Split merged_ui

modules/merged_ui/main.py

@@ -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 merged_ui.utils import generate_and_process_with_rvc, modified_get_vc
-from rvc_ui.main import build_rvc_ui, names, index_paths
+from rvc_ui.initialization import config
-from spark_ui.main import build_spark_ui, initialize_model, run_tts
+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"
+    Build the combined TTS-RVC UI interface using Gradio
-    device = 0
+    """
-    spark_model = initialize_model(model_dir, device=device)
-    
     # 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
+                # Connect generate function to button
-                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
                 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):
+                # Connect modified_get_vc function for dropdown change
-                    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 ""
-                
                 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],
                 )

modules/merged_ui/utils.py

@@ -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 ""