Update app.py

app.py

@@ -1,504 +1,508 @@
-import gradio as gr
-import requests
-import random
-import os
-import zipfile 
-import librosa
-import time
-from infer_rvc_python import BaseLoader
-from pydub import AudioSegment
-from tts_voice import tts_order_voice
-import edge_tts
-import tempfile
-from audio_separator.separator import Separator
-import model_handler
-import psutil
-import cpuinfo
-
-language_dict = tts_order_voice
-
-async def text_to_speech_edge(text, language_code):
-    voice = language_dict[language_code]
-    communicate = edge_tts.Communicate(text, voice)
-    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp_file:
-        tmp_path = tmp_file.name
-
-    await communicate.save(tmp_path)
-
-    return tmp_path
-
-try:
-    import spaces
-    spaces_status = True
-except ImportError:
-    spaces_status = False
-
-separator = Separator()
-converter = BaseLoader(only_cpu=False, hubert_path=None, rmvpe_path=None) # <- yeah so like this handles rvc
-
-global pth_file
-global index_file
-
-pth_file = "model.pth"
-index_file = "model.index"
-
-#CONFIGS
-TEMP_DIR = "temp"
-MODEL_PREFIX = "model"
-PITCH_ALGO_OPT = [
-    "pm",
-    "harvest",
-    "crepe",
-    "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"},
-]
-
-os.makedirs(TEMP_DIR, exist_ok=True)
-
-def unzip_file(file):
-    filename = os.path.basename(file).split(".")[0] 
-    with zipfile.ZipFile(file, 'r') as zip_ref:
-        zip_ref.extractall(os.path.join(TEMP_DIR, filename)) 
-    return True
-    
-
-def progress_bar(total, current):
-    return "[" + "=" * int(current / total * 20) + ">" + " " * (20 - int(current / total * 20)) + "] " + str(int(current / total * 100)) + "%"
-
-def download_from_url(url, name=None):
-    if name is None:
-        raise ValueError("The model name must be provided")
-    if "/blob/" in url:
-        url = url.replace("/blob/", "/resolve/") 
-    if "huggingface" not in url:
-        return ["The URL must be from huggingface", "Failed", "Failed"]
-    filename = os.path.join(TEMP_DIR, MODEL_PREFIX + str(random.randint(1, 1000)) + ".zip")
-    response = requests.get(url)
-    total = int(response.headers.get('content-length', 0)) 
-    if total > 500000000:
-
-        return ["The file is too large. You can only download files up to 500 MB in size.", "Failed", "Failed"]
-    current = 0
-    with open(filename, "wb") as f:
-        for data in response.iter_content(chunk_size=4096): 
-            f.write(data)
-            current += len(data)
-            print(progress_bar(total, current), end="\r") #
-    
-    
-
-    try:
-        unzip_file(filename)
-    except Exception as e:
-        return ["Failed to unzip the file", "Failed", "Failed"] 
-    unzipped_dir = os.path.join(TEMP_DIR, os.path.basename(filename).split(".")[0])
-    pth_files = []
-    index_files = []
-    for root, dirs, files in os.walk(unzipped_dir): 
-        for file in files:
-            if file.endswith(".pth"):
-                pth_files.append(os.path.join(root, file))
-            elif file.endswith(".index"):
-                index_files.append(os.path.join(root, file))
-    
-    print(pth_files, index_files) 
-    global pth_file
-    global index_file
-    pth_file = pth_files[0]
-    index_file = index_files[0]
-
-    print(pth_file)
-    print(index_file)
-
-    MODELS.append({"model": pth_file, "index": index_file, "model_name": name})
-    return ["Downloaded as " + name, pth_files[0], index_files[0]]
-
-def inference(audio, model_name):
-        output_data = inf_handler(audio, model_name)
-        vocals = output_data[0]
-        inst = output_data[1]
-
-        return vocals, inst
-
-if spaces_status:
-    @spaces.GPU()
-    def convert_now(audio_files, random_tag, converter):
-        return converter(
-            audio_files,
-            random_tag,
-            overwrite=False,
-            parallel_workers=8
-        )
-
-        
