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import torch, os, traceback, sys, warnings, shutil, numpy as np
import gradio as gr
import librosa
import asyncio
import rarfile
import edge_tts
import yt_dlp
import ffmpeg
import gdown
import subprocess
import wave
import soundfile as sf
from scipy.io import wavfile
from datetime import datetime
from urllib.parse import urlparse
from mega import Mega
from flask import Flask, request, jsonify
app = Flask(__name__)
now_dir = os.getcwd()
tmp = os.path.join(now_dir, "TEMP")
shutil.rmtree(tmp, ignore_errors=True)
os.makedirs(tmp, exist_ok=True)
os.environ["TEMP"] = tmp
from lib.infer_pack.models import (
SynthesizerTrnMs256NSFsid,
SynthesizerTrnMs256NSFsid_nono,
SynthesizerTrnMs768NSFsid,
SynthesizerTrnMs768NSFsid_nono,
)
from fairseq import checkpoint_utils
from vc_infer_pipeline import VC
from config import Config
config = Config()
tts_voice_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices())
voices = [f"{v['ShortName']}-{v['Gender']}" for v in tts_voice_list]
hubert_model = None
f0method_mode = ["pm", "harvest", "crepe"]
f0method_info = "PM is fast, Harvest is good but extremely slow, and Crepe effect is good but requires GPU (Default: PM)"
if os.path.isfile("rmvpe.pt"):
f0method_mode.insert(2, "rmvpe")
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)"
def load_hubert():
global hubert_model
models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
["hubert_base.pt"],
suffix="",
)
hubert_model = models[0]
hubert_model = hubert_model.to(config.device)
if config.is_half:
hubert_model = hubert_model.half()
else:
hubert_model = hubert_model.float()
hubert_model.eval()
load_hubert()
weight_root = "weights"
index_root = "weights/index"
weights_model = []
weights_index = []
for _, _, model_files in os.walk(weight_root):
for file in model_files:
if file.endswith(".pth"):
weights_model.append(file)
for _, _, index_files in os.walk(index_root):
for file in index_files:
if file.endswith('.index') and "trained" not in file:
weights_index.append(os.path.join(index_root, file))
def check_models():
weights_model = []
weights_index = []
for _, _, model_files in os.walk(weight_root):
for file in model_files:
if file.endswith(".pth"):
weights_model.append(file)
for _, _, index_files in os.walk(index_root):
for file in index_files:
if file.endswith('.index') and "trained" not in file:
weights_index.append(os.path.join(index_root, file))
return (
gr.Dropdown.update(choices=sorted(weights_model), value=weights_model[0]),
gr.Dropdown.update(choices=sorted(weights_index))
)
def clean():
return (
gr.Dropdown.update(value=""),
gr.Slider.update(visible=False)
)
@app.route('/convert_voice', methods=['POST'])
def convert_voice(spk_id, input_audio_path, voice_transform):
output_audio_path = vc_single(
sid=spk_id,
input_audio_path=input_audio_path,
f0_up_key=voice_transform, # Assuming voice_transform corresponds to f0_up_key
f0_file=None if not f0_file else f0_file,
f0_method="rmvpe",
file_index=None, # Assuming file_index_path corresponds to file_index
index_rate=0.75,
filter_radius=3,
resample_sr=0,
rms_mix_rate=0.25,
protect=0.33 # Adjusted from protect_rate to protect to match the function signature
)
return output_audio_path
def vc_single(
sid,
input_audio_path,
f0_up_key,
f0_file,
f0_method,
file_index,
index_rate,
filter_radius,
resample_sr,
rms_mix_rate,
protect
): # spk_item, input_audio0, vc_transform0,f0_file,f0method0
global tgt_sr, net_g, vc, hubert_model, version, cpt
try:
logs = []
print(f"Converting...")
logs.append(f"Converting...")
yield "\n".join(logs), None
f0_up_key = int(f0_up_key)
times = [0, 0, 0]
if hubert_model == None:
load_hubert()
if_f0 = cpt.get("f0", 1)
audio_opt = vc.pipeline(
hubert_model,
net_g,
sid,
audio,
input_audio_path,
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=f0_file
)
if resample_sr >= 16000 and tgt_sr != resample_sr:
tgt_sr = resample_sr
index_info = (
"Using index:%s." % file_index
if os.path.exists(file_index)
else "Index not used."
)
output_file_path = os.path.join("output", f"converted_audio_{sid}.wav") # Adjust path as needed
os.makedirs(os.path.dirname(output_file_path), exist_ok=True) # Create the output directory if it doesn't exist
# Save the audio file using the target sampling rate
sf.write(output_file_path, audio_opt, tgt_sr)
# Return the path to the saved file along with any other information
return (
f"Success. Audio saved to {output_file_path}\n{index_info}\nTime:\nnpy: %.2fs, f0: %.2fs, infer: %.2fs."
