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Parent(s):
62d6c2d
Update app.py
Browse files
app.py
CHANGED
@@ -1,504 +1,508 @@
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import gradio as gr
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import requests
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import random
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import os
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import zipfile
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import librosa
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import time
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from infer_rvc_python import BaseLoader
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from pydub import AudioSegment
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from tts_voice import tts_order_voice
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import edge_tts
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import tempfile
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from audio_separator.separator import Separator
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import model_handler
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import psutil
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import cpuinfo
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language_dict = tts_order_voice
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async def text_to_speech_edge(text, language_code):
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voice = language_dict[language_code]
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communicate = edge_tts.Communicate(text, voice)
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with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp_file:
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tmp_path = tmp_file.name
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await communicate.save(tmp_path)
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return tmp_path
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try:
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import spaces
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spaces_status = True
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except ImportError:
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spaces_status = False
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separator = Separator()
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converter = BaseLoader(only_cpu=False, hubert_path=None, rmvpe_path=None)
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global pth_file
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global index_file
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pth_file = "model.pth"
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index_file = "model.index"
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#CONFIGS
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TEMP_DIR = "temp"
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MODEL_PREFIX = "model"
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PITCH_ALGO_OPT = [
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"pm",
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"harvest",
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"crepe",
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"rmvpe",
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"rmvpe+",
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]
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UVR_5_MODELS = [
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{"model_name": "BS-Roformer-Viperx-1297", "checkpoint": "model_bs_roformer_ep_317_sdr_12.9755.ckpt"},
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{"model_name": "MDX23C-InstVoc HQ 2", "checkpoint": "MDX23C-8KFFT-InstVoc_HQ_2.ckpt"},
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{"model_name": "Kim Vocal 2", "checkpoint": "Kim_Vocal_2.onnx"},
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{"model_name": "5_HP-Karaoke", "checkpoint": "5_HP-Karaoke-UVR.pth"},
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{"model_name": "UVR-DeNoise by FoxJoy", "checkpoint": "UVR-DeNoise.pth"},
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{"model_name": "UVR-DeEcho-DeReverb by FoxJoy", "checkpoint": "UVR-DeEcho-DeReverb.pth"},
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]
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MODELS = [
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{"model": "model.pth", "index": "model.index", "model_name": "Test Model"},
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]
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os.makedirs(TEMP_DIR, exist_ok=True)
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def unzip_file(file):
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filename = os.path.basename(file).split(".")[0]
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with zipfile.ZipFile(file, 'r') as zip_ref:
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zip_ref.extractall(os.path.join(TEMP_DIR, filename))
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return True
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def progress_bar(total, current):
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return "[" + "=" * int(current / total * 20) + ">" + " " * (20 - int(current / total * 20)) + "] " + str(int(current / total * 100)) + "%"
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def download_from_url(url, name=None):
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if name is None:
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raise ValueError("The model name must be provided")
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if "/blob/" in url:
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url = url.replace("/blob/", "/resolve/")
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if "huggingface" not in url:
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return ["The URL must be from huggingface", "Failed", "Failed"]
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filename = os.path.join(TEMP_DIR, MODEL_PREFIX + str(random.randint(1, 1000)) + ".zip")
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response = requests.get(url)
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total = int(response.headers.get('content-length', 0))
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if total > 500000000:
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return ["The file is too large. You can only download files up to 500 MB in size.", "Failed", "Failed"]
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current = 0
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with open(filename, "wb") as f:
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for data in response.iter_content(chunk_size=4096):
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f.write(data)
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current += len(data)
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print(progress_bar(total, current), end="\r") #
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try:
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unzip_file(filename)
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except Exception as e:
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return ["Failed to unzip the file", "Failed", "Failed"]
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unzipped_dir = os.path.join(TEMP_DIR, os.path.basename(filename).split(".")[0])
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pth_files = []
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index_files = []
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for root, dirs, files in os.walk(unzipped_dir):
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for file in files:
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if file.endswith(".pth"):
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pth_files.append(os.path.join(root, file))
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elif file.endswith(".index"):
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index_files.append(os.path.join(root, file))
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print(pth_files, index_files)
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global pth_file
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global index_file
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pth_file = pth_files[0]
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index_file = index_files[0]
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print(pth_file)
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print(index_file)
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MODELS.append({"model": pth_file, "index": index_file, "model_name": name})
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return ["Downloaded as " + name, pth_files[0], index_files[0]]
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def inference(audio, model_name):
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output_data = inf_handler(audio, model_name)
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vocals = output_data[0]
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inst = output_data[1]
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return vocals, inst
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if spaces_status:
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@spaces.GPU()
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def convert_now(audio_files, random_tag, converter):
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return converter(
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audio_files,
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random_tag,
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overwrite=False,
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parallel_workers=8
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)
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else:
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def convert_now(audio_files, random_tag, converter):
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return converter(
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audio_files,
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random_tag,
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overwrite=False,
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parallel_workers=8
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)
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def calculate_remaining_time(epochs, seconds_per_epoch):
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total_seconds = epochs * seconds_per_epoch
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hours = total_seconds // 3600
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minutes = (total_seconds % 3600) // 60
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seconds = total_seconds % 60
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if hours == 0:
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return f"{int(minutes)} minutes"
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elif hours == 1:
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return f"{int(hours)} hour and {int(minutes)} minutes"
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else:
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return f"{int(hours)} hours and {int(minutes)} minutes"
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def inf_handler(audio, model_name):
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model_found = False
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for model_info in UVR_5_MODELS:
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if model_info["model_name"] == model_name:
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separator.load_model(model_info["checkpoint"])
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model_found = True
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break
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if not model_found:
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separator.load_model()
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output_files = separator.separate(audio)
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vocals = output_files[0]
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inst = output_files[1]
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return vocals, inst
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def run(
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model,
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audio_files,
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pitch_alg,
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pitch_lvl,
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index_inf,
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r_m_f,
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e_r,
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c_b_p,
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):
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if not audio_files:
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raise ValueError("The audio pls")
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if isinstance(audio_files, str):
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audio_files = [audio_files]
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try:
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duration_base = librosa.get_duration(filename=audio_files[0])
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print("Duration:", duration_base)
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except Exception as e:
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print(e)
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random_tag = "USER_"+str(random.randint(10000000, 99999999))
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file_m = model
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print("File model:", file_m)
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# get from MODELS
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for model in MODELS:
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if model["model_name"] == file_m:
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print(model)
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file_m = model["model"]
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file_index = model["index"]
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break
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if not file_m.endswith(".pth"):
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raise ValueError("The model file must be a .pth file")
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print("Random tag:", random_tag)
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print("File model:", file_m)
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print("Pitch algorithm:", pitch_alg)
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print("Pitch level:", pitch_lvl)
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print("File index:", file_index)
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print("Index influence:", index_inf)
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print("Respiration median filtering:", r_m_f)
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print("Envelope ratio:", e_r)
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converter.apply_conf(
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tag=random_tag,
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file_model=file_m,
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pitch_algo=pitch_alg,
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pitch_lvl=pitch_lvl,
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file_index=file_index,
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index_influence=index_inf,
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respiration_median_filtering=r_m_f,
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envelope_ratio=e_r,
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consonant_breath_protection=c_b_p,
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resample_sr=44100 if audio_files[0].endswith('.mp3') else 0,
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)
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time.sleep(0.1)
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result = convert_now(audio_files, random_tag, converter)
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print("Result:", result)
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return result[0]
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def upload_model(index_file, pth_file, model_name):
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pth_file = pth_file.name
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index_file = index_file.name
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MODELS.append({"model": pth_file, "index": index_file, "model_name": model_name})
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return "Uploaded!"
