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finetune_models/run/training/GPT_XTTS_FT-July-04-2024_01+29PM-44c61c9/best_model.pth

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finetune_models/run/training/GPT_XTTS_FT-July-04-2024_01+29PM-44c61c9/best_model_120.pth

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finetune_models/run/training/GPT_XTTS_FT-July-04-2024_01+29PM-44c61c9/config.json

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+{
+    "output_path": "output",
+    "logger_uri": null,
+    "run_name": "run",
+    "project_name": null,
+    "run_description": "\ud83d\udc38Coqui trainer run.",
+    "print_step": 25,
+    "plot_step": 100,
+    "model_param_stats": false,
+    "wandb_entity": null,
+    "dashboard_logger": "tensorboard",
+    "save_on_interrupt": true,
+    "log_model_step": null,
+    "save_step": 10000,
+    "save_n_checkpoints": 5,
+    "save_checkpoints": true,
+    "save_all_best": false,
+    "save_best_after": 10000,
+    "target_loss": null,
+    "print_eval": false,
+    "test_delay_epochs": 0,
+    "run_eval": true,
+    "run_eval_steps": null,
+    "distributed_backend": "nccl",
+    "distributed_url": "tcp://localhost:54321",
+    "mixed_precision": false,
+    "precision": "fp16",
+    "epochs": 1000,
+    "batch_size": 32,
+    "eval_batch_size": 16,
+    "grad_clip": 0.0,
+    "scheduler_after_epoch": true,
+    "lr": 0.001,
+    "optimizer": "radam",
+    "optimizer_params": null,
+    "lr_scheduler": null,
+    "lr_scheduler_params": {},
+    "use_grad_scaler": false,
+    "allow_tf32": false,
+    "cudnn_enable": true,
+    "cudnn_deterministic": false,
+    "cudnn_benchmark": false,
+    "training_seed": 54321,
+    "model": "xtts",
+    "num_loader_workers": 0,
+    "num_eval_loader_workers": 0,
+    "use_noise_augment": false,
+    "audio": {
+        "sample_rate": 22050,
+        "output_sample_rate": 24000
+    },
+    "use_phonemes": false,
+    "phonemizer": null,
+    "phoneme_language": null,
+    "compute_input_seq_cache": false,
+    "text_cleaner": null,
+    "enable_eos_bos_chars": false,
+    "test_sentences_file": "",
+    "phoneme_cache_path": null,
+    "characters": null,
+    "add_blank": false,
+    "batch_group_size": 0,
+    "loss_masking": null,
+    "min_audio_len": 1,
+    "max_audio_len": Infinity,
+    "min_text_len": 1,
+    "max_text_len": Infinity,
+    "compute_f0": false,
+    "compute_energy": false,
+    "compute_linear_spec": false,
+    "precompute_num_workers": 0,
+    "start_by_longest": false,
+    "shuffle": false,
+    "drop_last": false,
+    "datasets": [
+        {
+            "formatter": "",
+            "dataset_name": "",
+            "path": "",
+            "meta_file_train": "",
+            "ignored_speakers": null,
+            "language": "",
+            "phonemizer": "",
+            "meta_file_val": "",
+            "meta_file_attn_mask": ""
+        }
+    ],
+    "test_sentences": [],
+    "eval_split_max_size": null,
+    "eval_split_size": 0.01,
+    "use_speaker_weighted_sampler": false,
+    "speaker_weighted_sampler_alpha": 1.0,
+    "use_language_weighted_sampler": false,
+    "language_weighted_sampler_alpha": 1.0,
+    "use_length_weighted_sampler": false,
+    "length_weighted_sampler_alpha": 1.0,
+    "model_args": {
+        "gpt_batch_size": 1,
+        "enable_redaction": false,
+        "kv_cache": true,
+        "gpt_checkpoint": null,
+        "clvp_checkpoint": null,
+        "decoder_checkpoint": null,
+        "num_chars": 255,
+        "tokenizer_file": "",
+        "gpt_max_audio_tokens": 605,
+        "gpt_max_text_tokens": 402,
+        "gpt_max_prompt_tokens": 70,
+        "gpt_layers": 30,
+        "gpt_n_model_channels": 1024,
+        "gpt_n_heads": 16,
+        "gpt_number_text_tokens": 6681,
+        "gpt_start_text_token": null,
+        "gpt_stop_text_token": null,
+        "gpt_num_audio_tokens": 1026,
+        "gpt_start_audio_token": 1024,
+        "gpt_stop_audio_token": 1025,
+        "gpt_code_stride_len": 1024,
+        "gpt_use_masking_gt_prompt_approach": true,
+        "gpt_use_perceiver_resampler": true,
+        "input_sample_rate": 22050,
+        "output_sample_rate": 24000,
+        "output_hop_length": 256,
+        "decoder_input_dim": 1024,
+        "d_vector_dim": 512,
+        "cond_d_vector_in_each_upsampling_layer": true,
+        "duration_const": 102400
+    },
+    "model_dir": null,
+    "languages": [
+        "en",
+        "es",
+        "fr",
+        "de",
+        "it",
+        "pt",
+        "pl",
+        "tr",
+        "ru",
+        "nl",
