diff --git a/tts_server.py b/tts_server.py index 8e732e1..0cb65d9 100644 --- a/tts_server.py +++ b/tts_server.py @@ -1,193 +1,135 @@ -import os -import sys import json import time -import librosa -import logging -import importlib -import subprocess +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 contextlib import asynccontextmanager -from fastapi.staticfiles import StaticFiles -from fastapi.templating import Jinja2Templates -from fastapi.middleware.cors import CORSMiddleware -from fastapi import FastAPI, Form, Request, Response, Depends, HTTPException, Query +########################## +#### Webserver Imports#### +########################## +from fastapi import ( + FastAPI, + Form, + Request, + Response, + Depends, + HTTPException, + Query, +) from fastapi.responses import JSONResponse, HTMLResponse, RedirectResponse, FileResponse, StreamingResponse -########################## -# Generation API Imports # -########################## -import re -import html -import uuid -import hashlib -import numpy as np -import soundfile as sf -from typing import Union, Dict, List -from pydantic import BaseModel, ValidationError, Field, field_validator -######################################################################################## -# START-UP # Silence RVC warning about torch.nn.utils.weight_norm even though not used # -######################################################################################## -import warnings -warnings.filterwarnings("ignore", category=UserWarning, module="torch.nn.utils.weight_norm") -warnings.filterwarnings("ignore", category=UserWarning, module="torch.nn.functional", lineno=5476) -#################### -# Setup local path # -#################### -this_dir = Path(__file__).parent.resolve() # Set this_dir as the current alltalk_tts folder +from fastapi.middleware.cors import CORSMiddleware +from fastapi.templating import Jinja2Templates +from fastapi.staticfiles import StaticFiles +from contextlib import asynccontextmanager -#################################################### -# Load params and api_defailts from confignew.json # -#################################################### -def load_config(): - global branding, output_directory, debug_transcode, debug_tts, debug_tts_variables, engines_available, engine_loaded, debug_rvc, debug_concat, debug_openai - # Define the path to the confignew.json file & load in api_defaults & params - configfile_path = this_dir / "confignew.json" - with open(configfile_path, "r") as configfile: - configfile_data = json.load(configfile) - api_defaults = { - "api_text_filtering": configfile_data["api_def"]["api_text_filtering"], - "api_narrator_enabled": configfile_data["api_def"]["api_narrator_enabled"], - "api_text_not_inside": configfile_data["api_def"]["api_text_not_inside"], - "api_language": configfile_data["api_def"]["api_language"], - "api_output_file_name": configfile_data["api_def"]["api_output_file_name"], - "api_output_file_timestamp": configfile_data["api_def"]["api_output_file_timestamp"], - "api_autoplay": configfile_data["api_def"]["api_autoplay"], - "api_autoplay_volume": configfile_data["api_def"]["api_autoplay_volume"], - "api_port_number": configfile_data["api_def"]["api_port_number"], - "api_allowed_filter": configfile_data["api_def"]["api_allowed_filter"], - "api_length_stripping": configfile_data["api_def"]["api_length_stripping"], - "api_max_characters": configfile_data["api_def"]["api_max_characters"], - "api_use_legacy_api": configfile_data["api_def"]["api_use_legacy_api"], - "api_legacy_ip_address": configfile_data["api_def"]["api_legacy_ip_address"], - } - rvc_settings = { - "rvc_enabled": configfile_data["rvc_settings"]["rvc_enabled"], - "rvc_char_model_file": configfile_data["rvc_settings"]["rvc_char_model_file"], - "rvc_narr_model_file": configfile_data["rvc_settings"]["rvc_narr_model_file"], - "split_audio": configfile_data["rvc_settings"]["split_audio"], - "autotune": configfile_data["rvc_settings"]["autotune"], - "pitch": configfile_data["rvc_settings"]["pitch"], - "filter_radius": configfile_data["rvc_settings"]["filter_radius"], - "index_rate": configfile_data["rvc_settings"]["index_rate"], - "rms_mix_rate": configfile_data["rvc_settings"]["rms_mix_rate"], - "protect": configfile_data["rvc_settings"]["protect"], - "hop_length": configfile_data["rvc_settings"]["hop_length"], - "f0method": configfile_data["rvc_settings"]["f0method"], - "embedder_model": configfile_data["rvc_settings"]["embedder_model"], - "training_data_size": configfile_data["rvc_settings"]["training_data_size"], - } - branding = configfile_data.get("branding", "") - debug_transcode = configfile_data.get("debugging").get("debug_transcode") - debug_tts = configfile_data.get("debugging").get("debug_tts") - debug_tts_variables = configfile_data.get("debugging").get("debug_tts_variables") - debug_rvc = configfile_data.get("debugging").get("debug_rvc") - debug_concat = configfile_data.get("debugging").get("debug_concat") - debug_openai = configfile_data.get("debugging").get("debug_openai") - output_directory = this_dir / configfile_data.get("output_folder", "") - output_directory.mkdir(parents=True, exist_ok=True) - tts_engines_file = os.path.join(this_dir, "system", "tts_engines", "tts_engines.json") - with open(tts_engines_file, "r") as f: - tts_engines_data = json.load(f) - engines_available = [engine["name"] for engine in tts_engines_data["engines_available"]] # List of the available TTS engines from tts_engines.json - engine_loaded = tts_engines_data["engine_loaded"] # The currently set TTS engine from tts_engines.json - selected_model = tts_engines_data["selected_model"] - # Conditionally import infer_pipeline if rvc_enabled is true - if rvc_settings["rvc_enabled"]: - from system.tts_engines.rvc.infer.infer import infer_pipeline - else: - infer_pipeline = None - return configfile_data, api_defaults, rvc_settings, infer_pipeline +########################### +#### 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 -# Call to load in the params and api_defaults -params, api_defaults, rvc_settings, infer_pipeline = load_config() +################################################################# +#### 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 -#################### -# Check for FFMPEG # -#################### -def check_ffmpeg(this_dir): - if sys.platform == "win32": - # Check for ffmpeg.exe in the specified directory on Windows - ffmpeg_path = os.path.join(this_dir, "system", "win_ffmpeg", "ffmpeg.exe") - if os.path.exists(ffmpeg_path): - return True - else: - # Check for FFmpeg on Linux and macOS using the command line - try: - subprocess.run(['ffmpeg', '-version'], stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=True) - return True - except (subprocess.CalledProcessError, FileNotFoundError): - pass - return False -# Check if FFmpeg is installed -ffmpeg_installed = check_ffmpeg(this_dir) +# Define the path to the confignew.json file +configfile_path = this_dir / "confignew.json" -if not ffmpeg_installed: - print(f"[{branding}ENG] \033[92mTranscoding :\033[94m ffmpeg not found\033[0m") - print(f"[{branding}ENG] FFmpeg is not installed. Transcoding will be disabled.") - print(f"[{branding}ENG] Please install FFmpeg on your system.") - - if sys.platform == "win32": - print(f"[{branding}ENG] \033[92mTranscoding :\033[94m ffmpeg not found\033[0m") - print(f"[{branding}ENG] Installation instructions for Windows:") - print(f"[{branding}ENG] Copy the 'ffmpeg.exe' file to '{os.path.join(this_dir, 'system', 'win_ffmpeg')}'") - else: - print(f"[{branding}ENG] \033[92mTranscoding :\033[94m ffmpeg not found\033[0m") - print(f"[{branding}ENG] Installation instructions:") - print(f"[{branding}ENG] Linux (Debian-based systems): Run 'sudo apt-get install ffmpeg' in the terminal.") - print(f"[{branding}ENG] macOS: Run 'brew install ffmpeg' in the terminal (requires Homebrew).") - # You can choose to exit the script or continue without FFmpeg - # exit(1) +# 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"] -if ffmpeg_installed: - from ffmpeg.asyncio import FFmpeg - print(f"[{branding}ENG] \033[92mTranscoding :\033[93m ffmpeg found\033[0m") - -################################ -# Check for portaudio on Linux # -################################ -try: - import sounddevice as sd - sounddevice_installed=True -except OSError: - print(f"[{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"[{branding}Startup] \033[91mInfo\033[0m please install PortAudio. This will not affect any other features or use of Alltalk.") - print(f"[{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"[{branding}Startup] \033[91mInfo\033[0m On Linux, you can use the following command to install PortAudio:") - print(f"[{branding}Startup] \033[91mInfo\033[0m sudo apt-get install portaudio19-dev") +# Load values for temperature and repetition_penalty +temperature = params["local_temperature"] +repetition_penalty = params["local_repetition_penalty"] -####################################################################### -# Attempt to import the ModelLoader class for the selected TTS engine # -####################################################################### -if engine_loaded in engines_available: - loader_module = importlib.import_module(f"system.tts_engines.