import os import sys import time import requests 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 = './resources/app/models/ccby/' models_path = '/home/user/.cache/huggingface/hub/models--Pendrokar--xvapitch_nvidia/snapshots/61b10e60b22bc21c1e072f72f1108b9c2b21e94c/' voice_models = [ "ccby_nvidia_hifi_6670_M", "ccby_nv_hifi_11614_F", "ccby_nvidia_hifi_11697_F", "ccby_nvidia_hifi_12787_F", "ccby_nvidia_hifi_6097_M", "ccby_nvidia_hifi_6671_M", "ccby_nvidia_hifi_8051_F", "ccby_nvidia_hifi_9017_M", "ccby_nvidia_hifi_9136_F", "ccby_nvidia_hifi_92_F", ] current_voice_model = None # move models to a more persistant place # try: # for voice_model_name in voice_model_names: # os.rename(hf_cache_models_path + voice_model_name + '.pt', models_path + voice_model_name + '.pt') # os.rename(hf_cache_models_path + voice_model_name + '.json', models_path + voice_model_name + '.json') # os.rename(hf_cache_models_path + voice_model_name + '.wav', models_path + voice_model_name + '.wav') # except Exception as e: # print(f'Failed to move downloaded models, perhaps already moved: {e}') 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(voice_models[0]) current_voice_model = voice_models[0] # 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': '{}', } try: response = requests.post('http://0.0.0.0:8008/loadModel', json=data) response.raise_for_status() # If the response contains an HTTP error status code, raise an exception current_voice_model = voice_model_name except requests.exceptions.RequestException as err: print('Failed to load voice model!') return def predict(input_text, pacing, voice): # load voice model if not the current model if (current_voice_model != voice): load_model(voice) model_type = 'xVAPitch' pace = pacing if pacing else 1.0 save_path = '/tmp/xvapitch_audio_sample.wav' language = 'en' base_speaker_emb = 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use_sr = 0 use_cleanup = 0 data = { '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, } try: response = requests.post('http://0.0.0.0:8008/synthesize', json=data) 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 synthesize!') print('server.log contents:') with open('resources/app/server.log', 'r') as f: print(f.read()) return save_path input_textbox = gr.Textbox( label="Input Text", lines=1, max_lines=5, autofocus=True ) pacing_slider = gr.Slider(0.5, 2.0, value=1.0, step=0.1, label="Pacing") voice_radio = gr.Radio( voice_models, label="Voice", info="NVIDIA HIFI CC-BY-4.0 model" ) gradio_app = gr.Interface( predict, [ input_textbox, pacing_slider, voice_radio ], outputs=gr.Audio(label="22kHz audio", type="filepath"), title="xVASynth (WIP)" # 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()