+        "cs",
+        "ar",
+        "zh-cn",
+        "hu",
+        "ko",
+        "ja",
+        "hi"
+    ],
+    "temperature": 0.75,
+    "length_penalty": 1.0,
+    "repetition_penalty": 5.0,
+    "top_k": 50,
+    "top_p": 0.85,
+    "num_gpt_outputs": 1,
+    "gpt_cond_len": 30,
+    "gpt_cond_chunk_len": 4,
+    "max_ref_len": 30,
+    "sound_norm_refs": false
+}

finetune_models/run/training/GPT_XTTS_FT-July-04-2024_01+29PM-44c61c9/events.out.tfevents.1720099779.0426d3820a65.4247.0

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+size 62903

finetune_models/run/training/GPT_XTTS_FT-July-04-2024_01+29PM-44c61c9/trainer_0_log.txt

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+ > Training Environment:
+ | > Backend: Torch
+ | > Mixed precision: False
+ | > Precision: float32
+ | > Current device: 0
+ | > Num. of GPUs: 1
+ | > Num. of CPUs: 12
+ | > Num. of Torch Threads: 1
+ | > Torch seed: 1
+ | > Torch CUDNN: True
+ | > Torch CUDNN deterministic: False
+ | > Torch CUDNN benchmark: False
+ | > Torch TF32 MatMul: False
+ > Start Tensorboard: tensorboard --logdir=/content/xtts-v2/finetune_models/run/training/GPT_XTTS_FT-July-04-2024_01+29PM-44c61c9
+
+ > Model has 518442047 parameters
+
+ > EPOCH: 0/8
+ --> /content/xtts-v2/finetune_models/run/training/GPT_XTTS_FT-July-04-2024_01+29PM-44c61c9
+
+ > TRAINING (2024-07-04 13:29:40) 
+
+   --> TIME: 2024-07-04 13:29:45 -- STEP: 0/15 -- GLOBAL_STEP: 0
+     | > loss_text_ce: 0.03748854622244835  (0.03748854622244835)
+     | > loss_mel_ce: 3.7625863552093506  (3.7625863552093506)
+     | > loss: 3.800074815750122  (3.800074815750122)
+     | > grad_norm: 0  (0)
+     | > current_lr: 5e-06 
+     | > step_time: 1.2487  (1.248706579208374)
+     | > loader_time: 3.617  (3.6170296669006348)
+
+
+ > EVALUATION 
+
+
+  --> EVAL PERFORMANCE
+     | > avg_loader_time: 0.1386585235595703 (+0)
+     | > avg_loss_text_ce: 0.03846975229680538 (+0)
+     | > avg_loss_mel_ce: 3.401963472366333 (+0)
+     | > avg_loss: 3.440433144569397 (+0)
+
+ > BEST MODEL : /content/xtts-v2/finetune_models/run/training/GPT_XTTS_FT-July-04-2024_01+29PM-44c61c9/best_model_15.pth
+
+ > EPOCH: 1/8
+ --> /content/xtts-v2/finetune_models/run/training/GPT_XTTS_FT-July-04-2024_01+29PM-44c61c9
+
+ > TRAINING (2024-07-04 13:30:23) 
+
+ > EVALUATION 
+
+
+  --> EVAL PERFORMANCE
+     | > avg_loader_time: 0.12281334400177002 (-0.015845179557800293)
+     | > avg_loss_text_ce: 0.037664541974663734 (-0.0008052103221416473)
+     | > avg_loss_mel_ce: 3.2830638885498047 (-0.11889958381652832)
+     | > avg_loss: 3.3207284212112427 (-0.1197047233581543)
+
+ > BEST MODEL : /content/xtts-v2/finetune_models/run/training/GPT_XTTS_FT-July-04-2024_01+29PM-44c61c9/best_model_30.pth
+
+ > EPOCH: 2/8
+ --> /content/xtts-v2/finetune_models/run/training/GPT_XTTS_FT-July-04-2024_01+29PM-44c61c9
+
+ > TRAINING (2024-07-04 13:31:03) 
+
+ > EVALUATION 
+
+
+  --> EVAL PERFORMANCE
+     | > avg_loader_time: 0.1253896951675415 (+0.0025763511657714844)
+     | > avg_loss_text_ce: 0.03692051209509373 (-0.0007440298795700073)
+     | > avg_loss_mel_ce: 3.2056061029434204 (-0.07745778560638428)
+     | > avg_loss: 3.242526650428772 (-0.0782017707824707)
+
+ > BEST MODEL : /content/xtts-v2/finetune_models/run/training/GPT_XTTS_FT-July-04-2024_01+29PM-44c61c9/best_model_45.pth
+
+ > EPOCH: 3/8
+ --> /content/xtts-v2/finetune_models/run/training/GPT_XTTS_FT-July-04-2024_01+29PM-44c61c9
+
+ > TRAINING (2024-07-04 13:32:49) 
+
+   --> TIME: 2024-07-04 13:32:54 -- STEP: 5/15 -- GLOBAL_STEP: 50
+     | > loss_text_ce: 0.03478017821907997  (0.0374395526945591)
+     | > loss_mel_ce: 3.1205806732177734  (3.1422857284545898)
+     | > loss: 3.1553609371185303  (3.1797253131866454)
+     | > grad_norm: 0  (0.0)
+     | > current_lr: 5e-06 
+     | > step_time: 0.3306  (0.32296051979064944)
+     | > loader_time: 0.0082  (0.008426570892333984)
+
+
+ > EVALUATION 
+
+
+  --> EVAL PERFORMANCE
+     | > avg_loader_time: 0.1264183521270752 (+0.0010286569595336914)
+     | > avg_loss_text_ce: 0.03631771728396416 (-0.00060279481112957)
+     | > avg_loss_mel_ce: 3.1698708534240723 (-0.035735249519348145)
+     | > avg_loss: 3.2061885595321655 (-0.036338090896606445)
+
+ > BEST MODEL : /content/xtts-v2/finetune_models/run/training/GPT_XTTS_FT-July-04-2024_01+29PM-44c61c9/best_model_60.pth
+
+ > EPOCH: 4/8
+ --> /content/xtts-v2/finetune_models/run/training/GPT_XTTS_FT-July-04-2024_01+29PM-44c61c9
+
+ > TRAINING (2024-07-04 13:37:54) 
+
+ > EVALUATION 
+
+
+  --> EVAL PERFORMANCE
+     | > avg_loader_time: 0.1271202564239502 (+0.000701904296875)
