import os
import glob
import json
import argparse
import traceback
import logging
import gradio as gr
import numpy as np
import librosa
import torch
import asyncio
import edge_tts
from datetime import datetime
from fairseq import checkpoint_utils
from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono
from vc_infer_pipeline import VC
from config import Config
config = Config()
logging.getLogger("numba").setLevel(logging.WARNING)
limitation = os.getenv("SYSTEM") == "spaces" # limit audio length in huggingface spaces
def create_vc_fn(tgt_sr, net_g, vc, if_f0, file_index):
def vc_fn(
input_audio,
f0_up_key,
f0_method,
index_rate,
tts_mode,
tts_text,
tts_voice
):
try:
if tts_mode:
if len(tts_text) > 100 and limitation:
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
asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save("tts.mp3"))
audio, sr = librosa.load("tts.mp3", sr=16000, mono=True)
else:
if config.files:
audio, sr = librosa.load(input_audio, sr=16000, mono=True)
else:
if input_audio is None:
return "You need to upload an audio", None
sampling_rate, audio = input_audio
duration = audio.shape[0] / sampling_rate
if duration > 600 and limitation:
return "Please upload an audio file that is less than 600 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)
times = [0, 0, 0]
f0_up_key = int(f0_up_key)
audio_opt = vc.pipeline(
hubert_model,
net_g,
0,
audio,
times,
f0_up_key,
f0_method,
file_index,
index_rate,
if_f0,
f0_file=None,
)
print(
f"[{datetime.now().strftime('%Y-%m-%d %H:%M')}]: npy: {times[0]}, f0: {times[1]}s, infer: {times[2]}s"
)
return "Success", (tgt_sr, audio_opt)
except:
info = traceback.format_exc()
print(info)
return info, (None, None)
return vc_fn
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()
def change_to_tts_mode(tts_mode):
if tts_mode:
return gr.Audio.update(visible=False), gr.Textbox.update(visible=True), gr.Dropdown.update(visible=True)
else:
return gr.Audio.update(visible=True), gr.Textbox.update(visible=False), gr.Dropdown.update(visible=False)
if __name__ == '__main__':
load_hubert()
models = []
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]
folder_path = "weights"
for name in os.listdir(folder_path):
print("check folder: " + name)
if name.startswith("."): break
cover_path = glob.glob(f"{folder_path}/{name}/*.png") + glob.glob(f"{folder_path}/{name}/*.jpg")
index_path = glob.glob(f"{folder_path}/{name}/*.index")
checkpoint_path = glob.glob(f"{folder_path}/{name}/*.pth")
title = name
cover = cover_path[0]
index = index_path[0]
cpt = torch.load(checkpoint_path[0], 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)
if if_f0 == 1:
net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
else:
net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
del net_g.enc_q
print(net_g.load_state_dict(cpt["weight"], strict=False)) # 不加这一行清不干净, 真奇葩
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)
models.append((name, title, cover, create_vc_fn(tgt_sr, net_g, vc, if_f0, index)))
with gr.Blocks() as app:
gr.Markdown("# 🥳🎶🎡 - AI歌手，RVC歌声转换")
gr.Markdown("### 🤗 - 更快的训练过程，更好的训练效果；Powered by [RVC-Project](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI)")
gr.Markdown("### 更多精彩应用，敬请关注[滔滔AI](http://www.talktalkai.com)；滔滔AI，为爱滔滔！💕")
with gr.Tabs():
for (name, title, cover, vc_fn) in models:
with gr.TabItem(name):
with gr.Row():
gr.Markdown(
''
f'
{title}
\n'+
(f'
' if cover else "")+
'