import os import sys import time import requests import json from subprocess import Popen, PIPE import threading from huggingface_hub import hf_hub_download import gradio as gr hf_model_name = "Pendrokar/xvapitch_nvidia" hf_cache_models_path = '/home/user/.cache/huggingface/hub/models--Pendrokar--xvapitch_nvidia/snapshots/61b10e60b22bc21c1e072f72f1108b9c2b21e94c/' models_path = '/home/user/.cache/huggingface/hub/models--Pendrokar--xvapitch_nvidia/snapshots/61b10e60b22bc21c1e072f72f1108b9c2b21e94c/' # FIXME: currently hardcoded in DeepMoji code # try: # os.symlink('/home/user/.cache/huggingface/hub/models--Pendrokar--TorchMoji/snapshots/58217568daaf64d3621245dd5c88c94e651a08d6', '/home/user/app/resources/app/plugins/deepmoji_plugings/model', target_is_directory=True) # except: # print('Failed to create symlink to DeepMoji model, may already be there.') voice_models = [ ("Male #6671", "ccby_nvidia_hifi_6671_M"), ("Male #6670", "ccby_nvidia_hifi_6670_M"), ("Male #9017", "ccby_nvidia_hifi_9017_M"), ("Male #6097", "ccby_nvidia_hifi_6097_M"), ("Female #92", "ccby_nvidia_hifi_92_F"), ("Female #11697", "ccby_nvidia_hifi_11697_F"), ("Female #12787", "ccby_nvidia_hifi_12787_F"), ("Female #11614", "ccby_nv_hifi_11614_F"), ("Female #8051", "ccby_nvidia_hifi_8051_F"), ("Female #9136", "ccby_nvidia_hifi_9136_F"), ] current_voice_model = None base_speaker_emb = '' # order ranked by similarity to English due to the xVASynth's use of ARPAbet instead of IPA languages = [ ("🇬🇧 EN", "en"), ("🇩🇪 DE", "de"), ("🇪🇸 ES", "es"), ("🇮🇹 IT", "it"), ("🇳🇱 NL", "nl"), ("🇵🇹 PT", "pt"), ("🇵🇱 PL", "pl"), ("🇷🇴 RO", "ro"), ("🇸🇪 SV", "sv"), ("🇩🇰 DA", "da"), ("🇫🇮 FI", "fi"), ("🇭🇺 HU", "hu"), ("🇬🇷 EL", "el"), ("🇫🇷 FR", "fr"), ("🇷🇺 RU", "ru"), ("🇺🇦 UK", "uk"), ("🇹🇷 TR", "tr"), ("🇸🇦 AR", "ar"), ("🇮🇳 HI", "hi"), ("🇯🇵 JP", "jp"), ("🇰🇷 KO", "ko"), ("🇨🇳 ZH", "zh"), ("🇻🇳 VI", "vi"), ("🇻🇦 LA", "la"), ("HA", "ha"), ("SW", "sw"), ("🇳🇬 YO", "yo"), ("WO", "wo"), ] # Translated from English by DeepMind's Gemini Pro default_text = { "ar": "هذا هو صوتي.", "da": "Sådan lyder min stemme.", "de": "So klingt meine Stimme.", "el": "Έτσι ακούγεται η φωνή μου.", "en": "This is what my voice sounds like.", "es": "Así suena mi voz.", "fi": "Näin ääneni kuulostaa.", "fr": "Voici à quoi ressemble ma voix.", "ha": "Wannan ne muryata ke.", "hi": "यह मेरी आवाज़ कैसी लगती है।", "hu": "Így hangzik a hangom.", "it": "Così suona la mia voce.", "jp": "これが私の声です。", "ko": "여기 제 목소리가 어떤지 들어보세요.", "la": "Haec est vox mea sonans.", "nl": "Dit is hoe mijn stem klinkt.", "pl": "Tak brzmi mój głos.", "pt": "É assim que minha voz soa.", "ro": "Așa sună vocea mea.", "ru": "Вот как звучит мой голос.", "sv": "Såhär låter min röst.", "sw": "Sauti yangu inasikika hivi.", "tr": "Benim sesimin sesi böyle.", "uk": "Ось як звучить мій голос.", "vi": "Đây là giọng nói của tôi.", "wo": "Ndox li neen xewnaal ma.", "yo": "Ìyí ni ohùn mi ńlá.", "zh": "这是我的声音。", } def run_xvaserver(): # start the process without waiting for a response print('Running xVAServer subprocess...\n') xvaserver = Popen(['python', f'{os.path.dirname(os.path.abspath(__file__))}/resources/app/server.py'], stdout=PIPE, stderr=PIPE, cwd=f'{os.path.dirname(os.path.abspath(__file__))}/resources/app/') # Wait for a moment to ensure the server starts up time.sleep(10) # Check if the server is running if xvaserver.poll() is not None: print("Web server failed to start.") sys.exit(0) # contact local xVASynth server print('Attempting to connect to xVASynth...') try: response = requests.get('http://0.0.0.0:8008') response.raise_for_status() # If the response contains an HTTP error status code, raise an exception except requests.exceptions.RequestException as err: print('Failed to connect!') return print('xVAServer running on port 8008') # load default model load_model("ccby_nvidia_hifi_6671_M") # Wait for the process to exit xvaserver.wait() def load_model(voice_model_name): model_path = models_path + voice_model_name model_type = 'xVAPitch' language = 'en' data = { 'outputs': None, 'version': '3.0', 'model': model_path, 'modelType': model_type, 'base_lang': language, 'pluginsContext': '{}', } embs = base_speaker_emb try: response = requests.post('http://0.0.0.0:8008/loadModel', json=data, timeout=60) response.raise_for_status() # If the response contains an HTTP error status code, raise an