{engine_loaded}.model_engine") - tts_class = getattr(loader_module, "tts_class") - # Setup model_engine as the way to call the functions within the Class. - model_engine = tts_class() +# 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: - raise ValueError(f"Invalid TTS engine: {engine_loaded}") + # 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 -########################################## -# Run setup function in the model_engine # -########################################## @asynccontextmanager async def startup_shutdown(no_actual_value_it_demanded_something_be_here): - try: - await model_engine.setup() - except FileNotFoundError as e: - print(f"Error during setup: {e}. Continuing without the TTS model.") + await setup() yield # Shutdown logic -############################### -# Setup FastAPI with Lifespan # -############################### + +# Create FastAPI app with lifespan app = FastAPI(lifespan=startup_shutdown) # Allow all origins, and set other CORS options app.add_middleware( @@ -198,473 +140,685 @@ app.add_middleware( allow_headers=["*"], ) -############################## -# API Endpoint - /api/reload # -############################## -model_currently_changing = False +##################################### +#### 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 apifunction_reload(request: Request): - global model_currently_changing - if model_currently_changing == True: - return Response(content=json.dumps({"status": "model-is currently changing"}), media_type="application/json") - model_currently_changing == True - requested_model = request.query_params.get("tts_method") - if requested_model not in model_engine.available_models: - model_currently_changing == False +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"} - # Call the handle_tts_method_change method from the imported class - success = await model_engine.handle_tts_method_change(requested_model) - if success: - model_engine.current_model_loaded = requested_model - print(f"[{branding}ENG]") - print(f"[{branding}ENG] {branding} Server Ready.") - model_currently_changing == False - return Response(content=json.dumps({"status": "model-success"}), media_type="application/json") - else: - model_currently_changing == False - return Response(content=json.dumps({"status": "model-failure"}), media_type="application/json") - -#################################### -# API Endpoint - /api/enginereload # -#################################### -import asyncio -uvicorn_server = None -def restart_self(): - """Restart the current script.""" - #print(f"[{branding}ENG] Restarting subprocess...") - os.execv(sys.executable, ['python'] + sys.argv) + await handle_tts_method_change(tts_method) + return Response( + content=json.dumps({"status": "model-success"}), media_type="application/json" + ) -async def handle_restart(): - global uvicorn_server - if uvicorn_server: - print(f"[{branding}ENG] Stopping uvicorn server...") - uvicorn_server.should_exit = True - uvicorn_server.force_exit = True - # Check if the server has stopped - while not uvicorn_server.is_stopped: - await asyncio.sleep(0.1) # Check every 100ms if server is stopped - restart_self() -@app.post("/api/enginereload") -async def apifunction_reload(request: Request): - requested_engine = request.query_params.get("engine") - if requested_engine not in engines_available: - return {"status": "error", "message": "Invalid TTS engine specified"} - print(f"[{branding}ENG]") - print(f"[{branding}ENG] \033[94mChanging model loaded. Please wait.\033[00m") - print(f"[{branding}ENG]") - # Update the tts_engines.json file - tts_engines_file = os.path.join(this_dir, "system", "tts_engines", "tts_engines.json") - with open(tts_engines_file, "r") as f: - tts_engines_data = json.load(f) - for engine in tts_engines_data["engines_available"]: - if engine["name"] == requested_engine: - tts_engines_data["engine_loaded"] = requested_engine - tts_engines_data["selected_model"] = engine["selected_model"] - break - with open(tts_engines_file, "w") as f: - json.dump(tts_engines_data, f) - #print(f"[{branding}ENG] Reloading the engine...") - # Start the restart process in the background - asyncio.create_task(handle_restart()) - # Never goes past the above asyncio as the script is killed at that point - return Response(content=json.dumps({"status": "engine-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) -####################################### -# API Endpoint - /api/stop-generation # -####################################### -# When this endpoint it called it will set tts_stop_generation in the model_engine to True, which can be used to interrupt the current TTS generation. -@app.put("/api/stop-generation") -async def apifunction_stop_generation(): - if model_engine.tts_generating_lock and not model_engine.tts_stop_generation: - model_engine.tts_stop_generation = True - return {"message": "Cancelling current TTS generation"} -############################# -# API Endpoint - /api/audio # -############################# -# Gives web access to the output files as downloads -@app.get("/audio/{filename}") -async def apifunction_get_audio(filename: str): - audio_path = this_dir / output_directory / filename - return FileResponse(audio_path) - -################################## -# API Endpoint - /api/audiocache # -################################## -@app.get("/audiocache/{filename}") -async def apifunction_get_audio(filename: str): - audio_path = Path({output_directory}) / 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 - -############################## -# API Endpoint - /api/voices # -############################## -@app.get("/api/voices") -async def apifunction_get_voices(): - if not model_engine.multivoice_capable: - return {"status": "error", "message": f"The currently loaded TTS engine '{model_engine.engine_loaded}' does not support multiple voices."} - - available_voices = model_engine.voices_file_list() - return {"status": "success", "voices": available_voices} - -################################# -# API Endpoint - /api/rvcvoices # -################################# -@app.get("/api/rvcvoices") -async def apifunction_get_voices(): - global params, api_defaults, rvc_settings, infer_pipeline - params, api_defaults, rvc_settings, infer_pipeline = load_config() - if not rvc_settings["rvc_enabled"]: - return {"status": "success", "rvcvoices": ["Disabled"]} - # Define the directory path - directory = os.path.join(this_dir, "models", "rvc_voices") - # Check if the directory exists - if not os.path.exists(directory): - return {"status": "success", "rvcvoices": ["Disabled"]} - # List all .pth files recursively in the directory - pth_files = [] - for root, dirs, files in os.walk(directory): - for file in files: - if file.endswith(".pth"): - pth_files.append(os.path.relpath(os.path.join(root, file), start=directory)) - # If no .pth files found, return "Disabled" - if not pth_files: - return {"status": "success", "rvcvoices": ["Disabled"]} - # Sort the list alphabetically - pth_files.sort() - # Add "Disabled" at the beginning of the list - pth_files.insert(0, "Disabled") - return {"status": "success", "rvcvoices": pth_files} - -##################################### -# API Endpoint - /api/reload_config # -##################################### -@app.get("/api/reload_config") -async def apifunction_reload_config(): - global params, api_defaults, rvc_settings, infer_pipeline - model_engine.available_models = model_engine.scan_models_folder() - params, api_defaults, rvc_settings, infer_pipeline = load_config() - return Response("Config file reloaded successfully") - -############################# -# API Endpoint - /api/ready # -############################# -@app.get("/api/ready") -async def apifunction_ready(): - if model_engine.setup_has_run: - return Response("Ready") - else: - return Response("Unloaded") - -####################################### -# API Endpoint - /api/currentsettings # -####################################### -@app.get('/api/currentsettings') -def apifunction_get_current_settings(): - settings = { - "engines_available": engines_available, # Lists the currently available TTS engines - "current_engine_loaded": model_engine.engine_loaded, # Lists the currently loaded TTS engine - "models_available": [{"name": name} for name in model_engine.available_models.keys()], # Lists the currently available models that can be loaded on this TTS engine. - "current_model_loaded": model_engine.current_model_loaded, # Lists the currently loaded model - "manufacturer_name": model_engine.manufacturer_name, # The name of the person/company/group who made the current TTS engine - "audio_format": model_engine.audio_format, # The default audio format the the currently loaded TTS engine generates in - "deepspeed_capable": model_engine.deepspeed_capable, # Is the TTS engine capable of supporting DeepSpeed - "deepspeed_available": model_engine.deepspeed_available, # Was DeepSpeed detected as being available for use - "deepspeed_enabled": model_engine.deepspeed_enabled, # Is DeepSpeed current enabled or disabled - "generationspeed_capable": model_engine.generationspeed_capable, # Does the TTS engine support changing speed of generated TTS - "generationspeed_set": model_engine.generationspeed_set, # What is the currently set TTS speed - "lowvram_capable": model_engine.lowvram_capable, # Does the TTS engine support moving the model between VRAM and System Ram - "lowvram_enabled": model_engine.lowvram_enabled, # What is the currently enabled/disabled state of low vram - "pitch_capable": model_engine.pitch_capable, # Does the TTS engine support pitch on tts generation - "pitch_set": model_engine.pitch_set, # What is the currently set value for pitch - "repetitionpenalty_capable": model_engine.repetitionpenalty_capable, # Does the TTS engine support differnt repitition penalties - "repetitionpenalty_set": model_engine.repetitionpenalty_set, # What is the currently set repetition penalty - "streaming_capable": model_engine.streaming_capable, # Does the model support streaming TTS generation - "temperature_capable": model_engine.temperature_capable, # Does the TTS engine support model temperature settings - "temperature_set": model_engine.temperature_set, # What is the currently set temperature - "ttsengines_installed": model_engine.engine_installed, # - "languages_capable": model_engine.languages_capable, # Does the TTS engine support multi languages - "multivoice_capable": model_engine.multivoice_capable, # Does the TTS engine support multi voice models - "multimodel_capable": model_engine.multimodel_capable, # Does the engine support loading many different models - } - return settings - -###################################### -# API Endpoint - /api/lowvramsetting # -###################################### @app.post("/api/lowvramsetting") -async def apifunction_low_vram(request: Request, new_low_vram_value: bool): - if not model_engine.lowvram_capable: - return {"status": "error", "message": f"The currently loaded TTS engine '{model_engine.engine_loaded}' does not support lowvram."} +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 model_engine.lowvram_enabled == new_low_vram_value: - return Response(content=json.dumps({"status": "success", "message": f"[{branding}Model] LowVRAM is already {'enabled' if new_low_vram_value else 'disabled'}.",})) - model_engine.lowvram_enabled = new_low_vram_value - await model_engine.unload_model() - - if model_engine.cuda_is_available: - if model_engine.lowvram_enabled: - model_engine.device = "cpu" - print(f"[{branding}Engine] \033[94mLowVRAM Enabled.\033[0m Model will move between \033[93mVRAM(cuda) <> System RAM(cpu)\033[0m") + + 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: - model_engine.device = "cuda" - print(f"[{branding}Engine] \033[94mLowVRAM Disabled.