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with gr.Blocks(theme=gr.themes.Default(primary_hue="pink", secondary_hue="rose"), title="Ilaria RVC 💖") as demo:
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gr.Markdown("## Ilaria RVC 💖")
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with gr.Tab("Inference"):
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sound_gui = gr.Audio(value=None,type="filepath",autoplay=False,visible=True,)
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def update():
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print(MODELS)
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return gr.Dropdown(label="Model",choices=[model["model_name"] for model in MODELS],visible=True,interactive=True, value=MODELS[0]["model_name"],)
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with gr.Row():
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models_dropdown = gr.Dropdown(label="Model",choices=[model["model_name"] for model in MODELS],visible=True,interactive=True, value=MODELS[0]["model_name"],)
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refresh_button = gr.Button("Refresh Models")
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refresh_button.click(update, outputs=[models_dropdown])
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with gr.Accordion("Ilaria TTS", open=False):
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text_tts = gr.Textbox(label="Text", placeholder="Hello!", lines=3, interactive=True,)
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dropdown_tts = gr.Dropdown(label="Language and Model",choices=list(language_dict.keys()),interactive=True, value=list(language_dict.keys())[0])
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button_tts = gr.Button("Speak", variant="primary",)
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button_tts.click(text_to_speech_edge, inputs=[text_tts, dropdown_tts], outputs=[sound_gui])
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with gr.Accordion("Settings", open=False):
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pitch_algo_conf = gr.Dropdown(PITCH_ALGO_OPT,value=PITCH_ALGO_OPT[4],label="Pitch algorithm",visible=True,interactive=True,)
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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,)
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index_inf_conf = gr.Slider(minimum=0,maximum=1,label="Index influence -> How much accent is applied",value=0.75,)
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respiration_filter_conf = gr.Slider(minimum=0,maximum=7,label="Respiration median filtering",value=3,step=1,interactive=True,)
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envelope_ratio_conf = gr.Slider(minimum=0,maximum=1,label="Envelope ratio",value=0.25,interactive=True,)
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consonant_protec_conf = gr.Slider(minimum=0,maximum=0.5,label="Consonant breath protection",value=0.5,interactive=True,)
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button_conf = gr.Button("Convert",variant="primary",)
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output_conf = gr.Audio(type="filepath",label="Output",)
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button_conf.click(lambda :None, None, output_conf)
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button_conf.click(
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run,
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inputs=[
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models_dropdown,
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sound_gui,
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pitch_algo_conf,
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pitch_lvl_conf,
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index_inf_conf,
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respiration_filter_conf,
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envelope_ratio_conf,
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consonant_protec_conf,
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],
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outputs=[output_conf],
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)
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with gr.Tab("Model Loader (Download and Upload)"):
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with gr.Accordion("Model Downloader", open=False):
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gr.Markdown(
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"Download the model from the following URL and upload it here. (Huggingface RVC model)"
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)
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model = gr.Textbox(lines=1, label="Model URL")
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name = gr.Textbox(lines=1, label="Model Name", placeholder="Model Name")
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download_button = gr.Button("Download Model")
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status = gr.Textbox(lines=1, label="Status", placeholder="Waiting....", interactive=False)
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model_pth = gr.Textbox(lines=1, label="Model pth file", placeholder="Waiting....", interactive=False)
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index_pth = gr.Textbox(lines=1, label="Index pth file", placeholder="Waiting....", interactive=False)
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download_button.click(download_from_url, [model, name], outputs=[status, model_pth, index_pth])
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with gr.Accordion("Upload A Model", open=False):
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index_file_upload = gr.File(label="Index File (.index)")
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pth_file_upload = gr.File(label="Model File (.pth)")
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model_name = gr.Textbox(label="Model Name", placeholder="Model Name")
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upload_button = gr.Button("Upload Model")
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upload_status = gr.Textbox(lines=1, label="Status", placeholder="Waiting....", interactive=False)
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upload_button.click(upload_model, [index_file_upload, pth_file_upload, model_name], upload_status)
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with gr.Tab("Vocal Separator (UVR)"):
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gr.Markdown("Separate vocals and instruments from an audio file using UVR models. - This is only on CPU due to ZeroGPU being ZeroGPU :(")
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uvr5_audio_file = gr.Audio(label="Audio File",type="filepath")
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with gr.Row():
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uvr5_model = gr.Dropdown(label="Model", choices=[model["model_name"] for model in UVR_5_MODELS])
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uvr5_button = gr.Button("Separate Vocals", variant="primary",)
