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https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
synced 2025-04-26 14:38:58 +08:00
Split merged_ui
This commit is contained in:
VSlobolinskyi
parent
4287b7d577
commit
9a0af2de15
@ -1,20 +1,15 @@
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import os
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import gradio as gr
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import torch
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from time import sleep
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# Import modules from your packages
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from rvc_ui.initialization import now_dir, config, vc
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from rvc_ui.main import build_rvc_ui, names, index_paths
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from spark_ui.main import build_spark_ui, initialize_model, run_tts
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from merged_ui.utils import generate_and_process_with_rvc, modified_get_vc
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from rvc_ui.initialization import config
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from rvc_ui.main import names, index_paths
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from spark.sparktts.utils.token_parser import LEVELS_MAP_UI
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def build_merged_ui():
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# Initialize the Spark TTS model
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model_dir = "spark/pretrained_models/Spark-TTS-0.5B"
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device = 0
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spark_model = initialize_model(model_dir, device=device)
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"""
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Build the combined TTS-RVC UI interface using Gradio
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"""
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# Create the UI
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with gr.Blocks(title="Unified TTS-RVC Pipeline") as app:
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gr.Markdown("## Voice Generation and Conversion Pipeline")
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@ -138,39 +133,7 @@ def build_merged_ui():
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vc_output1 = gr.Textbox(label="Output information")
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vc_output2 = gr.Audio(label="Final converted audio")
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# Function to handle combined TTS and RVC processing
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def generate_and_process_with_rvc(
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text, prompt_text, prompt_wav_upload, prompt_wav_record,
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spk_item, vc_transform, f0method,
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file_index1, file_index2, index_rate, filter_radius,
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resample_sr, rms_mix_rate, protect
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):
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# First generate TTS audio
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prompt_speech = prompt_wav_upload if prompt_wav_upload else prompt_wav_record
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prompt_text_clean = None if not prompt_text or len(prompt_text) < 2 else prompt_text
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tts_path = run_tts(
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text,
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spark_model,
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prompt_text=prompt_text_clean,
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prompt_speech=prompt_speech
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)
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# Make sure we have a TTS file to process
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if not tts_path or not os.path.exists(tts_path):
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return "Failed to generate TTS audio", None
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# Call RVC processing function
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f0_file = None # We're not using an F0 curve file in this pipeline
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output_info, output_audio = vc.vc_single(
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spk_item, tts_path, vc_transform, f0_file, f0method,
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file_index1, file_index2, index_rate, filter_radius,
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resample_sr, rms_mix_rate, protect
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)
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return output_info, output_audio
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# Connect function to button
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# Connect generate function to button
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generate_with_rvc_button.click(
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generate_and_process_with_rvc,
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inputs=[
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@ -192,17 +155,9 @@ def build_merged_ui():
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outputs=[vc_output1, vc_output2],
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)
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def modified_get_vc(sid0_value, protect0_value):
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protect1_value = protect0_value
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outputs = vc.get_vc(sid0_value, protect0_value, protect1_value)
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if isinstance(outputs, tuple) and len(outputs) >= 3:
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return outputs[0], outputs[1], outputs[3]
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return 0, protect0_value, file_index2.choices[0] if file_index2.choices else ""
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# Connect modified_get_vc function for dropdown change
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sid0.change(
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fn=modified_get_vc,
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fn=lambda sid0_val, protect0_val: modified_get_vc(sid0_val, protect0_val, file_index2),
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inputs=[sid0, protect0],
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outputs=[spk_item, protect0, file_index2],
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)
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104
modules/merged_ui/utils.py
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104
modules/merged_ui/utils.py
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@ -0,0 +1,104 @@
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import os
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import shutil
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# Import modules from your packages
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from rvc_ui.initialization import vc
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from spark_ui.main import initialize_model, run_tts
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from spark.sparktts.utils.token_parser import LEVELS_MAP_UI
