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import subprocess, torch, os, traceback, sys, warnings, shutil, numpy as np |
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from mega import Mega |
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os.environ["no_proxy"] = "localhost, 127.0.0.1, ::1" |
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import threading |
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from time import sleep |
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from subprocess import Popen |
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import faiss |
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from random import shuffle |
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import json, datetime, requests |
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from gtts import gTTS |
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now_dir = os.getcwd() |
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sys.path.append(now_dir) |
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tmp = os.path.join(now_dir, "TEMP") |
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shutil.rmtree(tmp, ignore_errors=True) |
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shutil.rmtree("%s/runtime/Lib/site-packages/infer_pack" % (now_dir), ignore_errors=True) |
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os.makedirs(tmp, exist_ok=True) |
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os.makedirs(os.path.join(now_dir, "logs"), exist_ok=True) |
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os.makedirs(os.path.join(now_dir, "weights"), exist_ok=True) |
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os.environ["TEMP"] = tmp |
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warnings.filterwarnings("ignore") |
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torch.manual_seed(114514) |
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from i18n import I18nAuto |
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|
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import edge_tts, asyncio |
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from ilariatts import tts_order_voice |
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language_dict = tts_order_voice |
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ilariavoices = language_dict.keys() |
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|
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import signal |
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|
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import math |
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|
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from utils import load_audio, CSVutil |
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|
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global DoFormant, Quefrency, Timbre |
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|
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if not os.path.isdir('csvdb/'): |
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os.makedirs('csvdb') |
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frmnt, stp = open("csvdb/formanting.csv", 'w'), open("csvdb/stop.csv", 'w') |
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frmnt.close() |
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stp.close() |
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|
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try: |
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DoFormant, Quefrency, Timbre = CSVutil('csvdb/formanting.csv', 'r', 'formanting') |
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DoFormant = ( |
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lambda DoFormant: True if DoFormant.lower() == 'true' else (False if DoFormant.lower() == 'false' else DoFormant) |
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)(DoFormant) |
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except (ValueError, TypeError, IndexError): |
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DoFormant, Quefrency, Timbre = False, 1.0, 1.0 |
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CSVutil('csvdb/formanting.csv', 'w+', 'formanting', DoFormant, Quefrency, Timbre) |
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|
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def download_models(): |
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|
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if not os.path.isfile('./hubert_base.pt'): |
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response = requests.get('https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt') |
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|
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if response.status_code == 200: |
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with open('./hubert_base.pt', 'wb') as f: |
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f.write(response.content) |
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print("Downloaded hubert base model file successfully. File saved to ./hubert_base.pt.") |
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else: |
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raise Exception("Failed to download hubert base model file. Status code: " + str(response.status_code) + ".") |
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|
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|
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if not os.path.isfile('./rmvpe.pt'): |
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response = requests.get('https://drive.usercontent.google.com/download?id=1Hkn4kNuVFRCNQwyxQFRtmzmMBGpQxptI&export=download&authuser=0&confirm=t&uuid=0b3a40de-465b-4c65-8c41-135b0b45c3f7&at=APZUnTV3lA3LnyTbeuduura6Dmi2:1693724254058') |
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|
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if response.status_code == 200: |
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with open('./rmvpe.pt', 'wb') as f: |
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f.write(response.content) |
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print("Downloaded rmvpe model file successfully. File saved to ./rmvpe.pt.") |
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else: |
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raise Exception("Failed to download rmvpe model file. Status code: " + str(response.status_code) + ".") |
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|
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download_models() |
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|
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print("\n-------------------------------\nRVC v2 Easy GUI (Local Edition)\n-------------------------------\n") |
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|
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def formant_apply(qfrency, tmbre): |
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Quefrency = qfrency |
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Timbre = tmbre |
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DoFormant = True |
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CSVutil('csvdb/formanting.csv', 'w+', 'formanting', DoFormant, qfrency, tmbre) |
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|
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return ({"value": Quefrency, "__type__": "update"}, {"value": Timbre, "__type__": "update"}) |
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|
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def get_fshift_presets(): |
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fshift_presets_list = [] |
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for dirpath, _, filenames in os.walk("./formantshiftcfg/"): |
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for filename in filenames: |
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if filename.endswith(".txt"): |
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fshift_presets_list.append(os.path.join(dirpath,filename).replace('\\','/')) |
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|
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if len(fshift_presets_list) > 0: |
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return fshift_presets_list |
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else: |
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return '' |
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|
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def formant_enabled(cbox, qfrency, tmbre, frmntapply, formantpreset, formant_refresh_button): |
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|
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if (cbox): |
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|
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DoFormant = True |
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CSVutil('csvdb/formanting.csv', 'w+', 'formanting', DoFormant, qfrency, tmbre) |
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|
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return ( |
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{"value": True, "__type__": "update"}, |
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{"visible": True, "__type__": "update"}, |
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{"visible": True, "__type__": "update"}, |
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{"visible": True, "__type__": "update"}, |