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uvr5_output_voc = gr.Audio(type="filepath", label="Output 1",)
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uvr5_output_inst = gr.Audio(type="filepath", label="Output 2",)
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uvr5_button.click(inference, [uvr5_audio_file, uvr5_model], [uvr5_output_voc, uvr5_output_inst])
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with gr.Tab("Extra"):
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with gr.Accordion("Model Information", open=False):
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def json_to_markdown_table(json_data):
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table = "| Key | Value |\n| --- | --- |\n"
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for key, value in json_data.items():
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table += f"| {key} | {value} |\n"
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return table
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def model_info(name):
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for model in MODELS:
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if model["model_name"] == name:
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print(model["model"])
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info = model_handler.model_info(model["model"])
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info2 = {
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"Model Name": model["model_name"],
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"Model Config": info['config'],
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"Epochs Trained": info['epochs'],
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"Sample Rate": info['sr'],
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"Pitch Guidance": info['f0'],
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"Model Precision": info['size'],
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}
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return gr.Markdown(json_to_markdown_table(info2))
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return "Model not found"
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def update():
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print(MODELS)
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return gr.Dropdown(label="Model", choices=[model["model_name"] for model in MODELS])
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with gr.Row():
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model_info_dropdown = gr.Dropdown(label="Model", choices=[model["model_name"] for model in MODELS])
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refresh_button = gr.Button("Refresh Models")
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refresh_button.click(update, outputs=[model_info_dropdown])
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model_info_button = gr.Button("Get Model Information")
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model_info_output = gr.Textbox(value="Waiting...",label="Output", interactive=False)
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model_info_button.click(model_info, [model_info_dropdown], [model_info_output])
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372 |
-
|
373 |
-
|
374 |
-
|
375 |
-
with gr.Accordion("Training Time Calculator", open=False):
|
376 |
-
with gr.Column():
|
377 |
-
epochs_input = gr.Number(label="Number of Epochs")
|
378 |
-
seconds_input = gr.Number(label="Seconds per Epoch")
|
379 |
-
calculate_button = gr.Button("Calculate Time Remaining")
|
380 |
-
remaining_time_output = gr.Textbox(label="Remaining Time", interactive=False)
|
381 |
-
|
382 |
-
calculate_button.click(calculate_remaining_time,inputs=[epochs_input, seconds_input],outputs=[remaining_time_output])
|
383 |
-
|
384 |
-
with gr.Accordion("Model Fusion", open=False):
|
385 |
-
with gr.Group():
|
386 |
-
def merge(ckpt_a, ckpt_b, alpha_a, sr_, if_f0_, info__, name_to_save0, version_2):
|
387 |
-
for model in MODELS:
|
388 |
-
if model["model_name"] == ckpt_a:
|
389 |
-
ckpt_a = model["model"]
|
390 |
-
if model["model_name"] == ckpt_b:
|
391 |
-
ckpt_b = model["model"]
|
392 |
-
|
393 |
-
path = model_handler.merge(ckpt_a, ckpt_b, alpha_a, sr_, if_f0_, info__, name_to_save0, version_2)
|
394 |
-
if path == "Fail to merge the models. The model architectures are not the same.":
|
395 |
-
return "Fail to merge the models. The model architectures are not the same."
|
396 |
-
else:
|
397 |
-
MODELS.append({"model": path, "index": None, "model_name": name_to_save0})
|
398 |
-
return "Merged, saved as " + name_to_save0
|
399 |
-
|
400 |
-
gr.Markdown(value="Strongly suggested to use only very clean models.")
|
401 |
-
with gr.Row():
|
402 |
-
def update():
|
403 |
-
print(MODELS)
|
404 |
-
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])
|
405 |
-
refresh_button_fusion = gr.Button("Refresh Models")
|
406 |
-
ckpt_a = gr.Dropdown(label="Model A", choices=[model["model_name"] for model in MODELS])
|
407 |
-
ckpt_b = gr.Dropdown(label="Model B", choices=[model["model_name"] for model in MODELS])
|
408 |
-
refresh_button_fusion.click(update, outputs=[ckpt_a, ckpt_b])
|
409 |
-
alpha_a = gr.Slider(
|
410 |
-
minimum=0,
|
411 |
-
maximum=1,
|
412 |
-
label="Weight of the first model over the second",
|
413 |
-
value=0.5,
|
414 |
-
interactive=True,
|
415 |
-
)
|
416 |
-
with gr.Group():
|
417 |
-
with gr.Row():
|
418 |
-
sr_ = gr.Radio(
|
419 |
-
label="Sample rate of both models",
|
420 |
-
choices=["32k","40k", "48k"],
|
421 |
-
value="32k",
|
422 |
-
interactive=True,
|
423 |
-
)
|
424 |
-
if_f0_ = gr.Radio(
|
425 |
-
label="Pitch Guidance",
|
426 |
-
choices=["Yes", "Nah"],
|
427 |
-
value="Yes",
|
428 |
-
interactive=True,
|
429 |
-
)
|
430 |
-
info__ = gr.Textbox(
|
431 |
-
label="Add informations to the model",
|
432 |
-
value="",
|
433 |
-
max_lines=8,
|
434 |
-
interactive=True,
|
435 |
-
visible=False
|
436 |
-
)
|
437 |
-
name_to_save0 = gr.Textbox(
|
438 |
-
label="Final Model name",
|
439 |
-
value="",
|
440 |
-
max_lines=1,
|
441 |
-
interactive=True,
|
442 |
-
)
|
443 |
-
version_2 = gr.Radio(
|
444 |
-
label="Versions of the models",
|
445 |
-
choices=["v1", "v2"],
|
446 |
-
value="v2",
|
447 |
-
interactive=True,
|
448 |
-
)
|
449 |
-
with gr.Group():
|
450 |
-
with gr.Row():
|
451 |
-
but6 = gr.Button("Fuse the two models", variant="primary")
|
452 |
-
info4 = gr.Textbox(label="Output", value="", max_lines=8)
|
453 |
-
but6.click(
|
454 |
-
merge,
|
455 |
-
[ckpt_a,ckpt_b,alpha_a,sr_,if_f0_,info__,name_to_save0,version_2,],info4,api_name="ckpt_merge",)
|
456 |
-
|
457 |
-
with gr.Accordion("Model Quantization", open=False):
|
458 |
-
gr.Markdown("Quantize the model to a lower precision. - soon™ or never™ 😎")
|
459 |
-
|
460 |
-
with gr.Accordion("Debug", open=False):
|
461 |
-
def json_to_markdown_table(json_data):
|
462 |
-
table = "| Key | Value |\n| --- | --- |\n"
|
463 |
-
for key, value in json_data.items():
|
464 |
-
table += f"| {key} | {value} |\n"
|
465 |
-
return table
|
466 |
-
gr.Markdown("View the models that are currently loaded in the instance.")