-else:
-    def convert_now(audio_files, random_tag, converter):
-        return converter(
-            audio_files,
-            random_tag,
-            overwrite=False,
-            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(
-    model,
-    audio_files,
-    pitch_alg,
-    pitch_lvl,
-    index_inf,
-    r_m_f,
-    e_r,
-    c_b_p,
-):
-    if not audio_files:
-        raise ValueError("The audio pls")
-
-    if isinstance(audio_files, str):
-        audio_files = [audio_files]
-
-    try:
-        duration_base = librosa.get_duration(filename=audio_files[0])
-        print("Duration:", duration_base)
-    except Exception as e:
-        print(e)
-
-    random_tag = "USER_"+str(random.randint(10000000, 99999999))
-
-    file_m = model
-    print("File model:", file_m)
-
-    # get from MODELS
-    for model in MODELS:
-        if model["model_name"] == file_m:
-            print(model)
-            file_m = model["model"]
-            file_index = model["index"]
-            break
-    
-    if not file_m.endswith(".pth"):
-        raise ValueError("The model file must be a .pth file")
-
-
-    print("Random tag:", random_tag)
-    print("File model:", file_m)
-    print("Pitch algorithm:", pitch_alg)
-    print("Pitch level:", pitch_lvl)
-    print("File index:", file_index)
-    print("Index influence:", index_inf)
-    print("Respiration median filtering:", r_m_f)
-    print("Envelope ratio:", e_r)
-
-    converter.apply_conf(
-        tag=random_tag,
-        file_model=file_m,
-        pitch_algo=pitch_alg,
-        pitch_lvl=pitch_lvl,
-        file_index=file_index,
-        index_influence=index_inf,
-        respiration_median_filtering=r_m_f,
-        envelope_ratio=e_r,
-        consonant_breath_protection=c_b_p,
-        resample_sr=44100 if audio_files[0].endswith('.mp3') else 0, 
-    )
-    time.sleep(0.1)
-
-    result = convert_now(audio_files, random_tag, converter)
-    print("Result:", result)
-
-    return result[0]
-
-def upload_model(index_file, pth_file, model_name):
-    pth_file = pth_file.name
-    index_file = index_file.name
-    MODELS.append({"model": pth_file, "index": index_file, "model_name": model_name})
-    return "Uploaded!"  
-
-with gr.Blocks(theme=gr.themes.Default(primary_hue="pink", secondary_hue="rose"), title="Ilaria RVC 💖") as demo:
-    gr.Markdown("## Ilaria RVC 💖")
-    with gr.Tab("Inference"):
-        sound_gui = gr.Audio(value=None,type="filepath",autoplay=False,visible=True,)
-        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"],)
-        with gr.Row():
-            models_dropdown = gr.Dropdown(label="Model",choices=[model["model_name"] for model in MODELS],visible=True,interactive=True, value=MODELS[0]["model_name"],)
-            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,)
-            index_inf_conf =  gr.Slider(minimum=0,maximum=1,label="Index influence -> How much accent is applied",value=0.75,)
-            respiration_filter_conf = gr.Slider(minimum=0,maximum=7,label="Respiration median filtering",value=3,step=1,interactive=True,)
-            envelope_ratio_conf = gr.Slider(minimum=0,maximum=1,label="Envelope ratio",value=0.25,interactive=True,)
-            consonant_protec_conf = gr.Slider(minimum=0,maximum=0.5,label="Consonant breath protection",value=0.5,interactive=True,)
-
-        button_conf = gr.Button("Convert",variant="primary",)
-        output_conf = gr.Audio(type="filepath",label="Output",)
-    	
-        button_conf.click(lambda :None, None, output_conf)
-        button_conf.click(
-            run,
-            inputs=[
-                models_dropdown,
-                sound_gui,
-                pitch_algo_conf,
-                pitch_lvl_conf,
-                index_inf_conf,
-                respiration_filter_conf,
-                envelope_ratio_conf,
-                consonant_protec_conf,
-            ],
-            outputs=[output_conf],
