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import os
import io
import gradio as gr
import librosa
import numpy as np
import logging
import soundfile
import asyncio
import argparse
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):
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 get_speakers():
speakers = []
for _,dirs,_ in os.walk(CUSTOM_MODELS_DIR):
for folder in dirs:
cur_speaker = {}
# Look for G_****.pth
g = glob.glob(os.path.join(CUSTOM_MODELS_DIR,folder,'G_*.pth'))
if not len(g):
continue
cur_speaker["model_path"] = g[0]
cur_speaker["model_folder"] = folder
# Look for *.pt (clustering model)
clst = glob.glob(os.path.join(CUSTOM_MODELS_DIR,folder,'*.pt'))
if not len(clst):
cur_speaker["cluster_path"] = ""
else:
cur_speaker["cluster_path"] = clst[0]
# Look for config.json
cfg = glob.glob(os.path.join(CUSTOM_MODELS_DIR,folder,'*.json'))
if not len(cfg):
continue
cur_speaker["cfg_path"] = cfg[0]
with open(cur_speaker["cfg_path"]) as f:
try:
cfg_json = json.loads(f.read())
except Exception as e:
print("Malformed config json in "+folder)
for name, i in cfg_json["spk"].items():
cur_speaker["name"] = name
cur_speaker["id"] = i
if not name.startswith('.'):
speakers.append(copy.copy(cur_speaker))
return sorted(speakers, key=lambda x:x["name"].lower())
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--device', type=str, default='cpu')
parser.add_argument('--api', action="store_true", default=False)
parser.add_argument("--share", action="store_true", default=False, help="share gradio app")
args = parser.parse_args()
speakers = get_speakers()
speaker_list = [x["name"] for x in speakers]
models = []
voices = []
# !svc infer {NAME}.wav -c config.json -m G_riri_220.pth
# display(Audio(f"{NAME}.out.wav", autoplay=True))
with gr.Blocks() as app:
gr.Markdown(
"# <center> Sovits Chapay\n"
"## <center> The input audio should be clean and pure voice without background music.\n"
)
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)
voice = gr.Dropdown(choices=speaker_list, visible=True)
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], [vc_output1, vc_output2])
app.queue(concurrency_count=1, api_open=args.api).launch(share=args.share)
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