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cefd1e045baf15c68fbdcac15fb1978850f8987f
alltalk_tts/tts_server.py
T
S95Sedan cefd1e045b Temperature and Repetition_penalty modification 
Modified script.py to enable temperature and repetition_penalty to be parsed and tts_server.py to recieve those variables.
2024-01-15 21:51:00 +01:00

1181 lines
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Python
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import json
import time
import os
from pathlib import Path
import torch
import torchaudio
from TTS.tts.configs.xtts_config import XttsConfig
from TTS.tts.models.xtts import Xtts
import io
import wave
##########################
#### Webserver Imports####
##########################
from fastapi import (
FastAPI,
Form,
Request,
Response,
Depends,
HTTPException,
)
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 / "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"]
# Define the path to the JSON file
config_file_path = this_dir / "modeldownload.json"
#############################################
#### LOAD PARAMS FROM MODELDOWNLOAD.JSON ####
############################################
# This is used only in the instance that someone has changed their model path
# Define the path to the JSON file
modeldownload_config_file_path = this_dir / "modeldownload.json"
# Check if the JSON file exists
if modeldownload_config_file_path.exists():
with open(modeldownload_config_file_path, "r") as modeldownload_config_file:
modeldownload_settings = json.load(modeldownload_config_file)
# Extract settings from the loaded JSON
modeldownload_base_path = Path(modeldownload_settings.get("base_path", ""))
modeldownload_model_path = Path(modeldownload_settings.get("model_path", ""))
else:
# Default settings if the JSON file doesn't exist or is empty
print(
f"[{params['branding']}Startup] \033[91mWarning\033[0m modeldownload.config is missing so please re-download it and save it in the alltalk_tts main folder."
)
##################################################
#### Check to see if a finetuned model exists ####
##################################################
# Set the path to the directory
trained_model_directory = this_dir / "models" / "trainedmodel"
# Check if the directory "trainedmodel" exists
finetuned_model = trained_model_directory.exists()
# If the directory exists, check for the existence of the required files
if finetuned_model:
required_files = ["model.pth", "config.json", "vocab.json"]
finetuned_model = all((trained_model_directory / file).exists() for file in required_files)
########################
#### STARTUP CHECKS ####
########################
try:
from TTS.api import TTS
from TTS.utils.synthesizer import Synthesizer
except ModuleNotFoundError:
print(
f"[{params['branding']}Startup] \033[91mWarning\033[0m Could not find the TTS module. Make sure to install the requirements for the alltalk_tts extension.",
f"[{params['branding']}Startup] \033[91mWarning\033[0m Linux / Mac:\npip install -r extensions/alltalk_tts/requirements.txt\n",
f"[{params['branding']}Startup] \033[91mWarning\033[0m Windows:\npip install -r extensions\\alltalk_tts\\requirements.txt\n",
f"[{params['branding']}Startup] \033[91mWarning\033[0m If you used the one-click installer, paste the command above in the terminal window launched after running the cmd_ script. On Windows, that's cmd_windows.bat."
)
raise
# DEEPSPEED Import - Check for DeepSpeed and import it if it exists
deepspeed_available = False
try:
import deepspeed
deepspeed_available = True
except ImportError:
pass
if deepspeed_available:
print(f"[{params['branding']}Startup] DeepSpeed \033[93mDetected\033[0m")
print(f"[{params['branding']}Startup] Activate DeepSpeed in {params['branding']}settings")
else:
print(f"[{params['branding']}Startup] DeepSpeed \033[93mNot Detected\033[0m. See https://github.com/microsoft/DeepSpeed")
@asynccontextmanager
async def startup_shutdown(no_actual_value_it_demanded_something_be_here):
await setup()
yield
# Shutdown logic
# Create FastAPI app with lifespan
app = FastAPI(lifespan=startup_shutdown)
# Allow all origins, and set other CORS options
app.add_middleware(
CORSMiddleware,
allow_origins=["*"], # Set this to the specific origins you want to allow
