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 import anyio import asyncio from audio_separator.separator import Separator 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 # fucking dogshit toggle 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+", ] 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 get_training_info(audio_file): if audio_file is None: return 'Please provide an audio file!' duration = get_audio_duration(audio_file) sample_rate = wave.open(audio_file, 'rb').getframerate() training_info = { (0, 2): (150, 'OV2'), (2, 3): (200, 'OV2'), (3, 5): (250, 'OV2'), (5, 10): (300, 'Normal'), (10, 25): (500, 'Normal'), (25, 45): (700, 'Normal'), (45, 60): (1000, 'Normal') } for (min_duration, max_duration), (epochs, pretrain) in training_info.items(): if min_duration <= duration < max_duration: break else: return 'Duration is not within the specified range!' return f'You should use the **{pretrain}** pretrain with **{epochs}** epochs at **{sample_rate/1000}khz** sample rate.' def on_button_click(audio_file_path): return get_training_info(audio_file_path) def get_audio_duration(audio_file_path): audio_info = sf.info(audio_file_path) duration_minutes = audio_info.duration / 60 return duration_minutes def progress_bar(total, current): # best progress bar ever trust me sunglasses emoji 😎 return "[" + "=" * int(current / total * 20) + ">" + " " * (20 - int(current / total * 20)) + "] " + str(int(current / total * 100)) + "%" def download_from_url(url, filename=None): if "/blob/" in url: url = url.replace("/blob/", "/resolve/") # made it delik proof 😎 if "huggingface" not in url: return ["The URL must be from huggingface", "Failed", "Failed"] if filename is None: 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)) # bytes to download (length of the file) 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] pth_file_ui.value = pth_file index_file_ui.value = index_file print(pth_file_ui.value) print(index_file_ui.value) return ["Downloaded as " + filename, 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( 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 = pth_file_ui.value file_index = index_file_ui.value 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): pth_file = pth_file.name index_file = index_file.name pth_file_ui.value = pth_file index_file_ui.value = index_file 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) pth_file_ui = gr.Textbox(label="Model pth file", value=pth_file, visible=False, interactive=False) index_file_ui = gr.Textbox(label="Index pth file", value=index_file, visible=False, interactive=False) 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") # Rimuovi l'output_tts e usa solo sound_gui come output 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=[ 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. (Hugginface RVC model)" ) model = gr.Textbox(lines=1, label="Model URL") 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, 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)") 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], upload_status) with gr.Tab("Extra"): 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( fn=calculate_remaining_time, inputs=[epochs_input, seconds_input], outputs=[remaining_time_output] ) with gr.Accordion('Training Helper', open=False): with gr.Column(): audio_input = gr.Audio(type="filepath", label="Upload your audio file") gr.Text("Please note that these results are approximate and intended to provide a general idea for beginners.", label='Notice:') training_info_output = gr.Markdown(label="Training Information:") get_info_button = gr.Button("Get Training Info") get_info_button.click( fn=on_button_click, inputs=[audio_input], outputs=[training_info_output] ) 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) - i make this ui........ ## In loving memory of JLabDX 🕊️ """ ) demo.queue(api_open=False).launch(show_api=False)