\033[0m Model will stay in \033[93mVRAM(cuda)\033[0m") - await model_engine.setup() + # 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: - # Handle the case where CUDA is not available - print(f"[{branding}Engine] \033[91mError:\033[0m Nvidia CUDA is not available on this system. Unable to use LowVRAM mode.") - model_engine.lowvram_enabled = False - + 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)})) -################################# -# API Endpoint - /api/deepspeed # -################################# + +################### +#### 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): - if not model_engine.deepspeed_capable or not model_engine.deepspeed_available: - return {"status": "error", "message": f"The currently loaded TTS engine '{model_engine.engine_loaded}' does not support DeepSpeed or DeepSpeed is not available on this system."} try: if new_deepspeed_value is None: raise ValueError("Missing 'deepspeed' parameter") - if model_engine.deepspeed_enabled == new_deepspeed_value: + 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'}.",})) - model_engine.deepspeed_enabled = new_deepspeed_value - await model_engine.handle_deepspeed_change(new_deepspeed_value) + 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)})) -################################# -# API Endpoint - /api/voice2rvc # -################################# -@app.post("/api/voice2rvc") -async def voice2rvc(input_tts_path: str = Form(...), output_rvc_path: str = Form(...), pth_name: str = Form(...), pitch: str = Form(...), method: str = Form(...)): - try: - # Handle "Disabled" case - if pth_name.lower() in ["disabled", "disable"]: - print(f"[{branding}TTS] \033[94mVoice2RVC Convert: No voice was specified or the name was Disabled\033[0m") - return {"status": "error", "message": "No voice was specified or the name was Disabled"} - input_tts_path = Path(input_tts_path) - output_rvc_path = Path(output_rvc_path) - # Check if input file exists - if not input_tts_path.is_file(): - raise HTTPException(status_code=400, detail=f"Input file {input_tts_path} does not exist.") - # Define the model path based on the selected model name - pth_path = this_dir / "models" / "rvc_voices" / pth_name - # Check if model file exists - if not pth_path.is_file(): - raise HTTPException(status_code=400, detail=f"Model file {pth_path} does not exist.") - # Run the RVC conversion - result_path = run_voice2rvc(input_tts_path, output_rvc_path, pth_path, pitch, method) - if result_path: - return {"status": "success", "output_path": str(result_path)} - else: - raise HTTPException(status_code=500, detail="RVC conversion failed.") - except Exception as e: - print(f"Error during Voice2RVC conversion: {e}") - raise HTTPException(status_code=500, detail=str(e)) -# Define the run_rvc function -def run_voice2rvc(input_tts_path, output_rvc_path, pth_path, pitch, method): - print(f"[{branding}GEN] \033[94mVoice2RVC Convert: Started\033[0m") - generate_start_time = time.time() - f0up_key = pitch - filter_radius = rvc_settings["filter_radius"] - index_rate = rvc_settings["index_rate"] - rms_mix_rate = rvc_settings["rms_mix_rate"] - protect = rvc_settings["protect"] - hop_length = rvc_settings["hop_length"] - f0method = method - split_audio = rvc_settings["split_audio"] - f0autotune = rvc_settings["autotune"] - embedder_model = rvc_settings["embedder_model"] - training_data_size = rvc_settings["training_data_size"] - # Convert path variables to strings - input_tts_path = str(input_tts_path) - pth_path = str(pth_path) - # Check if the model file exists - if not os.path.isfile(pth_path): - print(f"Model file {pth_path} does not exist. Exiting.") - return - # Get the directory of the model file - model_dir = os.path.dirname(pth_path) - # Find all .index files in the model directory - index_files = [file for file in os.listdir(model_dir) if file.endswith(".index")] - if len(index_files) == 1: - index_path = str(os.path.join(model_dir, index_files[0])) - else: - index_path = "" - # Call the infer_pipeline function - infer_pipeline(f0up_key, filter_radius, index_rate, rms_mix_rate, protect, hop_length, f0method, - input_tts_path, output_rvc_path, pth_path, index_path, split_audio, f0autotune, embedder_model, training_data_size, debug_rvc) - generate_end_time = time.time() - generate_elapsed_time = generate_end_time - generate_start_time - print(f"[{branding}GEN] \033[94mVoice2RVC Convert: \033[91m{generate_elapsed_time:.2f} seconds.\033[0m") - return output_rvc_path +######################## +#### TTS GENERATION #### +######################## +debug_generate_audio = False +tts_stop_generation = False # Called to stop generation of the current text at whatever stage its at. currently only set for streaming. +tts_generation_lock = False # Tracks locking of the generaetion process. +tts_narrator_generatingtts = False # Tracks if the current tts processes are narrator based, to avoid moving model on each chunk of text generated. +# Not in use tts_queue_behaviour = False # True is to queue the current generation request. False is to cancel the current generation and start a new one. -################################## -# Transcode between file formats # -################################## -async def transcode_audio(input_file, output_format): - print(f"[{branding}Debug] *************************************************",) if debug_transcode else None - print(f"[{branding}Debug] transcode_audio function called (debug_transcode)",) if debug_transcode else None - print(f"[{branding}Debug] *************************************************",) if debug_transcode else None - print(f"[{branding}Debug] Input file : {input_file}") if debug_transcode else None - print(f"[{branding}Debug] Output format : {output_format}") if debug_transcode else None - if output_format == "Disabled": - print(f"[{branding}Debug] Transcode format is set to Disabled so skipping transcode.") if debug_transcode else None - return input_file - if not ffmpeg_installed: - print(f"[{branding}TTS] FFmpeg is not installed. Format conversion is not possible.") - raise Exception("FFmpeg is not installed. Format conversion is not possible.") - # Get the file extension of the input file - input_extension = os.path.splitext(input_file)[1][1:].lower() - print(f"[{branding}Debug] Input file extension: {input_extension}") if debug_transcode else None - # Check if the input extension matches the requested output format - if input_extension == output_format.lower(): - print(f"[{branding}Debug] Input file is already in the requested format: {output_format}") if debug_transcode else None - return input_file - output_file = os.path.splitext(input_file)[0] + f".{output_format}" - print(f"[{branding}Debug] Output file: {output_file}") if debug_transcode else None - ffmpeg_path = "ffmpeg" # Default path for Linux and macOS - if sys.platform == "win32": - ffmpeg_path = os.path.join(this_dir, "system", "win_ffmpeg", "ffmpeg.exe") - ffmpeg = (FFmpeg(ffmpeg_path).option("y").input(input_file).output(output_file)) - print(f"[{branding}Debug] Transcoding to {output_format}") if debug_transcode else None - if output_format == "opus": - print(f"[{branding}Debug] Configuring Opus options") if debug_transcode else None - ffmpeg.output(output_file, {"codec:a": "libopus", "b:a": "128k", "vbr": "on", "compression_level": 10, "frame_duration": 60, "application": "voip"}) - elif output_format == "aac": - print(f"[{branding}Debug] Configuring AAC options") if debug_transcode else None - ffmpeg.output(output_file, {"codec:a": "aac", "b:a": "192k"}) - elif output_format == "flac": - print(f"[{branding}Debug] Configuring FLAC options") if debug_transcode else None - ffmpeg.output(output_file, {"codec:a": "flac", "compression_level": 8}) - elif output_format == "wav": - print(f"[{branding}Debug] Configuring WAV options") if debug_transcode else None - ffmpeg.output(output_file, {"codec:a": "pcm_s16le"}) - elif output_format == "mp3": - print(f"[{branding}Debug] Configuring MP3 options") if debug_transcode else None - ffmpeg.output(output_file, {"codec:a": "libmp3lame", "b:a": "192k"}) - else: - print(f"[{branding}TTS] Unsupported output format: {output_format}") - raise ValueError(f"Unsupported output format: {output_format}") - try: - print(f"[{branding}Debug] Starting transcoding process") if debug_transcode else None - await ffmpeg.execute() - print(f"[{branding}Debug] Transcoding completed successfully") if debug_transcode else None - except Exception as e: - print(f"[{branding}TTS] Error occurred during transcoding: {str(e)}") - raise - print(f"[{branding}Debug] Deleting original input file") if debug_transcode else None - os.remove(input_file) - print(f"[{branding}Debug] Transcoding process completed") if debug_transcode else None - print(f"[{branding}Debug] Transcoded file: {output_file}") if debug_transcode else None - return output_file - -############################## -# Central Transcode function # -############################## -async def transcode_audio_if_necessary(output_file, model_audio_format, output_audio_format): - if debug_transcode: - print(f"[{branding}Debug] model_engine.audio_format is:", model_audio_format) - print(f"[{branding}Debug] audio format is:", output_audio_format) - print(f"[{branding}Debug] Entering the transcode condition") - try: - if debug_transcode: - print(f"[{branding}Debug] Calling transcode_audio function") - output_file = await transcode_audio(output_file, output_audio_format) - if debug_transcode: - print(f"[{branding}Debug] Transcode completed successfully") - except Exception as e: - print(f"[{branding}Debug] Error occurred during transcoding:", str(e)) - raise - if debug_transcode: - print(f"[{branding}Debug] Transcode condition completed") - # Update the output file path and URLs based on the transcoded file - if debug_tts: - print(f"[{branding}Debug] Updating output file paths and URLs") - if params["api_def"]["api_use_legacy_api"]: - output_file_url = f'http://{api_defaults["api_legacy_ip_address"]}:{api_defaults["api_port_number"]}/audio/{os.path.basename(output_file)}' - output_cache_url = f'http://{api_defaults["api_legacy_ip_address"]}:{api_defaults["api_port_number"]}/audiocache/{os.path.basename(output_file)}' - else: - output_file_url = f'/audio/{os.path.basename(output_file)}' - output_cache_url = f'/audiocache/{os.path.basename(output_file)}' - if debug_tts: - print(f"[{branding}Debug] Output file paths and URLs updated") - return output_file, output_file_url, output_cache_url - -############################## -#### Streaming Generation #### -############################## -@app.get("/api/tts-generate-streaming", response_class=StreamingResponse) -async def apifunction_generate_streaming(text: str, voice: str, language: str, output_file: str): - if model_engine.streaming_capable == False: - print(f"[{branding}GEN] Streaming is unavailable for this TTS Engine") - try: - output_file_path = f'{this_dir / output_directory / output_file}.