Spaces:
Running
on
CPU Upgrade
Running
on
CPU Upgrade
skip web server
Browse files- app.py +18 -15
- resources/app/no_server.py +202 -0
app.py
CHANGED
@@ -7,6 +7,7 @@ from subprocess import Popen, PIPE
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import threading
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from huggingface_hub import hf_hub_download
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import gradio as gr
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hf_model_name = "Pendrokar/xvapitch_nvidia"
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hf_cache_models_path = '/home/user/.cache/huggingface/hub/models--Pendrokar--xvapitch_nvidia/snapshots/61b10e60b22bc21c1e072f72f1108b9c2b21e94c/'
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@@ -146,8 +147,9 @@ def load_model(voice_model_name):
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print('Loading voice model...')
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try:
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current_voice_model = voice_model_name
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with open(model_path + '.json', 'r', encoding='utf-8') as f:
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@@ -211,9 +213,10 @@ def predict(
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print('Synthesizing...')
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try:
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except requests.exceptions.RequestException as err:
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print('FAILED to synthesize: {err}')
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save_path = ''
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@@ -487,18 +490,18 @@ with gr.Blocks(css=".arpabet {display: inline-block; background-color: gray; bor
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if __name__ == "__main__":
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# Run the web server in a separate thread
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print('Attempting to connect to local xVASynth server...')
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try:
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except requests.exceptions.RequestException as err:
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print('running Gradio interface')
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demo.launch()
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# Wait for the web server thread to finish (shouldn't be reached in normal execution)
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web_server_thread.join()
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import threading
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from huggingface_hub import hf_hub_download
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import gradio as gr
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import resources.app.no_server as xvaserver
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hf_model_name = "Pendrokar/xvapitch_nvidia"
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hf_cache_models_path = '/home/user/.cache/huggingface/hub/models--Pendrokar--xvapitch_nvidia/snapshots/61b10e60b22bc21c1e072f72f1108b9c2b21e94c/'
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print('Loading voice model...')
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try:
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xvaserver.loadModel(data)
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# response = requests.post('http://0.0.0.0:8008/loadModel', json=data, timeout=60)
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# response.raise_for_status() # If the response contains an HTTP error status code, raise an exception
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current_voice_model = voice_model_name
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with open(model_path + '.json', 'r', encoding='utf-8') as f:
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print('Synthesizing...')
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try:
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xvaserver.synthesize(data)
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# response = requests.post('http://0.0.0.0:8008/synthesize', json=data, timeout=60)
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# response.raise_for_status() # If the response contains an HTTP error status code, raise an exception
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# json_data = json.loads(response.text)
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except requests.exceptions.RequestException as err:
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print('FAILED to synthesize: {err}')
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save_path = ''
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if __name__ == "__main__":
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# Run the web server in a separate thread
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# print('Attempting to connect to local xVASynth server...')
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# try:
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# response = requests.get('http://0.0.0.0:8008')
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# response.raise_for_status() # If the response contains an HTTP error status code, raise an exception
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# except requests.exceptions.RequestException as err:
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# print('Failed to connect to xVASynth!')
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# web_server_thread = threading.Thread(target=run_xvaserver)
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# print('Starting xVAServer thread')
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# web_server_thread.start()
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print('running Gradio interface')
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demo.launch()
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# Wait for the web server thread to finish (shouldn't be reached in normal execution)
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# web_server_thread.join()
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resources/app/no_server.py
ADDED
@@ -0,0 +1,202 @@
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import os
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import sys
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import traceback
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import multiprocessing
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import json
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torch_dml_device = None
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multiprocessing.freeze_support()
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# PROD = 'xVASynth.exe' in os.listdir(".")