-        )
-
-
-    with gr.Tab("Model Loader (Download and Upload)"):
-        with gr.Accordion("Model Downloader", open=False):
-            gr.Markdown(
-                "Download the model from the following URL and upload it here. (Huggingface RVC model)"
-            )
-            model = gr.Textbox(lines=1, label="Model URL")
-            name = gr.Textbox(lines=1, label="Model Name", placeholder="Model Name")
-            download_button = gr.Button("Download Model")
-            status = gr.Textbox(lines=1, label="Status", placeholder="Waiting....", interactive=False)
-            model_pth = gr.Textbox(lines=1, label="Model pth file", placeholder="Waiting....", interactive=False)
-            index_pth = gr.Textbox(lines=1, label="Index pth file", placeholder="Waiting....", interactive=False)
-            download_button.click(download_from_url, [model, name], outputs=[status, model_pth, index_pth])
-        with gr.Accordion("Upload A Model", open=False):
-            index_file_upload = gr.File(label="Index File (.index)")
-            pth_file_upload = gr.File(label="Model File (.pth)")
-
-            model_name = gr.Textbox(label="Model Name", placeholder="Model Name")
-            upload_button = gr.Button("Upload Model")
-            upload_status = gr.Textbox(lines=1, label="Status", placeholder="Waiting....", interactive=False)
-
-            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(
-            """
-            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 💖)
-
-            made with ❤️ by [mikus](https://github.com/cappuch) - made the ui!
-
-            ## In loving memory of JLabDX 🕊️
-            """
-        )
-
-demo.queue(api_open=False).launch(show_api=False) # idk ilaria if you want or dont want to
+import gradio as gr
+import requests
+import random
+import os
+import zipfile 
+import librosa
+import time
+from infer_rvc_python import BaseLoader
+from pydub import AudioSegment
+from tts_voice import tts_order_voice
+import edge_tts
+import tempfile
+from audio_separator.separator import Separator
+import model_handler
+import psutil
+import cpuinfo
+
+language_dict = tts_order_voice
+
+async def text_to_speech_edge(text, language_code):
+    voice = language_dict[language_code]
+    communicate = edge_tts.Communicate(text, voice)
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp_file:
+        tmp_path = tmp_file.name
+
+    await communicate.save(tmp_path)
+
+    return tmp_path
+
+try:
+    import spaces
+    spaces_status = True
+except ImportError:
+    spaces_status = False
+
+separator = Separator()
+converter = BaseLoader(only_cpu=False, hubert_path=None, rmvpe_path=None)
+
+global pth_file
+global index_file
+
+pth_file = "model.pth"
+index_file = "model.index"
+
+#CONFIGS
+TEMP_DIR = "temp"
+MODEL_PREFIX = "model"
+PITCH_ALGO_OPT = [
+    "pm",
+    "harvest",
+    "crepe",
+    "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"},
+]
+
+os.makedirs(TEMP_DIR, exist_ok=True)
+
+def unzip_file(file):
+    filename = os.path.basename(file).split(".")[0] 
+    with zipfile.ZipFile(file, 'r') as zip_ref:
+        zip_ref.extractall(os.path.join(TEMP_DIR, filename)) 
+    return True
+    
+
+def progress_bar(total, current):
+    return "[" + "=" * int(current / total * 20) + ">" + " " * (20 - int(current / total * 20)) + "] " + str(int(current / total * 100)) + "%"
+
+def download_from_url(url, name=None):
+    if name is None:
+        raise ValueError("The model name must be provided")
+    if "/blob/" in url:
+        url = url.replace("/blob/", "/resolve/") 
+    if "huggingface" not in url:
+        return ["The URL must be from huggingface", "Failed", "Failed"]
+    filename = os.path.join(TEMP_DIR, MODEL_PREFIX + str(random.randint(1, 1000)) + ".zip")
+    response = requests.get(url)
+    total = int(response.headers.get('content-length', 0)) 
+    if total > 500000000:
+
+        return ["The file is too large. You can only download files up to 500 MB in size.", "Failed", "Failed"]
+    current = 0
+    with open(filename, "wb") as f:
+        for data in response.iter_content(chunk_size=4096): 
+            f.write(data)
+            current += len(data)
+            print(progress_bar(total, current), end="\r") #
+    
+    
+
+    try:
+        unzip_file(filename)
+    except Exception as e:
+        return ["Failed to unzip the file", "Failed", "Failed"] 
+    unzipped_dir = os.path.join(TEMP_DIR, os.path.basename(filename).split(".")[0])
+    pth_files = []
+    index_files = []
+    for root, dirs, files in os.walk(unzipped_dir): 
+        for file in files:
+            if file.endswith(".pth"):
+                pth_files.append(os.path.join(root, file))
+            elif file.endswith(".index"):
+                index_files.append(os.path.join(root, file))
+    
+    print(pth_files, index_files) 
+    global pth_file
+    global index_file
+    pth_file = pth_files[0]
+    index_file = index_files[0]
+
+    print(pth_file)
+    print(index_file)
+
+    MODELS.append({"model": pth_file, "index": index_file, "model_name": name})
+    return ["Downloaded as " + name, pth_files[0], index_files[0]]
+
+def inference(audio, model_name):
+        output_data = inf_handler(audio, model_name)
+        vocals = output_data[0]
+        inst = output_data[1]
+
+        return vocals, inst
+
+if spaces_status:
+    @spaces.GPU()
+    def convert_now(audio_files, random_tag, converter):
+        return converter(
+            audio_files,
+            random_tag,
+            overwrite=False,
+            parallel_workers=8
+        )
+
+        
+else:
+    def convert_now(audio_files, random_tag, converter):
+        return converter(
+            audio_files,
+            random_tag,
+            overwrite=False,
+            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(
+    model,
+    audio_files,
+    pitch_alg,
+    pitch_lvl,
+    index_inf,
+    r_m_f,
+    e_r,
+    c_b_p,
+):
+    if not audio_files:
+        raise ValueError("The audio pls")
+
+    if isinstance(audio_files, str):
+        audio_files = [audio_files]
+
+    try:
+        duration_base = librosa.get_duration(filename=audio_files[0])
+        print("Duration:", duration_base)
+    except Exception as e:
+        print(e)
+
+    random_tag = "USER_"+str(random.randint(10000000, 99999999))
+
+    file_m = model
+    print("File model:", file_m)
+
+    # get from MODELS
+    for model in MODELS:
+        if model["model_name"] == file_m:
+            print(model)
+            file_m = model["model"]
+            file_index = model["index"]
+            break
+    
+    if not file_m.endswith(".pth"):
+        raise ValueError("The model file must be a .pth file")
+
+
+    print("Random tag:", random_tag)
+    print("File model:", file_m)
+    print("Pitch algorithm:", pitch_alg)
+    print("Pitch level:", pitch_lvl)
+    print("File index:", file_index)
+    print("Index influence:", index_inf)
+    print("Respiration median filtering:", r_m_f)
+    print("Envelope ratio:", e_r)
+
+    converter.apply_conf(
+        tag=random_tag,
+        file_model=file_m,
+        pitch_algo=pitch_alg,
+        pitch_lvl=pitch_lvl,
+        file_index=file_index,
+        index_influence=index_inf,
+        respiration_median_filtering=r_m_f,
+        envelope_ratio=e_r,
+        consonant_breath_protection=c_b_p,
+        resample_sr=44100 if audio_files[0].endswith('.mp3') else 0, 
+    )
+    time.sleep(0.1)
+
+    result = convert_now(audio_files, random_tag, converter)
+    print("Result:", result)
+
+    return result[0]
+
+def upload_model(index_file, pth_file, model_name):
+    pth_file = pth_file.name
+    index_file = index_file.name
+    MODELS.append({"model": pth_file, "index": index_file, "model_name": model_name})
+    return "Uploaded!"  