+     | > avg_loss_text_ce: 0.035877423360943794 (-0.00044029392302036285)
+     | > avg_loss_mel_ce: 3.1362961530685425 (-0.033574700355529785)
+     | > avg_loss: 3.1721736192703247 (-0.03401494026184082)
+
+ > BEST MODEL : /content/xtts-v2/finetune_models/run/training/GPT_XTTS_FT-July-04-2024_01+29PM-44c61c9/best_model_75.pth
+
+ > EPOCH: 5/8
+ --> /content/xtts-v2/finetune_models/run/training/GPT_XTTS_FT-July-04-2024_01+29PM-44c61c9
+
+ > TRAINING (2024-07-04 13:48:50) 
+
+ > EVALUATION 
+
+
+  --> EVAL PERFORMANCE
+     | > avg_loader_time: 0.12177145481109619 (-0.005348801612854004)
+     | > avg_loss_text_ce: 0.03552030771970749 (-0.00035711564123630524)
+     | > avg_loss_mel_ce: 3.130112409591675 (-0.006183743476867676)
+     | > avg_loss: 3.165632724761963 (-0.006540894508361816)
+
+ > BEST MODEL : /content/xtts-v2/finetune_models/run/training/GPT_XTTS_FT-July-04-2024_01+29PM-44c61c9/best_model_90.pth
+
+ > EPOCH: 6/8
+ --> /content/xtts-v2/finetune_models/run/training/GPT_XTTS_FT-July-04-2024_01+29PM-44c61c9
+
+ > TRAINING (2024-07-04 13:54:05) 
+
+   --> TIME: 2024-07-04 13:54:13 -- STEP: 10/15 -- GLOBAL_STEP: 100
+     | > loss_text_ce: 0.03132708743214607  (0.03531287014484406)
+     | > loss_mel_ce: 2.6794981956481934  (2.865787482261658)
+     | > loss: 2.710825204849243  (2.9011003494262697)
+     | > grad_norm: 0  (0.0)
+     | > current_lr: 5e-06 
+     | > step_time: 0.3346  (0.3178673505783081)
+     | > loader_time: 0.0077  (0.008596587181091308)
+
+
+ > EVALUATION 
+
+
+  --> EVAL PERFORMANCE
+     | > avg_loader_time: 0.12420761585235596 (+0.0024361610412597656)
+     | > avg_loss_text_ce: 0.03527705930173397 (-0.00024324841797351837)
+     | > avg_loss_mel_ce: 3.1189099550247192 (-0.011202454566955566)
+     | > avg_loss: 3.154186964035034 (-0.011445760726928711)
+
+ > BEST MODEL : /content/xtts-v2/finetune_models/run/training/GPT_XTTS_FT-July-04-2024_01+29PM-44c61c9/best_model_105.pth
+
+ > EPOCH: 7/8
+ --> /content/xtts-v2/finetune_models/run/training/GPT_XTTS_FT-July-04-2024_01+29PM-44c61c9
+
+ > TRAINING (2024-07-04 14:01:23) 
+
+ > EVALUATION 
+
+
+  --> EVAL PERFORMANCE
+     | > avg_loader_time: 0.12220907211303711 (-0.0019985437393188477)
+     | > avg_loss_text_ce: 0.035058384761214256 (-0.00021867454051971436)
+     | > avg_loss_mel_ce: 3.100669741630554 (-0.01824021339416504)
+     | > avg_loss: 3.1357282400131226 (-0.01845872402191162)
+
+ > BEST MODEL : /content/xtts-v2/finetune_models/run/training/GPT_XTTS_FT-July-04-2024_01+29PM-44c61c9/best_model_120.pth

finetune_models/run/training/GPT_XTTS_FT-July-04-2024_01+29PM-44c61c9/webui_xtts.py

@@ -0,0 +1,1115 @@
+import os,shutil,sys,pdb,re
+now_dir = os.getcwd()
+sys.path.append(now_dir)
+import json,yaml,warnings,torch
+import platform
+import psutil
+import signal
+from pathlib import Path
+
+warnings.filterwarnings("ignore")
+torch.manual_seed(233333)
+tmp = os.path.join(now_dir, "TEMP")
+os.makedirs(tmp, exist_ok=True)
+os.environ["TEMP"] = tmp
+if(os.path.exists(tmp)):
+    for name in os.listdir(tmp):
+        if(name=="jieba.cache"):continue
+        path="%s/%s"%(tmp,name)
+        delete=os.remove if os.path.isfile(path) else shutil.rmtree
+        try:
+            delete(path)
+        except Exception as e:
+            print(str(e))
+            pass
+import site
+site_packages_roots = []
+for path in site.getsitepackages():
+    if "packages" in path:
+        site_packages_roots.append(path)
+if(site_packages_roots==[]):site_packages_roots=["%s/runtime/Lib/site-packages" % now_dir]
+#os.environ["OPENBLAS_NUM_THREADS"] = "4"
+os.environ["no_proxy"] = "localhost, 127.0.0.1, ::1"
+os.environ["all_proxy"] = ""
+for site_packages_root in site_packages_roots:
+    if os.path.exists(site_packages_root):
+        try:
+            with open("%s/users.pth" % (site_packages_root), "w") as f:
+                f.write(
+                    "%s\n%s/tools\n%s/tools/damo_asr\n%s/GPT_SoVITS\n%s/tools/uvr5"
+                    % (now_dir, now_dir, now_dir, now_dir, now_dir)
+                )
+            break
+        except PermissionError:
+            pass
+from tools import my_utils
+import traceback
+import shutil
+import pdb
+import gradio as gr
+from subprocess import Popen
+import signal
+from config import python_exec,infer_device,is_half,exp_root,webui_port_main,webui_port_infer_tts,webui_port_uvr5,webui_port_subfix,is_share
+from tools.i18n.i18n import I18nAuto
+i18n = I18nAuto()
+from scipy.io import wavfile
+from tools.my_utils import load_audio
+from multiprocessing import cpu_count
+
+import argparse
+import os
+import sys
+import tempfile
+
+import gradio as gr