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PROD = True
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sys.path.append("./resources/app")
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# Saves me having to do backend re-compilations for every little UI hotfix
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with open(f'{"./resources/app" if PROD else "."}/javascript/script.js', encoding="utf8") as f:
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lines = f.read().split("\n")
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APP_VERSION = lines[1].split('"v')[1].split('"')[0]
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# Imports and logger setup
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# ========================
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try:
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# import python.pyinstaller_imports
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import numpy
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import logging
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from logging.handlers import RotatingFileHandler
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import json
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from socketserver import ThreadingMixIn
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from python.audio_post import run_audio_post, prepare_input_audio, mp_ffmpeg_output, normalize_audio, start_microphone_recording, move_recorded_file
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import ffmpeg
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except:
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print(traceback.format_exc())
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with open("./DEBUG_err_imports.txt", "w+") as f:
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f.write(traceback.format_exc())
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# Pyinstaller hack
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# ================
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try:
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def script_method(fn, _rcb=None):
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return fn
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def script(obj, optimize=True, _frames_up=0, _rcb=None):
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return obj
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import torch.jit
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torch.jit.script_method = script_method
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torch.jit.script = script
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import torch
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import tqdm
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import regex
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except:
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with open("./DEBUG_err_import_torch.txt", "w+") as f:
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f.write(traceback.format_exc())
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# ================
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# CPU_ONLY = not torch.cuda.is_available()
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CPU_ONLY = True
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try:
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logger = logging.getLogger('serverLog')
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logger.setLevel(logging.DEBUG)
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server_log_path = f'{os.path.dirname(os.path.realpath(__file__))}/{"../../../" if PROD else ""}/server.log'
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fh = RotatingFileHandler(server_log_path, maxBytes=2*1024*1024, backupCount=5)
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fh.setLevel(logging.DEBUG)
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ch = logging.StreamHandler()
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ch.setLevel(logging.ERROR)
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formatter = logging.Formatter('%(asctime)s - %(message)s')
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fh.setFormatter(formatter)
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ch.setFormatter(formatter)
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logger.addHandler(fh)
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logger.addHandler(ch)
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logger.info(f'New session. Version: {APP_VERSION}. Installation: {"CPU" if CPU_ONLY else "CPU+GPU"} | Prod: {PROD} | Log path: {server_log_path}')
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logger.orig_info = logger.info
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def prefixed_log (msg):
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logger.info(f'{logger.logging_prefix}{msg}')
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def set_logger_prefix (prefix=""):
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if len(prefix):
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logger.logging_prefix = f'[{prefix}]: '
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logger.log = prefixed_log
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else:
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logger.log = logger.orig_info
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logger.set_logger_prefix = set_logger_prefix
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logger.set_logger_prefix("")
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except:
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with open("./DEBUG_err_logger.txt", "w+") as f:
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f.write(traceback.format_exc())
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try:
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logger.info(traceback.format_exc())
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except:
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pass
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# if CPU_ONLY:
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# torch_dml_device = torch.device("cpu")
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try:
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from python.plugins_manager import PluginManager
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plugin_manager = PluginManager(APP_VERSION, PROD, CPU_ONLY, logger)
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active_plugins = plugin_manager.get_active_plugins_count()
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logger.info(f'Plugin manager loaded. {active_plugins} active plugins.')
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except:
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logger.info("Plugin manager FAILED.")