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
#####################################
#### MODEL LOADING AND UNLOADING ####
#####################################
# MODEL LOADERS Picker For API TTS, API Local, XTTSv2 Local, XTTSv2 FT
async def setup():
global device
# Set a timer to calculate load times
generate_start_time = time.time() # Record the start time of loading the model
# Start loading the correct model as set by "tts_method_api_tts", "tts_method_api_local" or "tts_method_xtts_local" being True/False
if params["tts_method_api_tts"]:
print(
f"[{params['branding']}Model] \033[94mAPI TTS Loading\033[0m {params['tts_model_name']} \033[94minto\033[93m",
device,
"\033[0m",
)
model = await api_load_model()
elif params["tts_method_api_local"]:
print(
f"[{params['branding']}Model] \033[94mAPI Local Loading\033[0m {modeldownload_model_path} \033[94minto\033[93m",
device,
"\033[0m",
)
model = await api_manual_load_model()
elif params["tts_method_xtts_local"]:
print(
f"[{params['branding']}Model] \033[94mXTTSv2 Local Loading\033[0m {modeldownload_model_path} \033[94minto\033[93m",
device,
"\033[0m",
)
model = await xtts_manual_load_model()
elif tts_method_xtts_ft:
print(
f"[{params['branding']}Model] \033[94mXTTSv2 FT Loading\033[0m /models/fintuned/model.pth \033[94minto\033[93m",
device,
"\033[0m",
)
model = await xtts_ft_manual_load_model()
# Create an end timer for calculating load times
generate_end_time = time.time()
# Calculate start time minus end time
generate_elapsed_time = generate_end_time - generate_start_time
# Print out the result of the load time
print(
f"[{params['branding']}Model] \033[94mModel Loaded in \033[93m{generate_elapsed_time:.2f} seconds.\033[0m"
)
# Set "tts_model_loaded" to true
params["tts_model_loaded"] = True
# Set the output path for wav files
output_directory = this_dir / params["output_folder_wav_standalone"]
output_directory.mkdir(parents=True, exist_ok=True)
#Path(f'this_folder/outputs/').mkdir(parents=True, exist_ok=True)
# MODEL LOADER For "API TTS"
async def api_load_model():
global model
model = TTS(params["tts_model_name"]).to(device)
return model
# MODEL LOADER For "API Local"
async def api_manual_load_model():
global model
# check to see if a custom path has been set in modeldownload.json and use that path to load the model if so
if str(modeldownload_base_path) == "models":
model = TTS(
model_path=this_dir / "models" / modeldownload_model_path,
config_path=this_dir / "models" / modeldownload_model_path / "config.json",
).to(device)
else:
print(
f"[{params['branding']}Model] \033[94mInfo\033[0m Loading your custom model set in \033[93mmodeldownload.json\033[0m:",
modeldownload_base_path / modeldownload_model_path,
)
model = TTS(
model_path=modeldownload_base_path / modeldownload_model_path,
config_path=modeldownload_base_path / modeldownload_model_path / "config.json",
).to(device)
return model
# MODEL LOADER For "XTTSv2 Local"
async def xtts_manual_load_model():
global model
config = XttsConfig()
# check to see if a custom path has been set in modeldownload.json and use that path to load the model if so
if str(modeldownload_base_path) == "models":
config_path = this_dir / "models" / modeldownload_model_path / "config.json"
vocab_path_dir = this_dir / "models" / modeldownload_model_path / "vocab.json"
checkpoint_dir = this_dir / "models" / modeldownload_model_path
else:
print(
f"[{params['branding']}Model] \033[94mInfo\033[0m Loading your custom model set in \033[93mmodeldownload.json\033[0m:",
modeldownload_base_path / modeldownload_model_path,
)
config_path = modeldownload_base_path / modeldownload_model_path / "config.json"
vocab_path_dir = modeldownload_base_path / modeldownload_model_path / "vocab.json"
checkpoint_dir = modeldownload_base_path / modeldownload_model_path
config.load_json(str(config_path))
model = Xtts.init_from_config(config)
model.load_checkpoint(
config,
checkpoint_dir=str(checkpoint_dir),
vocab_path=str(vocab_path_dir),
use_deepspeed=params["deepspeed_activate"],
)
model.to(device)
return model
# MODEL LOADER For "XTTSv2 FT"
async def xtts_ft_manual_load_model():
global model
config = XttsConfig()
config_path = this_dir / "models" / "trainedmodel" / "config.json"