% (*times,),
output_file_path,
)
except:
info = traceback.format_exc()
logger.warning(info)
return info, (None, None)
def get_vc(sid, to_return_protect0):
global n_spk, tgt_sr, net_g, vc, cpt, version, weights_index
if sid == "" or sid == []:
global hubert_model
if hubert_model is not None: # 考虑到轮询, 需要加个判断看是否 sid 是由有模型切换到无模型的
print("clean_empty_cache")
del net_g, n_spk, vc, hubert_model, tgt_sr # ,cpt
hubert_model = net_g = n_spk = vc = hubert_model = tgt_sr = None
if torch.cuda.is_available():
torch.cuda.empty_cache()
###楼下不这么折腾清理不干净
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"])
elif version == "v2":
if if_f0 == 1:
net_g = SynthesizerTrnMs768NSFsid(
*cpt["config"], is_half=config.is_half
)
else:
net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
del net_g, cpt
if torch.cuda.is_available():
torch.cuda.empty_cache()
cpt = None
return (
gr.Slider.update(maximum=2333, visible=False),
gr.Slider.update(visible=True),
gr.Dropdown.update(choices=sorted(weights_index), value=""),
gr.Markdown.update(value="# <center> No model selected")
)
print(f"Loading {sid} model...")
selected_model = sid[:-4]
cpt = torch.load(os.path.join(weight_root, sid), map_location="cpu")
tgt_sr = cpt["config"][-1]
cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]
if_f0 = cpt.get("f0", 1)
if if_f0 == 0:
to_return_protect0 = {
"visible": False,
"value": 0.5,
"__type__": "update",
}
else:
to_return_protect0 = {
"visible": True,
"value": to_return_protect0,
"__type__": "update",
}
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"])
elif version == "v2":
if if_f0 == 1:
net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
else:
net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
del net_g.enc_q
print(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)
n_spk = cpt["config"][-3]
weights_index = []
for _, _, index_files in os.walk(index_root):
for file in index_files:
if file.endswith('.index') and "trained" not in file:
weights_index.append(os.path.join(index_root, file))
if weights_index == []:
selected_index = gr.Dropdown.update(value="")
else:
selected_index = gr.Dropdown.update(value=weights_index[0])
for index, model_index in enumerate(weights_index):
if selected_model in model_index:
selected_index = gr.Dropdown.update(value=weights_index[index])
break
return (
gr.Slider.update(maximum=n_spk, visible=True),
to_return_protect0,
selected_index,
gr.Markdown.update(
f'## <center> {selected_model}\n'+
f'### <center> RVC {version} Model'
)
)
def find_audio_files(folder_path, extensions):
audio_files = []
for root, dirs, files in os.walk(folder_path):
for file in files:
if any(file.endswith(ext) for ext in extensions):
audio_files.append(file)
return audio_files
def vc_multi(
spk_item,
vc_input,
vc_output,
vc_transform0,
f0method0,
file_index,
index_rate,
filter_radius,
resample_sr,
rms_mix_rate,
protect,
):
global tgt_sr, net_g, vc, hubert_model, version, cpt
logs = []
logs.append("Converting...")
yield "\n".join(logs)
print()
try:
if os.path.exists(vc_input):
folder_path = vc_input
extensions = [".mp3", ".wav", ".flac", ".ogg"]
audio_files = find_audio_files(folder_path, extensions)
for index, file in enumerate(audio_files, start=1):
audio, sr = librosa.load(os.path.join(folder_path, file), sr=16000, mono=True)
input_audio_path = folder_path, file
f0_up_key = int(vc_transform0)
times = [0, 0, 0]
if hubert_model == None:
load_hubert()
if_f0 = cpt.get("f0", 1)
audio_opt = vc.pipeline(
hubert_model,
net_g,
spk_item,
audio,
input_audio_path,
times,
f0_up_key,
f0method0,
file_index,
index_rate,
if_f0,
filter_radius,
tgt_sr,
resample_sr,
rms_mix_rate,
version,
protect,
f0_file=None
)
if resample_sr >= 16000 and tgt_sr != resample_sr:
tgt_sr = resample_sr
output_path = f"{os.path.join(vc_output, file)}"
os.makedirs(os.path.join(vc_output), exist_ok=True)
sf.write(
output_path,
audio_opt,
tgt_sr,
)
info = f"{index} / {len(audio_files)} | {file}"
print(info)
logs.append(info)
yield "\n".join(logs)
else:
logs.append("Folder not found or path doesn't exist.")
yield "\n".join(logs)
except:
info = traceback.format_exc()
print(info)
logs.append(info)
yield "\n".join(logs)
def download_audio(url, audio_provider):
logs = []
os.makedirs("dl_audio", exist_ok=True)
if url == "":
logs.append("URL required!")
yield None, "\n".join(logs)
return None, "\n".join(logs)
if audio_provider == "Youtube":
logs.append("Downloading the audio...")
yield None, "\n".join(logs)
ydl_opts = {
'noplaylist': True,
'format': 'bestaudio/best',
'postprocessors': [{
'key': 'FFmpegExtractAudio',
'preferredcodec': 'wav',
}],
"outtmpl": 'result/dl_audio/audio',
}
audio_path = "result/dl_audio/audio.wav"
with yt_dlp.YoutubeDL(ydl_opts) as ydl:
ydl.download([url])
logs.append("Download Complete.")
