LOAD PARAMS FROM confignew.json - REQUIRED FOR BRANDING #### -################################################################# -# STARTUP VARIABLE - Create "this_dir" variable as the current script directory -this_dir = Path(__file__).parent.resolve() - - -# load standard config file in and get settings -def load_config(file_path): - with open(file_path, "r") as config_file: - config = json.load(config_file) - return config - - -config_file_path = this_dir / "confignew.json" -params = load_config(config_file_path) - -# Define the path to the modeldownload config file file -modeldownload_config_file_path = this_dir / "modeldownload.json" - -# Check if the JSON file exists -if modeldownload_config_file_path.exists(): - with open(modeldownload_config_file_path, "r") as config_file: - settings = json.load(config_file) - - # Extract settings from the loaded JSON - base_path = Path(settings.get("base_path", "")) - model_path = Path(settings.get("model_path", "")) - files_to_download = settings.get("files_to_download", {}) -else: - # Default settings if the JSON file doesn't exist or is empty - print( - f"[{params['branding']}Startup] \033[91mWarning\033[0m modeldownload.json is missing so please re-download it and save it in the coquii_tts main folder." - ) - print( - f"[{params['branding']}Startup] \033[91mWarning\033[0m API Local and XTTSv2 Local will error unless this is corrected." - ) - -# Read the version specifier from requirements_nvidia.txt -with open(this_dir / "system" / "requirements" / "requirements_standalone.txt", "r") as req_file: - requirements = req_file.readlines() - -tts_version_required = None -for req in requirements: - if req.startswith("TTS=="): - tts_version_required = req.strip().split("==")[1] - break - -if tts_version_required is None: - raise ValueError( - f"[{params['branding']}Startup] \033[91mWarning\033[0m Could not find TTS version specifier in requirements file" - ) - - -def create_directory_if_not_exists(directory): - if not directory.exists(): - directory.mkdir(parents=True) - - -def download_file(url, destination): - response = requests.get(url, stream=True) - total_size_in_bytes = int(response.headers.get("content-length", 0)) - block_size = 1024 # 1 Kibibyte - - progress_bar = tqdm(total=total_size_in_bytes, unit="iB", unit_scale=True) - - with open(destination, "wb") as file: - for data in response.iter_content(block_size): - progress_bar.update(len(data)) - file.write(data) - - progress_bar.close() - - -def check_tts_version(): - try: - tts_version = metadata.version("tts") - print( - f"[{params['branding']}Startup] TTS version installed: \033[93m" - + tts_version - + "\033[0m" - ) - - if version.parse(tts_version) < version.parse(tts_version_required): - print( - f"[{params['branding']}Startup] \033[91mWarning\033[0m TTS version is too old. Please upgrade to version \033[93m" - + tts_version_required - + "\033[0m or later.\033[0m" - ) - print( - f"[{params['branding']}Startup] \033[91mWarning\033[0m At your terminal/command prompt \033[94mpip install --upgrade tts\033[0m" - ) - else: - print(f"[{params['branding']}Startup] TTS version is up to date.") - except metadata.PackageNotFoundError: - print( - f"[{params['branding']}Startup] \033[91mWarning\033[0m TTS is not installed." - ) - - -# Check and create directories -if str(base_path) == "models": - create_directory_if_not_exists(this_dir / base_path / model_path) -else: - create_directory_if_not_exists(base_path / model_path) - print( - f"[{params['branding']}Startup] \033[94mInfo\033[0m Custom path set in \033[93mmodeldownload.json\033[0m. Using the following settings:" - ) - print( - f"[{params['branding']}Startup] \033[94mInfo\033[0m Base folder Path:\033[93m", - base_path, - "\033[0m", - ) - print( - f"[{params['branding']}Startup] \033[94mInfo\033[0m Model folder Path:\033[93m", - model_path, - "\033[0m", - ) - print( - f"[{params['branding']}Startup] \033[94mInfo\033[0m Full Path:\033[93m", - base_path / model_path, - "\033[0m", - ) - -# Download files if they don't exist -print("[" + params["branding"] + "Startup] Checking Model is Downloaded.") -for filename, url in files_to_download.items(): - if str(base_path) == "models": - destination = this_dir / base_path / model_path / filename - else: - destination = Path(base_path) / model_path / filename - - if not destination.exists(): - print(f"[{params['branding']}Startup] Downloading " + filename + "...") - download_file(url, destination) - -check_tts_version() diff --git a/script.py b/script.py index 5b4066d..e69de29 100644 --- a/script.py +++ b/script.py @@ -1,1073 +0,0 @@ -import html -import json -import random -import subprocess -import time -import os -import requests -import threading -import signal -import sys -import atexit -from pathlib import Path -from datetime import datetime, timedelta -import re -import numpy as np -import soundfile as sf -import uuid - -###################################### -#### ALLTALK ALLOWED STARTUP TIME #### -###################################### -startup_wait_time = 120 - -# You can change the above setting to a larger number to allow AllTAlk more time to start up. The default setting is 120 seconds (2 minutes). -# On some older systems you may need to allow AllTalk more time. So you could set it to 240 (4 minutes) which will give AllTalk more to load. - -################################################################# -#### LOAD PARAMS FROM confignew.json - REQUIRED FOR BRANDING #### -################################################################# -# STARTUP VARIABLE - Create "this_dir" variable as the current script directory -this_dir = Path(__file__).parent.resolve() - - -# load config file in and get settings -def load_config(file_path): - with open(file_path, "r") as config_file: - config = json.load(config_file) - return config - - -config_file_path = this_dir / "confignew.json" -# Load the params dictionary from the confignew.json file -params = load_config(config_file_path) - -# Required for sentence splitting -try: - from TTS.api import TTS - from TTS.utils.synthesizer import Synthesizer -except ModuleNotFoundError: - logger.error( - f"[{params['branding']}]\033[91mWarning\033[0m Could not find the TTS module. Make sure to install the requirements for the {params['branding']} extension." - f"[{params['branding']}]\033[91mWarning\033[0m Linux / Mac:\npip install -r /alltalk_tts/requirements.txt\n" - f"[{params['branding']}]\033[91mWarning\033[0m Windows:\npip install -r \\alltalk_tts\\requirements.txt\n" - f"[{params['branding']}]\033[91mWarning\033[0m If you used the one-click installer, paste the command above in the terminal window launched after running the cmd_ script. On Windows, that's cmd_windows.bat." - ) - raise - -# IMPORT - Attempt Importing DeepSpeed (required for displaying Deepspeed checkbox in gradio) -try: - import deepspeed - - deepspeed_installed = True -except ImportError: - deepspeed_installed = False - -# Import gradio if being used within text generation webUI -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 - - # This is set to check if the script is being run within text generation webui or as a standalone script. False is running as part of text gen web ui or a gradio interface - running_in_standalone = False - output_folder_wav = params["output_folder_wav"] -except ModuleNotFoundError: - output_folder_wav = params["output_folder_wav_standalone"] - print(f"[{params['branding']}Startup] Running script.py in standalone mode") - # This is set to check if the script is being run within text generation webui or as a standalone script. true means standalone - running_in_standalone = True - -########################### -#### STARTUP VARIABLES #### -########################### -# STARTUP VARIABLE - Import languges file for Gradio to be able to display them in the interface -with open(this_dir / "system" / "config" / "languages.json", encoding="utf8") as f: - languages = json.load(f) -# Create a global lock -process_lock = threading.Lock() -# Base setting for a possible FineTuned model existing and the loader being available -tts_method_xtts_ft = False - - -# Gather the voice files -def get_available_voices(): - return sorted([voice.name for voice in Path(f"{this_dir}/voices").glob("*.wav")]) - - -######################### -#### LICENSE DISPLAY #### -######################### -# STARTUP Display Licence Information -print(f"[{params['branding']}Startup] \033[94mCoqui Public Model License\033[0m") -print(f"[{params['branding']}Startup] \033[94mhttps://coqui.ai/cpml.txt\033[0m") - - -############################################ -#### DELETE OLD OUTPUT WAV FILES IF SET #### -############################################ -def delete_old_files(folder_path, days_to_keep): - current_time = datetime.now() - print( - f"[{params['branding']}Startup] Deletion of old output folder WAV files is currently enabled and set at", - delete_output_wavs_setting, - ) - 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) - - -# Extract settings using params dictionary -delete_output_wavs_setting = params["delete_output_wavs"] -output_folder_wav = os.path.normpath(output_folder_wav) - -# Check and perform file deletion -if delete_output_wavs_setting.lower() == "disabled": - print( - "[" - + params["branding"] - + "Startup] Old output wav file deletion is set to disabled." - ) -else: - try: - days_to_keep = int(delete_output_wavs_setting.split()[0]) - delete_old_files(output_folder_wav, days_to_keep) - except ValueError: - print( - f"[{params['branding']}Startup] Invalid setting for deleting old wav files. Please use 'Disabled' or 'X Days' format." - ) - - -######################## -#### STARTUP CHECKS #### -######################## -# STARTUP Checks routine -def check_required_files(): - this_dir = Path(__file__).parent.resolve() - download_script_path = this_dir / "modeldownload.py" - subprocess.run(["python", str(download_script_path)]) - print(f"[{params['branding']}Startup] All required files are present.") - - -# STARTUP Call Check routine -check_required_files() - -################################################## -#### Check to see if a finetuned model exists #### -################################################## -# Set the path to the directory -trained_model_directory = this_dir / "models" / "trainedmodel" -# Check if the directory "trainedmodel" exists -finetuned_model = trained_model_directory.exists() -# If the directory exists, check for the existence of the required files -# If true, this will add a extra option in the Gradio interface for loading Xttsv2 FT -if finetuned_model: - required_files = ["model.pth", "config.json", "vocab.json"] - finetuned_model = all( - (trained_model_directory / file).exists() for file in required_files - ) -if finetuned_model: - print(f"[{params['branding']}Startup] Finetuned model \033[93mDetected\033[0m") - -#################################################### -#### SET GRADIO BUTTONS BASED ON confignew.json #### -#################################################### - -if params["tts_method_api_tts"] == True: - gr_modelchoice = "API TTS" -elif params["tts_method_api_local"] == True: - gr_modelchoice = "API Local" -elif params["tts_method_xtts_local"] == True: - gr_modelchoice = "XTTSv2 Local" - -# Set the default for Narrated text without asterisk or quotes to be Narrator -non_quoted_text_is = True - - -###################### -#### GRADIO STUFF #### -###################### -def remove_tts_from_history(history): - for i, entry in enumerate(history["internal"]): - history["visible"][i] = [history["visible"][i][0], entry[1]] - return history - - -def toggle_text_in_history(history): - for i, entry in enumerate(history["visible"]): - visible_reply = entry[1] - if visible_reply.startswith("')[0]}\n\n{reply}", - ] - else: - history["visible"][i] = [ - history["visible"][i][0], - f"{visible_reply.split('')[0]}", - ] - return history - - -def history_modifier(history): - # Remove autoplay from the last reply - if len(history["internal"]) > 0: - history["visible"][-1] = [ - history["visible"][-1][0], - history["visible"][-1][1].replace("controls autoplay>", "controls>"), - ] - - return history - - -###################################### -#### SUBPROCESS/WEBSERVER STARTUP #### -###################################### -base_url = f"http://{params['ip_address']}:{params['port_number']}" -script_path = this_dir / "tts_server.py" - - -def signal_handler(sig, frame): - print( - f"[{params['branding']}Shutdown] \033[94mReceived Ctrl+C, terminating subprocess\033[92m" - ) - if process.poll() is None: - process.terminate() - process.wait() # Wait for the subprocess to finish - sys.exit(0) - - -# Attach the signal handler to the SIGINT signal (Ctrl+C) -signal.signal(signal.SIGINT, signal_handler) -# Check if we're running in docker -if os.path.isfile("/.dockerenv"): - print( - f"[{params['branding']}Startup] \033[94mRunning in Docker. Please wait.\033[0m" - ) -else: - # Start the subprocess - process = subprocess.Popen(["python", script_path]) - # Check if the subprocess has started successfully - if process.poll() is None: - print(f"[{params['branding']}Startup] TTS Subprocess starting") - print(f"[{params['branding']}Startup]") - print( - f"[{params['branding']}Startup] \033[94m {params['branding']}Settings & Documentation:\033[00m", - f"\033[92mhttp://{params['ip_address']}:{params['port_number']}\033[00m", - ) - print(f"[{params['branding']}Startup]") - else: - print( - f"[{params['branding']}Startup] \033[91mWarning\033[0m TTS Subprocess Webserver failing to start process" - ) - print( - f"[{params['branding']}Startup] \033[91mWarning\033[0m It could be that you have something on port:", - params["port_number"], - ) - print( - f"[{params['branding']}Startup] \033[91mWarning\033[0m Or you have not started in a Python environement with all the necesssary bits installed" - ) - print( - f"[{params['branding']}Startup] \033[91mWarning\033[0m Check you are starting Text-generation-webui with either the start_xxxxx file or the Python environment with cmd_xxxxx file." - ) - print( - f"[{params['branding']}Startup] \033[91mWarning\033[0m xxxxx is the type of OS you are on e.g. windows, linux or mac." - ) - print( - f"[{params['branding']}Startup] \033[91mWarning\033[0m Alternatively, you could check no other Python processes are running that shouldnt be e.g. Restart your computer is the simple way." - ) - # 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 - - # Introduce a delay before starting the check loop - time.sleep(26) # Wait 26 secs before checking if the tts_server.py has started up. - start_time = time.time() - while time.time() - start_time < timeout: - try: - response = requests.get(f"{base_url}/ready") - if response.status_code == 200: - break - except requests.RequestException as e: - # Print the exception for debugging purposes - print( - f"[{params['branding']}Startup] \033[91mWarning\033[0m TTS Subprocess has NOT started up yet, Will keep trying for {timeout} seconds maximum. Please wait." - ) - time.sleep(5) - else: - print( - f"\n[{params['branding']}Startup] Startup timed out. Full help available here \033[92mhttps://github.com/erew123/alltalk_tts#-help-with-problems\033[0m" - ) - print( - f"[{params['branding']}Startup] On older system you may wish to open and edit \033[94mscript.py\033[0m with a text editor and changing the" - ) - print( - f"[{params['branding']}Startup] \033[94mstartup_wait_time = 120\033[0m setting to something like \033[94mstartup_wait_time = 240\033[0m as this will allow" - ) - print( - f"[{params['branding']}Startup] AllTalk more time to try load the model into your VRAM. Otherise please visit the Github for" - ) - print( - f"[{params['branding']}Startup] 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) - - -##################################### -#### MODEL LOADING AND UNLOADING #### -##################################### -# MODEL - Swap model based on Gradio selection API TTS, API Local, XTTSv2 Local -def send_reload_request(tts_method): - global tts_method_xtts_ft - try: - params["tts_model_loaded"] = False - url = f"{base_url}/api/reload" - payload = {"tts_method": tts_method} - response = requests.post(url, params=payload) - response.raise_for_status() # Raises an HTTPError for bad responses - json_response = response.json() - # Check if the reload operation was successful - if json_response.get("status") == "model-success": - # Update tts_tts_model_loaded to True if the reload was successful - params["tts_model_loaded"] = True - # Update local script parameters based on the tts_method - if tts_method == "API TTS": - params["tts_method_api_local"] = False - params["tts_method_xtts_local"] = False - params["tts_method_api_tts"] = True - params["deepspeed_activate"] = False - audio_path = this_dir / "system" / "at_sounds" / "apitts.wav" - tts_method_xtts_ft = False - elif tts_method == "API Local": - params["tts_method_api_tts"] = False - params["tts_method_xtts_local"] = False - params["tts_method_api_local"] = True - params["deepspeed_activate"] = False - audio_path = this_dir / "system" / "at_sounds" / "apilocal.wav" - tts_method_xtts_ft = False - elif tts_method == "XTTSv2 Local": - params["tts_method_api_tts"] = False - params["tts_method_api_local"] = False - params["tts_method_xtts_local"] = True - audio_path = this_dir / "system" / "at_sounds" / "xttslocal.wav" - tts_method_xtts_ft = False - elif tts_method == "XTTSv2 FT": - params["tts_method_api_tts"] = False - params["tts_method_api_local"] = False - params["tts_method_xtts_local"] = False - audio_path = this_dir / "system" / "at_sounds" / "xttsfinetuned.wav" - tts_method_xtts_ft = True - return f'' - except requests.exceptions.RequestException as e: - # Handle the HTTP request error - print( - f"[{params['branding']}Server] \033[91mWarning\033[0m Error during request to webserver process: {e}" - ) - return {"status": "error", "message": str(e)} - - -################## -#### LOW VRAM #### -################## -# LOW VRAM - Gradio Checkbox handling -def send_lowvram_request(low_vram): - try: - params["tts_model_loaded"] = False - if low_vram: - audio_path = this_dir / "system" / "at_sounds" / "lowvramenabled.wav" - else: - audio_path = this_dir / "system" / "at_sounds" / "lowvramdisabled.wav" - url = f"{base_url}/api/lowvramsetting?new_low_vram_value={low_vram}" - headers = {"Content-Type": "application/json"} - response = requests.post(url, headers=headers) - response.raise_for_status() # Raises an HTTPError for bad responses - json_response = response.json() - # Check if the low VRAM request was successful - if json_response.get("status") == "lowvram-success": - # Update any relevant variables or perform other actions on success - params["tts_model_loaded"] = True - return f'' - except requests.exceptions.RequestException as e: - # Handle the HTTP request error - print( - f"[{params['branding']}Server] \033[91mWarning\033[0m Error during request to webserver process: {e}" - ) - return {"status": "error", "message": str(e)} - - -################### -#### DeepSpeed #### -################### -# DEEPSPEED - Reload the model when DeepSpeed checkbox is enabled/disabled -def send_deepspeed_request(deepspeed_param): - try: - params["tts_model_loaded"] = False - if deepspeed_param: - audio_path = this_dir / "system" / "at_sounds" / "deepspeedenabled.wav" - else: - audio_path = this_dir / "system" / "at_sounds" / "deepspeeddisabled.wav" - - url = f"{base_url}/api/deepspeed?new_deepspeed_value={deepspeed_param}" - headers = {"Content-Type": "application/json"} - response = requests.post(url, headers=headers) - response.raise_for_status() # Raises an HTTPError for bad responses - json_response = response.json() - # Check if the deepspeed request was successful - if json_response.get("status") == "deepspeed-success": - # Update any relevant variables or perform other actions on success - params["tts_model_loaded"] = True - return f'' - except requests.exceptions.RequestException as e: - # Handle the HTTP request error - print( - f"[{params['branding']}Server] \033[91mWarning\033[0m Error during request to webserver process: {e}" - ) - return {"status": "error", "message": str(e)} - - -# DEEPSPEED - Display DeepSpeed Checkbox Yes or No -deepspeed_condition = params["tts_method_xtts_local"] == "True" and deepspeed_installed - - -############################################################# -#### TTS STRING CLEANING & PROCESSING PRE SENDING TO TTS #### -############################################################# -def new_split_into_sentences(self, text): - sentences = self.seg.segment(text) - if params["remove_trailing_dots"]: - sentences_without_dots = [] - for sentence in sentences: - if sentence.endswith(".") and not sentence.endswith("..."): - sentence = sentence[:-1] - - sentences_without_dots.append(sentence) - - return sentences_without_dots - else: - return sentences - - -Synthesizer.split_into_sentences = new_split_into_sentences - - -# Check model is loaded and string isnt empty, before sending a TTS request. -def before_audio_generation(string, params): - # Check Model is loaded into cuda or cpu and error if not - if not params["tts_model_loaded"]: - print( - f"[{params['branding']}Model] \033[91mWarning\033[0m Model is still loading, please wait before trying to generate TTS" - ) - return - string = html.unescape(string) or random_sentence() - if string == "": - return "*Empty string*" - return string - - -################## -#### Narrator #### -################## -def combine(audio_files, output_folder, state): - audio = np.array([]) - - for audio_file in audio_files: - audio_data, sample_rate = sf.read(audio_file) - # Ensure all audio files have the same sample rate - if audio.size == 0: - audio = audio_data - else: - audio = np.concatenate((audio, audio_data)) - - # Save the combined audio to a file with a specified sample rate - if "character_menu" in state: - output_file_path = os.path.join( - output_folder, f'{state["character_menu"]}_{int(time.time())}_combined.wav' - ) - else: - output_file_path = os.path.join( - output_folder, f"TTSOUT_{int(time.time())}_combined.wav" - ) - sf.write(output_file_path, audio, samplerate=sample_rate) - # Clean up unnecessary files - for audio_file in audio_files: - os.remove(audio_file) - - return output_file_path - - -################################ -#### TTS PREVIEW GENERATION #### -################################ -# PREVIEW VOICE - Generate Random Sentence if Voice Preview box is empty -def random_sentence(): - with open(this_dir / "system" / "config" / "harvard_sentences.txt") as f: - return random.choice(list(f)) - - -# PREVIEW VOICE- Generate TTS Function -def voice_preview(string): - if not params["activate"]: - return string - # Clean the string, capture model not loaded, and move model to cuda if needed - cleaned_string = before_audio_generation(string, params) - if cleaned_string is None: - return - string = cleaned_string - # Setup the output file - output_file = Path(params["output_folder_wav"]) / "voice_preview.wav" - # Generate the audio - language_code = languages.get(params["language"]) - temperature = params["local_temperature"] - repetition_penalty = params["local_repetition_penalty"] - # Convert the WindowsPath object to a string before using it in JSON payload - output_file_str = output_file.as_posix() - # Lock before making the generate request - with process_lock: - generate_response = send_generate_request( - string, - params["voice"], - language_code, - temperature, - repetition_penalty, - output_file_str, - ) - # Check if lock is already acquired - if process_lock.locked(): - print( - f"[{params['branding']}Model] \033[91mWarning\033[0m Audio generation is already in progress. Please wait." - ) - return - if generate_response.get("status") == "generate-success": - # Handle Gradio and playback - autoplay = "autoplay" if params["autoplay"] else "" - return f'' - else: - # Handle the case where audio generation was not successful - return f"[{params['branding']}Server] Audio generation failed. Status: {generate_response.get('status')}" - - -####################### -#### TEXT CLEANING #### -####################### - -def process_text(text): - # Normalize HTML encoded quotes - text = html.unescape(text) - - # Replace ellipsis with a single dot - text = re.sub(r"\.