exception current_voice_model = voice_model_name with open(model_path + '.json', 'r', encoding='utf-8') as f: voice_model_json = json.load(f) embs = voice_model_json['games'][0]['base_speaker_emb'] except requests.exceptions.RequestException as err: print('Failed to load voice model!') return embs def predict( input_text, voice, lang, pacing, pitch, energy, anger, happy, sad, surprise, use_deepmoji ): # grab only the first 1000 characters input_text = input_text[:1000] # load voice model if not the current model if (current_voice_model != voice): base_speaker_emb = load_model(voice) model_type = 'xVAPitch' pace = pacing if pacing else 1.0 save_path = '/tmp/xvapitch_audio_sample.wav' language = lang use_sr = 0 use_cleanup = 0 pluginsContext = {} pluginsContext["mantella_settings"] = { "emAngry": (anger if anger > 0 else 0), "emHappy": (happy if happy > 0 else 0), "emSad": (sad if sad > 0 else 0), "emSurprise": (surprise if surprise > 0 else 0), "run_model": use_deepmoji } data = { 'pluginsContext': json.dumps(pluginsContext), 'modelType': model_type, # pad with whitespaces as a workaround to avoid cutoffs 'sequence': input_text.center(len(input_text) + 2, ' '), 'pace': pace, 'outfile': save_path, 'vocoder': 'n/a', 'base_lang': language, 'base_emb': base_speaker_emb, 'useSR': use_sr, 'useCleanup': use_cleanup, } print('Synthesizing...') try: response = requests.post('http://0.0.0.0:8008/synthesize', json=data, timeout=60) response.raise_for_status() # If the response contains an HTTP error status code, raise an exception # response_data = json.loads(response.text) except requests.exceptions.RequestException as err: print('Failed to synthesize!') save_path = '' response = {text: 'Failed'} print('server.log contents:') with open('resources/app/server.log', 'r') as f: print(f.read()) return [save_path, response.text] input_textbox = gr.Textbox( label="Input Text", value="This is what my voice sounds like.", info="Also accepts ARPAbet symbols placed within {} brackets.", lines=1, max_lines=5, autofocus=True ) pacing_slider = gr.Slider(0.5, 2.0, value=1.0, step=0.1, label="Duration") pitch_slider = gr.Slider(0, 1.0, value=0.5, step=0.05, label="Pitch", visible=False) energy_slider = gr.Slider(0.1, 1.0, value=1.0, step=0.05, label="Energy", visible=False) anger_slider = gr.Slider(0, 1.0, value=0, step=0.05, label="😠 Anger", info="Tread lightly beyond 0.9") happy_slider = gr.Slider(0, 1.0, value=0, step=0.05, label="😃 Happiness", info="Tread lightly beyond 0.7") sad_slider = gr.Slider(0, 1.0, value=0, step=0.05, label="😭 Sadness", info="Duration increased when beyond 0.2") surprise_slider = gr.Slider(0, 1.0, value=0, step=0.05, label="😮 Surprise", info="Does not play well with Happiness with either being beyond 0.3") voice_radio = gr.Radio( voice_models, value="ccby_nvidia_hifi_6671_M", label="Voice", info="NVIDIA HIFI CC-BY-4.0 xVAPitch voice model" ) def set_default_text(lang): input_textbox = gr.Textbox( label="Input Text", value=default_text[lang], lines=1, max_lines=5, autofocus=True ) language_radio = gr.Radio( languages, value="en", label="Language", info="Will be more monotone and have an English accent. Tested mostly by a native Briton." ) # language_radio.change(set_default_text) deepmoji_checkbox = gr.Checkbox(label="Use DeepMoji", info="Auto adjust emotional values") gradio_app = gr.Interface( predict, [ input_textbox, voice_radio, language_radio, pacing_slider, pitch_slider, energy_slider, anger_slider, happy_slider, sad_slider, surprise_slider, deepmoji_checkbox ], outputs=[ gr.Audio(label="22kHz audio output", type="filepath"), gr.Textbox(label="xVASynth Server Response") ], title="xVASynth (WIP)", clear_btn=gr.Button(visible=False) # examples=[ # ["Once, I headed in much deeper. But I doubt I'll ever do that again.", 1], # ["You love hurting me, huh?", 1.5], # ["Ah, I see. Well, I'm afraid I can't help with that.", 1], # ["Embrace your demise!", 1], # ["Never come back!", 1] # ], # cache_examples=None ) if __name__ == "__main__": # Run the web server in a separate thread web_server_thread = threading.Thread(target=run_xvaserver) print('Starting xVAServer thread') web_server_thread.start() print('running Gradio interface') gradio_app.launch() # Wait for the web server thread to finish (shouldn't be reached in normal execution) web_server_thread.join()