|
467 |
-
|
468 |
-
gr.Markdown(json_to_markdown_table({"Models": len(MODELS), "UVR Models": len(UVR_5_MODELS)}))
|
469 |
-
|
470 |
-
gr.Markdown("View the current status of the instance.")
|
471 |
-
status = {
|
472 |
-
"Status": "Running", # duh lol
|
473 |
-
"Models": len(MODELS),
|
474 |
-
"UVR Models": len(UVR_5_MODELS),
|
475 |
-
"CPU Usage": f"{psutil.cpu_percent()}%",
|
476 |
-
"RAM Usage": f"{psutil.virtual_memory().percent}%",
|
477 |
-
"CPU": f"{cpuinfo.get_cpu_info()['brand_raw']}",
|
478 |
-
"System Uptime": f"{round(time.time() - psutil.boot_time(), 2)} seconds",
|
479 |
-
"System Load Average": f"{psutil.getloadavg()}",
|
480 |
-
"====================": "====================",
|
481 |
-
"CPU Cores": psutil.cpu_count(),
|
482 |
-
"CPU Threads": psutil.cpu_count(logical=True),
|
483 |
-
"RAM Total": f"{round(psutil.virtual_memory().total / 1024**3, 2)} GB",
|
484 |
-
"RAM Used": f"{round(psutil.virtual_memory().used / 1024**3, 2)} GB",
|
485 |
-
"CPU Frequency": f"{psutil.cpu_freq().current} MHz",
|
486 |
-
"====================": "====================",
|
487 |
-
"GPU": "A100 - Do a request (Inference, you won't see it either way)",
|
488 |
-
}
|
489 |
-
gr.Markdown(json_to_markdown_table(status))
|
490 |
-
|
491 |
-
with gr.Tab("Credits"):
|
492 |
-
gr.Markdown(
|
493 |
-
"""
|
494 |
-
Ilaria RVC made by [Ilaria](https://huggingface.co/TheStinger) suport her on [ko-fi](https://ko-fi.com/ilariaowo)
|
495 |
-
|
496 |
-
The Inference code is made by [r3gm](https://huggingface.co/r3gm) (his module helped form this space 💖)
|
497 |
-
|
498 |
-
made with ❤️ by [mikus](https://github.com/cappuch) - made the ui!
|
499 |
-
|
500 |
-
## In loving memory of JLabDX 🕊️
|
501 |
-
"""
|
502 |
-
)
|
503 |
-
|
504 |
-
|
|
|
|
|
|
|
|
|
|
1 |
+
import gradio as gr
|
2 |
+
import requests
|
3 |
+
import random
|
4 |
+
import os
|
5 |
+
import zipfile
|
6 |
+
import librosa
|
7 |
+
import time
|
8 |
+
from infer_rvc_python import BaseLoader
|
9 |
+
from pydub import AudioSegment
|
10 |
+
from tts_voice import tts_order_voice
|
11 |
+
import edge_tts
|
12 |
+
import tempfile
|
13 |
+
from audio_separator.separator import Separator
|
14 |
+
import model_handler
|
15 |
+
import psutil
|
16 |
+
import cpuinfo
|
17 |
+
|
18 |
+
language_dict = tts_order_voice
|
19 |
+
|
20 |
+
async def text_to_speech_edge(text, language_code):
|
21 |
+
voice = language_dict[language_code]
|
22 |
+
communicate = edge_tts.Communicate(text, voice)
|
23 |
+
with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp_file:
|
24 |
+
tmp_path = tmp_file.name
|
25 |
+
|
26 |
+
await communicate.save(tmp_path)
|
27 |
+
|
28 |
+
return tmp_path
|
29 |
+
|
30 |
+
try:
|
31 |
+
import spaces
|
32 |
+
spaces_status = True
|
33 |
+
except ImportError:
|
34 |
+
spaces_status = False
|
35 |
+
|
36 |
+
separator = Separator()
|
37 |
+
converter = BaseLoader(only_cpu=False, hubert_path=None, rmvpe_path=None)
|
38 |
+
|
39 |
+
global pth_file
|
40 |
+
global index_file
|
41 |
+
|
42 |
+
pth_file = "model.pth"
|
43 |
+
index_file = "model.index"
|
44 |
+
|
45 |
+
#CONFIGS
|
46 |
+
TEMP_DIR = "temp"
|
47 |
+
MODEL_PREFIX = "model"
|
48 |
+
PITCH_ALGO_OPT = [
|
49 |
+
"pm",
|
50 |
+
"harvest",
|
51 |
+
"crepe",
|
52 |
+
"rmvpe",
|
53 |
+
"rmvpe+",
|
54 |
+
]
|
55 |
+
UVR_5_MODELS = [
|
56 |
+
{"model_name": "BS-Roformer-Viperx-1297", "checkpoint": "model_bs_roformer_ep_317_sdr_12.9755.ckpt"},
|
57 |
+
{"model_name": "MDX23C-InstVoc HQ 2", "checkpoint": "MDX23C-8KFFT-InstVoc_HQ_2.ckpt"},
|
58 |
+
{"model_name": "Kim Vocal 2", "checkpoint": "Kim_Vocal_2.onnx"},
|
59 |
+
{"model_name": "5_HP-Karaoke", "checkpoint": "5_HP-Karaoke-UVR.pth"},
|
60 |
+
{"model_name": "UVR-DeNoise by FoxJoy", "checkpoint": "UVR-DeNoise.pth"},
|
61 |
+
{"model_name": "UVR-DeEcho-DeReverb by FoxJoy", "checkpoint": "UVR-DeEcho-DeReverb.pth"},
|
62 |
+
]
|
63 |
+
MODELS = [
|
64 |
+
{"model": "model.pth", "index": "model.index", "model_name": "Test Model"},
|
65 |
+
]
|
66 |
+
|
67 |
+
os.makedirs(TEMP_DIR, exist_ok=True)
|
68 |
+
|
69 |
+
def unzip_file(file):
|
70 |
+
filename = os.path.basename(file).split(".")[0]
|
71 |
+
with zipfile.ZipFile(file, 'r') as zip_ref:
|
72 |
+
zip_ref.extractall(os.path.join(TEMP_DIR, filename))
|
73 |
+
return True
|
74 |
+
|
75 |
+
|
76 |
+
def progress_bar(total, current):
|
77 |
+
return "[" + "=" * int(current / total * 20) + ">" + " " * (20 - int(current / total * 20)) + "] " + str(int(current / total * 100)) + "%"
|
78 |
+
|
79 |
+
def download_from_url(url, name=None):
|
80 |
+
if name is None:
|
81 |
+
