mirror of
https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
synced 2025-04-04 03:38:58 +08:00
Split merged_ui
This commit is contained in:
VSlobolinskyi
parent
4287b7d577
commit
9a0af2de15
@ -1,20 +1,15 @@
|
||||
import os
|
||||
import gradio as gr
|
||||
import torch
|
||||
from time import sleep
|
||||
|
||||
# Import modules from your packages
|
||||
from rvc_ui.initialization import now_dir, config, vc
|
||||
from rvc_ui.main import build_rvc_ui, names, index_paths
|
||||
from spark_ui.main import build_spark_ui, initialize_model, run_tts
|
||||
from merged_ui.utils import generate_and_process_with_rvc, modified_get_vc
|
||||
from rvc_ui.initialization import config
|
||||
from rvc_ui.main import names, index_paths
|
||||
from spark.sparktts.utils.token_parser import LEVELS_MAP_UI
|
||||
|
||||
def build_merged_ui():
|
||||
# Initialize the Spark TTS model
|
||||
model_dir = "spark/pretrained_models/Spark-TTS-0.5B"
|
||||
device = 0
|
||||
spark_model = initialize_model(model_dir, device=device)
|
||||
|
||||
"""
|
||||
Build the combined TTS-RVC UI interface using Gradio
|
||||
"""
|
||||
# Create the UI
|
||||
with gr.Blocks(title="Unified TTS-RVC Pipeline") as app:
|
||||
gr.Markdown("## Voice Generation and Conversion Pipeline")
|
||||
@ -138,39 +133,7 @@ def build_merged_ui():
|
||||
vc_output1 = gr.Textbox(label="Output information")
|
||||
vc_output2 = gr.Audio(label="Final converted audio")
|
||||
|
||||
# Function to handle combined TTS and RVC processing
|
||||
def generate_and_process_with_rvc(
|
||||
text, prompt_text, prompt_wav_upload, prompt_wav_record,
|
||||
spk_item, vc_transform, f0method,
|
||||
file_index1, file_index2, index_rate, filter_radius,
|
||||
resample_sr, rms_mix_rate, protect
|
||||
):
|
||||
# First generate TTS audio
|
||||
prompt_speech = prompt_wav_upload if prompt_wav_upload else prompt_wav_record
|
||||
prompt_text_clean = None if not prompt_text or len(prompt_text) < 2 else prompt_text
|
||||
|
||||
tts_path = run_tts(
|
||||
text,
|
||||
spark_model,
|
||||
prompt_text=prompt_text_clean,
|
||||
prompt_speech=prompt_speech
|
||||
)
|
||||
|
||||
# Make sure we have a TTS file to process
|
||||
if not tts_path or not os.path.exists(tts_path):
|
||||
return "Failed to generate TTS audio", None
|
||||
|
||||
# Call RVC processing function
|
||||
f0_file = None # We're not using an F0 curve file in this pipeline
|
||||
output_info, output_audio = vc.vc_single(
|
||||
spk_item, tts_path, vc_transform, f0_file, f0method,
|
||||
file_index1, file_index2, index_rate, filter_radius,
|
||||
resample_sr, rms_mix_rate, protect
|
||||
)
|
||||
|
||||
return output_info, output_audio
|
||||
|
||||
# Connect function to button
|
||||
# Connect generate function to button
|
||||
generate_with_rvc_button.click(
|
||||
generate_and_process_with_rvc,
|
||||
inputs=[
|
||||
@ -192,17 +155,9 @@ def build_merged_ui():
|
||||
outputs=[vc_output1, vc_output2],
|
||||
)
|
||||
|
||||
def modified_get_vc(sid0_value, protect0_value):
|
||||
protect1_value = protect0_value
|
||||
outputs = vc.get_vc(sid0_value, protect0_value, protect1_value)
|
||||
|
||||
if isinstance(outputs, tuple) and len(outputs) >= 3:
|
||||
return outputs[0], outputs[1], outputs[3]
|
||||
|
||||
return 0, protect0_value, file_index2.choices[0] if file_index2.choices else ""
|
||||
|
||||
# Connect modified_get_vc function for dropdown change
|
||||
sid0.change(
|
||||
fn=modified_get_vc,
|
||||
fn=lambda sid0_val, protect0_val: modified_get_vc(sid0_val, protect0_val, file_index2),
|
||||
inputs=[sid0, protect0],
|
||||
outputs=[spk_item, protect0, file_index2],
|
||||
)
|
||||
|
||||
104
modules/merged_ui/utils.py
Normal file
104
modules/merged_ui/utils.py
Normal file
@ -0,0 +1,104 @@
|
||||
import os
|
||||
import shutil
|
||||
|
||||
# Import modules from your packages
|
||||
from rvc_ui.initialization import vc
|
||||
from spark_ui.main import initialize_model, run_tts
|
||||
from spark.sparktts.utils.token_parser import LEVELS_MAP_UI
|
||||
|
||||
