import os import sys import traceback import multiprocessing import json torch_dml_device = None if __name__ == '__main__': server = None multiprocessing.freeze_support() PROD = 'xVASynth.exe' in os.listdir(".") # 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 http.server import BaseHTTPRequestHandler, HTTPServer 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: with open("./DEBUG_err_import_torch.txt", "w+") as f: f.write(traceback.format_exc()) # ================ CPU_ONLY = not torch.cuda.is_available() try: 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("") except: with open("./DEBUG_err_logger.txt", "w+") as f: f.write(traceback.format_exc()) try: logger.info(traceback.format_exc()) except: pass if CPU_ONLY: torch_dml_device = torch.device("cpu") try: 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.') except: logger.info("Plugin manager FAILED.") logger.info(traceback.format_exc()) plugin_manager.run_plugins(plist=plugin_manager.plugins["start"]["pre"], event="pre start", data=None) # ======================== Models manager modelsPaths = {} try: from python.models_manager import ModelsManager models_manager = ModelsManager(logger, PROD, device="cpu") except: logger.info("Models manager failed to initialize") logger.info(traceback.format_exc()) # ======================== print("Models ready") logger.info("Models ready") # Server class ThreadedHTTPServer(ThreadingMixIn, HTTPServer): pass class Handler(BaseHTTPRequestHandler): def _set_response(self): self.send_response(200) self.send_header("Content-Type", "application/json") self.end_headers() def do_GET(self): returnString = "[DEBUG] Get request for {}".format(self.path).encode("utf-8") logger.info(returnString) self._set_response() self.wfile.write(returnString) def do_POST(self): global modelsPaths post_data = "" try: content_length = int(self.headers['Content-Length']) post_data = json.loads(self.rfile.read(content_length).decode('utf-8')) if content_length else {} req_response = "POST request for {}".format(self.path) print("POST") print(self.path) # For headless mode if self.path == "/setAvailableVoices": modelsPaths = json.loads(post_data["modelsPaths"]) if self.path == "/getAvailableVoices": models = {} for gameId in modelsPaths.keys(): models[gameId] = [] modelJSONs = sorted(os.listdir(modelsPaths[gameId])) for fname in modelJSONs: if fname.endswith(".json"): with open(f'{modelsPaths[gameId]}/{fname}', "r") as f: jsons = f.read() metadata = json.loads(jsons) models[gameId].append({ "modelType": metadata["modelType"], "author": metadata["author"] if "author" in metadata else "", "emb_size": metadata["emb_size"] if "emb_size" in metadata else 1, "voiceId": metadata["games"][0]["voiceId"], "voiceName": metadata["games"][0]["voiceName"], "gender": metadata["games"][0]["gender"] if "gender" in metadata["games"][0] else "other", "emb_i": metadata["games"][0]["emb_i"] if "emb_i" in metadata["games"][0] else 0 }) req_response = json.dumps(models) if self.path == "/setVocoder": logger.info("POST {}".format(self.path)) logger.info(post_data) vocoder = post_data["vocoder"] modelPath = post_data["modelPath"] hifi_gan = "waveglow" not in vocoder if vocoder=="qnd": req_response = models_manager.load_model("hifigan", f'{"./resources/app" if PROD else "."}/python/hifigan/hifi.pt') elif not hifi_gan: req_response = models_manager.load_model(vocoder, modelPath) req_response = "" if req_response is None else req_response if self.path == "/stopServer": logger.info("POST {}".format(self.path)) logger.info("STOPPING SERVER") server.shutdown() sys.exit() if self.path == "/normalizeAudio": input_path = post_data["input_path"] output_path = post_data["output_path"] req_response = normalize_audio(input_path, output_path) if self.path == "/customEvent": logger.info("POST {}".format(self.path)) plugin_manager.run_plugins(plist=plugin_manager.plugins["custom-event"], event="custom-event", data=post_data) if self.path == "/setDevice": logger.info("POST {}".format(self.path)) logger.info(post_data) if post_data["device"] == "cpu": logger.info("Setting torch device to CPU") device = torch.device("cpu") elif CPU_ONLY: logger.info("Setting torch device to DirectML") device = torch_dml_device else: logger.info("Setting torch device to CUDA") device = torch.device("cuda:0") models_manager.set_device(device) if self.path == "/loadModel": logger.info("POST {}".format(self.path)) 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": models_manager.models_bank["fastpitch1_1"][instance_index].init_arpabet_dicts() if self.path == "/getG2P": text = post_data["text"] base_lang = post_data["base_lang"] model = models_manager.models("xVAPitch", instance_index=0) returnString = model.getG2P(text, base_lang) req_response = returnString if self.path == "/synthesizeSimple": logger.info("POST {}".format(self.path)) text = post_data["sequence"] instance_index = post_data["instance_index"] if "instance_index" in post_data else 0 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 useCleanup = post_data["useCleanup"] if "useCleanup" in post_data else None model = models_manager.models("xvapitch", instance_index=instance_index) req_response = model.infer(plugin_manager, text, out_path, vocoder=None, \ speaker_i=None, editor_data=None, pace=None, old_sequence=None, \ globalAmplitudeModifier=None, base_lang=base_lang, base_emb=base_emb, useSR=False, useCleanup=useCleanup) if self.path == "/synthesize": logger.info("POST {}".format(self.path)) 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) if self.path == "/synthesize_batch": post_data["pluginsContext"] = json.loads(post_data["pluginsContext"]) plugin_manager.set_context(post_data["pluginsContext"]) plugin_manager.run_plugins(plist=plugin_manager.plugins["batch-synth-line"]["pre"], event="pre batch-synth-line", data=post_data) modelType = post_data["modelType"] linesBatch = post_data["linesBatch"] speaker_i = post_data["speaker_i"] vocoder = post_data["vocoder"] outputJSON = post_data["outputJSON"] useSR = post_data["useSR"] useCleanup = post_data["useCleanup"] with torch.no_grad(): try: model = models_manager.models(modelType.lower().replace(".", "_").replace(" ", "")) req_response = model.infer_batch(plugin_manager, linesBatch, outputJSON=outputJSON, vocoder=vocoder, speaker_i=speaker_i, useSR=useSR, useCleanup=useCleanup) except RuntimeError as e: if "CUDA out of memory" in str(e): req_response = "CUDA OOM" else: req_response = traceback.format_exc() logger.info(req_response) except: e = traceback.format_exc() if "CUDA out of memory" in str(e): req_response = "CUDA OOM" else: req_response = e logger.info(e) post_data["req_response"] = req_response plugin_manager.run_plugins(plist=plugin_manager.plugins["batch-synth-line"]["post"], event="post batch-synth-line", data=post_data) if self.path == "/runSpeechToSpeech": logger.info("POST {}".format(self.path)) input_path = post_data["input_path"] style_emb = post_data["style_emb"] options = post_data["options"] audio_out_path = post_data["audio_out_path"] useSR = post_data["useSR"] useCleanup = post_data["useCleanup"] vc_strength = post_data["vc_strength"] removeNoise = post_data["removeNoise"] removeNoiseStrength = post_data["removeNoiseStrength"] final_path = prepare_input_audio(PROD, logger, input_path, removeNoise, removeNoiseStrength) models_manager.init_model("speaker_rep") models_manager.load_model("speaker_rep", f'{"./resources/app" if PROD else "."}/python/xvapitch/speaker_rep/speaker_rep.pt') try: out = models_manager.models("xvapitch").run_speech_to_speech(final_path, audio_out_path.replace(".wav", "_tempS2S.wav"), style_emb, models_manager, plugin_manager, vc_strength=vc_strength, useSR=useSR, useCleanup=useCleanup) if out=="TOO_SHORT": req_response = "TOO_SHORT" else: data_out = "" req_response = data_out # For use by /outputAudio post_data["input_path"] = audio_out_path.replace(".wav", "_tempS2S.wav") post_data["output_path"] = audio_out_path except ValueError: req_response = traceback.format_exc() logger.info(req_response) except RuntimeError: req_response = traceback.format_exc() logger.info(req_response) except Exception as e: req_response = traceback.format_exc() logger.info(req_response) logger.info(repr(e)) if self.path == "/batchOutputAudio": input_paths = post_data["input_paths"] output_paths = post_data["output_paths"] processes = post_data["processes"] options = json.loads(post_data["options"]) # For plugins extraInfo = {} if "extraInfo" in post_data: extraInfo = json.loads(post_data["extraInfo"]) extraInfo["pluginsContext"] = json.loads(post_data["pluginsContext"]) extraInfo["audio_options"] = options