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 ๐Ÿ’–") as demo: gr.Markdown("## Ilaria RVC ๐Ÿ’–") 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. - soonโ„ข or neverโ„ข ๐Ÿ˜Ž") 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 ๐Ÿ’–) made with โค๏ธ by [mikus](https://github.com/cappuch) - made the ui! ## In loving memory of JLabDX ๐Ÿ•Š๏ธ """ ) demo.queue(api_open=False).launch(show_api=False) # idk ilaria if you want or dont want to