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from random import shuffle
import gradio as gr
import zipfile
import os
import tempfile
import shutil
from glob import glob
from infer.modules.train.preprocess import PreProcess
from infer.modules.train.extract.extract_f0_rmvpe import FeatureInput
from infer.modules.train.train import train
from infer.lib.train.process_ckpt import extract_small_model
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)
pp.logfile.seek(0)
log = pp.logfile.read()
return temp_dir, f"Preprocessed {len(audio_files)} audio files.\n{log}"
@zero(duration=120)
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
def write_filelist(exp_dir: str) -> None:
if_f0_3 = True
spk_id5 = 0
gt_wavs_dir = "%s/0_gt_wavs" % (exp_dir)
feature_dir = "%s/3_feature768" % (exp_dir)
if if_f0_3:
f0_dir = "%s/2a_f0" % (exp_dir)
f0nsf_dir = "%s/2b-f0nsf" % (exp_dir)
names = (
set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)])
& set([name.split(".")[0] for name in os.listdir(feature_dir)])
& set([name.split(".")[0] for name in os.listdir(f0_dir)])
& set([name.split(".")[0] for name in os.listdir(f0nsf_dir)])
)
else:
names = set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)]) & set(
[name.split(".")[0] for name in os.listdir(feature_dir)]
)
opt = []
for name in names:
if if_f0_3:
opt.append(
"%s/%s.wav|%s/%s.npy|%s/%s.wav.npy|%s/%s.wav.npy|%s"
% (
gt_wavs_dir.replace("\\", "\\\\"),
name,
feature_dir.replace("\\", "\\\\"),
name,
f0_dir.replace("\\", "\\\\"),
name,
f0nsf_dir.replace("\\", "\\\\"),
name,
spk_id5,
)
)
else:
opt.append(
"%s/%s.wav|%s/%s.npy|%s"
% (
gt_wavs_dir.replace("\\", "\\\\"),
name,
feature_dir.replace("\\", "\\\\"),
name,
spk_id5,
)
)
fea_dim = 768
now_dir = os.getcwd()
sr2 = "40k"
if if_f0_3:
for _ in range(2):
opt.append(
"%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s"
% (now_dir, sr2, now_dir, fea_dim, now_dir, now_dir, spk_id5)
)
else:
for _ in range(2):
opt.append(
"%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s"
% (now_dir, sr2, now_dir, fea_dim, spk_id5)
)
shuffle(opt)
with open("%s/filelist.txt" % exp_dir, "w") as f:
f.write("\n".join(opt))
@zero(duration=300)
def train_model(exp_dir: str) -> str:
shutil.copy("config.json", exp_dir)
write_filelist(exp_dir)
train(exp_dir)
models = glob(f"{exp_dir}/G_*.pth")
if not models:
raise gr.Error("No model found")
latest_model = max(models, key=os.path.getctime)
return latest_model
def download_weight(exp_dir: str) -> str:
models = glob(f"{exp_dir}/G_*.pth")
if not models:
raise gr.Error("No model found")
latest_model = max(models, key=os.path.getctime)
name = os.path.basename(exp_dir)
extract_small_model(
latest_model, name, "40k", True, "Model trained by ZeroGPU.", "v2"
)
return "assets/weights/%s.pth" % name
def download_expdir(exp_dir: str) -> str:
shutil.make_archive(exp_dir, "zip", exp_dir)
return f"{exp_dir}.zip"
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():
train_btn = gr.Button(value="Train", variant="primary")
with gr.Column():
latest_model = gr.File(label="Latest model")
with gr.Row():
with gr.Column():
download_weight_btn = gr.Button(
value="Download latest model", variant="primary"
)
with gr.Column():
download_weight_output = gr.File(label="Download latest model")
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],
)
train_btn.click(
fn=train_model,
inputs=[exp_dir],
outputs=[latest_model],
)
download_weight_btn.click(
fn=download_weight,
inputs=[exp_dir],
outputs=[download_weight_output],
)
download_expdir_btn.click(
fn=download_expdir,
inputs=[exp_dir],
outputs=[download_expdir_output],
)
app.launch()
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