{model_engine.audio_format}' - stream = await generate_audio(text, voice, language, model_engine.temperature_set, model_engine.repetitionpenalty_set, 1.0, 1.0, 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(...)): - if model_engine.streaming_capable == False: - print(f"[{branding}GEN] Streaming is unavailable for this TTS Engine") - try: - output_file_path = f'{this_dir / output_directory / output_file}.{model_engine.audio_format}' - await generate_audio(text, voice, language, model_engine.temperature_set, model_engine.repetitionpenalty_set, "1.0", "1.0", 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) - -################################### -# Central generate_audio function # -################################### -async def generate_audio(text, voice, language, temperature, repetition_penalty, speed, pitch, output_file, streaming=False): +# 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 = model_engine.generate_tts(text, voice, language, temperature, repetition_penalty, speed, pitch, output_file, streaming) + 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, tts_stop_generation, tts_generation_lock + tts_generation_lock = True + 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): + if tts_stop_generation: + print(f"[{params['branding']}TTSGen] Stopping audio generation.") + file_chunks.clear() # Clear the file_chunks list + tts_stop_generation = False + tts_generation_lock = False + break + 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() + print(f"[{params['branding']}Debug] Stream audio generation: Yielded audio chunk {i}.") if debug_generate_audio else None + 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=float(temperature), + length_penalty=float(model.config.length_penalty), + repetition_penalty=float(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" and tts_narrator_generatingtts == False: + await switch_device() + tts_generation_lock = False + 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 apifunction_preview_voice(request: Request, voice: str = Form(...)): +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 / output_directory / f'{output_file_name}.{model_engine.audio_format}' - await generate_audio(text, voice, language, model_engine.temperature_set, model_engine.repetitionpenalty_set, 1.0, 0, output_file_path, streaming=False) + 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'/audio/{output_file_name}.{model_engine.audio_format}' + 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={ @@ -678,194 +832,96 @@ async def apifunction_preview_voice(request: Request, voice: str = Form(...)): print(f"An error occurred: {e}") return JSONResponse(content={"error": "An error occurred"}, status_code=500) -################################################################## -# API Endpoint - OpenAI Speech API compatable endpoint Validator # -################################################################## -class OpenAIInput(BaseModel): - model: str = Field(..., description="The TTS model to use. Currently ignored.") - input: str = Field(..., max_length=4096, description="The text to generate audio for.") - voice: str = Field(..., description="The voice to use when generating the audio.") - response_format: str = Field(default="wav", description="The format of the audio. Currently only 'wav' is supported.") - speed: float = Field(default=1.0, ge=0.25, le=4.0, description="The speed of the generated audio.") +######################## +#### GENERATION API #### +######################## +import html +import re +import uuid +import numpy as np +import soundfile as sf +import sys +import hashlib - @field_validator('voice') - def validate_voice(cls, value): - supported_voices = ["alloy", "echo", "fable", "nova", "onyx", "shimmer"] - if value not in supported_voices: - raise ValueError(f'Voice must be one of {supported_voices}') - return value +############################## +#### Streaming Generation #### +############################## -class OpenAIGenerator: - @staticmethod - def validate_input(json_data: dict) -> Union[None, str]: - try: - OpenAIInput(**json_data) - return None - except ValidationError as e: - errors = [] - for err in e.errors(): - field = err['loc'][0] - message = err['msg'] - description = OpenAIInput.model_fields[field].field_info.description - errors.append(f"Error in field '{field}': {message}. Description: {description}") - return ', '.join(errors) - -######################################################################### -# API Endpoint - OpenAI Speech API compatable endpoint /v1/audio/speech # -######################################################################### -@app.post("/v1/audio/speech", response_class=JSONResponse) -async def openai_tts_generate(request: Request): +@app.get("/api/tts-generate-streaming", response_class=StreamingResponse) +async def tts_generate_streaming(text: str, voice: str, language: str, output_file: str): try: - json_data = await request.json() - print(f"[{branding}Debug] Received JSON data: {json_data}") if debug_openai else None - validation_error = OpenAIGenerator.validate_input(json_data) - if validation_error: - print(f"Validation error: {validation_error}") if debug_openai else None - return JSONResponse(content={"error": validation_error}, status_code=400) - model = json_data["model"] # Currently ignored - input_text = json_data["input"] - voice = json_data["voice"] - response_format = json_data["response_format"].lower() - speed = json_data["speed"] - print(f"[{branding}Debug] Input text: {input_text}") if debug_openai else None - print(f"[{branding}Debug] Voice: {voice}") if debug_openai else None - print(f"[{branding}Debug] Speed: {speed}") if debug_openai else None - cleaned_string = html.unescape(standard_filtering(input_text)) - # Map the OpenAI voice to the corresponding internal voice - voice_mapping = { - "alloy": model_engine.openai_alloy, - "echo": model_engine.openai_echo, - "fable": model_engine.openai_fable, - "nova": model_engine.openai_nova, - "onyx": model_engine.openai_onyx, - "shimmer": model_engine.openai_shimmer - } - mapped_voice = voice_mapping.get(voice) - if not mapped_voice: - print(f"Unsupported voice: {voice}") - return JSONResponse(content={"error": "Unsupported voice"}, status_code=400) - print(f"[{branding}Debug]Mapped voice: {mapped_voice}") if debug_openai else None - # Generate the audio - # Generate a unique filename - unique_id = uuid.uuid4() - timestamp = int(time.time()) - output_file_path = f'{this_dir / output_directory / f"openai_output_{unique_id}_{timestamp}.{model_engine.audio_format}"}' - await generate_audio(cleaned_string, mapped_voice, "en", model_engine.temperature_set, - model_engine.repetitionpenalty_set, speed, model_engine.pitch_set, - output_file_path, streaming=False) - print(f"[{branding}Debug] Audio generated at: {output_file_path}") if debug_openai else None - if rvc_settings["rvc_enabled"]: - if rvc_settings["rvc_char_model_file"].lower() in ["disabled", "disable"]: - print(f"[{branding}Debug] Pass rvccharacter_voice_gen") if debug_openai else None - pass # Skip RVC processing for character part - else: - print(f"[{branding}Debug] send to rvc") if debug_openai else None - pth_path = this_dir / "models" / "rvc_voices" / rvc_settings["rvc_char_model_file"] - run_rvc(output_file_path, pth_path, infer_pipeline) - # Transcode the audio to the requested format - transcoded_file_path = await transcode_for_openai(output_file_path, response_format) - print(f"[{branding}Debug] Audio transcoded to: {transcoded_file_path}") if debug_openai else None - # Return the audio file - return FileResponse(transcoded_file_path, media_type=f"audio/{response_format}", filename=f"output.{response_format}") + 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"[{branding}Debug] An error occurred: {str(e)}") if debug_openai else None + print(f"An error occurred: {e}") return JSONResponse(content={"error": "An error occurred"}, status_code=500) -########################################################################### -# API Endpoint - OpenAI Speech API compatable endpoint Transcode Function # -########################################################################### -async def transcode_for_openai(input_file, output_format): - print(f"[{branding}Debug] ************************************") if debug_openai else None - print(f"[{branding}Debug] transcode_for_openai function called") if debug_openai else None - print(f"[{branding}Debug] ************************************") if debug_openai else None - print(f"[{branding}Debug] Input file : {input_file}") if debug_openai else None - print(f"[{branding}Debug] Output format : {output_format}") if debug_openai else None - if not ffmpeg_installed: - print(f"[{branding}TTS] FFmpeg is not installed. Format conversion is not possible.") - raise Exception("FFmpeg is not installed. Format conversion is not possible.") - # Get the file extension of the input file - input_extension = os.path.splitext(input_file)[1][1:].lower() - print(f"[{branding}Debug] Input file extension: {input_extension}") if debug_openai else None - # Check if the input extension matches the requested output format - if input_extension == output_format.lower(): - print(f"[{branding}Debug] Input file is already in the requested format: {output_format}") if debug_openai else None - return input_file - output_file = os.path.splitext(input_file)[0] + f".