{3,}", ".", text) - - # Pattern to identify combined narrator and character speech - combined_pattern = r'(\*[^*"]+\*|"[^"*]+")' - - # List to hold parts of speech along with their type - ordered_parts = [] - - # Track the start of the next segment - start = 0 - - # Find all matches - for match in re.finditer(combined_pattern, text): - # Add the text before the match, if any, as ambiguous - if start < match.start(): - ambiguous_text = text[start : match.start()].strip() - if ambiguous_text: - ordered_parts.append(("ambiguous", ambiguous_text)) - - # Add the matched part as either narrator or character - matched_text = match.group(0) - if matched_text.startswith("*") and matched_text.endswith("*"): - ordered_parts.append(("narrator", matched_text.strip("*").strip())) - elif matched_text.startswith('"') and matched_text.endswith('"'): - ordered_parts.append(("character", matched_text.strip('"').strip())) - else: - # In case of mixed or improperly formatted parts - if "*" in matched_text: - ordered_parts.append(("narrator", matched_text.strip("*").strip('"'))) - else: - ordered_parts.append(("character", matched_text.strip('"').strip("*"))) - - # Update the start of the next segment - start = match.end() - - # Add any remaining text after the last match as ambiguous - if start < len(text): - ambiguous_text = text[start:].strip() - if ambiguous_text: - ordered_parts.append(("ambiguous", ambiguous_text)) - - return ordered_parts - - -######################## -#### IMAGE CLEANING #### -######################## - -img_pattern = r']*src\s*=\s*["\'][^"\'>]+["\'][^>]*>' - -def extract_and_remove_images(text): - """ - Extracts all image data from the text and removes it for clean TTS processing. - Returns the cleaned text and the extracted image data. - """ - img_matches = re.findall(img_pattern, text) - img_info = "\n".join(img_matches) # Store extracted image data - cleaned_text = re.sub(img_pattern, '', text) # Remove images from text - return cleaned_text, img_info - -def reinsert_images(text, img_info): - """ - Reinserts the previously extracted image data back into the text. - """ - if img_info: # Check if there are images to reinsert - text += f"\n\n{img_info}" - return text - -################################# -#### TTS STANDARD GENERATION #### -################################# -# STANDARD VOICE - Generate TTS Function -def output_modifier(string, state): - if not params["activate"]: - return string - img_info = "" - cleaned_text, img_info = extract_and_remove_images(string) - # print("Cleaned STRING IS:", cleaned_text) - cleaned_string = before_audio_generation(cleaned_text, params) - if cleaned_string is None: - return - language_code = languages.get(params["language"]) - temperature = params["local_temperature"] - repetition_penalty = params["local_repetition_penalty"] - # Create a list to store generated audio paths - audio_files = [] - if process_lock.acquire(blocking=False): - try: - if params["narrator_enabled"]: - processed_parts = process_text(cleaned_string) - - audio_files_all_paragraphs = [] - for part_type, part in processed_parts: - # Skip parts that are too short - if len(part.strip()) <= 3: - continue - - # Determine the voice to use based on the part type - if part_type == "narrator": - voice_to_use = params["narrator_voice"] - print( - f"[{params['branding']}TTSGen] \033[92mNarrator\033[0m" - ) # Green - elif part_type == "character": - voice_to_use = params["voice"] - print( - f"[{params['branding']}TTSGen] \033[36mCharacter\033[0m" - ) # Yellow - else: - # Handle ambiguous parts based on user preference - voice_to_use = ( - params["voice"] - if non_quoted_text_is - else params["narrator_voice"] - ) - voice_description = ( - "\033[36mCharacter (Text-not-inside)\033[0m" - if non_quoted_text_is - else "\033[92mNarrator (Text-not-inside)\033[0m" - ) - print(f"[{params['branding']}TTSGen] {voice_description}") - - # Replace multiple exclamation marks, question marks, or other punctuation with a single instance - cleaned_part = re.sub(r"([!?.])\1+", r"\1", part) - # Further clean to remove any other unwanted characters - cleaned_part = re.sub(r'[^a-zA-Z0-9\s\.,;:!?\-\'"\u0100-\u017F\u0400-\u04FFÀ-ÿ\u0150\u0151\u0170\u0171\u4E00-\u9FFF\u3400-\u4DBF\u20000-\u2A6DF\u3000-\u303F\$]', '', cleaned_part) - # Remove all newline characters (single or multiple) - cleaned_part = re.sub(r"\n+", " ", cleaned_part) - - # Generate TTS and output to a file - output_filename = get_output_filename(state) - generate_response = send_generate_request( - cleaned_part, - voice_to_use, - language_code, - temperature, - repetition_penalty, - output_filename, - ) - audio_path = generate_response.get("data", {}).get("audio_path") - audio_files_all_paragraphs.append(audio_path) - - # Combine audio files across paragraphs - final_output_file = combine( - audio_files_all_paragraphs, params["output_folder_wav"], state - ) - else: - # Decode HTML entities first - cleaned_part = html.unescape(cleaned_string) - # Replace multiple instances of certain punctuation marks with a single instance - cleaned_part = re.sub(r"([!?.])\1+", r"\1", cleaned_part) - # Further clean to remove any other unwanted characters - cleaned_part = re.sub(r'[^a-zA-Z0-9\s\.,;:!?\-\'"\u0100-\u017F\u0400-\u04FFÀ-ÿ\u0150\u0151\u0170\u0171\u4E00-\u9FFF\u3400-\u4DBF\u20000-\u2A6DF\u3000-\u303F\$]', '', cleaned_part) - # Remove all newline characters (single or multiple) - cleaned_part = re.sub(r"\n+", " ", cleaned_part) - # Process the part and give it a non-character name if being used vai API or standalone. - if "character_menu" in state: - output_file = Path( - f'{params["output_folder_wav"]}/{state["character_menu"]}_{int(time.time())}.wav' - ) - else: - output_file = Path( - f'{params["output_folder_wav"]}/TTSOUT_{int(time.time())}.wav' - ) - output_file_str = output_file.as_posix() - output_file = get_output_filename(state) - generate_response = send_generate_request( - cleaned_part, - params["voice"], - language_code, - temperature, - repetition_penalty, - output_file_str, - ) - audio_path = generate_response.get("data", {}).get("audio_path") - final_output_file = audio_path - finally: - # Always release the lock, whether an exception occurs or not - process_lock.release() - else: - # The lock is already acquired - print( - f"[{params['branding']}Model] \033[91mWarning\033[0m Audio generation is already in progress. Please wait." - ) - return - - if generate_response.get("status") == "generate-success": - audio_path = generate_response.get("data", {}).get("audio_path") - if audio_path: - # Handle Gradio and playback - autoplay = "autoplay" if params["autoplay"] else "" - string = (f'') - if params["show_text"]: - string += reinsert_images(cleaned_string, img_info) - shared.processing_message = "*Is typing...*" - return string - else: - print(f"[{params['branding']}Server] \033[91mWarning\033[0m No audio path in the response.") - else: - print(f"[{params['branding']}Server] \033[91mWarning\033[0m Audio generation failed. Status:", generate_response.get("message"),) - -def get_output_filename(state): - if "character_menu" in state: - return Path( - f'{params["output_folder_wav"]}/{state["character_menu"]}_{str(uuid.uuid4())[:8]}.wav' - ).as_posix() - else: - return Path( - f'{params["output_folder_wav"]}/TTSOUT_{str(uuid.uuid4())[:8]}.wav' - ).as_posix() - - -############################################### -#### SEND GENERATION REQUEST TO TTS ENGINE #### -############################################### -def send_generate_request( - text, voice, language, temperature, repetition_penalty, output_file -): - url = f"{base_url}/api/generate" - payload = { - "text": text, - "voice": voice, - "language": language, - "temperature": temperature, - "repetition_penalty": repetition_penalty, - "output_file": output_file, - } - headers = {"Content-Type": "application/json"} - response = requests.post(url, json=payload, headers=headers) - return response.json() - - -################################ -#### SUBPORCESS TERMINATION #### -################################ -# Register the termination code to be executed at exit -atexit.register(lambda: process.terminate() if process.poll() is None else None) - - -###################### -#### GRADIO STUFF #### -###################### -def state_modifier(state): - if not params["activate"]: - return state - - state["stream"] = False - return state - - -def update_narrator_enabled(value): - if value == "Enabled": - params["narrator_enabled"] = True - elif value == "Disabled": - params["narrator_enabled"] = False - - -def update_non_quoted_text_is(value): - global non_quoted_text_is - if value == "Narrator": - non_quoted_text_is = False - elif value == "Char": - non_quoted_text_is = True - - -def input_modifier(string, state): - if not params["activate"]: - return string - - shared.processing_message = "*Is recording a voice message...*" - return string - - -def ui(): - with gr.Accordion(params["branding"] + " TTS (XTTSv2)"): - # Activate alltalk_tts, Enable autoplay, Show text - with gr.Row(): - activate = gr.Checkbox(value=params["activate"], label="Enable TTS") - autoplay = gr.Checkbox(value=params["autoplay"], label="Autoplay TTS") - show_text = gr.Checkbox(value=params["show_text"], label="Show Text") - - # Low vram enable, Deepspeed enable, Remove trailing dots - with gr.Row(): - low_vram = gr.Checkbox( - value=params["low_vram"], label="Enable Low VRAM Mode" - ) - low_vram_play = gr.HTML(visible=False) - deepspeed_checkbox = gr.Checkbox( - value=params["deepspeed_activate"], - label="Enable DeepSpeed", - visible=deepspeed_installed, - ) - deepspeed_checkbox_play = gr.HTML(visible=False) - remove_trailing_dots = gr.Checkbox( - value=params["remove_trailing_dots"], label='Remove trailing "."' - ) - - # TTS method, Character voice selection - with gr.Row(): - model_loader_choices = ["API TTS", "API Local", "XTTSv2 Local"] - if finetuned_model: - model_loader_choices.append("XTTSv2 FT") - tts_radio_buttons = gr.Radio( - choices=model_loader_choices, - label="TTS Method (Each method sounds slightly different)", - value=gr_modelchoice, # Set the default value - ) - tts_radio_buttons_play = gr.HTML(visible=False) - with gr.Row(): - available_voices = get_available_voices() - default_voice = params[ - "voice" - ] # Check if the default voice is in the list of available voices - - if default_voice not in available_voices: - default_voice = available_voices[ - 0 - ] # Choose the first available voice as the default - # Add allow_custom_value=True to the Dropdown - voice = gr.Dropdown( - available_voices, - label="Character Voice", - value=default_voice, - allow_custom_value=True, - ) - create_refresh_button( - voice, - lambda: None, - lambda: { - "choices": get_available_voices(), - "value": params["voice"], - }, - "refresh-button", - ) - - # Language, Narrator voice - with gr.Row(): - language = gr.Dropdown( - languages.keys(), label="Language", value=params["language"] - ) - with gr.Row(): - narrator_voice_gr = gr.Dropdown( - get_available_voices(), - label="Narrator Voice", - allow_custom_value=True, - value=params["narrator_voice"], - ) - create_refresh_button( - narrator_voice_gr, - lambda: None, - lambda: { - "choices": get_available_voices(), - "value": params["narrator_voice"], - }, - "refresh-button", - ) - - # Temperature, Repetition Penalty - with gr.Row(): - local_temperature_gr = gr.Slider( - minimum=0.05, - maximum=1, - step=0.05, - label="Temperature", - value=params["local_temperature"], - ) - local_repetition_penalty_gr = gr.Slider( - minimum=0.5, - maximum=20, - step=0.5, - label="Repetition Penalty", - value=params["local_repetition_penalty"], - ) - - # Narrator enable, Non quoted text, Explanation text - with gr.Row(): - with gr.Row(): - narrator_enabled_gr = gr.Radio( - choices={"Enabled": "true", "Disabled": "false"}, - label="Narrator", - value="Enabled" if params.get("narrator_enabled") else "Disabled", - ) - non_quoted_text_is_gr = gr.Radio( - choices={"Character": "true", "Narrator": "false"}, - label='Unmarked text NOT inside of * or " is', - value="Character" if non_quoted_text_is else "Narrator", - ) - explanation_text = gr.HTML( - f"

⚙️ Settings and Documentation Page⚙️
- Low VRAM Mode and Deepspeed take 15 seconds to be enabled or disabled.
- The DeepSpeed checkbox is only visible if DeepSpeed is present.

" - ) - - # Preview speech - with gr.Row(): - preview_text = gr.Text( - show_label=False, - placeholder="Preview text", - elem_id="silero_preview_text", - ) - preview_play = gr.Button("Preview") - preview_audio = gr.HTML(visible=False) - - with gr.Row(): - convert = gr.Button("Permanently replace audios with the message texts") - convert_cancel = gr.Button("Cancel", visible=False) - convert_confirm = gr.Button( - "Confirm (cannot be undone)", variant="stop", visible=False - ) - - # Convert history with confirmation - convert_arr = [convert_confirm, convert, convert_cancel] - convert.click( - lambda: [ - gr.update(visible=True), - gr.update(visible=False), - gr.update(visible=True), - ], - None, - convert_arr, - ) - convert_confirm.click( - lambda: [ - gr.update(visible=False), - gr.update(visible=True), - gr.update(visible=False), - ], - None, - convert_arr, - ).then(remove_tts_from_history, gradio("history"), gradio("history")).then( - chat.save_history, - gradio("history", "unique_id", "character_menu", "mode"), - None, - ).then( - chat.redraw_html, gradio(ui_chat.reload_arr), gradio("display") - ) - - convert_cancel.click( - lambda: [ - gr.update(visible=False), - gr.update(visible=True), - gr.update(visible=False), - ], - None, - convert_arr, - ) - - # Toggle message text in history - show_text.change(lambda x: params.update({"show_text": x}), show_text, None).then( - toggle_text_in_history, gradio("history"), gradio("history") - ).then( - chat.save_history, - gradio("history", "unique_id", "character_menu", "mode"), - None, - ).then( - chat.redraw_html, gradio(ui_chat.reload_arr), gradio("display") - ) - - # Event functions to update the parameters in the backend - activate.change(lambda x: params.update({"activate": x}), activate, None) - autoplay.change(lambda x: params.update({"autoplay": x}), autoplay, None) - low_vram.change(lambda x: params.update({"low_vram": x}), low_vram, None) - low_vram.change(lambda x: send_lowvram_request(x), low_vram, low_vram_play, None) - tts_radio_buttons.change( - send_reload_request, tts_radio_buttons, tts_radio_buttons_play, None - ) - deepspeed_checkbox.change( - send_deepspeed_request, deepspeed_checkbox, deepspeed_checkbox_play, None - ) - remove_trailing_dots.change( - lambda x: params.update({"remove_trailing_dots": x}), remove_trailing_dots, None - ) - voice.change(lambda x: params.update({"voice": x}), voice, None) - language.change(lambda x: params.update({"language": x}), language, None) - - # TSS Settings - local_temperature_gr.change( - lambda x: params.update({"local_temperature": x}), local_temperature_gr, None - ) - local_repetition_penalty_gr.change( - lambda x: params.update({"local_repetition_penalty": x}), - local_repetition_penalty_gr, - None, - ) - - # Narrator selection actions - narrator_enabled_gr.change(update_narrator_enabled, narrator_enabled_gr, None) - non_quoted_text_is_gr.change(update_non_quoted_text_is, non_quoted_text_is_gr, None) - narrator_voice_gr.change( - lambda x: params.update({"narrator_voice": x}), narrator_voice_gr, None - ) - - # Play preview - preview_text.submit(voice_preview, preview_text, preview_audio) - preview_play.click(voice_preview, preview_text, preview_audio) - - -########################################## -#### STANDALONE MODE LOOP TERMINATION #### -########################################## -## Loop to keep the script from exiting out if its being run as a standalone script and not part of text-generation-webui -if running_in_standalone: - while True: - try: - time.sleep(1) # Add a small delay to avoid high CPU usage - except KeyboardInterrupt: - break # Allow graceful exit on Ctrl+C diff --git a/tts_server.py b/tts_server.py index 1f7cba3..e69de29 100644 --- a/tts_server.py +++ b/tts_server.py @@ -1,1247 +0,0 @@ -import json -import time -import os -from pathlib import Path -import torch -import torchaudio -from TTS.tts.configs.xtts_config import XttsConfig -from TTS.tts.models.xtts import Xtts -import io -import wave - -########################## -#### Webserver Imports#### -########################## -from fastapi import ( - FastAPI, - Form, - Request, - Response, - Depends, - HTTPException, - Query, -) -from fastapi.responses import JSONResponse, HTMLResponse, RedirectResponse, FileResponse, StreamingResponse -from fastapi.middleware.cors import CORSMiddleware -from fastapi.templating import Jinja2Templates -from fastapi.staticfiles import StaticFiles -from contextlib import asynccontextmanager - -########################### -#### STARTUP VARIABLES #### -########################### -# STARTUP VARIABLE - Create "this_dir" variable as the current script directory -this_dir = Path(__file__).parent.resolve() -# STARTUP VARIABLE - Set "device" to cuda if exists, otherwise cpu -device = "cuda" if torch.cuda.is_available() else "cpu" -# STARTUP VARIABLE - Import languges file for Gradio to be able to display them in the interface -with open(this_dir / "system" / "config" / "languages.json", encoding="utf8") as f: - languages = json.load(f) -# Base setting for a possible FineTuned model existing and the loader being available -tts_method_xtts_ft = False - -################################################################# -#### LOAD PARAMS FROM confignew.json - REQUIRED FOR BRANDING #### -################################################################# -# Load config file and get settings -def load_config(file_path): - with open(file_path, "r") as configfile_path: - configfile_data = json.load(configfile_path) - return configfile_data - - -# Define the path to the confignew.json file -configfile_path = this_dir / "confignew.json" - -# Load confignew.json and assign it to a different variable (config_data) -params = load_config(configfile_path) -# check someone hasnt enabled lowvram on a system thats not cuda enabled -params["low_vram"] = "false" if not torch.cuda.is_available() else params["low_vram"] - -# Load values for temperature and repetition_penalty -temperature = params["local_temperature"] -repetition_penalty = params["local_repetition_penalty"] - -# Define the path to the JSON file -config_file_path = this_dir / "modeldownload.json" - -############################################# -#### LOAD PARAMS FROM MODELDOWNLOAD.JSON #### -############################################ -# This is used only in the instance that someone has changed their model path -# Define the path to the JSON file -modeldownload_config_file_path = this_dir / "modeldownload.json" - -# Check if the JSON file exists -if modeldownload_config_file_path.exists(): - with open(modeldownload_config_file_path, "r") as modeldownload_config_file: - modeldownload_settings = json.load(modeldownload_config_file) - - # Extract settings from the loaded JSON - modeldownload_base_path = Path(modeldownload_settings.get("base_path", "")) - modeldownload_model_path = Path(modeldownload_settings.get("model_path", "")) -else: - # Default settings if the JSON file doesn't exist or is empty - print( - f"[{params['branding']}Startup] \033[91mWarning\033[0m modeldownload.config is missing so please re-download it and save it in the alltalk_tts main folder." - ) - -################################################## -#### Check to see if a finetuned model exists #### -################################################## -# Set the path to the directory -trained_model_directory = this_dir / "models" / "trainedmodel" -# Check if the directory "trainedmodel" exists -finetuned_model = trained_model_directory.exists() -# If the directory exists, check for the existence of the required files -if finetuned_model: - required_files = ["model.pth", "config.json", "vocab.json"] - finetuned_model = all((trained_model_directory / file).exists() for file in required_files) - -######################## -#### STARTUP CHECKS #### -######################## -try: - from TTS.api import TTS - from TTS.utils.synthesizer import Synthesizer -except ModuleNotFoundError: - print( - f"[{params['branding']}Startup] \033[91mWarning\033[0m Could not find the TTS module. Make sure to install the requirements for the alltalk_tts extension.", - f"[{params['branding']}Startup] \033[91mWarning\033[0m Linux / Mac:\npip install -r extensions/alltalk_tts/requirements.txt\n", - f"[{params['branding']}Startup] \033[91mWarning\033[0m Windows:\npip install -r extensions\\alltalk_tts\\requirements.txt\n", - f"[{params['branding']}Startup] \033[91mWarning\033[0m If you used the one-click installer, paste the command above in the terminal window launched after running the cmd_ script. On Windows, that's cmd_windows.bat." - ) - raise - -# DEEPSPEED Import - Check for DeepSpeed and import it if it exists -deepspeed_available = False -try: - import deepspeed - deepspeed_available = True -except ImportError: - pass -if deepspeed_available: - print(f"[{params['branding']}Startup] DeepSpeed \033[93mDetected\033[0m") - print(f"[{params['branding']}Startup] Activate DeepSpeed in {params['branding']}settings") -else: - print(f"[{params['branding']}Startup] DeepSpeed \033[93mNot Detected\033[0m. See https://github.com/microsoft/DeepSpeed") - - -@asynccontextmanager -async def startup_shutdown(no_actual_value_it_demanded_something_be_here): - await setup() - yield - # Shutdown logic - - -# Create FastAPI app with lifespan -app = FastAPI(lifespan=startup_shutdown) -# Allow all origins, and set other CORS options -app.add_middleware( - CORSMiddleware, - allow_origins=["*"], # Set this to the specific origins you want to allow - allow_credentials=True, - allow_methods=["*"], - allow_headers=["*"], -) - -##################################### -#### MODEL LOADING AND UNLOADING #### -##################################### -# MODEL LOADERS Picker For API TTS, API Local, XTTSv2 Local, XTTSv2 FT -async def setup(): - global device - # Set a timer to calculate load times - generate_start_time = time.time() # Record the start time of loading the model - # Start loading the correct model as set by "tts_method_api_tts", "tts_method_api_local" or "tts_method_xtts_local" being True/False - if params["tts_method_api_tts"]: - print( - f"[{params['branding']}Model] \033[94mAPI TTS Loading\033[0m {params['tts_model_name']} \033[94minto\033[93m", - device, - "\033[0m", - ) - model = await api_load_model() - elif params["tts_method_api_local"]: - print( - f"[{params['branding']}Model] \033[94mAPI Local Loading\033[0m {modeldownload_model_path} \033[94minto\033[93m", - device, - "\033[0m", - ) - model = await api_manual_load_model() - elif params["tts_method_xtts_local"]: - print( - f"[{params['branding']}Model] \033[94mXTTSv2 Local Loading\033[0m {modeldownload_model_path} \033[94minto\033[93m", - device, - "\033[0m", - ) - model = await xtts_manual_load_model() - elif tts_method_xtts_ft: - print( - f"[{params['branding']}Model] \033[94mXTTSv2 FT Loading\033[0m /models/fintuned/model.pth \033[94minto\033[93m", - device, - "\033[0m", - ) - model = await xtts_ft_manual_load_model() - # Create an end timer for calculating load times - generate_end_time = time.time() - # Calculate start time minus end time - generate_elapsed_time = generate_end_time - generate_start_time - # Print out the result of the load time - print( - f"[{params['branding']}Model] \033[94mModel Loaded in \033[93m{generate_elapsed_time:.2f} seconds.