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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 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 > 20 and limitation:
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)
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 (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()
categories = []
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]
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 model_name, info in models_info.items():
if not info['enable']:
continue
model_title = info['title']
model_author = info.get("author", None)
model_cover = f"weights/{category_folder}/{model_name}/{info['cover']}"
model_index = f"weights/{category_folder}/{model_name}/{info['feature_retrieval_library']}"
cpt = torch.load(f"weights/{category_folder}/{model_name}/{model_name}.pth", 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))
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)
print(f"Model loaded: {model_name}")
models.append((model_name, model_title, model_author, model_cover, create_vc_fn(tgt_sr, net_g, vc, if_f0, model_index)))
categories.append([category_title, category_folder, description, models])
with gr.Blocks() as app:
gr.Markdown(
"# <center> RVC Models\n"
"## <center> The input audio should be clean and pure voice without background music.\n"
"## <center> [Recommended to use google colab for more feature](https://colab.research.google.com/drive/110kiMZTdP6Ri1lY9-NbQf17GVPPhHyeT?usp=sharing)\n"
"### <center> This project was inspired by [zomehwh](https://huggingface.co/spaces/zomehwh/rvc-models) and [ardha27](https://huggingface.co/spaces/ardha27/rvc-models)\n"
"[](https://colab.research.google.com/drive/110kiMZTdP6Ri1lY9-NbQf17GVPPhHyeT?usp=sharing)\n\n"
"[](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI)"
)
for (folder_title, folder, 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 added the model or fix your model path.")
continue
with gr.Tabs():
for (name, title, author, cover, vc_fn) in models:
with gr.TabItem(name):
with gr.Row():
gr.Markdown(
'<div align="center">'
f'<div>{title}</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():
with gr.Column():
vc_input = gr.Audio(label="Input audio"+' (less than 20 seconds)' if limitation else '')
vc_transpose = gr.Number(label="Transpose", value=0, info='Type "12" to change from male to female voice. Type "-12" to change female to male voice')
vc_f0method = gr.Radio(
label="Pitch extraction algorithm",
choices=["pm", "harvest"],
value="pm",
interactive=True,
info="PM is fast but Harvest is better for low frequencies. (Default: PM)"
)
vc_index_ratio = gr.Slider(
minimum=0,
maximum=1,
label="Retrieval feature ratio",
value=0.6,
interactive=True,
info="(Default: 0.6)"
)
tts_mode = gr.Checkbox(label="tts (use edge-tts as input)", value=False)
tts_text = gr.Textbox(visible=False,label="TTS text (100 words limitation)" if limitation else "TTS text")
tts_voice = gr.Dropdown(label="Edge-tts speaker", choices=voices, visible=False, allow_custom_value=False, value="en-US-AnaNeural-Female")
vc_submit = gr.Button("Generate", variant="primary")
with gr.Column():
vc_output = gr.Audio(label="Output Audio")
vc_submit.click(vc_fn, [vc_input, vc_transpose, vc_f0method, vc_index_ratio, tts_mode, tts_text, tts_voice], [vc_output])
tts_mode.change(change_to_tts_mode, [tts_mode], [vc_input, tts_text, tts_voice])
app.queue(concurrency_count=1, max_size=20, api_open=config.api).launch(share=config.colab) |