From 51b5a95563c01be668faa87530aefef5d139cacc Mon Sep 17 00:00:00 2001 From: erew123 <35898566+erew123@users.noreply.github.com> Date: Thu, 6 Jun 2024 15:27:29 +0100 Subject: [PATCH] Delete tts_server.py --- tts_server.py | 1247 ------------------------------------------------- 1 file changed, 1247 deletions(-) delete mode 100644 tts_server.py diff --git a/tts_server.py b/tts_server.py deleted file mode 100644 index fc9e0ac..0000000 --- a/tts_server.py +++ /dev/null @@ -1,1247 +0,0 @@ -import json -import time -import os -from pathlib import Path -import torch -import torchaudio -from TTS.tts.configs.xtts_config import XttsConfig -from TTS.tts.models.xtts import Xtts -import io -import wave -import logging -logging.disable(logging.WARNING) -########################## -#### Webserver Imports#### -########################## -from fastapi import ( - FastAPI, - Form, - Request, - Response, - Depends, - HTTPException, - Query, -) -from fastapi.responses import JSONResponse, HTMLResponse, RedirectResponse, FileResponse, StreamingResponse -from fastapi.middleware.cors import CORSMiddleware -from fastapi.templating import Jinja2Templates -from fastapi.staticfiles import StaticFiles -from contextlib import asynccontextmanager - -########################### -#### STARTUP VARIABLES #### -########################### -# STARTUP VARIABLE - Create "this_dir" variable as the current script directory -this_dir = Path(__file__).parent.resolve() -# STARTUP VARIABLE - Set "device" to cuda if exists, otherwise cpu -device = "cuda" if torch.cuda.is_available() else "cpu" -# STARTUP VARIABLE - Import languges file for Gradio to be able to display them in the interface -with open(this_dir / "system" / "config" / "languages.json", encoding="utf8") as f: - languages = json.load(f) -# Base setting for a possible FineTuned model existing and the loader being available -tts_method_xtts_ft = False - -################################################################# -#### LOAD PARAMS FROM confignew.json - REQUIRED FOR BRANDING #### -################################################################# -# Load config file and get settings -def load_config(file_path): - with open(file_path, "r") as configfile_path: - configfile_data = json.load(configfile_path) - return configfile_data - - -# Define the path to the confignew.json file -configfile_path = this_dir / "confignew.json" - -# Load confignew.json and assign it to a different variable (config_data) -params = load_config(configfile_path) -# check someone hasnt enabled lowvram on a system thats not cuda enabled -params["low_vram"] = "false" if not torch.cuda.is_available() else params["low_vram"] - -# Load values for temperature and repetition_penalty -temperature = params["local_temperature"] -repetition_penalty = params["local_repetition_penalty"] - -# Define the path to the JSON file -config_file_path = this_dir / "modeldownload.json" - -############################################# -#### LOAD PARAMS FROM MODELDOWNLOAD.JSON #### -############################################ -# This is used only in the instance that someone has changed their model path -# Define the path to the JSON file -modeldownload_config_file_path = this_dir / "modeldownload.json" - -# Check if the JSON file exists -if modeldownload_config_file_path.exists(): - with open(modeldownload_config_file_path, "r") as modeldownload_config_file: - modeldownload_settings = json.load(modeldownload_config_file) - - # Extract settings from the loaded JSON - modeldownload_base_path = Path(modeldownload_settings.get("base_path", "")) - modeldownload_model_path = Path(modeldownload_settings.get("model_path", "")) -else: - # Default settings if the JSON file doesn't exist or is empty - print( - f"[{params['branding']}Startup] \033[91mWarning\033[0m modeldownload.config is missing so please re-download it and save it in the alltalk_tts main folder." - ) - -################################################## -#### Check to see if a finetuned model exists #### -################################################## -# Set the path to the directory -trained_model_directory = this_dir / "models" / "trainedmodel" -# Check if the directory "trainedmodel" exists -finetuned_model = trained_model_directory.exists() -# If the directory exists, check for the existence of the required files -if finetuned_model: - required_files = ["model.pth", "config.json", "vocab.json"] - finetuned_model = all((trained_model_directory / file).exists() for file in required_files) - -######################## -#### STARTUP CHECKS #### -######################## -try: - from TTS.api import TTS - from TTS.utils.synthesizer import Synthesizer -except ModuleNotFoundError: - print( - f"[{params['branding']}Startup] \033[91mWarning\033[0m Could not find the TTS module. Make sure to install the requirements for the alltalk_tts extension.", - f"[{params['branding']}Startup] \033[91mWarning\033[0m Linux / Mac:\npip install -r extensions/alltalk_tts/requirements.txt\n", - f"[{params['branding']}Startup] \033[91mWarning\033[0m Windows:\npip install -r extensions\\alltalk_tts\\requirements.txt\n", - f"[{params['branding']}Startup] \033[91mWarning\033[0m If you used the one-click installer, paste the command above in the terminal window launched after running the cmd_ script. On Windows, that's cmd_windows.bat." - ) - raise - -# DEEPSPEED Import - Check for DeepSpeed and import it if it exists -deepspeed_available = False -try: - import deepspeed - deepspeed_available = True -except ImportError: - pass - -@asynccontextmanager -async def startup_shutdown(no_actual_value_it_demanded_something_be_here): - await setup() - yield - # Shutdown logic - - -# Create FastAPI app with lifespan -app = FastAPI(lifespan=startup_shutdown) -# Allow all origins, and set other CORS options -app.add_middleware( - CORSMiddleware, - allow_origins=["*"], # Set this to the specific origins you want to allow - allow_credentials=True, - allow_methods=["*"], - allow_headers=["*"], -) - -##################################### -#### MODEL LOADING AND UNLOADING #### -##################################### -# MODEL