\033[0m" - ) - # Set "tts_model_loaded" to true - params["tts_model_loaded"] = True - # Set the output path for wav files - output_directory = this_dir / params["output_folder_wav_standalone"] - output_directory.mkdir(parents=True, exist_ok=True) - #Path(f'this_folder/outputs/').mkdir(parents=True, exist_ok=True) - - -# MODEL LOADER For "API TTS" -async def api_load_model(): - global model - model = TTS(params["tts_model_name"]).to(device) - return model - - -# MODEL LOADER For "API Local" -async def api_manual_load_model(): - global model - # check to see if a custom path has been set in modeldownload.json and use that path to load the model if so - if str(modeldownload_base_path) == "models": - model = TTS( - model_path=this_dir / "models" / modeldownload_model_path, - config_path=this_dir / "models" / modeldownload_model_path / "config.json", - ).to(device) - else: - print( - f"[{params['branding']}Model] \033[94mInfo\033[0m Loading your custom model set in \033[93mmodeldownload.json\033[0m:", - modeldownload_base_path / modeldownload_model_path, - ) - model = TTS( - model_path=modeldownload_base_path / modeldownload_model_path, - config_path=modeldownload_base_path / modeldownload_model_path / "config.json", - ).to(device) - return model - - -# MODEL LOADER For "XTTSv2 Local" -async def xtts_manual_load_model(): - global model - config = XttsConfig() - # check to see if a custom path has been set in modeldownload.json and use that path to load the model if so - if str(modeldownload_base_path) == "models": - config_path = this_dir / "models" / modeldownload_model_path / "config.json" - vocab_path_dir = this_dir / "models" / modeldownload_model_path / "vocab.json" - checkpoint_dir = this_dir / "models" / modeldownload_model_path - else: - print( - f"[{params['branding']}Model] \033[94mInfo\033[0m Loading your custom model set in \033[93mmodeldownload.json\033[0m:", - modeldownload_base_path / modeldownload_model_path, - ) - config_path = modeldownload_base_path / modeldownload_model_path / "config.json" - vocab_path_dir = modeldownload_base_path / modeldownload_model_path / "vocab.json" - checkpoint_dir = modeldownload_base_path / modeldownload_model_path - config.load_json(str(config_path)) - model = Xtts.init_from_config(config) - model.load_checkpoint( - config, - checkpoint_dir=str(checkpoint_dir), - vocab_path=str(vocab_path_dir), - use_deepspeed=params["deepspeed_activate"], - ) - model.to(device) - return model - -# MODEL LOADER For "XTTSv2 FT" -async def xtts_ft_manual_load_model(): - global model - config = XttsConfig() - config_path = this_dir / "models" / "trainedmodel" / "config.json" - vocab_path_dir = this_dir / "models" / "trainedmodel" / "vocab.json" - checkpoint_dir = this_dir / "models" / "trainedmodel" - config.load_json(str(config_path)) - model = Xtts.init_from_config(config) - model.load_checkpoint( - config, - checkpoint_dir=str(checkpoint_dir), - vocab_path=str(vocab_path_dir), - use_deepspeed=params["deepspeed_activate"], - ) - model.to(device) - return model - -# MODEL UNLOADER -async def unload_model(model): - print(f"[{params['branding']}Model] \033[94mUnloading model \033[0m") - del model - if torch.cuda.is_available(): - torch.cuda.empty_cache() - params["tts_model_loaded"] = False - return None - - -# MODEL - Swap model based on Gradio selection API TTS, API Local, XTTSv2 Local -async def handle_tts_method_change(tts_method): - global model - global tts_method_xtts_ft - # Update the params dictionary based on the selected radio button - print( - f"[{params['branding']}Model] \033[94mChanging model \033[92m(Please wait 15 seconds)\033[0m" - ) - # Set other parameters to False - if tts_method == "API TTS": - params["tts_method_api_local"] = False - params["tts_method_xtts_local"] = False - params["tts_method_api_tts"] = True - params["deepspeed_activate"] = False - tts_method_xtts_ft = False - elif tts_method == "API Local": - params["tts_method_api_tts"] = False - params["tts_method_xtts_local"] = False - params["tts_method_api_local"] = True - params["deepspeed_activate"] = False - tts_method_xtts_ft = False - elif tts_method == "XTTSv2 Local": - params["tts_method_api_tts"] = False - params["tts_method_api_local"] = False - params["tts_method_xtts_local"] = True - tts_method_xtts_ft = False - elif tts_method == "XTTSv2 FT": - tts_method_xtts_ft = True - params["tts_method_api_tts"] = False - params["tts_method_api_local"] = False - params["tts_method_xtts_local"] = False - - # Unload the current model - model = await unload_model(model) - - # Load the correct model based on the updated params - await setup() - - -# MODEL WEBSERVER- API Swap Between Models -@app.route("/api/reload", methods=["POST"]) -async def reload(request: Request): - tts_method = request.query_params.get("tts_method") - if tts_method not in ["API TTS", "API Local", "XTTSv2 Local", "XTTSv2 FT"]: - return {"status": "error", "message": "Invalid TTS method specified"} - await handle_tts_method_change(tts_method) - return Response( - content=json.dumps({"status": "model-success"}), media_type="application/json" - ) - - -################## -#### LOW VRAM #### -################## -# LOW VRAM - MODEL MOVER VRAM(cuda)<>System RAM(cpu) for Low VRAM setting -async def switch_device(): - global model, device - # Check if CUDA is available before performing GPU-related operations - if torch.cuda.is_available(): - if device == "cuda": - device = "cpu" - model.to(device) - torch.cuda.empty_cache() - else: - device == "cpu" - device = "cuda" - model.to(device) - - -@app.post("/api/lowvramsetting") -async def set_low_vram(request: Request, new_low_vram_value: bool): - global device - try: - if new_low_vram_value is None: - raise ValueError("Missing 'low_vram' parameter") - - if params["low_vram"] == new_low_vram_value: - return Response( - content=json.dumps( - { - "status": "success", - "message": f"[{params['branding']}Model] LowVRAM is already {'enabled' if new_low_vram_value else 'disabled'}.", - } - ) - ) - params["low_vram"] = new_low_vram_value - if params["low_vram"]: - await unload_model(model) - if torch.cuda.is_available(): - device = "cpu" - print( - f"[{params['branding']}Model] \033[94mChanging model \033[92m(Please wait 15 seconds)\033[0m" - ) - print( - f"[{params['branding']}Model] \033[94mLowVRAM Enabled.\033[0m Model will move between \033[93mVRAM(cuda) <> System RAM(cpu)\033[0m" - ) - await setup() - else: - # Handle the case where CUDA is not available - print( - f"[{params['branding']}Model] \033[91mError:\033[0m Nvidia CUDA is not available on this system. Unable to use LowVRAM mode." - ) - params["low_vram"] = False - else: - await unload_model(model) - if torch.cuda.is_available(): - device = "cuda" - print( - f"[{params['branding']}Model] \033[94mChanging model \033[92m(Please wait 15 seconds)\033[0m" - ) - print( - f"[{params['branding']}Model] \033[94mLowVRAM Disabled.\033[0m Model will stay in \033[93mVRAM(cuda)\033[0m" - ) - await setup() - else: - # Handle the case where CUDA is not available - print( - f"[{params['branding']}Model] \033[91mError:\033[0m Nvidia CUDA is not available on this system. Unable to use LowVRAM mode." - ) - params["low_vram"] = False - return Response(content=json.dumps({"status": "lowvram-success"})) - except Exception as e: - return Response(content=json.dumps({"status": "error", "message": str(e)})) - - -################### -#### DeepSpeed #### -################### -# DEEPSPEED - Reload the model when DeepSpeed checkbox is enabled/disabled -async def handle_deepspeed_change(value): - global model - if value: - # DeepSpeed enabled - print(f"[{params['branding']}Model] \033[93mDeepSpeed Activating\033[0m") - - print( - f"[{params['branding']}Model] \033[94mChanging model \033[92m(DeepSpeed can take 30 seconds to activate)\033[0m" - ) - print( - f"[{params['branding']}Model] \033[91mInformation\033[0m If you have not set CUDA_HOME path, DeepSpeed may fail to load/activate" - ) - print( - f"[{params['branding']}Model] \033[91mInformation\033[0m DeepSpeed needs to find nvcc from the CUDA Toolkit. Please check your CUDA_HOME path is" - ) - print( - f"[{params['branding']}Model] \033[91mInformation\033[0m pointing to the correct location and use 'set CUDA_HOME=putyoutpathhere' (Windows) or" - ) - print( - f"[{params['branding']}Model] \033[91mInformation\033[0m 'export CUDA_HOME=putyoutpathhere' (Linux) within your Python Environment" - ) - model = await unload_model(model) - params["tts_method_api_tts"] = False - params["tts_method_api_local"] = False - params["tts_method_xtts_local"] = True - params["deepspeed_activate"] = True - await setup() - else: - # DeepSpeed disabled - print(f"[{params['branding']}Model] \033[93mDeepSpeed De-Activating\033[0m") - print( - f"[{params['branding']}Model] \033[94mChanging model \033[92m(Please wait 15 seconds)\033[0m" - ) - params["deepspeed_activate"] = False - model = await unload_model(model) - await setup() - - return value # Return new checkbox value - - -# DEEPSPEED WEBSERVER- API Enable/Disable DeepSpeed -@app.post("/api/deepspeed") -async def deepspeed(request: Request, new_deepspeed_value: bool): - try: - if new_deepspeed_value is None: - raise ValueError("Missing 'deepspeed' parameter") - if params["deepspeed_activate"] == new_deepspeed_value: - return Response( - content=json.dumps( - { - "status": "success", - "message": f"DeepSpeed is already {'enabled' if new_deepspeed_value else 'disabled'}.", - } - ) - ) - params["deepspeed_activate"] = new_deepspeed_value - await handle_deepspeed_change(params["deepspeed_activate"]) - return Response(content=json.dumps({"status": "deepspeed-success"})) - except Exception as e: - return Response(content=json.dumps({"status": "error", "message": str(e)})) - - -######################## -#### TTS GENERATION #### -######################## - -# TTS VOICE GENERATION METHODS (called from voice_preview and output_modifer) -async def generate_audio(text, voice, language, temperature, repetition_penalty, output_file, streaming=False): - # Get the async generator from the internal function - response = generate_audio_internal(text, voice, language, temperature, repetition_penalty, output_file, streaming) - # If streaming, then return the generator as-is, otherwise just exhaust it and return - if streaming: - return response - async for _ in response: - pass - -async def generate_audio_internal(text, voice, language, temperature, repetition_penalty, output_file, streaming): - global model - if params["low_vram"] and device == "cpu": - await switch_device() - generate_start_time = time.time() # Record the start time of generating TTS - - # XTTSv2 LOCAL & Xttsv2 FT Method - if params["tts_method_xtts_local"] or tts_method_xtts_ft: - print(f"[{params['branding']}TTSGen] {text}") - gpt_cond_latent, speaker_embedding = model.get_conditioning_latents( - audio_path=[f"{this_dir}/voices/{voice}"], - gpt_cond_len=model.config.gpt_cond_len, - max_ref_length=model.config.max_ref_len, - sound_norm_refs=model.config.sound_norm_refs, - ) - - # Common arguments for both functions - common_args = { - "text": text, - "language": language, - "gpt_cond_latent": gpt_cond_latent, - "speaker_embedding": speaker_embedding, - "temperature": float(temperature), - "length_penalty": float(model.config.length_penalty), - "repetition_penalty": float(repetition_penalty), - "top_k": int(model.config.top_k), - "top_p": float(model.config.top_p), - "enable_text_splitting": True - } - - # Determine the correct inference function and add streaming specific argument if needed - inference_func = model.inference_stream if streaming else model.inference - if streaming: - common_args["stream_chunk_size"] = 20 - - # Call the appropriate function - output = inference_func(**common_args) - - # Process the output based on streaming or non-streaming - if streaming: - # Streaming-specific operations - file_chunks = [] - wav_buf = io.BytesIO() - with wave.open(wav_buf, "wb") as vfout: - vfout.setnchannels(1) - vfout.setsampwidth(2) - vfout.setframerate(24000) - vfout.writeframes(b"") - wav_buf.seek(0) - yield wav_buf.read() - - for i, chunk in enumerate(output): - file_chunks.append(chunk) - if isinstance(chunk, list): - chunk = torch.cat(chunk, dim=0) - chunk = chunk.clone().detach().cpu().numpy() - chunk = chunk[None, : int(chunk.shape[0])] - chunk = np.clip(chunk, -1, 1) - chunk = (chunk * 32767).astype(np.int16) - yield chunk.tobytes() - else: - # Non-streaming-specific operation - torchaudio.save(output_file, torch.tensor(output["wav"]).unsqueeze(0), 24000) - - - # API LOCAL Methods - elif params["tts_method_api_local"]: - # Streaming only allowed for XTTSv2 local - if streaming: - raise ValueError("Streaming is only supported in XTTSv2 local") - - # Set the correct output path (different from the if statement) - print(f"[{params['branding']}TTSGen] Using API Local") - model.tts_to_file( - text=text, - file_path=output_file, - speaker_wav=[f"{this_dir}/voices/{voice}"], - language=language, - temperature=temperature, - length_penalty=model.config.length_penalty, - repetition_penalty=repetition_penalty, - top_k=model.config.top_k, - top_p=model.config.top_p, - ) - - # API TTS - elif params["tts_method_api_tts"]: - # Streaming only allowed for XTTSv2 local - if streaming: - raise ValueError("Streaming is only supported in XTTSv2 local") - - print(f"[{params['branding']}TTSGen] Using API TTS") - model.tts_to_file( - text=text, - file_path=output_file, - speaker_wav=[f"{this_dir}/voices/{voice}"], - language=language, - ) - - # Print Generation time and settings - generate_end_time = time.time() # Record the end time to generate TTS - generate_elapsed_time = generate_end_time - generate_start_time - print( - f"[{params['branding']}TTSGen] \033[93m{generate_elapsed_time:.2f} seconds. \033[94mLowVRAM: \033[33m{params['low_vram']} \033[94mDeepSpeed: \033[33m{params['deepspeed_activate']}\033[0m" - ) - # Move model back to cpu system ram if needed. - if params["low_vram"] and device == "cuda": - await switch_device() - return - - -# TTS VOICE GENERATION METHODS - generate TTS API -@app.route("/api/generate", methods=["POST"]) -async def generate(request: Request): - try: - # Get parameters from JSON body - data = await request.json() - text = data["text"] - voice = data["voice"] - language = data["language"] - temperature = data["temperature"] - repetition_penalty = data["repetition_penalty"] - output_file = data["output_file"] - streaming = False - # Generation logic - response = await generate_audio(text, voice, language, temperature, repetition_penalty, output_file, streaming) - if streaming: - return StreamingResponse(response, media_type="audio/wav") - return JSONResponse( - content={"status": "generate-success", "data": {"audio_path": output_file}} - ) - except Exception as e: - return JSONResponse(content={"status": "error", "message": str(e)}) - - -################################################### -#### POPULATE FILES LIST FROM VOICES DIRECTORY #### -################################################### -# List files in the "voices" directory -def list_files(directory): - files = [ - f - for f in os.listdir(directory) - if os.path.isfile(os.path.join(directory, f)) and f.endswith(".wav") - ] - return files - -############################# -#### JSON CONFIG UPDATER #### -############################# - -# Create an instance of Jinja2Templates for rendering HTML templates -templates = Jinja2Templates(directory=this_dir / "system") - -# Create a dependency to get the current JSON data -def get_json_data(): - with open(this_dir / "confignew.json", "r") as json_file: - data = json.load(json_file) - return data - - -# Define an endpoint function -@app.get("/settings") -async def get_settings(request: Request): - wav_files = list_files(this_dir / "voices") - # Render the template with the current JSON data and list of WAV files - return templates.TemplateResponse( - "/at_admin/at_settings.html", - { - "request": request, - "data": get_json_data(), - "modeldownload_model_path": modeldownload_model_path, - "wav_files": wav_files, - }, - ) - -# Define an endpoint to serve static files -app.mount("/static", StaticFiles(directory=str(this_dir / "system")), name="static") - -@app.post("/update-settings") -async def update_settings( - request: Request, - activate: bool = Form(...), - autoplay: bool = Form(...), - deepspeed_activate: bool = Form(...), - delete_output_wavs: str = Form(...), - ip_address: str = Form(...), - language: str = Form(...), - local_temperature: str = Form(...), - local_repetition_penalty: str = Form(...), - low_vram: bool = Form(...), - tts_model_loaded: bool = Form(...), - tts_model_name: str = Form(...), - narrator_enabled: bool = Form(...), - narrator_voice: str = Form(...), - output_folder_wav: str = Form(...), - port_number: str = Form(...), - remove_trailing_dots: bool = Form(...), - show_text: bool = Form(...), - tts_method: str = Form(...), - voice: str = Form(...), - data: dict = Depends(get_json_data), -): - # Update the settings based on the form values - data["activate"] = activate - data["autoplay"] = autoplay - data["deepspeed_activate"] = deepspeed_activate - data["delete_output_wavs"] = delete_output_wavs - data["ip_address"] = ip_address - data["language"] = language - data["local_temperature"] = local_temperature - data["local_repetition_penalty"] = local_repetition_penalty - data["low_vram"] = low_vram - data["tts_model_loaded"] = tts_model_loaded - data["tts_model_name"] = tts_model_name - data["narrator_enabled"] = narrator_enabled - data["narrator_voice"] = narrator_voice - data["output_folder_wav"] = output_folder_wav - data["port_number"] = port_number - data["remove_trailing_dots"] = remove_trailing_dots - data["show_text"] = show_text - data["tts_method_api_local"] = tts_method == "api_local" - data["tts_method_api_tts"] = tts_method == "api_tts" - data["tts_method_xtts_local"] = tts_method == "xtts_local" - data["voice"] = voice - - # Save the updated settings back to the JSON file - with open(this_dir / "confignew.json", "w") as json_file: - json.dump(data, json_file) - - # Redirect to the settings page to display the updated settings - return RedirectResponse(url="/settings", status_code=303) - - -################################## -#### SETTINGS PAGE DEMO VOICE #### -################################## - -@app.get("/tts-demo-request", response_class=StreamingResponse) -async def tts_demo_request_streaming(text: str, voice: str, language: str, output_file: str): - try: - output_file_path = this_dir / "outputs" / output_file - stream = await generate_audio(text, voice, language, temperature, repetition_penalty, output_file_path, streaming=True) - return StreamingResponse(stream, media_type="audio/wav") - except Exception as e: - print(f"An error occurred: {e}") - return JSONResponse(content={"error": "An error occurred"}, status_code=500) - -@app.post("/tts-demo-request", response_class=JSONResponse) -async def tts_demo_request(request: Request, text: str = Form(...), voice: str = Form(...), language: str = Form(...), output_file: str = Form(...)): - try: - output_file_path = this_dir / "outputs" / output_file - await generate_audio(text, voice, language, temperature, repetition_penalty, output_file_path, streaming=False) - return JSONResponse(content={"output_file_path": str(output_file)}, status_code=200) - except Exception as e: - print(f"An error occurred: {e}") - return JSONResponse(content={"error": "An error occurred"}, status_code=500) - - -##################### -#### Audio feeds #### -##################### - -# Gives web access to the output files -@app.get("/audio/{filename}") -async def get_audio(filename: str): - audio_path = this_dir / "outputs" / filename - return FileResponse(audio_path) - -@app.get("/audiocache/{filename}") -async def get_audio(filename: str): - audio_path = Path("outputs") / filename - if not audio_path.is_file(): - raise HTTPException(status_code=404, detail="File not found") - - response = FileResponse( - path=audio_path, - media_type='audio/wav', - filename=filename - ) - # Set caching headers - response.headers["Cache-Control"] = "public, max-age=604800" # Cache for one week - response.headers["ETag"] = str(audio_path.stat().st_mtime) # Use the file's last modified time as a simple ETag - - return response - -######################### -#### VOICES LIST API #### -######################### -# Define the new endpoint -@app.get("/api/voices") -async def get_voices(): - wav_files = list_files(this_dir / "voices") - return {"voices": wav_files} - -########################### -#### PREVIEW VOICE API #### -########################### -@app.post("/api/previewvoice/", response_class=JSONResponse) -async def preview_voice(request: Request, voice: str = Form(...)): - try: - # Hardcoded settings - language = "en" - output_file_name = "api_preview_voice" - - # Clean the voice filename for inclusion in the text - clean_voice_filename = re.sub(r'\.wav$', '', voice.replace(' ', '_')) - clean_voice_filename = re.sub(r'[^a-zA-Z0-9]', ' ', clean_voice_filename) - - # Generate the audio - text = f"Hello, this is a preview of voice {clean_voice_filename}." - - # Generate the audio - output_file_path = this_dir / "outputs" / f"{output_file_name}.wav" - await generate_audio(text, voice, language, temperature, repetition_penalty, output_file_path, streaming=False) - - # Generate the URL - output_file_url = f'http://{params["ip_address"]}:{params["port_number"]}/audio/{output_file_name}.wav' - - # Return the response with both local file path and URL - return JSONResponse( - content={ - "status": "generate-success", - "output_file_path": str(output_file_path), - "output_file_url": str(output_file_url), - }, - status_code=200, - ) - except Exception as e: - print(f"An error occurred: {e}") - return JSONResponse(content={"error": "An error occurred"}, status_code=500) - -######################## -#### GENERATION API #### -######################## -import html -import re -import uuid -import numpy as np -import soundfile as sf -import sys -import hashlib - -############################## -#### Streaming Generation #### -############################## - -@app.get("/api/tts-generate-streaming", response_class=StreamingResponse) -async def tts_generate_streaming(text: str, voice: str, language: str, output_file: str): - try: - output_file_path = this_dir / "outputs" / output_file - stream = await generate_audio(text, voice, language, temperature, repetition_penalty, output_file_path, streaming=True) - return StreamingResponse(stream, media_type="audio/wav") - except Exception as e: - print(f"An error occurred: {e}") - return JSONResponse(content={"error": "An error occurred"}, status_code=500) - -@app.post("/api/tts-generate-streaming", response_class=JSONResponse) -async def tts_generate_streaming(request: Request, text: str = Form(...), voice: str = Form(...), language: str = Form(...), output_file: str = Form(...)): - try: - output_file_path = this_dir / "outputs" / output_file - await generate_audio(text, voice, language, temperature, repetition_penalty, output_file_path, streaming=False) - return JSONResponse(content={"output_file_path": str(output_file)}, status_code=200) - except Exception as e: - print(f"An error occurred: {e}") - return JSONResponse(content={"error": "An error occurred"}, status_code=500) - -############################## -#### Standard Generation #### -############################## - -# Check for PortAudio library on Linux -try: - import sounddevice as sd - sounddevice_installed=True -except OSError: - print(f"[{params['branding']}Startup] \033[91mInfo\033[0m PortAudio library not found. If you wish to play TTS in standalone mode through the API suite") - print(f"[{params['branding']}Startup] \033[91mInfo\033[0m please install PortAudio. This will not affect any other features or use of Alltalk.") - print(f"[{params['branding']}Startup] \033[91mInfo\033[0m If you don't know what the API suite is, then this message is nothing to worry about.") - sounddevice_installed=False - if sys.platform.startswith('linux'): - print(f"[{params['branding']}Startup] \033[91mInfo\033[0m On Linux, you can use the following command to install PortAudio:") - print(f"[{params['branding']}Startup] \033[91mInfo\033[0m sudo apt-get install portaudio19-dev") - -from typing import Union, Dict, List -from pydantic import BaseModel, ValidationError, Field - -def play_audio(file_path, volume): - data, fs = sf.read(file_path) - sd.play(volume * data, fs) - sd.wait() - -class Request(BaseModel): - # Define the structure of the 'Request' class if needed - pass - -class JSONInput(BaseModel): - text_input: str = Field(..., max_length=2000, description="text_input needs to be 2000 characters or less.") - text_filtering: str = Field(..., pattern="^(none|standard|html)$", description="text_filtering needs to be 'none', 'standard' or 'html'.") - character_voice_gen: str = Field(..., pattern="^.*\.wav$", description="character_voice_gen needs to be the name of a wav file e.g. mysample.wav.") - narrator_enabled: bool = Field(..., description="narrator_enabled needs to be true or false.") - narrator_voice_gen: str = Field(..., pattern="^.*\.wav$", description="narrator_voice_gen needs to be the name of a wav file e.g. mysample.wav.") - text_not_inside: str = Field(..., pattern="^(character|narrator)$", description="text_not_inside needs to be 'character' or 'narrator'.") - language: str = Field(..., pattern="^(ar|zh-cn|cs|nl|en|fr|de|hu|it|ja|ko|pl|pt|ru|es|tr)$", description="language needs to be one of the following ar|zh-cn|cs|nl|en|fr|de|hu|it|ja|ko|pl|pt|ru|es|tr.") - output_file_name: str = Field(..., pattern="^[a-zA-Z0-9_]+$", description="output_file_name needs to be the name without any special characters or file extension e.g. 'filename'") - output_file_timestamp: bool = Field(..., description="output_file_timestamp needs to be true or false.") - autoplay: bool = Field(..., description="autoplay needs to be a true or false value.") - autoplay_volume: float = Field(..., ge=0.1, le=1.0, description="autoplay_volume needs to be from 0.1 to 1.0") - - @classmethod - def validate_autoplay_volume(cls, value): - if not (0.1 <= value <= 1.0): - raise ValueError("Autoplay volume must be between 0.1 and 1.0") - return value - - -class TTSGenerator: - @staticmethod - def validate_json_input(json_data: Union[Dict, str]) -> Union[None, str]: - try: - if isinstance(json_data, str): - json_data = json.loads(json_data) - JSONInput(**json_data) - return None # JSON is valid - except ValidationError as e: - return str(e) - -def process_text(text): - # Normalize HTML encoded quotes - text = html.unescape(text) - # Replace ellipsis with a single dot - text = re.sub(r'\.