extraInfo["input_paths"] = input_paths extraInfo["output_paths"] = output_paths extraInfo["processes"] = processes extraInfo["ffmpeg"] = ffmpeg plugin_manager.run_plugins(plist=plugin_manager.plugins["mp-output-audio"]["pre"], event="pre mp-output-audio", data=extraInfo) req_response = mp_ffmpeg_output(PROD, logger, processes, input_paths, output_paths, options) plugin_manager.run_plugins(plist=plugin_manager.plugins["mp-output-audio"]["post"], event="post mp-output-audio", data=extraInfo) if self.path == "/outputAudio" or (self.path == "/runSpeechToSpeech" and req_response==""): isBatchMode = post_data["isBatchMode"] if not isBatchMode: logger.info("POST /outputAudio") input_path = post_data["input_path"] output_path = post_data["output_path"] options = json.loads(post_data["options"]) # For plugins extraInfo = {} if "extraInfo" in post_data: extraInfo = json.loads(post_data["extraInfo"]) extraInfo["pluginsContext"] = json.loads(post_data["pluginsContext"]) extraInfo["audio_options"] = options extraInfo["input_path"] = input_path extraInfo["output_path"] = output_path extraInfo["ffmpeg"] = ffmpeg plugin_manager.run_plugins(plist=plugin_manager.plugins["output-audio"]["pre"], event="pre output-audio", data=extraInfo) input_path = post_data["input_path"] output_path = post_data["output_path"] req_response = run_audio_post(PROD, None if isBatchMode else logger, input_path, output_path, options) plugin_manager.run_plugins(plist=plugin_manager.plugins["output-audio"]["post"], event="post output-audio", data=extraInfo) if self.path == "/refreshPlugins": logger.info("POST {}".format(self.path)) status = plugin_manager.refresh_active_plugins() logger.info("status") logger.info(status) req_response = ",".join(status) if self.path == "/getWavV3StyleEmb": logger.info("POST {}".format(self.path)) wav_path = post_data["wav_path"] models_manager.init_model("speaker_rep") load_resp = models_manager.load_model("speaker_rep", f'{"./resources/app" if PROD else "."}/python/xvapitch/speaker_rep/speaker_rep.pt') if load_resp=="ENOENT": req_response = "ENOENT" else: style_emb = models_manager.models("speaker_rep").compute_embedding(wav_path).squeeze().cpu().detach().numpy() req_response = ",".join([str(v) for v in style_emb]) if self.path == "/computeEmbsAndDimReduction": logger.info("POST {}".format(self.path)) models_manager.init_model("speaker_rep") load_resp = models_manager.load_model("speaker_rep", f'{"./resources/app" if PROD else "."}/python/xvapitch/speaker_rep/speaker_rep.pt') embs = models_manager.models("speaker_rep").reduce_data_dimension(post_data["mappings"], post_data["includeAllVoices"], post_data["onlyInstalled"], post_data["algorithm"]) req_response = embs if self.path == "/checkReady": modelsPaths = json.loads(post_data["modelsPaths"]) device = torch.device("cpu") if post_data["device"]=="cpu" else (torch_dml_device if CPU_ONLY else torch.device("cuda:0")) models_manager.set_device(device) req_response = "ready" if self.path == "/updateARPABet": if "fastpitch1_1" in list(models_manager.models_bank.keys()): models_manager.models_bank["fastpitch1_1"].refresh_arpabet_dicts() if self.path == "/start_microphone_recording": start_microphone_recording(logger, models_manager, f'{"./resources/app" if PROD else "."}') req_response = "" if self.path == "/move_recorded_file": file_path = post_data["file_path"] move_recorded_file(PROD, logger, models_manager, f'{"./resources/app" if PROD else "."}', file_path) self._set_response() self.wfile.write(json.dumps(req_response).encode('utf-8')) except Exception as e: with open("./DEBUG_request.txt", "w+") as f: f.write(traceback.format_exc()) f.write(str(post_data)) logger.info("Post Error:\n {}".format(repr(e))) print(traceback.format_exc()) logger.info(traceback.format_exc()) try: # server = HTTPServer(("",8008), Handler) server = ThreadedHTTPServer(("",8008), Handler) # Prevent issues with socket reuse server.allow_reuse_address = True except: with open("./DEBUG_server_error.txt", "w+") as f: f.write(traceback.format_exc()) logger.info(traceback.format_exc()) try: plugin_manager.run_plugins(plist=plugin_manager.plugins["start"]["post"], event="post start", data=None) print("Server ready") logger.info("Server ready") server.serve_forever() except KeyboardInterrupt: pass server.server_close()