import gradio as gr
import requests
import random
import os
import zipfile
import librosa
import time
from infer_rvc_python import BaseLoader
from pydub import AudioSegment
from tts_voice import tts_order_voice
import edge_tts
import tempfile
from audio_separator.separator import Separator
import model_handler
import psutil
import cpuinfo
language_dict = tts_order_voice
async def text_to_speech_edge(text, language_code):
voice = language_dict[language_code]
communicate = edge_tts.Communicate(text, voice)
with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp_file:
tmp_path = tmp_file.name
await communicate.save(tmp_path)
return tmp_path
try:
import spaces
spaces_status = True
except ImportError:
spaces_status = False
separator = Separator()
converter = BaseLoader(only_cpu=False, hubert_path=None, rmvpe_path=None)
global pth_file
global index_file
pth_file = "model.pth"
index_file = "model.index"
#CONFIGS
TEMP_DIR = "temp"
MODEL_PREFIX = "model"
PITCH_ALGO_OPT = [
"pm",
"harvest",
"crepe",
"rmvpe",
"rmvpe+",
]
UVR_5_MODELS = [
{"model_name": "BS-Roformer-Viperx-1297", "checkpoint": "model_bs_roformer_ep_317_sdr_12.9755.ckpt"},
{"model_name": "MDX23C-InstVoc HQ 2", "checkpoint": "MDX23C-8KFFT-InstVoc_HQ_2.ckpt"},
{"model_name": "Kim Vocal 2", "checkpoint": "Kim_Vocal_2.onnx"},
{"model_name": "5_HP-Karaoke", "checkpoint": "5_HP-Karaoke-UVR.pth"},
{"model_name": "UVR-DeNoise by FoxJoy", "checkpoint": "UVR-DeNoise.pth"},
{"model_name": "UVR-DeEcho-DeReverb by FoxJoy", "checkpoint": "UVR-DeEcho-DeReverb.pth"},
]
MODELS = [
{"model": "model.pth", "index": "model.index", "model_name": "Test Model"},
]
os.makedirs(TEMP_DIR, exist_ok=True)
def unzip_file(file):
filename = os.path.basename(file).split(".")[0]
with zipfile.ZipFile(file, 'r') as zip_ref:
zip_ref.extractall(os.path.join(TEMP_DIR, filename))
return True
def progress_bar(total, current):
return "[" + "=" * int(current / total * 20) + ">" + " " * (20 - int(current / total * 20)) + "] " + str(int(current / total * 100)) + "%"
def contains_bad_word(text, bad_words):
text_lower = text.lower()
for word in bad_words:
if word.lower() in text_lower:
return True
return False
bad_words = ['puttana', 'whore', 'badword3', 'badword4']
class BadWordError(Exception):
def __init__(self, msg):
super().__init__(msg)
self.word = word
def download_from_url(url, name=None):
if name is None:
raise ValueError("The model name must be provided")
if "/blob/" in url:
url = url.replace("/blob/", "/resolve/")
if "huggingface" not in url:
return ["The URL must be from huggingface", "Failed", "Failed"]
if contains_bad_word(url, bad_words):
return BadWordError("The file url has a bad word.")
if contains_bad_word(name, bad_words):
return BadWordError("The file name has a bad word.")