LOADERS Picker For API TTS, API Local, XTTSv2 Local, XTTSv2 FT -async def setup(): - global device - # Set a timer to calculate load times - generate_start_time = time.time() # Record the start time of loading the model - # Start loading the correct model as set by "tts_method_api_tts", "tts_method_api_local" or "tts_method_xtts_local" being True/False - if params["tts_method_api_tts"]: - print( - f"[{params['branding']}Model] \033[94mAPI TTS Loading\033[0m {params['tts_model_name']} \033[94minto\033[93m", - device, - "\033[0m", - ) - model = await api_load_model() - elif params["tts_method_api_local"]: - print( - f"[{params['branding']}Model] \033[94mAPI Local Loading\033[0m {modeldownload_model_path} \033[94minto\033[93m", - device, - "\033[0m", - ) - model = await api_manual_load_model() - elif params["tts_method_xtts_local"]: - print( - f"[{params['branding']}Model] \033[94mXTTSv2 Local Loading\033[0m {modeldownload_model_path} \033[94minto\033[93m", - device, - "\033[0m", - ) - model = await xtts_manual_load_model() - elif tts_method_xtts_ft: - print( - f"[{params['branding']}Model] \033[94mXTTSv2 FT Loading\033[0m /models/fintuned/model.pth \033[94minto\033[93m", - device, - "\033[0m", - ) - model = await xtts_ft_manual_load_model() - # Create an end timer for calculating load times - generate_end_time = time.time() - # Calculate start time minus end time - generate_elapsed_time = generate_end_time - generate_start_time - # Print out the result of the load time - print(f"[{params['branding']}Model] \033[94mModel Loaded in \033[93m{generate_elapsed_time:.2f} seconds.\033[0m") - print(f"[{params['branding']}Model] Ready") - # Set "tts_model_loaded" to true - params["tts_model_loaded"] = True - # Set the output path for wav files - output_directory = this_dir / params["output_folder_wav_standalone"] - output_directory.mkdir(parents=True, exist_ok=True) - #Path(f'this_folder/outputs/').mkdir(parents=True, exist_ok=True) - - -# MODEL LOADER For "API TTS" -async def api_load_model(): - global model - model = TTS(params["tts_model_name"]).to(device) - return model - - -# MODEL LOADER For "API Local" -async def api_manual_load_model(): - global model - # check to see if a custom path has been set in modeldownload.json and use that path to load the model if so - if str(modeldownload_base_path) == "models": - model = TTS( - model_path=this_dir / "models" / modeldownload_model_path, - config_path=this_dir / "models" / modeldownload_model_path / "config.json", - ).to(device) - else: - print( - f"[{params['branding']}Model] \033[94mInfo\033[0m Loading your custom model set in \033[93mmodeldownload.json\033[0m:", - modeldownload_base_path / modeldownload_model_path, - ) - model = TTS( - model_path=modeldownload_base_path / modeldownload_model_path, - config_path=modeldownload_base_path / modeldownload_model_path / "config.json", - ).to(device) - print(f"[{params['branding']}Model] \033[94mCoqui Public Model License\033[0m") - print(f"[{params['branding']}Model] \033[94mhttps://coqui.ai/cpml.txt\033[0m") - return model - - -# MODEL LOADER For "XTTSv2 Local" -async def xtts_manual_load_model(): - global model - config = XttsConfig() - # check to see if a custom path has been set in modeldownload.json and use that path to load the model if so - if str(modeldownload_base_path) == "models": - config_path = this_dir / "models" / modeldownload_model_path / "config.json" - vocab_path_dir = this_dir / "models" / modeldownload_model_path / "vocab.json" - checkpoint_dir = this_dir / "models" / modeldownload_model_path - else: - print( - f"[{params['branding']}Model] \033[94mInfo\033[0m Loading your custom model set in \033[93mmodeldownload.json\033[0m:", - modeldownload_base_path / modeldownload_model_path, - ) - config_path = modeldownload_base_path / modeldownload_model_path / "config.json" - vocab_path_dir = modeldownload_base_path / modeldownload_model_path / "vocab.json" - checkpoint_dir = modeldownload_base_path / modeldownload_model_path - config.load_json(str(config_path)) - model = Xtts.init_from_config(config) - model.load_checkpoint( - config, - checkpoint_dir=str(checkpoint_dir), - vocab_path=str(vocab_path_dir), - use_deepspeed=params["deepspeed_activate"], - ) - model.to(device) - print(f"[{params['branding']}Model] \033[94mCoqui Public Model License\033[0m") - print(f"[{params['branding']}Model] \033[94mhttps://coqui.ai/cpml.txt\033[0m") - return model - -# MODEL LOADER For "XTTSv2 FT" -async def xtts_ft_manual_load_model(): - global model - config = XttsConfig() - config_path = this_dir / "models" / "trainedmodel" / "config.json" - vocab_path_dir = this_dir / "models" / "trainedmodel" / "vocab.json" - checkpoint_dir = this_dir / "models" / "trainedmodel" - config.load_json(str(config_path)) - model = Xtts.init_from_config(config) - model.load_checkpoint( - config, - checkpoint_dir=str(checkpoint_dir), - vocab_path=str(vocab_path_dir), - use_deepspeed=params["deepspeed_activate"], - ) - model.to(device) - print(f"[{params['branding']}Model] \033[94mCoqui Public Model License\033[0m") - print(f"[{params['branding']}Model] \033[94mhttps://coqui.ai/cpml.txt\033[0m") - return model - -# MODEL UNLOADER -async def unload_model(model): - print(f"[{params['branding']}Model] \033[94mUnloading model \033[0m") - del model - if torch.cuda.is_available(): - torch.cuda.empty_cache() - params["tts_model_loaded"] = False - return None - - -# MODEL - Swap model based on Gradio selection API TTS, API Local, XTTSv2 Local -async def handle_tts_method_change(tts_method): - global model - global tts_method_xtts_ft - # Update the params dictionary based on the selected radio button - print( - f"[{params['branding']}Model] \033[94mChanging model \033[92m(Please wait 15 seconds)\033[0m" - ) - # Set other parameters to False - if tts_method == "API TTS": - params["tts_method_api_local"] = False - params["tts_method_xtts_local"] = False - params["tts_method_api_tts"] = True - params["deepspeed_activate"] = False - tts_method_xtts_ft = False - elif tts_method == "API Local": - params["tts_method_api_tts"] = False - params["tts_method_xtts_local"] = False - params["tts_method_api_local"] = True - params["deepspeed_activate"] = False - tts_method_xtts_ft = False - elif tts_method == "XTTSv2 Local": - params["tts_method_api_tts"] = False - params["tts_method_api_local"] = False - params["tts_method_xtts_local"] = True - tts_method_xtts_ft = False - elif tts_method == "XTTSv2 FT": - tts_method_xtts_ft = True - params["tts_method_api_tts"] = False - params["tts_method_api_local"] = False - params["tts_method_xtts_local"] = False - - # Unload the current model - model = await unload_model(model) - - # Load the correct model based on the updated params - await setup() - - -# MODEL WEBSERVER- API Swap Between Models -@app.route("/api/reload", methods=["POST"]) -async def reload(request: Request): - tts_method = request.query_params.get("tts_method") - if tts_method not in ["API TTS", "API Local", "XTTSv2 Local", "XTTSv2 FT"]: - return {"status": "error", "message": "Invalid TTS method specified"} - await handle_tts_method_change(tts_method) - return Response( - content=json.dumps({"status": "model-success"}), media_type="application/json" - ) - - -################## -#### LOW VRAM #### -################## -# LOW VRAM - MODEL MOVER VRAM(cuda)<>System RAM(cpu) for Low VRAM setting -async def switch_device(): - global model, device - # Check if CUDA is available before performing GPU-related operations - if torch.cuda.is_available(): - if device == "cuda": - device = "cpu" - model.to(device) - torch.cuda.empty_cache() - else: - device == "cpu" - device = "cuda" - model.to(device) - - -@app.post("/api/lowvramsetting") -async def set_low_vram(request: Request, new_low_vram_value: bool): - global device - try: - if new_low_vram_value is None: - raise ValueError("Missing 'low_vram' parameter") - - if params["low_vram"] == new_low_vram_value: - return Response( - content=json.dumps( - { - "status": "success", - "message": f"[{params['branding']}Model] LowVRAM is already {'enabled' if new_low_vram_value else 'disabled'}.", - } - ) - ) - params["low_vram"] = new_low_vram_value - if params["low_vram"]: - await unload_model(model) - if torch.cuda.is_available(): - device = "cpu" - print( - f"[{params['branding']}Model] \033[94mChanging model \033[92m(Please wait 15 seconds)\033[0m" - ) - print( - f"[{params['branding']}Model] \033[94mLowVRAM Enabled.\033[0m Model will move between \033[93mVRAM(cuda) <> System RAM(cpu)\033[0m" - ) - await setup() - else: - # Handle the case where CUDA is not available - print( - f"[{params['branding']}Model] \033[91mError:\033[0m Nvidia CUDA is not available on this system. Unable to use LowVRAM mode." - ) - params["low_vram"] = False - else: - await unload_model(model) - if torch.cuda.is_available(): - device = "cuda" - print( - f"[{params['branding']}Model] \033[94mChanging model \033[92m(Please wait 15 seconds)\033[0m" - ) - print( - f"[{params['branding']}Model] \033[94mLowVRAM Disabled.\033[0m Model will stay in \033[93mVRAM(cuda)\033[0m" - ) - await setup() - else: - # Handle the case where CUDA is not available - print( - f"[{params['branding']}Model] \033[91mError:\033[0m Nvidia CUDA is not available on this system. Unable to use LowVRAM mode." - ) - params["low_vram"] = False - return Response(content=json.dumps({"status": "lowvram-success"})) - except Exception as e: - return Response(content=json.dumps({"status": "error", "message": str(e)})) - - -################### -#### DeepSpeed #### -################### -# DEEPSPEED - Reload the model when DeepSpeed checkbox is enabled/disabled -async def handle_deepspeed_change(value): - global model - if value: - # DeepSpeed enabled - print(f"[{params['branding']}Model] \033[93mDeepSpeed Activating\033[0m") - - print( - f"[{params['branding']}Model] \033[94mChanging model \033[92m(DeepSpeed can take 30 seconds to activate)\033[0m" - ) - print( - f"[{params['branding']}Model] \033[91mInformation\033[0m If you have not set CUDA_HOME path, DeepSpeed may fail to load/activate" - ) - print( - f"[{params['branding']}Model] \033[91mInformation\033[0m DeepSpeed needs to find nvcc from the CUDA Toolkit. Please check your CUDA_HOME path is" - ) - print( - f"[{params['branding']}Model] \033[91mInformation\033[0m pointing to the correct location and use 'set CUDA_HOME=putyoutpathhere' (Windows) or" - ) - print( - f"[{params['branding']}Model] \033[91mInformation\033[0m 'export CUDA_HOME=putyoutpathhere' (Linux) within your Python Environment" - ) - model = await unload_model(model) - params["tts_method_api_tts"] = False - params["tts_method_api_local"] = False - params["tts_method_xtts_local"] = True - params["deepspeed_activate"] = True - await setup() - else: - # DeepSpeed disabled - print(f"[{params['branding']}Model] \033[93mDeepSpeed De-Activating\033[0m") - print( - f"[{params['branding']}Model] \033[94mChanging model \033[92m(Please wait 15 seconds)\033[0m" - ) - params["deepspeed_activate"] = False - model = await unload_model(model) - await setup() - - return value # Return new checkbox value - - -# DEEPSPEED WEBSERVER- API Enable/Disable DeepSpeed -@app.post("/api/deepspeed") -async def deepspeed(request: Request, new_deepspeed_value: bool): - try: - if new_deepspeed_value is None: - raise ValueError("Missing 'deepspeed' parameter") - if params["deepspeed_activate"] == new_deepspeed_value: - return Response( - content=json.dumps( - { - "status": "success", - "message": f"DeepSpeed is already {'enabled' if new_deepspeed_value else 'disabled'}.", - } - ) - ) - params["deepspeed_activate"] = new_deepspeed_value - await handle_deepspeed_change(params["deepspeed_activate"]) - return Response(content=json.dumps({"status": "deepspeed-success"})) - except Exception as e: - return Response(content=json.dumps({"status": "error", "message": str(e)})) - - -######################## -#### TTS GENERATION #### -######################## - -# TTS VOICE GENERATION METHODS (called from voice_preview and output_modifer) -async def generate_audio(text, voice, language, temperature, repetition_penalty, output_file, streaming=False): - # Get the