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CPU Upgrade
File size: 7,949 Bytes
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import os
import sys
import traceback
import multiprocessing
import json
torch_dml_device = None
multiprocessing.freeze_support()
# PROD = 'xVASynth.exe' in os.listdir(".")
PROD = True
sys.path.append("./resources/app")
sys.path.append("./resources/app/python")
sys.path.append("./resources/app/python/text")
sys.path.append("./resources/app/python/text/h2p_parser")
sys.path.append("./resources/app/deepmoji_plugin")
# Saves me having to do backend re-compilations for every little UI hotfix
with open(f'{"./resources/app" if PROD else "."}/javascript/script.js', encoding="utf8") as f:
lines = f.read().split("\n")
APP_VERSION = lines[1].split('"v')[1].split('"')[0]
# Imports and logger setup
# ========================
try:
# import python.pyinstaller_imports
import numpy
import logging
from logging.handlers import RotatingFileHandler
import json
# from socketserver import ThreadingMixIn
# from python.audio_post import run_audio_post, prepare_input_audio, mp_ffmpeg_output, normalize_audio, start_microphone_recording, move_recorded_file
# import ffmpeg
except:
print(traceback.format_exc())
with open("./DEBUG_err_imports.txt", "w+") as f:
f.write(traceback.format_exc())
# Pyinstaller hack
# ================
try:
def script_method(fn, _rcb=None):
return fn
def script(obj, optimize=True, _frames_up=0, _rcb=None):
return obj
import torch.jit
torch.jit.script_method = script_method
torch.jit.script = script
import torch
import tqdm
import regex
except:
print(traceback.format_exc())
with open("./DEBUG_err_import_torch.txt", "w+") as f:
f.write(traceback.format_exc())
# ================
# CPU_ONLY = not torch.cuda.is_available()
CPU_ONLY = True
logger = logging.getLogger('serverLog')
logger.setLevel(logging.DEBUG)
server_log_path = f'{os.path.dirname(os.path.realpath(__file__))}/{"../../../" if PROD else ""}/server.log'
fh = RotatingFileHandler(server_log_path, maxBytes=2*1024*1024, backupCount=5)
fh.setLevel(logging.DEBUG)
ch = logging.StreamHandler()
ch.setLevel(logging.ERROR)
formatter = logging.Formatter('%(asctime)s - %(message)s')
fh.setFormatter(formatter)
ch.setFormatter(formatter)
logger.addHandler(fh)
logger.addHandler(ch)
logger.info(f'New session. Version: {APP_VERSION}. Installation: {"CPU" if CPU_ONLY else "CPU+GPU"} | Prod: {PROD} | Log path: {server_log_path}')
logger.orig_info = logger.info
def prefixed_log (msg):
logger.info(f'{logger.logging_prefix}{msg}')
def set_logger_prefix (prefix=""):
if len(prefix):
logger.logging_prefix = f'[{prefix}]: '
logger.log = prefixed_log
else:
logger.log = logger.orig_info
logger.set_logger_prefix = set_logger_prefix
logger.set_logger_prefix("")
if CPU_ONLY:
torch_dml_device = torch.device("cpu")
from python.plugins_manager import PluginManager
plugin_manager = PluginManager(APP_VERSION, PROD, CPU_ONLY, logger)
active_plugins = plugin_manager.get_active_plugins_count()
logger.info(f'Plugin manager loaded. {active_plugins} active plugins.')