vocab_path_dir = this_dir / "models" / "trainedmodel" / "vocab.json"
checkpoint_dir = this_dir / "models" / "trainedmodel"
config.load_json(str(config_path))
model = Xtts.init_from_config(config)
model.load_checkpoint(
config,
checkpoint_dir=str(checkpoint_dir),
vocab_path=str(vocab_path_dir),
use_deepspeed=params["deepspeed_activate"],
)
model.to(device)
return model
# MODEL UNLOADER
async def unload_model(model):
print(f"[{params['branding']}Model] \033[94mUnloading model \033[0m")
del model
if torch.cuda.is_available():
torch.cuda.empty_cache()
params["tts_model_loaded"] = False
return None
# MODEL - Swap model based on Gradio selection API TTS, API Local, XTTSv2 Local
async def handle_tts_method_change(tts_method):
global model
global tts_method_xtts_ft
# Update the params dictionary based on the selected radio button
print(
f"[{params['branding']}Model] \033[94mChanging model \033[92m(Please wait 15 seconds)\033[0m"
)
# Set other parameters to False
if tts_method == "API TTS":
params["tts_method_api_local"] = False
params["tts_method_xtts_local"] = False
params["tts_method_api_tts"] = True
params["deepspeed_activate"] = False
tts_method_xtts_ft = False
elif tts_method == "API Local":
params["tts_method_api_tts"] = False
params["tts_method_xtts_local"] = False
params["tts_method_api_local"] = True
params["deepspeed_activate"] = False
tts_method_xtts_ft = False
elif tts_method == "XTTSv2 Local":
params["tts_method_api_tts"] = False
params["tts_method_api_local"] = False
params["tts_method_xtts_local"] = True
tts_method_xtts_ft = False
elif tts_method == "XTTSv2 FT":
tts_method_xtts_ft = True
params["tts_method_api_tts"] = False
params["tts_method_api_local"] = False
params["tts_method_xtts_local"] = False
# Unload the current model
model = await unload_model(model)
# Load the correct model based on the updated params
await setup()
# MODEL WEBSERVER- API Swap Between Models
@app.route("/api/reload", methods=["POST"])
async def reload(request: Request):
tts_method = request.query_params.get("tts_method")
if tts_method not in ["API TTS", "API Local", "XTTSv2 Local", "XTTSv2 FT"]:
return {"status": "error", "message": "Invalid TTS method specified"}
await handle_tts_method_change(tts_method)
return Response(
content=json.dumps({"status": "model-success"}), media_type="application/json"
)
##################
#### LOW VRAM ####
##################
# LOW VRAM - MODEL MOVER VRAM(cuda)<>System RAM(cpu) for Low VRAM setting
async def switch_device():
global model, device
# Check if CUDA is available before performing GPU-related operations
if torch.cuda.is_available():
if device == "cuda":
device = "cpu"
model.to(device)
torch.cuda.empty_cache()
else:
device == "cpu"
device = "cuda"
model.to(device)
@app.post("/api/lowvramsetting")
async def set_low_vram(request: Request, new_low_vram_value: bool):
global device
try:
if new_low_vram_value is None:
raise ValueError("Missing 'low_vram' parameter")
if params["low_vram"] == new_low_vram_value:
return Response(
content=json.dumps(
{
"status": "success",
"message": f"[{params['branding']}Model] LowVRAM is already {'enabled' if new_low_vram_value else 'disabled'}.",
}
)
)
params["low_vram"] = new_low_vram_value
if params["low_vram"]:
await unload_model(model)
if torch.cuda.is_available():
device = "cpu"
print(
f"[{params['branding']}Model] \033[94mChanging model \033[92m(Please wait 15 seconds)\033[0m"
)
print(
f"[{params['branding']}Model] \033[94mLowVRAM Enabled.\033[0m Model will move between \033[93mVRAM(cuda) <> System RAM(cpu)\033[0m"
)
await setup()
else:
# Handle the case where CUDA is not available
print(
f"[{params['branding']}Model] \033[91mError:\033[0m Nvidia CUDA is not available on this system. Unable to use LowVRAM mode."
)
params["low_vram"] = False
else:
await unload_model(model)
if torch.cuda.is_available():
device = "cuda"
print(
f"[{params['branding']}Model] \033[94mChanging model \033[92m(Please wait 15 seconds)\033[0m"
)
print(
f"[{params['branding']}Model] \033[94mLowVRAM Disabled.\033[0m Model will stay in \033[93mVRAM(cuda)\033[0m"
)
await setup()
else:
# Handle the case where CUDA is not available
print(
f"[{params['branding']}Model] \033[91mError:\033[0m Nvidia CUDA is not available on this system. Unable to use LowVRAM mode."