async generator from the internal function - response = generate_audio_internal(text, voice, language, temperature, repetition_penalty, output_file, streaming) - # If streaming, then return the generator as-is, otherwise just exhaust it and return - if streaming: - return response - async for _ in response: - pass - -async def generate_audio_internal(text, voice, language, temperature, repetition_penalty, output_file, streaming): - global model - if params["low_vram"] and device == "cpu": - await switch_device() - generate_start_time = time.time() # Record the start time of generating TTS - - # XTTSv2 LOCAL & Xttsv2 FT Method - if params["tts_method_xtts_local"] or tts_method_xtts_ft: - print(f"[{params['branding']}TTSGen] {text}") - gpt_cond_latent, speaker_embedding = model.get_conditioning_latents( - audio_path=[f"{this_dir}/voices/{voice}"], - gpt_cond_len=model.config.gpt_cond_len, - max_ref_length=model.config.max_ref_len, - sound_norm_refs=model.config.sound_norm_refs, - ) - - # Common arguments for both functions - common_args = { - "text": text, - "language": language, - "gpt_cond_latent": gpt_cond_latent, - "speaker_embedding": speaker_embedding, - "temperature": float(temperature), - "length_penalty": float(model.config.length_penalty), - "repetition_penalty": float(repetition_penalty), - "top_k": int(model.config.top_k), - "top_p": float(model.config.top_p), - "enable_text_splitting": True - } - - # Determine the correct inference function and add streaming specific argument if needed - inference_func = model.inference_stream if streaming else model.inference - if streaming: - common_args["stream_chunk_size"] = 20 - - # Call the appropriate function - output = inference_func(**common_args) - - # Process the output based on streaming or non-streaming - if streaming: - # Streaming-specific operations - file_chunks = [] - wav_buf = io.BytesIO() - with wave.open(wav_buf, "wb") as vfout: - vfout.setnchannels(1) - vfout.setsampwidth(2) - vfout.setframerate(24000) - vfout.writeframes(b"") - wav_buf.seek(0) - yield wav_buf.read() - - for i, chunk in enumerate(output): - file_chunks.append(chunk) - if isinstance(chunk, list): - chunk = torch.cat(chunk, dim=0) - chunk = chunk.clone().detach().cpu().numpy() - chunk = chunk[None, : int(chunk.shape[0])] - chunk = np.clip(chunk, -1, 1) - chunk = (chunk * 32767).astype(np.int16) - yield chunk.tobytes() - else: - # Non-streaming-specific operation - torchaudio.save(output_file, torch.tensor(output["wav"]).unsqueeze(0), 24000) - - - # API LOCAL Methods - elif params["tts_method_api_local"]: - # Streaming only allowed for XTTSv2 local - if streaming: - raise ValueError("Streaming is only supported in XTTSv2 local") - - # Set the correct output path (different from the if statement) - print(f"[{params['branding']}TTSGen] Using API Local") - model.tts_to_file( - text=text, - file_path=output_file, - speaker_wav=[f"{this_dir}/voices/{voice}"], - language=language, - temperature=temperature, - length_penalty=model.config.length_penalty, - repetition_penalty=repetition_penalty, - top_k=model.config.top_k, - top_p=model.config.top_p, - ) - - # API TTS - elif params["tts_method_api_tts"]: - # Streaming only allowed for XTTSv2 local - if streaming: - raise ValueError("Streaming is only supported in XTTSv2 local") - - print(f"[{params['branding']}TTSGen] Using API TTS") - model.tts_to_file( - text=text, - file_path=output_file, - speaker_wav=[f"{this_dir}/voices/{voice}"], - language=language, - ) - - # Print Generation time and settings - generate_end_time = time.time() # Record the end time to generate TTS - generate_elapsed_time = generate_end_time - generate_start_time - print( - f"[{params['branding']}TTSGen] \033[93m{generate_elapsed_time:.2f} seconds. \033[94mLowVRAM: \033[33m{params['low_vram']} \033[94mDeepSpeed: \033[33m{params['deepspeed_activate']}\033[0m" - ) - # Move model back to cpu system ram if needed. - if params["low_vram"] and device == "cuda": - await switch_device() - return - - -# TTS VOICE GENERATION METHODS - generate TTS API -@app.route("/api/generate", methods=["POST"]) -async def generate(request: Request): - try: - # Get parameters from JSON body - data = await request.json() - text = data["text"] - voice = data["voice"] - language = data["language"] - temperature = data["temperature"] - repetition_penalty = data["repetition_penalty"] - output_file = data["output_file"] - streaming = False - # Generation logic - response = await generate_audio(text, voice, language, temperature, repetition_penalty, output_file, streaming) - if streaming: - return StreamingResponse(response, media_type="audio/wav") - return JSONResponse( - content={"status": "generate-success", "data": {"audio_path": output_file}} - ) - except Exception as e: - return JSONResponse(content={"status": "error", "message": str(e)}) - - -################################################### -#### POPULATE FILES LIST FROM VOICES DIRECTORY #### -################################################### -# List files in the "voices" directory -def list_files(directory): - files = [ - f - for f in os.listdir(directory) - if os.path.isfile(os.path.join(directory, f)) and f.endswith(".wav") - ] - return files - -############################# -#### JSON CONFIG UPDATER #### -############################# - -# Create an instance of Jinja2Templates for rendering HTML templates -templates = Jinja2Templates(directory=this_dir / "system") - -# Create a dependency to get the current JSON data -def get_json_data(): - with open(this_dir / "confignew.json", "r") as json_file: - data = json.load(json_file) - return data - - -# Define an endpoint function -@app.get("/settings") -async def get_settings(request: Request): - wav_files = list_files(this_dir / "voices") - # Render the template with the current JSON data and list of WAV files - return templates.TemplateResponse( - "/at_admin/at_settings.html", - { - "request": request, - "data": get_json_data(), - "modeldownload_model_path": modeldownload_model_path, - "wav_files": wav_files, - }, - ) - -# Define an endpoint to serve static files -app.mount("/static", StaticFiles(directory=str(this_dir / "system")), name="static") - -@app.post("/update-settings") -async def update_settings( - request: Request, - activate: bool = Form(...), - autoplay: bool = Form(...), - deepspeed_activate: bool = Form(...), - delete_output_wavs: str = Form(...), - ip_address: str = Form(...), - language: str = Form(...), - local_temperature: str = Form(...), - local_repetition_penalty: str = Form(...), - low_vram: bool = Form(...), - tts_model_loaded: bool = Form(...), - tts_model_name: str = Form(...), - narrator_enabled: bool = Form(...), - narrator_voice: str = Form(...), - output_folder_wav: str = Form(...), - port_number: str = Form(...), - remove_trailing_dots: bool = Form(...), - show_text: bool = Form(...), - tts_method: str = Form(...), - voice: str = Form(...), - data: dict = Depends(get_json_data), -): - # Update the settings based on the form values - data["activate"] = activate - data["autoplay"] = autoplay - data["deepspeed_activate"] = deepspeed_activate - data["delete_output_wavs"] = delete_output_wavs - data["ip_address"] = ip_address - data["language"] = language - data["local_temperature"] = local_temperature - data["local_repetition_penalty"] = local_repetition_penalty - data["low_vram"] = low_vram - data["tts_model_loaded"] = tts_model_loaded - data["tts_model_name"] = tts_model_name - data["narrator_enabled"] = narrator_enabled - data["narrator_voice"] = narrator_voice - data["output_folder_wav"] = output_folder_wav - data["port_number"] = port_number - data["remove_trailing_dots"] = remove_trailing_dots - data["show_text"] = show_text - data["tts_method_api_local"] = tts_method == "api_local" - data["tts_method_api_tts"] = tts_method == "api_tts" - data["tts_method_xtts_local"] = tts_method == "xtts_local" - data["voice"] = voice - - # Save the updated settings back to the JSON file - with open(this_dir / "confignew.json", "w") as json_file: - json.dump(data, json_file) - - # Redirect to the settings page to display the updated settings - return RedirectResponse(url="/settings", status_code=303) - - -################################## -#### SETTINGS PAGE DEMO VOICE #### -################################## - -@app.get("/tts-demo-request", response_class=StreamingResponse) -async def tts_demo_request_streaming(text: str, voice: str, language: str, output_file: str): - try: - output_file_path = this_dir / "outputs" / output_file - stream = await generate_audio(text, voice, language, temperature, repetition_penalty, output_file_path, streaming=True) - return StreamingResponse(stream, media_type="audio/wav") - except Exception as e: - print(f"An error occurred: {e}") - return JSONResponse(content={"error": "An error occurred"}, status_code=500) - -@app.post("/tts-demo-request", response_class=JSONResponse) -async def tts_demo_request(request: Request, text: str = Form(...), voice: str = Form(...), language: str = Form(...), output_file: str = Form(...)): - try: - output_file_path = this_dir / "outputs" / output_file - await generate_audio(text, voice, language, temperature, repetition_penalty, output_file_path, streaming=False) - return JSONResponse(content={"output_file_path": str(output_file)}, status_code=200) - except Exception as e: - print(f"An error occurred: {e}") - return JSONResponse(content={"error": "An error occurred"}, status_code=500) - - -##################### -#### Audio feeds #### -##################### - -# Gives web access to the output files -@app.get("/audio/{filename}") -async def get_audio(filename: str): - audio_path = this_dir / "outputs" / filename - return FileResponse(audio_path) - -@app.get("/audiocache/{filename}") -async def get_audio(filename: str): - audio_path = Path("outputs") / filename - if not audio_path.is_file(): - raise HTTPException(status_code=404, detail="File not found") - - response = FileResponse( - path=audio_path, - media_type='audio/wav', - filename=filename - ) - # Set caching headers - response.headers["Cache-Control"] = "public, max-age=604800" # Cache for one week - response.headers["ETag"] = str(audio_path.stat().st_mtime) # Use the file's last modified time as a simple ETag - - return response - -######################### -#### VOICES LIST API #### -######################### -# Define the new endpoint -@app.get("/api/voices") -async def get_voices(): - wav_files = list_files(this_dir / "voices") - return {"voices": wav_files} - -########################### -#### PREVIEW VOICE API #### -########################### -@app.post("/api/previewvoice/", response_class=JSONResponse) -async def preview_voice(request: Request, voice: str = Form(...)): - try: - # Hardcoded settings - language = "en" - output_file_name = "api_preview_voice" - - # Clean the voice filename for inclusion in the text - clean_voice_filename = re.sub(r'\.wav$', '', voice.replace(' ', '_')) - clean_voice_filename = re.sub(r'[^a-zA-Z0-9]', ' ', clean_voice_filename) - - # Generate the audio - text = f"Hello, this is a preview of voice {clean_voice_filename}." - - # Generate the audio - output_file_path = this_dir / "outputs" / f"{output_file_name}.wav" - await generate_audio(text, voice, language, temperature, repetition_penalty, output_file_path, streaming=False) - - # Generate the URL - output_file_url = f'http://{params["ip_address"]}:{params["port_number"]}/audio/{output_file_name}.wav' - - # Return the response with both local file path and URL - return JSONResponse( - content={ - "status": "generate-success", - "output_file_path": str(output_file_path), - "output_file_url": str(output_file_url), - }, - status_code=200, - ) - except Exception as e: - print(f"An error occurred: {e}") - return JSONResponse(content={"error": "An error occurred"}, status_code=500) - -######################## -#### GENERATION API #### -######################## -import html -import re -import uuid -import numpy as np -import soundfile as sf -import sys -import hashlib - -############################## -#### Streaming Generation #### -############################## - -@app.get("/api/tts-generate-streaming", response_class=StreamingResponse) -async