raise ValueError("The model name must be provided")
|
82 |
+
if "/blob/" in url:
|
83 |
+
url = url.replace("/blob/", "/resolve/")
|
84 |
+
if "huggingface" not in url:
|
85 |
+
return ["The URL must be from huggingface", "Failed", "Failed"]
|
86 |
+
filename = os.path.join(TEMP_DIR, MODEL_PREFIX + str(random.randint(1, 1000)) + ".zip")
|
87 |
+
response = requests.get(url)
|
88 |
+
total = int(response.headers.get('content-length', 0))
|
89 |
+
if total > 500000000:
|
90 |
+
|
91 |
+
return ["The file is too large. You can only download files up to 500 MB in size.", "Failed", "Failed"]
|
92 |
+
current = 0
|
93 |
+
with open(filename, "wb") as f:
|
94 |
+
for data in response.iter_content(chunk_size=4096):
|
95 |
+
f.write(data)
|
96 |
+
current += len(data)
|
97 |
+
print(progress_bar(total, current), end="\r") #
|
98 |
+
|
99 |
+
|
100 |
+
|
101 |
+
try:
|
102 |
+
unzip_file(filename)
|
103 |
+
except Exception as e:
|
104 |
+
return ["Failed to unzip the file", "Failed", "Failed"]
|
105 |
+
unzipped_dir = os.path.join(TEMP_DIR, os.path.basename(filename).split(".")[0])
|
106 |
+
pth_files = []
|
107 |
+
index_files = []
|
108 |
+
for root, dirs, files in os.walk(unzipped_dir):
|
109 |
+
for file in files:
|
110 |
+
if file.endswith(".pth"):
|
111 |
+
pth_files.append(os.path.join(root, file))
|
112 |
+
elif file.endswith(".index"):
|
113 |
+
index_files.append(os.path.join(root, file))
|
114 |
+
|
115 |
+
print(pth_files, index_files)
|
116 |
+
global pth_file
|
117 |
+
global index_file
|
118 |
+
pth_file = pth_files[0]
|
119 |
+
index_file = index_files[0]
|
120 |
+
|
121 |
+
print(pth_file)
|
122 |
+
print(index_file)
|
123 |
+
|
124 |
+
MODELS.append({"model": pth_file, "index": index_file, "model_name": name})
|
125 |
+
return ["Downloaded as " + name, pth_files[0], index_files[0]]
|
126 |
+
|
127 |
+
def inference(audio, model_name):
|
128 |
+
output_data = inf_handler(audio, model_name)
|
129 |
+
vocals = output_data[0]
|
130 |
+
inst = output_data[1]
|
131 |
+
|
132 |
+
return vocals, inst
|
133 |
+
|
134 |
+
if spaces_status:
|
135 |
+
@spaces.GPU()
|
136 |
+
def convert_now(audio_files, random_tag, converter):
|
137 |
+
return converter(
|
138 |
+
audio_files,
|
139 |
+
random_tag,
|
140 |
+
overwrite=False,
|
141 |
+
parallel_workers=8
|
142 |
+
)
|
143 |
+
|
144 |
+
|
145 |
+
else:
|
146 |
+
def convert_now(audio_files, random_tag, converter):
|
147 |
+
return converter(
|
148 |
+
audio_files,
|
149 |
+
random_tag,
|
150 |
+
overwrite=False,
|
151 |
+
parallel_workers=8
|
152 |
+
)
|
153 |
+
|
154 |
+
def calculate_remaining_time(epochs, seconds_per_epoch):
|
155 |
+
total_seconds = epochs * seconds_per_epoch
|
156 |
+
|
157 |
+
hours = total_seconds // 3600
|
158 |
+
minutes = (total_seconds % 3600) // 60
|
159 |
+
seconds = total_seconds % 60
|
160 |
+
|
161 |
+
if hours == 0:
|
162 |
+
return f"{int(minutes)} minutes"
|
163 |
+
elif hours == 1:
|
164 |
+
return f"{int(hours)} hour and {int(minutes)} minutes"
|
165 |
+
else:
|
166 |
+
return f"{int(hours)} hours and {int(minutes)} minutes"
|
167 |
+
|
168 |
+
def inf_handler(audio, model_name):
|
169 |
+
model_found = False
|
170 |
+
for model_info in UVR_5_MODELS:
|
171 |
+
if model_info["model_name"] == model_name:
|
172 |
+
separator.load_model(model_info["checkpoint"])
|
173 |
+
model_found = True
|
174 |
+
break
|
175 |
+
if not model_found:
|
176 |
+
separator.load_model()
|
177 |
+
output_files = separator.separate(audio)
|
178 |
+
vocals = output_files[0]
|
179 |
+
inst = output_files[1]
|
180 |
+
return vocals, inst
|
181 |
+
|
182 |
+
|
183 |
+
def run(
|
184 |
+
model,
|
185 |
+
audio_files,
|
186 |
+
pitch_alg,
|
187 |
+
pitch_lvl,
|
188 |
+
index_inf,
|
189 |
+
r_m_f,
|
190 |
+
e_r,
|
191 |
+
c_b_p,
|
192 |
+
):
|
193 |
+
if not audio_files:
|
194 |
+
raise ValueError("The audio pls")
|
195 |
+
|
196 |
+
if isinstance(audio_files, str):
|
197 |
+
audio_files = [audio_files]
|
198 |
+
|
199 |
+
try:
|
200 |
+
duration_base = librosa.get_duration(filename=audio_files[0])
|
201 |
+
print("Duration:", duration_base)
|
202 |
+
except Exception as e:
|
203 |
+
print(e)
|
204 |
+
|
205 |
+
random_tag = "USER_"+str(random.randint(10000000, 99999999))
|
206 |
+
|
207 |
+
file_m = model
|
208 |
+
print("File model:", file_m)
|
209 |
+
|
210 |
+
# get from MODELS
|
211 |
+
for model in MODELS:
|
212 |
+
if model["model_name"] == file_m:
|
213 |
+
print(model)
|
214 |
+
file_m = model["model"]
|
215 |
+
file_index = model["index"]
|
216 |
+
break
|
217 |
+
|
218 |
+
if not file_m.endswith(".pth"):
|
219 |
+
raise ValueError("The model file must be a .pth file")