{3,}', '.', text) - # Pattern to identify combined narrator and character speech - combined_pattern = r'(\*[^*"]+\*|"[^"*]+")' - # List to hold parts of speech along with their type - ordered_parts = [] - # Track the start of the next segment - start = 0 - # Find all matches - for match in re.finditer(combined_pattern, text): - # Add the text before the match, if any, as ambiguous - if start < match.start(): - ambiguous_text = text[start:match.start()].strip() - if ambiguous_text: - ordered_parts.append(('ambiguous', ambiguous_text)) - # Add the matched part as either narrator or character - matched_text = match.group(0) - if matched_text.startswith('*') and matched_text.endswith('*'): - ordered_parts.append(('narrator', matched_text.strip('*').strip())) - elif matched_text.startswith('"') and matched_text.endswith('"'): - ordered_parts.append(('character', matched_text.strip('"').strip())) - else: - # In case of mixed or improperly formatted parts - if '*' in matched_text: - ordered_parts.append(('narrator', matched_text.strip('*').strip('"'))) - else: - ordered_parts.append(('character', matched_text.strip('"').strip('*'))) - # Update the start of the next segment - start = match.end() - # Add any remaining text after the last match as ambiguous - if start < len(text): - ambiguous_text = text[start:].strip() - if ambiguous_text: - ordered_parts.append(('ambiguous', ambiguous_text)) - return ordered_parts - -def standard_filtering(text_input): - text_output = (text_input - .replace("***", "") - .replace("**", "") - .replace("*", "") - .replace("\n\n", "\n") - .replace("'", "'") - ) - return text_output - -def combine(output_file_timestamp, output_file_name, audio_files): - audio = np.array([]) - sample_rate = None - try: - for audio_file in audio_files: - audio_data, current_sample_rate = sf.read(audio_file) - if audio.size == 0: - audio = audio_data - sample_rate = current_sample_rate - elif sample_rate == current_sample_rate: - audio = np.concatenate((audio, audio_data)) - else: - raise ValueError("Sample rates of input files are not consistent.") - except Exception as e: - # Handle exceptions (e.g., file not found, invalid audio format) - return None, None - if output_file_timestamp: - timestamp = int(time.time()) - output_file_path = os.path.join(this_dir / "outputs" / f'{output_file_name}_{timestamp}_combined.wav') - output_file_url = f'http://{params["ip_address"]}:{params["port_number"]}/audio/{output_file_name}_{timestamp}_combined.wav' - output_cache_url = f'http://{params["ip_address"]}:{params["port_number"]}/audiocache/{output_file_name}_{timestamp}_combined.wav' - else: - output_file_path = os.path.join(this_dir / "outputs" / f'{output_file_name}_combined.wav') - output_file_url = f'http://{params["ip_address"]}:{params["port_number"]}/audio/{output_file_name}_combined.wav' - output_cache_url = f'http://{params["ip_address"]}:{params["port_number"]}/audiocache/{output_file_name}_combined.wav' - try: - sf.write(output_file_path, audio, samplerate=sample_rate) - # Clean up unnecessary files - for audio_file in audio_files: - os.remove(audio_file) - except Exception as e: - # Handle exceptions (e.g., failed to write output file) - return None, None - return output_file_path, output_file_url, output_cache_url - -# Generation API (separate from text-generation-webui) -@app.post("/api/tts-generate", response_class=JSONResponse) -async def tts_generate( - text_input: str = Form(...), - text_filtering: str = Form(...), - character_voice_gen: str = Form(...), - narrator_enabled: bool = Form(...), - narrator_voice_gen: str = Form(...), - text_not_inside: str = Form(...), - language: str = Form(...), - output_file_name: str = Form(...), - output_file_timestamp: bool = Form(...), - autoplay: bool = Form(...), - autoplay_volume: float = Form(...), - streaming: bool = Form(False), -): - try: - json_input_data = { - "text_input": text_input, - "text_filtering": text_filtering, - "character_voice_gen": character_voice_gen, - "narrator_enabled": narrator_enabled, - "narrator_voice_gen": narrator_voice_gen, - "text_not_inside": text_not_inside, - "language": language, - "output_file_name": output_file_name, - "output_file_timestamp": output_file_timestamp, - "autoplay": autoplay, - "autoplay_volume": autoplay_volume, - "streaming": streaming, - } - JSONresult = TTSGenerator.validate_json_input(json_input_data) - if JSONresult is None: - pass - else: - return JSONResponse(content={"error": JSONresult}, status_code=400) - if narrator_enabled: - processed_parts = process_text(text_input) - audio_files_all_paragraphs = [] - for part_type, part in processed_parts: - # Skip parts that are too short - if len(part.strip()) <= 3: - continue - # Determine the voice to use based on the part type - if part_type == 'narrator': - voice_to_use = narrator_voice_gen - print(f"[{params['branding']}TTSGen] \033[92mNarrator\033[0m") # Green - elif part_type == 'character': - voice_to_use = character_voice_gen - print(f"[{params['branding']}TTSGen] \033[36mCharacter\033[0m") # Yellow - else: - # Handle ambiguous parts based on user preference - voice_to_use = character_voice_gen if text_not_inside == "character" else narrator_voice_gen - voice_description = "\033[36mCharacter (Text-not-inside)\033[0m" if text_not_inside == "character" else "\033[92mNarrator (Text-not-inside)\033[0m" - print(f"[{params['branding']}TTSGen] {voice_description}") - # Replace multiple exclamation marks, question marks, or other punctuation with a single instance - cleaned_part = re.sub(r'([!?.])\1+', r'\1', part) - # Further clean to remove any other unwanted characters - cleaned_part = re.sub(r'[^a-zA-Z0-9\s\.,;:!?\-\'"\u0400-\u04FFÀ-ÿ\u0150\u0151\u0170\u0171]\$', '', cleaned_part) - # Remove all newline characters (single or multiple) - cleaned_part = re.sub(r'\n+', ' ', cleaned_part) - output_file = this_dir / "outputs" / f"{output_file_name}_{uuid.uuid4()}_{int(time.time())}.wav" - output_file_str = output_file.as_posix() - response = await generate_audio(cleaned_part, voice_to_use, language,temperature, repetition_penalty, output_file_str, streaming) - audio_path = output_file_str - audio_files_all_paragraphs.append(audio_path) - # Combine audio files across paragraphs - output_file_path, output_file_url, output_cache_url = combine(output_file_timestamp, output_file_name, audio_files_all_paragraphs) - else: - if output_file_timestamp: - timestamp = int(time.time()) - # Generate a standard UUID - original_uuid = uuid.uuid4() - # Hash the UUID using SHA-256 - hash_object = hashlib.sha256(str(original_uuid).encode()) - hashed_uuid = hash_object.hexdigest() - # Truncate to the desired length, for example, 16 characters - short_uuid = hashed_uuid[:5] - output_file_path = this_dir / "outputs" / f"{output_file_name}_{timestamp}{short_uuid}.wav" - output_file_url = f'http://{params["ip_address"]}:{params["port_number"]}/audio/{output_file_name}_{timestamp}{short_uuid}.wav' - output_cache_url = f'http://{params["ip_address"]}:{params["port_number"]}/audiocache/{output_file_name}_{timestamp}{short_uuid}.wav' - else: - output_file_path = this_dir / "outputs" / f"{output_file_name}.wav" - output_file_url = f'http://{params["ip_address"]}:{params["port_number"]}/audio/{output_file_name}.wav' - output_cache_url = f'http://{params["ip_address"]}:{params["port_number"]}/audiocache/{output_file_name}.wav' - if text_filtering == "html": - cleaned_string = html.unescape(standard_filtering(text_input)) - cleaned_string = re.sub(r'([!?.])\1+', r'\1', text_input) - # Further clean to remove any other unwanted characters - cleaned_string = re.sub(r'[^a-zA-Z0-9\s\.,;:!?\-\'"\u0400-\u04FFÀ-ÿ\u0150\u0151\u0170\u0171]\$', '', cleaned_string) - # Remove all newline characters (single or multiple) - cleaned_string = re.sub(r'\n+', ' ', cleaned_string) - elif text_filtering == "standard": - cleaned_string = re.sub(r'([!?.])\1+', r'\1', text_input) - # Further clean to remove any other unwanted characters - cleaned_string = re.sub(r'[^a-zA-Z0-9\s\.,;:!?\-\'"\u0400-\u04FFÀ-ÿ\u0150\u0151\u0170\u0171]\$', '', cleaned_string) - # Remove all newline characters (single or multiple) - cleaned_string = re.sub(r'\n+', ' ', cleaned_string) - else: - cleaned_string = text_input - response = await generate_audio(cleaned_string, character_voice_gen, language, temperature, repetition_penalty, output_file_path, streaming) - if sounddevice_installed == False or streaming == True: - autoplay = False - if autoplay: - play_audio(output_file_path, autoplay_volume) - if streaming: - return StreamingResponse(response, media_type="audio/wav") - return JSONResponse(content={"status": "generate-success", "output_file_path": str(output_file_path), "output_file_url": str(output_file_url), "output_cache_url": str(output_cache_url)}, status_code=200) - except Exception as e: - return JSONResponse(content={"status": "generate-failure", "error": "An error occurred"}, status_code=500) - - -########################## -#### Current Settings #### -########################## -# Define the available models -models_available = [ - {"name": "Coqui", "model_name": "API TTS"}, - {"name": "Coqui", "model_name": "API Local"}, - {"name": "Coqui", "model_name": "XTTSv2 Local"} -] - -@app.get('/api/currentsettings') -def get_current_settings(): - # Determine the current model loaded - if params["tts_method_api_tts"]: - current_model_loaded = "API TTS" - elif params["tts_method_api_local"]: - current_model_loaded = "API Local" - elif params["tts_method_xtts_local"]: - current_model_loaded = "XTTSv2 Local" - else: - current_model_loaded = None # or a default value if no method is active - - settings = { - "models_available": models_available, - "current_model_loaded": current_model_loaded, - "deepspeed_available": deepspeed_available, - "deepspeed_status": params["deepspeed_activate"], - "low_vram_status": params["low_vram"], - "finetuned_model": finetuned_model - } - return settings # Automatically converted to JSON by Fas - -############################# -#### Word Add-in Sharing #### -############################# -# Mount the static files from the 'word_addin' directory -app.mount("/api/word_addin", StaticFiles(directory=os.path.join(this_dir / 'system' / 'word_addin')), name="word_addin") - -############################################# -#### TTS Generator Comparision Endpoints #### -############################################# -import subprocess -import aiofiles - -class TTSItem(BaseModel): - id: int - fileUrl: str - text: str - characterVoice: str - language: str - -class TTSData(BaseModel): - ttsList: List[TTSItem] - -@app.post("/api/save-tts-data") -async def save_tts_data(tts_data: List[TTSItem]): - # Convert the list of Pydantic models to a list of dictionaries - tts_data_list = [item.dict() for item in tts_data] - # Serialize the list of dictionaries to a JSON string - tts_data_json = json.dumps(tts_data_list, indent=4) - async with aiofiles.open(this_dir / "outputs" / "ttsList.json", 'w') as f: - await f.write(tts_data_json) - return {"message": "Data saved successfully"} - -import sys - -@app.get("/api/trigger-analysis") -async def trigger_analysis(threshold: int = Query(default=98)): - venv_path = sys.prefix - env = os.environ.copy() - env["PATH"] = os.path.join(venv_path, "bin") + ":" + env["PATH"] - ttslist_path = this_dir / "outputs" / "ttsList.json" - wavfile_path = this_dir / "outputs" - subprocess.run(["python", "tts_diff.py", f"--threshold={threshold}", f"--ttslistpath={ttslist_path}", f"--wavfilespath={wavfile_path}"], cwd=this_dir / "system" / "tts_diff", env=env) - # Read the analysis summary - try: - with open(this_dir / "outputs" / "analysis_summary.json", "r") as summary_file: - summary_data = json.load(summary_file) - except FileNotFoundError: - summary_data = {"error": "Analysis summary file not found."} - return {"message": "Analysis Completed", "summary": summary_data} - -################################################# -#### TTS Generator SRT Subtitiles generation #### -################################################# -@app.get("/api/srt-generation") -async def srt_generation(): - venv_path = sys.prefix - env = os.environ.copy() - env["PATH"] = os.path.join(venv_path, "bin") + ":" + env["PATH"] - ttslist_path = this_dir / "outputs" / "ttsList.json" - wavfile_path = this_dir / "outputs" - subprocess.run(["python", "tts_srt.py", f"--ttslistpath={ttslist_path}", f"--wavfilespath={wavfile_path}"], cwd=this_dir / "system" / "tts_srt", env=env) - - srt_file_path = this_dir / "outputs" / "subtitles.srt" - if not srt_file_path.exists(): - raise HTTPException(status_code=404, detail="Subtitle file not found.") - - return FileResponse(path=srt_file_path, filename="subtitles.srt", media_type='application/octet-stream') - -################################################### -#### Webserver Startup & Initial model Loading #### -################################################### - -# Get the admin interface template -template = templates.get_template("admin.html") -# Render the template with the dynamic values -rendered_html = template.render(params=params) - -############################### -#### Internal script ready #### -############################### -@app.get("/ready") -async def ready(): - return Response("Ready endpoint") - -############################ -#### External API ready #### -############################ -@app.get("/api/ready") -async def ready(): - return Response("Ready") - -@app.get("/") -async def read_root(): - return HTMLResponse(content=rendered_html, status_code=200) - -# Start Uvicorn Webserver -host_parameter = params["ip_address"] -port_parameter = int(params["port_number"]) - -if __name__ == "__main__": - import uvicorn - - uvicorn.run(app, host=host_parameter, port=port_parameter, log_level="warning") From 6815ba0f2e3bcd61a341e7704eded35dd440e5a4 Mon Sep 17 00:00:00 2001 From: erew123 <35898566+erew123@users.noreply.github.com> Date: Mon, 1 Apr 2024 11:00:38 +0100 Subject: [PATCH 2/9] Minor documentation update --- system/admin.html | 1 + 1 file changed, 1 insertion(+) diff --git a/system/admin.html b/system/admin.html index 90a85e6..d7f5d07 100644 --- a/system/admin.html +++ b/system/admin.html @@ -1357,6 +1357,7 @@

🟨 Help with problems

     🔄 Minor updates/bug fixes list can be found here

+

      Please check Github for the most up to date help list here

🟨 How to make a diagnostics report file From e7996ebc7a1ea81247e7c98cab653b717b215dd1 Mon Sep 17 00:00:00 2001 From: erew123 <35898566+erew123@users.noreply.github.com> Date: Mon, 1 Apr 2024 12:26:42 +0100 Subject: [PATCH 3/9] Add files via upload --- system/config/at_configdowngrade.json | 2 ++ system/config/at_configupdate.json | 2 ++ 2 files changed, 4 insertions(+) create mode 100644 system/config/at_configdowngrade.json create mode 100644 system/config/at_configupdate.json diff --git a/system/config/at_configdowngrade.json b/system/config/at_configdowngrade.json new file mode 100644 index 0000000..e02aef2 --- /dev/null +++ b/system/config/at_configdowngrade.json @@ -0,0 +1,2 @@ +{ +} \ No newline at end of file diff --git a/system/config/at_configupdate.json b/system/config/at_configupdate.json new file mode 100644 index 0000000..e02aef2 --- /dev/null +++ b/system/config/at_configupdate.json @@ -0,0 +1,2 @@ +{ +} \ No newline at end of file From cead53a808d453c14e068c6e3fcccdcc4e4c02f3 Mon Sep 17 00:00:00 2001 From: erew123 <35898566+erew123@users.noreply.github.com> Date: Mon, 1 Apr 2024 12:28:32 +0100 Subject: [PATCH 4/9] Config file checker & clean start-up Cleaned up the start-up screen & added mini diags Added a config file updater that can be used to add/remove things from the confignew.json file if ever needed. --- script.py | 1082 +++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1082 insertions(+) diff --git a/script.py b/script.py index e69de29..df1f4ce 100644 --- a/script.py +++ b/script.py @@ -0,0 +1,1082 @@ +import html +import json +import random +import subprocess +import time +import os +import requests +import threading +import signal +import sys +import atexit +import shutil +from pathlib import Path +from datetime import datetime, timedelta +import re +import numpy as np +import soundfile as sf +import uuid +import logging +# Store the current disable level +current_disable_level = logging.getLogger().manager.disable + +###################################### +#### ALLTALK ALLOWED STARTUP TIME #### +###################################### +startup_wait_time = 120 + +# You can change the above setting to a larger number to allow AllTAlk more time to start up. The default setting is 120 seconds (2 minutes). +# On some older systems you may need to allow AllTalk more time. So you could set it to 240 (4 minutes) which will give AllTalk more to load. + +################################################################# +#### LOAD PARAMS FROM confignew.json - REQUIRED FOR BRANDING #### +################################################################# +# STARTUP VARIABLE - Create "this_dir" variable as the current script directory +this_dir = Path(__file__).parent.resolve() + +# load config file in and get settings +def load_config(file_path): + with open(file_path, "r") as config_file: + config = json.load(config_file) + return config + +config_file_path = this_dir / "confignew.json" +# Load the params dictionary from the confignew.json file +params = load_config(config_file_path) + +print(f"[{params['branding']}Startup]\033[94m _ _ _ \033[1;35m_____ _ _ \033[0m _____ _____ ____ ") +print(f"[{params['branding']}Startup]\033[94m / \ | | |\033[1;35m_ _|_ _| | | __ \033[0m |_ _|_ _/ ___| ") +print(f"[{params['branding']}Startup]\033[94m / _ \ | | |\033[1;35m | |/ _` | | |/ / \033[0m | | | | \___ \ ") +print(f"[{params['branding']}Startup]\033[94m / ___ \| | |\033[1;35m | | (_| | | < \033[0m | | | | ___) |") +print(f"[{params['branding']}Startup]\033[94m /_/ \_\_|_|\033[1;35m |_|\__,_|_|_|\_\ \033[0m |_| |_| |____/ ") +print(f"[{params['branding']}Startup]") + +############################################## +#### Update any changes to confignew.json #### +############################################## + +update_config_path = this_dir / "system" / "config" / "at_configupdate.json" +downgrade_config_path = this_dir / "system" / "config" / "at_configdowngrade.json" + +def changes_needed(main_config, update_config, downgrade_config): + """Check if there are any changes to be made to the main configuration.""" + for key in downgrade_config.keys(): + if key in main_config: + return True + for key, value in update_config.items(): + if key not in main_config: + return True + return False + +def update_config(config_file_path, update_config_path, downgrade_config_path): + try: + with open(config_file_path, 'r') as file: + main_config = json.load(file) + with open(update_config_path, 'r') as file: + update_config = json.load(file) + with open(downgrade_config_path, 'r') as file: + downgrade_config = json.load(file) + + # Determine if changes are needed + if changes_needed(main_config, update_config, downgrade_config): + # Backup with timestamp to avoid overwriting + timestamp = datetime.now().strftime("%Y%m%d%H%M%S") + backup_path = config_file_path.with_suffix(f".{timestamp}.bak") + logging.info(f"Creating backup of the main config to {backup_path}") + shutil.copy(config_file_path, backup_path) + + # Proceed with updates and downgrades + for key, value in update_config.items(): + if key not in main_config: + main_config[key] = value + for key in downgrade_config.keys(): + if key in main_config: + del main_config[key] + + # Save the updated configuration + with open(config_file_path, 'w') as file: + json.dump(main_config, file, indent=4) + + print(f"[{params['branding']}Startup] \033[92mConfig file check : \033[91mUpdates applied\033[0m") + else: + print(f"[{params['branding']}Startup] \033[92mConfig file check : \033[93mNo Updates required\033[0m") + + except Exception as e: + print(f"[{params['branding']}Startup] \033[92mConfig file check : \033[91mError updating\033[0m") + +# Update the configuration +update_config(config_file_path, update_config_path, downgrade_config_path) +# Re-Load the params dictionary from the confignew.json file +params = load_config(config_file_path) + +######################################### +#### Continue on with Startup Checks #### +######################################### + +# Required for sentence splitting +try: + from TTS.api import TTS + from TTS.utils.synthesizer import Synthesizer +except ModuleNotFoundError: + # Inform the user about the missing module and suggest next steps + print(f"[{params['branding']}]\033[91mWarning\033[0m Could not find the TTS module. Make sure to install the requirements for the {params['branding']} extension.") + print(f"[{params['branding']}]\033[91mWarning\033[0m Please use the ATSetup utility or check the Github installation instructions.") + # Re-raise the ModuleNotFoundError to stop the program and print the traceback + raise + +# Suppress logging +logging.disable(logging.ERROR) +try: + import deepspeed + deepspeed_installed = True +except ImportError: + deepspeed_installed = False +# Restore previous logging level +logging.disable(current_disable_level) + +# Import gradio if being used within text generation webUI +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 + + # This is set to check if the script is being run within text generation webui or as a standalone script. False is running as part of text gen web ui or a gradio interface + running_in_standalone = False + output_folder_wav = params["output_folder_wav"] + print(f"[{params['branding']}Startup] \033[92m{params['branding']}startup Mode : \033[93mText-Gen-webui mode\033[0m") +except ModuleNotFoundError: + output_folder_wav = params["output_folder_wav_standalone"] + print(f"[{params['branding']}Startup] \033[92m{params['branding']}startup Mode : \033[93mStandalone mode\033[0m") + # This is set to check if the script is being run within text generation webui or as a standalone script. true means standalone + running_in_standalone = True + +########################### +#### STARTUP VARIABLES #### +########################### +# STARTUP VARIABLE - Import languges file for Gradio to be able to display them in the interface +with open(this_dir / "system" / "config" / "languages.json", encoding="utf8") as f: + languages = json.load(f) +# Create a global lock +process_lock = threading.Lock() +# Base setting for a possible FineTuned model existing and the loader being available +tts_method_xtts_ft = False + + +# Gather the voice files +def get_available_voices(): + return sorted([voice.name for voice in Path(f"{this_dir}/voices").glob("*.wav")]) + + +############################################ +#### DELETE OLD OUTPUT WAV FILES IF SET #### +############################################ +def delete_old_files(folder_path, days_to_keep): + current_time = datetime.now() + print(f"[{params['branding']}Startup] \033[92mWAV file deletion :\033[93m {formatted_date}\033[0m",delete_output_wavs_setting,) + 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) + + +# Extract settings using params dictionary +delete_output_wavs_setting = params["delete_output_wavs"] +output_folder_wav = os.path.normpath(output_folder_wav) + +# Check and perform file deletion +if delete_output_wavs_setting.lower() == "disabled": + print("["+ params["branding"]+"Startup] \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_wav, days_to_keep) + except ValueError: + print(f"[{params['branding']}Startup] \033[92mWAV file deletion :\033[93m Invalid setting for deleting old wav files. Please use 'Disabled' or 'X Days' format\033[0m") + +if deepspeed_installed: + print(f"[{params['branding']}Startup] \033[92mDeepSpeed version :\033[93m",deepspeed.__version__,"\033[0m") +else: + print(f"[{params['branding']}Startup] \033[92mDeepSpeed version :\033[93m Not Detected\033[0m") + +######################## +#### STARTUP CHECKS #### +######################## +# STARTUP Checks routine +def check_required_files(): + this_dir = Path(__file__).parent.resolve() + download_script_path = this_dir / "modeldownload.py" + subprocess.run(["python", str(download_script_path)]) + +# STARTUP Call Check routine +check_required_files() + +################################################## +#### Check to see if a finetuned model exists #### +################################################## +# Set the path to the directory +trained_model_directory = this_dir / "models" / "trainedmodel" +# Check if the directory "trainedmodel" exists +finetuned_model = trained_model_directory.exists() +# If the directory exists, check for the existence of the required files +# If true, this will add a extra option in the Gradio interface for loading Xttsv2 FT +if finetuned_model: + required_files = ["model.pth", "config.json", "vocab.json"] + finetuned_model = all( + (trained_model_directory / file).exists() for file in required_files + ) +if finetuned_model: + print(f"[{params['branding']}Startup] Finetuned model \033[93mDetected\033[0m") + +#################################################### +#### SET GRADIO BUTTONS BASED ON confignew.json #### +#################################################### + +if params["tts_method_api_tts"] == True: + gr_modelchoice = "API TTS" +elif params["tts_method_api_local"] == True: + gr_modelchoice = "API Local" +elif params["tts_method_xtts_local"] == True: + gr_modelchoice = "XTTSv2 Local" + +# Set the default for Narrated text without asterisk or quotes to be Narrator +non_quoted_text_is = True + +###################### +#### GRADIO STUFF #### +###################### +def remove_tts_from_history(history): + for i, entry in enumerate(history["internal"]): + history["visible"][i] = [history["visible"][i][0], entry[1]] + return history + + +def toggle_text_in_history(history): + for i, entry in enumerate(history["visible"]): + visible_reply = entry[1] + if visible_reply.startswith("')[0]}\n\n{reply}", + ] + else: + history["visible"][i] = [ + history["visible"][i][0], + f"{visible_reply.split('')[0]}", + ] + return history + + +def history_modifier(history): + # Remove autoplay from the last reply + if len(history["internal"]) > 0: + history["visible"][-1] = [ + history["visible"][-1][0], + history["visible"][-1][1].replace("controls autoplay>", "controls>"), + ] + + return history + + +###################################### +#### SUBPROCESS/WEBSERVER STARTUP #### +###################################### +base_url = f"http://{params['ip_address']}:{params['port_number']}" +script_path = this_dir / "tts_server.py" + + +def signal_handler(sig, frame): + print(f"[{params['branding']}Shutdown] \033[94mReceived Ctrl+C, terminating subprocess\033[92m") + if process.poll() is None: + process.terminate() + process.wait() # Wait for the subprocess to finish + sys.exit(0) + + +# Attach the signal handler to the SIGINT signal (Ctrl+C) +signal.signal(signal.SIGINT, signal_handler) +# Check if we're running in docker +if os.path.isfile("/.dockerenv"): + print( + f"[{params['branding']}Startup] \033[94mRunning in Docker. Please wait.