{output_format}" - print(f"[{branding}Debug] Output file: {output_file}") if debug_openai else None - ffmpeg_path = "ffmpeg" # Default path for Linux and macOS - if sys.platform == "win32": - ffmpeg_path = os.path.join(this_dir, "system", "win_ffmpeg", "ffmpeg.exe") - ffmpeg = (FFmpeg(ffmpeg_path).option("y").input(input_file).output(output_file)) - print(f"[{branding}Debug] Transcoding to {output_format}") if debug_openai else None - if output_format == "opus": - print(f"[{branding}Debug] Configuring Opus options") if debug_openai else None - ffmpeg.output(output_file, {"codec:a": "libopus", "b:a": "128k", "vbr": "on", "compression_level": 10, "frame_duration": 60, "application": "voip"}) - elif output_format == "aac": - print(f"[{branding}Debug] Configuring AAC options") if debug_openai else None - ffmpeg.output(output_file, {"codec:a": "aac", "b:a": "192k"}) - elif output_format == "flac": - print(f"[{branding}Debug] Configuring FLAC options") if debug_openai else None - ffmpeg.output(output_file, {"codec:a": "flac", "compression_level": 8}) - elif output_format == "wav": - print(f"[{branding}Debug] Configuring WAV options") if debug_openai else None - ffmpeg.output(output_file, {"codec:a": "pcm_s16le"}) - elif output_format == "mp3": - print(f"[{branding}Debug] Configuring MP3 options") if debug_openai else None - ffmpeg.output(output_file, {"codec:a": "libmp3lame", "b:a": "192k"}) - elif output_format in ["ogg", "m4a"]: - if output_format == "ogg": - print(f"[{branding}Debug] Configuring OGG options") if debug_openai else None - ffmpeg.output(output_file, {"codec:a": "libvorbis"}) - elif output_format == "m4a": - print(f"[{branding}Debug] Configuring M4A options") if debug_openai else None - ffmpeg.output(output_file, {"codec:a": "aac", "b:a": "192k"}) - else: - print(f"[{branding}TTS] Unsupported output format: {output_format}") - raise ValueError(f"Unsupported output format: {output_format}") +@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: - print(f"[{branding}Debug] Starting transcoding process") if debug_openai else None - await ffmpeg.execute() - print(f"[{branding}Debug] Transcoding completed successfully") if debug_openai else None + 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"[{branding}TTS] Error occurred during transcoding: {str(e)}") - raise - print(f"[{branding}Debug] Transcoding process completed") if debug_openai else None - print(f"[{branding}Debug] Transcoded file: {output_file}") if debug_openai else None - return output_file + print(f"An error occurred: {e}") + return JSONResponse(content={"error": "An error occurred"}, status_code=500) +@app.put("/api/stop-generation") +async def stop_generation_endpoint(): + global tts_stop_generation, tts_generation_lock + if tts_generation_lock and not tts_stop_generation: + tts_stop_generation = True + return {"message": "Generation stopped"} + +############################## +#### 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 -####################### -# Play at the console # -####################### def play_audio(file_path, volume): - print(f"[{branding}GEN] \033[94m File path is: {file_path}") if debug_tts else None - print(f"[{branding}GEN] \033[94m Volume is: {volume}") if debug_tts else None - normalized_file_path = os.path.normpath(file_path) - print(f"[{branding}GEN] \033[94m Normalized file path is: {normalized_file_path}") if debug_tts else None - directory = os.path.dirname(normalized_file_path) - if os.path.isdir(directory): - print(f"[{branding}GEN] \033[94m Directory contents: {os.listdir(directory)}") if debug_tts else None - else: - print(f"[{branding}GEN] \033[94mError: Directory does not exist: {directory}") if debug_tts else None - if not os.path.isfile(normalized_file_path): - print(f"[{branding}GEN] \033[94mError: File does not exist: {normalized_file_path}\033[0m") if debug_tts else None - return - # Check for AAC format - if normalized_file_path.lower().endswith('.aac'): - print(f"[{branding}GEN] \033[94mPlay Audio : \033[0mAAC format files cannot be played at the console. Please choose another format") - return - try: - print(f"[{branding}GEN] \033[94mPlay Audio : \033[0mPlaying audio at console") - data, fs = sf.read(normalized_file_path) - sd.play(volume * data, fs) - sd.wait() - except Exception as e: - print(f"[{branding}GEN] \033[94mError playing audio file: {e}\033[0m") + 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)$", description="text_not_inside needs to be 'character' or 'narrator'.") + language: str = Field(..., pattern="^(ar|zh-cn|cs|nl|en|fr|de|hu|hi|it|ja|ko|pl|pt|ru|es|tr)$", description="language needs to be one of the following ar|zh-cn|cs|nl|en|fr|de|hu|hi|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 @@ -878,9 +934,6 @@ class TTSGenerator: except ValidationError as e: return str(e) -################################################################# -# /api/tts-generate Generation API Endpoint Narration Filtering # -################################################################# def process_text(text): # Normalize HTML encoded quotes text = html.unescape(text) @@ -930,367 +983,64 @@ def standard_filtering(text_input): ) return text_output -################################################################# -# /api/tts-generate Generation API Endpoint Narration Combining # -################################################################# -def combine(output_file_timestamp, output_file_name, audio_files, target_sample_rate=44100): - audio = np.array([]) +def combine(output_file_timestamp, output_file_name, audio_files): + audio = np.array([]) + sample_rate = None try: for audio_file in audio_files: - normalized_audio_file = os.path.normpath(audio_file) - print(f"[{branding}Debug] Processing file: {normalized_audio_file}") if debug_concat else None - # Check if the file exists - if not os.path.isfile(normalized_audio_file): - print(f"[{branding}Debug] Error: File does not exist: {normalized_audio_file}") if debug_concat else None - return None, None - # Read the audio file - audio_data, current_sample_rate = sf.read(normalized_audio_file) - print(f"[{branding}Debug] Read file: {normalized_audio_file}, Sample rate: {current_sample_rate}, Data shape: {audio_data.shape}") if debug_concat else None - # Resample if necessary - if current_sample_rate != target_sample_rate: - print(f"[{branding}Debug] Resampling file from {current_sample_rate} to {target_sample_rate} Hz") if debug_concat else None - audio_data = librosa.resample(audio_data, orig_sr=current_sample_rate, target_sr=target_sample_rate) - # Concatenate audio data + audio_data, current_sample_rate = sf.read(audio_file) if audio.size == 0: audio = audio_data - else: + sample_rate = current_sample_rate + elif sample_rate == current_sample_rate: audio = np.concatenate((audio, audio_data)) - if output_file_timestamp: - timestamp = int(time.time()) - output_file_path = os.path.join(this_dir / "outputs" / f'{output_file_name}_{timestamp}_combined.wav') - sf.write(output_file_path, audio, target_sample_rate) - # Legacy API or New API return - if params["api_def"]["api_use_legacy_api"]: - output_file_url = f"http://{api_defaults['api_legacy_ip_address']}:{params['api_def']['api_port_number']}/audio/{output_file_name}_{timestamp}_combined.wav" - output_cache_url = f"http://{api_defaults['api_legacy_ip_address']}:{params['api_def']['api_port_number']}/audiocache/{output_file_name}_{timestamp}_combined.wav" else: - output_file_url = f'/audio/{output_file_name}_{timestamp}_combined.wav' - output_cache_url = f'/audiocache/{output_file_name}_{timestamp}_combined.wav' - else: - output_file_path = os.path.join(this_dir / "outputs" / f'{output_file_name}_combined.wav') - sf.write(output_file_path, audio, target_sample_rate) - # Legacy API or New API return - if params["api_def"]["api_use_legacy_api"]: - output_file_url = f"http://{api_defaults['api_legacy_ip_address']}:{api_defaults['api_port_number']}/audio/{output_file_name}_combined.wav" - output_cache_url = f"http://{api_defaults['api_legacy_ip_address']}:{api_defaults['api_port_number']}/audiocache/{output_file_name}_combined.wav" - else: - output_file_url = f'/audio/{output_file_name}_combined.wav' - output_cache_url = f'/audiocache/{output_file_name}_combined.wav' - print(f"[{branding}Debug] Output file changed to:", output_file_path) if debug_concat else None - print(f"[{branding}Debug] Output file changed to:", output_file_url) if debug_concat else None - print(f"[{branding}Debug] Output file changed to:", output_cache_url) if debug_concat else None + raise ValueError("Sample rates of input files are not consistent.") except Exception as e: - print(f"Error occurred: {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 -################################## -# Central RVC Generation request # -################################## -def run_rvc(input_tts_path, pth_path, pitch, infer_pipeline): - generate_start_time = time.time() - f0up_key = pitch - filter_radius = rvc_settings["filter_radius"] - index_rate = rvc_settings["index_rate"] - rms_mix_rate = rvc_settings["rms_mix_rate"] - protect = rvc_settings["protect"] - hop_length = rvc_settings["hop_length"] - f0method = rvc_settings["f0method"] - split_audio = rvc_settings["split_audio"] - f0autotune = rvc_settings["autotune"] - embedder_model = rvc_settings["embedder_model"] - training_data_size = rvc_settings["training_data_size"] - # Convert path variables to strings - input_tts_path = str(input_tts_path) - pth_path = str(pth_path) - # Check if the model file exists - if not os.path.isfile(pth_path): - print(f"Model file {pth_path} does not exist. Exiting.") - return - # Get the directory of the model file - model_dir = os.path.dirname(pth_path) - # Get the filename of pth_path - pth_filename = os.path.basename(pth_path) - # Find all .index files in the model directory - index_files = [file for file in os.listdir(model_dir) if file.endswith(".index")] - if len(index_files) == 1: - index_path = str(os.path.join(model_dir, index_files[0])) - # Get the filename of index_path - index_filename = os.path.basename(index_path) - index_filename_print = index_filename - index_size_print = training_data_size - elif len(index_files) > 1: - print(f"[{branding}GEN] \033[94mRVC Convert :\033[0m Multiple RVC index files found in the models folder where \033[93m{pth_filename}\033[0m is") - print(f"[{branding}GEN] \033[94mRVC Convert :\033[0m located. Unable to determine which index to use. Continuing without an index file.") - index_path = "" - index_filename = None - index_filename_print = "None used" - index_size_print = "N/A" - else: - index_path = "" - index_filename = None - index_filename_print = "None used" - index_size_print = "N/A" - # Set the output path to be the same as the input path - output_rvc_path = input_tts_path - if debug_rvc: - debug_lines = [ - f"[{branding}Debug] ************************************", - f"[{branding}Debug] run_rvc function called (debug_rvc)", - f"[{branding}Debug] ***********************************", - f"[{branding}Debug] f0up_key : {f0up_key}", - f"[{branding}Debug] filter_radius : {filter_radius}", - f"[{branding}Debug] index_rate : {index_rate}", - f"[{branding}Debug] rms_mix_rate : {rms_mix_rate}", - f"[{branding}Debug] protect : {protect}", - f"[{branding}Debug] hop_length : {hop_length}", - f"[{branding}Debug] f0method : {f0method}", - f"[{branding}Debug] input_tts_path : {input_tts_path}", - f"[{branding}Debug] output_rvc_path : {output_rvc_path}", - f"[{branding}Debug] pth_path : {pth_path}", - f"[{branding}Debug] index_path : {index_path}", - f"[{branding}Debug] split_audio : {split_audio}", - f"[{branding}Debug] f0autotune : {f0autotune}", - f"[{branding}Debug] embedder_model : {embedder_model}", - f"[{branding}Debug] training_data_size: {training_data_size}" - ] - debug_info = "\n".join(debug_lines) - print(debug_info) - # Call the infer_pipeline function - infer_pipeline(f0up_key, filter_radius, index_rate, rms_mix_rate, protect, hop_length, f0method, - input_tts_path, output_rvc_path, pth_path, index_path, split_audio, f0autotune, embedder_model, training_data_size, debug_rvc) - generate_end_time = time.time() - generate_elapsed_time = generate_end_time - generate_start_time - print(f"[{branding}GEN] \033[94mRVC Convert : \033[94mModel: \033[93m{pth_filename} \033[94mIndex: \033[93m{index_filename_print}\033[0m") - print(f"[{branding}GEN] \033[94mRVC Convert : \033[93m{generate_elapsed_time:.2f} seconds. \033[94mMethod: \033[93m{f0method} \033[94mIndex size used \033[93m{index_size_print}\033[0m") - return - -#################################################################### -# /api/tts-generate Generation API Endpoint Narration RVC handling # -#################################################################### -def process_rvc_narrator(part_type, voice_gen, pitch, default_model_file, output_file, infer_pipeline): - if voice_gen.lower() in ["disabled", "disable"]: - return - else: - voice_gen_path = this_dir / "models" / "rvc_voices" / voice_gen - pth_path = voice_gen_path if voice_gen else default_model_file - run_rvc(output_file, pth_path, pitch, infer_pipeline) - -############################################################# -# /api/tts-generate Generation API Endpoint JSON validation # -############################################################# -class JSONInput(BaseModel): - text_input: str = Field(..., max_length=int(api_defaults["api_max_characters"]), description=f"text_input needs to be {api_defaults['api_max_characters']} 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=r'^[\(\)a-zA-Z0-9\_\-./\s]+$', description="character_voice_gen needs to be the name of a valid voice for the loaded TTS engine.") - rvccharacter_voice_gen: str = Field(..., description="rvccharacter_voice_gen needs to be the name of a valid pth file in the 'folder\\file.pth' format or the word 'Disabled'.") - rvccharacter_pitch: float = Field(..., description="RVC Character pitch needs to be a number between -24 and 24") - narrator_enabled: str = Field(..., pattern="^(true|false|silent)$", description="narrator_enabled needs to be 'true', 'false' or 'silent'.") - narrator_voice_gen: str = Field(..., pattern=r'^[\(\)a-zA-Z0-9\_\-./\s]+$', description="character_voice_gen needs to be the name of a valid voice for the loaded TTS engine.") - rvcnarrator_voice_gen: str = Field(..., description="rvcnarrator_voice_gen needs to be the name of a valid pth file in the 'folder\\file.pth' format or the word 'Disabled'.") - rvcnarrator_pitch: float = Field(..., description="RVC Narrator pitch needs to be a number between -24 and 24") - text_not_inside: str = Field(..., pattern="^(character|narrator|silent)$", description="text_not_inside needs to be 'character', 'narrator' or 'silent'.") - language: str = Field(..., pattern="^(ar|zh-cn|cs|nl|en|fr|de|hu|hi|it|ja|ko|pl|pt|ru|es|tr)$", description="language needs to be one of the following: ar, zh-cn, cs, nl, en, fr, de, hu, hi, it, ja, ko, pl, pt, ru, es, tr.") - output_file_name: str = Field(..., pattern="^[a-zA-Z0-9_]+$", 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.") - speed: float = Field(..., ge=0.25, le=2.0, description="speed needs to be between 0.25 and 2.0.") - pitch: float = Field(..., ge=-10, le=10, description="pitch needs to be between -10 and 10.") - temperature: float = Field(..., ge=0.1, le=1.0, description="temperature needs to be between 0.1 and 1.0.") - repetition_penalty: float = Field(..., ge=1.0, le=20.0, description="repetition_penalty needs to be between 1.0 and 20.0.") - - @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 - - def validate_rvccharacter_voice_gen(cls, v): - if v.lower() == "disabled": - return v - pattern = re.compile(r'^.*\.(pth)$') - if not pattern.match(v): - raise ValueError("rvccharacter_voice_gen needs to be the name of a valid pth file in the 'folder\\file.pth' format or 'Disabled'.") - return v - - def validate_rvnarrator_voice_gen(cls, v): - if v.lower() == "disabled": - return v - pattern = re.compile(r'^.*\.(pth)$') - if not pattern.match(v): - raise ValueError("rvcnarrator_voice_gen needs to be the name of a valid pth file in the 'folder\\file.pth' format or 'Disabled'.") - return v - - def validate_pitches(cls, value): - try: - num_value = float(value) - except ValueError: - raise ValueError("Pitch must be a number or a string representing a number.") - if not -24 <= num_value <= 24: - raise ValueError("Pitch needs to be a number between -24 and 24.") - return value - -def validate_json_input(json_input_data): - try: - JSONInput(**json_input_data) - except ValidationError as e: - error_messages = [] - for error in e.errors(): - field = error["loc"][0] - description = JSONInput.__fields__[field].description - error_messages.append(f"{field}: {description}") - error_message = "\n".join(error_messages) - print(f"[{branding}API] \033[91mError with API request:\033[0m", error_message) - return error_message - return None - -############################################################### -# /api/tts-generate Generation API Endpoint pull fresh values # -############################################################### -def get_api_text_filtering(): - return api_defaults["api_text_filtering"] -def get_api_narrator_enabled(): - return api_defaults["api_narrator_enabled"] -def get_api_text_not_inside(): - return api_defaults["api_text_not_inside"] -def get_api_language(): - return api_defaults["api_language"] -def get_api_output_file_name(): - return api_defaults["api_output_file_name"] -def get_api_output_file_timestamp(): - return api_defaults["api_output_file_timestamp"] -def get_api_autoplay(): - return api_defaults["api_autoplay"] -def get_api_autoplay_volume(): - return api_defaults["api_autoplay_volume"] -def get_params_speed(): - return model_engine.generationspeed_set -def get_params_temperature(): - return model_engine.temperature_set -def get_params_repetition(): - return model_engine.repetitionpenalty_set -def get_params_pitch(): - return model_engine.pitch_set -def get_character_voice_gen(): - return model_engine.def_character_voice -def get_rvccharacter_voice_gen(): - return rvc_settings["rvc_char_model_file"] -def get_rvccharacter_pitch(): - return rvc_settings["pitch"] -def get_narrator_voice_gen(): - return model_engine.def_narrator_voice -def get_rvcnarrator_voice_gen(): - return rvc_settings["rvc_narr_model_file"] -def get_rvcnarrator_pitch(): - return rvc_settings["pitch"] - -############################################# -# /api/tts-generate Generation API Endpoint # -############################################# +# Generation API (separate from text-generation-webui) @app.post("/api/tts-generate", response_class=JSONResponse) -async def apifunction_generate_tts_standard( +async def tts_generate( text_input: str = Form(...), - text_filtering: str = Form(None), - character_voice_gen: str = Form(None), - rvccharacter_voice_gen: str = Form(None), - rvccharacter_pitch: float = Form(None), - narrator_enabled: str = Form(None), - narrator_voice_gen: str = Form(None), - rvcnarrator_voice_gen: str = Form(None), - rvcnarrator_pitch: float = Form(None), - text_not_inside: str = Form(None), - language: str = Form(None), - output_file_name: str = Form(None), - output_file_timestamp: bool = Form(None), - autoplay: bool = Form(None), - autoplay_volume: float = Form(None), + 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), - speed: float = Form(None), - temperature: float = Form(None), - repetition_penalty: float = Form(None), - pitch: float = Form(None), - _text_filtering: str = Depends(get_api_text_filtering), - _character_voice_gen: str = Depends(get_character_voice_gen), - _rvccharacter_voice_gen: str = Depends(get_rvccharacter_voice_gen), - _rvccharacter_pitch: str = Depends(get_rvccharacter_pitch), - _narrator_enabled: str = Depends(get_api_narrator_enabled), - _narrator_voice_gen: str = Depends(get_narrator_voice_gen), - _rvcnarrator_voice_gen: str = Depends(get_rvcnarrator_voice_gen), - _rvcnarrator_pitch: str = Depends(get_rvcnarrator_pitch), - _text_not_inside: str = Depends(get_api_text_not_inside), - _language: str = Depends(get_api_language), - _output_file_name: str = Depends(get_api_output_file_name), - _output_file_timestamp: bool = Depends(get_api_output_file_timestamp), - _autoplay: bool = Depends(get_api_autoplay), - _autoplay_volume: float = Depends(get_api_autoplay_volume), - _speed: float = Depends(get_params_speed), - _temperature: float = Depends(get_params_temperature), - _repetition_penalty: float = Depends(get_params_repetition), - _pitch: float = Depends(get_params_pitch), ): - if debug_tts or debug_tts_variables: - debug_lines = [ - f"[{branding}Debug] *******************************************************", - f"[{branding}Debug] /api/tts_generate function called (debug_tts_variables)", - f"[{branding}Debug] **** PRE validation checks ****", - f"[{branding}Debug] *******************************************************", - f"[{branding}Debug] Defaults will be pulled from Settings if not specified in API Request!", - f"[{branding}Debug] max_characters : {api_defaults['api_max_characters']}", - f"[{branding}Debug] length_stripping : {api_defaults['api_length_stripping']}", - f"[{branding}Debug] legacy_api : {api_defaults['api_use_legacy_api']}", - f"[{branding}Debug] legacy_api IPadd : {api_defaults['api_legacy_ip_address']}", - f"[{branding}Debug] allowed_filter : {api_defaults['api_allowed_filter']}", - f"[{branding}Debug] text_filtering : {text_filtering}", - f"[{branding}Debug] character_voice_gen : {character_voice_gen}", - f"[{branding}Debug] rvccharacter_voice_gen : {rvccharacter_voice_gen}", - f"[{branding}Debug] rvccharacter_pitch : {rvccharacter_pitch}", - f"[{branding}Debug] narrator_enabled : {narrator_enabled}", - f"[{branding}Debug] narrator_voice_gen : {narrator_voice_gen}", - f"[{branding}Debug] rvcnarrator_voice_gen : {rvcnarrator_voice_gen}", - f"[{branding}Debug] rvcnarrator_pitch : {rvcnarrator_pitch}", - f"[{branding}Debug] text_not_inside : {text_not_inside}", - f"[{branding}Debug] language : {language}", - f"[{branding}Debug] output_file_name : {output_file_name}", - f"[{branding}Debug] output_file_timestamp : {output_file_timestamp}", - f"[{branding}Debug] autoplay : {autoplay}", - f"[{branding}Debug] autoplay_volume : {autoplay_volume}", - f"[{branding}Debug] streaming : {streaming}", - f"[{branding}Debug] speed : {speed}", - f"[{branding}Debug] temperature : {temperature}", - f"[{branding}Debug] repetition_penalty : {repetition_penalty}", - f"[{branding}Debug] pitch : {pitch}" - ] - debug_info = "\n".join(debug_lines) - print(debug_info) - text_filtering = text_filtering or _text_filtering - character_voice_gen = character_voice_gen or _character_voice_gen - rvccharacter_voice_gen = rvccharacter_voice_gen or _rvccharacter_voice_gen - rvccharacter_pitch = rvccharacter_pitch if rvccharacter_pitch is not None else _rvccharacter_pitch - narrator_enabled = narrator_enabled or _narrator_enabled # NEED TO COME BACK AND LOOK AT THIS!!! - narrator_voice_gen = narrator_voice_gen or _narrator_voice_gen - rvcnarrator_voice_gen = rvcnarrator_voice_gen or _rvcnarrator_voice_gen - rvcnarrator_pitch = rvcnarrator_pitch if rvcnarrator_pitch is not None else _rvcnarrator_pitch - text_not_inside = text_not_inside or _text_not_inside - language = language or _language - output_file_name = output_file_name or _output_file_name - output_file_timestamp = output_file_timestamp if output_file_timestamp is not None else _output_file_timestamp - autoplay = autoplay if autoplay is not None else _autoplay - autoplay_volume = autoplay_volume or _autoplay_volume - speed = speed or _speed - temperature = temperature or _temperature - repetition_penalty = repetition_penalty or _repetition_penalty - pitch = pitch or _pitch try: json_input_data = { "text_input": text_input, "text_filtering": text_filtering, "character_voice_gen": character_voice_gen, - "rvccharacter_voice_gen": rvccharacter_voice_gen, - "rvccharacter_pitch": rvccharacter_pitch, "narrator_enabled": narrator_enabled, "narrator_voice_gen": narrator_voice_gen, - "rvcnarrator_voice_gen": rvcnarrator_voice_gen, - "rvcnarrator_pitch": rvcnarrator_pitch, "text_not_inside": text_not_inside, "language": language, "output_file_name": output_file_name, @@ -1298,131 +1048,56 @@ async def apifunction_generate_tts_standard( "autoplay": autoplay, "autoplay_volume": autoplay_volume, "streaming": streaming, - "speed": speed, - "temperature": temperature, - "repetition_penalty": repetition_penalty, - "pitch": pitch, } - JSONresult = validate_json_input(json_input_data) + global tts_narrator_generatingtts + JSONresult = TTSGenerator.validate_json_input(json_input_data) if JSONresult is None: pass else: + print(f"[{params['branding']}API] \033[91mError with API request:\033[0m", JSONresult) return JSONResponse(content={"error": JSONresult}, status_code=400) - - if debug_tts or debug_tts_variables: - debug_lines = [ - f"[{branding}Debug] *******************************************************", - f"[{branding}Debug] /api/tts_generate function called (debug_tts_variables)", - f"[{branding}Debug] **** POST validation checks ****", - f"[{branding}Debug] *******************************************************", - f"[{branding}Debug] Defaults will be pulled from Settings if not specified in API Request!", - f"[{branding}Debug] max_characters : {api_defaults['api_max_characters']}", - f"[{branding}Debug] length_stripping : {api_defaults['api_length_stripping']}", - f"[{branding}Debug] legacy_api : {api_defaults['api_use_legacy_api']}", - f"[{branding}Debug] legacy_api IPadd : {api_defaults['api_legacy_ip_address']}", - f"[{branding}Debug] allowed_filter : {api_defaults['api_allowed_filter']}", - f"[{branding}Debug] text_filtering : {text_filtering}", - f"[{branding}Debug] character_voice_gen : {character_voice_gen}", - f"[{branding}Debug] rvccharacter_voice_gen : {rvccharacter_voice_gen}", - f"[{branding}Debug] rvccharacter_pitch : {rvccharacter_pitch}", - f"[{branding}Debug] narrator_enabled : {narrator_enabled}", - f"[{branding}Debug] narrator_voice_gen : {narrator_voice_gen}", - f"[{branding}Debug] rvcnarrator_voice_gen : {rvcnarrator_voice_gen}", - f"[{branding}Debug] rvcnarrator_pitch : {rvcnarrator_pitch}", - f"[{branding}Debug] text_not_inside : {text_not_inside}", - f"[{branding}Debug] language : {language}", - f"[{branding}Debug] output_file_name : {output_file_name}", - f"[{branding}Debug] output_file_timestamp : {output_file_timestamp}", - f"[{branding}Debug] autoplay : {autoplay}", - f"[{branding}Debug] autoplay_volume : {autoplay_volume}", - f"[{branding}Debug] streaming : {streaming}", - f"[{branding}Debug] speed : {speed}", - f"[{branding}Debug] temperature : {temperature}", - f"[{branding}Debug] repetition_penalty : {repetition_penalty}", - f"[{branding}Debug] pitch : {pitch}" - ] - debug_info = "\n".join(debug_lines) - print(debug_info) - if narrator_enabled.lower() == "silent" and text_not_inside.lower() == "silent": - print(f"[{branding}GEN] \033[92mBoth Narrator & Text-not-inside are set to \033[91msilent\033[92m. If you get no TTS, this is why.\033[0m") - if narrator_enabled.lower() in ["true", "silent"]: - if model_engine.lowvram_enabled and model_engine.device == "cpu": - await model_engine.handle_lowvram_change() - model_engine.tts_narrator_generatingtts = True - print(f"[{branding}Debug] Moved into Narrator") if debug_tts else None - print(f"[{branding}Debug] original text : {text_input}") if debug_tts else None + if narrator_enabled: + if params["low_vram"] and device == "cpu": + await switch_device() + tts_narrator_generatingtts = True 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()) <= int(params["api_def"]["api_length_stripping"]): + if len(part.strip()) <= 3: continue # Determine the voice to use based on the part type if part_type == 'narrator': - if narrator_enabled.lower() == "silent": - print(f"[{branding}GEN] \033[95mNarrator Silent:\033[0m", part) # Green - continue # Skip generating audio for narrator parts if set to "silent" voice_to_use = narrator_voice_gen - print(f"[{branding}GEN] \033[92mNarrator:\033[0m", part) # Purple + print(f"[{params['branding']}TTSGen] \033[92mNarrator\033[0m") # Green elif part_type == 'character': voice_to_use = character_voice_gen - print(f"[{branding}GEN] \033[36mCharacter:\033[0m", part) # Yellow + print(f"[{params['branding']}TTSGen] \033[36mCharacter\033[0m") # Yellow else: # Handle ambiguous parts based on user preference - if text_not_inside == "silent": - print(f"[{branding}GEN] \033[95mText-not-inside Silent:\033[0m", part) # Purple - continue # Skip generating audio for ambiguous parts if set to "silent" 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"[{branding}GEN] {voice_description}:", part) + 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) + cleaned_part = re.sub(r'([!?.\u3002\uFF1F\uFF01\uFF0C])\1+', r'\1', part) + # Replace "Chinese ellipsis" with a single dot + cleaned_part = re.sub(r"\u2026{1,2}", ". ", cleaned_part) # Further clean to remove any other unwanted characters - cleaned_part = re.sub(rf'{api_defaults["api_allowed_filter"]}', '', cleaned_part) - print(f"[{branding}Debug] text after api_allowed: {text_input}") if debug_tts else None + cleaned_part = re.sub(r'[^a-zA-Z0-9\s.,;:!?\-\'"$\u0400-\u04FF\u00C0-\u00FF\u0150\u0151\u0170\u0171\u0900-\u097F\u2018\u2019\u201C\u201D\u3001\u3002\u3040-\u309F\u30A0-\u30FF\u4E00-\u9FFF\u3400-\u4DBF\uF900-\uFAFF\u0600-\u06FF\u0750-\u077F\uFB50-\uFDFF\uFE70-\uFEFF\uAC00-\uD7A3\u1100-\u11FF\u3130-\u318F\uFF01\uFF0c\uFF1A\uFF1B\uFF1F]', '', cleaned_part) # Remove all newline characters (single or multiple) cleaned_part = re.sub(r'\n+', ' ', cleaned_part) - output_file = this_dir / output_directory / f'{output_file_name}_{uuid.uuid4()}_{int(time.time())}.{model_engine.audio_format}' + 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, speed, pitch, output_file_str, streaming) + response = await generate_audio(cleaned_part, voice_to_use, language,temperature, repetition_penalty, output_file_str, streaming) audio_path = output_file_str - if rvc_settings["rvc_enabled"]: - if part_type == 'character': - process_rvc_narrator(part_type, rvccharacter_voice_gen, rvccharacter_pitch, rvc_settings["rvc_char_model_file"], output_file, infer_pipeline) - elif part_type == 'narrator': - process_rvc_narrator(part_type, rvcnarrator_voice_gen, rvcnarrator_pitch, rvc_settings["rvc_narr_model_file"], output_file, infer_pipeline) - else: - if text_not_inside == 'character': - process_rvc_narrator('character', rvccharacter_voice_gen, rvccharacter_pitch, rvc_settings["rvc_char_model_file"], output_file, infer_pipeline) - elif text_not_inside == 'narrator': - process_rvc_narrator('narrator', rvcnarrator_voice_gen, rvcnarrator_pitch, rvc_settings["rvc_narr_model_file"], output_file, infer_pipeline) - print(f"[{branding}Debug] Appending audio path to list") if debug_tts else None - audio_files_all_paragraphs.append(audio_path) + audio_files_all_paragraphs.append(audio_path) # Combine audio files across paragraphs - model_engine.tts_narrator_generatingtts = False - print(f"[{branding}Debug] Narrator sending to combine") if debug_tts else None output_file_path, output_file_url, output_cache_url = combine(output_file_timestamp, output_file_name, audio_files_all_paragraphs) - # Transcode audio if necessary - model_audio_format = str(model_engine.audio_format).lower() - output_audio_format = str(params["transcode_audio_format"]).lower() - if output_audio_format == "disabled": - pass - else: - if model_audio_format != output_audio_format and not model_engine.tts_narrator_generatingtts: - print(f"[{branding}Debug] Pre Transcode call - Output file: {output_file_path}") if debug_tts else None - output_file_path, output_file_url, output_cache_url = await transcode_audio_if_necessary(output_file_path, model_audio_format, output_audio_format) - print(f"[{branding}Debug] Post Transcode call - Output file: {output_file_path}") if debug_tts else None - if sounddevice_installed == False: - autoplay = False - if autoplay: - play_audio(output_file_path, autoplay_volume) + tts_narrator_generatingtts = False # Move model back to cpu system ram if needed. - if model_engine.lowvram_enabled and model_engine.device == "cuda" and model_engine.lowvram_capable: - await model_engine.handle_lowvram_change() - 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) + if params["low_vram"] and device == "cuda": + await switch_device() else: - print(f"[{branding}Debug] Moved into Standard generation") if debug_tts else None - print(f"[{branding}Debug] original text : {text_input}") if debug_tts else None if output_file_timestamp: timestamp = int(time.time()) # Generate a standard UUID @@ -1432,79 +1107,76 @@ async def apifunction_generate_tts_standard( hashed_uuid = hash_object.hexdigest() # Truncate to the desired length, for example, 16 characters short_uuid = hashed_uuid[:5] - output_file_path = this_dir / output_directory / f'{output_file_name}_{timestamp}{short_uuid}.