Update app.py

app.py

@@ -11,6 +11,7 @@ from tts_voice import tts_order_voice
 import edge_tts
 import tempfile
 from audio_separator.separator import Separator
+from animalesepy import *
 import model_handler
 import psutil
 import cpuinfo
@@ -54,14 +55,7 @@ PITCH_ALGO_OPT = [
     "rmvpe",
     "rmvpe+",
 ]
-UVR_5_MODELS = [
-    {"model_name": "BS-Roformer-Viperx-1297", "checkpoint": "model_bs_roformer_ep_317_sdr_12.9755.ckpt"},
-    {"model_name": "MDX23C-InstVoc HQ 2", "checkpoint": "MDX23C-8KFFT-InstVoc_HQ_2.ckpt"},
-    {"model_name": "Kim Vocal 2", "checkpoint": "Kim_Vocal_2.onnx"},
-    {"model_name": "5_HP-Karaoke", "checkpoint": "5_HP-Karaoke-UVR.pth"},
-    {"model_name": "UVR-DeNoise by FoxJoy", "checkpoint": "UVR-DeNoise.pth"},
-    {"model_name": "UVR-DeEcho-DeReverb by FoxJoy", "checkpoint": "UVR-DeEcho-DeReverb.pth"},
-]
+
 MODELS = [
     {"model": "model.pth", "index": "model.index", "model_name": "Test Model"},
 ]
@@ -174,33 +168,6 @@ else:
             parallel_workers=8
         )
 
-def calculate_remaining_time(epochs, seconds_per_epoch):
-    total_seconds = epochs * seconds_per_epoch
-
-    hours = total_seconds // 3600
-    minutes = (total_seconds % 3600) // 60
-    seconds = total_seconds % 60
-
-    if hours == 0:
-        return f"{int(minutes)} minutes"
-    elif hours == 1:
-        return f"{int(hours)} hour and {int(minutes)} minutes"
-    else:
-        return f"{int(hours)} hours and {int(minutes)} minutes"
-
-def inf_handler(audio, model_name): 
-    model_found = False
-    for model_info in UVR_5_MODELS:
-        if model_info["model_name"] == model_name:
-            separator.load_model(model_info["checkpoint"])
-            model_found = True
-            break
-    if not model_found:
-        separator.load_model()
-    output_files = separator.separate(audio)
-    vocals = output_files[0]
-    inst = output_files[1]
-    return vocals, inst
 
     
 def run(
@@ -277,10 +244,17 @@ def upload_model(index_file, pth_file, model_name):
     return "Uploaded!"  
 
 with gr.Blocks(theme=gr.themes.Default(primary_hue="pink", secondary_hue="rose"), title="Ilaria RVC 💖") as app:
-    gr.Markdown("## Ilaria RVC 💖")
-    gr.Markdown("**Help keeping up the GPU donating on [Ko-Fi](https://ko-fi.com/ilariaowo)**")
+    gr.Markdown("## Animalese RVC 💖")
+    gr.Markdown("**this project is forked of Ilaria RVC!**")
     with gr.Tab("Inference"):
-        sound_gui = gr.Audio(value=None,type="filepath",autoplay=False,visible=True,)
+        text_input = gr.Textbox(label="Input Text", placeholder="Enter text to convert to Animalese")
+        shorten_input = gr.Checkbox(label="Shorten Words")
+        pitch_input = gr.Slider(minimum=0.2, maximum=2.0, step=0.1, value=1.0, label="Pitch")
+        preview_button = gr.Button("Preview!")
+        sound_gui = gr.Audio(value=None,type="filepath",autoplay=False,visible=True)
+        preview_button.click(fn=lambda text, shorten, pitch: preview_audio(generate_audio(text, shorten, pitch)),
+                         inputs=[text_input, shorten_input, pitch_input],
+                         outputs=sound_gui)
         def update():
             print(MODELS)
             return gr.Dropdown(label="Model",choices=[model["model_name"] for model in MODELS],visible=True,interactive=True, value=MODELS[0]["model_name"],)
@@ -289,13 +263,7 @@ with gr.Blocks(theme=gr.themes.Default(primary_hue="pink", secondary_hue="rose")
             refresh_button = gr.Button("Refresh Models")
             refresh_button.click(update, outputs=[models_dropdown])
 