\033[0m" + ) +else: + # Start the subprocess + process = subprocess.Popen(["python", script_path]) + # Check if the subprocess has started successfully + if process.poll() is None: + print(f"[{params['branding']}Startup] \033[92mTTS Subprocess :\033[93m Starting up\033[0m") + print(f"[{params['branding']}Startup]") + print( + f"[{params['branding']}Startup] \033[94m{params['branding']}Settings & Documentation:\033[00m", + f"\033[92mhttp://{params['ip_address']}:{params['port_number']}\033[00m", + ) + print(f"[{params['branding']}Startup]") + else: + print(f"[{params['branding']}Startup] \033[91mWarning\033[0m TTS Subprocess Webserver failing to start process") + print(f"[{params['branding']}Startup] \033[91mWarning\033[0m It could be that you have something on port:",params["port_number"],) + print(f"[{params['branding']}Startup] \033[91mWarning\033[0m Or you have not started in a Python environement with all the necesssary bits installed") + print(f"[{params['branding']}Startup] \033[91mWarning\033[0m Check you are starting Text-generation-webui with either the start_xxxxx file or the Python environment with cmd_xxxxx file.") + print(f"[{params['branding']}Startup] \033[91mWarning\033[0m xxxxx is the type of OS you are on e.g. windows, linux or mac.") + print(f"[{params['branding']}Startup] \033[91mWarning\033[0m Alternatively, you could check no other Python processes are running that shouldnt be e.g. Restart your computer is the simple way.") + # 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 + + # Introduce a delay before starting the check loop + time.sleep(26) # Wait 26 secs before checking if the tts_server.py has started up. + start_time = time.time() + while time.time() - start_time < timeout: + try: + response = requests.get(f"{base_url}/ready") + if response.status_code == 200: + break + except requests.RequestException as e: + # Print the exception for debugging purposes + print(f"[{params['branding']}Startup] \033[91mWarning\033[0m TTS Subprocess has NOT started up yet, Will keep trying for {timeout} seconds maximum. Please wait.") + time.sleep(5) + else: + print(f"\n[{params['branding']}Startup] Startup timed out. Full help available here \033[92mhttps://github.com/erew123/alltalk_tts#-help-with-problems\033[0m") + print(f"[{params['branding']}Startup] On older system you may wish to open and edit \033[94mscript.py\033[0m with a text editor and changing the") + print(f"[{params['branding']}Startup] \033[94mstartup_wait_time = 120\033[0m setting to something like \033[94mstartup_wait_time = 240\033[0m as this will allow") + print(f"[{params['branding']}Startup] AllTalk more time to try load the model into your VRAM. Otherise please visit the Github for") + print(f"[{params['branding']}Startup] 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) + + +##################################### +#### MODEL LOADING AND UNLOADING #### +##################################### +# MODEL - Swap model based on Gradio selection API TTS, API Local, XTTSv2 Local +def send_reload_request(tts_method): + global tts_method_xtts_ft + try: + params["tts_model_loaded"] = False + url = f"{base_url}/api/reload" + payload = {"tts_method": tts_method} + response = requests.post(url, params=payload) + response.raise_for_status() # Raises an HTTPError for bad responses + json_response = response.json() + # Check if the reload operation was successful + if json_response.get("status") == "model-success": + # Update tts_tts_model_loaded to True if the reload was successful + params["tts_model_loaded"] = True + # Update local script parameters based on the tts_method + if tts_method == "API TTS": + params["tts_method_api_local"] = False + params["tts_method_xtts_local"] = False + params["tts_method_api_tts"] = True + params["deepspeed_activate"] = False + audio_path = this_dir / "system" / "at_sounds" / "apitts.wav" + tts_method_xtts_ft = False + elif tts_method == "API Local": + params["tts_method_api_tts"] = False + params["tts_method_xtts_local"] = False + params["tts_method_api_local"] = True + params["deepspeed_activate"] = False + audio_path = this_dir / "system" / "at_sounds" / "apilocal.wav" + tts_method_xtts_ft = False + elif tts_method == "XTTSv2 Local": + params["tts_method_api_tts"] = False + params["tts_method_api_local"] = False + params["tts_method_xtts_local"] = True + audio_path = this_dir / "system" / "at_sounds" / "xttslocal.wav" + tts_method_xtts_ft = False + elif tts_method == "XTTSv2 FT": + params["tts_method_api_tts"] = False + params["tts_method_api_local"] = False + params["tts_method_xtts_local"] = False + audio_path = this_dir / "system" / "at_sounds" / "xttsfinetuned.wav" + tts_method_xtts_ft = True + return f'' + except requests.exceptions.RequestException as e: + # Handle the HTTP request error + print(f"[{params['branding']}Server] \033[91mWarning\033[0m Error during request to webserver process: {e}") + return {"status": "error", "message": str(e)} + + +################## +#### LOW VRAM #### +################## +# LOW VRAM - Gradio Checkbox handling +def send_lowvram_request(low_vram): + try: + params["tts_model_loaded"] = False + if low_vram: + audio_path = this_dir / "system" / "at_sounds" / "lowvramenabled.wav" + else: + audio_path = this_dir / "system" / "at_sounds" / "lowvramdisabled.wav" + url = f"{base_url}/api/lowvramsetting?new_low_vram_value={low_vram}" + headers = {"Content-Type": "application/json"} + response = requests.post(url, headers=headers) + response.raise_for_status() # Raises an HTTPError for bad responses + json_response = response.json() + # Check if the low VRAM request was successful + if json_response.get("status") == "lowvram-success": + # Update any relevant variables or perform other actions on success + params["tts_model_loaded"] = True + return f'' + except requests.exceptions.RequestException as e: + # Handle the HTTP request error + print(f"[{params['branding']}Server] \033[91mWarning\033[0m Error during request to webserver process: {e}") + return {"status": "error", "message": str(e)} + + +################### +#### DeepSpeed #### +################### +# DEEPSPEED - Reload the model when DeepSpeed checkbox is enabled/disabled +def send_deepspeed_request(deepspeed_param): + try: + params["tts_model_loaded"] = False + if deepspeed_param: + audio_path = this_dir / "system" / "at_sounds" / "deepspeedenabled.wav" + else: + audio_path = this_dir / "system" / "at_sounds" / "deepspeeddisabled.wav" + url = f"{base_url}/api/deepspeed?new_deepspeed_value={deepspeed_param}" + headers = {"Content-Type": "application/json"} + response = requests.post(url, headers=headers) + response.raise_for_status() # Raises an HTTPError for bad responses + json_response = response.json() + # Check if the deepspeed request was successful + if json_response.get("status") == "deepspeed-success": + # Update any relevant variables or perform other actions on success + params["tts_model_loaded"] = True + return f'' + except requests.exceptions.RequestException as e: + # Handle the HTTP request error + print(f"[{params['branding']}Server] \033[91mWarning\033[0m Error during request to webserver process: {e}") + return {"status": "error", "message": str(e)} + + +# DEEPSPEED - Display DeepSpeed Checkbox Yes or No +deepspeed_condition = params["tts_method_xtts_local"] == "True" and deepspeed_installed + + +############################################################# +#### TTS STRING CLEANING & PROCESSING PRE SENDING TO TTS #### +############################################################# +def new_split_into_sentences(self, text): + sentences = self.seg.segment(text) + if params["remove_trailing_dots"]: + sentences_without_dots = [] + for sentence in sentences: + if sentence.endswith(".") and not sentence.endswith("..."): + sentence = sentence[:-1] + + sentences_without_dots.append(sentence) + + return sentences_without_dots + else: + return sentences + + +Synthesizer.split_into_sentences = new_split_into_sentences + + +# Check model is loaded and string isnt empty, before sending a TTS request. +def before_audio_generation(string, params): + # Check Model is loaded into cuda or cpu and error if not + if not params["tts_model_loaded"]: + print(f"[{params['branding']}Model] \033[91mWarning\033[0m Model is still loading, please wait before trying to generate TTS") + return + string = html.unescape(string) or random_sentence() + if string == "": + return "*Empty string*" + return string + + +################## +#### Narrator #### +################## +def combine(audio_files, output_folder, state): + audio = np.array([]) + + for audio_file in audio_files: + audio_data, sample_rate = sf.read(audio_file) + # Ensure all audio files have the same sample rate + if audio.size == 0: + audio = audio_data + else: + audio = np.concatenate((audio, audio_data)) + + # Save the combined audio to a file with a specified sample rate + if "character_menu" in state: + output_file_path = os.path.join(output_folder, f'{state["character_menu"]}_{int(time.time())}_combined.wav') + else: + output_file_path = os.path.join(output_folder, f"TTSOUT_{int(time.time())}_combined.wav") + sf.write(output_file_path, audio, samplerate=sample_rate) + # Clean up unnecessary files + for audio_file in audio_files: + os.remove(audio_file) + + return output_file_path + + +################################ +#### TTS PREVIEW GENERATION #### +################################ +# PREVIEW VOICE - Generate Random Sentence if Voice Preview box is empty +def random_sentence(): + with open(this_dir / "system" / "config" / "harvard_sentences.txt") as f: + return random.choice(list(f)) + + +# PREVIEW VOICE- Generate TTS Function +def voice_preview(string): + if not params["activate"]: + return string + # Clean the string, capture model not loaded, and move model to cuda if needed + cleaned_string = before_audio_generation(string, params) + if cleaned_string is None: + return + string = cleaned_string + # Setup the output file + output_file = Path(params["output_folder_wav"]) / "voice_preview.wav" + # Generate the audio + language_code = languages.get(params["language"]) + temperature = params["local_temperature"] + repetition_penalty = params["local_repetition_penalty"] + # Convert the WindowsPath object to a string before using it in JSON payload + output_file_str = output_file.as_posix() + # Lock before making the generate request + with process_lock: + generate_response = send_generate_request( + string, + params["voice"], + language_code, + temperature, + repetition_penalty, + output_file_str, + ) + # Check if lock is already acquired + if process_lock.locked(): + print(f"[{params['branding']}Model] \033[91mWarning\033[0m Audio generation is already in progress. Please wait.") + return + if generate_response.get("status") == "generate-success": + # Handle Gradio and playback + autoplay = "autoplay" if params["autoplay"] else "" + return f'' + else: + # Handle the case where audio generation was not successful + return f"[{params['branding']}Server] Audio generation failed. Status: {generate_response.get('status')}" + + +####################### +#### TEXT CLEANING #### +####################### + +def process_text(text): + # Normalize HTML encoded quotes + text = html.unescape(text) + + # Replace ellipsis with a single dot + text = re.sub(r"\.{3,}", ".", text) + + # Pattern to identify combined narrator and character speech + combined_pattern = r'(\*[^*"]+\*|"[^"*]+")' + + # List to hold parts of speech along with their type + ordered_parts = [] + + # Track the start of the next segment + start = 0 + + # Find all matches + for match in re.finditer(combined_pattern, text): + # Add the text before the match, if any, as ambiguous + if start < match.start(): + ambiguous_text = text[start : match.start()].strip() + if ambiguous_text: + ordered_parts.append(("ambiguous", ambiguous_text)) + + # Add the matched part as either narrator or character + matched_text = match.group(0) + if matched_text.startswith("*") and matched_text.endswith("*"): + ordered_parts.append(("narrator", matched_text.strip("*").strip())) + elif matched_text.startswith('"') and matched_text.endswith('"'): + ordered_parts.append(("character", matched_text.strip('"').strip())) + else: + # In case of mixed or improperly formatted parts + if "*" in matched_text: + ordered_parts.append(("narrator", matched_text.strip("*").strip('"'))) + else: + ordered_parts.append(("character", matched_text.strip('"').strip("*"))) + + # Update the start of the next segment + start = match.end() + + # Add any remaining text after the last match as ambiguous + if start < len(text): + ambiguous_text = text[start:].strip() + if ambiguous_text: + ordered_parts.append(("ambiguous", ambiguous_text)) + + return ordered_parts + + +######################## +#### IMAGE CLEANING #### +######################## + +img_pattern = r']*src\s*=\s*["\'][^"\'>]+["\'][^>]*>' + +def extract_and_remove_images(text): + """ + Extracts all image data from the text and removes it for clean TTS processing. + Returns the cleaned text and the extracted image data. + """ + img_matches = re.findall(img_pattern, text) + img_info = "\n".join(img_matches) # Store extracted image data + cleaned_text = re.sub(img_pattern, '', text) # Remove images from text + return cleaned_text, img_info + +def reinsert_images(text, img_info): + """ + Reinserts the previously extracted image data back into the text. + """ + if img_info: # Check if there are images to reinsert + text += f"\n\n{img_info}" + return text + +################################# +#### TTS STANDARD GENERATION #### +################################# +# STANDARD VOICE - Generate TTS Function +def output_modifier(string, state): + if not params["activate"]: + return string + img_info = "" + cleaned_text, img_info = extract_and_remove_images(string) + # print("Cleaned STRING IS:", cleaned_text) + cleaned_string = before_audio_generation(cleaned_text, params) + if cleaned_string is None: + return + language_code = languages.get(params["language"]) + temperature = params["local_temperature"] + repetition_penalty = params["local_repetition_penalty"] + # Create a list to store generated audio paths + audio_files = [] + if process_lock.acquire(blocking=False): + try: + if params["narrator_enabled"]: + processed_parts = process_text(cleaned_string) + + audio_files_all_paragraphs = [] + for part_type, part in processed_parts: + # Skip parts that are too short + if len(part.strip()) <= 3: + continue + + # Determine the voice to use based on the part type + if part_type == "narrator": + voice_to_use = params["narrator_voice"] + print(f"[{params['branding']}TTSGen] \033[92mNarrator\033[0m") # Green + elif part_type == "character": + voice_to_use = params["voice"] + print(f"[{params['branding']}TTSGen] \033[36mCharacter\033[0m") # Yellow + else: + # Handle ambiguous parts based on user preference + voice_to_use = ( + params["voice"] + if non_quoted_text_is + else params["narrator_voice"] + ) + voice_description = ( + "\033[36mCharacter (Text-not-inside)\033[0m" + if non_quoted_text_is + else "\033[92mNarrator (Text-not-inside)\033[0m" + ) + print(f"[{params['branding']}TTSGen] {voice_description}") + + # Replace multiple exclamation marks, question marks, or other punctuation with a single instance + cleaned_part = re.sub(r"([!?.])\1+", r"\1", part) + # Further clean to remove any other unwanted characters + cleaned_part = re.sub(r'[^a-zA-Z0-9\s\.,;:!?\-\'"\u0100-\u017F\u0400-\u04FFÀ-ÿ\u0150\u0151\u0170\u0171\u4E00-\u9FFF\u3400-\u4DBF\u20000-\u2A6DF\u3000-\u303F\$]', '', cleaned_part) + # Remove all newline characters (single or multiple) + cleaned_part = re.sub(r"\n+", " ", cleaned_part) + + # Generate TTS and output to a file + output_filename = get_output_filename(state) + generate_response = send_generate_request( + cleaned_part, + voice_to_use, + language_code, + temperature, + repetition_penalty, + output_filename, + ) + audio_path = generate_response.get("data", {}).get("audio_path") + audio_files_all_paragraphs.append(audio_path) + + # Combine audio files across paragraphs + final_output_file = combine( + audio_files_all_paragraphs, params["output_folder_wav"], state + ) + else: + # Decode HTML entities first + cleaned_part = html.unescape(cleaned_string) + # Replace multiple instances of certain punctuation marks with a single instance + cleaned_part = re.sub(r"([!?.])\1+", r"\1", cleaned_part) + # Further clean to remove any other unwanted characters + cleaned_part = re.sub(r'[^a-zA-Z0-9\s\.,;:!?\-\'"\u0100-\u017F\u0400-\u04FFÀ-ÿ\u0150\u0151\u0170\u0171\u4E00-\u9FFF\u3400-\u4DBF\u20000-\u2A6DF\u3000-\u303F\$]', '', cleaned_part) + # Remove all newline characters (single or multiple) + cleaned_part = re.sub(r"\n+", " ", cleaned_part) + # Process the part and give it a non-character name if being used vai API or standalone. + if "character_menu" in state: + output_file = Path( + f'{params["output_folder_wav"]}/{state["character_menu"]}_{int(time.time())}.wav' + ) + else: + output_file = Path( + f'{params["output_folder_wav"]}/TTSOUT_{int(time.time())}.wav' + ) + output_file_str = output_file.as_posix() + output_file = get_output_filename(state) + generate_response = send_generate_request( + cleaned_part, + params["voice"], + language_code, + temperature, + repetition_penalty, + output_file_str, + ) + audio_path = generate_response.get("data", {}).get("audio_path") + final_output_file = audio_path + finally: + # Always release the lock, whether an exception occurs or not + process_lock.release() + else: + # The lock is already acquired + print( + f"[{params['branding']}Model] \033[91mWarning\033[0m Audio generation is already in progress. Please wait." + ) + return + + if generate_response.get("status") == "generate-success": + audio_path = generate_response.get("data", {}).get("audio_path") + if audio_path: + # Handle Gradio and playback + autoplay = "autoplay" if params["autoplay"] else "" + string = (f'') + if params["show_text"]: + string += reinsert_images(cleaned_string, img_info) + shared.processing_message = "*Is typing...*" + return string + else: + print(f"[{params['branding']}Server] \033[91mWarning\033[0m No audio path in the response.") + else: + print(f"[{params['branding']}Server] \033[91mWarning\033[0m Audio generation failed. Status:", generate_response.get("message"),) + +def get_output_filename(state): + if "character_menu" in state: + return Path( + f'{params["output_folder_wav"]}/{state["character_menu"]}_{str(uuid.uuid4())[:8]}.wav' + ).as_posix() + else: + return Path( + f'{params["output_folder_wav"]}/TTSOUT_{str(uuid.uuid4())[:8]}.wav' + ).as_posix() + + +############################################### +#### SEND GENERATION REQUEST TO TTS ENGINE #### +############################################### +def send_generate_request( + text, voice, language, temperature, repetition_penalty, output_file +): + url = f"{base_url}/api/generate" + payload = { + "text": text, + "voice": voice, + "language": language, + "temperature": temperature, + "repetition_penalty": repetition_penalty, + "output_file": output_file, + } + headers = {"Content-Type": "application/json"} + response = requests.post(url, json=payload, headers=headers) + return response.json() + + +################################ +#### SUBPORCESS TERMINATION #### +################################ +# Register the termination code to be executed at exit +atexit.register(lambda: process.terminate() if process.poll() is None else None) + + +###################### +#### GRADIO STUFF #### +###################### +def state_modifier(state): + if not params["activate"]: + return state + + state["stream"] = False + return state + + +def update_narrator_enabled(value): + if value == "Enabled": + params["narrator_enabled"] = True + elif value == "Disabled": + params["narrator_enabled"] = False + + +def update_non_quoted_text_is(value): + global non_quoted_text_is + if value == "Narrator": + non_quoted_text_is = False + elif value == "Char": + non_quoted_text_is = True + + +def input_modifier(string, state): + if not params["activate"]: + return string + + shared.processing_message = "*Is recording a voice message...*" + return string + + +def ui(): + with gr.Accordion(params["branding"] + " TTS (XTTSv2)"): + # Activate alltalk_tts, Enable autoplay, Show text + with gr.Row(): + activate = gr.Checkbox(value=params["activate"], label="Enable TTS") + autoplay = gr.Checkbox(value=params["autoplay"], label="Autoplay TTS") + show_text = gr.Checkbox(value=params["show_text"], label="Show Text") + + # Low vram enable, Deepspeed enable, Remove trailing dots + with gr.Row(): + low_vram = gr.Checkbox( + value=params["low_vram"], label="Enable Low VRAM Mode" + ) + low_vram_play = gr.HTML(visible=False) + deepspeed_checkbox = gr.Checkbox( + value=params["deepspeed_activate"], + label="Enable DeepSpeed", + visible=deepspeed_installed, + ) + deepspeed_checkbox_play = gr.HTML(visible=False) + remove_trailing_dots = gr.Checkbox( + value=params["remove_trailing_dots"], label='Remove trailing "."' + ) + + # TTS method, Character voice selection + with gr.Row(): + model_loader_choices = ["API TTS", "API Local", "XTTSv2 Local"] + if finetuned_model: + model_loader_choices.append("XTTSv2 FT") + tts_radio_buttons = gr.Radio( + choices=model_loader_choices, + label="TTS Method (Each method sounds slightly different)", + value=gr_modelchoice, # Set the default value + ) + tts_radio_buttons_play = gr.HTML(visible=False) + with gr.Row(): + available_voices = get_available_voices() + default_voice = params[ + "voice" + ] # Check if the default voice is in the list of available voices + + if default_voice not in available_voices: + default_voice = available_voices[ + 0 + ] # Choose the first available voice as the default + # Add allow_custom_value=True to the Dropdown + voice = gr.Dropdown( + available_voices, + label="Character Voice", + value=default_voice, + allow_custom_value=True, + ) + create_refresh_button( + voice, + lambda: None, + lambda: { + "choices": get_available_voices(), + "value": params["voice"], + }, + "refresh-button", + ) + + # Language, Narrator voice + with gr.Row(): + language = gr.Dropdown( + languages.keys(), label="Language", value=params["language"] + ) + with gr.Row(): + narrator_voice_gr = gr.Dropdown( + get_available_voices(), + label="Narrator Voice", + allow_custom_value=True, + value=params["narrator_voice"], + ) + create_refresh_button( + narrator_voice_gr, + lambda: None, + lambda: { + "choices": get_available_voices(), + "value": params["narrator_voice"], + }, + "refresh-button", + ) + + # Temperature, Repetition Penalty + with gr.Row(): + local_temperature_gr = gr.Slider( + minimum=0.05, + maximum=1, + step=0.05, + label="Temperature", + value=params["local_temperature"], + ) + local_repetition_penalty_gr = gr.Slider( + minimum=0.5, + maximum=20, + step=0.5, + label="Repetition Penalty", + value=params["local_repetition_penalty"], + ) + + # Narrator enable, Non quoted text, Explanation text + with gr.Row(): + with gr.Row(): + narrator_enabled_gr = gr.Radio( + choices={"Enabled": "true", "Disabled": "false"}, + label="Narrator", + value="Enabled" if params.get("narrator_enabled") else "Disabled", + ) + non_quoted_text_is_gr = gr.Radio( + choices={"Character": "true", "Narrator": "false"}, + label='Unmarked text NOT inside of * or " is', + value="Character" if non_quoted_text_is else "Narrator", + ) + explanation_text = gr.HTML( + f"

⚙️ Settings and Documentation Page⚙️
- Low VRAM Mode and Deepspeed take 15 seconds to be enabled or disabled.