filename = os.path.join(TEMP_DIR, MODEL_PREFIX + str(random.randint(1, 1000)) + ".zip")
response = requests.get(url)
total = int(response.headers.get('content-length', 0))
if total > 500000000:
return ["The file is too large. You can only download files up to 500 MB in size.", "Failed", "Failed"]
current = 0
with open(filename, "wb") as f:
for data in response.iter_content(chunk_size=4096):
f.write(data)
current += len(data)
print(progress_bar(total, current), end="\r") #
try:
unzip_file(filename)
except Exception as e:
return ["Failed to unzip the file", "Failed", "Failed"]
unzipped_dir = os.path.join(TEMP_DIR, os.path.basename(filename).split(".")[0])
pth_files = []
index_files = []
for root, dirs, files in os.walk(unzipped_dir):
for file in files:
if file.endswith(".pth"):
pth_files.append(os.path.join(root, file))
elif file.endswith(".index"):
index_files.append(os.path.join(root, file))
print(pth_files, index_files)
global pth_file
global index_file
pth_file = pth_files[0]
index_file = index_files[0]
print(pth_file)
print(index_file)
if name == "":
name = pth_file.split(".")[0]
MODELS.append({"model": pth_file, "index": index_file, "model_name": name})
return ["Downloaded as " + name, pth_files[0], index_files[0]]
def inference(audio, model_name):
output_data = inf_handler(audio, model_name)
vocals = output_data[0]
inst = output_data[1]
return vocals, inst
if spaces_status:
@spaces.GPU()
def convert_now(audio_files, random_tag, converter):
return converter(
audio_files,
random_tag,
overwrite=False,
parallel_workers=8
)
else:
def convert_now(audio_files, random_tag, converter):
return converter(
audio_files,
random_tag,
overwrite=False,
parallel_workers=8
)
def inf_handler(audio, model_name):
model_found = False
for model_info in UVR_5_MODELS:
if model_info["model_name"] == model_name:
separator.load_model(model_info["checkpoint"])
model_found = True
break
if not model_found:
separator.load_model()
output_files = separator.separate(audio)
vocals = output_files[0]
inst = output_files[1]
return vocals, inst
def run(
model,
audio_files,
pitch_alg,
pitch_lvl,
index_inf,
r_m_f,
e_r,
c_b_p,
):
if not audio_files:
raise ValueError("The audio pls")
if isinstance(audio_files, str):
audio_files = [audio_files]
try:
duration_base = librosa.get_duration(filename=audio_files[0])
print("Duration:", duration_base)
except Exception as e:
print(e)
random_tag = "USER_"+str(random.randint(10000000, 99999999))
file_m = model
print("File model:", file_m)
# get from MODELS
for model in MODELS:
if model["model_name"] == file_m:
print(model)
file_m = model["model"]
file_index = model["index"]
break
if not file_m.endswith(".pth"):
raise ValueError("The model file must be a .pth file")
print("Random tag:", random_tag)
print("File model:", file_m)
print("Pitch algorithm:", pitch_alg)
print("Pitch level:", pitch_lvl)
print("File index:", file_index)
print("Index influence:", index_inf)
print("Respiration median filtering:", r_m_f)
print("Envelope ratio:", e_r)
converter.apply_conf(
tag=random_tag,
file_model=file_m,
pitch_algo=pitch_alg,
pitch_lvl=pitch_lvl,
file_index=file_index,
index_influence=index_inf,
respiration_median_filtering=r_m_f,
envelope_ratio=e_r,
consonant_breath_protection=c_b_p,
resample_sr=44100 if audio_files[0].endswith('.mp3') else 0,
)
time.sleep(0.1)
result = convert_now(audio_files, random_tag, converter)
print("Result:", result)
return result[0]
def upload_model(index_file, pth_file, model_name):
pth_file = pth_file.name
index_file = index_file.name
MODELS.append({"model": pth_file, "index": index_file, "model_name": model_name})
return "Uploaded!"
with gr.Blocks(
title="Ilqria RVC UI mod",
css="footer{display:none !important}",
theme=gr.themes.Default(
primary_hue="rose",
secondary_hue="rose",
spacing_size=gr.themes.sizes.spacing_sm,
radius_size=gr.themes.sizes.radius_none,
)) as app:
gr.Markdown("## Ilaria RVC Mod💖")
with gr.Tab("Inference"):
sound_gui = gr.Audio(value=None,type="filepath",autoplay=False,visible=True,)
def update():
print(MODELS)
return gr.Dropdown(label="Model",choices=[model["model_name"] for model in MODELS],visible=True,interactive=True, value=MODELS[0]["model_name"],)