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logger.info(traceback.format_exc())
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plugin_manager.run_plugins(plist=plugin_manager.plugins["start"]["pre"], event="pre start", data=None)
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# ======================== Models manager
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# modelsPaths = {}
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try:
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from python.models_manager import ModelsManager
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models_manager = ModelsManager(logger, PROD, device="cpu")
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except:
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logger.info("Models manager failed to initialize")
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logger.info(traceback.format_exc())
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# ========================
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print("Models ready")
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logger.info("Models ready")
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global modelsPaths
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post_data = ""
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def loadModel(post_data):
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logger.info("Direct: loadModel")
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logger.info(post_data)
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ckpt = post_data["model"]
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modelType = post_data["modelType"]
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instance_index = post_data["instance_index"] if "instance_index" in post_data else 0
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modelType = modelType.lower().replace(".", "_").replace(" ", "")
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post_data["pluginsContext"] = json.loads(post_data["pluginsContext"])
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n_speakers = post_data["model_speakers"] if "model_speakers" in post_data else None
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base_lang = post_data["base_lang"] if "base_lang" in post_data else None
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plugin_manager.run_plugins(plist=plugin_manager.plugins["load-model"]["pre"], event="pre load-model", data=post_data)
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models_manager.load_model(modelType, ckpt+".pt", instance_index=instance_index, n_speakers=n_speakers, base_lang=base_lang)
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plugin_manager.run_plugins(plist=plugin_manager.plugins["load-model"]["post"], event="post load-model", data=post_data)
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if modelType=="fastpitch1_1":
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models_manager.models_bank["fastpitch1_1"][instance_index].init_arpabet_dicts()
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return req_response
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def synthesize(post_data):
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logger.info("Direct: synthesize")
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post_data["pluginsContext"] = json.loads(post_data["pluginsContext"])
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instance_index = post_data["instance_index"] if "instance_index" in post_data else 0
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# 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
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continue_synth = True
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if "waveglow" in post_data["vocoder"]:
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waveglowPath = post_data["waveglowPath"]
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req_response = models_manager.load_model(post_data["vocoder"], waveglowPath, instance_index=instance_index)
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if req_response=="ENOENT":
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continue_synth = False
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device = post_data["device"] if "device" in post_data else models_manager.device_label
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device = torch.device("cpu") if device=="cpu" else (torch_dml_device if CPU_ONLY else torch.device("cuda:0"))
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models_manager.set_device(device, instance_index=instance_index)
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if continue_synth:
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plugin_manager.set_context(post_data["pluginsContext"])
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plugin_manager.run_plugins(plist=plugin_manager.plugins["synth-line"]["pre"], event="pre synth-line", data=post_data)
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modelType = post_data["modelType"]
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text = post_data["sequence"]
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pace = float(post_data["pace"])
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out_path = post_data["outfile"]
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base_lang = post_data["base_lang"] if "base_lang" in post_data else None
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base_emb = post_data["base_emb"] if "base_emb" in post_data else None
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pitch = post_data["pitch"] if "pitch" in post_data else None
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energy = post_data["energy"] if "energy" in post_data else None
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emAngry = post_data["emAngry"] if "emAngry" in post_data else None
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emHappy = post_data["emHappy"] if "emHappy" in post_data else None
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emSad = post_data["emSad"] if "emSad" in post_data else None
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emSurprise = post_data["emSurprise"] if "emSurprise" in post_data else None
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editorStyles = post_data["editorStyles"] if "editorStyles" in post_data else None
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duration = post_data["duration"] if "duration" in post_data else None
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speaker_i = post_data["speaker_i"] if "speaker_i" in post_data else None
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useSR = post_data["useSR"] if "useSR" in post_data else None
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useCleanup = post_data["useCleanup"] if "useCleanup" in post_data else None
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vocoder = post_data["vocoder"]
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globalAmplitudeModifier = float(post_data["globalAmplitudeModifier"]) if "globalAmplitudeModifier" in post_data else None
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editor_data = [pitch, duration, energy, emAngry, emHappy, emSad, emSurprise, editorStyles]
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old_sequence = post_data["old_sequence"] if "old_sequence" in post_data else None
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model = models_manager.models(modelType.lower().replace(".", "_").replace(" ", ""), instance_index=instance_index)
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req_response = model.infer(plugin_manager, text, out_path, vocoder=vocoder, \
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speaker_i=speaker_i, editor_data=editor_data, pace=pace, old_sequence=old_sequence, \
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globalAmplitudeModifier=globalAmplitudeModifier, base_lang=base_lang, base_emb=base_emb, useSR=useSR, useCleanup=useCleanup)
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plugin_manager.run_plugins(plist=plugin_manager.plugins["synth-line"]["post"], event="post synth-line", data=post_data)
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return req_response
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