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{"visible": True, "__type__": "update"}, |
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{"visible": True, "__type__": "update"}, |
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) |
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|
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else: |
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|
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DoFormant = False |
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CSVutil('csvdb/formanting.csv', 'w+', 'formanting', DoFormant, qfrency, tmbre) |
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|
|
|
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return ( |
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{"value": False, "__type__": "update"}, |
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{"visible": False, "__type__": "update"}, |
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{"visible": False, "__type__": "update"}, |
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{"visible": False, "__type__": "update"}, |
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{"visible": False, "__type__": "update"}, |
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{"visible": False, "__type__": "update"}, |
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{"visible": False, "__type__": "update"}, |
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) |
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|
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def preset_apply(preset, qfer, tmbr): |
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if str(preset) != '': |
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with open(str(preset), 'r') as p: |
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content = p.readlines() |
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qfer, tmbr = content[0].split('\n')[0], content[1] |
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|
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formant_apply(qfer, tmbr) |
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else: |
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pass |
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return ({"value": qfer, "__type__": "update"}, {"value": tmbr, "__type__": "update"}) |
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|
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def update_fshift_presets(preset, qfrency, tmbre): |
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|
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qfrency, tmbre = preset_apply(preset, qfrency, tmbre) |
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|
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if (str(preset) != ''): |
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with open(str(preset), 'r') as p: |
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content = p.readlines() |
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qfrency, tmbre = content[0].split('\n')[0], content[1] |
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|
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formant_apply(qfrency, tmbre) |
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else: |
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pass |
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return ( |
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{"choices": get_fshift_presets(), "__type__": "update"}, |
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{"value": qfrency, "__type__": "update"}, |
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{"value": tmbre, "__type__": "update"}, |
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) |
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|
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i18n = I18nAuto() |
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|
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|
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ngpu = torch.cuda.device_count() |
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gpu_infos = [] |
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mem = [] |
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if (not torch.cuda.is_available()) or ngpu == 0: |
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if_gpu_ok = False |
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else: |
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if_gpu_ok = False |
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for i in range(ngpu): |
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gpu_name = torch.cuda.get_device_name(i) |
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if ( |
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"10" in gpu_name |
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or "16" in gpu_name |
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or "20" in gpu_name |
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or "30" in gpu_name |
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or "40" in gpu_name |
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or "A2" in gpu_name.upper() |
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or "A3" in gpu_name.upper() |
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or "A4" in gpu_name.upper() |
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or "P4" in gpu_name.upper() |
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or "A50" in gpu_name.upper() |
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or "A60" in gpu_name.upper() |
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or "70" in gpu_name |
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or "80" in gpu_name |
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or "90" in gpu_name |
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or "M4" in gpu_name.upper() |
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or "T4" in gpu_name.upper() |
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or "TITAN" in gpu_name.upper() |
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): |
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if_gpu_ok = True |
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gpu_infos.append("%s\t%s" % (i, gpu_name)) |
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mem.append( |
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int( |
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torch.cuda.get_device_properties(i).total_memory |
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/ 1024 |
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/ 1024 |
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/ 1024 |
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+ 0.4 |
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) |
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) |
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if if_gpu_ok == True and len(gpu_infos) > 0: |
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gpu_info = "\n".join(gpu_infos) |
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default_batch_size = min(mem) // 2 |
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else: |
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gpu_info = i18n("很遗憾您这没有能用的显卡来支持您训练") |
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default_batch_size = 1 |
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gpus = "-".join([i[0] for i in gpu_infos]) |
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from lib.infer_pack.models import ( |
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SynthesizerTrnMs256NSFsid, |
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SynthesizerTrnMs256NSFsid_nono, |
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SynthesizerTrnMs768NSFsid, |
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SynthesizerTrnMs768NSFsid_nono, |
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) |
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import soundfile as sf |
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from fairseq import checkpoint_utils |
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import gradio as gr |
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import logging |
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from vc_infer_pipeline import VC |
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from config import Config |
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|
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config = Config() |
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|
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logging.getLogger("numba").setLevel(logging.WARNING) |
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|
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hubert_model = None |
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|
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def load_hubert(): |
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global hubert_model |
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models, _, _ = checkpoint_utils.load_model_ensemble_and_task( |
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["hubert_base.pt"], |
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suffix="", |
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) |
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hubert_model = models[0] |
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hubert_model = hubert_model.to(config.device) |
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if config.is_half: |
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hubert_model = hubert_model.half() |