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# Initialize the Spark TTS model (moved outside function to avoid reinitializing)
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model_dir = "spark/pretrained_models/Spark-TTS-0.5B"
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device = 0
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spark_model = initialize_model(model_dir, device=device)
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def generate_and_process_with_rvc(
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text, prompt_text, prompt_wav_upload, prompt_wav_record,
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spk_item, vc_transform, f0method,
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file_index1, file_index2, index_rate, filter_radius,
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resample_sr, rms_mix_rate, protect
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):
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"""
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Handle combined TTS and RVC processing and save outputs to TEMP directories
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"""
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# Ensure TEMP directories exist
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os.makedirs("./TEMP/spark", exist_ok=True)
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os.makedirs("./TEMP/rvc", exist_ok=True)
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# Get next fragment number
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fragment_num = 1
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while (os.path.exists(f"./TEMP/spark/fragment_{fragment_num}.wav") or
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os.path.exists(f"./TEMP/rvc/fragment_{fragment_num}.wav")):
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fragment_num += 1
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# First generate TTS audio
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prompt_speech = prompt_wav_upload if prompt_wav_upload else prompt_wav_record
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prompt_text_clean = None if not prompt_text or len(prompt_text) < 2 else prompt_text
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tts_path = run_tts(
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text,
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spark_model,
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prompt_text=prompt_text_clean,
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prompt_speech=prompt_speech
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)
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# Make sure we have a TTS file to process
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if not tts_path or not os.path.exists(tts_path):
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return "Failed to generate TTS audio", None
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# Save Spark output to TEMP/spark
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spark_output_path = f"./TEMP/spark/fragment_{fragment_num}.wav"
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shutil.copy2(tts_path, spark_output_path)
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# Call RVC processing function
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f0_file = None # We're not using an F0 curve file in this pipeline
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output_info, output_audio = vc.vc_single(
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spk_item, tts_path, vc_transform, f0_file, f0method,
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file_index1, file_index2, index_rate, filter_radius,
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resample_sr, rms_mix_rate, protect
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)
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# Save RVC output to TEMP/rvc directory
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rvc_output_path = f"./TEMP/rvc/fragment_{fragment_num}.wav"
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rvc_saved = False
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# Try different ways to save the RVC output based on common formats
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try:
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if isinstance(output_audio, str) and os.path.exists(output_audio):
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# Case 1: output_audio is a file path string
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shutil.copy2(output_audio, rvc_output_path)
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rvc_saved = True
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elif isinstance(output_audio, tuple) and len(output_audio) >= 2:
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# Case 2: output_audio might be (sample_rate, audio_data)
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try:
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import soundfile as sf
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sf.write(rvc_output_path, output_audio[1], output_audio[0])
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rvc_saved = True
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except Exception as inner_e:
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output_info += f"\nFailed to save RVC tuple format: {str(inner_e)}"
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elif hasattr(output_audio, 'name') and os.path.exists(output_audio.name):
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# Case 3: output_audio might be a file-like object
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shutil.copy2(output_audio.name, rvc_output_path)
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rvc_saved = True
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except Exception as e:
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output_info += f"\nError saving RVC output: {str(e)}"
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# Add file paths to output info
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output_info += f"\nSpark output saved to: {spark_output_path}"
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if rvc_saved:
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output_info += f"\nRVC output saved to: {rvc_output_path}"
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else:
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output_info += f"\nCould not automatically save RVC output to {rvc_output_path}"
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return output_info, output_audio
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def modified_get_vc(sid0_value, protect0_value, file_index2_component):
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"""
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Modified function to get voice conversion parameters
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"""
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protect1_value = protect0_value
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outputs = vc.get_vc(sid0_value, protect0_value, protect1_value)
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if isinstance(outputs, tuple) and len(outputs) >= 3:
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return outputs[0], outputs[1], outputs[3]
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return 0, protect0_value, file_index2_component.choices[0] if file_index2_component.choices else ""
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