+import librosa.display
+import numpy as np
+
+import torch
+import torchaudio
+import traceback
+from TTS.demos.xtts_ft_demo.utils.formatter import format_audio_list
+from TTS.demos.xtts_ft_demo.utils.gpt_train import train_gpt
+
+from TTS.tts.configs.xtts_config import XttsConfig
+from TTS.tts.models.xtts import Xtts
+
+# from .list to .csv
+import pandas as pd
+from sklearn.model_selection import train_test_split
+
+def split_csv(input_csv, train_csv, eval_csv, eval_size=0.15):
+    # Load the data from the CSV file
+    data = pd.read_csv(input_csv, delimiter='|', header=0)
+
+    # Split the data into training and evaluation sets
+    train_data, eval_data = train_test_split(data, test_size=eval_size, random_state=42)
+
+    # Save the training data to a CSV file
+    train_data.to_csv(train_csv, index=False, sep='|')
+
+    # Save the evaluation data to a CSV file
+    eval_data.to_csv(eval_csv, index=False, sep='|')
+
+    print("CSV files have been successfully split.")
+
+
+def convert_list_to_csv(input_file, output_file):
+    try:
+        # Open the input .list file to read
+        with open(input_file, 'r', encoding='utf-8') as infile:
+            # Open the output .csv file to write
+            with open(output_file, 'w', encoding='utf-8') as outfile:
+                # Write the header to the CSV
+                outfile.write("audio_file|text|speaker_name\n")
+                # Process each line in the input file
+                for line in infile:
+                    parts = line.strip().split('|')
+                    if len(parts) == 4:
+                        # Extract relevant parts: WAV file path and transcription
+                        wav_path = parts[0]
+                        transcription = parts[3]
+                        # Write the formatted line to the CSV file
+                        outfile.write(f"{wav_path}|{transcription}|coqui\n")
+        print("Conversion to CSV completed successfully.")
+        split_csv(output_file, "train.csv", "eval.csv")
+        print("Split completed successfully")
+        return "train.csv", "eval.csv"
+    except Exception as e:
+        print(f"An error occurred: {e}")
+
+
+def clear_gpu_cache():
+    # clear the GPU cache
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+
+XTTS_MODEL = None
+def load_model(xtts_checkpoint, xtts_config, xtts_vocab):
+    global XTTS_MODEL
+    clear_gpu_cache()
+    if not xtts_checkpoint or not xtts_config or not xtts_vocab:
+        return "You need to run the previous steps or manually set the `XTTS checkpoint path`, `XTTS config path`, and `XTTS vocab path` fields !!"
+    config = XttsConfig()
+    config.load_json(xtts_config)
+    XTTS_MODEL = Xtts.init_from_config(config)
+    print("Loading XTTS model! ")
+    XTTS_MODEL.load_checkpoint(config, checkpoint_path=xtts_checkpoint, vocab_path=xtts_vocab, use_deepspeed=False)
+    if torch.cuda.is_available():
+        XTTS_MODEL.cuda()
+
+    print("模型已成功加载！")
+    return "模型已成功加载！"
+
+def run_tts(lang, tts_text, speaker_audio_file):
+    if XTTS_MODEL is None or not speaker_audio_file:
+        return "您需要先执行第5步 - 加载模型", None, None
+
+    speaker_audio_file = "".join([item for item in speaker_audio_file.strip().split("\n") if item != ""])
+    gpt_cond_latent, speaker_embedding = XTTS_MODEL.get_conditioning_latents(audio_path=speaker_audio_file, gpt_cond_len=XTTS_MODEL.config.gpt_cond_len, max_ref_length=XTTS_MODEL.config.max_ref_len, sound_norm_refs=XTTS_MODEL.config.sound_norm_refs)
+    out = XTTS_MODEL.inference(
+        text=tts_text.strip(),
+        language=lang,
+        gpt_cond_latent=gpt_cond_latent,
+        speaker_embedding=speaker_embedding,
+        temperature=XTTS_MODEL.config.temperature, # Add custom parameters here
+        length_penalty=XTTS_MODEL.config.length_penalty,
+        repetition_penalty=XTTS_MODEL.config.repetition_penalty,
+        top_k=XTTS_MODEL.config.top_k,
+        top_p=XTTS_MODEL.config.top_p,
+    )
+
+    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp:
+        out["wav"] = torch.tensor(out["wav"]).unsqueeze(0)
+        out_path = fp.name
+        torchaudio.save(out_path, out["wav"], 24000)
+
+    return "推理成功，快来听听吧！", out_path, speaker_audio_file
+
+
+
+
+# define a logger to redirect 
+class Logger:
+    def __init__(self, filename="log.out"):
+        self.log_file = filename
+        self.terminal = sys.stdout
+        self.log = open(self.log_file, "w")
+
+    def write(self, message):
+        self.terminal.write(message)
+        self.log.write(message)
+
+    def flush(self):
+        self.terminal.flush()