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else: |
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hubert_model = hubert_model.float() |
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hubert_model.eval() |
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|
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|
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weight_root = "weights" |
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index_root = "logs" |
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names = [] |
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for name in os.listdir(weight_root): |
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if name.endswith(".pth"): |
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names.append(name) |
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index_paths = [] |
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for root, dirs, files in os.walk(index_root, topdown=False): |
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for name in files: |
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if name.endswith(".index") and "trained" not in name: |
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index_paths.append("%s/%s" % (root, name)) |
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|
|
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|
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def vc_single( |
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sid, |
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input_audio_path, |
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f0_up_key, |
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f0_file, |
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f0_method, |
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file_index, |
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|
|
|
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index_rate, |
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filter_radius, |
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resample_sr, |
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rms_mix_rate, |
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protect, |
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crepe_hop_length, |
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): |
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global tgt_sr, net_g, vc, hubert_model, version |
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if input_audio_path is None: |
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return "You need to upload an audio", None |
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f0_up_key = int(f0_up_key) |
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try: |
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audio = load_audio(input_audio_path, 16000, DoFormant, Quefrency, Timbre) |
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audio_max = np.abs(audio).max() / 0.95 |
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if audio_max > 1: |
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audio /= audio_max |
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times = [0, 0, 0] |
|
if hubert_model == None: |
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load_hubert() |
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if_f0 = cpt.get("f0", 1) |
|
file_index = ( |
|
( |
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file_index.strip(" ") |
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.strip('"') |
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.strip("\n") |
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.strip('"') |
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.strip(" ") |
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.replace("trained", "added") |
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) |
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) |
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|
|
|
|
|
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audio_opt = vc.pipeline( |
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hubert_model, |
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net_g, |
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sid, |
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audio, |
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input_audio_path, |
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times, |
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f0_up_key, |
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f0_method, |
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file_index, |
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|
|
index_rate, |
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if_f0, |
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filter_radius, |
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tgt_sr, |
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resample_sr, |
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rms_mix_rate, |
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version, |
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protect, |
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crepe_hop_length, |
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f0_file=f0_file, |
|
) |
|
if resample_sr >= 16000 and tgt_sr != resample_sr: |
|
tgt_sr = resample_sr |
|
index_info = ( |
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"Using index:%s." % file_index |
|
if os.path.exists(file_index) |
|
else "Index not used." |
|
) |
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return "Success.\n %s\nTime:\n npy:%ss, f0:%ss, infer:%ss" % ( |
|
index_info, |
|
times[0], |
|
times[1], |
|
times[2], |
|
), (tgt_sr, audio_opt) |
|
except: |
|
info = traceback.format_exc() |
|
print(info) |
|
return info, (None, None) |
|
|
|
|
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def vc_multi( |
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sid, |
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dir_path, |
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opt_root, |
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paths, |
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f0_up_key, |
|
f0_method, |
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file_index, |
|
file_index2, |
|
|
|
index_rate, |
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filter_radius, |
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resample_sr, |
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rms_mix_rate, |
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protect, |
|
format1, |
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crepe_hop_length, |
|
): |
|
try: |
|
dir_path = ( |
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dir_path.strip(" ").strip('"').strip("\n").strip('"').strip(" ") |
|
) |
|
opt_root = opt_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ") |
|
os.makedirs(opt_root, exist_ok=True) |
|
try: |
|
if dir_path != "": |
|
paths = [os.path.join(dir_path, name) for name in os.listdir(dir_path)] |
|
else: |
|
paths = [path.name for path in paths] |
|
except: |
|
traceback.print_exc() |
|
paths = [path.name for path in paths] |
|
infos = [] |
|
for path in paths: |
|
info, opt = vc_single( |
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sid, |
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path, |
|
f0_up_key, |
|
None, |
|
f0_method, |
|
file_index, |
|
|
|
index_rate, |
|
filter_radius, |
|
resample_sr, |
|
rms_mix_rate, |
|
protect, |
|
crepe_hop_length |
|
) |
|
if "Success" in info: |
|
try: |
|
tgt_sr, audio_opt = opt |
|
if format1 in ["wav", "flac"]: |
|
sf.write( |
|
"%s/%s.%s" % (opt_root, os.path.basename(path), format1), |
|
audio_opt, |
|
tgt_sr, |
|
) |
|
else: |
|
path = "%s/%s.wav" % (opt_root, os.path.basename(path)) |
|
sf.write( |
|
path, |
|
audio_opt, |
|
tgt_sr, |
|
) |
|
if os.path.exists(path): |
|
os.system( |
|
"ffmpeg -i %s -vn %s -q:a 2 -y" |
|
% (path, path[:-4] + ".%s" % format1) |
|
) |
|
except: |
|
info += traceback.format_exc() |
|
infos.append("%s->%s" % (os.path.basename(path), info)) |
|
yield "\n".join(infos) |
|
yield "\n".join(infos) |
|
except: |
|
yield traceback.format_exc() |
|
|
|
|
|
def get_vc(sid): |
|
global n_spk, tgt_sr, net_g, vc, cpt, version |
|
if sid == "" or sid == []: |
|
global hubert_model |
|
if hubert_model != None: |
|
print("clean_empty_cache") |
|
del net_g, n_spk, vc, hubert_model, tgt_sr |
|
hubert_model = net_g = n_spk = vc = hubert_model = tgt_sr = None |
|
if torch.cuda.is_available(): |
|
torch.cuda.empty_cache() |
|
|
|
if_f0 = cpt.get("f0", 1) |
|
version = cpt.get("version", "v1") |
|
if version == "v1": |
|
if if_f0 == 1: |
|
net_g = SynthesizerTrnMs256NSFsid( |
|
*cpt["config"], is_half=config.is_half |
|
) |
|
else: |
|
net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"]) |
|
elif version == "v2": |
|
if if_f0 == 1: |
|
net_g = SynthesizerTrnMs768NSFsid( |
|
*cpt["config"], is_half=config.is_half |
|
) |
|
else: |
|
net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"]) |
|
del net_g, cpt |
|
if torch.cuda.is_available(): |
|
torch.cuda.empty_cache() |
|
cpt = None |
|
return {"visible": False, "__type__": "update"} |
|
person = "%s/%s" % (weight_root, sid) |
|
print("loading %s" % person) |
|
cpt = torch.load(person, map_location="cpu") |
|
tgt_sr = cpt["config"][-1] |
|
cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] |
|
if_f0 = cpt.get("f0", 1) |
|
version = cpt.get("version", "v1") |
|
if version == "v1": |
|
if if_f0 == 1: |
|
