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import os
import glob
import json
import traceback
import logging
import gradio as gr
import numpy as np
import librosa
import torch
import asyncio
import edge_tts
import sys
import io
from datetime import datetime
from lib.config.config import Config
from lib.vc.vc_infer_pipeline import VC
from lib.vc.settings import change_audio_mode
from lib.vc.audio import load_audio
from lib.infer_pack.models import (
SynthesizerTrnMs256NSFsid,
SynthesizerTrnMs256NSFsid_nono,
SynthesizerTrnMs768NSFsid,
SynthesizerTrnMs768NSFsid_nono,
)
from lib.vc.utils import (
combine_vocal_and_inst,
cut_vocal_and_inst,
download_audio,
load_hubert
)
config = Config()
logging.getLogger("numba").setLevel(logging.WARNING)
logger = logging.getLogger(__name__)
spaces = os.getenv("SYSTEM") == "spaces"
force_support = None
if config.unsupported is False:
if config.device == "mps" or config.device == "cpu":
force_support = False
else:
force_support = True
audio_mode = []
f0method_mode = []
f0method_info = ""
hubert_model = load_hubert(config)
if force_support is False or spaces is True:
if spaces is True:
audio_mode = ["Upload audio", "TTS Audio"]
else:
audio_mode = ["Input path", "Upload audio", "TTS Audio"]
f0method_mode = ["pm", "harvest"]
f0method_info = "PM is fast, Harvest is good but extremely slow, Rvmpe is alternative to harvest (might be better). (Default: PM)"
else:
audio_mode = ["Input path", "Upload audio", "Youtube", "TTS Audio"]
f0method_mode = ["pm", "harvest", "crepe"]
f0method_info = "PM is fast, Harvest is good but extremely slow, Rvmpe is alternative to harvest (might be better), and Crepe effect is good but requires GPU (Default: PM)"
if os.path.isfile("rmvpe.pt"):
f0method_mode.insert(2, "rmvpe")
def create_vc_fn(model_name, tgt_sr, net_g, vc, if_f0, version, file_index):
def vc_fn(
vc_audio_mode,
vc_input,
vc_upload,
tts_text,
tts_voice,
f0_up_key,
f0_method,
index_rate,
filter_radius,
resample_sr,
rms_mix_rate,
protect,
):
try:
logs = []
logger.info(f"Converting using {model_name}...")
logs.append(f"Converting using {model_name}...")
yield "\n".join(logs), None
if vc_audio_mode == "Input path" or "Youtube" and vc_input != "":
audio = load_audio(vc_input, 16000)
audio_max = np.abs(audio).max() / 0.95
if audio_max > 1:
audio /= audio_max
elif vc_audio_mode == "Upload audio":
if vc_upload is None:
return "You need to upload an audio", None
sampling_rate, audio = vc_upload
duration = audio.shape[0] / sampling_rate
if duration > 20 and spaces:
return "Please upload an audio file that is less than 20 seconds. If you need to generate a longer audio file, please use Colab.", None
audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
if len(audio.shape) > 1:
audio = librosa.to_mono(audio.transpose(1, 0))
if sampling_rate != 16000:
audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
elif vc_audio_mode == "TTS Audio":
if len(tts_text) > 100 and spaces:
return "Text is too long", None
if tts_text is None or tts_voice is None:
return "You need to enter text and select a voice", None
os.makedirs("output", exist_ok=True)
os.makedirs(os.path.join("output", "tts"), exist_ok=True)
asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save(os.path.join("output", "tts", "tts.mp3")))
audio, sr = librosa.load(os.path.join("output", "tts", "tts.mp3"), sr=16000, mono=True)
vc_input = os.path.join("output", "tts", "tts.mp3")
times = [0, 0, 0]
f0_up_key = int(f0_up_key)
audio_opt = vc.pipeline(
hubert_model,
net_g,
0,
audio,
vc_input,
times,
f0_up_key,
f0_method,
file_index,
# file_big_npy,
index_rate,
if_f0,
filter_radius,
tgt_sr,
resample_sr,