- The DeepSpeed checkbox is only visible if DeepSpeed is present.

" + ) + + # Preview speech + with gr.Row(): + preview_text = gr.Text( + show_label=False, + placeholder="Preview text", + elem_id="silero_preview_text", + ) + preview_play = gr.Button("Preview") + preview_audio = gr.HTML(visible=False) + + with gr.Row(): + convert = gr.Button("Permanently replace audios with the message texts") + convert_cancel = gr.Button("Cancel", visible=False) + convert_confirm = gr.Button( + "Confirm (cannot be undone)", variant="stop", visible=False + ) + + # Convert history with confirmation + convert_arr = [convert_confirm, convert, convert_cancel] + convert.click( + lambda: [ + gr.update(visible=True), + gr.update(visible=False), + gr.update(visible=True), + ], + None, + convert_arr, + ) + convert_confirm.click( + lambda: [ + gr.update(visible=False), + gr.update(visible=True), + gr.update(visible=False), + ], + None, + convert_arr, + ).then(remove_tts_from_history, gradio("history"), gradio("history")).then( + chat.save_history, + gradio("history", "unique_id", "character_menu", "mode"), + None, + ).then( + chat.redraw_html, gradio(ui_chat.reload_arr), gradio("display") + ) + + convert_cancel.click( + lambda: [ + gr.update(visible=False), + gr.update(visible=True), + gr.update(visible=False), + ], + None, + convert_arr, + ) + + # Toggle message text in history + show_text.change(lambda x: params.update({"show_text": x}), show_text, None).then( + toggle_text_in_history, gradio("history"), gradio("history") + ).then( + chat.save_history, + gradio("history", "unique_id", "character_menu", "mode"), + None, + ).then( + chat.redraw_html, gradio(ui_chat.reload_arr), gradio("display") + ) + + # Event functions to update the parameters in the backend + activate.change(lambda x: params.update({"activate": x}), activate, None) + autoplay.change(lambda x: params.update({"autoplay": x}), autoplay, None) + low_vram.change(lambda x: params.update({"low_vram": x}), low_vram, None) + low_vram.change(lambda x: send_lowvram_request(x), low_vram, low_vram_play, None) + tts_radio_buttons.change( + send_reload_request, tts_radio_buttons, tts_radio_buttons_play, None + ) + deepspeed_checkbox.change( + send_deepspeed_request, deepspeed_checkbox, deepspeed_checkbox_play, None + ) + remove_trailing_dots.change( + lambda x: params.update({"remove_trailing_dots": x}), remove_trailing_dots, None + ) + voice.change(lambda x: params.update({"voice": x}), voice, None) + language.change(lambda x: params.update({"language": x}), language, None) + + # TSS Settings + local_temperature_gr.change( + lambda x: params.update({"local_temperature": x}), local_temperature_gr, None + ) + local_repetition_penalty_gr.change( + lambda x: params.update({"local_repetition_penalty": x}), + local_repetition_penalty_gr, + None, + ) + + # Narrator selection actions + narrator_enabled_gr.change(update_narrator_enabled, narrator_enabled_gr, None) + non_quoted_text_is_gr.change(update_non_quoted_text_is, non_quoted_text_is_gr, None) + narrator_voice_gr.change( + lambda x: params.update({"narrator_voice": x}), narrator_voice_gr, None + ) + + # Play preview + preview_text.submit(voice_preview, preview_text, preview_audio) + preview_play.click(voice_preview, preview_text, preview_audio) + + +########################################## +#### STANDALONE MODE LOOP TERMINATION #### +########################################## +## Loop to keep the script from exiting out if its being run as a standalone script and not part of text-generation-webui +if running_in_standalone: + while True: + try: + time.sleep(1) # Add a small delay to avoid high CPU usage + except KeyboardInterrupt: + break # Allow graceful exit on Ctrl+C From 2c7feb31e517ba76cd745d4d899d271599d62004 Mon Sep 17 00:00:00 2001 From: erew123 <35898566+erew123@users.noreply.github.com> Date: Mon, 1 Apr 2024 12:34:09 +0100 Subject: [PATCH 5/9] Add files via upload --- modeldownload.py | 193 +++++++++++++++++++++++++++++++++++++++++++++++ script.py | 2 +- 2 files changed, 194 insertions(+), 1 deletion(-) diff --git a/modeldownload.py b/modeldownload.py index e69de29..d2db7f0 100644 --- a/modeldownload.py +++ b/modeldownload.py @@ -0,0 +1,193 @@ +import os +import sys +from pathlib import Path +import requests +from requests.exceptions import ConnectionError +from tqdm import tqdm +import importlib.metadata as metadata +import json +from packaging import version +from datetime import datetime + +################################################################# +#### LOAD PARAMS FROM confignew.json - REQUIRED FOR BRANDING #### +################################################################# +# STARTUP VARIABLE - Create "this_dir" variable as the current script directory +this_dir = Path(__file__).parent.resolve() + +# load standard config file in and get settings +def load_config(file_path): + with open(file_path, "r") as config_file: + config = json.load(config_file) + return config + +config_file_path = this_dir / "confignew.json" +params = load_config(config_file_path) + +# Define the path to the modeldownload config file file +modeldownload_config_file_path = this_dir / "modeldownload.json" + +# Check if the JSON file exists +if modeldownload_config_file_path.exists(): + with open(modeldownload_config_file_path, "r") as config_file: + settings = json.load(config_file) + + # Extract settings from the loaded JSON + base_path = Path(settings.get("base_path", "")) + model_path = Path(settings.get("model_path", "")) + files_to_download = settings.get("files_to_download", {}) +else: + # Default settings if the JSON file doesn't exist or is empty + print(f"[{params['branding']}Startup] \033[91mWarning\033[0m modeldownload.json is missing so please re-download it and save it in the coquii_tts main folder.") + print(f"[{params['branding']}Startup] \033[91mWarning\033[0m API Local and XTTSv2 Local will error unless this is corrected.") + +# Read the version specifier from requirements_nvidia.txt +with open(this_dir / "system" / "requirements" / "requirements_standalone.txt", "r") as req_file: + requirements = req_file.readlines() + +tts_version_required = None +for req in requirements: + if req.startswith("TTS=="): + tts_version_required = req.strip().split("==")[1] + break + +if tts_version_required is None: + raise ValueError(f"[{params['branding']}Startup] \033[91mWarning\033[0m Could not find TTS version specifier in requirements file") + +def create_directory_if_not_exists(directory): + if not directory.exists(): + directory.mkdir(parents=True) + +def download_file(url, destination): + response = requests.get(url, stream=True) + total_size_in_bytes = int(response.headers.get("content-length", 0)) + block_size = 1024 # 1 Kibibyte + + progress_bar = tqdm(total=total_size_in_bytes, unit="iB", unit_scale=True) + + with open(destination, "wb") as file: + for data in response.iter_content(block_size): + progress_bar.update(len(data)) + file.write(data) + + progress_bar.close() + + +def check_tts_version(): + try: + tts_version = metadata.version("tts") + print(f"[{params['branding']}Startup] \033[92mCurrent TTS Version : \033[93m"+tts_version+"\033[0m") + + if version.parse(tts_version) < version.parse(tts_version_required): + print(f"[{params['branding']}Startup] \033[91mWarning\033[0m TTS version is too old. Please upgrade to version \033[93m"+tts_version_required+"\033[0m or later.\033[0m") + print(f"[{params['branding']}Startup] \033[91mWarning\033[0m At your terminal/command prompt \033[94mpip install --upgrade tts\033[0m") + else: + print(f"[{params['branding']}Startup] \033[92mCurrent TTS Version is :\033[93m Up to date\033[0m") + except metadata.PackageNotFoundError: + print(f"[{params['branding']}Startup] \033[91mWarning\033[0m TTS is not installed") + +def check_torch_version(): + import torch + pytorch_version = torch.__version__ + cuda_version = torch.version.cuda + major, minor, micro = sys.version_info[:3] + python_version = f"{major}.{minor}.{micro}" + print(f"[{params['branding']}Startup] \033[92mCurrent Python Version :\033[93m {python_version}\033[0m") + print(f"[{params['branding']}Startup] \033[92mCurrent PyTorch Version:\033[93m {pytorch_version}\033[0m") + if cuda_version is None: + print(f"[{params['branding']}Startup] \033[92mCurrent CUDA Version :\033[91m CUDA is not available\033[0m") + else: + print(f"[{params['branding']}Startup] \033[92mCurrent CUDA Version :\033[93m {cuda_version}\033[0m") + +def ordinal(n): + return "%d%s" % (n, "th" if 4 <= n % 100 <= 20 else {1: "st", 2: "nd", 3: "rd"}.get(n % 10, "th")) + +def format_datetime(iso_str): + # Parse the ISO 8601 string to datetime + dt = datetime.strptime(iso_str, "%Y-%m-%dT%H:%M:%SZ") + # Format the datetime into a more readable string + return dt.strftime(f"{ordinal(dt.day)} %B %Y at %H:%M") + +def fetch_latest_commit_sha_and_date(owner, repo): + url = f"https://api.github.com/repos/{owner}/{repo}/commits" + try: + response = requests.get(url) + if response.status_code == 200: + latest_commit = response.json()[0] + latest_commit_sha = latest_commit['sha'] + latest_commit_date = latest_commit['commit']['committer']['date'] + return latest_commit_sha, latest_commit_date + else: + print(f"[{params['branding']}Startup] \033[92m{params['branding']}Github updated :\033[91m Failed to fetch the latest commits due to an unexpected response from GitHub") + return None, None + except ConnectionError: + # This block is executed when a connection error occurs + print(f"[{params['branding']}Startup] \033[92m{params['branding']}Github updated :\033[91m Could not reach GitHub to check for updates\033[0m") + return None, None + +def read_or_initialize_sha(file_path): + 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(github_site, github_repository) + 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 + else: + # Handle the case where GitHub couldn't be reached + return None + +def update_sha_file(file_path, new_sha): + with open(file_path, 'w') as file: + json.dump({"last_known_commit_sha": new_sha}, file) + +# Check and create directories +if str(base_path) == "models": + create_directory_if_not_exists(this_dir / base_path / model_path) +else: + create_directory_if_not_exists(base_path / model_path) + print(f"[{params['branding']}Startup] \033[94mInfo\033[0m Custom path set in \033[93mmodeldownload.json\033[0m. Using the following settings:") + print(f"[{params['branding']}Startup] \033[94mInfo\033[0m Base folder Path:\033[93m",base_path,"\033[0m",) + print(f"[{params['branding']}Startup] \033[94mInfo\033[0m Model folder Path:\033[93m",model_path,"\033[0m",) + print(f"[{params['branding']}Startup] \033[94mInfo\033[0m Full Path:\033[93m",base_path / model_path,"\033[0m",) + +# Download files if they don't exist +print(f"[{params['branding']}Startup] \033[92mModel is available :\033[93m Checking\033[0m") +for filename, url in files_to_download.items(): + if str(base_path) == "models": + destination = this_dir / base_path / model_path / filename + else: + destination = Path(base_path) / model_path / filename + if not destination.exists(): + print(f"[{params['branding']}Startup] Downloading " + filename + "...") + download_file(url, destination) +print(f"[{params['branding']}Startup] \033[92mModel is available :\033[93m Checked\033[0m") + +github_site = "erew123" +github_repository = "alltalk_tts" + +check_torch_version() +check_tts_version() +# 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 (adjusted for handling both SHA and date) +last_known_commit_sha = read_or_initialize_sha(sha_file_path) # Assuming adjustment for date + +# Assuming you have fetched the latest commit SHA and date +latest_commit_sha, latest_commit_date = fetch_latest_commit_sha_and_date(github_site, github_repository) + +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(f"There's an update available for alltalk_tts.") + print(f"[{params['branding']}Startup] \033[92m{params['branding']}Github updated :\033[93m {formatted_date}\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(f"Your alltalk_tts software is up to date.") + print(f"[{params['branding']}Startup] \033[92m{params['branding']}Github updated :\033[93m {formatted_date}\033[0m") \ No newline at end of file diff --git a/script.py b/script.py index df1f4ce..9ba158d 100644 --- a/script.py +++ b/script.py @@ -201,7 +201,7 @@ else: if deepspeed_installed: print(f"[{params['branding']}Startup] \033[92mDeepSpeed version :\033[93m",deepspeed.__version__,"\033[0m") else: - print(f"[{params['branding']}Startup] \033[92mDeepSpeed version :\033[93m Not Detected\033[0m") + print(f"[{params['branding']}Startup] \033[92mDeepSpeed version :\033[91m Not Detected\033[0m") ######################## #### STARTUP CHECKS #### From 09977956f5c3235871958b0a1e0570efc5decf29 Mon Sep 17 00:00:00 2001 From: erew123 <35898566+erew123@users.noreply.github.com> Date: Mon, 1 Apr 2024 12:44:22 +0100 Subject: [PATCH 6/9] Tidy up model download message --- modeldownload.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modeldownload.py b/modeldownload.py index d2db7f0..f9baab4 100644 --- a/modeldownload.py +++ b/modeldownload.py @@ -163,7 +163,7 @@ for filename, url in files_to_download.items(): else: destination = Path(base_path) / model_path / filename if not destination.exists(): - print(f"[{params['branding']}Startup] Downloading " + filename + "...") + print(f"[{params['branding']}Startup] \033[92mModel Downloading file : \033[93m" + filename + "\033[0m") download_file(url, destination) print(f"[{params['branding']}Startup] \033[92mModel is available :\033[93m Checked\033[0m") From 856c0c1baa541a0ce8dc9aa97b395951492a7086 Mon Sep 17 00:00:00 2001 From: erew123 <35898566+erew123@users.noreply.github.com> Date: Mon, 1 Apr 2024 13:04:24 +0100 Subject: [PATCH 7/9] Add files via upload --- tts_server.py | 1247 +++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1247 insertions(+) diff --git a/tts_server.py b/tts_server.py index e69de29..fc9e0ac 100644 --- a/tts_server.py +++ b/tts_server.py @@ -0,0 +1,1247 @@ +import json +import time +import os +from pathlib import Path +import torch +import torchaudio +from TTS.tts.configs.xtts_config import XttsConfig +from TTS.tts.models.xtts import Xtts +import io +import wave +import logging +logging.disable(logging.WARNING) +########################## +#### Webserver Imports#### +########################## +from fastapi import ( + FastAPI, + Form, + Request, + Response, + Depends, + HTTPException, + Query, +) +from fastapi.responses import JSONResponse, HTMLResponse, RedirectResponse, FileResponse, StreamingResponse +from fastapi.middleware.cors import CORSMiddleware +from fastapi.templating import Jinja2Templates +from fastapi.staticfiles import StaticFiles +from contextlib import asynccontextmanager + +########################### +#### STARTUP VARIABLES #### +########################### +# STARTUP VARIABLE - Create "this_dir" variable as the current script directory +this_dir = Path(__file__).parent.resolve() +# STARTUP VARIABLE - Set "device" to cuda if exists, otherwise cpu +device = "cuda" if torch.cuda.is_available() else "cpu" +# STARTUP VARIABLE - Import languges file for Gradio to be able to display them in the interface +with open(this_dir / "system" / "config" / "languages.json", encoding="utf8") as f: + languages = json.load(f) +# Base setting for a possible FineTuned model existing and the loader being available +tts_method_xtts_ft = False + +################################################################# +#### LOAD PARAMS FROM confignew.json - REQUIRED FOR BRANDING #### +################################################################# +# Load config file and get settings +def load_config(file_path): + with open(file_path, "r") as configfile_path: + configfile_data = json.load(configfile_path) + return configfile_data + + +# Define the path to the confignew.json file +configfile_path = this_dir / "confignew.json" + +# Load confignew.json and assign it to a different variable (config_data) +params = load_config(configfile_path) +# check someone hasnt enabled lowvram on a system thats not cuda enabled +params["low_vram"] = "false" if not torch.cuda.is_available() else params["low_vram"] + +# Load values for temperature and repetition_penalty +temperature = params["local_temperature"] +repetition_penalty = params["local_repetition_penalty"] + +# Define the path to the JSON file +config_file_path = this_dir / "modeldownload.json" + +############################################# +#### LOAD PARAMS FROM MODELDOWNLOAD.JSON #### +############################################ +# This is used only in the instance that someone has changed their model path +# Define the path to the JSON file +modeldownload_config_file_path = this_dir / "modeldownload.json" + +# Check if the JSON file exists +if modeldownload_config_file_path.exists(): + with open(modeldownload_config_file_path, "r") as modeldownload_config_file: + modeldownload_settings = json.load(modeldownload_config_file) + + # Extract settings from the loaded JSON + modeldownload_base_path = Path(modeldownload_settings.get("base_path", "")) + modeldownload_model_path = Path(modeldownload_settings.get("model_path", "")) +else: + # Default settings if the JSON file doesn't exist or is empty + print( + f"[{params['branding']}Startup] \033[91mWarning\033[0m modeldownload.config is missing so please re-download it and save it in the alltalk_tts main folder." + ) + +################################################## +#### Check to see if a finetuned model exists #### +################################################## +# Set the path to the directory +trained_model_directory = this_dir / "models" / "trainedmodel" +# Check if the directory "trainedmodel" exists +finetuned_model = trained_model_directory.exists() +# If the directory exists, check for the existence of the required files +if finetuned_model: + required_files = ["model.pth", "config.json", "vocab.json"] + finetuned_model = all((trained_model_directory / file).exists() for file in required_files) + +######################## +#### STARTUP CHECKS #### +######################## +try: + from TTS.api import TTS + from TTS.utils.synthesizer import Synthesizer +except ModuleNotFoundError: + print( + f"[{params['branding']}Startup] \033[91mWarning\033[0m Could not find the TTS module. Make sure to install the requirements for the alltalk_tts extension.", + f"[{params['branding']}Startup] \033[91mWarning\033[0m Linux / Mac:\npip install -r extensions/alltalk_tts/requirements.txt\n", + f"[{params['branding']}Startup] \033[91mWarning\033[0m Windows:\npip install -r extensions\\alltalk_tts\\requirements.txt\n", + f"[{params['branding']}Startup] \033[91mWarning\033[0m If you used the one-click installer, paste the command above in the terminal window launched after running the cmd_ script. On Windows, that's cmd_windows.bat." + ) + raise + +# DEEPSPEED Import - Check for DeepSpeed and import it if it exists +deepspeed_available = False +try: + import deepspeed + deepspeed_available = True +except ImportError: + pass + +@asynccontextmanager +async def startup_shutdown(no_actual_value_it_demanded_something_be_here): + await setup() + yield + # Shutdown logic + + +# Create FastAPI app with lifespan +app = FastAPI(lifespan=startup_shutdown) +# Allow all origins, and set other CORS options +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], # Set this to the specific origins you want to allow + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + +##################################### +#### MODEL LOADING AND UNLOADING #### +##################################### +# MODEL LOADERS Picker For API TTS, API Local, XTTSv2 Local, XTTSv2 FT +async def setup(): + global device + # Set a timer to calculate load times + generate_start_time = time.time() # Record the start time of loading the model + # Start loading the correct model as set by "tts_method_api_tts", "tts_method_api_local" or "tts_method_xtts_local" being True/False + if params["tts_method_api_tts"]: + print( + f"[{params['branding']}Model] \033[94mAPI TTS Loading\033[0m {params['tts_model_name']} \033[94minto\033[93m", + device, + "\033[0m", + ) + model = await api_load_model() + elif params["tts_method_api_local"]: + print( + f"[{params['branding']}Model] \033[94mAPI Local Loading\033[0m {modeldownload_model_path} \033[94minto\033[93m", + device, + "\033[0m", + ) + model = await api_manual_load_model() + elif params["tts_method_xtts_local"]: + print( + f"[{params['branding']}Model] \033[94mXTTSv2 Local Loading\033[0m {modeldownload_model_path} \033[94minto\033[93m", + device, + "\033[0m", + ) + model = await xtts_manual_load_model() + elif tts_method_xtts_ft: + print( + f"[{params['branding']}Model] \033[94mXTTSv2 FT Loading\033[0m /models/fintuned/model.pth \033[94minto\033[93m", + device, + "\033[0m", + ) + model = await xtts_ft_manual_load_model() + # Create an end timer for calculating load times + generate_end_time = time.time() + # Calculate start time minus end time + generate_elapsed_time = generate_end_time - generate_start_time + # Print out the result of the load time + print(f"[{params['branding']}Model] \033[94mModel Loaded in \033[93m{generate_elapsed_time:.2f} seconds.