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import io
import os
os.system("wget -P hubert/ https://huggingface.co/spaces/innnky/nanami/resolve/main/checkpoint_best_legacy_500.pt")
import gradio as gr
import librosa
import numpy as np
import soundfile
from inference.infer_tool import Svc
import logging
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)
model = Svc("logs/44k/G_199200.pth", "logs/44k/config.json", cluster_model_path="logs/44k/kmeans_10000.pt")
def predict(input_audio, not_singing):
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 > 45:
return "Please upload audio less than 45 seconds", 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)
print(audio.shape)
out_wav_path = "temp.wav"
soundfile.write(out_wav_path, audio, 16000, format="wav")
out_audio, out_sr = model.infer("aimodel", 0, out_wav_path,
cluster_infer_ratio=0,
auto_predict_f0=not_singing,
noice_scale=0.4
)
return (44100, out_audio.numpy())
audio_input = gr.Audio(label="Upload Audio")
not_singing = gr.Checkbox(label="Check this box if this audio is not singing", value=False)
audio_output = gr.Audio(label="Output Audio")
demo = gr.Interface(predict, inputs=[audio_input, not_singing], outputs=[audio_output])
# app = gr.Blocks()
# with app:
# audio_input = gr.Audio(label="Upload Audio")
# not_singing = gr.Checkbox(label="Check this box if this audio is not singing", value=False)
# audio_output = gr.Audio(label="Output Audio")
# submit_btn = gr.Button("Submit", variant="primary")
# submit_btn.click(predict, [audio_input, not_singing], [audio_output], api_name="predict")
demo.launch()
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