net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half) |
|
else: |
|
net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"]) |
|
elif version == "v2": |
|
if if_f0 == 1: |
|
net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half) |
|
else: |
|
net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"]) |
|
del net_g.enc_q |
|
print(net_g.load_state_dict(cpt["weight"], strict=False)) |
|
net_g.eval().to(config.device) |
|
if config.is_half: |
|
net_g = net_g.half() |
|
else: |
|
net_g = net_g.float() |
|
vc = VC(tgt_sr, config) |
|
n_spk = cpt["config"][-3] |
|
return {"visible": False, "maximum": n_spk, "__type__": "update"} |
|
|
|
|
|
def change_choices(): |
|
names = [] |
|
for name in os.listdir(weight_root): |
|
if name.endswith(".pth"): |
|
names.append(name) |
|
index_paths = [] |
|
for root, dirs, files in os.walk(index_root, topdown=False): |
|
for name in files: |
|
if name.endswith(".index") and "trained" not in name: |
|
index_paths.append("%s/%s" % (root, name)) |
|
return {"choices": sorted(names), "__type__": "update"}, { |
|
"choices": sorted(index_paths), |
|
"__type__": "update", |
|
} |
|
|
|
|
|
def clean(): |
|
return {"value": "", "__type__": "update"} |
|
|
|
|
|
sr_dict = { |
|
"32k": 32000, |
|
"40k": 40000, |
|
"48k": 48000, |
|
} |
|
|
|
|
|
def if_done(done, p): |
|
while 1: |
|
if p.poll() == None: |
|
sleep(0.5) |
|
else: |
|
break |
|
done[0] = True |
|
|
|
|
|
def if_done_multi(done, ps): |
|
while 1: |
|
|
|
|
|
flag = 1 |
|
for p in ps: |
|
if p.poll() == None: |
|
flag = 0 |
|
sleep(0.5) |
|
break |
|
if flag == 1: |
|
break |
|
done[0] = True |
|
|
|
|
|
def preprocess_dataset(trainset_dir, exp_dir, sr, n_p): |
|
sr = sr_dict[sr] |
|
os.makedirs("%s/logs/%s" % (now_dir, exp_dir), exist_ok=True) |
|
f = open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "w") |
|
f.close() |
|
cmd = ( |
|
config.python_cmd |
|
+ " trainset_preprocess_pipeline_print.py %s %s %s %s/logs/%s " |
|
% (trainset_dir, sr, n_p, now_dir, exp_dir) |
|
+ str(config.noparallel) |
|
) |
|
print(cmd) |
|
p = Popen(cmd, shell=True) |
|
|
|
done = [False] |
|
threading.Thread( |
|
target=if_done, |
|
args=( |
|
done, |
|
p, |
|
), |
|
).start() |
|
while 1: |
|
with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "r") as f: |
|
yield (f.read()) |
|
sleep(1) |
|
if done[0] == True: |
|
break |
|
with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "r") as f: |
|
log = f.read() |
|
print(log) |
|
yield log |
|
|
|
|
|
def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19, echl): |
|
gpus = gpus.split("-") |
|
os.makedirs("%s/logs/%s" % (now_dir, exp_dir), exist_ok=True) |
|
f = open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "w") |
|
f.close() |
|
if if_f0: |
|
cmd = config.python_cmd + " extract_f0_print.py %s/logs/%s %s %s %s" % ( |
|
now_dir, |
|
exp_dir, |
|
n_p, |
|
f0method, |
|
echl, |
|
) |
|
print(cmd) |
|
p = Popen(cmd, shell=True, cwd=now_dir) |
|
|
|
done = [False] |
|
threading.Thread( |
|
target=if_done, |
|
args=( |
|
done, |
|
p, |
|
), |
|
).start() |
|
while 1: |
|
with open( |
|
"%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r" |
|
) as f: |
|
yield (f.read()) |
|
sleep(1) |
|
if done[0] == True: |
|
break |
|
with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f: |
|
log = f.read() |
|
print(log) |
|
yield log |
|
|
|
""" |
|
n_part=int(sys.argv[1]) |
|
i_part=int(sys.argv[2]) |
|
i_gpu=sys.argv[3] |
|
exp_dir=sys.argv[4] |
|
os.environ["CUDA_VISIBLE_DEVICES"]=str(i_gpu) |
|
""" |
|
leng = len(gpus) |
|
ps = [] |
|
for idx, n_g in enumerate(gpus): |
|
cmd = ( |
|
config.python_cmd |
|
+ " extract_feature_print.py %s %s %s %s %s/logs/%s %s" |
|
% ( |
|
config.device, |
|
leng, |
|
idx, |
|
n_g, |
|
now_dir, |
|
exp_dir, |
|
version19, |
|
) |
|
) |
|
print(cmd) |
|
p = Popen( |
|
cmd, shell=True, cwd=now_dir |
|
) |
|
ps.append(p) |
|
|
|
done = [False] |
|
threading.Thread( |
|
target=if_done_multi, |
|
args=( |
|
done, |
|
ps, |
|
), |
|
).start() |
|
while 1: |
|
with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f: |
|
yield (f.read()) |
|
sleep(1) |
|
if done[0] == True: |
|
break |
|
with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f: |
|
log = f.read() |
|
print(log) |
|
yield log |
|
|
|
|
|
def change_sr2(sr2, if_f0_3, version19): |
|
path_str = "" if version19 == "v1" else "_v2" |
|
f0_str = "f0" if if_f0_3 else "" |
|
if_pretrained_generator_exist = os.access("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK) |
|
if_pretrained_discriminator_exist = os.access("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK) |
|
if (if_pretrained_generator_exist == False): |
|
print("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model") |
|
if (if_pretrained_discriminator_exist == False): |
|
print("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model") |
|
return ( |
|
("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_generator_exist else "", |
|
("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_discriminator_exist else "", |
|
{"visible": True, "__type__": "update"} |
|
) |
|
|
|
def change_version19(sr2, if_f0_3, version19): |
|
path_str = "" if version19 == "v1" else "_v2" |
|
f0_str = "f0" if if_f0_3 else "" |
|
if_pretrained_generator_exist = os.access("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK) |
|
if_pretrained_discriminator_exist = os.access("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK) |
|
if (if_pretrained_generator_exist == False): |
|
print("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model") |
|
if (if_pretrained_discriminator_exist == False): |
|
print("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model") |
|
return ( |
|
("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_generator_exist else "", |
|
("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_discriminator_exist else "", |
|
) |
|
|
|
|
|
def change_f0(if_f0_3, sr2, version19): |
|
path_str = "" if version19 == "v1" else "_v2" |
|
if_pretrained_generator_exist = os.access("pretrained%s/f0G%s.pth" % (path_str, sr2), os.F_OK) |
|
if_pretrained_discriminator_exist = os.access("pretrained%s/f0D%s.pth" % (path_str, sr2), os.F_OK) |
|
if (if_pretrained_generator_exist == False): |
|
print("pretrained%s/f0G%s.pth" % (path_str, sr2), "not exist, will not use pretrained model") |
|
if (if_pretrained_discriminator_exist == False): |
|
print("pretrained%s/f0D%s.pth" % (path_str, sr2), "not exist, will not use pretrained model") |
|
if if_f0_3: |
|
return ( |
|
{"visible": True, "__type__": "update"}, |
|
"pretrained%s/f0G%s.pth" % (path_str, sr2) if if_pretrained_generator_exist else "", |
|
"pretrained%s/f0D%s.pth" % (path_str, sr2) if if_pretrained_discriminator_exist else "", |
|
) |
|
return ( |
|
{"visible": False, "__type__": "update"}, |
|
("pretrained%s/G%s.pth" % (path_str, sr2)) if if_pretrained_generator_exist else "", |
|
("pretrained%s/D%s.pth" % (path_str, sr2)) if if_pretrained_discriminator_exist else "", |
|
) |
|
|
|
|
|
global log_interval |
|
|
|
|
|
def set_log_interval(exp_dir, batch_size12): |
|
log_interval = 1 |
|
|
|
folder_path = os.path.join(exp_dir, "1_16k_wavs") |
|
|
|
if os.path.exists(folder_path) and os.path.isdir(folder_path): |
|
wav_files = [f for f in os.listdir(folder_path) if f.endswith(".wav")] |
|
if wav_files: |
|
sample_size = len(wav_files) |
|
log_interval = math.ceil(sample_size / batch_size12) |
|
if log_interval > 1: |
|
log_interval += 1 |
|
return log_interval |
|
|
|
|
|
def click_train( |
|
exp_dir1, |
|
sr2, |
|
if_f0_3, |
|
spk_id5, |
|
save_epoch10, |
|
total_epoch11, |
|
batch_size12, |
|
if_save_latest13, |
|
pretrained_G14, |
|
pretrained_D15, |
|
gpus16, |
|
if_cache_gpu17, |
|
if_save_every_weights18, |
|
version19, |
|
): |
|
CSVutil('csvdb/stop.csv', 'w+', 'formanting', False) |
|
|
|
exp_dir = "%s/logs/%s" % (now_dir, exp_dir1) |
|
os.makedirs(exp_dir, exist_ok=True) |
|
gt_wavs_dir = "%s/0_gt_wavs" % (exp_dir) |
|
feature_dir = ( |
|
"%s/3_feature256" % (exp_dir) |
|
if version19 == "v1" |
|
else "%s/3_feature768" % (exp_dir) |
|
) |
|
|
|
log_interval = set_log_interval(exp_dir, batch_size12) |
|
|
|
if if_f0_3: |
|
f0_dir = "%s/2a_f0" % (exp_dir) |
|
f0nsf_dir = "%s/2b-f0nsf" % (exp_dir) |
|
names = ( |
|
set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)]) |
|
& set([name.split(".")[0] for name in os.listdir(feature_dir)]) |
|
& set([name.split(".")[0] for name in os.listdir(f0_dir)]) |
|
& set([name.split(".")[0] for name in os.listdir(f0nsf_dir)]) |
|
) |
|
else: |
|
names = set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)]) & set( |
|
[name.split(".")[0] for name in os.listdir(feature_dir)] |
|
) |
|
opt = [] |
|
for name in names: |
|
if if_f0_3: |
|
opt.append( |
|
"%s/%s.wav|%s/%s.npy|%s/%s.wav.npy|%s/%s.wav.npy|%s" |
|
% ( |
|
gt_wavs_dir.replace("\\", "\\\\"), |
|
name, |
|
feature_dir.replace("\\", "\\\\"), |
|
name, |
|
f0_dir.replace("\\", "\\\\"), |
|
name, |
|
f0nsf_dir.replace("\\", "\\\\"), |
|
name, |
|
spk_id5, |
|
) |
|
) |
|
else: |
|
opt.append( |
|
"%s/%s.wav|%s/%s.npy|%s" |
|
% ( |
|
gt_wavs_dir.replace("\\", "\\\\"), |
|
name, |
|
feature_dir.replace("\\", "\\\\"), |
|
name, |
|
spk_id5, |
|
) |
|
) |
|
fea_dim = 256 if version19 == "v1" else 768 |
|
if if_f0_3: |
|
for _ in range(2): |
|
opt.append( |
|
"%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s" |
|
% (now_dir, sr2, now_dir, fea_dim, now_dir, now_dir, spk_id5) |
|
) |
|
else: |
|
for _ in range(2): |
|
opt.append( |
|
"%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s" |
|
% (now_dir, sr2, now_dir, fea_dim, spk_id5) |
|
) |
|
shuffle(opt) |
|
with open("%s/filelist.txt" % exp_dir, "w") as f: |
|
f.write("\n".join(opt)) |
|
print("write filelist done") |
|
|
|
|
|
print("use gpus:", gpus16) |