+        self.log.flush()
+
+    def isatty(self):
+        return False
+
+# redirect stdout and stderr to a file
+sys.stdout = Logger()
+sys.stderr = sys.stdout
+
+
+# logging.basicConfig(stream=sys.stdout, level=logging.INFO)
+import logging
+logging.basicConfig(
+    level=logging.WARNING,
+    format="%(asctime)s [%(levelname)s] %(message)s",
+    handlers=[
+        logging.StreamHandler(sys.stdout)
+    ]
+)
+
+def read_logs():
+    sys.stdout.flush()
+    with open(sys.stdout.log_file, "r") as f:
+        return f.read()
+
+
+os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1' # 当遇到mps不支持的步骤时使用cpu
+
+n_cpu=cpu_count()
+           
+ngpu = torch.cuda.device_count()
+gpu_infos = []
+mem = []
+if_gpu_ok = False
+
+# 判断是否有能用来训练和加速推理的N卡
+if torch.cuda.is_available() or ngpu != 0:
+    for i in range(ngpu):
+        gpu_name = torch.cuda.get_device_name(i)
+        if any(value in gpu_name.upper()for value in ["10","16","20","30","40","A2","A3","A4","P4","A50","500","A60","70","80","90","M4","T4","TITAN","L4","4060"]):
+            # A10#A100#V100#A40#P40#M40#K80#A4500
+            if_gpu_ok = True  # 至少有一张能用的N卡
+            gpu_infos.append("%s\t%s" % (i, gpu_name))
+            mem.append(int(torch.cuda.get_device_properties(i).total_memory/ 1024/ 1024/ 1024+ 0.4))
+# 判断是否支持mps加速
+if torch.backends.mps.is_available():
+    if_gpu_ok = True
+    gpu_infos.append("%s\t%s" % ("0", "Apple GPU"))
+    mem.append(psutil.virtual_memory().total/ 1024 / 1024 / 1024) # 实测使用系统内存作为显存不会爆显存
+
+if if_gpu_ok and len(gpu_infos) > 0:
+    gpu_info = "\n".join(gpu_infos)
+    default_batch_size = min(mem) // 2
+else:
+    gpu_info = i18n("很遗憾您这没有能用的显卡来支持您训练")
+    default_batch_size = 1
+gpus = "-".join([i[0] for i in gpu_infos])
+
+pretrained_sovits_name="GPT_SoVITS/pretrained_models/s2G488k.pth"
+pretrained_gpt_name="GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
+def get_weights_names():
+    SoVITS_names = [pretrained_sovits_name]
+    for name in os.listdir(SoVITS_weight_root):
+        if name.endswith(".pth"):SoVITS_names.append(name)
+    GPT_names = [pretrained_gpt_name]
+    for name in os.listdir(GPT_weight_root):
+        if name.endswith(".ckpt"): GPT_names.append(name)
+    return SoVITS_names,GPT_names
+SoVITS_weight_root="SoVITS_weights"
+GPT_weight_root="GPT_weights"
+os.makedirs(SoVITS_weight_root,exist_ok=True)
+os.makedirs(GPT_weight_root,exist_ok=True)
+SoVITS_names,GPT_names = get_weights_names()
+
+def custom_sort_key(s):
+    # 使用正则表达式提取字符串中的数字部分和非数字部分
+    parts = re.split('(\d+)', s)
+    # 将数字部分转换为整数，非数字部分保持不变
+    parts = [int(part) if part.isdigit() else part for part in parts]
+    return parts
+
+def change_choices():
+    SoVITS_names, GPT_names = get_weights_names()
+    return {"choices": sorted(SoVITS_names,key=custom_sort_key), "__type__": "update"}, {"choices": sorted(GPT_names,key=custom_sort_key), "__type__": "update"}
+
+p_label=None
+p_uvr5=None
+p_asr=None
+p_denoise=None
+p_tts_inference=None
+
+def kill_proc_tree(pid, including_parent=True):  
+    try:
+        parent = psutil.Process(pid)
+    except psutil.NoSuchProcess:
+        # Process already terminated
+        return
+
+    children = parent.children(recursive=True)
+    for child in children:
+        try:
+            os.kill(child.pid, signal.SIGTERM)  # or signal.SIGKILL
+        except OSError:
+            pass
+    if including_parent:
+        try:
+            os.kill(parent.pid, signal.SIGTERM)  # or signal.SIGKILL
+        except OSError:
+            pass
+
+system=platform.system()
+def kill_process(pid):
+    if(system=="Windows"):
+        cmd = "taskkill /t /f /pid %s" % pid
+        os.system(cmd)
+    else:
+        kill_proc_tree(pid)
+    
+
+def change_label(if_label,path_list):
+    global p_label
+    if(if_label==True and p_label==None):
+        path_list=my_utils.clean_path(path_list)
+        cmd = '"%s" tools/subfix_webui.py --load_list "%s" --webui_port %s --is_share %s'%(python_exec,path_list,webui_port_subfix,is_share)
+        yield i18n("打标工具WebUI已开启")
+        print(cmd)
+        p_label = Popen(cmd, shell=True)
+    elif(if_label==False and p_label!=None):
+        kill_process(p_label.pid)
+        p_label=None
+        yield i18n("打标工具WebUI已关闭")
+
+def change_uvr5(if_uvr5):
+    global p_uvr5
+    if(if_uvr5==True and p_uvr5==None):
+        cmd = '"%s" tools/uvr5/webui.py "%s" %s %s %s'%(python_exec,infer_device,is_half,webui_port_uvr5,is_share)
+        yield i18n("UVR5已开启")
+        print(cmd)
+        p_uvr5 = Popen(cmd, shell=True)
+    elif(if_uvr5==False and p_uvr5!=None):
+        kill_process(p_uvr5.pid)