def tts_generate_streaming(text: str, voice: str, language: str, output_file: str): - try: - output_file_path = this_dir / "outputs" / output_file - stream = await generate_audio(text, voice, language, temperature, repetition_penalty, output_file_path, streaming=True) - return StreamingResponse(stream, media_type="audio/wav") - except Exception as e: - print(f"An error occurred: {e}") - return JSONResponse(content={"error": "An error occurred"}, status_code=500) - -@app.post("/api/tts-generate-streaming", response_class=JSONResponse) -async def tts_generate_streaming(request: Request, text: str = Form(...), voice: str = Form(...), language: str = Form(...), output_file: str = Form(...)): - try: - output_file_path = this_dir / "outputs" / output_file - await generate_audio(text, voice, language, temperature, repetition_penalty, output_file_path, streaming=False) - return JSONResponse(content={"output_file_path": str(output_file)}, status_code=200) - except Exception as e: - print(f"An error occurred: {e}") - return JSONResponse(content={"error": "An error occurred"}, status_code=500) - -############################## -#### Standard Generation #### -############################## - -# Check for PortAudio library on Linux -try: - import sounddevice as sd - sounddevice_installed=True -except OSError: - print(f"[{params['branding']}Startup] \033[91mInfo\033[0m PortAudio library not found. If you wish to play TTS in standalone mode through the API suite") - print(f"[{params['branding']}Startup] \033[91mInfo\033[0m please install PortAudio. This will not affect any other features or use of Alltalk.") - print(f"[{params['branding']}Startup] \033[91mInfo\033[0m If you don't know what the API suite is, then this message is nothing to worry about.") - sounddevice_installed=False - if sys.platform.startswith('linux'): - print(f"[{params['branding']}Startup] \033[91mInfo\033[0m On Linux, you can use the following command to install PortAudio:") - print(f"[{params['branding']}Startup] \033[91mInfo\033[0m sudo apt-get install portaudio19-dev") - -from typing import Union, Dict, List -from pydantic import BaseModel, ValidationError, Field - -def play_audio(file_path, volume): - data, fs = sf.read(file_path) - sd.play(volume * data, fs) - sd.wait() - -class Request(BaseModel): - # Define the structure of the 'Request' class if needed - pass - -class JSONInput(BaseModel): - text_input: str = Field(..., max_length=2000, description="text_input needs to be 2000 characters or less.") - text_filtering: str = Field(..., pattern="^(none|standard|html)$", description="text_filtering needs to be 'none', 'standard' or 'html'.") - character_voice_gen: str = Field(..., pattern="^.*\.wav$", description="character_voice_gen needs to be the name of a wav file e.g. mysample.wav.") - narrator_enabled: bool = Field(..., description="narrator_enabled needs to be true or false.") - narrator_voice_gen: str = Field(..., pattern="^.*\.wav$", description="narrator_voice_gen needs to be the name of a wav file e.g. mysample.wav.") - text_not_inside: str = Field(..., pattern="^(character|narrator)$", description="text_not_inside needs to be 'character' or 'narrator'.") - language: str = Field(..., pattern="^(ar|zh-cn|cs|nl|en|fr|de|hu|it|ja|ko|pl|pt|ru|es|tr)$", description="language needs to be one of the following ar|zh-cn|cs|nl|en|fr|de|hu|it|ja|ko|pl|pt|ru|es|tr.") - output_file_name: str = Field(..., pattern="^[a-zA-Z0-9_]+$", description="output_file_name needs to be the name without any special characters or file extension e.g. 'filename'") - output_file_timestamp: bool = Field(..., description="output_file_timestamp needs to be true or false.") - autoplay: bool = Field(..., description="autoplay needs to be a true or false value.") - autoplay_volume: float = Field(..., ge=0.1, le=1.0, description="autoplay_volume needs to be from 0.1 to 1.0") - - @classmethod - def validate_autoplay_volume(cls, value): - if not (0.1 <= value <= 1.0): - raise ValueError("Autoplay volume must be between 0.1 and 1.0") - return value - - -class TTSGenerator: - @staticmethod - def validate_json_input(json_data: Union[Dict, str]) -> Union[None, str]: - try: - if isinstance(json_data, str): - json_data = json.loads(json_data) - JSONInput(**json_data) - return None # JSON is valid - except ValidationError as e: - return str(e) - -def process_text(text): - # Normalize HTML encoded quotes - text = html.unescape(text) - # Replace ellipsis with a single dot - text = re.sub(r'\.{3,}', '.', text) - # Pattern to identify combined narrator and character speech - combined_pattern = r'(\*[^*"]+\*|"[^"*]+")' - # List to hold parts of speech along with their type - ordered_parts = [] - # Track the start of the next segment - start = 0 - # Find all matches - for match in re.finditer(combined_pattern, text): - # Add the text before the match, if any, as ambiguous - if start < match.start(): - ambiguous_text = text[start:match.start()].strip() - if ambiguous_text: - ordered_parts.append(('ambiguous', ambiguous_text)) - # Add the matched part as either narrator or character - matched_text = match.group(0) - if matched_text.startswith('*') and matched_text.endswith('*'): - ordered_parts.append(('narrator', matched_text.strip('*').strip())) - elif matched_text.startswith('"') and matched_text.endswith('"'): - ordered_parts.append(('character', matched_text.strip('"').strip())) - else: - # In case of mixed or improperly formatted parts - if '*' in matched_text: - ordered_parts.append(('narrator', matched_text.strip('*').strip('"'))) - else: - ordered_parts.append(('character', matched_text.strip('"').strip('*'))) - # Update the start of the next segment - start = match.end() - # Add any remaining text after the last match as ambiguous - if start < len(text): - ambiguous_text = text[start:].strip() - if ambiguous_text: - ordered_parts.append(('ambiguous', ambiguous_text)) - return ordered_parts - -def standard_filtering(text_input): - text_output = (text_input - .replace("***", "") - .replace("**", "") - .replace("*", "") - .replace("\n\n", "\n") - .replace("'", "'") - ) - return