|
if pretrained_G14 == "": |
|
print("no pretrained Generator") |
|
if pretrained_D15 == "": |
|
print("no pretrained Discriminator") |
|
if gpus16: |
|
cmd = ( |
|
config.python_cmd |
|
+ " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s -li %s" |
|
% ( |
|
exp_dir1, |
|
sr2, |
|
1 if if_f0_3 else 0, |
|
batch_size12, |
|
gpus16, |
|
total_epoch11, |
|
save_epoch10, |
|
("-pg %s" % pretrained_G14) if pretrained_G14 != "" else "", |
|
("-pd %s" % pretrained_D15) if pretrained_D15 != "" else "", |
|
1 if if_save_latest13 == True else 0, |
|
1 if if_cache_gpu17 == True else 0, |
|
1 if if_save_every_weights18 == True else 0, |
|
version19, |
|
log_interval, |
|
) |
|
) |
|
else: |
|
cmd = ( |
|
config.python_cmd |
|
+ " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s -li %s" |
|
% ( |
|
exp_dir1, |
|
sr2, |
|
1 if if_f0_3 else 0, |
|
batch_size12, |
|
total_epoch11, |
|
save_epoch10, |
|
("-pg %s" % pretrained_G14) if pretrained_G14 != "" else "\b", |
|
("-pd %s" % pretrained_D15) if pretrained_D15 != "" else "\b", |
|
1 if if_save_latest13 == True else 0, |
|
1 if if_cache_gpu17 == True else 0, |
|
1 if if_save_every_weights18 == True else 0, |
|
version19, |
|
log_interval, |
|
) |
|
) |
|
print(cmd) |
|
p = Popen(cmd, shell=True, cwd=now_dir) |
|
global PID |
|
PID = p.pid |
|
p.wait() |
|
return ("训练结束, 您可查看控制台训练日志或实验文件夹下的train.log", {"visible": False, "__type__": "update"}, {"visible": True, "__type__": "update"}) |
|
|
|
|
|
|
|
def train_index(exp_dir1, version19): |
|
exp_dir = "%s/logs/%s" % (now_dir, exp_dir1) |
|
os.makedirs(exp_dir, exist_ok=True) |
|
feature_dir = ( |
|
"%s/3_feature256" % (exp_dir) |
|
if version19 == "v1" |
|
else "%s/3_feature768" % (exp_dir) |
|
) |
|
if os.path.exists(feature_dir) == False: |
|
return "请先进行特征提取!" |
|
listdir_res = list(os.listdir(feature_dir)) |
|
if len(listdir_res) == 0: |
|
return "请先进行特征提取!" |
|
npys = [] |
|
for name in sorted(listdir_res): |
|
phone = np.load("%s/%s" % (feature_dir, name)) |
|
npys.append(phone) |
|
big_npy = np.concatenate(npys, 0) |
|
big_npy_idx = np.arange(big_npy.shape[0]) |
|
np.random.shuffle(big_npy_idx) |
|
big_npy = big_npy[big_npy_idx] |
|
np.save("%s/total_fea.npy" % exp_dir, big_npy) |
|
|
|
n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39) |
|
infos = [] |
|
infos.append("%s,%s" % (big_npy.shape, n_ivf)) |
|
yield "\n".join(infos) |
|
index = faiss.index_factory(256 if version19 == "v1" else 768, "IVF%s,Flat" % n_ivf) |
|
|
|
infos.append("training") |
|
yield "\n".join(infos) |
|
index_ivf = faiss.extract_index_ivf(index) |
|
index_ivf.nprobe = 1 |
|
index.train(big_npy) |
|
faiss.write_index( |
|
index, |
|
"%s/trained_IVF%s_Flat_nprobe_%s_%s_%s.index" |
|
% (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19), |
|
) |
|
|
|
infos.append("adding") |
|
yield "\n".join(infos) |
|
batch_size_add = 8192 |
|
for i in range(0, big_npy.shape[0], batch_size_add): |
|
index.add(big_npy[i : i + batch_size_add]) |
|
faiss.write_index( |
|
index, |
|
"%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index" |
|
% (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19), |
|
) |
|
infos.append( |
|
"成功构建索引,added_IVF%s_Flat_nprobe_%s_%s_%s.index" |
|
% (n_ivf, index_ivf.nprobe, exp_dir1, version19) |
|
) |
|
|
|
|
|
yield "\n".join(infos) |
|
|
|
|
|
|
|
def train1key( |
|
exp_dir1, |
|
sr2, |
|
if_f0_3, |
|
trainset_dir4, |
|
spk_id5, |
|
np7, |
|
f0method8, |
|
save_epoch10, |
|
total_epoch11, |
|
batch_size12, |
|
if_save_latest13, |
|
pretrained_G14, |
|
pretrained_D15, |
|
gpus16, |
|
if_cache_gpu17, |
|
if_save_every_weights18, |
|
version19, |
|
echl |
|
): |
|
infos = [] |
|
|
|
def get_info_str(strr): |
|
infos.append(strr) |
|
return "\n".join(infos) |
|
|
|
model_log_dir = "%s/logs/%s" % (now_dir, exp_dir1) |
|
preprocess_log_path = "%s/preprocess.log" % model_log_dir |
|
extract_f0_feature_log_path = "%s/extract_f0_feature.log" % model_log_dir |
|
gt_wavs_dir = "%s/0_gt_wavs" % model_log_dir |
|
feature_dir = ( |
|
"%s/3_feature256" % model_log_dir |
|
if version19 == "v1" |
|
else "%s/3_feature768" % model_log_dir |
|
) |
|
|
|
os.makedirs(model_log_dir, exist_ok=True) |
|
|
|
open(preprocess_log_path, "w").close() |
|
cmd = ( |
|
config.python_cmd |
|
+ " trainset_preprocess_pipeline_print.py %s %s %s %s " |
|
% (trainset_dir4, sr_dict[sr2], np7, model_log_dir) |
|
+ str(config.noparallel) |
|
) |
|
yield get_info_str(i18n("step1:正在处理数据")) |
|
yield get_info_str(cmd) |
|
p = Popen(cmd, shell=True) |
|
p.wait() |
|
with open(preprocess_log_path, "r") as f: |
|
print(f.read()) |
|
|
|
open(extract_f0_feature_log_path, "w") |
|
if if_f0_3: |
|
yield get_info_str("step2a:正在提取音高") |
|
cmd = config.python_cmd + " extract_f0_print.py %s %s %s %s" % ( |
|
model_log_dir, |
|
np7, |
|
f0method8, |
|
echl |
|
) |
|
yield get_info_str(cmd) |
|
p = Popen(cmd, shell=True, cwd=now_dir) |
|
p.wait() |
|
with open(extract_f0_feature_log_path, "r") as f: |
|
print(f.read()) |
|
else: |
|
yield get_info_str(i18n("step2a:无需提取音高")) |
|
|
|
yield get_info_str(i18n("step2b:正在提取特征")) |
|
gpus = gpus16.split("-") |
|
leng = len(gpus) |
|
ps = [] |
|
for idx, n_g in enumerate(gpus): |
|
cmd = config.python_cmd + " extract_feature_print.py %s %s %s %s %s %s" % ( |
|
config.device, |
|
leng, |
|
idx, |
|
n_g, |
|
model_log_dir, |
|
version19, |
|
) |
|
yield get_info_str(cmd) |
|
p = Popen( |
|
cmd, shell=True, cwd=now_dir |
|
) |
|
ps.append(p) |
|
for p in ps: |
|
p.wait() |
|
with open(extract_f0_feature_log_path, "r") as f: |
|
print(f.read()) |
|
|
|
yield get_info_str(i18n("step3a:正在训练模型")) |
|
|
|
if if_f0_3: |
|
f0_dir = "%s/2a_f0" % model_log_dir |
|
f0nsf_dir = "%s/2b-f0nsf" % model_log_dir |
|
names = ( |
|
set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)]) |
|
& set([name.split(".")[0] for name in os.listdir(feature_dir)]) |
|
& set([name.split(".")[0] for name in os.listdir(f0_dir)]) |
|
& set([name.split(".")[0] for name in os.listdir(f0nsf_dir)]) |
|
) |
|
else: |
|
names = set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)]) & set( |
|
[name.split(".")[0] for name in os.listdir(feature_dir)] |
|
) |
|
opt = [] |
|
for name in names: |
|
if if_f0_3: |
|
opt.append( |
|
"%s/%s.wav|%s/%s.npy|%s/%s.wav.npy|%s/%s.wav.npy|%s" |
|
% ( |
|
gt_wavs_dir.replace("\\", "\\\\"), |
|
name, |
|
feature_dir.replace("\\", "\\\\"), |
|
name, |
|
f0_dir.replace("\\", "\\\\"), |
|
name, |
|
f0nsf_dir.replace("\\", "\\\\"), |
|
name, |
|
spk_id5, |
|
) |
|
) |
|
else: |
|
opt.append( |
|
"%s/%s.wav|%s/%s.npy|%s" |
|
% ( |
|
gt_wavs_dir.replace("\\", "\\\\"), |
|
name, |
|
feature_dir.replace("\\", "\\\\"), |
|
name, |
|
spk_id5, |
|
) |
|
) |
|
fea_dim = 256 if version19 == "v1" else 768 |
|
if if_f0_3: |
|
for _ in range(2): |
|
opt.append( |
|
"%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s" |
|
% (now_dir, sr2, now_dir, fea_dim, now_dir, now_dir, spk_id5) |
|
) |
|
else: |
|
for _ in range(2): |
|
opt.append( |
|
"%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s" |
|
% (now_dir, sr2, now_dir, fea_dim, spk_id5) |
|
) |
|
shuffle(opt) |
|
with open("%s/filelist.txt" % model_log_dir, "w") as f: |
|
f.write("\n".join(opt)) |
|
yield get_info_str("write filelist done") |
|
if gpus16: |
|
cmd = ( |
|
config.python_cmd |
|
+" train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s" |
|
% ( |
|
exp_dir1, |
|
sr2, |
|
1 if if_f0_3 else 0, |
|
batch_size12, |
|
gpus16, |
|
total_epoch11, |
|
save_epoch10, |
|
("-pg %s" % pretrained_G14) if pretrained_G14 != "" else "", |
|
("-pd %s" % pretrained_D15) if pretrained_D15 != "" else "", |
|
1 if if_save_latest13 == True else 0, |
|
1 if if_cache_gpu17 == True else 0, |
|
1 if if_save_every_weights18 == True else 0, |
|
version19, |
|
) |
|
) |
|
else: |
|
cmd = ( |
|
config.python_cmd |
|
+ " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s" |
|
% ( |
|
exp_dir1, |
|
sr2, |
|
1 if if_f0_3 else 0, |
|
batch_size12, |
|
total_epoch11, |
|
save_epoch10, |
|
("-pg %s" % pretrained_G14) if pretrained_G14 != "" else "", |
|
("-pd %s" % pretrained_D15) if pretrained_D15 != "" else "", |
|
1 if if_save_latest13 == True else 0, |
|
1 if if_cache_gpu17 == True else 0, |
|
1 if if_save_every_weights18 == True else 0, |
|
version19, |
|
) |
|
) |
|
yield get_info_str(cmd) |
|
p = Popen(cmd, shell=True, cwd=now_dir) |
|
p.wait() |
|
yield get_info_str(i18n("训练结束, 您可查看控制台训练日志或实验文件夹下的train.log")) |
|
|
|
npys = [] |
|
listdir_res = list(os.listdir(feature_dir)) |
|
for name in sorted(listdir_res): |
|
phone = np.load("%s/%s" % (feature_dir, name)) |
|
npys.append(phone) |
|
big_npy = np.concatenate(npys, 0) |
|
|
|
big_npy_idx = np.arange(big_npy.shape[0]) |
|
np.random.shuffle(big_npy_idx) |
|
big_npy = big_npy[big_npy_idx] |
|
np.save("%s/total_fea.npy" % model_log_dir, big_npy) |
|
|
|
|
|
n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39) |
|
yield get_info_str("%s,%s" % (big_npy.shape, n_ivf)) |
|
index = faiss.index_factory(256 if version19 == "v1" else 768, "IVF%s,Flat" % n_ivf) |
|
yield get_info_str("training index") |
|
index_ivf = faiss.extract_index_ivf(index) |
|
index_ivf.nprobe = 1 |
|
index.train(big_npy) |
|
faiss.write_index( |
|
index, |
|
"%s/trained_IVF%s_Flat_nprobe_%s_%s_%s.index" |
|
% (model_log_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19), |
|
) |
|
yield get_info_str("adding index") |
|
batch_size_add = 8192 |
|
for i in range(0, big_npy.shape[0], batch_size_add): |
|
index.add(big_npy[i : i + batch_size_add]) |
|
faiss.write_index( |
|
index, |
|
"%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index" |
|
% (model_log_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19), |
|
) |
|
yield get_info_str( |
|
"成功构建索引, added_IVF%s_Flat_nprobe_%s_%s_%s.index" |
|