plugin_manager.run_plugins(plist=plugin_manager.plugins["start"]["pre"], event="pre start", data=None)
# ======================== Models manager
modelsPaths = {}
from python.models_manager import ModelsManager
models_manager = ModelsManager(logger, PROD, device="cpu")
# ========================
print("Models ready")
logger.info("Models ready")
post_data = ""
def loadModel(post_data):
req_response = {}
logger.info("Direct: loadModel")
logger.info(post_data)
ckpt = post_data["model"]
modelType = post_data["modelType"]
instance_index = post_data["instance_index"] if "instance_index" in post_data else 0
modelType = modelType.lower().replace(".", "_").replace(" ", "")
post_data["pluginsContext"] = json.loads(post_data["pluginsContext"])
n_speakers = post_data["model_speakers"] if "model_speakers" in post_data else None
base_lang = post_data["base_lang"] if "base_lang" in post_data else None
plugin_manager.run_plugins(plist=plugin_manager.plugins["load-model"]["pre"], event="pre load-model", data=post_data)
models_manager.load_model(modelType, ckpt+".pt", instance_index=instance_index, n_speakers=n_speakers, base_lang=base_lang)
plugin_manager.run_plugins(plist=plugin_manager.plugins["load-model"]["post"], event="post load-model", data=post_data)
if (
modelType=="fastpitch1_1"
or modelType=="xvapitch"
):
models_manager.models_bank[modelType][instance_index].init_arpabet_dicts()
return req_response
def synthesize(post_data, stream=False):
req_response = {}
logger.info("Direct: synthesize")
post_data["pluginsContext"] = json.loads(post_data["pluginsContext"])
instance_index = post_data["instance_index"] if "instance_index" in post_data else 0
# Handle the case where the vocoder remains selected on app start-up, with auto-HiFi turned off, but no setVocoder call is made before synth
continue_synth = True
if "waveglow" in post_data["vocoder"]:
waveglowPath = post_data["waveglowPath"]
req_response = models_manager.load_model(post_data["vocoder"], waveglowPath, instance_index=instance_index)
if req_response=="ENOENT":
continue_synth = False
device = post_data["device"] if "device" in post_data else models_manager.device_label
device = torch.device("cpu") if device=="cpu" else (torch_dml_device if CPU_ONLY else torch.device("cuda:0"))
models_manager.set_device(device, instance_index=instance_index)
if continue_synth:
plugin_manager.set_context(post_data["pluginsContext"])
plugin_manager.run_plugins(plist=plugin_manager.plugins["synth-line"]["pre"], event="pre synth-line", data=post_data)
modelType = post_data["modelType"]
text = post_data["sequence"]
pace = float(post_data["pace"])
out_path = post_data["outfile"]
base_lang = post_data["base_lang"] if "base_lang" in post_data else None
base_emb = post_data["base_emb"] if "base_emb" in post_data else None
pitch = post_data["pitch"] if "pitch" in post_data else None
energy = post_data["energy"] if "energy" in post_data else None
emAngry = post_data["emAngry"] if "emAngry" in post_data else None
emHappy = post_data["emHappy"] if "emHappy" in post_data else None
emSad = post_data["emSad"] if "emSad" in post_data else None
emSurprise = post_data["emSurprise"] if "emSurprise" in post_data else None
editorStyles = post_data["editorStyles"] if "editorStyles" in post_data else None
duration = post_data["duration"] if "duration" in post_data else None
speaker_i = post_data["speaker_i"] if "speaker_i" in post_data else None
useSR = post_data["useSR"] if "useSR" in post_data else None
useCleanup = post_data["useCleanup"] if "useCleanup" in post_data else None
vocoder = post_data["vocoder"]
globalAmplitudeModifier = float(post_data["globalAmplitudeModifier"]) if "globalAmplitudeModifier" in post_data else None
editor_data = [pitch, duration, energy, emAngry, emHappy, emSad, emSurprise, editorStyles]
old_sequence = post_data["old_sequence"] if "old_sequence" in post_data else None
model = models_manager.models(modelType.lower().replace(".", "_").replace(" ", ""), instance_index=instance_index)
req_response = model.infer(plugin_manager, text, out_path, vocoder=vocoder, \
speaker_i=speaker_i, editor_data=editor_data, pace=pace, old_sequence=old_sequence, \
globalAmplitudeModifier=globalAmplitudeModifier, base_lang=base_lang, base_emb=base_emb, useSR=useSR, useCleanup=useCleanup)
plugin_manager.run_plugins(plist=plugin_manager.plugins["synth-line"]["post"], event="post synth-line", data=post_data)
return req_response |