|
220 |
+
|
221 |
+
|
222 |
+
print("Random tag:", random_tag)
|
223 |
+
print("File model:", file_m)
|
224 |
+
print("Pitch algorithm:", pitch_alg)
|
225 |
+
print("Pitch level:", pitch_lvl)
|
226 |
+
print("File index:", file_index)
|
227 |
+
print("Index influence:", index_inf)
|
228 |
+
print("Respiration median filtering:", r_m_f)
|
229 |
+
print("Envelope ratio:", e_r)
|
230 |
+
|
231 |
+
converter.apply_conf(
|
232 |
+
tag=random_tag,
|
233 |
+
file_model=file_m,
|
234 |
+
pitch_algo=pitch_alg,
|
235 |
+
pitch_lvl=pitch_lvl,
|
236 |
+
file_index=file_index,
|
237 |
+
index_influence=index_inf,
|
238 |
+
respiration_median_filtering=r_m_f,
|
239 |
+
envelope_ratio=e_r,
|
240 |
+
consonant_breath_protection=c_b_p,
|
241 |
+
resample_sr=44100 if audio_files[0].endswith('.mp3') else 0,
|
242 |
+
)
|
243 |
+
time.sleep(0.1)
|
244 |
+
|
245 |
+
result = convert_now(audio_files, random_tag, converter)
|
246 |
+
print("Result:", result)
|
247 |
+
|
248 |
+
return result[0]
|
249 |
+
|
250 |
+
def upload_model(index_file, pth_file, model_name):
|
251 |
+
pth_file = pth_file.name
|
252 |
+
index_file = index_file.name
|
253 |
+
MODELS.append({"model": pth_file, "index": index_file, "model_name": model_name})
|
254 |
+
return "Uploaded!"
|
255 |
+
|
256 |
+
with gr.Blocks(theme=gr.themes.Default(primary_hue="pink", secondary_hue="rose"), title="Ilaria RVC 💖") as demo:
|
257 |
+
gr.Markdown("## Ilaria RVC 💖")
|
258 |
+
with gr.Tab("Inference"):
|
259 |
+
sound_gui = gr.Audio(value=None,type="filepath",autoplay=False,visible=True,)
|
260 |
+
def update():
|
261 |
+
print(MODELS)
|
262 |
+
return gr.Dropdown(label="Model",choices=[model["model_name"] for model in MODELS],visible=True,interactive=True, value=MODELS[0]["model_name"],)
|
263 |
+
with gr.Row():
|
264 |
+
models_dropdown = gr.Dropdown(label="Model",choices=[model["model_name"] for model in MODELS],visible=True,interactive=True, value=MODELS[0]["model_name"],)
|
265 |
+
refresh_button = gr.Button("Refresh Models")
|
266 |
+
refresh_button.click(update, outputs=[models_dropdown])
|
267 |
+
|
268 |
+
with gr.Accordion("Ilaria TTS", open=False):
|
269 |
+
text_tts = gr.Textbox(label="Text", placeholder="Hello!", lines=3, interactive=True,)
|
270 |
+
dropdown_tts = gr.Dropdown(label="Language and Model",choices=list(language_dict.keys()),interactive=True, value=list(language_dict.keys())[0])
|
271 |
+
|
272 |
+
button_tts = gr.Button("Speak", variant="primary",)
|
273 |
+
button_tts.click(text_to_speech_edge, inputs=[text_tts, dropdown_tts], outputs=[sound_gui])
|
274 |
+
|
275 |
+
with gr.Accordion("Settings", open=False):
|
276 |
+
pitch_algo_conf = gr.Dropdown(PITCH_ALGO_OPT,value=PITCH_ALGO_OPT[4],label="Pitch algorithm",visible=True,interactive=True,)
|
277 |
+
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,)
|
278 |
+
index_inf_conf = gr.Slider(minimum=0,maximum=1,label="Index influence -> How much accent is applied",value=0.75,)
|
279 |
+
respiration_filter_conf = gr.Slider(minimum=0,maximum=7,label="Respiration median filtering",value=3,step=1,interactive=True,)
|
280 |
+
envelope_ratio_conf = gr.Slider(minimum=0,maximum=1,label="Envelope ratio",value=0.25,interactive=True,)
|
281 |
+
consonant_protec_conf = gr.Slider(minimum=0,maximum=0.5,label="Consonant breath protection",value=0.5,interactive=True,)
|
282 |
+
|
283 |
+
button_conf = gr.Button("Convert",variant="primary",)
|
284 |
+
output_conf = gr.Audio(type="filepath",label="Output",)
|
285 |
+
|
286 |
+
button_conf.click(lambda :None, None, output_conf)
|
287 |
+
button_conf.click(
|
288 |
+
run,
|
289 |
+
inputs=[
|
290 |
+
models_dropdown,
|
291 |
+
sound_gui,
|
292 |
+
pitch_algo_conf,
|
293 |
+
pitch_lvl_conf,
|
294 |
+
index_inf_conf,
|
295 |
+
respiration_filter_conf,
|
296 |
+
envelope_ratio_conf,
|
297 |
+
consonant_protec_conf,
|
298 |
+
],
|
299 |
+
outputs=[output_conf],
|
300 |
+
)
|
301 |
+
|
302 |
+
|
303 |
+
with gr.Tab("Model Loader (Download and Upload)"):
|
304 |
+
with gr.Accordion("Model Downloader", open=False):
|
305 |
+
gr.Markdown(
|
306 |
+
"Download the model from the following URL and upload it here. (Huggingface RVC model)"
|
307 |
+
)
|
308 |
+
model = gr.Textbox(lines=1, label="Model URL")
|
309 |
+
name = gr.Textbox(lines=1, label="Model Name", placeholder="Model Name")
|
310 |
+
download_button = gr.Button("Download Model")
|
311 |
+
status = gr.Textbox(lines=1, label="Status", placeholder="Waiting....", interactive=False)
|
312 |
+
model_pth = gr.Textbox(lines=1, label="Model pth file", placeholder="Waiting....", interactive=False)