# Initialize the Spark TTS model (moved outside function to avoid reinitializing)
|
||||
model_dir = "spark/pretrained_models/Spark-TTS-0.5B"
|
||||
device = 0
|
||||
spark_model = initialize_model(model_dir, device=device)
|
||||
|
||||
def generate_and_process_with_rvc(
|
||||
text, prompt_text, prompt_wav_upload, prompt_wav_record,
|
||||
spk_item, vc_transform, f0method,
|
||||
file_index1, file_index2, index_rate, filter_radius,
|
||||
resample_sr, rms_mix_rate, protect
|
||||
):
|
||||
"""
|
||||
Handle combined TTS and RVC processing and save outputs to TEMP directories
|
||||
"""
|
||||
# Ensure TEMP directories exist
|
||||
os.makedirs("./TEMP/spark", exist_ok=True)
|
||||
os.makedirs("./TEMP/rvc", exist_ok=True)
|
||||
|
||||
# Get next fragment number
|
||||
fragment_num = 1
|
||||
while (os.path.exists(f"./TEMP/spark/fragment_{fragment_num}.wav") or
|
||||
os.path.exists(f"./TEMP/rvc/fragment_{fragment_num}.wav")):
|
||||
fragment_num += 1
|
||||
|
||||
# First generate TTS audio
|
||||
prompt_speech = prompt_wav_upload if prompt_wav_upload else prompt_wav_record
|
||||
prompt_text_clean = None if not prompt_text or len(prompt_text) < 2 else prompt_text
|
||||
|
||||
tts_path = run_tts(
|
||||
text,
|
||||
spark_model,
|
||||
prompt_text=prompt_text_clean,
|
||||
prompt_speech=prompt_speech
|
||||
)
|
||||
|
||||
# Make sure we have a TTS file to process
|
||||
if not tts_path or not os.path.exists(tts_path):
|
||||
return "Failed to generate TTS audio", None
|
||||
|
||||
# Save Spark output to TEMP/spark
|
||||
spark_output_path = f"./TEMP/spark/fragment_{fragment_num}.wav"
|
||||
shutil.copy2(tts_path, spark_output_path)
|
||||
|
||||
# Call RVC processing function
|
||||
f0_file = None # We're not using an F0 curve file in this pipeline
|
||||
output_info, output_audio = vc.vc_single(
|
||||
spk_item, tts_path, vc_transform, f0_file, f0method,
|
||||
file_index1, file_index2, index_rate, filter_radius,
|
||||
resample_sr, rms_mix_rate, protect
|
||||
)
|
||||
|
||||
# Save RVC output to TEMP/rvc directory
|
||||
rvc_output_path = f"./TEMP/rvc/fragment_{fragment_num}.wav"
|
||||
rvc_saved = False
|
||||
|
||||
# Try different ways to save the RVC output based on common formats
|
||||
try:
|
||||
if isinstance(output_audio, str) and os.path.exists(output_audio):
|
||||
# Case 1: output_audio is a file path string
|
||||
shutil.copy2(output_audio, rvc_output_path)
|
||||
rvc_saved = True
|
||||
elif isinstance(output_audio, tuple) and len(output_audio) >= 2:
|
||||
# Case 2: output_audio might be (sample_rate, audio_data)
|
||||
try:
|
||||
import soundfile as sf
|
||||
sf.write(rvc_output_path, output_audio[1], output_audio[0])
|
||||
rvc_saved = True
|
||||
except Exception as inner_e:
|
||||
output_info += f"\nFailed to save RVC tuple format: {str(inner_e)}"
|
||||
elif hasattr(output_audio, 'name') and os.path.exists(output_audio.name):
|
||||
# Case 3: output_audio might be a file-like object
|
||||
shutil.copy2(output_audio.name, rvc_output_path)
|
||||
rvc_saved = True
|
||||
except Exception as e:
|
||||
output_info += f"\nError saving RVC output: {str(e)}"
|
||||
|
||||
# Add file paths to output info
|
||||
output_info += f"\nSpark output saved to: {spark_output_path}"
|
||||
if rvc_saved:
|
||||
output_info += f"\nRVC output saved to: {rvc_output_path}"
|
||||
else:
|
||||
output_info += f"\nCould not automatically save RVC output to {rvc_output_path}"
|
||||
|
||||
return output_info, output_audio
|
||||
|
||||
def modified_get_vc(sid0_value, protect0_value, file_index2_component):
|
||||
"""
|
||||
Modified function to get voice conversion parameters
|
||||
"""
|
||||
protect1_value = protect0_value
|
||||
outputs = vc.get_vc(sid0_value, protect0_value, protect1_value)
|
||||
|
||||
if isinstance(outputs, tuple) and len(outputs) >= 3:
|
||||
return outputs[0], outputs[1], outputs[3]
|
||||
|
||||
return 0, protect0_value, file_index2_component.choices[0] if file_index2_component.choices else ""
|
||||
Loading…
x
Reference in New Issue
Block a user