xVASynth-TTS / app.py
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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()