-        with gr.Accordion("Ilaria TTS", open=False):
-            text_tts = gr.Textbox(label="Text", placeholder="Hello!", lines=3, interactive=True,)
-            dropdown_tts = gr.Dropdown(label="Language and Model",choices=list(language_dict.keys()),interactive=True, value=list(language_dict.keys())[0])
-
-            button_tts = gr.Button("Speak", variant="primary",)
-            button_tts.click(text_to_speech_edge, inputs=[text_tts, dropdown_tts], outputs=[sound_gui])
-
+        
         with gr.Accordion("Settings", open=False):
             pitch_algo_conf = gr.Dropdown(PITCH_ALGO_OPT,value=PITCH_ALGO_OPT[4],label="Pitch algorithm",visible=True,interactive=True,)
             pitch_lvl_conf = gr.Slider(label="Pitch level (lower -> 'male' while higher -> 'female')",minimum=-24,maximum=24,step=1,value=0,visible=True,interactive=True,)
@@ -347,181 +315,18 @@ with gr.Blocks(theme=gr.themes.Default(primary_hue="pink", secondary_hue="rose")
             upload_button.click(upload_model, [index_file_upload, pth_file_upload, model_name], upload_status)
     
 
-    with gr.Tab("Vocal Separator (UVR)"):
-        gr.Markdown("Separate vocals and instruments from an audio file using UVR models. - This is only on CPU due to ZeroGPU being ZeroGPU :(")
-        uvr5_audio_file = gr.Audio(label="Audio File",type="filepath")
-
-        with gr.Row():
-            uvr5_model = gr.Dropdown(label="Model", choices=[model["model_name"] for model in UVR_5_MODELS])
-            uvr5_button = gr.Button("Separate Vocals", variant="primary",)
-
-        uvr5_output_voc = gr.Audio(type="filepath", label="Output 1",)
-        uvr5_output_inst = gr.Audio(type="filepath", label="Output 2",)
-
-        uvr5_button.click(inference, [uvr5_audio_file, uvr5_model], [uvr5_output_voc, uvr5_output_inst])
-    
-    with gr.Tab("Extra"):
-        with gr.Accordion("Model Information", open=False):
-            def json_to_markdown_table(json_data):
-                table = "| Key | Value |\n| --- | --- |\n"
-                for key, value in json_data.items():
-                    table += f"| {key} | {value} |\n"
-                return table
-            def model_info(name):
-                for model in MODELS:
-                    if model["model_name"] == name:
-                        print(model["model"])
-                        info = model_handler.model_info(model["model"])
-                        info2 = {
-                            "Model Name": model["model_name"],
-                            "Model Config": info['config'],
-                            "Epochs Trained": info['epochs'],
-                            "Sample Rate": info['sr'],
-                            "Pitch Guidance": info['f0'],
-                            "Model Precision": info['size'],
-                        }
-                        return gr.Markdown(json_to_markdown_table(info2))
-
-                return "Model not found"
-            def update():
-                print(MODELS)
-                return gr.Dropdown(label="Model", choices=[model["model_name"] for model in MODELS])
-            with gr.Row():
-                model_info_dropdown = gr.Dropdown(label="Model", choices=[model["model_name"] for model in MODELS])
-                refresh_button = gr.Button("Refresh Models")
-                refresh_button.click(update, outputs=[model_info_dropdown])
-            model_info_button = gr.Button("Get Model Information")
-            model_info_output = gr.Textbox(value="Waiting...",label="Output", interactive=False)
-            model_info_button.click(model_info, [model_info_dropdown], [model_info_output])
-            
-
-
-        with gr.Accordion("Training Time Calculator", open=False):
-            with gr.Column():
-                epochs_input = gr.Number(label="Number of Epochs")
-                seconds_input = gr.Number(label="Seconds per Epoch")
-                calculate_button = gr.Button("Calculate Time Remaining")
-                remaining_time_output = gr.Textbox(label="Remaining Time", interactive=False)
-                
-                calculate_button.click(calculate_remaining_time,inputs=[epochs_input, seconds_input],outputs=[remaining_time_output])
-
-        with gr.Accordion("Model Fusion", open=False): 
-                    with gr.Group():
-                        def merge(ckpt_a, ckpt_b, alpha_a, sr_, if_f0_, info__, name_to_save0, version_2):
-                            for model in MODELS:
-                                if model["model_name"] == ckpt_a:
-                                    ckpt_a = model["model"]
-                                if model["model_name"] == ckpt_b:
-                                    ckpt_b = model["model"]
-                            
-                            path = model_handler.merge(ckpt_a, ckpt_b, alpha_a, sr_, if_f0_, info__, name_to_save0, version_2)
-                            if path == "Fail to merge the models. The model architectures are not the same.":
-                                return "Fail to merge the models. The model architectures are not the same."
-                            else:
-                                MODELS.append({"model": path, "index": None, "model_name": name_to_save0})
-                                return "Merged, saved as " + name_to_save0
-
-                        gr.Markdown(value="Strongly suggested to use only very clean models.")
-                        with gr.Row():
-                            def update():
-                                print(MODELS)