% (n_ivf, index_ivf.nprobe, exp_dir1, version19) |
|
) |
|
yield get_info_str(i18n("全流程结束!")) |
|
|
|
|
|
def whethercrepeornah(radio): |
|
mango = True if radio == 'mangio-crepe' or radio == 'mangio-crepe-tiny' else False |
|
return ({"visible": mango, "__type__": "update"}) |
|
|
|
|
|
def change_info_(ckpt_path): |
|
if ( |
|
os.path.exists(ckpt_path.replace(os.path.basename(ckpt_path), "train.log")) |
|
== False |
|
): |
|
return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"} |
|
try: |
|
with open( |
|
ckpt_path.replace(os.path.basename(ckpt_path), "train.log"), "r" |
|
) as f: |
|
info = eval(f.read().strip("\n").split("\n")[0].split("\t")[-1]) |
|
sr, f0 = info["sample_rate"], info["if_f0"] |
|
version = "v2" if ("version" in info and info["version"] == "v2") else "v1" |
|
return sr, str(f0), version |
|
except: |
|
traceback.print_exc() |
|
return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"} |
|
|
|
|
|
from lib.infer_pack.models_onnx import SynthesizerTrnMsNSFsidM |
|
|
|
|
|
def export_onnx(ModelPath, ExportedPath, MoeVS=True): |
|
cpt = torch.load(ModelPath, map_location="cpu") |
|
cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] |
|
hidden_channels = 256 if cpt.get("version","v1")=="v1"else 768 |
|
|
|
test_phone = torch.rand(1, 200, hidden_channels) |
|
test_phone_lengths = torch.tensor([200]).long() |
|
test_pitch = torch.randint(size=(1, 200), low=5, high=255) |
|
test_pitchf = torch.rand(1, 200) |
|
test_ds = torch.LongTensor([0]) |
|
test_rnd = torch.rand(1, 192, 200) |
|
|
|
device = "cpu" |
|
|
|
|
|
net_g = SynthesizerTrnMsNSFsidM( |
|
*cpt["config"], is_half=False,version=cpt.get("version","v1") |
|
) |
|
net_g.load_state_dict(cpt["weight"], strict=False) |
|
input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds", "rnd"] |
|
output_names = [ |
|
"audio", |
|
] |
|
|
|
torch.onnx.export( |
|
net_g, |
|
( |
|
test_phone.to(device), |
|
test_phone_lengths.to(device), |
|
test_pitch.to(device), |
|
test_pitchf.to(device), |
|
test_ds.to(device), |
|
test_rnd.to(device), |
|
), |
|
ExportedPath, |
|
dynamic_axes={ |
|
"phone": [1], |
|
"pitch": [1], |
|
"pitchf": [1], |
|
"rnd": [2], |
|
}, |
|
do_constant_folding=False, |
|
opset_version=16, |
|
verbose=False, |
|
input_names=input_names, |
|
output_names=output_names, |
|
) |
|
return "Finished" |
|
|
|
|
|
|
|
def get_presets(): |
|
data = None |
|
with open('../inference-presets.json', 'r') as file: |
|
data = json.load(file) |
|
preset_names = [] |
|
for preset in data['presets']: |
|
preset_names.append(preset['name']) |
|
|
|
return preset_names |
|
|
|
def change_choices2(): |
|
audio_files=[] |
|
for filename in os.listdir("./audios"): |
|
if filename.endswith(('.wav','.mp3','.ogg','.flac','.m4a','.aac','.mp4')): |
|
audio_files.append(os.path.join('./audios',filename).replace('\\', '/')) |
|
return {"choices": sorted(audio_files), "__type__": "update"}, {"__type__": "update"} |
|
|
|
audio_files=[] |
|
for filename in os.listdir("./audios"): |
|
if filename.endswith(('.wav','.mp3','.ogg','.flac','.m4a','.aac','.mp4')): |
|
audio_files.append(os.path.join('./audios',filename).replace('\\', '/')) |
|
|
|
def get_index(): |
|
if check_for_name() != '': |
|
chosen_model=sorted(names)[0].split(".")[0] |
|
logs_path="./logs/"+chosen_model |
|
if os.path.exists(logs_path): |
|
for file in os.listdir(logs_path): |
|
if file.endswith(".index"): |
|
return os.path.join(logs_path, file) |
|
return '' |
|
else: |
|
return '' |
|
|
|
def get_indexes(): |
|
indexes_list=[] |
|
for dirpath, dirnames, filenames in os.walk("./logs/"): |
|
for filename in filenames: |
|
if filename.endswith(".index"): |
|
indexes_list.append(os.path.join(dirpath,filename)) |
|
if len(indexes_list) > 0: |
|
return indexes_list |
|
else: |
|
return '' |
|
|
|
def get_name(): |
|
if len(audio_files) > 0: |
|
return sorted(audio_files)[0] |
|
else: |
|
return '' |
|
|
|
def save_to_wav(record_button): |
|
if record_button is None: |
|
pass |
|
else: |
|
path_to_file=record_button |
|
new_name = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")+'.wav' |
|
new_path='./audios/'+new_name |
|
shutil.move(path_to_file,new_path) |
|
return new_path |
|
|
|
def save_to_wav2(dropbox): |
|
file_path=dropbox.name |
|
shutil.move(file_path,'./audios') |
|
return os.path.join('./audios',os.path.basename(file_path)) |
|
|
|
def match_index(sid0): |
|
folder=sid0.split(".")[0] |
|
parent_dir="./logs/"+folder |
|
if os.path.exists(parent_dir): |
|
for filename in os.listdir(parent_dir): |
|
if filename.endswith(".index"): |
|
index_path=os.path.join(parent_dir,filename) |
|
return index_path |
|
else: |
|
return '' |
|
|
|
def check_for_name(): |
|
if len(names) > 0: |
|
return sorted(names)[0] |
|
else: |
|
return '' |
|
|
|
def download_from_url(url, model): |
|
if url == '': |
|
return "URL cannot be left empty." |
|
if model =='': |
|
return "You need to name your model. For example: My-Model" |
|
url = url.strip() |
|
zip_dirs = ["zips", "unzips"] |
|
for directory in zip_dirs: |
|
if os.path.exists(directory): |
|
shutil.rmtree(directory) |
|
os.makedirs("zips", exist_ok=True) |
|
os.makedirs("unzips", exist_ok=True) |
|
zipfile = model + '.zip' |
|
zipfile_path = './zips/' + zipfile |
|
try: |
|
if "drive.google.com" in url: |
|
subprocess.run(["gdown", url, "--fuzzy", "-O", zipfile_path]) |
|
elif "mega.nz" in url: |
|
m = Mega() |
|
m.download_url(url, './zips') |
|
else: |
|
subprocess.run(["wget", url, "-O", zipfile_path]) |
|
for filename in os.listdir("./zips"): |
|
if filename.endswith(".zip"): |
|
zipfile_path = os.path.join("./zips/",filename) |
|
shutil.unpack_archive(zipfile_path, "./unzips", 'zip') |
|
else: |
|
return "No zipfile found." |
|
for root, dirs, files in os.walk('./unzips'): |
|
for file in files: |
|
file_path = os.path.join(root, file) |
|
if file.endswith(".index"): |
|
os.mkdir(f'./logs/{model}') |
|
shutil.copy2(file_path,f'./logs/{model}') |
|
elif "G_" not in file and "D_" not in file and file.endswith(".pth"): |
|
shutil.copy(file_path,f'./weights/{model}.pth') |
|
shutil.rmtree("zips") |
|
shutil.rmtree("unzips") |
|
return "Model downloaded, you can go back to the inference page!" |
|
except: |
|
return "ERROR - The download failed. Check if the link is valid." |
|
def success_message(face): |
|
return f'{face.name} has been uploaded.', 'None' |
|
def mouth(size, face, voice, faces): |
|
if size == 'Half': |
|
size = 2 |
|
else: |
|
size = 1 |
|
if faces == 'None': |
|
character = face.name |
|
else: |
|
if faces == 'Ben Shapiro': |
|
character = '/content/wav2lip-HD/inputs/ben-shapiro-10.mp4' |
|
elif faces == 'Andrew Tate': |
|
character = '/content/wav2lip-HD/inputs/tate-7.mp4' |
|
command = "python inference.py " \ |
|
"--checkpoint_path checkpoints/wav2lip.pth " \ |
|
f"--face {character} " \ |
|
f"--audio {voice} " \ |
|
"--pads 0 20 0 0 " \ |
|
"--outfile /content/wav2lip-HD/outputs/result.mp4 " \ |
|
"--fps 24 " \ |
|
f"--resize_factor {size}" |
|
process = subprocess.Popen(command, shell=True, cwd='/content/wav2lip-HD/Wav2Lip-master') |
|
stdout, stderr = process.communicate() |
|
return '/content/wav2lip-HD/outputs/result.mp4', 'Animation completed.' |
|
eleven_voices = ['Adam','Antoni','Josh','Arnold','Sam','Bella','Rachel','Domi','Elli'] |
|
eleven_voices_ids=['pNInz6obpgDQGcFmaJgB','ErXwobaYiN019PkySvjV','TxGEqnHWrfWFTfGW9XjX','VR6AewLTigWG4xSOukaG','yoZ06aMxZJJ28mfd3POQ','EXAVITQu4vr4xnSDxMaL','21m00Tcm4TlvDq8ikWAM','AZnzlk1XvdvUeBnXmlld','MF3mGyEYCl7XYWbV9V6O'] |
|
chosen_voice = dict(zip(eleven_voices, eleven_voices_ids)) |
|
|
|
def stoptraining(mim): |
|
if int(mim) == 1: |
|
try: |
|
CSVutil('csvdb/stop.csv', 'w+', 'stop', 'True') |
|
os.kill(PID, signal.SIGTERM) |
|
except Exception as e: |
|
print(f"Couldn't click due to {e}") |
|
return ( |
|
{"visible": False, "__type__": "update"}, |
|
{"visible": True, "__type__": "update"}, |
|
) |
|
|
|
|
|
def elevenTTS(xiapi, text, id, lang): |
|
if xiapi!= '' and id !='': |
|
choice = chosen_voice[id] |
|
CHUNK_SIZE = 1024 |
|
url = f"https://api.elevenlabs.io/v1/text-to-speech/{choice}" |
|
headers = { |
|
"Accept": "audio/mpeg", |
|
"Content-Type": "application/json", |
|
"xi-api-key": xiapi |
|
} |
|
if lang == 'en': |
|
data = { |
|
"text": text, |
|
"model_id": "eleven_monolingual_v1", |
|
"voice_settings": { |
|
"stability": 0.5, |
|
"similarity_boost": 0.5 |
|
} |
|
} |
|
else: |
|
data = { |
|
"text": text, |
|
"model_id": "eleven_multilingual_v1", |
|
"voice_settings": { |
|
"stability": 0.5, |
|
"similarity_boost": 0.5 |
|
} |
|
} |
|
|
|
response = requests.post(url, json=data, headers=headers) |
|
with open('./temp_eleven.mp3', 'wb') as f: |
|
for chunk in response.iter_content(chunk_size=CHUNK_SIZE): |
|
if chunk: |
|
f.write(chunk) |
|
aud_path = save_to_wav('./temp_eleven.mp3') |
|
return aud_path, aud_path |
|
else: |
|
tts = gTTS(text, lang=lang) |
|
tts.save('./temp_gTTS.mp3') |
|
aud_path = save_to_wav('./temp_gTTS.mp3') |
|
return aud_path, aud_path |
|
|
|
def ilariaTTS(text, ttsvoice): |
|
vo=language_dict[ttsvoice] |
|
asyncio.run(edge_tts.Communicate(text, vo).save("./temp_ilaria.mp3")) |
|
aud_path = save_to_wav('./temp_ilaria.mp3') |
|
return aud_path, aud_path |
|
|
|
def upload_to_dataset(files, dir): |
|
if dir == '': |
|
dir = './dataset' |
|
if not os.path.exists(dir): |
|
os.makedirs(dir) |
|
count = 0 |
|
for file in files: |
|
path=file.name |
|
shutil.copy2(path,dir) |
|
count += 1 |
|
return f' {count} files uploaded to {dir}.' |
|
|
|
def zip_downloader(model): |
|
if not os.path.exists(f'./weights/{model}.pth'): |
|
return {"__type__": "update"}, f'Make sure the Voice Name is correct. I could not find {model}.pth' |
|
index_found = False |
|
for file in os.listdir(f'./logs/{model}'): |