rms_mix_rate,
version,
protect,
f0_file=None,
)
info = f"[{datetime.now().strftime('%Y-%m-%d %H:%M')}]: npy: {times[0]}, f0: {times[1]}s, infer: {times[2]}s"
logger.info(f"{model_name} | {info}")
logs.append(f"Successfully Convert {model_name}\n{info}")
yield "\n".join(logs), (tgt_sr, audio_opt)
except Exception as err:
info = traceback.format_exc()
logger.error(info)
logger.error(f"Error when using {model_name}.\n{str(err)}")
yield info, None
return vc_fn
def load_model():
categories = []
category_count = 0
if os.path.isfile("weights/folder_info.json"):
with open("weights/folder_info.json", "r", encoding="utf-8") as f:
folder_info = json.load(f)
for category_name, category_info in folder_info.items():
if not category_info['enable']:
continue
category_title = category_info['title']
category_folder = category_info['folder_path']
description = category_info['description']
models = []
with open(f"weights/{category_folder}/model_info.json", "r", encoding="utf-8") as f:
models_info = json.load(f)
for character_name, info in models_info.items():
if not info['enable']:
continue
model_title = info['title']
model_name = info['model_path']
model_author = info.get("author", None)
model_cover = f"weights/{category_folder}/{character_name}/{info['cover']}"
model_index = f"weights/{category_folder}/{character_name}/{info['feature_retrieval_library']}"
cpt = torch.load(f"weights/{category_folder}/{character_name}/{model_name}", map_location="cpu")
tgt_sr = cpt["config"][-1]
cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
if_f0 = cpt.get("f0", 1)
version = cpt.get("version", "v1")
if version == "v1":
if if_f0 == 1:
net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
else:
net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
model_version = "V1"
elif version == "v2":
if if_f0 == 1:
net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
else:
net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
model_version = "V2"
del net_g.enc_q
logger.info(net_g.load_state_dict(cpt["weight"], strict=False))
net_g.eval().to(config.device)
if config.is_half:
net_g = net_g.half()
else:
net_g = net_g.float()
vc = VC(tgt_sr, config)
logger.info(f"Model loaded: {character_name} / {info['feature_retrieval_library']} | ({model_version})")
models.append((character_name, model_title, model_author, model_cover, model_version, create_vc_fn(model_name, tgt_sr, net_g, vc, if_f0, version, model_index)))
category_count += 1
categories.append([category_title, description, models])
elif os.path.exists("weights"):
models = []
for w_root, w_dirs, _ in os.walk("weights"):
model_count = 1
for sub_dir in w_dirs:
pth_files = glob.glob(f"weights/{sub_dir}/*.pth")
index_files = glob.glob(f"weights/{sub_dir}/*.index")
if pth_files == []:
logger.debug(f"Model [{model_count}/{len(w_dirs)}]: No Model file detected, skipping...")
continue
cpt = torch.load(pth_files[0])
tgt_sr = cpt["config"][-1]
cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
if_f0 = cpt.get("f0", 1)
version = cpt.get("version", "v1")
if version == "v1":
if if_f0 == 1:
net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
else:
net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
model_version = "V1"
elif version == "v2":
if if_f0 == 1:
net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
else:
net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
model_version = "V2"
del net_g.enc_q
logger.info(net_g.load_state_dict(cpt["weight"], strict=False))
net_g.eval().to(config.device)
if config.is_half:
net_g = net_g.half()
else:
net_g = net_g.float()
vc = VC(tgt_sr, config)
if index_files == []:
logger.warning("No Index file detected!")