\033[0m") + print(f"[{params['branding']}Model] Ready") + # Set "tts_model_loaded" to true + params["tts_model_loaded"] = True + # Set the output path for wav files + output_directory = this_dir / params["output_folder_wav_standalone"] + output_directory.mkdir(parents=True, exist_ok=True) + #Path(f'this_folder/outputs/').mkdir(parents=True, exist_ok=True) + + +# MODEL LOADER For "API TTS" +async def api_load_model(): + global model + model = TTS(params["tts_model_name"]).to(device) + return model + + +# MODEL LOADER For "API Local" +async def api_manual_load_model(): + global model + # check to see if a custom path has been set in modeldownload.json and use that path to load the model if so + if str(modeldownload_base_path) == "models": + model = TTS( + model_path=this_dir / "models" / modeldownload_model_path, + config_path=this_dir / "models" / modeldownload_model_path / "config.json", + ).to(device) + else: + print( + f"[{params['branding']}Model] \033[94mInfo\033[0m Loading your custom model set in \033[93mmodeldownload.json\033[0m:", + modeldownload_base_path / modeldownload_model_path, + ) + model = TTS( + model_path=modeldownload_base_path / modeldownload_model_path, + config_path=modeldownload_base_path / modeldownload_model_path / "config.json", + ).to(device) + print(f"[{params['branding']}Model] \033[94mCoqui Public Model License\033[0m") + print(f"[{params['branding']}Model] \033[94mhttps://coqui.ai/cpml.txt\033[0m") + return model + + +# MODEL LOADER For "XTTSv2 Local" +async def xtts_manual_load_model(): + global model + config = XttsConfig() + # check to see if a custom path has been set in modeldownload.json and use that path to load the model if so + if str(modeldownload_base_path) == "models": + config_path = this_dir / "models" / modeldownload_model_path / "config.json" + vocab_path_dir = this_dir / "models" / modeldownload_model_path / "vocab.json" + checkpoint_dir = this_dir / "models" / modeldownload_model_path + else: + print( + f"[{params['branding']}Model] \033[94mInfo\033[0m Loading your custom model set in \033[93mmodeldownload.json\033[0m:", + modeldownload_base_path / modeldownload_model_path, + ) + config_path = modeldownload_base_path / modeldownload_model_path / "config.json" + vocab_path_dir = modeldownload_base_path / modeldownload_model_path / "vocab.json" + checkpoint_dir = modeldownload_base_path / modeldownload_model_path + config.load_json(str(config_path)) + model = Xtts.init_from_config(config) + model.load_checkpoint( + config, + checkpoint_dir=str(checkpoint_dir), + vocab_path=str(vocab_path_dir), + use_deepspeed=params["deepspeed_activate"], + ) + model.to(device) + print(f"[{params['branding']}Model] \033[94mCoqui Public Model License\033[0m") + print(f"[{params['branding']}Model] \033[94mhttps://coqui.ai/cpml.txt\033[0m") + return model + +# MODEL LOADER For "XTTSv2 FT" +async def xtts_ft_manual_load_model(): + global model + config = XttsConfig() + config_path = this_dir / "models" / "trainedmodel" / "config.json" + vocab_path_dir = this_dir / "models" / "trainedmodel" / "vocab.json" + checkpoint_dir = this_dir / "models" / "trainedmodel" + config.load_json(str(config_path)) + model = Xtts.init_from_config(config) + model.load_checkpoint( + config, + checkpoint_dir=str(checkpoint_dir), + vocab_path=str(vocab_path_dir), + use_deepspeed=params["deepspeed_activate"], + ) + model.to(device) + print(f"[{params['branding']}Model] \033[94mCoqui Public Model License\033[0m") + print(f"[{params['branding']}Model] \033[94mhttps://coqui.ai/cpml.txt\033[0m") + return model + +# MODEL UNLOADER +async def unload_model(model): + print(f"[{params['branding']}Model] \033[94mUnloading model \033[0m") + del model + if torch.cuda.is_available(): + torch.cuda.empty_cache() + params["tts_model_loaded"] = False + return None + + +# MODEL - Swap model based on Gradio selection API TTS, API Local, XTTSv2 Local +async def handle_tts_method_change(tts_method): + global model + global tts_method_xtts_ft + # Update the params dictionary based on the selected radio button + print( + f"[{params['branding']}Model] \033[94mChanging model \033[92m(Please wait 15 seconds)\033[0m" + ) + # Set other parameters to False + if tts_method == "API TTS": + params["tts_method_api_local"] = False + params["tts_method_xtts_local"] = False + params["tts_method_api_tts"] = True + params["deepspeed_activate"] = False + tts_method_xtts_ft = False + elif tts_method == "API Local": + params["tts_method_api_tts"] = False + params["tts_method_xtts_local"] = False + params["tts_method_api_local"] = True + params["deepspeed_activate"] = False + tts_method_xtts_ft = False + elif tts_method == "XTTSv2 Local": + params["tts_method_api_tts"] = False + params["tts_method_api_local"] = False + params["tts_method_xtts_local"] = True + tts_method_xtts_ft = False + elif tts_method == "XTTSv2 FT": + tts_method_xtts_ft = True + params["tts_method_api_tts"] = False + params["tts_method_api_local"] = False + params["tts_method_xtts_local"] = False + + # Unload the current model + model = await unload_model(model) + + # Load the correct model based on the updated params + await setup() + + +# MODEL WEBSERVER- API Swap Between Models +@app.route("/api/reload", methods=["POST"]) +async def reload(request: Request): + tts_method = request.query_params.get("tts_method") + if tts_method not in ["API TTS", "API Local", "XTTSv2 Local", "XTTSv2 FT"]: + return {"status": "error", "message": "Invalid TTS method specified"} + await handle_tts_method_change(tts_method) + return Response( + content=json.dumps({"status": "model-success"}), media_type="application/json" + ) + + +################## +#### LOW VRAM #### +################## +# LOW VRAM - MODEL MOVER VRAM(cuda)<>System RAM(cpu) for Low VRAM setting +async def switch_device(): + global model, device + # Check if CUDA is available before performing GPU-related operations + if torch.cuda.is_available(): + if device == "cuda": + device = "cpu" + model.to(device) + torch.cuda.empty_cache() + else: + device == "cpu" + device = "cuda" + model.to(device) + + +@app.post("/api/lowvramsetting") +async def set_low_vram(request: Request, new_low_vram_value: bool): + global device + try: + if new_low_vram_value is None: + raise ValueError("Missing 'low_vram' parameter") + + if params["low_vram"] == new_low_vram_value: + return Response( + content=json.dumps( + { + "status": "success", + "message": f"[{params['branding']}Model] LowVRAM is already {'enabled' if new_low_vram_value else 'disabled'}.", + } + ) + ) + params["low_vram"] = new_low_vram_value + if params["low_vram"]: + await unload_model(model) + if torch.cuda.is_available(): + device = "cpu" + print( + f"[{params['branding']}Model] \033[94mChanging model \033[92m(Please wait 15 seconds)\033[0m" + ) + print( + f"[{params['branding']}Model] \033[94mLowVRAM Enabled.\033[0m Model will move between \033[93mVRAM(cuda) <> System RAM(cpu)\033[0m" + ) + await setup() + else: + # Handle the case where CUDA is not available + print( + f"[{params['branding']}Model] \033[91mError:\033[0m Nvidia CUDA is not available on this system. Unable to use LowVRAM mode." + ) + params["low_vram"] = False + else: + await unload_model(model) + if torch.cuda.is_available(): + device = "cuda" + print( + f"[{params['branding']}Model] \033[94mChanging model \033[92m(Please wait 15 seconds)\033[0m" + ) + print( + f"[{params['branding']}Model] \033[94mLowVRAM Disabled.\033[0m Model will stay in \033[93mVRAM(cuda)\033[0m" + ) + await setup() + else: + # Handle the case where CUDA is not available + print( + f"[{params['branding']}Model] \033[91mError:\033[0m Nvidia CUDA is not available on this system. Unable to use LowVRAM mode." + ) + params["low_vram"] = False + return Response(content=json.dumps({"status": "lowvram-success"})) + except Exception as e: + return Response(content=json.dumps({"status": "error", "message": str(e)})) + + +################### +#### DeepSpeed #### +################### +# DEEPSPEED - Reload the model when DeepSpeed checkbox is enabled/disabled +async def handle_deepspeed_change(value): + global model + if value: + # DeepSpeed enabled + print(f"[{params['branding']}Model] \033[93mDeepSpeed Activating\033[0m") + + print( + f"[{params['branding']}Model] \033[94mChanging model \033[92m(DeepSpeed can take 30 seconds to activate)\033[0m" + ) + print( + f"[{params['branding']}Model] \033[91mInformation\033[0m If you have not set CUDA_HOME path, DeepSpeed may fail to load/activate" + ) + print( + f"[{params['branding']}Model] \033[91mInformation\033[0m DeepSpeed needs to find nvcc from the CUDA Toolkit. Please check your CUDA_HOME path is" + ) + print( + f"[{params['branding']}Model] \033[91mInformation\033[0m pointing to the correct location and use 'set CUDA_HOME=putyoutpathhere' (Windows) or" + ) + print( + f"[{params['branding']}Model] \033[91mInformation\033[0m 'export CUDA_HOME=putyoutpathhere' (Linux) within your Python Environment" + ) + model = await unload_model(model) + params["tts_method_api_tts"] = False + params["tts_method_api_local"] = False + params["tts_method_xtts_local"] = True + params["deepspeed_activate"] = True + await setup() + else: + # DeepSpeed disabled + print(f"[{params['branding']}Model] \033[93mDeepSpeed De-Activating\033[0m") + print( + f"[{params['branding']}Model] \033[94mChanging model \033[92m(Please wait 15 seconds)\033[0m" + ) + params["deepspeed_activate"] = False + model = await unload_model(model) + await setup() + + return value # Return new checkbox value + + +# DEEPSPEED WEBSERVER- API Enable/Disable DeepSpeed +@app.post("/api/deepspeed") +async def deepspeed(request: Request, new_deepspeed_value: bool): + try: + if new_deepspeed_value is None: + raise ValueError("Missing 'deepspeed' parameter") + if params["deepspeed_activate"] == new_deepspeed_value: + return Response( + content=json.dumps( + { + "status": "success", + "message": f"DeepSpeed is already {'enabled' if new_deepspeed_value else 'disabled'}.", + } + ) + ) + params["deepspeed_activate"] = new_deepspeed_value + await handle_deepspeed_change(params["deepspeed_activate"]) + return Response(content=json.dumps({"status": "deepspeed-success"})) + except Exception as e: + return Response(content=json.dumps({"status": "error", "message": str(e)})) + + +######################## +#### TTS GENERATION #### +######################## + +# TTS VOICE GENERATION METHODS (called from voice_preview and output_modifer) +async def generate_audio(text, voice, language, temperature, repetition_penalty, output_file, streaming=False): + # Get the async generator from the internal function + response = generate_audio_internal(text, voice, language, temperature, repetition_penalty, output_file, streaming) + # If streaming, then return the generator as-is, otherwise just exhaust it and return + if streaming: + return response + async for _ in response: + pass + +async def generate_audio_internal(text, voice, language, temperature, repetition_penalty, output_file, streaming): + global model + if params["low_vram"] and device == "cpu": + await switch_device() + generate_start_time = time.time() # Record the start time of generating TTS + + # XTTSv2 LOCAL & Xttsv2 FT Method + if params["tts_method_xtts_local"] or tts_method_xtts_ft: + print(f"[{params['branding']}TTSGen] {text}") + gpt_cond_latent, speaker_embedding = model.get_conditioning_latents( + audio_path=[f"{this_dir}/voices/{voice}"], + gpt_cond_len=model.config.gpt_cond_len, + max_ref_length=model.config.max_ref_len, + sound_norm_refs=model.config.sound_norm_refs, + ) + + # Common arguments for both functions + common_args = { + "text": text, + "language": language, + "gpt_cond_latent": gpt_cond_latent, + "speaker_embedding": speaker_embedding, + "temperature": float(temperature), + "length_penalty": float(model.config.length_penalty), + "repetition_penalty": float(repetition_penalty), + "top_k": int(model.config.top_k), + "top_p": float(model.config.top_p), + "enable_text_splitting": True + } + + # Determine the correct inference function and add streaming specific argument if needed + inference_func = model.inference_stream if streaming else model.inference + if streaming: + common_args["stream_chunk_size"] = 20 + + # Call the appropriate function + output = inference_func(**common_args) + + # Process the output based on streaming or non-streaming + if streaming: + # Streaming-specific operations + file_chunks = [] + wav_buf = io.BytesIO() + with wave.open(wav_buf, "wb") as vfout: + vfout.setnchannels(1) + vfout.setsampwidth(2) + vfout.setframerate(24000) + vfout.writeframes(b"") + wav_buf.seek(0) + yield wav_buf.read() + + for i, chunk in enumerate(output): + file_chunks.append(chunk) + if isinstance(chunk, list): + chunk = torch.cat(chunk, dim=0) + chunk = chunk.clone().detach().cpu().numpy() + chunk = chunk[None, : int(chunk.shape[0])] + chunk = np.clip(chunk, -1, 1) + chunk = (chunk * 32767).astype(np.int16) + yield chunk.tobytes() + else: + # Non-streaming-specific operation + torchaudio.save(output_file, torch.tensor(output["wav"]).unsqueeze(0), 24000) + + + # API LOCAL Methods + elif params["tts_method_api_local"]: + # Streaming only allowed for XTTSv2 local + if streaming: + raise ValueError("Streaming is only supported in XTTSv2 local") + + # Set the correct output path (different from the if statement) + print(f"[{params['branding']}TTSGen] Using API Local") + model.tts_to_file( + text=text, + file_path=output_file, + speaker_wav=[f"{this_dir}/voices/{voice}"], + language=language, + temperature=temperature, + length_penalty=model.config.length_penalty, + repetition_penalty=repetition_penalty, + top_k=model.config.top_k, + top_p=model.config.top_p, + ) + + # API TTS + elif params["tts_method_api_tts"]: + # Streaming only allowed for XTTSv2 local + if streaming: + raise ValueError("Streaming is only supported in XTTSv2 local") + + print(f"[{params['branding']}TTSGen] Using API TTS") + model.tts_to_file( + text=text, + file_path=output_file, + speaker_wav=[f"{this_dir}/voices/{voice}"], + language=language, + ) + + # Print Generation time and settings + generate_end_time = time.time() # Record the end time to generate TTS + generate_elapsed_time = generate_end_time - generate_start_time + print( + f"[{params['branding']}TTSGen] \033[93m{generate_elapsed_time:.2f} seconds. \033[94mLowVRAM: \033[33m{params['low_vram']} \033[94mDeepSpeed: \033[33m{params['deepspeed_activate']}\033[0m" + ) + # Move model back to cpu system ram if needed. + if params["low_vram"] and device == "cuda": + await switch_device() + return + + +# TTS VOICE GENERATION METHODS - generate TTS API +@app.route("/api/generate", methods=["POST"]) +async def generate(request: Request): + try: + # Get parameters from JSON body + data = await request.json() + text = data["text"] + voice = data["voice"] + language = data["language"] + temperature = data["temperature"] + repetition_penalty = data["repetition_penalty"] + output_file = data["output_file"] + streaming = False + # Generation logic + response = await generate_audio(text, voice, language, temperature, repetition_penalty, output_file, streaming) + if streaming: + return StreamingResponse(response, media_type="audio/wav") + return JSONResponse( + content={"status": "generate-success", "data": {"audio_path": output_file}} + ) + except Exception as e: + return JSONResponse(content={"status": "error", "message": str(e)}) + + +################################################### +#### POPULATE FILES LIST FROM VOICES DIRECTORY #### +################################################### +# List files in the "voices" directory +def list_files(directory): + files = [ + f + for f in os.listdir(directory) + if os.path.isfile(os.path.join(directory, f)) and f.endswith(".wav") + ] + return files + +############################# +#### JSON CONFIG UPDATER #### +############################# + +# Create an instance of Jinja2Templates for rendering HTML templates +templates = Jinja2Templates(directory=this_dir / "system") + +# Create a dependency to get the current JSON data +def get_json_data(): + with open(this_dir / "confignew.json", "r") as json_file: + data = json.load(json_file) + return data + + +# Define an endpoint function +@app.get("/settings") +async def get_settings(request: Request): + wav_files = list_files(this_dir / "voices") + # Render the template with the current JSON data and list of WAV files + return templates.TemplateResponse( + "/at_admin/at_settings.html", + { + "request": request, + "data": get_json_data(), + "modeldownload_model_path": modeldownload_model_path, + "wav_files": wav_files, + }, + ) + +# Define an endpoint to serve static files +app.mount("/static", StaticFiles(directory=str(this_dir / "system")), name="static") + +@app.post("/update-settings") +async def update_settings( + request: Request, + activate: bool = Form(...), + autoplay: bool = Form(...), + deepspeed_activate: bool = Form(...), + delete_output_wavs: str = Form(...), + ip_address: str = Form(...), + language: str = Form(...), + local_temperature: str = Form(...), + local_repetition_penalty: str = Form(...), + low_vram: bool = Form(...), + tts_model_loaded: bool = Form(...), + tts_model_name: str = Form(...), + narrator_enabled: bool = Form(...), + narrator_voice: str = Form(...), + output_folder_wav: str = Form(...), + port_number: str = Form(...), + remove_trailing_dots: bool = Form(...), + show_text: bool = Form(...), + tts_method: str = Form(...), + voice: str = Form(...), + data: dict = Depends(get_json_data), +): + # Update the settings based on the form values + data["activate"] = activate + data["autoplay"] = autoplay + data["deepspeed_activate"] = deepspeed_activate + data["delete_output_wavs"] = delete_output_wavs + data["ip_address"] = ip_address + data["language"] = language + data["local_temperature"] = local_temperature + data["local_repetition_penalty"] = local_repetition_penalty + data["low_vram"] = low_vram + data["tts_model_loaded"] = tts_model_loaded + data["tts_model_name"] = tts_model_name + data["narrator_enabled"] = narrator_enabled + data["narrator_voice"] = narrator_voice + data["output_folder_wav"] = output_folder_wav + data["port_number"] = port_number + data["remove_trailing_dots"] = remove_trailing_dots + data["show_text"] = show_text + data["tts_method_api_local"] = tts_method == "api_local" + data["tts_method_api_tts"] = tts_method == "api_tts" + data["tts_method_xtts_local"] = tts_method == "xtts_local" + data["voice"] = voice + + # Save the updated settings back to the JSON file + with open(this_dir / "confignew.json", "w") as json_file: + json.dump(data, json_file) + + # Redirect to the settings page to display the updated settings + return RedirectResponse(url="/settings", status_code=303) + + +################################## +#### SETTINGS PAGE DEMO VOICE #### +################################## + +@app.get("/tts-demo-request", response_class=StreamingResponse) +async def tts_demo_request_streaming(text: str, voice: str, language: str, output_file: str): + try: + output_file_path = this_dir / "outputs" / output_file + stream = await generate_audio(text, voice, language, temperature, repetition_penalty, output_file_path, streaming=True) + return StreamingResponse(stream, media_type="audio/wav") + except Exception as e: + print(f"An error occurred: {e}") + return JSONResponse(content={"error": "An error occurred"}, status_code=500) + +@app.post("/tts-demo-request", response_class=JSONResponse) +async def tts_demo_request(request: Request, text: str = Form(...), voice: str = Form(...), language: str = Form(...), output_file: str = Form(...)): + try: + output_file_path = this_dir / "outputs" / output_file + await generate_audio(text, voice, language, temperature, repetition_penalty, output_file_path, streaming=False) + return JSONResponse(content={"output_file_path": str(output_file)}, status_code=200) + except Exception as e: + print(f"An error occurred: {e}") + return JSONResponse(content={"error": "An error occurred"}, status_code=500) + + +##################### +#### Audio feeds #### +##################### + +# Gives web access to the output files +@app.get("/audio/{filename}") +async def get_audio(filename: str): + audio_path = this_dir / "outputs" / filename + return FileResponse(audio_path) + +@app.get("/audiocache/{filename}") +async def get_audio(filename: str): + audio_path = Path("outputs") / filename + if not audio_path.is_file(): + raise HTTPException(status_code=404, detail="File not found") + + response = FileResponse( + path=audio_path, + media_type='audio/wav', + filename=filename + ) + # Set caching headers + response.headers["Cache-Control"] = "public, max-age=604800" # Cache for one week + response.headers["ETag"] = str(audio_path.stat().st_mtime) # Use the file's last modified time as a simple ETag + + return response + +######################### +#### VOICES LIST API #### +######################### +# Define the new endpoint +@app.get("/api/voices") +async def get_voices(): + wav_files = list_files(this_dir / "voices") + return {"voices": wav_files} + +########################### +#### PREVIEW VOICE API #### +########################### +@app.post("/api/previewvoice/", response_class=JSONResponse) +async def preview_voice(request: Request, voice: str = Form(...)): + try: + # Hardcoded settings + language = "en" + output_file_name = "api_preview_voice" + + # Clean the voice filename for inclusion in the text + clean_voice_filename = re.sub(r'\.wav$', '', voice.replace(' ', '_')) + clean_voice_filename = re.sub(r'[^a-zA-Z0-9]', ' ', clean_voice_filename) + + # Generate the audio + text = f"Hello, this is a preview of voice {clean_voice_filename}." + + # Generate the audio + output_file_path = this_dir / "outputs" / f"{output_file_name}.wav" + await generate_audio(text, voice, language, temperature, repetition_penalty, output_file_path, streaming=False) + + # Generate the URL + output_file_url = f'http://{params["ip_address"]}:{params["port_number"]}/audio/{output_file_name}.wav' + + # Return the response with both local file path and URL + return JSONResponse( + content={ + "status": "generate-success", + "output_file_path": str(output_file_path), + "output_file_url": str(output_file_url), + }, + status_code=200, + ) + except Exception as e: + print(f"An error occurred: {e}") + return JSONResponse(content={"error": "An error occurred"}, status_code=500) + +######################## +#### GENERATION API #### +######################## +import html +import re +import uuid +import numpy as np +import soundfile as sf +import sys +import hashlib + +############################## +#### Streaming Generation #### +############################## + +@app.get("/api/tts-generate-streaming", response_class=StreamingResponse) +async def tts_generate_streaming(text: str, voice: str, language: str, output_file: str): + try: + output_file_path = this_dir / "outputs" / output_file + stream = await generate_audio(text, voice, language, temperature, repetition_penalty, output_file_path, streaming=True) + return StreamingResponse(stream, media_type="audio/wav") + except Exception as e: + print(f"An error occurred: {e}") + return JSONResponse(content={"error": "An error occurred"}, status_code=500) + +@app.post("/api/tts-generate-streaming", response_class=JSONResponse) +async def tts_generate_streaming(request: Request, text: str = Form(...), voice: str = Form(...), language: str = Form(...), output_file: str = Form(...)): + try: + output_file_path = this_dir / "outputs" / output_file + await generate_audio(text, voice, language, temperature, repetition_penalty, output_file_path, streaming=False) + return JSONResponse(content={"output_file_path": str(output_file)}, status_code=200) + except Exception as e: + print(f"An error occurred: {e}") + return JSONResponse(content={"error": "An error occurred"}, status_code=500) + +############################## +#### Standard Generation #### +############################## + +# Check for PortAudio library on Linux +try: + import sounddevice as sd + sounddevice_installed=True +except OSError: + print(f"[{params['branding']}Startup] \033[91mInfo\033[0m PortAudio library not found. If you wish to play TTS in standalone mode through the API suite") + print(f"[{params['branding']}Startup] \033[91mInfo\033[0m please install PortAudio. This will not affect any other features or use of Alltalk.") + print(f"[{params['branding']}Startup] \033[91mInfo\033[0m If you don't know what the API suite is, then this message is nothing to worry about.") + sounddevice_installed=False + if sys.platform.startswith('linux'): + print(f"[{params['branding']}Startup] \033[91mInfo\033[0m On Linux, you can use the following command to install PortAudio:") + print(f"[{params['branding']}Startup] \033[91mInfo\033[0m sudo apt-get install portaudio19-dev") + +from typing import Union, Dict, List +from pydantic import BaseModel, ValidationError, Field + +def play_audio(file_path, volume): + data, fs = sf.read(file_path) + sd.play(volume * data, fs) + sd.wait() + +class Request(BaseModel): + # Define the structure of the 'Request' class if needed + pass + +class JSONInput(BaseModel): + text_input: str = Field(..., max_length=2000, description="text_input needs to be 2000 characters or less.") + text_filtering: str = Field(..., pattern="^(none|standard|html)$", description="text_filtering needs to be 'none', 'standard' or 'html'.") + character_voice_gen: str = Field(..., pattern="^.