|
if file.endswith('.index') and 'added' in file: |
|
log_file = file |
|
index_found = True |
|
if index_found: |
|
return [f'./weights/{model}.pth', f'./logs/{model}/{log_file}'], "Done" |
|
else: |
|
return f'./weights/{model}.pth', "Could not find Index file." |
|
|
|
fakeapplio = gr.themes.Base( |
|
primary_hue="violet", |
|
secondary_hue="violet", |
|
font=[gr.themes.GoogleFont('sans-serif'), 'ui-sans-serif', 'system-ui', 'sans-serif'], |
|
).set( |
|
border_color_accent_dark='*primary_600', |
|
border_color_primary_dark='*neutral_900', |
|
color_accent_soft_dark='*neutral_950', |
|
block_background_fill_dark='*neutral_950', |
|
block_border_color_dark='*neutral_950', |
|
checkbox_background_color_dark='*neutral_950', |
|
checkbox_border_color_dark='*primary_950', |
|
checkbox_label_background_fill_dark='*button_primary_background_fill', |
|
checkbox_label_background_fill_selected_dark='*button_primary_background_fill', |
|
input_background_fill='*neutral_200', |
|
input_background_fill_dark='*neutral_900', |
|
slider_color_dark='*button_primary_background_fill' |
|
) |
|
|
|
with gr.Blocks(theme=fakeapplio) as app: |
|
with gr.Tabs(): |
|
with gr.TabItem("Inference"): |
|
gr.HTML("<h1> Applio-RVC-Fork V3 🍏 </h1>") |
|
gr.HTML("<h10> If you need voice models you can use Applio Web: https://applio.org/models </h10>") |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
with gr.Row(): |
|
sid0 = gr.Dropdown(label="Choose the model.", choices=sorted(names), value=check_for_name()) |
|
refresh_button = gr.Button("Refresh", variant="primary") |
|
if check_for_name() != '': |
|
get_vc(sorted(names)[0]) |
|
vc_transform0 = gr.Number(label="Pitch: 0 from man to man (or woman to woman); 12 from man to woman and -12 from woman to man.", value=0) |
|
|
|
spk_item = gr.Slider( |
|
minimum=0, |
|
maximum=2333, |
|
step=1, |
|
label=i18n("请选择说话人id"), |
|
value=0, |
|
visible=False, |
|
interactive=True, |
|
) |
|
|
|
sid0.change( |
|
fn=get_vc, |
|
inputs=[sid0], |
|
outputs=[spk_item], |
|
) |
|
but0 = gr.Button("Convert", variant="primary") |
|
with gr.Row(): |
|
with gr.Column(): |
|
with gr.Row(): |
|
dropbox = gr.File(label="Drag your audio file and click refresh.") |
|
with gr.Row(): |
|
record_button=gr.Audio(source="microphone", label="Or you can use your microphone!", type="filepath") |
|
with gr.Row(): |
|
input_audio0 = gr.Dropdown( |
|
label="Choose the audio file.", |
|
value="./audios/Test_Audio.mp3", |
|
choices=audio_files |
|
) |
|
dropbox.upload(fn=save_to_wav2, inputs=[dropbox], outputs=[input_audio0]) |
|
dropbox.upload(fn=change_choices2, inputs=[], outputs=[input_audio0]) |
|
refresh_button2 = gr.Button("Refresh", variant="primary", size='sm') |
|
record_button.change(fn=save_to_wav, inputs=[record_button], outputs=[input_audio0]) |
|
record_button.change(fn=change_choices2, inputs=[], outputs=[input_audio0]) |
|
with gr.Row(): |
|
with gr.Accordion('ElevenLabs / Google TTS', open=False): |
|
with gr.Column(): |
|
lang = gr.Radio(label='Chinese & Japanese do not work with ElevenLabs currently.',choices=['en','it','es','fr','pt','zh-CN','de','hi','ja'], value='en') |
|
api_box = gr.Textbox(label="Enter your API Key for ElevenLabs, or leave empty to use GoogleTTS", value='') |
|
elevenid=gr.Dropdown(label="Voice:", choices=eleven_voices) |
|
with gr.Column(): |
|
tfs = gr.Textbox(label="Input your Text", interactive=True, value="This is a test.") |
|
tts_button = gr.Button(value="Speak") |
|
tts_button.click(fn=elevenTTS, inputs=[api_box,tfs, elevenid, lang], outputs=[record_button, input_audio0]) |
|
with gr.Row(): |
|
with gr.Accordion('Wav2Lip', open=False, visible=False): |
|
with gr.Row(): |
|
size = gr.Radio(label='Resolution:',choices=['Half','Full']) |
|
face = gr.UploadButton("Upload A Character",type='file') |
|
faces = gr.Dropdown(label="OR Choose one:", choices=['None','Ben Shapiro','Andrew Tate']) |
|
with gr.Row(): |
|
preview = gr.Textbox(label="Status:",interactive=False) |
|
face.upload(fn=success_message,inputs=[face], outputs=[preview, faces]) |
|
with gr.Row(): |
|
animation = gr.Video(type='filepath') |
|
refresh_button2.click(fn=change_choices2, inputs=[], outputs=[input_audio0, animation]) |
|
with gr.Row(): |
|
animate_button = gr.Button('Animate') |
|
|
|
with gr.Column(): |
|
vc_output2 = gr.Audio( |
|
label="Final Result! (Click on the three dots to download the audio)", |
|
type='filepath', |
|
interactive=False, |
|
) |
|
|
|
with gr.Accordion('TTS', open=True): |
|
with gr.Column(): |
|
ilariaid=gr.Dropdown(label="Voice:", choices=ilariavoices, value="English-Jenny (Female)") |
|
ilariatext = gr.Textbox(label="Input your Text", interactive=True, value="This is a test.") |
|
ilariatts_button = gr.Button(value="Speak") |
|
ilariatts_button.click(fn=ilariaTTS, inputs=[ilariatext, ilariaid], outputs=[record_button, input_audio0]) |
|
|
|
|
|
with gr.Accordion("Index Settings", open=False): |
|
|
|
|
|
file_index1 = gr.Dropdown( |
|
label="3. Choose the index file (in case it wasn't automatically found.)", |
|
choices=get_indexes(), |
|
value=get_index(), |
|
interactive=True, |
|
) |
|
sid0.change(fn=match_index, inputs=[sid0],outputs=[file_index1]) |
|
refresh_button.click( |
|
fn=change_choices, inputs=[], outputs=[sid0, file_index1] |
|
) |
|
|
|
|
|
|
|
|
|
|
|
index_rate1 = gr.Slider( |
|
minimum=0, |
|
maximum=1, |
|
label=i18n("检索特征占比"), |
|
value=0.66, |
|
interactive=True, |
|
) |
|
|
|
animate_button.click(fn=mouth, inputs=[size, face, vc_output2, faces], outputs=[animation, preview]) |
|
|
|
with gr.Accordion("Advanced Options", open=False): |
|
f0method0 = gr.Radio( |
|
label="Optional: Change the Pitch Extraction Algorithm. Extraction methods are sorted from 'worst quality' to 'best quality'. If you don't know what you're doing, leave rmvpe.", |
|
choices=["pm", "dio", "crepe-tiny", "mangio-crepe-tiny", "crepe", "harvest", "mangio-crepe", "rmvpe"], |
|
value="rmvpe", |
|
interactive=True, |
|
) |
|
|
|
crepe_hop_length = gr.Slider( |
|
minimum=1, |
|
maximum=512, |
|
step=1, |
|
label="Mangio-Crepe Hop Length. Higher numbers will reduce the chance of extreme pitch changes but lower numbers will increase accuracy. 64-192 is a good range to experiment with.", |
|
value=120, |
|
interactive=True, |
|
visible=False, |
|
) |
|
f0method0.change(fn=whethercrepeornah, inputs=[f0method0], outputs=[crepe_hop_length]) |
|
filter_radius0 = gr.Slider( |
|
minimum=0, |
|
maximum=7, |
|
label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波,数值为滤波半径,使用可以削弱哑音"), |
|
value=3, |
|
step=1, |
|
interactive=True, |
|
) |
|
resample_sr0 = gr.Slider( |
|
minimum=0, |
|
maximum=48000, |
|
label=i18n("后处理重采样至最终采样率,0为不进行重采样"), |
|
value=0, |
|
step=1, |
|
interactive=True, |
|
visible=False |
|
) |
|
rms_mix_rate0 = gr.Slider( |
|
minimum=0, |
|
maximum=1, |
|
label=i18n("输入源音量包络替换输出音量包络融合比例,越靠近1越使用输出包络"), |
|
value=0.21, |
|
interactive=True, |
|
) |
|
protect0 = gr.Slider( |
|
minimum=0, |
|
maximum=0.5, |
|
label=i18n("保护清辅音和呼吸声,防止电音撕裂等artifact,拉满0.5不开启,调低加大保护力度但可能降低索引效果"), |
|
value=0.33, |
|
step=0.01, |
|
interactive=True, |
|
) |
|
formanting = gr.Checkbox( |
|
value=bool(DoFormant), |
|
label="[EXPERIMENTAL] Formant shift inference audio", |
|
info="Used for male to female and vice-versa conversions", |
|
interactive=True, |
|
visible=True, |
|
) |
|
|
|
formant_preset = gr.Dropdown( |
|
value='', |
|
choices=get_fshift_presets(), |
|
label="browse presets for formanting", |
|
visible=bool(DoFormant), |
|
) |
|
formant_refresh_button = gr.Button( |
|
value='\U0001f504', |
|
visible=bool(DoFormant), |
|
variant='primary', |
|
) |
|
|
|
|
|
|
|
qfrency = gr.Slider( |
|
value=Quefrency, |
|
info="Default value is 1.0", |
|
label="Quefrency for formant shifting", |
|
minimum=0.0, |
|
maximum=16.0, |
|
step=0.1, |
|
visible=bool(DoFormant), |
|
interactive=True, |
|
) |
|
tmbre = gr.Slider( |
|
value=Timbre, |
|
info="Default value is 1.0", |
|
label="Timbre for formant shifting", |
|
minimum=0.0, |
|
maximum=16.0, |
|
step=0.1, |
|
visible=bool(DoFormant), |
|
interactive=True, |
|
) |
|
|
|
formant_preset.change(fn=preset_apply, inputs=[formant_preset, qfrency, tmbre], outputs=[qfrency, tmbre]) |
|
frmntbut = gr.Button("Apply", variant="primary", visible=bool(DoFormant)) |
|
formanting.change(fn=formant_enabled,inputs=[formanting,qfrency,tmbre,frmntbut,formant_preset,formant_refresh_button],outputs=[formanting,qfrency,tmbre,frmntbut,formant_preset,formant_refresh_button]) |
|
frmntbut.click(fn=formant_apply,inputs=[qfrency, tmbre], outputs=[qfrency, tmbre]) |
|
formant_refresh_button.click(fn=update_fshift_presets,inputs=[formant_preset, qfrency, tmbre],outputs=[formant_preset, qfrency, tmbre]) |
|
|
|
with gr.Row(): |
|
vc_output1 = gr.Textbox("") |
|
f0_file = gr.File(label=i18n("F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调"), visible=False) |
|
|
|
but0.click( |
|
vc_single, |
|
[ |
|
spk_item, |
|
input_audio0, |
|
vc_transform0, |
|
f0_file, |
|
f0method0, |
|
file_index1, |
|
|
|
|
|
index_rate1, |
|
filter_radius0, |
|
resample_sr0, |
|
rms_mix_rate0, |
|
protect0, |
|
crepe_hop_length |
|
], |
|
[vc_output1, vc_output2], |
|
) |
|
|
|
with gr.Accordion("Batch Conversion",open=False, visible=False): |
|
with gr.Row(): |
|
with gr.Column(): |
|
vc_transform1 = gr.Number( |
|
label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0 |
|
) |
|
opt_input = gr.Textbox(label=i18n("指定输出文件夹"), value="opt") |
|
f0method1 = gr.Radio( |
|
label=i18n( |
|
"选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU" |
|
), |
|
choices=["pm", "harvest", "crepe", "rmvpe"], |
|
value="rmvpe", |
|
interactive=True, |
|
) |
|
filter_radius1 = gr.Slider( |
|
minimum=0, |
|
maximum=7, |
|
label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波,数值为滤波半径,使用可以削弱哑音"), |