index_info = "None"
model_index = ""
else:
index_info = index_files[0]
model_index = index_files[0]
logger.info(f"Model loaded [{model_count}/{len(w_dirs)}]: {index_files[0]} / {index_info} | ({model_version})")
model_count += 1
models.append((index_files[0][:-4], index_files[0][:-4], "", "", model_version, create_vc_fn(index_files[0], tgt_sr, net_g, vc, if_f0, version, model_index)))
categories.append(["Models", "", models])
else:
categories = []
return categories
if __name__ == '__main__':
categories = load_model()
tts_voice_list = asyncio.new_event_loop().run_until_complete(edge_tts.list_voices())
voices = [f"{v['ShortName']}-{v['Gender']}" for v in tts_voice_list]
with gr.Blocks(theme='Hev832/emerald') as app:
gr.Markdown(
"<div align='center'>\n\n"+
"# Multi Model RVC Inference\n\n"+
"[](https://github.com/ArkanDash/Multi-Model-RVC-Inference)\n\n"+
"</div>"
)
if categories == []:
gr.Markdown(
"<div align='center'>\n\n"+
"## No model found, please add the model into weights folder\n\n"+
"</div>"
)
for (folder_title, description, models) in categories:
with gr.TabItem(folder_title):
if description:
gr.Markdown(f"### <center> {description}")
with gr.Tabs():
if not models:
gr.Markdown("# <center> No Model Loaded.")
gr.Markdown("## <center> Please add the model or fix your model path.")
continue
for (name, title, author, cover, model_version, vc_fn) in models:
with gr.TabItem(name):
with gr.Row():
gr.Markdown(
'<div align="center">'
f'<div>{title}</div>\n'+
f'<div>RVC {model_version} Model</div>\n'+
(f'<div>Model author: {author}</div>' if author else "")+
(f'<img style="width:auto;height:300px;" src="file/{cover}">' if cover else "")+
'</div>'
)
with gr.Row():
if spaces is False:
with gr.TabItem("Input"):
with gr.Row():
with gr.Column():
vc_audio_mode = gr.Dropdown(label="Input voice", choices=audio_mode, allow_custom_value=False, value="Upload audio")
# Input
vc_input = gr.Textbox(label="Input audio path", visible=False)
# Upload
vc_upload = gr.Audio(label="Upload audio file", sources=["upload", "microphone"], visible=True, interactive=True)
# Youtube
vc_download_audio = gr.Dropdown(label="Provider", choices=["Youtube"], allow_custom_value=False, visible=False, value="Youtube", info="Select provider (Default: Youtube)")
vc_link = gr.Textbox(label="Youtube URL", visible=False, info="Example: https://www.youtube.com/watch?v=Nc0sB1Bmf-A", placeholder="https://www.youtube.com/watch?v=...")
vc_log_yt = gr.Textbox(label="Output Information", visible=False, interactive=False)
vc_download_button = gr.Button("Download Audio", variant="primary", visible=False)
vc_audio_preview = gr.Audio(label="Audio Preview", visible=False)
# TTS
tts_text = gr.Textbox(label="TTS text", info="Text to speech input", visible=False)
tts_voice = gr.Dropdown(label="Edge-tts speaker", choices=voices, visible=False, allow_custom_value=False, value="en-US-AnaNeural-Female")
with gr.Column():
vc_split_model = gr.Dropdown(label="Splitter Model", choices=["hdemucs_mmi", "htdemucs", "htdemucs_ft", "mdx", "mdx_q", "mdx_extra_q"], allow_custom_value=False, visible=False, value="htdemucs", info="Select the splitter model (Default: htdemucs)")