|
313 |
+
index_pth = gr.Textbox(lines=1, label="Index pth file", placeholder="Waiting....", interactive=False)
|
314 |
+
download_button.click(download_from_url, [model, name], outputs=[status, model_pth, index_pth])
|
315 |
+
with gr.Accordion("Upload A Model", open=False):
|
316 |
+
index_file_upload = gr.File(label="Index File (.index)")
|
317 |
+
pth_file_upload = gr.File(label="Model File (.pth)")
|
318 |
+
|
319 |
+
model_name = gr.Textbox(label="Model Name", placeholder="Model Name")
|
320 |
+
upload_button = gr.Button("Upload Model")
|
321 |
+
upload_status = gr.Textbox(lines=1, label="Status", placeholder="Waiting....", interactive=False)
|
322 |
+
|
323 |
+
upload_button.click(upload_model, [index_file_upload, pth_file_upload, model_name], upload_status)
|
324 |
+
|
325 |
+
|
326 |
+
with gr.Tab("Vocal Separator (UVR)"):
|
327 |
+
gr.Markdown("Separate vocals and instruments from an audio file using UVR models. - This is only on CPU due to ZeroGPU being ZeroGPU :(")
|
328 |
+
uvr5_audio_file = gr.Audio(label="Audio File",type="filepath")
|
329 |
+
|
330 |
+
with gr.Row():
|
331 |
+
uvr5_model = gr.Dropdown(label="Model", choices=[model["model_name"] for model in UVR_5_MODELS])
|
332 |
+
uvr5_button = gr.Button("Separate Vocals", variant="primary",)
|
333 |
+
|
334 |
+
uvr5_output_voc = gr.Audio(type="filepath", label="Output 1",)
|
335 |
+
uvr5_output_inst = gr.Audio(type="filepath", label="Output 2",)
|
336 |
+
|
337 |
+
uvr5_button.click(inference, [uvr5_audio_file, uvr5_model], [uvr5_output_voc, uvr5_output_inst])
|
338 |
+
|
339 |
+
with gr.Tab("Extra"):
|
340 |
+
with gr.Accordion("Model Information", open=False):
|
341 |
+
def json_to_markdown_table(json_data):
|
342 |
+
table = "| Key | Value |\n| --- | --- |\n"
|
343 |
+
for key, value in json_data.items():
|
344 |
+
table += f"| {key} | {value} |\n"
|
345 |
+
return table
|
346 |
+
def model_info(name):
|
347 |
+
for model in MODELS:
|
348 |
+
if model["model_name"] == name:
|
349 |
+
print(model["model"])
|
350 |
+
info = model_handler.model_info(model["model"])
|
351 |
+
info2 = {
|
352 |
+
"Model Name": model["model_name"],
|
353 |
+
"Model Config": info['config'],
|
354 |
+
"Epochs Trained": info['epochs'],
|
355 |
+
"Sample Rate": info['sr'],
|
356 |
+
"Pitch Guidance": info['f0'],
|
357 |
+
"Model Precision": info['size'],
|
358 |
+
}
|
359 |
+
return gr.Markdown(json_to_markdown_table(info2))
|
360 |
+
|
361 |
+
return "Model not found"
|
362 |
+
def update():
|
363 |
+
print(MODELS)
|
364 |
+
return gr.Dropdown(label="Model", choices=[model["model_name"] for model in MODELS])
|
365 |
+
with gr.Row():
|
366 |
+
model_info_dropdown = gr.Dropdown(label="Model", choices=[model["model_name"] for model in MODELS])
|
367 |
+
refresh_button = gr.Button("Refresh Models")
|
368 |
+
refresh_button.click(update, outputs=[model_info_dropdown])
|
369 |
+
model_info_button = gr.Button("Get Model Information")
|
370 |
+
model_info_output = gr.Textbox(value="Waiting...",label="Output", interactive=False)
|
371 |
+
model_info_button.click(model_info, [model_info_dropdown], [model_info_output])
|
372 |
+
|
373 |
+
|
374 |
+
|
375 |
+
with gr.Accordion("Training Time Calculator", open=False):
|
376 |
+
with gr.Column():
|
377 |
+
epochs_input = gr.Number(label="Number of Epochs")
|
378 |
+
seconds_input = gr.Number(label="Seconds per Epoch")
|
379 |
+
calculate_button = gr.Button("Calculate Time Remaining")
|
380 |
+
remaining_time_output = gr.Textbox(label="Remaining Time", interactive=False)
|
381 |
+
|
382 |
+
calculate_button.click(calculate_remaining_time,inputs=[epochs_input, seconds_input],outputs=[remaining_time_output])
|
383 |
+
|
384 |
+
with gr.Accordion("Model Fusion", open=False):
|
385 |
+
with gr.Group():
|
386 |
+
def merge(ckpt_a, ckpt_b, alpha_a, sr_, if_f0_, info__, name_to_save0, version_2):
|
387 |
+
for model in MODELS:
|
388 |
+
if model["model_name"] == ckpt_a:
|
389 |
+
ckpt_a = model["model"]
|
390 |
+
if model["model_name"] == ckpt_b:
|
391 |
+
ckpt_b = model["model"]
|
392 |
+
|
393 |
+
path = model_handler.merge(ckpt_a, ckpt_b, alpha_a, sr_, if_f0_, info__, name_to_save0, version_2)
|
394 |
+
if path == "Fail to merge the models. The model architectures are not the same.":
|
395 |
+
return "Fail to merge the models. The model architectures are not the same."
|
396 |
+
else:
|
397 |
+
MODELS.append({"model": path, "index": None, "model_name": name_to_save0})
|
398 |
+
return "Merged, saved as " + name_to_save0
|
399 |
+
|
400 |
+
gr.Markdown(value="Strongly suggested to use only very clean models.")
