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 contains_bad_word(text, bad_words): text_lower = text.lower() for word in bad_words: if word.lower() in text_lower: return True return False bad_words = ['puttana', 'whore', 'badword3', 'badword4'] class BadWordError(Exception): def __init__(self, msg): super().__init__(msg) self.word = word 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"] if contains_bad_word(url, bad_words): return BadWordError("The file url has a bad word.") if contains_bad_word(name, bad_words): return BadWordError("The file name has a bad word.") 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) if name == "": name = pth_file.split(".")[0] 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 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( title="Ilqria RVC UI mod", css="footer{display:none !important}", theme=gr.themes.Default( primary_hue="rose", secondary_hue="rose", spacing_size=gr.themes.sizes.spacing_sm, radius_size=gr.themes.sizes.radius_none, )) as app: gr.Markdown("## Ilaria RVC Mod💖") 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("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) ''') app.launch(share=True)