vc_split_log = gr.Textbox(label="Output Information", visible=False, interactive=False)
vc_split = gr.Button("Split Audio", variant="primary", visible=False)
vc_vocal_preview = gr.Audio(label="Vocal Preview", visible=False)
vc_inst_preview = gr.Audio(label="Instrumental Preview", visible=False)
with gr.TabItem("Convert"):
with gr.Row():
with gr.Column():
vc_transform0 = gr.Number(label="Transpose", value=0, info='Type "12" to change from male to female voice. Type "-12" to change female to male voice')
f0method0 = gr.Radio(
label="Pitch extraction algorithm",
info=f0method_info,
choices=f0method_mode,
value="pm",
interactive=True
)
index_rate1 = gr.Slider(
minimum=0,
maximum=1,
label="Retrieval feature ratio",
info="(Default: 0.7)",
value=0.7,
interactive=True,
)
filter_radius0 = gr.Slider(
minimum=0,
maximum=7,
label="Apply Median Filtering",
info="The value represents the filter radius and can reduce breathiness.",
value=3,
step=1,
interactive=True,
)
resample_sr0 = gr.Slider(
minimum=0,
maximum=48000,
label="Resample the output audio",
info="Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling",
value=0,
step=1,
interactive=True,
)
rms_mix_rate0 = gr.Slider(
minimum=0,
maximum=1,
label="Volume Envelope",
info="Use the volume envelope of the input to replace or mix with the volume envelope of the output. The closer the ratio is to 1, the more the output envelope is used",
value=1,
interactive=True,
)
protect0 = gr.Slider(
minimum=0,
maximum=0.5,
label="Voice Protection",
info="Protect voiceless consonants and breath sounds to prevent artifacts such as tearing in electronic music. Set to 0.5 to disable. Decrease the value to increase protection, but it may reduce indexing accuracy",
value=0.5,
step=0.01,
interactive=True,
)
with gr.Column():
vc_log = gr.Textbox(label="Output Information", interactive=False)
vc_output = gr.Audio(label="Output Audio", interactive=False)
vc_convert = gr.Button("Convert", variant="primary")
vc_vocal_volume = gr.Slider(
minimum=0,
maximum=10,
label="Vocal volume",
value=1,
interactive=True,
step=1,
info="Adjust vocal volume (Default: 1}",
visible=False
)
vc_inst_volume = gr.Slider(
minimum=0,
maximum=10,
label="Instrument volume",
value=1,
interactive=True,
step=1,
info="Adjust instrument volume (Default: 1}",
visible=False
)
vc_combined_output = gr.Audio(label="Output Combined Audio", visible=False)
vc_combine = gr.Button("Combine",variant="primary", visible=False)
else:
with gr.Column():
vc_audio_mode = gr.Dropdown(label="Input voice", choices=audio_mode, allow_custom_value=False, value="Upload audio")
# Input
vc_input = gr.Textbox(label="Input audio path", visible=False)
# Upload
vc_upload = gr.Audio(label="Upload audio file", sources=["upload", "microphone"], visible=True, interactive=True)
# Youtube
vc_download_audio = gr.Dropdown(label="Provider", choices=["Youtube"], allow_custom_value=False, visible=False, value="Youtube", info="Select provider (Default: Youtube)")
vc_link = gr.Textbox(label="Youtube URL", visible=False, info="Example: https://www.youtube.com/watch?v=Nc0sB1Bmf-A", placeholder="https://www.youtube.com/watch?v=...")