with gr.Row():
models_dropdown = gr.Dropdown(label="Model",choices=[model["model_name"] for model in MODELS],visible=True,interactive=True, value=MODELS[0]["model_name"],)
refresh_button = gr.Button("Refresh Models")
refresh_button.click(update, outputs=[models_dropdown])
with gr.Accordion("Ilaria TTS", open=False):
text_tts = gr.Textbox(label="Text", placeholder="Hello!", lines=3, interactive=True,)
dropdown_tts = gr.Dropdown(label="Language and Model",choices=list(language_dict.keys()),interactive=True, value=list(language_dict.keys())[0])
button_tts = gr.Button("Speak", variant="primary",)
button_tts.click(text_to_speech_edge, inputs=[text_tts, dropdown_tts], outputs=[sound_gui])
with gr.Accordion("Settings", open=False):
pitch_algo_conf = gr.Dropdown(PITCH_ALGO_OPT,value=PITCH_ALGO_OPT[4],label="Pitch algorithm",visible=True,interactive=True,)
pitch_lvl_conf = gr.Slider(label="Pitch level (lower -> 'male' while higher -> 'female')",minimum=-24,maximum=24,step=1,value=0,visible=True,interactive=True,)
index_inf_conf = gr.Slider(minimum=0,maximum=1,label="Index influence -> How much accent is applied",value=0.75,)
respiration_filter_conf = gr.Slider(minimum=0,maximum=7,label="Respiration median filtering",value=3,step=1,interactive=True,)
envelope_ratio_conf = gr.Slider(minimum=0,maximum=1,label="Envelope ratio",value=0.25,interactive=True,)
consonant_protec_conf = gr.Slider(minimum=0,maximum=0.5,label="Consonant breath protection",value=0.5,interactive=True,)
button_conf = gr.Button("Convert",variant="primary",)
output_conf = gr.Audio(type="filepath",label="Output",)
button_conf.click(lambda :None, None, output_conf)
button_conf.click(
run,
inputs=[
models_dropdown,
sound_gui,
pitch_algo_conf,
pitch_lvl_conf,
index_inf_conf,
respiration_filter_conf,
envelope_ratio_conf,
consonant_protec_conf,
],
outputs=[output_conf],
)
with gr.Tab("Model Loader (Download and Upload)"):
with gr.Accordion("Model Downloader", open=False):
gr.Markdown(
"Download the model from the following URL and upload it here. (Huggingface RVC model)"
)
model = gr.Textbox(lines=1, label="Model URL")
name = gr.Textbox(lines=1, label="Model Name", placeholder="Model Name")
download_button = gr.Button("Download Model")
status = gr.Textbox(lines=1, label="Status", placeholder="Waiting....", interactive=False)
model_pth = gr.Textbox(lines=1, label="Model pth file", placeholder="Waiting....", interactive=False)
index_pth = gr.Textbox(lines=1, label="Index pth file", placeholder="Waiting....", interactive=False)
download_button.click(download_from_url, [model, name], outputs=[status, model_pth, index_pth])
with gr.Accordion("Upload A Model", open=False):
index_file_upload = gr.File(label="Index File (.index)")
pth_file_upload = gr.File(label="Model File (.pth)")
model_name = gr.Textbox(label="Model Name", placeholder="Model Name")
upload_button = gr.Button("Upload Model")
upload_status = gr.Textbox(lines=1, label="Status", placeholder="Waiting....", interactive=False)
upload_button.click(upload_model, [index_file_upload, pth_file_upload, model_name], upload_status)
with gr.Tab("Vocal Separator (UVR)"):
gr.Markdown("Separate vocals and instruments from an audio file using UVR models. - This is only on CPU due to ZeroGPU being ZeroGPU :(")
uvr5_audio_file = gr.Audio(label="Audio File",type="filepath")
with gr.Row():
uvr5_model = gr.Dropdown(label="Model", choices=[model["model_name"] for model in UVR_5_MODELS])
uvr5_button = gr.Button("Separate Vocals", variant="primary",)
uvr5_output_voc = gr.Audio(type="filepath", label="Output 1",)
uvr5_output_inst = gr.Audio(type="filepath", label="Output 2",)
uvr5_button.click(inference, [uvr5_audio_file, uvr5_model], [uvr5_output_voc, uvr5_output_inst])
with gr.Tab("Credits"):
gr.Markdown(
"""
Ilaria RVC made by [Ilaria](https://huggingface.co/TheStinger) suport her on [ko-fi](https://ko-fi.com/ilariaowo)
The Inference code is made by [r3gm](https://huggingface.co/r3gm) (his module helped form this space 💖)
made with ❤️ by [mikus](https://github.com/cappuch) - made the ui!
## In loving memory of JLabDX 🕊️
"""
)
with gr.Tab(("")):
gr.Markdown('''
''')
app.launch(share=True)