+
+with gr.Blocks(theme=gr.themes.Default(primary_hue="pink", secondary_hue="rose"), title="Ilaria RVC 💖") as demo:
+    gr.Markdown("## Ilaria RVC 💖")
+    with gr.Tab("Inference"):
+        sound_gui = gr.Audio(value=None,type="filepath",autoplay=False,visible=True,)
+        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"],)
+        with gr.Row():
+            models_dropdown = gr.Dropdown(label="Model",choices=[model["model_name"] for model in MODELS],visible=True,interactive=True, value=MODELS[0]["model_name"],)
+            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,)
+            index_inf_conf =  gr.Slider(minimum=0,maximum=1,label="Index influence -> How much accent is applied",value=0.75,)
+            respiration_filter_conf = gr.Slider(minimum=0,maximum=7,label="Respiration median filtering",value=3,step=1,interactive=True,)
+            envelope_ratio_conf = gr.Slider(minimum=0,maximum=1,label="Envelope ratio",value=0.25,interactive=True,)
+            consonant_protec_conf = gr.Slider(minimum=0,maximum=0.5,label="Consonant breath protection",value=0.5,interactive=True,)
+
+        button_conf = gr.Button("Convert",variant="primary",)
+        output_conf = gr.Audio(type="filepath",label="Output",)
+    	
+        button_conf.click(lambda :None, None, output_conf)
+        button_conf.click(
+            run,
+            inputs=[
+                models_dropdown,
+                sound_gui,
+                pitch_algo_conf,
+                pitch_lvl_conf,
+                index_inf_conf,
+                respiration_filter_conf,
+                envelope_ratio_conf,
+                consonant_protec_conf,
+            ],
+            outputs=[output_conf],
+        )
+
+
+    with gr.Tab("Model Loader (Download and Upload)"):
+        with gr.Accordion("Model Downloader", open=False):
+            gr.Markdown(
+                "Download the model from the following URL and upload it here. (Huggingface RVC model)"
+            )
+            model = gr.Textbox(lines=1, label="Model URL")
+            name = gr.Textbox(lines=1, label="Model Name", placeholder="Model Name")
+            download_button = gr.Button("Download Model")
+            status = gr.Textbox(lines=1, label="Status", placeholder="Waiting....", interactive=False)
+            model_pth = gr.Textbox(lines=1, label="Model pth file", placeholder="Waiting....", interactive=False)
+            index_pth = gr.Textbox(lines=1, label="Index pth file", placeholder="Waiting....", interactive=False)
+            download_button.click(download_from_url, [model, name], outputs=[status, model_pth, index_pth])
+        with gr.Accordion("Upload A Model", open=False):
+            index_file_upload = gr.File(label="Index File (.index)")
+            pth_file_upload = gr.File(label="Model File (.pth)")
+
+            model_name = gr.Textbox(label="Model Name", placeholder="Model Name")
+            upload_button = gr.Button("Upload Model")
+            upload_status = gr.Textbox(lines=1, label="Status", placeholder="Waiting....", interactive=False)
+
+            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(
+            """
+            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 💖)
+
+            made with ❤️ by [mikus](https://github.com/cappuch) - made the ui!
+
+            ## In loving memory of JLabDX 🕊️
+            """
+        )
+    with gr.Tab(("")):
+        gr.Markdown('''
+            ![ilaria](https://i.ytimg.com/vi/5PWqt2Wg-us/maxresdefault.jpg)
+        ''')
+
+demo.queue(api_open=False).launch(show_api=False)