text_output - -def combine(output_file_timestamp, output_file_name, audio_files): - audio = np.array([]) - sample_rate = None - try: - for audio_file in audio_files: - audio_data, current_sample_rate = sf.read(audio_file) - if audio.size == 0: - audio = audio_data - sample_rate = current_sample_rate - elif sample_rate == current_sample_rate: - audio = np.concatenate((audio, audio_data)) - else: - raise ValueError("Sample rates of input files are not consistent.") - except Exception as e: - # Handle exceptions (e.g., file not found, invalid audio format) - return None, None - if output_file_timestamp: - timestamp = int(time.time()) - output_file_path = os.path.join(this_dir / "outputs" / f'{output_file_name}_{timestamp}_combined.wav') - output_file_url = f'http://{params["ip_address"]}:{params["port_number"]}/audio/{output_file_name}_{timestamp}_combined.wav' - output_cache_url = f'http://{params["ip_address"]}:{params["port_number"]}/audiocache/{output_file_name}_{timestamp}_combined.wav' - else: - output_file_path = os.path.join(this_dir / "outputs" / f'{output_file_name}_combined.wav') - output_file_url = f'http://{params["ip_address"]}:{params["port_number"]}/audio/{output_file_name}_combined.wav' - output_cache_url = f'http://{params["ip_address"]}:{params["port_number"]}/audiocache/{output_file_name}_combined.wav' - try: - sf.write(output_file_path, audio, samplerate=sample_rate) - # Clean up unnecessary files - for audio_file in audio_files: - os.remove(audio_file) - except Exception as e: - # Handle exceptions (e.g., failed to write output file) - return None, None - return output_file_path, output_file_url, output_cache_url - -# Generation API (separate from text-generation-webui) -@app.post("/api/tts-generate", response_class=JSONResponse) -async def tts_generate( - text_input: str = Form(...), - text_filtering: str = Form(...), - character_voice_gen: str = Form(...), - narrator_enabled: bool = Form(...), - narrator_voice_gen: str = Form(...), - text_not_inside: str = Form(...), - language: str = Form(...), - output_file_name: str = Form(...), - output_file_timestamp: bool = Form(...), - autoplay: bool = Form(...), - autoplay_volume: float = Form(...), - streaming: bool = Form(False), -): - try: - json_input_data = { - "text_input": text_input, - "text_filtering": text_filtering, - "character_voice_gen": character_voice_gen, - "narrator_enabled": narrator_enabled, - "narrator_voice_gen": narrator_voice_gen, - "text_not_inside": text_not_inside, - "language": language, - "output_file_name": output_file_name, - "output_file_timestamp": output_file_timestamp, - "autoplay": autoplay, - "autoplay_volume": autoplay_volume, - "streaming": streaming, - } - JSONresult = TTSGenerator.validate_json_input(json_input_data) - if JSONresult is None: - pass - else: - return JSONResponse(content={"error": JSONresult}, status_code=400) - if narrator_enabled: - processed_parts = process_text(text_input) - audio_files_all_paragraphs = [] - for part_type, part in processed_parts: - # Skip parts that are too short - if len(part.strip()) <= 3: - continue - # Determine the voice to use based on the part type - if part_type == 'narrator': - voice_to_use = narrator_voice_gen - print(f"[{params['branding']}TTSGen] \033[92mNarrator\033[0m") # Green - elif part_type == 'character': - voice_to_use = character_voice_gen - print(f"[{params['branding']}TTSGen] \033[36mCharacter\033[0m") # Yellow - else: - # Handle ambiguous parts based on user preference - voice_to_use = character_voice_gen if text_not_inside == "character" else narrator_voice_gen - voice_description = "\033[36mCharacter (Text-not-inside)\033[0m" if text_not_inside == "character" else "\033[92mNarrator (Text-not-inside)\033[0m" - print(f"[{params['branding']}TTSGen] {voice_description}") - # Replace multiple exclamation marks, question marks, or other punctuation with a single instance - cleaned_part = re.sub(r'([!?.])\1+', r'\1', part) - # Further clean to remove any other unwanted characters - cleaned_part = re.sub(r'[^a-zA-Z0-9\s\.,;:!?\-\'"\u0400-\u04FFÀ-ÿ\u0150\u0151\u0170\u0171]\$', '', cleaned_part) - # Remove all newline characters (single or multiple) - cleaned_part = re.sub(r'\n+', ' ', cleaned_part) - output_file = this_dir / "outputs" / f"{output_file_name}_{uuid.uuid4()}_{int(time.time())}.wav" - output_file_str = output_file.as_posix() - response = await generate_audio(cleaned_part, voice_to_use, language,temperature, repetition_penalty, output_file_str, streaming) - audio_path = output_file_str - audio_files_all_paragraphs.append(audio_path) - # Combine audio files across paragraphs - output_file_path, output_file_url, output_cache_url = combine(output_file_timestamp, output_file_name, audio_files_all_paragraphs) - else: - if output_file_timestamp: - timestamp = int(time.time()) - # Generate a standard UUID - original_uuid = uuid.uuid4() - # Hash the UUID using SHA-256 - hash_object = hashlib.sha256(str(original_uuid).encode()) - hashed_uuid = hash_object.hexdigest() - # Truncate to the desired length, for example, 16 characters - short_uuid = hashed_uuid[:5] - output_file_path = this_dir / "outputs" / f"{output_file_name}_{timestamp}{short_uuid}.wav" - output_file_url = f'http://{params["ip_address"]}:{params["port_number"]}/audio/{output_file_name}_{timestamp}{short_uuid}.wav' - output_cache_url = f'http://{params["ip_address"]}:{params["port_number"]}/audiocache/{output_file_name}_{timestamp}{short_uuid}.wav' - else: - output_file_path = this_dir / "outputs" / f"{output_file_name}.wav" - output_file_url = f'http://{params["ip_address"]}:{params["port_number"]}/audio/{output_file_name}.wav' - output_cache_url = f'http://{params["ip_address"]}:{params["port_number"]}/audiocache/{output_file_name}.wav' - if text_filtering == "html": - cleaned_string = html.unescape(standard_filtering(text_input)) - cleaned_string = re.sub(r'([!?.])\1+', r'\1', text_input) - # Further clean to remove any other unwanted characters - cleaned_string = re.sub(r'[^a-zA-Z0-9\s\.,;:!?