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import gradio as gr
import zipfile
import os
import tempfile
import shutil
from infer.modules.train.preprocess import PreProcess, preprocess_trainset
from infer.modules.train.extract.extract_f0_rmvpe import FeatureInput
from zero import zero
def extract_audio_files(zip_file: str, target_dir: str) -> list[str]:
with zipfile.ZipFile(zip_file, "r") as zip_ref:
zip_ref.extractall(target_dir)
audio_files = [
os.path.join(target_dir, f)
for f in os.listdir(target_dir)
if f.endswith((".wav", ".mp3", ".ogg"))
]
if not audio_files:
raise gr.Error("No audio files found at the top level of the zip file")
return audio_files
def train_rvc_model(audio_files: list[str]) -> str:
return "model_path"
def preprocess(zip_file: str) -> str:
temp_dir = tempfile.mkdtemp()
print(f"Using exp dir: {temp_dir}")
data_dir = os.path.join(temp_dir, "_data")
os.makedirs(data_dir)
audio_files = extract_audio_files(zip_file, data_dir)
pp = PreProcess(48000, temp_dir, 3.0, False)
pp.pipeline_mp_inp_dir(data_dir, 4)
with open("%s/preprocess.log" % temp_dir, "w") as f:
log = f.read()
return temp_dir, f"Preprocessed {len(audio_files)} audio files.\n{log}"
def download_expdir(exp_dir: str) -> str:
shutil.make_archive(exp_dir, "zip", exp_dir)
return f"{exp_dir}.zip"
@zero(duration=120)
def extract_features(exp_dir: str) -> str:
err = None
try:
fi = FeatureInput(exp_dir)
fi.run()
except Exception as e:
err = e
with open("%s/extract_f0_feature.log" % exp_dir, "w") as f:
log = f.read()
if err:
log = f"Error: {err}\n{log}"
return log
with gr.Blocks() as app:
with gr.Row():
with gr.Column():
zip_file = gr.File(
label="Upload a zip file containing audio files for training",
file_types=["zip"],
)
exp_dir = gr.Textbox(label="Experiment directory", visible=True)
preprocess_btn = gr.Button(value="Preprocess", variant="primary")
with gr.Column():
preprocess_output = gr.Textbox(label="Preprocessing output", lines=5)
with gr.Row():
with gr.Column():
extract_features_btn = gr.Button(
value="Extract features", variant="primary"
)
with gr.Column():
extract_features_output = gr.Textbox(
label="Feature extraction output", lines=5
)
with gr.Row():
with gr.Column():
download_expdir_btn = gr.Button(
value="Download experiment directory", variant="primary"
)
with gr.Column():
download_expdir_output = gr.File(label="Download experiment directory")
preprocess_btn.click(
fn=preprocess,
inputs=[zip_file],
outputs=[exp_dir, preprocess_output],
)
extract_features_btn.click(
fn=extract_features,
inputs=[exp_dir],
outputs=[extract_features_output],
)
download_expdir_btn.click(
fn=download_expdir,
inputs=[exp_dir],
outputs=[download_expdir_output],
)
app.launch()
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