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import glob
import os
import traceback
import gradio as gr
from modules import models, ui
from modules.ui import Tab
def inference_options_ui(show_out_dir=True):
with gr.Row(equal_height=False):
with gr.Column():
source_audio = gr.Textbox(label="Source Audio")
out_dir = gr.Textbox(
label="Out folder",
visible=show_out_dir,
placeholder=models.AUDIO_OUT_DIR,
)
with gr.Column():
transpose = gr.Slider(
minimum=-20, maximum=20, value=0, step=1, label="Transpose"
)
pitch_extraction_algo = gr.Radio(
choices=["dio", "harvest", "mangio-crepe", "crepe"],
value="crepe",
label="Pitch Extraction Algorithm",
)
embedding_model = gr.Radio(
choices=["auto", *models.EMBEDDINGS_LIST.keys()],
value="auto",
label="Embedder Model",
)
embedding_output_layer = gr.Radio(
choices=["auto", "9", "12"],
value="auto",
label="Embedder Output Layer",
)
with gr.Column():
auto_load_index = gr.Checkbox(value=False, label="Auto Load Index")
faiss_index_file = gr.Textbox(value="", label="Faiss Index File Path")
retrieval_feature_ratio = gr.Slider(
minimum=0,
maximum=1,
value=1,
step=0.01,
label="Retrieval Feature Ratio",
)
with gr.Column():
fo_curve_file = gr.File(label="F0 Curve File")
return (
source_audio,
out_dir,
transpose,
embedding_model,
embedding_output_layer,
pitch_extraction_algo,
auto_load_index,
faiss_index_file,
retrieval_feature_ratio,
fo_curve_file,
)
class Inference(Tab):
def title(self):
return "Inference"
def sort(self):
return 1
def ui(self, outlet):
def infer(
sid,
input_audio,
out_dir,
embedder_model,
embedding_output_layer,
f0_up_key,
f0_file,
f0_method,
auto_load_index,
faiss_index_file,
index_rate,
):
model = models.vc_model
try:
yield "Infering...", None
if out_dir == "":
out_dir = models.AUDIO_OUT_DIR
if "*" in input_audio:
assert (
out_dir is not None
), "Out folder is required for batch processing"
files = glob.glob(input_audio, recursive=True)
elif os.path.isdir(input_audio):
assert (
out_dir is not None
), "Out folder is required for batch processing"
files = glob.glob(
os.path.join(input_audio, "**", "*.wav"), recursive=True
)
else:
files = [input_audio]
for file in files:
audio = model.single(
sid,
file,
embedder_model,
embedding_output_layer,
f0_up_key,
f0_file,
f0_method,
auto_load_index,
faiss_index_file,
index_rate,
output_dir=out_dir,
)
yield "Success", (model.tgt_sr, audio) if len(files) == 1 else None
except:
yield "Error: " + traceback.format_exc(), None
with gr.Group():
with gr.Box():
with gr.Column():
_, speaker_id = ui.create_model_list_ui()
(
source_audio,
out_dir,
transpose,
embedder_model,
embedding_output_layer,
pitch_extraction_algo,
auto_load_index,
faiss_index_file,
retrieval_feature_ratio,
f0_curve_file,
) = inference_options_ui()
with gr.Row(equal_height=False):
with gr.Column():
status = gr.Textbox(value="", label="Status")
output = gr.Audio(label="Output", interactive=False)
with gr.Row():
infer_button = gr.Button("Infer", variant="primary")
infer_button.click(
infer,
inputs=[
speaker_id,
source_audio,
out_dir,
embedder_model,
embedding_output_layer,
transpose,
f0_curve_file,
pitch_extraction_algo,
auto_load_index,
faiss_index_file,
retrieval_feature_ratio,
],
outputs=[status, output],
queue=True,
)
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