\-\'"\u0400-\u04FFÀ-ÿ\u0150\u0151\u0170\u0171]\$', '', cleaned_string) - # Remove all newline characters (single or multiple) - cleaned_string = re.sub(r'\n+', ' ', cleaned_string) - elif text_filtering == "standard": - cleaned_string = re.sub(r'([!?.])\1+', r'\1', text_input) - # Further clean to remove any other unwanted characters - cleaned_string = re.sub(r'[^a-zA-Z0-9\s\.,;:!?\-\'"\u0400-\u04FFÀ-ÿ\u0150\u0151\u0170\u0171]\$', '', cleaned_string) - # Remove all newline characters (single or multiple) - cleaned_string = re.sub(r'\n+', ' ', cleaned_string) - else: - cleaned_string = text_input - response = await generate_audio(cleaned_string, character_voice_gen, language, temperature, repetition_penalty, output_file_path, streaming) - if sounddevice_installed == False or streaming == True: - autoplay = False - if autoplay: - play_audio(output_file_path, autoplay_volume) - if streaming: - return StreamingResponse(response, media_type="audio/wav") - return JSONResponse(content={"status": "generate-success", "output_file_path": str(output_file_path), "output_file_url": str(output_file_url), "output_cache_url": str(output_cache_url)}, status_code=200) - except Exception as e: - return JSONResponse(content={"status": "generate-failure", "error": "An error occurred"}, status_code=500) - - -########################## -#### Current Settings #### -########################## -# Define the available models -models_available = [ - {"name": "Coqui", "model_name": "API TTS"}, - {"name": "Coqui", "model_name": "API Local"}, - {"name": "Coqui", "model_name": "XTTSv2 Local"} -] - -@app.get('/api/currentsettings') -def get_current_settings(): - # Determine the current model loaded - if params["tts_method_api_tts"]: - current_model_loaded = "API TTS" - elif params["tts_method_api_local"]: - current_model_loaded = "API Local" - elif params["tts_method_xtts_local"]: - current_model_loaded = "XTTSv2 Local" - else: - current_model_loaded = None # or a default value if no method is active - - settings = { - "models_available": models_available, - "current_model_loaded": current_model_loaded, - "deepspeed_available": deepspeed_available, - "deepspeed_status": params["deepspeed_activate"], - "low_vram_status": params["low_vram"], - "finetuned_model": finetuned_model - } - return settings # Automatically converted to JSON by Fas - -############################# -#### Word Add-in Sharing #### -############################# -# Mount the static files from the 'word_addin' directory -app.mount("/api/word_addin", StaticFiles(directory=os.path.join(this_dir / 'system' / 'word_addin')), name="word_addin") - -############################################# -#### TTS Generator Comparision Endpoints #### -############################################# -import subprocess -import aiofiles - -class TTSItem(BaseModel): - id: int - fileUrl: str - text: str - characterVoice: str - language: str - -class TTSData(BaseModel): - ttsList: List[TTSItem] - -@app.post("/api/save-tts-data") -async def save_tts_data(tts_data: List[TTSItem]): - # Convert the list of Pydantic models to a list of dictionaries - tts_data_list = [item.dict() for item in tts_data] - # Serialize the list of dictionaries to a JSON string - tts_data_json = json.dumps(tts_data_list, indent=4) - async with aiofiles.open(this_dir / "outputs" / "ttsList.json", 'w') as f: - await f.write(tts_data_json) - return {"message": "Data saved successfully"} - -import sys - -@app.get("/api/trigger-analysis") -async def trigger_analysis(threshold: int = Query(default=98)): - venv_path = sys.prefix - env = os.environ.copy() - env["PATH"] = os.path.join(venv_path, "bin") + ":" + env["PATH"] - ttslist_path = this_dir / "outputs" / "ttsList.json" - wavfile_path = this_dir / "outputs" - subprocess.run(["python", "tts_diff.py", f"--threshold={threshold}", f"--ttslistpath={ttslist_path}", f"--wavfilespath={wavfile_path}"], cwd=this_dir / "system" / "tts_diff", env=env) - # Read the analysis summary - try: - with open(this_dir / "outputs" / "analysis_summary.json", "r") as summary_file: - summary_data = json.load(summary_file) - except FileNotFoundError: - summary_data = {"error": "Analysis summary file not found."} - return {"message": "Analysis Completed", "summary": summary_data} - -################################################# -#### TTS Generator SRT Subtitiles generation #### -################################################# -@app.get("/api/srt-generation") -async def srt_generation(): - venv_path = sys.prefix - env = os.environ.copy() - env["PATH"] = os.path.join(venv_path, "bin") + ":" + env["PATH"] - ttslist_path = this_dir / "outputs" / "ttsList.json" - wavfile_path = this_dir / "outputs" - subprocess.run(["python", "tts_srt.py", f"--ttslistpath={ttslist_path}", f"--wavfilespath={wavfile_path}"], cwd=this_dir / "system" / "tts_srt", env=env) - - srt_file_path = this_dir / "outputs" / "subtitles.srt" - if not srt_file_path.exists(): - raise HTTPException(status_code=404, detail="Subtitle file not found.") - - return FileResponse(path=srt_file_path, filename="subtitles.srt", media_type='application/octet-stream') - -################################################### -#### Webserver Startup & Initial model Loading #### -################################################### - -# Get the admin interface template -template = templates.get_template("admin.html") -# Render the template with the dynamic values -rendered_html = template.render(params=params) - -############################### -#### Internal script ready #### -############################### -@app.get("/ready") -async def ready(): - return Response("Ready endpoint") - -############################ -#### External API ready #### -############################ -@app.get("/api/ready") -async def ready(): - return Response("Ready") - -@app.get("/") -async def read_root(): - return HTMLResponse(content=rendered_html, status_code=200) - -# Start Uvicorn Webserver -host_parameter = params["ip_address"] -port_parameter = int(params["port_number"]) - -if __name__ == "__main__": - import uvicorn - - uvicorn.run(app, host=host_parameter, port=port_parameter, log_level="warning")