yield audio_path, "\n".join(logs)
def cut_vocal_and_inst_yt(split_model):
logs = []
logs.append("Starting the audio splitting process...")
yield "\n".join(logs), None, None, None
command = f"demucs --two-stems=vocals -n {split_model} result/dl_audio/audio.wav -o output"
result = subprocess.Popen(command.split(), stdout=subprocess.PIPE, text=True)
for line in result.stdout:
logs.append(line)
yield "\n".join(logs), None, None, None
print(result.stdout)
vocal = f"output/{split_model}/audio/vocals.wav"
inst = f"output/{split_model}/audio/no_vocals.wav"
logs.append("Audio splitting complete.")
yield "\n".join(logs), vocal, inst, vocal
def cut_vocal_and_inst(split_model, audio_data):
logs = []
vocal_path = "output/result/audio.wav"
os.makedirs("output/result", exist_ok=True)
wavfile.write(vocal_path, audio_data[0], audio_data[1])
logs.append("Starting the audio splitting process...")
yield "\n".join(logs), None, None
command = f"demucs --two-stems=vocals -n {split_model} {vocal_path} -o output"
result = subprocess.Popen(command.split(), stdout=subprocess.PIPE, text=True)
for line in result.stdout:
logs.append(line)
yield "\n".join(logs), None, None
print(result.stdout)
vocal = f"output/{split_model}/audio/vocals.wav"
inst = f"output/{split_model}/audio/no_vocals.wav"
logs.append("Audio splitting complete.")
yield "\n".join(logs), vocal, inst
def combine_vocal_and_inst(audio_data, vocal_volume, inst_volume, split_model):
os.makedirs("output/result", exist_ok=True)
vocal_path = "output/result/output.wav"
output_path = "output/result/combine.mp3"
inst_path = f"output/{split_model}/audio/no_vocals.wav"
wavfile.write(vocal_path, audio_data[0], audio_data[1])
command = f'ffmpeg -y -i {inst_path} -i {vocal_path} -filter_complex [0:a]volume={inst_volume}[i];[1:a]volume={vocal_volume}[v];[i][v]amix=inputs=2:duration=longest[a] -map [a] -b:a 320k -c:a libmp3lame {output_path}'
result = subprocess.run(command.split(), stdout=subprocess.PIPE)
print(result.stdout.decode())
return output_path
def download_and_extract_models(urls):
logs = []
os.makedirs("zips", exist_ok=True)
os.makedirs(os.path.join("zips", "extract"), exist_ok=True)
os.makedirs(os.path.join(weight_root), exist_ok=True)
os.makedirs(os.path.join(index_root), exist_ok=True)
for link in urls.splitlines():
url = link.strip()
if not url:
raise gr.Error("URL Required!")
return "No URLs provided."
model_zip = urlparse(url).path.split('/')[-2] + '.zip'
model_zip_path = os.path.join('zips', model_zip)
logs.append(f"Downloading...")
yield "\n".join(logs)
if "drive.google.com" in url:
gdown.download(url, os.path.join("zips", "extract"), quiet=False)
elif "mega.nz" in url:
m = Mega()
m.download_url(url, 'zips')
else:
os.system(f"wget {url} -O {model_zip_path}")
logs.append(f"Extracting...")
yield "\n".join(logs)
for filename in os.listdir("zips"):
archived_file = os.path.join("zips", filename)
if filename.endswith(".zip"):
shutil.unpack_archive(archived_file, os.path.join("zips", "extract"), 'zip')
elif filename.endswith(".rar"):
with rarfile.RarFile(archived_file, 'r') as rar:
rar.extractall(os.path.join("zips", "extract"))
for _, dirs, files in os.walk(os.path.join("zips", "extract")):
logs.append(f"Searching Model and Index...")
yield "\n".join(logs)
model = False
index = False
if files:
for file in files:
if file.endswith(".pth"):
basename = file[:-4]
shutil.move(os.path.join("zips", "extract", file), os.path.join(weight_root, file))
model = True
if file.endswith('.index') and "trained" not in file:
shutil.move(os.path.join("zips", "extract", file), os.path.join(index_root, file))
index = True
else:
logs.append("No model in main folder.")
yield "\n".join(logs)
logs.append("Searching in subfolders...")
yield "\n".join(logs)
for sub_dir in dirs:
for _, _, sub_files in os.walk(os.path.join("zips", "extract", sub_dir)):
for file in sub_files:
if file.endswith(".pth"):
basename = file[:-4]
shutil.move(os.path.join("zips", "extract", sub_dir, file), os.path.join(weight_root, file))
model = True
if file.endswith('.index') and "trained" not in file:
shutil.move(os.path.join("zips", "extract", sub_dir, file), os.path.join(index_root, file))
index = True
shutil.rmtree(os.path.join("zips", "extract", sub_dir))
if index is False:
logs.append("Model only file, no Index file detected.")
yield "\n".join(logs)
logs.append("Download Completed!")
yield "\n".join(logs)
logs.append("Successfully download all models! Refresh your model list to load the model")
yield "\n".join(logs)
if __name__ == '__main__':
app.run(debug=True, port=5000) |