*\.wav$", description="character_voice_gen needs to be the name of a wav file e.g. mysample.wav.") + narrator_enabled: bool = Field(..., description="narrator_enabled needs to be true or false.") + narrator_voice_gen: str = Field(..., pattern="^.*\.wav$", description="narrator_voice_gen needs to be the name of a wav file e.g. mysample.wav.") + text_not_inside: str = Field(..., pattern="^(character|narrator)$", description="text_not_inside needs to be 'character' or 'narrator'.") + language: str = Field(..., pattern="^(ar|zh-cn|cs|nl|en|fr|de|hu|it|ja|ko|pl|pt|ru|es|tr)$", description="language needs to be one of the following ar|zh-cn|cs|nl|en|fr|de|hu|it|ja|ko|pl|pt|ru|es|tr.") + output_file_name: str = Field(..., pattern="^[a-zA-Z0-9_]+$", description="output_file_name needs to be the name without any special characters or file extension e.g. 'filename'") + output_file_timestamp: bool = Field(..., description="output_file_timestamp needs to be true or false.") + autoplay: bool = Field(..., description="autoplay needs to be a true or false value.") + autoplay_volume: float = Field(..., ge=0.1, le=1.0, description="autoplay_volume needs to be from 0.1 to 1.0") + + @classmethod + def validate_autoplay_volume(cls, value): + if not (0.1 <= value <= 1.0): + raise ValueError("Autoplay volume must be between 0.1 and 1.0") + return value + + +class TTSGenerator: + @staticmethod + def validate_json_input(json_data: Union[Dict, str]) -> Union[None, str]: + try: + if isinstance(json_data, str): + json_data = json.loads(json_data) + JSONInput(**json_data) + return None # JSON is valid + except ValidationError as e: + return str(e) + +def process_text(text): + # Normalize HTML encoded quotes + text = html.unescape(text) + # Replace ellipsis with a single dot + text = re.sub(r'\.{3,}', '.', text) + # Pattern to identify combined narrator and character speech + combined_pattern = r'(\*[^*"]+\*|"[^"*]+")' + # List to hold parts of speech along with their type + ordered_parts = [] + # Track the start of the next segment + start = 0 + # Find all matches + for match in re.finditer(combined_pattern, text): + # Add the text before the match, if any, as ambiguous + if start < match.start(): + ambiguous_text = text[start:match.start()].strip() + if ambiguous_text: + ordered_parts.append(('ambiguous', ambiguous_text)) + # Add the matched part as either narrator or character + matched_text = match.group(0) + if matched_text.startswith('*') and matched_text.endswith('*'): + ordered_parts.append(('narrator', matched_text.strip('*').strip())) + elif matched_text.startswith('"') and matched_text.endswith('"'): + ordered_parts.append(('character', matched_text.strip('"').strip())) + else: + # In case of mixed or improperly formatted parts + if '*' in matched_text: + ordered_parts.append(('narrator', matched_text.strip('*').strip('"'))) + else: + ordered_parts.append(('character', matched_text.strip('"').strip('*'))) + # Update the start of the next segment + start = match.end() + # Add any remaining text after the last match as ambiguous + if start < len(text): + ambiguous_text = text[start:].strip() + if ambiguous_text: + ordered_parts.append(('ambiguous', ambiguous_text)) + return ordered_parts + +def standard_filtering(text_input): + text_output = (text_input + .replace("***", "") + .replace("**", "") + .replace("*", "") + .replace("\n\n", "\n") + .replace("'", "'") + ) + return text_output + +def combine(output_file_timestamp, output_file_name, audio_files): + audio = np.array([]) + sample_rate = None + try: + for audio_file in audio_files: + audio_data, current_sample_rate = sf.read(audio_file) + if audio.size == 0: + audio = audio_data + sample_rate = current_sample_rate + elif sample_rate == current_sample_rate: + audio = np.concatenate((audio, audio_data)) + else: + raise ValueError("Sample rates of input files are not consistent.") + except Exception as e: + # Handle exceptions (e.g., file not found, invalid audio format) + return None, None + if output_file_timestamp: + timestamp = int(time.time()) + output_file_path = os.path.join(this_dir / "outputs" / f'{output_file_name}_{timestamp}_combined.wav') + output_file_url = f'http://{params["ip_address"]}:{params["port_number"]}/audio/{output_file_name}_{timestamp}_combined.wav' + output_cache_url = f'http://{params["ip_address"]}:{params["port_number"]}/audiocache/{output_file_name}_{timestamp}_combined.wav' + else: + output_file_path = os.path.join(this_dir / "outputs" / f'{output_file_name}_combined.wav') + output_file_url = f'http://{params["ip_address"]}:{params["port_number"]}/audio/{output_file_name}_combined.wav' + output_cache_url = f'http://{params["ip_address"]}:{params["port_number"]}/audiocache/{output_file_name}_combined.wav' + try: + sf.write(output_file_path, audio, samplerate=sample_rate) + # Clean up unnecessary files + for audio_file in audio_files: + os.remove(audio_file) + except Exception as e: + # Handle exceptions (e.g., failed to write output file) + return None, None + return output_file_path, output_file_url, output_cache_url + +# Generation API (separate from text-generation-webui) +@app.post("/api/tts-generate", response_class=JSONResponse) +async def tts_generate( + text_input: str = Form(...), + text_filtering: str = Form(...), + character_voice_gen: str = Form(...), + narrator_enabled: bool = Form(...), + narrator_voice_gen: str = Form(...), + text_not_inside: str = Form(...), + language: str = Form(...), + output_file_name: str = Form(...), + output_file_timestamp: bool = Form(...), + autoplay: bool = Form(...), + autoplay_volume: float = Form(...), + streaming: bool = Form(False), +): + try: + json_input_data = { + "text_input": text_input, + "text_filtering": text_filtering, + "character_voice_gen": character_voice_gen, + "narrator_enabled": narrator_enabled, + "narrator_voice_gen": narrator_voice_gen, + "text_not_inside": text_not_inside, + "language": language, + "output_file_name": output_file_name, + "output_file_timestamp": output_file_timestamp, + "autoplay": autoplay, + "autoplay_volume": autoplay_volume, + "streaming": streaming, + } + JSONresult = TTSGenerator.validate_json_input(json_input_data) + if JSONresult is None: + pass + else: + return JSONResponse(content={"error": JSONresult}, status_code=400) + if narrator_enabled: + processed_parts = process_text(text_input) + audio_files_all_paragraphs = [] + for part_type, part in processed_parts: + # Skip parts that are too short + if len(part.strip()) <= 3: + continue + # Determine the voice to use based on the part type + if part_type == 'narrator': + voice_to_use = narrator_voice_gen + print(f"[{params['branding']}TTSGen] \033[92mNarrator\033[0m") # Green + elif part_type == 'character': + voice_to_use = character_voice_gen + print(f"[{params['branding']}TTSGen] \033[36mCharacter\033[0m") # Yellow + else: + # Handle ambiguous parts based on user preference + voice_to_use = character_voice_gen if text_not_inside == "character" else narrator_voice_gen + voice_description = "\033[36mCharacter (Text-not-inside)\033[0m" if text_not_inside == "character" else "\033[92mNarrator (Text-not-inside)\033[0m" + print(f"[{params['branding']}TTSGen] {voice_description}") + # Replace multiple exclamation marks, question marks, or other punctuation with a single instance + cleaned_part = re.sub(r'([!?.])\1+', r'\1', part) + # Further clean to remove any other unwanted characters + cleaned_part = re.sub(r'[^a-zA-Z0-9\s\.,;:!?\-\'"\u0400-\u04FFÀ-ÿ\u0150\u0151\u0170\u0171]\$', '', cleaned_part) + # Remove all newline characters (single or multiple) + cleaned_part = re.sub(r'\n+', ' ', cleaned_part) + output_file = this_dir / "outputs" / f"{output_file_name}_{uuid.uuid4()}_{int(time.time())}.wav" + output_file_str = output_file.as_posix() + response = await generate_audio(cleaned_part, voice_to_use, language,temperature, repetition_penalty, output_file_str, streaming) + audio_path = output_file_str + audio_files_all_paragraphs.append(audio_path) + # Combine audio files across paragraphs + output_file_path, output_file_url, output_cache_url = combine(output_file_timestamp, output_file_name, audio_files_all_paragraphs) + else: + if output_file_timestamp: + timestamp = int(time.time()) + # Generate a standard UUID + original_uuid = uuid.uuid4() + # Hash the UUID using SHA-256 + hash_object = hashlib.sha256(str(original_uuid).encode()) + hashed_uuid = hash_object.hexdigest() + # Truncate to the desired length, for example, 16 characters + short_uuid = hashed_uuid[:5] + output_file_path = this_dir / "outputs" / f"{output_file_name}_{timestamp}{short_uuid}.wav" + output_file_url = f'http://{params["ip_address"]}:{params["port_number"]}/audio/{output_file_name}_{timestamp}{short_uuid}.wav' + output_cache_url = f'http://{params["ip_address"]}:{params["port_number"]}/audiocache/{output_file_name}_{timestamp}{short_uuid}.wav' + else: + output_file_path = this_dir / "outputs" / f"{output_file_name}.wav" + output_file_url = f'http://{params["ip_address"]}:{params["port_number"]}/audio/{output_file_name}.wav' + output_cache_url = f'http://{params["ip_address"]}:{params["port_number"]}/audiocache/{output_file_name}.wav' + if text_filtering == "html": + cleaned_string = html.unescape(standard_filtering(text_input)) + cleaned_string = re.sub(r'([!?.])\1+', r'\1', text_input) + # Further clean to remove any other unwanted characters + cleaned_string = re.sub(r'[^a-zA-Z0-9\s\.,;:!?\-\'"\u0400-\u04FFÀ-ÿ\u0150\u0151\u0170\u0171]\$', '', cleaned_string) + # Remove all newline characters (single or multiple) + cleaned_string = re.sub(r'\n+', ' ', cleaned_string) + elif text_filtering == "standard": + cleaned_string = re.sub(r'([!?.])\1+', r'\1', text_input) + # Further clean to remove any other unwanted characters + cleaned_string = re.sub(r'[^a-zA-Z0-9\s\.,;:!?\-\'"\u0400-\u04FFÀ-ÿ\u0150\u0151\u0170\u0171]\$', '', cleaned_string) + # Remove all newline characters (single or multiple) + cleaned_string = re.sub(r'\n+', ' ', cleaned_string) + else: + cleaned_string = text_input + response = await generate_audio(cleaned_string, character_voice_gen, language, temperature, repetition_penalty, output_file_path, streaming) + if sounddevice_installed == False or streaming == True: + autoplay = False + if autoplay: + play_audio(output_file_path, autoplay_volume) + if streaming: + return StreamingResponse(response, media_type="audio/wav") + return JSONResponse(content={"status": "generate-success", "output_file_path": str(output_file_path), "output_file_url": str(output_file_url), "output_cache_url": str(output_cache_url)}, status_code=200) + except Exception as e: + return JSONResponse(content={"status": "generate-failure", "error": "An error occurred"}, status_code=500) + + +########################## +#### Current Settings #### +########################## +# Define the available models +models_available = [ + {"name": "Coqui", "model_name": "API TTS"}, + {"name": "Coqui", "model_name": "API Local"}, + {"name": "Coqui", "model_name": "XTTSv2 Local"} +] + +@app.get('/api/currentsettings') +def get_current_settings(): + # Determine the current model loaded + if params["tts_method_api_tts"]: + current_model_loaded = "API TTS" + elif params["tts_method_api_local"]: + current_model_loaded = "API Local" + elif params["tts_method_xtts_local"]: + current_model_loaded = "XTTSv2 Local" + else: + current_model_loaded = None # or a default value if no method is active + + settings = { + "models_available": models_available, + "current_model_loaded": current_model_loaded, + "deepspeed_available": deepspeed_available, + "deepspeed_status": params["deepspeed_activate"], + "low_vram_status": params["low_vram"], + "finetuned_model": finetuned_model + } + return settings # Automatically converted to JSON by Fas + +############################# +#### Word Add-in Sharing #### +############################# +# Mount the static files from the 'word_addin' directory +app.mount("/api/word_addin", StaticFiles(directory=os.path.join(this_dir / 'system' / 'word_addin')), name="word_addin") + +############################################# +#### TTS Generator Comparision Endpoints #### +############################################# +import subprocess +import aiofiles + +class TTSItem(BaseModel): + id: int + fileUrl: str + text: str + characterVoice: str + language: str + +class TTSData(BaseModel): + ttsList: List[TTSItem] + +@app.post("/api/save-tts-data") +async def save_tts_data(tts_data: List[TTSItem]): + # Convert the list of Pydantic models to a list of dictionaries + tts_data_list = [item.dict() for item in tts_data] + # Serialize the list of dictionaries to a JSON string + tts_data_json = json.dumps(tts_data_list, indent=4) + async with aiofiles.open(this_dir / "outputs" / "ttsList.json", 'w') as f: + await f.write(tts_data_json) + return {"message": "Data saved successfully"} + +import sys + +@app.get("/api/trigger-analysis") +async def trigger_analysis(threshold: int = Query(default=98)): + venv_path = sys.prefix + env = os.environ.copy() + env["PATH"] = os.path.join(venv_path, "bin") + ":" + env["PATH"] + ttslist_path = this_dir / "outputs" / "ttsList.json" + wavfile_path = this_dir / "outputs" + subprocess.run(["python", "tts_diff.py", f"--threshold={threshold}", f"--ttslistpath={ttslist_path}", f"--wavfilespath={wavfile_path}"], cwd=this_dir / "system" / "tts_diff", env=env) + # Read the analysis summary + try: + with open(this_dir / "outputs" / "analysis_summary.json", "r") as summary_file: + summary_data = json.load(summary_file) + except FileNotFoundError: + summary_data = {"error": "Analysis summary file not found."} + return {"message": "Analysis Completed", "summary": summary_data} + +################################################# +#### TTS Generator SRT Subtitiles generation #### +################################################# +@app.get("/api/srt-generation") +async def srt_generation(): + venv_path = sys.prefix + env = os.environ.copy() + env["PATH"] = os.path.join(venv_path, "bin") + ":" + env["PATH"] + ttslist_path = this_dir / "outputs" / "ttsList.json" + wavfile_path = this_dir / "outputs" + subprocess.run(["python", "tts_srt.py", f"--ttslistpath={ttslist_path}", f"--wavfilespath={wavfile_path}"], cwd=this_dir / "system" / "tts_srt", env=env) + + srt_file_path = this_dir / "outputs" / "subtitles.srt" + if not srt_file_path.exists(): + raise HTTPException(status_code=404, detail="Subtitle file not found.") + + return FileResponse(path=srt_file_path, filename="subtitles.srt", media_type='application/octet-stream') + +################################################### +#### Webserver Startup & Initial model Loading #### +################################################### + +# Get the admin interface template +template = templates.get_template("admin.html") +# Render the template with the dynamic values +rendered_html = template.render(params=params) + +############################### +#### Internal script ready #### +############################### +@app.get("/ready") +async def ready(): + return Response("Ready endpoint") + +############################ +#### External API ready #### +############################ +@app.get("/api/ready") +async def ready(): + return Response("Ready") + +@app.get("/") +async def read_root(): + return HTMLResponse(content=rendered_html, status_code=200) + +# Start Uvicorn Webserver +host_parameter = params["ip_address"] +port_parameter = int(params["port_number"]) + +if __name__ == "__main__": + import uvicorn + + uvicorn.run(app, host=host_parameter, port=port_parameter, log_level="warning") From 21cd1717196e798af61bd4f1e3b1031ce183fe83 Mon Sep 17 00:00:00 2001 From: erew123 <35898566+erew123@users.noreply.github.com> Date: Mon, 1 Apr 2024 13:11:09 +0100 Subject: [PATCH 8/9] Add files via upload --- system/admin.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/system/admin.html b/system/admin.html index d7f5d07..7a988dc 100644 --- a/system/admin.html +++ b/system/admin.html @@ -474,7 +474,7 @@ 22050Hz, Mono, 16 bit and between 6 to 30 seconds long, though 8 to 10 seconds is usually good enough. The model can handle up to 30 second samples, however I've not noticed any improvement in voice - output from a much longer clips. + output from much longer clips.

You want to find a nice clear selection of audio, so lets say you wanted to clone your favourite celebrity. You may go looking for an interview where they are talking. Pay From af4c63f1eeb95ba6546a625b2987cf611ae93f31 Mon Sep 17 00:00:00 2001 From: erew123 <35898566+erew123@users.noreply.github.com> Date: Mon, 1 Apr 2024 13:53:10 +0100 Subject: [PATCH 9/9] Update README.md --- README.md | 46 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 46 insertions(+) diff --git a/README.md b/README.md index 31ae1e9..c870c4d 100644 --- a/README.md +++ b/README.md @@ -131,6 +131,7 @@ To perform a Standalone installation of AllTalk:

Refer to `🟩 Other installation notes` for further details, including information on additional voices, changing IP, character card notes etc. +> If you wish to understand AllTalks start-up screen, please read **Understanding the AllTalk start-up screen** in the Help section. --- @@ -793,6 +794,51 @@ As far as I am aware, these are to do with the chrome browser the gradio text-ge ### Startup, Performance and Compatibility Issues +
+ 🟨 Understanding the AllTalk start-up screen
+ +The AllTalk start-up screen provides various bits of information about the detected Python environment and errors. + +![image](https://github.com/erew123/screenshots/raw/main/alltalkstartup.jpg) + +**Config file check**
+- Sometimes I need to add/remove something to your existing configuration file settings. Obviously, I don’t want to impact your existing settings, however any new features may need these settings to be created before AllTalk starts up. Ive added extra code that checks `alltalk_tts/system/config/at_configupdate.json` and `alltalk_tts/system/config/at_configdowngrade.json`, either adding or removing items to your configuration as necessary. If a change is made, you will be notified and a backup of the previous configuration file will be created in the `alltalk_tts` folder. + +**AllTalk startup Mode**
+- informational. This will state if AllTalk has detected it is running as part of Text-generation-webui or as a Standalone Application. + +**WAV file deletion**
+- If you have set deletion of old generated WAV files, this will state the time frame after which they are purged. + +**DeepSpeed version**
+- What version of DeepSpeed is installed/detected. This will **not** tell you if the version of DeepSpeed is compiled for your Python, PyTorch or CUDA version. Its important to remember that DeepSpeed has to be compiled for the exact version of Python, PyTorch and CUDA that you are using, so please ensure you have the correct DeepSpeed version installed if necessary. + +**Model is available**
+- AllTalk is checking if your model files exist. This is not a validity check of the actual model files, they can still be corrupted. If files are missing, AllTalk will attempt to download them from Huggingface, however, if Huggingface has an outage/issue or your internet connection has issues, its possible corrupted or incomplete files will be downloaded. Please read `RuntimeError: PytorchStreamReader failed reading zip archive: failed finding central directory` if you need to confirm your model files are ok. + +**Current Python Version**
+- Informational. Literally tells you the version of Python running in your Python environment. + +**Current PyTorch Version**
+- Informational. Tell tells you the version of **PyTorch** running in your Python environment, however if you have an Nvidia card, you should be running a CUDA based version of Pytorch. This is indicated with a `+cXXX` after the PyTorch version e.g. `2.2.2+cu121` would be PyTorch version 2.2.2 with CUDA 12.1 extensions. If you don’t have the PyTorch CUDA extensions installed, but you do have an Nvidia card, you may need to re-install PyTorch. + +**Current CUDA Version**
+- Informational. This is linked to the Current PyTorch Version, as detailed above. + +**Current TTS Version**
+- Informational. The current version of the TTS engine that is running. + +**AllTalk Github updated**
+- As long as you have an internet connection, this will tell you last time AllTalk was updated on Github. It is checking the [commit list](https://github.com/erew123/alltalk_tts/commits/main/) to see when the last commit was made. As such, this could be simply a documentation update, a bug fix or new features. Its simply there as a guide to let you know the last time something was changed on AllTalk's Github. + +**TTS Subprocess**
+- When AllTalk reaches this stage, the subprocess that loads in the AI model is starting. This is most likely where an error could occur with loading the TTS model, just after the documentation message. + +**AllTalk Settings & Documentation: http ://x.x.x.x**
+- The link where you can reach AllTalks built in settings and documentation page. The TTS model will be loading immediately after this is displayed. +

+
+
🟨 AllTalk is only loading into CPU, but I have an Nvidia GPU so it should be loading into CUDA