-                                return gr.Dropdown(label="Model A", choices=[model["model_name"] for model in MODELS]), gr.Dropdown(label="Model B", choices=[model["model_name"] for model in MODELS])
-                            refresh_button_fusion = gr.Button("Refresh Models")
-                            ckpt_a = gr.Dropdown(label="Model A", choices=[model["model_name"] for model in MODELS])
-                            ckpt_b = gr.Dropdown(label="Model B", choices=[model["model_name"] for model in MODELS])
-                            refresh_button_fusion.click(update, outputs=[ckpt_a, ckpt_b])
-                            alpha_a = gr.Slider(
-                                minimum=0,
-                                maximum=1,
-                                label="Weight of the first model over the second",
-                                value=0.5,
-                                interactive=True,
-                            )
-                    with gr.Group():
-                        with gr.Row():
-                            sr_ = gr.Radio(
-                                label="Sample rate of both models",
-                                choices=["32k","40k", "48k"],
-                                value="32k",
-                                interactive=True,
-                            )
-                            if_f0_ = gr.Radio(
-                                label="Pitch Guidance",
-                                choices=["Yes", "Nah"],
-                                value="Yes",
-                                interactive=True,
-                            )
-                            info__ = gr.Textbox(
-                                label="Add informations to the model",
-                                value="",
-                                max_lines=8,
-                                interactive=True,
-                                visible=False
-                            )
-                            name_to_save0 = gr.Textbox(
-                                label="Final Model name",
-                                value="",
-                                max_lines=1,
-                                interactive=True,
-                            )
-                            version_2 = gr.Radio(
-                                label="Versions of the models",
-                                choices=["v1", "v2"],
-                                value="v2",
-                                interactive=True,
-                            )
-                    with gr.Group():
-                        with gr.Row():
-                            but6 = gr.Button("Fuse the two models", variant="primary")
-                            info4 = gr.Textbox(label="Output", value="", max_lines=8)
-                        but6.click(
-                            merge,
-                            [ckpt_a,ckpt_b,alpha_a,sr_,if_f0_,info__,name_to_save0,version_2,],info4,api_name="ckpt_merge",)
-
-        with gr.Accordion("Model Quantization", open=False):
-            gr.Markdown("Quantize the model to a lower precision. - soon™ or never™ 😎")
-
-        with gr.Accordion("Debug", open=False):
-            def json_to_markdown_table(json_data):
-                table = "| Key | Value |\n| --- | --- |\n"
-                for key, value in json_data.items():
-                    table += f"| {key} | {value} |\n"
-                return table
-            gr.Markdown("View the models that are currently loaded in the instance.")
-
-            gr.Markdown(json_to_markdown_table({"Models": len(MODELS), "UVR Models": len(UVR_5_MODELS)}))
-
-            gr.Markdown("View the current status of the instance.")
-            status = {
-                "Status": "Running", # duh lol
-                "Models": len(MODELS),
-                "UVR Models": len(UVR_5_MODELS),
-                "CPU Usage": f"{psutil.cpu_percent()}%",
-                "RAM Usage": f"{psutil.virtual_memory().percent}%",
-                "CPU": f"{cpuinfo.get_cpu_info()['brand_raw']}",
-                "System Uptime": f"{round(time.time() - psutil.boot_time(), 2)} seconds",
-                "System Load Average": f"{psutil.getloadavg()}",
-                "====================": "====================",
-                "CPU Cores": psutil.cpu_count(),
-                "CPU Threads": psutil.cpu_count(logical=True),
-                "RAM Total": f"{round(psutil.virtual_memory().total / 1024**3, 2)} GB",
-                "RAM Used": f"{round(psutil.virtual_memory().used / 1024**3, 2)} GB",
-                "CPU Frequency": f"{psutil.cpu_freq().current} MHz",
-                "====================": "====================",
-                "GPU": "A100 - Do a request (Inference, you won't see it either way)",
-            }
-            gr.Markdown(json_to_markdown_table(status))
-
     with gr.Tab("Credits"):
         gr.Markdown(
             """
+            Animalese RVC made by [Blane187](https://huggingface.co/Blane187)
+           
             Ilaria RVC made by [Ilaria](https://huggingface.co/TheStinger) suport her on [ko-fi](https://ko-fi.com/ilariaowo)
             
-            The Inference code is made by [r3gm](https://huggingface.co/r3gm) (his module helped form this space 💖)
+            The modules made by [r3gm](https://huggingface.co/r3gm) (
 
             made with ❤️ by [mikus](https://github.com/cappuch) - made the ui!
 
-            ## In loving memory of JLabDX 🕊️
+            
             """
         )
     with gr.Tab(("")):