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import gradio as gr
from utils.hparams import hparams
from preprocessing.data_gen_utils import get_pitch_parselmouth,get_pitch_crepe
import numpy as np
import matplotlib.pyplot as plt
import IPython.display as ipd
import utils
import librosa
import torchcrepe
from infer import *
import logging
from infer_tools.infer_tool import *
import io
import tempfile
##Render function
def render_audio(audio_file):
print(audio_file)
print(ckpt)
print(yaml)
############
logging.getLogger('numba').setLevel(logging.WARNING)
global wav_gen
project_name = "Unnamed"
model_path = ckpt
config_path= yaml
hubert_gpu=True
svc_model = Svc(project_name,config_path,hubert_gpu, model_path)
print('model loaded')
wav_fn = audio_file
demoaudio, sr = librosa.load(wav_fn)
key = 0
pndm_speedup = 20
wav_gen='queeeeee.wav'#直接改后缀可以保存不同格式音频，如flac可无损压缩
f0_tst, f0_pred, audio = run_clip(svc_model,file_path=wav_fn, key=key, acc=pndm_speedup, use_crepe=True, use_pe=True, thre=0.05,
use_gt_mel=False, add_noise_step=500,project_name=project_name,out_path=wav_gen)
# Play the audio file in the output component
output_audio.play(wav_gen)
return wav_gen
############################################
# Create the output components
#Transform ckpt binary into .ckpt
def transform_binary(ckpt_file):
# Create a temporary file and write the binary contents to it
temp_file = tempfile.NamedTemporaryFile(suffix='.ckpt', delete=False)
temp_file.write(ckpt_file)
print("CKPT Path is:", temp_file.name)
global ckpt
ckpt = temp_file.name
print(ckpt)
print(ckpt)
print(ckpt)
return temp_file.name
#Transform yaml binary into .yaml
def transform_binary2(yaml_file):
# Create a temporary file and write the binary contents to it
temp_file = tempfile.NamedTemporaryFile(suffix='.yaml', delete=False)
temp_file.write(yaml_file)
print("YAML Path is:", temp_file.name)
global yaml
yaml = temp_file.name
print(yaml)
print(yaml)
return temp_file.name
#Play audio
def play(audio_file):
print(audio_file)
upload_input = gr.inputs.File()
output_label = gr.outputs.Label()
demo = gr.Blocks()
with demo:
gr.Markdown("# **<p align='center'>DIFF-SVC Inference</p>**")
gr.Markdown(
"""
<p style='text-align: center'>
Render whatever model you want with this space!
</p>
"""
)
ckpt_file = gr.File(label= 'Load your CKPT', type="binary")
yaml_file = gr.File(label= 'Load your YAML', type="binary")
audio_file = gr.Audio(label = 'Load your WAV', type="filepath")
#Button 1
b1 = gr.Button("Decompile CKPT")
b1.click(transform_binary, inputs=ckpt_file)
#Button 2
b2 = gr.Button("Decompile YAML")
b2.click(transform_binary2, inputs=yaml_file)
#Button 4
b4 = gr.Button("Render")
b4.click(fn=render_audio, inputs=[audio_file])
def spam():
print(yaml)
print(ckpt)
#b5 = gr.Button("SPAM ME")
#b5.click(fn=spam)
demo.launch()
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