{model_engine.audio_format}' - #Legacy API or New API return - if params["api_def"]["api_use_legacy_api"]: - output_file_url = f'http://{api_defaults["api_legacy_ip_address"]}:{api_defaults["api_port_number"]}/audio/{output_file_name}_{timestamp}{short_uuid}.{model_engine.audio_format}' - output_cache_url = f'http://{api_defaults["api_legacy_ip_address"]}:{api_defaults["api_port_number"]}/audiocache/{output_file_name}_{timestamp}{short_uuid}.{model_engine.audio_format}' - else: - output_file_url = f'/audio/{output_file_name}_{timestamp}{short_uuid}.{model_engine.audio_format}' - output_cache_url = f'/audiocache/{output_file_name}_{timestamp}{short_uuid}.{model_engine.audio_format}' + 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 / output_directory / f"{output_file_name}.{model_engine.audio_format}" - # Legacy API or New API return - if params["api_def"]["api_use_legacy_api"]: - output_file_url = f'http://{api_defaults["api_legacy_ip_address"]}:{api_defaults["api_port_number"]}/audio/{output_file_name}.{model_engine.audio_format}' - output_cache_url = f'http://{api_defaults["api_legacy_ip_address"]}:{api_defaults["api_port_number"]}/audiocache/{output_file_name}.{model_engine.audio_format}' - else: - output_file_url = f'/audio/{output_file_name}.{model_engine.audio_format}' - output_cache_url = f'/audiocache/{output_file_name}.{model_engine.audio_format}' + 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', cleaned_string) + cleaned_string = re.sub(r'([!?.\u3002\uFF1F\uFF01\uFF0C])\1+', r'\1', text_input) + # Replace "Chinese ellipsis" with a single dot + cleaned_string = re.sub(r"\u2026{1,2}", ". ", cleaned_string) # Further clean to remove any other unwanted characters - cleaned_string = re.sub(rf'{api_defaults["api_allowed_filter"]}', '', cleaned_string) - print(f"[{branding}Debug] HTML Filtering - text after api_allowed: {cleaned_string}") if debug_tts else None + cleaned_string = re.sub(r'[^a-zA-Z0-9\s.,;:!?\-\'"$\u0400-\u04FF\u00C0-\u00FF\u0150\u0151\u0170\u0171\u0900-\u097F\u2018\u2019\u201C\u201D\u3001\u3002\u3040-\u309F\u30A0-\u30FF\u4E00-\u9FFF\u3400-\u4DBF\uF900-\uFAFF\u0600-\u06FF\u0750-\u077F\uFB50-\uFDFF\uFE70-\uFEFF\uAC00-\uD7A3\u1100-\u11FF\u3130-\u318F\uFF01\uFF0c\uFF1A\uFF1B\uFF1F]', '', 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) + cleaned_string = re.sub(r'([!?.\u3002\uFF1F\uFF01\uFF0C])\1+', r'\1', text_input) + # Replace "Chinese ellipsis" with a single dot + cleaned_string = re.sub(r"\u2026{1,2}", ". ", cleaned_string) # Further clean to remove any other unwanted characters - cleaned_string = re.sub(rf'{api_defaults["api_allowed_filter"]}', '', cleaned_string) - print(f"[{branding}Debug] Standard Filtering - text after api_allowed: {cleaned_string}") if debug_tts else None + cleaned_string = re.sub(r'[^a-zA-Z0-9\s.,;:!?\-\'"$\u0400-\u04FF\u00C0-\u00FF\u0150\u0151\u0170\u0171\u0900-\u097F\u2018\u2019\u201C\u201D\u3001\u3002\u3040-\u309F\u30A0-\u30FF\u4E00-\u9FFF\u3400-\u4DBF\uF900-\uFAFF\u0600-\u06FF\u0750-\u077F\uFB50-\uFDFF\uFE70-\uFEFF\uAC00-\uD7A3\u1100-\u11FF\u3130-\u318F\uFF01\uFF0c\uFF1A\uFF1B\uFF1F]', '', cleaned_string) # Remove all newline characters (single or multiple) cleaned_string = re.sub(r'\n+', ' ', cleaned_string) else: - print(f"[{branding}Debug] No filtering text : {text_input}") if debug_tts else None cleaned_string = text_input - print(f"[{branding}GEN]", cleaned_string) - print(f"[{branding}Debug] Sending request to generate_audio in tts_server.py") if debug_tts else None - try: - if streaming: - response = await generate_audio(cleaned_string, character_voice_gen, language, temperature, repetition_penalty, speed, pitch, output_file_path, streaming) - return StreamingResponse(response, media_type="audio/wav") - else: - response = await generate_audio(cleaned_string, character_voice_gen, language, temperature, repetition_penalty, speed, pitch, output_file_path, streaming) - if rvc_settings["rvc_enabled"]: - if rvccharacter_voice_gen.lower() in ["disabled", "disable"]: - print(f"[{branding}Debug] Pass rvccharacter_voice_gen") if debug_tts else None - pass # Skip RVC processing for character part - else: - print(f"[{branding}Debug] send to rvc") if debug_tts else None - rvccharacter_voice_gen = this_dir / "models" / "rvc_voices" / rvccharacter_voice_gen - pth_path = rvccharacter_voice_gen if rvccharacter_voice_gen else rvc_settings["rvc_char_model_file"] - run_rvc(output_file_path, pth_path, rvccharacter_pitch, infer_pipeline) - # Transcode audio if necessary - model_audio_format = str(model_engine.audio_format).lower() - output_audio_format = str(params["transcode_audio_format"]).lower() - print(f"[{branding}Debug] At Model audio format") if debug_tts else None - if output_audio_format == "disabled": - pass - else: - if model_audio_format != output_audio_format and not model_engine.tts_narrator_generatingtts: - print(f"[{branding}Debug] Pre Transcode call - Output file: {output_file_path}") if debug_tts else None - output_file_path, output_file_url, output_cache_url = await transcode_audio_if_necessary(output_file_path, model_audio_format, output_audio_format) - print(f"[{branding}Debug] Post Transcode call - Output file: {output_file_path}") if debug_tts else None - if sounddevice_installed == False: - autoplay = False - if autoplay: - play_audio(output_file_path, autoplay_volume) - 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) + 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 #### ############################# @@ -1514,7 +1186,9 @@ app.mount("/api/word_addin", StaticFiles(directory=os.path.join(this_dir / 'syst ############################################# #### TTS Generator Comparision Endpoints #### ############################################# +import subprocess import aiofiles + class TTSItem(BaseModel): id: int fileUrl: str @@ -1526,114 +1200,83 @@ class TTSData(BaseModel): ttsList: List[TTSItem] @app.post("/api/save-tts-data") -async def apifunction_save_tts_data(tts_data: List[TTSItem]): +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 / output_directory / "ttsList.json", 'w') as f: + async with aiofiles.open(this_dir / "outputs" / "ttsList.json", 'w') as f: await f.write(tts_data_json) return {"message": "Data saved successfully"} -######################################## -# Trigger TTS Gen Text/Speech Analysis # -######################################## +import sys + @app.get("/api/trigger-analysis") -async def apifunction_trigger_analysis(threshold: int = Query(default=98)): +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 / output_directory / "ttsList.json" - wavfile_path = this_dir / output_directory + 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 / output_directory / "analysis_summary.json", "r") as summary_file: + 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 # -########################################### +################################################# +#### TTS Generator SRT Subtitiles generation #### +################################################# @app.get("/api/srt-generation") -async def apifunction_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 / output_directory / "ttsList.json" - wavfile_path = this_dir / output_directory + 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 / output_directory / "subtitles.srt" + + 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') -################################# -# Static Mount for file serving # -################################# -app.mount("/static", StaticFiles(directory=str(this_dir / "system")), name="static") - -######################################## -# Legacy JSON update settings function # -######################################## -# Setup logging -logging.basicConfig(level=logging.DEBUG) -def get_json_data(): - with open(this_dir / "confignew.json", "r") as json_file: - data = json.load(json_file) - return data +################################################### +#### Webserver Startup & Initial model Loading #### +################################################### -@app.get("/settings") -async def get_settings(request: Request): - data = get_json_data() - return templates.TemplateResponse("admin.html", {"request": request, "data": data}) - -@app.get("/settings-json") -async def get_settings_json(): - return get_json_data() - -@app.post("/update-settings") -async def update_settings( - request: Request, - delete_output_wavs: str = Form(...), - gradio_interface: str = Form(...), - gradio_port_number: int = Form(...), - api_port_number: int = Form(...), -): - data = get_json_data() - # Update the settings based on the form values - data["delete_output_wavs"] = delete_output_wavs - data["gradio_interface"] = gradio_interface.lower() == 'true' - data["gradio_port_number"] = gradio_port_number - data["api_def"]["api_port_number"] = api_port_number - # Save the updated settings back to the JSON file - with open(this_dir / "confignew.json", "w") as json_file: - json.dump(data, json_file, indent=4) - # Redirect to the settings page to display the updated settings - return templates.TemplateResponse("admin.html", {"request": request, "data": data}) - -# Create an instance of Jinja2Templates for rendering HTML templates -templates = Jinja2Templates(directory=this_dir / "system") # Get the admin interface template template = templates.get_template("admin.html") # Render the template with the dynamic values rendered_html = template.render(params=params) - -################################################### -#### Webserver Startup & Initial model Loading #### -################################################### + +############################### +#### 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 -port_parameter = int(params["api_def"]["api_port_number"]) +host_parameter = params["ip_address"] +port_parameter = int(params["port_number"]) if __name__ == "__main__": import uvicorn - # Start Uvicorn Webserver - uvicorn_server = uvicorn.run(app, host="0.0.0.0", port=port_parameter, log_level="debug") - + uvicorn.run(app, host=host_parameter, port=port_parameter, log_level="warning")