+        p_uvr5=None
+        yield i18n("UVR5已关闭")
+
+def change_tts_inference(if_tts,bert_path,cnhubert_base_path,gpu_number,gpt_path,sovits_path):
+    global p_tts_inference
+    if(if_tts==True and p_tts_inference==None):
+        os.environ["gpt_path"]=gpt_path if "/" in gpt_path else "%s/%s"%(GPT_weight_root,gpt_path)
+        os.environ["sovits_path"]=sovits_path if "/"in sovits_path else "%s/%s"%(SoVITS_weight_root,sovits_path)
+        os.environ["cnhubert_base_path"]=cnhubert_base_path
+        os.environ["bert_path"]=bert_path
+        os.environ["_CUDA_VISIBLE_DEVICES"]=gpu_number
+        os.environ["is_half"]=str(is_half)
+        os.environ["infer_ttswebui"]=str(webui_port_infer_tts)
+        os.environ["is_share"]=str(is_share)
+        cmd = '"%s" GPT_SoVITS/inference_webui.py'%(python_exec)
+        yield i18n("TTS推理进程已开启")
+        print(cmd)
+        p_tts_inference = Popen(cmd, shell=True)
+    elif(if_tts==False and p_tts_inference!=None):
+        kill_process(p_tts_inference.pid)
+        p_tts_inference=None
+        yield i18n("TTS推理进程已关闭")
+
+from tools.asr.config import asr_dict
+def open_asr(asr_inp_dir, asr_opt_dir, asr_model, asr_model_size, asr_lang):
+    global p_asr
+    if(p_asr==None):
+        asr_inp_dir=my_utils.clean_path(asr_inp_dir)
+        cmd = f'"{python_exec}" tools/asr/{asr_dict[asr_model]["path"]}'
+        cmd += f' -i "{asr_inp_dir}"'
+        cmd += f' -o "{asr_opt_dir}"'
+        cmd += f' -s {asr_model_size}'
+        cmd += f' -l {asr_lang}'
+        cmd += " -p %s"%("float16"if is_half==True else "float32")
+
+        yield "ASR任务开启：%s"%cmd,{"__type__":"update","visible":False},{"__type__":"update","visible":True}
+        print(cmd)
+        p_asr = Popen(cmd, shell=True)
+        p_asr.wait()
+        p_asr=None
+        yield f"ASR任务完成, 查看终端进行下一步",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
+    else:
+        yield "已有正在进行的ASR任务，需先终止才能开启下一次任务",{"__type__":"update","visible":False},{"__type__":"update","visible":True}
+        # return None
+
+def close_asr():
+    global p_asr
+    if(p_asr!=None):
+        kill_process(p_asr.pid)
+        p_asr=None
+    return "已终止ASR进程",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
+def open_denoise(denoise_inp_dir, denoise_opt_dir):
+    global p_denoise
+    if(p_denoise==None):
+        denoise_inp_dir=my_utils.clean_path(denoise_inp_dir)
+        denoise_opt_dir=my_utils.clean_path(denoise_opt_dir)
+        cmd = '"%s" tools/cmd-denoise.py -i "%s" -o "%s" -p %s'%(python_exec,denoise_inp_dir,denoise_opt_dir,"float16"if is_half==True else "float32")
+
+        yield "语音降噪任务开启：%s"%cmd,{"__type__":"update","visible":False},{"__type__":"update","visible":True}
+        print(cmd)
+        p_denoise = Popen(cmd, shell=True)
+        p_denoise.wait()
+        p_denoise=None
+        yield f"语音降噪任务完成, 查看终端进行下一步",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
+    else:
+        yield "已有正在进行的语音降噪任务，需先终止才能开启下一次任务",{"__type__":"update","visible":False},{"__type__":"update","visible":True}
+        # return None
+
+def close_denoise():
+    global p_denoise
+    if(p_denoise!=None):
+        kill_process(p_denoise.pid)
+        p_denoise=None
+    return "已终止语音降噪进程",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
+
+p_train_SoVITS=None
+def open1Ba(batch_size,total_epoch,exp_name,text_low_lr_rate,if_save_latest,if_save_every_weights,save_every_epoch,gpu_numbers1Ba,pretrained_s2G,pretrained_s2D):
+    global p_train_SoVITS
+    if(p_train_SoVITS==None):
+        with open("GPT_SoVITS/configs/s2.json")as f:
+            data=f.read()
+            data=json.loads(data)
+        s2_dir="%s/%s"%(exp_root,exp_name)
+        os.makedirs("%s/logs_s2"%(s2_dir),exist_ok=True)
+        if(is_half==False):
+            data["train"]["fp16_run"]=False
+            batch_size=max(1,batch_size//2)
+        data["train"]["batch_size"]=batch_size
+        data["train"]["epochs"]=total_epoch
+        data["train"]["text_low_lr_rate"]=text_low_lr_rate
+        data["train"]["pretrained_s2G"]=pretrained_s2G
+        data["train"]["pretrained_s2D"]=pretrained_s2D
+        data["train"]["if_save_latest"]=if_save_latest
+        data["train"]["if_save_every_weights"]=if_save_every_weights
+        data["train"]["save_every_epoch"]=save_every_epoch
+        data["train"]["gpu_numbers"]=gpu_numbers1Ba
+        data["data"]["exp_dir"]=data["s2_ckpt_dir"]=s2_dir
+        data["save_weight_dir"]=SoVITS_weight_root
+        data["name"]=exp_name
+        tmp_config_path="%s/tmp_s2.json"%tmp
+        with open(tmp_config_path,"w")as f:f.write(json.dumps(data))