|
401 |
+
with gr.Row():
|
402 |
+
def update():
|
403 |
+
print(MODELS)
|
404 |
+
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])
|
405 |
+
refresh_button_fusion = gr.Button("Refresh Models")
|
406 |
+
ckpt_a = gr.Dropdown(label="Model A", choices=[model["model_name"] for model in MODELS])
|
407 |
+
ckpt_b = gr.Dropdown(label="Model B", choices=[model["model_name"] for model in MODELS])
|
408 |
+
refresh_button_fusion.click(update, outputs=[ckpt_a, ckpt_b])
|
409 |
+
alpha_a = gr.Slider(
|
410 |
+
minimum=0,
|
411 |
+
maximum=1,
|
412 |
+
label="Weight of the first model over the second",
|
413 |
+
value=0.5,
|
414 |
+
interactive=True,
|
415 |
+
)
|
416 |
+
with gr.Group():
|
417 |
+
with gr.Row():
|
418 |
+
sr_ = gr.Radio(
|
419 |
+
label="Sample rate of both models",
|
420 |
+
choices=["32k","40k", "48k"],
|
421 |
+
value="32k",
|
422 |
+
interactive=True,
|
423 |
+
)
|
424 |
+
if_f0_ = gr.Radio(
|
425 |
+
label="Pitch Guidance",
|
426 |
+
choices=["Yes", "Nah"],
|
427 |
+
value="Yes",
|
428 |
+
interactive=True,
|
429 |
+
)
|
430 |
+
info__ = gr.Textbox(
|
431 |
+
label="Add informations to the model",
|
432 |
+
value="",
|
433 |
+
max_lines=8,
|
434 |
+
interactive=True,
|
435 |
+
visible=False
|
436 |
+
)
|
437 |
+
name_to_save0 = gr.Textbox(
|
438 |
+
label="Final Model name",
|
439 |
+
value="",
|
440 |
+
max_lines=1,
|
441 |
+
interactive=True,
|
442 |
+
)
|
443 |
+
version_2 = gr.Radio(
|
444 |
+
label="Versions of the models",
|
445 |
+
choices=["v1", "v2"],
|
446 |
+
value="v2",
|
447 |
+
interactive=True,
|
448 |
+
)
|
449 |
+
with gr.Group():
|
450 |
+
with gr.Row():
|
451 |
+
but6 = gr.Button("Fuse the two models", variant="primary")
|
452 |
+
info4 = gr.Textbox(label="Output", value="", max_lines=8)
|
453 |
+
but6.click(
|
454 |
+
merge,
|
455 |
+
[ckpt_a,ckpt_b,alpha_a,sr_,if_f0_,info__,name_to_save0,version_2,],info4,api_name="ckpt_merge",)
|
456 |
+
|
457 |
+
with gr.Accordion("Model Quantization", open=False):
|
458 |
+
gr.Markdown("Quantize the model to a lower precision. - soon™ or never™ 😎")
|
459 |
+
|
460 |
+
with gr.Accordion("Debug", open=False):
|
461 |
+
def json_to_markdown_table(json_data):
|
462 |
+
table = "| Key | Value |\n| --- | --- |\n"
|
463 |
+
for key, value in json_data.items():
|
464 |
+
table += f"| {key} | {value} |\n"
|
465 |
+
return table
|
466 |
+
gr.Markdown("View the models that are currently loaded in the instance.")
|
467 |
+
|
468 |
+
gr.Markdown(json_to_markdown_table({"Models": len(MODELS), "UVR Models": len(UVR_5_MODELS)}))
|
469 |
+
|
470 |
+
gr.Markdown("View the current status of the instance.")
|
471 |
+
status = {
|
472 |
+
"Status": "Running", # duh lol
|
473 |
+
"Models": len(MODELS),
|
474 |
+
"UVR Models": len(UVR_5_MODELS),
|
475 |
+
"CPU Usage": f"{psutil.cpu_percent()}%",
|
476 |
+
"RAM Usage": f"{psutil.virtual_memory().percent}%",
|
477 |
+
"CPU": f"{cpuinfo.get_cpu_info()['brand_raw']}",
|
478 |
+
"System Uptime": f"{round(time.time() - psutil.boot_time(), 2)} seconds",
|
479 |
+
"System Load Average": f"{psutil.getloadavg()}",
|
480 |
+
"====================": "====================",
|
481 |
+
"CPU Cores": psutil.cpu_count(),
|
482 |
+
"CPU Threads": psutil.cpu_count(logical=True),
|
483 |
+
"RAM Total": f"{round(psutil.virtual_memory().total / 1024**3, 2)} GB",
|
484 |
+
"RAM Used": f"{round(psutil.virtual_memory().used / 1024**3, 2)} GB",
|
485 |
+
"CPU Frequency": f"{psutil.cpu_freq().current} MHz",
|
486 |
+
"====================": "====================",
|
487 |
+
"GPU": "A100 - Do a request (Inference, you won't see it either way)",
|
488 |
+
}
|
489 |
+
gr.Markdown(json_to_markdown_table(status))
|
490 |
+
|
491 |
+
with gr.Tab("Credits"):
|
492 |
+
gr.Markdown(
|
493 |
+
"""
|
494 |
+
Ilaria RVC made by [Ilaria](https://huggingface.co/TheStinger) suport her on [ko-fi](https://ko-fi.com/ilariaowo)
|
495 |
+
|
496 |
+
The Inference code is made by [r3gm](https://huggingface.co/r3gm) (his module helped form this space 💖)
|
497 |
+
|
498 |
+
made with ❤️ by [mikus](https://github.com/cappuch) - made the ui!
|
499 |
+
|
500 |
+
## In loving memory of JLabDX 🕊️
|
501 |
+
"""
|
502 |
+
)
|
503 |
+
with gr.Tab(("")):
|
504 |
+
gr.Markdown('''
|
505 |
+
![ilaria](https://i.ytimg.com/vi/5PWqt2Wg-us/maxresdefault.jpg)
|
506 |
+
''')
|
507 |
+
|
508 |
+
demo.queue(api_open=False).launch(show_api=False)
|