vc_log_yt = gr.Textbox(label="Output Information", visible=False, interactive=False)
vc_download_button = gr.Button("Download Audio", variant="primary", visible=False)
vc_audio_preview = gr.Audio(label="Audio Preview", visible=False)
# Splitter
vc_split_model = gr.Dropdown(label="Splitter Model", choices=["hdemucs_mmi", "htdemucs", "htdemucs_ft", "mdx", "mdx_q", "mdx_extra_q"], allow_custom_value=False, visible=False, value="htdemucs", info="Select the splitter model (Default: htdemucs)")
vc_split_log = gr.Textbox(label="Output Information", visible=False, interactive=False)
vc_split = gr.Button("Split Audio", variant="primary", visible=False)
vc_vocal_preview = gr.Audio(label="Vocal Preview", visible=False)
vc_inst_preview = gr.Audio(label="Instrumental Preview", visible=False)
# TTS
tts_text = gr.Textbox(label="TTS text", info="Text to speech input", visible=False)
tts_voice = gr.Dropdown(label="Edge-tts speaker", choices=voices, visible=False, allow_custom_value=False, value="en-US-AnaNeural-Female")
with gr.Column():
vc_transform0 = gr.Number(label="Transpose", value=0, info='Type "12" to change from male to female voice. Type "-12" to change female to male voice')
f0method0 = gr.Radio(
label="Pitch extraction algorithm",
info=f0method_info,
choices=f0method_mode,
value="pm",
interactive=True
)
index_rate1 = gr.Slider(
minimum=0,
maximum=1,
label="Retrieval feature ratio",
info="(Default: 0.7)",
value=0.7,
interactive=True,
)
filter_radius0 = gr.Slider(
minimum=0,
maximum=7,
label="Apply Median Filtering",
info="The value represents the filter radius and can reduce breathiness.",
value=3,
step=1,
interactive=True,
)
resample_sr0 = gr.Slider(
minimum=0,
maximum=48000,
label="Resample the output audio",
info="Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling",
value=0,
step=1,
interactive=True,
)
rms_mix_rate0 = gr.Slider(
minimum=0,
maximum=1,
label="Volume Envelope",
info="Use the volume envelope of the input to replace or mix with the volume envelope of the output. The closer the ratio is to 1, the more the output envelope is used",
value=1,
interactive=True,
)
protect0 = gr.Slider(
minimum=0,
maximum=0.5,
label="Voice Protection",
info="Protect voiceless consonants and breath sounds to prevent artifacts such as tearing in electronic music. Set to 0.5 to disable. Decrease the value to increase protection, but it may reduce indexing accuracy",
value=0.5,
step=0.01,
interactive=True,
)
with gr.Column():
vc_log = gr.Textbox(label="Output Information", interactive=False)
vc_output = gr.Audio(label="Output Audio", interactive=False)
vc_convert = gr.Button("Convert", variant="primary")
vc_vocal_volume = gr.Slider(
minimum=0,
maximum=10,
label="Vocal volume",
value=1,
interactive=True,
step=1,
info="Adjust vocal volume (Default: 1}",
visible=False
)
vc_inst_volume = gr.Slider(
minimum=0,
maximum=10,
label="Instrument volume",
value=1,
interactive=True,
step=1,
info="Adjust instrument volume (Default: 1}",
visible=False
)
vc_combined_output = gr.Audio(label="Output Combined Audio", visible=False)
vc_combine = gr.Button("Combine",variant="primary", visible=False)
vc_convert.click(
fn=vc_fn,
inputs=[
vc_audio_mode,
vc_input,
vc_upload,
tts_text,
tts_voice,
vc_transform0,
f0method0,
index_rate1,
filter_radius0,
resample_sr0,
rms_mix_rate0,
protect0,
],
outputs=[vc_log ,vc_output]
)
vc_download_button.click(
fn=download_audio,
inputs=[vc_link, vc_download_audio],
outputs=[vc_audio_preview, vc_log_yt]
)
vc_split.click(
fn=cut_vocal_and_inst,
inputs=[vc_split_model],
outputs=[vc_split_log, vc_vocal_preview, vc_inst_preview, vc_input]
)
vc_combine.click(
fn=combine_vocal_and_inst,
inputs=[vc_output, vc_vocal_volume, vc_inst_volume, vc_split_model],
outputs=[vc_combined_output]
)
vc_audio_mode.change(
fn=change_audio_mode,
inputs=[vc_audio_mode],
outputs=[
vc_input,
vc_upload,
vc_download_audio,
vc_link,
vc_log_yt,
vc_download_button,
vc_split_model,
vc_split_log,
vc_split,
vc_audio_preview,
vc_vocal_preview,
vc_inst_preview,
vc_vocal_volume,
vc_inst_volume,
vc_combined_output,
vc_combine,
tts_text,
tts_voice
]
)
app.queue(
max_size=20,
api_open=config.api,
).launch(
share=config.share,
max_threads=1,
allowed_paths=["weights"]
) |