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| 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: | |
| 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) | |