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import io | |
import gradio as gr | |
import librosa | |
import numpy as np | |
import utils | |
from inference.infer_tool import Svc | |
import logging | |
import soundfile | |
import asyncio | |
import argparse | |
import edge_tts | |
import gradio.processing_utils as gr_processing_utils | |
logging.getLogger('numba').setLevel(logging.WARNING) | |
logging.getLogger('markdown_it').setLevel(logging.WARNING) | |
logging.getLogger('urllib3').setLevel(logging.WARNING) | |
logging.getLogger('matplotlib').setLevel(logging.WARNING) | |
limitation = os.getenv("SYSTEM") == "spaces" # limit audio length in huggingface spaces | |
audio_postprocess_ori = gr.Audio.postprocess | |
def audio_postprocess(self, y): | |
data = audio_postprocess_ori(self, y) | |
if data is None: | |
return None | |
return gr_processing_utils.encode_url_or_file_to_base64(data["name"]) | |
gr.Audio.postprocess = audio_postprocess | |
def create_vc_fn(model, sid): | |
def vc_fn(input_audio, vc_transform, auto_f0, tts_text, tts_voice, tts_mode): | |
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) | |
raw_path = io.BytesIO() | |
soundfile.write(raw_path, audio, 16000, format="wav") | |
raw_path.seek(0) | |
out_audio, out_sr = model.infer(sid, vc_transform, raw_path, | |
auto_predict_f0=auto_f0, | |
) | |
return "Success", (44100, out_audio.cpu().numpy()) | |
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) | |
raw_path = io.BytesIO() | |
soundfile.write(raw_path, audio, 16000, format="wav") | |
raw_path.seek(0) | |
out_audio, out_sr = model.infer(sid, vc_transform, raw_path, | |
auto_predict_f0=auto_f0, | |
) | |
return "Success", (44100, out_audio.cpu().numpy()) | |
return vc_fn | |
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), gr.Checkbox.update(value=True) | |
else: | |
return gr.Audio.update(visible=True), gr.Textbox.update(visible=False), gr.Dropdown.update(visible=False), gr.Checkbox.update(value=False) | |
if __name__ == '__main__': | |
parser = argparse.ArgumentParser() | |
parser.add_argument('--device', type=str, default='cpu') | |
parser.add_argument('--api', action="store_true", default=True) | |
parser.add_argument("--share", action="store_true", default=False, help="share gradio app") | |
args = parser.parse_args() | |
hubert_model = utils.get_hubert_model().to(args.device) | |
models = [] | |
others = {} | |
voices = [] | |
tts_voice_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices()) | |
for r in tts_voice_list: | |
voices.append(f"{r['ShortName']}-{r['Gender']}") | |
for f in os.listdir("models"): | |
name = f | |
model = Svc(fr"models/{f}/{f}.pth", f"models/{f}/config.json", device=args.device) | |
cover = f"models/{f}/cover.png" if os.path.exists(f"models/{f}/cover.png") else None | |
models.append((name, cover, create_vc_fn(model, name))) | |
with gr.Blocks() as app: | |
gr.Markdown( | |
"# <center> Sovits Models\n" | |
"## <center> The input audio should be clean and pure voice without background music.\n" | |
"![visitor badge](https://visitor-badge.glitch.me/badge?page_id=sayashi.Sovits-Umamusume)\n\n" | |
"[![image](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1wfsBbMzmtLflOJeqc5ZnJiLY7L239hJW?usp=share_link)\n\n" | |
"[![Duplicate this Space](https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-sm-dark.svg)](https://huggingface.co/spaces/sayashi/sovits-models?duplicate=true)\n\n" | |
"[![Original Repo](https://badgen.net/badge/icon/github?icon=github&label=Original%20Repo)](https://github.com/svc-develop-team/so-vits-svc)" | |
) | |
with gr.Tabs(): | |
for (name, cover, vc_fn) in models: | |
with gr.TabItem(name): | |
with gr.Row(): | |
gr.Markdown( | |
'<div align="center">' | |
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_transform = gr.Number(label="vc_transform", value=0) | |
auto_f0 = gr.Checkbox(label="auto_f0", value=False) | |
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(choices=voices, visible=False) | |
vc_submit = gr.Button("Generate", variant="primary") | |
with gr.Column(): | |
vc_output1 = gr.Textbox(label="Output Message") | |
vc_output2 = gr.Audio(label="Output Audio") | |
vc_submit.click(vc_fn, [vc_input, vc_transform, auto_f0, tts_text, tts_voice, tts_mode], [vc_output1, vc_output2], api_name=f"{name.lower().strip()}") | |
tts_mode.change(change_to_tts_mode, [tts_mode], [vc_input, tts_text, tts_voice, auto_f0]) | |
app.queue(concurrency_count=1, api_open=args.api).launch(share=args.share) | |