)
params["low_vram"] = False
return Response(content=json.dumps({"status": "lowvram-success"}))
except Exception as e:
return Response(content=json.dumps({"status": "error", "message": str(e)}))
###################
#### DeepSpeed ####
###################
# DEEPSPEED - Reload the model when DeepSpeed checkbox is enabled/disabled
async def handle_deepspeed_change(value):
global model
if value:
# DeepSpeed enabled
print(f"[{params['branding']}Model] \033[93mDeepSpeed Activating\033[0m")
print(
f"[{params['branding']}Model] \033[94mChanging model \033[92m(DeepSpeed can take 30 seconds to activate)\033[0m"
)
print(
f"[{params['branding']}Model] \033[91mInformation\033[0m If you have not set CUDA_HOME path, DeepSpeed may fail to load/activate"
)
print(
f"[{params['branding']}Model] \033[91mInformation\033[0m DeepSpeed needs to find nvcc from the CUDA Toolkit. Please check your CUDA_HOME path is"
)
print(
f"[{params['branding']}Model] \033[91mInformation\033[0m pointing to the correct location and use 'set CUDA_HOME=putyoutpathhere' (Windows) or"
)
print(
f"[{params['branding']}Model] \033[91mInformation\033[0m 'export CUDA_HOME=putyoutpathhere' (Linux) within your Python Environment"
)
model = await unload_model(model)
params["tts_method_api_tts"] = False
params["tts_method_api_local"] = False
params["tts_method_xtts_local"] = True
params["deepspeed_activate"] = True
await setup()
else:
# DeepSpeed disabled
print(f"[{params['branding']}Model] \033[93mDeepSpeed De-Activating\033[0m")
print(
f"[{params['branding']}Model] \033[94mChanging model \033[92m(Please wait 15 seconds)\033[0m"
)
params["deepspeed_activate"] = False
model = await unload_model(model)
await setup()
return value # Return new checkbox value
# DEEPSPEED WEBSERVER- API Enable/Disable DeepSpeed
@app.post("/api/deepspeed")
async def deepspeed(request: Request, new_deepspeed_value: bool):
try:
if new_deepspeed_value is None:
raise ValueError("Missing 'deepspeed' parameter")
if params["deepspeed_activate"] == new_deepspeed_value:
return Response(
content=json.dumps(
{
"status": "success",
"message": f"DeepSpeed is already {'enabled' if new_deepspeed_value else 'disabled'}.",
}
)
)
params["deepspeed_activate"] = new_deepspeed_value
await handle_deepspeed_change(params["deepspeed_activate"])
return Response(content=json.dumps({"status": "deepspeed-success"}))
except Exception as e:
return Response(content=json.dumps({"status": "error", "message": str(e)}))
########################
#### TTS GENERATION ####
########################
# TTS VOICE GENERATION METHODS (called from voice_preview and output_modifer)
async def generate_audio(text, voice, language, temperature, repetition_penalty, output_file, streaming=False):
# Get the async generator from the internal function
response = generate_audio_internal(text, voice, language, temperature, repetition_penalty, output_file, streaming)
# If streaming, then return the generator as-is, otherwise just exhaust it and return
if streaming:
return response
async for _ in response:
pass
async def generate_audio_internal(text, voice, language, temperature, repetition_penalty, output_file, streaming):
global model
if params["low_vram"] and device == "cpu":
await switch_device()
generate_start_time = time.time() # Record the start time of generating TTS
# XTTSv2 LOCAL & Xttsv2 FT Method
if params["tts_method_xtts_local"] or tts_method_xtts_ft:
print(f"[{params['branding']}TTSGen] {text}")
gpt_cond_latent, speaker_embedding = model.get_conditioning_latents(
audio_path=[f"{this_dir}/voices/{voice}"],
gpt_cond_len=model.config.gpt_cond_len,
max_ref_length=model.config.max_ref_len,
sound_norm_refs=model.config.sound_norm_refs,
)
# Common arguments for both functions
common_args = {
"text": text,
"language": language,
"gpt_cond_latent": gpt_cond_latent,
"speaker_embedding": speaker_embedding,
"temperature": float(temperature),
"length_penalty": float(model.config.length_penalty),
"repetition_penalty": float(repetition_penalty),
"top_k": int(model.config.top_k),
"top_p": float(model.config.top_p),
"enable_text_splitting": True
}
# Determine the correct inference function and add streaming specific argument if needed
inference_func = model.inference_stream if streaming else model.inference
if streaming:
common_args["stream_chunk_size"] = 20
# Call the appropriate function
output = inference_func(**common_args)
# Process the output based on streaming or non-streaming
if streaming:
# Streaming-specific operations
file_chunks = []
wav_buf = io.BytesIO()
with wave.open(wav_buf, "wb") as vfout:
vfout.setnchannels(1)
vfout.setsampwidth(2)
vfout.setframerate(24000)
vfout.writeframes(b"")
wav_buf.seek(0)
yield wav_buf.read()
for i, chunk in enumerate(output):
file_chunks.append(chunk)
if isinstance(chunk, list):
chunk = torch.cat(chunk, dim=0)
chunk = chunk.clone().detach().cpu().numpy()
chunk = chunk[None, : int(chunk.shape[0])]
chunk = np.clip(chunk, -1, 1)
chunk = (chunk * 32767).astype(np.int16)
yield chunk.tobytes()
else:
# Non-streaming-specific operation
torchaudio.save(output_file, torch.tensor(output["wav"]).unsqueeze(0), 24000)
# API LOCAL Methods
elif params["tts_method_api_local"]:
# Streaming only allowed for XTTSv2 local
if streaming:
raise ValueError("Streaming is only supported in XTTSv2 local")
# Set the correct output path (different from the if statement)
print(f"[{params['branding']}TTSGen] Using API Local")
model.tts_to_file(
text=text,
file_path=output_file,
speaker_wav=[f"{this_dir}/voices/{voice}"],
language=language,
temperature=temperature,
length_penalty=model.config.length_penalty,
repetition_penalty=repetition_penalty,
top_k=model.config.top_k,
top_p=model.config.top_p,
)
# API TTS
elif params["tts_method_api_tts"]:
# Streaming only allowed for XTTSv2 local
if streaming:
raise ValueError("Streaming is only supported in XTTSv2 local")
print(f"[{params['branding']}TTSGen] Using API TTS")
model.tts_to_file(
text=text,
file_path=output_file,
speaker_wav=[f"{this_dir}/voices/{voice}"],
language=language,
)
# Print Generation time and settings
generate_end_time = time.time() # Record the end time to generate TTS
generate_elapsed_time = generate_end_time - generate_start_time
print(
f"[{params['branding']}TTSGen] \033[93m{generate_elapsed_time:.2f} seconds. \033[94mLowVRAM: \033[33m{params['low_vram']} \033[94mDeepSpeed: \033[33m{params['deepspeed_activate']}\033[0m"
)
# Move model back to cpu system ram if needed.
if params["low_vram"] and device == "cuda":
await switch_device()
return
# TTS VOICE GENERATION METHODS - generate TTS API
@app.route("/api/generate", methods=["POST"])
async def generate(request: Request):
try:
# Get parameters from JSON body
data = await request.json()
text = data["text"]
voice = data["voice"]
language = data["language"]
temperature = data["temperature"]
repetition_penalty = data["repetition_penalty"]
output_file = data["output_file"]
streaming = False
# Generation logic
response = await generate_audio(text, voice, language, temperature, repetition_penalty, output_file, streaming)
if streaming:
return StreamingResponse(response, media_type="audio/wav")
return JSONResponse(
content={"status": "generate-success", "data": {"audio_path": output_file}}
)
except Exception as e:
return JSONResponse(content={"status": "error", "message": str(e)})
###################################################
#### POPULATE FILES LIST FROM VOICES DIRECTORY ####
###################################################
# List files in the "voices" directory
def list_files(directory):
files = [
f
for f in os.listdir(directory)
if os.path.isfile(os.path.join(directory, f)) and f.endswith(".wav")
]
return files
#############################
#### JSON CONFIG UPDATER ####
#############################
# Create an instance of Jinja2Templates for rendering HTML templates
templates = Jinja2Templates(directory=this_dir / "templates")
# 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(
"generate_form.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 / "templates")), 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, 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, 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, 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, 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, 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
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=1000, description="text_input needs to be 1000 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("&#x27;", "'")
)
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, 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, 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 / 'templates' / 'word_addin')), name="word_addin")
###################################################
#### 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")
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