app.py

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 = '/tmp/hfcache/models--Pendrokar--xvapitch_nvidia/snapshots/61b10e60b22bc21c1e072f72f1108b9c2b21e94c'

models_path = './resources/app/models/ccby/'

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('Failed to move downloaded models, perhaps already moved')

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()

	# Wait for the process to exit
	xvaserver.wait()

def load_model():
	model_path =  models_path + voice_models[0]

	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
	except requests.exceptions.RequestException as err:
		print('Failed to load voice model!')
	return

def predict(input_text, pacing):

	# load voice model if not the current model
	# if (current_voice_model != voice_model)
	# 	load_model()

	model_type = 'xVAPitch'
	pace = pacing if pacing else 1.0
	save_path = '/tmp/xvapitch_audio_sample.wav'
	language = 'en'
	base_speaker_emb = "0.02059412143946074,0.008201584451707952,-0.05277030320936666,-0.039501296572360664,0.03819673617993138,0.040965055419247845,0.023416176201646512,0.0738048970470502,-0.022488680839208613,0.004742570652413852,0.08258309825838091,-0.029896974180596347,0.007623321898076605,-0.014385980884378709,-0.04895588345028929,-0.03173952258108633,0.06192960956524358,0.0024332545144194343,0.03269641523992302,0.025787167165119314,0.029786330252299206,0.06342059792605999,0.025040897510403375,-0.033921410832297355,-0.03109066728659562,0.005312140046451575,0.005451900488461911,0.04885825827158006,0.04651092324831847,0.04154541535128829,0.015740028257454398,0.07411105939487676,-0.04705822052194547,0.08234144021402753,-0.007609069202950745,-0.04202066055572556,-0.015052768978905475,0.018694231043643515,0.029143727266584075,0.10079553395035755,-0.013802278590248252,0.007599227800692346,-0.02139346933674926,-0.045930129824978,-0.002763870069305383,-0.02819758760639864,0.033784053053219305,-0.008986875977842606,-0.040692288661311804,-0.01590705578861926,0.048330252381842985,0.05027618679116613,-0.002831847005020155,-0.024023675035413982,-0.02443027882665902,0.004039959747256932,0.07551178626291195,0.029589468027455566,-0.0015828541818794495,0.04676929206444622,-0.020613106888042514,0.042098865959990336,0.09546128130911928,0.020703315059659565,-0.011249538079616667,-0.07540832834600718,0.03465820367046974,0.00393294581254368,0.053209329078733186,-0.03021388709092682,-0.01951826801479834,-0.05977936920461946,0.008797915463121605,-0.05712115565482911,0.04131273251034018,-0.09273814899968984,-0.10207227659178059,0.058790477853312324,0.01897804391189814,-0.02178261259176716,-0.021484194506276158,-0.08229631100545559,-0.05138027446698438,-0.00027628880637762317,0.015647992154012472,0.005354931579772256,0.08213169780215862,-0.047810609907234625,-0.05414263731160324,-0.038296023264480346,-0.007579790276616037,-0.03646887578494079,-0.04008997937172555,0.03678530506896434,-0.09368463678472914,-0.05199597782090462,-0.036921057160887616,0.029692581331897193,0.016386972778861173,0.06202756289126103,0.05066110052307285,0.027814329759178042,-0.007281413355143866,-0.03759511055890598,0.08385584525884529,-0.017281195804149618,0.06913154615865104,0.01315304627673114,-0.017020608851213877,0.06319093348804176,-0.03986924834602306,0.09147952597619971,-0.024315902884245753,-0.06426214898980127,0.03070522487696974,0.007921454523906421,-0.002303376761462218,0.04705365155222134,0.024035771629399676,0.005804419645614805,0.05944420597029526,0.041190475921322904,0.05472544337333494,0.0681096299295807,0.003283463596832385,0.012816178237446085,0.026948904272056224,0.006394830216742351,-0.05120212424670069,-0.039777774021692414,0.05101289164503741,-0.018439937818314864,0.011897065237903383,-0.09698980388851842,0.0047267140457017045,0.022849590908084076,-0.015288020578465004,0.03749845410723472,0.02220631416812867,-0.02319304866090999,0.04008733320760122,0.030397735504018062,-0.008541387401820474,-0.03605930499783825,0.06196725351288037,-0.11121911417017709,-0.04725924740469605,-0.07899495961733442,-0.04244266485396749,-0.027248625321533895,0.05066213618646282,-0.03506564923310191,-0.005840301218127498,0.004162213410093819,0.04909547090209646,0.06071786370157997,-0.05474646558435032,0.07962362481521716,0.07091592467528665,0.05937644418646688,-0.02911951892934846,-0.03254295913596573,-0.022591876345155616,0.0008537834699139989,-0.014769368427959144,0.009630182049977329,0.009811974880308653,-0.0542164803580909,-0.017230848620820638,0.024878364927052834,-0.02575894071886462,-0.043100961132885696,0.04117734547005268,0.03140134792124991,0.08679875127270445,-0.02462917350431534,-0.003768949563922518,0.03494913383390341,0.02756011486463656,0.018993482183639197,0.12100332289166019,0.05367585477269294,-0.07146326847903536,-0.020866162142183346,-0.09688478454024067,0.05966068745682935,-0.06886393149376473,-0.011604896745240395,0.02478997662226238,-0.01309686984815536,0.07455265846421104,-0.014204533134263597,0.012980711683124951,-0.01195490683579427,-0.011568376921449849,0.07520772829704946,0.06424759474690483,-0.05513639092416767,0.04627532131785045,0.05297611672982825,0.005834491664910714,-0.030645055896297464,0.028803855721750933,0.026741277795573754,-0.017403122514379046,0.0623772399556704,-0.045581046657594285,-0.03186385889481072,-0.07502207317506247,-0.02076766956408014,0.054792314306389735,-0.006492469279425618,0.07371799293001495,-0.053286731833541254,0.03164880561816261,-0.03494070488630127,-0.03544574287747602,0.045445782147385726,0.026391253902760294,0.000211523792895327,-0.05624092817469954,0.019716588188751345,-0.06794026260110232,0.03469104941185243,0.06745962027694244,-0.03902418382432907,0.047196376212513304,0.01882557501922099,0.014702989353749826,0.08243897184831722,-0.05324926561095518,-0.03501195345812069,-1.3509955095127642e-05,-0.0015714049399960489,-0.04396534584077557,-0.01538779513563676,0.01766822271506532,0.040906586077407575,-0.03636189620324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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")

gradio_app = gr.Interface(
	predict,
	[
		input_textbox,
		pacing_slider
	],
	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()