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import gradio as gr
from infer.modules.train.extract.extract_f0_rmvpe import FeatureInput
from infer.modules.train.extract_feature_print import HubertFeatureExtractor
from zero import zero
@zero(duration=300)
def extract_features(exp_dir: str) -> str:
err = None
fi = FeatureInput(exp_dir)
try:
fi.run()
except Exception as e:
err = e
fi.logfile.seek(0)
log = fi.logfile.read()
if err:
log = f"Error: {err}\n{log}"
return log
hfe = HubertFeatureExtractor(exp_dir)
try:
hfe.run()
except Exception as e:
err = e
hfe.logfile.seek(0)
log += hfe.logfile.read()
if err:
log = f"Error: {err}\n{log}"
return log
class FeatureExtractionTab:
def __init__(self):
pass
def ui(self):
gr.Markdown("# Feature Extraction")
gr.Markdown(
"Before training, you need to extract features from the audio files. "
"This process may take a while, depending on the number of audio files. "
"Under the hood, this process extracts speech features using HuBERT and extracts F0 by RMVPE."
)
with gr.Row():
self.extract_features_btn = gr.Button(
value="Extract features", variant="primary"
)
with gr.Row():
self.extract_features_log = gr.Textbox(
label="Feature extraction log", lines=10
)
def build(self, exp_dir: gr.Textbox):
self.extract_features_btn.click(
fn=extract_features,
inputs=[exp_dir],
outputs=[self.extract_features_log],
)
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