|
value=3, |
|
step=1, |
|
interactive=True, |
|
) |
|
with gr.Column(): |
|
file_index3 = gr.Textbox( |
|
label=i18n("特征检索库文件路径,为空则使用下拉的选择结果"), |
|
value="", |
|
interactive=True, |
|
) |
|
file_index4 = gr.Dropdown( |
|
label=i18n("自动检测index路径,下拉式选择(dropdown)"), |
|
choices=sorted(index_paths), |
|
interactive=True, |
|
) |
|
refresh_button.click( |
|
fn=lambda: change_choices()[1], |
|
inputs=[], |
|
outputs=file_index4, |
|
) |
|
|
|
|
|
|
|
|
|
|
|
index_rate2 = gr.Slider( |
|
minimum=0, |
|
maximum=1, |
|
label=i18n("检索特征占比"), |
|
value=1, |
|
interactive=True, |
|
) |
|
with gr.Column(): |
|
resample_sr1 = gr.Slider( |
|
minimum=0, |
|
maximum=48000, |
|
label=i18n("后处理重采样至最终采样率,0为不进行重采样"), |
|
value=0, |
|
step=1, |
|
interactive=True, |
|
) |
|
rms_mix_rate1 = gr.Slider( |
|
minimum=0, |
|
maximum=1, |
|
label=i18n("输入源音量包络替换输出音量包络融合比例,越靠近1越使用输出包络"), |
|
value=1, |
|
interactive=True, |
|
) |
|
protect1 = gr.Slider( |
|
minimum=0, |
|
maximum=0.5, |
|
label=i18n( |
|
"保护清辅音和呼吸声,防止电音撕裂等artifact,拉满0.5不开启,调低加大保护力度但可能降低索引效果" |
|
), |
|
value=0.33, |
|
step=0.01, |
|
interactive=True, |
|
) |
|
with gr.Column(): |
|
dir_input = gr.Textbox( |
|
label=i18n("输入待处理音频文件夹路径(去文件管理器地址栏拷就行了)"), |
|
value="E:\codes\py39\\test-20230416b\\todo-songs", |
|
) |
|
inputs = gr.File( |
|
file_count="multiple", label=i18n("也可批量输入音频文件, 二选一, 优先读文件夹") |
|
) |
|
with gr.Row(): |
|
format1 = gr.Radio( |
|
label=i18n("导出文件格式"), |
|
choices=["wav", "flac", "mp3", "m4a"], |
|
value="flac", |
|
interactive=True, |
|
) |
|
but1 = gr.Button(i18n("转换"), variant="primary") |
|
vc_output3 = gr.Textbox(label=i18n("输出信息")) |
|
but1.click( |
|
vc_multi, |
|
[ |
|
spk_item, |
|
dir_input, |
|
opt_input, |
|
inputs, |
|
vc_transform1, |
|
f0method1, |
|
file_index3, |
|
file_index4, |
|
|
|
index_rate2, |
|
filter_radius1, |
|
resample_sr1, |
|
rms_mix_rate1, |
|
protect1, |
|
format1, |
|
crepe_hop_length, |
|
], |
|
[vc_output3], |
|
) |
|
but1.click(fn=lambda: easy_uploader.clear()) |
|
with gr.TabItem("Download"): |
|
with gr.Row(): |
|
url=gr.Textbox(label="Huggingface Link:") |
|
with gr.Row(): |
|
model = gr.Textbox(label="Name of the model (without spaces):") |
|
download_button=gr.Button("Download") |
|
with gr.Row(): |
|
status_bar=gr.Textbox(label="Download Status") |
|
download_button.click(fn=download_from_url, inputs=[url, model], outputs=[status_bar]) |
|
with gr.Row(): |
|
gr.Markdown( |
|
""" |
|
Made with 🍏 by Blaise 😸 |
|
""" |
|
) |
|
|
|
def has_two_files_in_pretrained_folder(): |
|
pretrained_folder = "./pretrained/" |
|
if not os.path.exists(pretrained_folder): |
|
return False |
|
|
|
files_in_folder = os.listdir(pretrained_folder) |
|
num_files = len(files_in_folder) |
|
return num_files >= 2 |
|
|
|
if has_two_files_in_pretrained_folder(): |
|
print("Pretrained weights are downloaded. Training tab enabled!\n-------------------------------") |
|
with gr.TabItem("Train", visible=False): |
|
with gr.Row(): |
|
with gr.Column(): |
|
exp_dir1 = gr.Textbox(label="Voice Name:", value="My-Voice") |
|
sr2 = gr.Radio( |
|
label=i18n("目标采样率"), |
|
choices=["40k", "48k"], |
|
value="40k", |
|
interactive=True, |
|
visible=False |
|
) |
|
if_f0_3 = gr.Radio( |
|
label=i18n("模型是否带音高指导(唱歌一定要, 语音可以不要)"), |
|
choices=[True, False], |
|
value=True, |
|
interactive=True, |
|
visible=False |
|
) |
|
version19 = gr.Radio( |
|
label="RVC version", |
|
choices=["v1", "v2"], |
|
value="v2", |
|
interactive=True, |
|
visible=False, |
|
) |
|
np7 = gr.Slider( |
|
minimum=0, |
|
maximum=config.n_cpu, |
|
step=1, |
|
label="# of CPUs for data processing (Leave as it is)", |
|
value=config.n_cpu, |
|
interactive=True, |
|
visible=True |
|
) |
|
trainset_dir4 = gr.Textbox(label="Path to your dataset (audios, not zip):", value="./dataset") |
|
easy_uploader = gr.Files(label='OR Drop your audios here. They will be uploaded in your dataset path above.',file_types=['audio']) |
|
but1 = gr.Button("1. Process The Dataset", variant="primary") |
|
info1 = gr.Textbox(label="Status (wait until it says 'end preprocess'):", value="") |
|
easy_uploader.upload(fn=upload_to_dataset, inputs=[easy_uploader, trainset_dir4], outputs=[info1]) |
|
but1.click( |
|
preprocess_dataset, [trainset_dir4, exp_dir1, sr2, np7], [info1] |
|
) |
|
with gr.Column(): |
|
spk_id5 = gr.Slider( |
|
minimum=0, |
|
maximum=4, |
|
step=1, |
|
label=i18n("请指定说话人id"), |
|
value=0, |
|
interactive=True, |
|
visible=False |
|
) |
|
with gr.Accordion('GPU Settings', open=False, visible=False): |
|
gpus6 = gr.Textbox( |
|
label=i18n("以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2"), |
|
value=gpus, |
|
interactive=True, |
|
visible=False |
|
) |
|
gpu_info9 = gr.Textbox(label=i18n("显卡信息"), value=gpu_info) |
|
f0method8 = gr.Radio( |
|
label=i18n( |
|
"选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢" |
|
), |
|
choices=["harvest","crepe", "mangio-crepe", "rmvpe"], |
|
value="rmvpe", |
|
interactive=True, |
|
) |
|
|
|
extraction_crepe_hop_length = gr.Slider( |
|
minimum=1, |
|
maximum=512, |
|
step=1, |
|
label=i18n("crepe_hop_length"), |
|
value=128, |
|
interactive=True, |
|
visible=False, |
|
) |
|
f0method8.change(fn=whethercrepeornah, inputs=[f0method8], outputs=[extraction_crepe_hop_length]) |
|
but2 = gr.Button("2. Pitch Extraction", variant="primary") |
|
info2 = gr.Textbox(label="Status(Check the Colab Notebook's cell output):", value="", max_lines=8) |
|
but2.click( |
|
extract_f0_feature, |
|
[gpus6, np7, f0method8, if_f0_3, exp_dir1, version19, extraction_crepe_hop_length], |
|
[info2], |
|
) |
|
with gr.Row(): |
|
with gr.Column(): |
|
total_epoch11 = gr.Slider( |
|
minimum=1, |
|
maximum=5000, |
|
step=10, |
|
label="Total # of training epochs (IF you choose a value too high, your model will sound horribly overtrained.):", |
|
value=250, |
|
interactive=True, |
|
) |
|
butstop = gr.Button( |
|
"Stop Training", |
|
variant='primary', |
|
visible=False, |
|
) |
|
but3 = gr.Button("3. Train Model", variant="primary", visible=True) |
|
|
|
but3.click(fn=stoptraining, inputs=[gr.Number(value=0, visible=False)], outputs=[but3, butstop]) |
|
butstop.click(fn=stoptraining, inputs=[gr.Number(value=1, visible=False)], outputs=[butstop, but3]) |
|
|
|
|
|
but4 = gr.Button("4.Train Index", variant="primary") |
|
info3 = gr.Textbox(label="Status(Check the Colab Notebook's cell output):", value="", max_lines=10) |
|
with gr.Accordion("Training Preferences (You can leave these as they are)", open=False): |
|
|
|
with gr.Column(): |
|
save_epoch10 = gr.Slider( |
|
minimum=1, |
|
maximum=200, |
|
step=1, |
|
label="Backup every X amount of epochs:", |
|
value=10, |
|
interactive=True, |
|
) |
|
batch_size12 = gr.Slider( |
|
minimum=1, |
|
maximum=40, |
|
step=1, |
|
label="Batch Size (LEAVE IT unless you know what you're doing!):", |
|
value=default_batch_size, |
|
interactive=True, |
|
) |
|
if_save_latest13 = gr.Checkbox( |
|
label="Save only the latest '.ckpt' file to save disk space.", |
|
value=True, |
|
interactive=True, |
|
) |
|
if_cache_gpu17 = gr.Checkbox( |
|
label="Cache all training sets to GPU memory. Caching small datasets (less than 10 minutes) can speed up training, but caching large datasets will consume a lot of GPU memory and may not provide much speed improvement.", |
|
value=False, |
|
interactive=True, |
|
) |
|
if_save_every_weights18 = gr.Checkbox( |
|
label="Save a small final model to the 'weights' folder at each save point.", |
|
value=True, |
|
interactive=True, |
|
) |
|
zip_model = gr.Button('5. Download Model') |
|
zipped_model = gr.Files(label='Your Model and Index file can be downloaded here:') |
|
zip_model.click(fn=zip_downloader, inputs=[exp_dir1], outputs=[zipped_model, info3]) |
|
with gr.Group(): |
|
with gr.Accordion("Base Model Locations:", open=False, visible=False): |
|
pretrained_G14 = gr.Textbox( |
|
label=i18n("加载预训练底模G路径"), |
|
value="pretrained_v2/f0G40k.pth", |
|
interactive=True, |
|
) |
|
pretrained_D15 = gr.Textbox( |
|
label=i18n("加载预训练底模D路径"), |
|
value="pretrained_v2/f0D40k.pth", |
|
interactive=True, |
|
) |
|
gpus16 = gr.Textbox( |
|
label=i18n("以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2"), |
|
value=gpus, |
|
interactive=True, |
|
) |
|
sr2.change( |
|
change_sr2, |
|
[sr2, if_f0_3, version19], |
|
[pretrained_G14, pretrained_D15, version19], |
|
) |
|
version19.change( |
|
change_version19, |
|
[sr2, if_f0_3, version19], |
|
[pretrained_G14, pretrained_D15], |
|
) |
|
if_f0_3.change( |
|
change_f0, |
|
[if_f0_3, sr2, version19], |
|
[f0method8, pretrained_G14, pretrained_D15], |
|
) |
|
but5 = gr.Button(i18n("一键训练"), variant="primary", visible=False) |
|
but3.click( |
|
click_train, |
|
[ |
|
exp_dir1, |
|
sr2, |
|
if_f0_3, |
|
spk_id5, |
|
save_epoch10, |
|
total_epoch11, |
|
batch_size12, |
|
if_save_latest13, |
|
pretrained_G14, |
|
pretrained_D15, |
|
gpus16, |
|
if_cache_gpu17, |
|
if_save_every_weights18, |
|
version19, |
|
], |
|
[ |
|
info3, |
|
butstop, |
|
but3, |
|
], |
|
) |
|
but4.click(train_index, [exp_dir1, version19], info3) |
|
but5.click( |
|
train1key, |
|
[ |
|
exp_dir1, |
|
sr2, |
|
if_f0_3, |
|
trainset_dir4, |
|
spk_id5, |
|
np7, |
|
f0method8, |
|
save_epoch10, |
|
total_epoch11, |
|
batch_size12, |
|
if_save_latest13, |
|
pretrained_G14, |
|
pretrained_D15, |
|
gpus16, |
|
if_cache_gpu17, |
|
if_save_every_weights18, |
|
version19, |
|
extraction_crepe_hop_length |
|
], |
|
info3, |
|
) |
|
|
|
else: |
|
print( |
|
"Pretrained weights not downloaded. Disabling training tab.\n" |
|
"Wondering how to train a voice? Join AI HUB Discord Server! https://discord.gg/aihub\n" |
|
"-------------------------------\n" |
|
) |
|
|
|
app.queue(concurrency_count=511, max_size=1022).launch(share=False, quiet=False) |
|
|