+
+        cmd = '"%s" GPT_SoVITS/s2_train.py --config "%s"'%(python_exec,tmp_config_path)
+        yield "SoVITS训练开始：%s"%cmd,{"__type__":"update","visible":False},{"__type__":"update","visible":True}
+        print(cmd)
+        p_train_SoVITS = Popen(cmd, shell=True)
+        p_train_SoVITS.wait()
+        p_train_SoVITS=None
+        yield "SoVITS训练完成",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
+    else:
+        yield "已有正在进行的SoVITS训练任务，需先终止才能开启下一次任务",{"__type__":"update","visible":False},{"__type__":"update","visible":True}
+
+def close1Ba():
+    global p_train_SoVITS
+    if(p_train_SoVITS!=None):
+        kill_process(p_train_SoVITS.pid)
+        p_train_SoVITS=None
+    return "已终止SoVITS训练",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
+
+p_train_GPT=None
+def open1Bb(batch_size,total_epoch,exp_name,if_dpo,if_save_latest,if_save_every_weights,save_every_epoch,gpu_numbers,pretrained_s1):
+    global p_train_GPT
+    if(p_train_GPT==None):
+        with open("GPT_SoVITS/configs/s1longer.yaml")as f:
+            data=f.read()
+            data=yaml.load(data, Loader=yaml.FullLoader)
+        s1_dir="%s/%s"%(exp_root,exp_name)
+        os.makedirs("%s/logs_s1"%(s1_dir),exist_ok=True)
+        if(is_half==False):
+            data["train"]["precision"]="32"
+            batch_size = max(1, batch_size // 2)
+        data["train"]["batch_size"]=batch_size
+        data["train"]["epochs"]=total_epoch
+        data["pretrained_s1"]=pretrained_s1
+        data["train"]["save_every_n_epoch"]=save_every_epoch
+        data["train"]["if_save_every_weights"]=if_save_every_weights
+        data["train"]["if_save_latest"]=if_save_latest
+        data["train"]["if_dpo"]=if_dpo
+        data["train"]["half_weights_save_dir"]=GPT_weight_root
+        data["train"]["exp_name"]=exp_name
+        data["train_semantic_path"]="%s/6-name2semantic.tsv"%s1_dir
+        data["train_phoneme_path"]="%s/2-name2text.txt"%s1_dir
+        data["output_dir"]="%s/logs_s1"%s1_dir
+
+        os.environ["_CUDA_VISIBLE_DEVICES"]=gpu_numbers.replace("-",",")
+        os.environ["hz"]="25hz"
+        tmp_config_path="%s/tmp_s1.yaml"%tmp
+        with open(tmp_config_path, "w") as f:f.write(yaml.dump(data, default_flow_style=False))
+        # cmd = '"%s" GPT_SoVITS/s1_train.py --config_file "%s" --train_semantic_path "%s/6-name2semantic.tsv" --train_phoneme_path "%s/2-name2text.txt" --output_dir "%s/logs_s1"'%(python_exec,tmp_config_path,s1_dir,s1_dir,s1_dir)
+        cmd = '"%s" GPT_SoVITS/s1_train.py --config_file "%s" '%(python_exec,tmp_config_path)
+        yield "GPT训练开始：%s"%cmd,{"__type__":"update","visible":False},{"__type__":"update","visible":True}
+        print(cmd)
+        p_train_GPT = Popen(cmd, shell=True)
+        p_train_GPT.wait()
+        p_train_GPT=None
+        yield "GPT训练完成",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
+    else:
+        yield "已有正在进行的GPT训练任务，需先终止才能开启下一次任务",{"__type__":"update","visible":False},{"__type__":"update","visible":True}
+
+def close1Bb():
+    global p_train_GPT
+    if(p_train_GPT!=None):
+        kill_process(p_train_GPT.pid)
+        p_train_GPT=None
+    return "已终止GPT训练",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
+
+ps_slice=[]
+def open_slice(inp,opt_root,threshold,min_length,min_interval,hop_size,max_sil_kept,_max,alpha,n_parts):
+    global ps_slice
+    inp = my_utils.clean_path(inp)
+    opt_root = my_utils.clean_path(opt_root)
+    if(os.path.exists(inp)==False):
+        yield "输入路径不存在",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
+        return
+    if os.path.isfile(inp):n_parts=1
+    elif os.path.isdir(inp):pass
+    else:
+        yield "输入路径存在但既不是文件也不是文件夹",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
+        return
+    if (ps_slice == []):
+        for i_part in range(n_parts):
+            cmd = '"%s" tools/slice_audio.py "%s" "%s" %s %s %s %s %s %s %s %s %s''' % (python_exec,inp, opt_root, threshold, min_length, min_interval, hop_size, max_sil_kept, _max, alpha, i_part, n_parts)
+            print(cmd)
+            p = Popen(cmd, shell=True)
+            ps_slice.append(p)
+        yield "切割执行中", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
+        for p in ps_slice:
+            p.wait()
+        ps_slice=[]
+        yield "切割结束",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
+    else:
+        yield "已有正在进行的切割任务，需先终止才能开启下一次任务", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
+
+def close_slice():
+    global ps_slice
+    if (ps_slice != []):
+        for p_slice in ps_slice:
+            try:
+                kill_process(p_slice.pid)
+            except:
+                traceback.print_exc()
+        ps_slice=[]
+    return "已终止所有切割进程", {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False}
+
+ps1a=[]
+def open1a(inp_text,inp_wav_dir,exp_name,gpu_numbers,bert_pretrained_dir):
+    global ps1a
+    inp_text = my_utils.clean_path(inp_text)
+    inp_wav_dir = my_utils.clean_path(inp_wav_dir)
+    if (ps1a == []):
+        opt_dir="%s/%s"%(exp_root,exp_name)
+        config={
+            "inp_text":inp_text,
+            "inp_wav_dir":inp_wav_dir,
+            "exp_name":exp_name,
+            "opt_dir":opt_dir,
+            "bert_pretrained_dir":bert_pretrained_dir,
+        }
+        gpu_names=gpu_numbers.split("-")
+        all_parts=len(gpu_names)
+        for i_part in range(all_parts):
+            config.update(
+                {
+                    "i_part": str(i_part),
+                    "all_parts": str(all_parts),
+                    "_CUDA_VISIBLE_DEVICES": gpu_names[i_part],
+                    "is_half": str(is_half)
+                }
+            )
+            os.environ.update(config)
+            cmd = '"%s" GPT_SoVITS/prepare_datasets/1-get-text.py'%python_exec
+            print(cmd)
+            p = Popen(cmd, shell=True)
+            ps1a.append(p)
+        yield "文本进程执行中", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
+        for p in ps1a:
+            p.wait()
+        opt = []
+        for i_part in range(all_parts):
+            txt_path = "%s/2-name2text-%s.txt" % (opt_dir, i_part)
+            with open(txt_path, "r", encoding="utf8") as f:
+                opt += f.read().strip("\n").split("\n")
+            os.remove(txt_path)
+        path_text = "%s/2-name2text.txt" % opt_dir
+        with open(path_text, "w", encoding="utf8") as f:
+            f.write("\n".join(opt) + "\n")
+        ps1a=[]
+        yield "文本进程结束",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
+    else:
+        yield "已有正在进行的文本任务，需先终止才能开启下一次任务", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
+
+def close1a():
+    global ps1a
+    if (ps1a != []):
+        for p1a in ps1a:
+            try:
+                kill_process(p1a.pid)
+            except:
+                traceback.print_exc()
+        ps1a=[]
+    return "已终止所有1a进程", {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False}
+
+ps1b=[]
+def open1b(inp_text,inp_wav_dir,exp_name,gpu_numbers,ssl_pretrained_dir):
+    global ps1b
+    inp_text = my_utils.clean_path(inp_text)
+    inp_wav_dir = my_utils.clean_path(inp_wav_dir)
+    if (ps1b == []):
+        config={
+            "inp_text":inp_text,
+            "inp_wav_dir":inp_wav_dir,
+            "exp_name":exp_name,
+            "opt_dir":"%s/%s"%(exp_root,exp_name),
+            "cnhubert_base_dir":ssl_pretrained_dir,
+            "is_half": str(is_half)
+        }
+        gpu_names=gpu_numbers.split("-")
+        all_parts=len(gpu_names)
+        for i_part in range(all_parts):
+            config.update(
+                {
+                    "i_part": str(i_part),
+                    "all_parts": str(all_parts),
+                    "_CUDA_VISIBLE_DEVICES": gpu_names[i_part],
+                }
+            )
+            os.environ.update(config)
+            cmd = '"%s" GPT_SoVITS/prepare_datasets/2-get-hubert-wav32k.py'%python_exec
+            print(cmd)
+            p = Popen(cmd, shell=True)
+            ps1b.append(p)
+        yield "SSL提取进程执行中", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
+        for p in ps1b:
+            p.wait()
+        ps1b=[]
+        yield "SSL提取进程结束",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
+    else:
+        yield "已有正在进行的SSL提取任务，需先终止才能开启下一次任务", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
+
+def close1b():
+    global ps1b
+    if (ps1b != []):
+        for p1b in ps1b:
+            try:
+                kill_process(p1b.pid)
+            except:
+                traceback.print_exc()
+        ps1b=[]
+    return "已终止所有1b进程", {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False}
+
+ps1c=[]
+def open1c(inp_text,exp_name,gpu_numbers,pretrained_s2G_path):
+    global ps1c
+    inp_text = my_utils.clean_path(inp_text)
+    if (ps1c == []):
+        opt_dir="%s/%s"%(exp_root,exp_name)
+        config={
+            "inp_text":inp_text,
+            "exp_name":exp_name,
+            "opt_dir":opt_dir,
+            "pretrained_s2G":pretrained_s2G_path,
+            "s2config_path":"GPT_SoVITS/configs/s2.json",
+            "is_half": str(is_half)
+        }
+        gpu_names=gpu_numbers.split("-")
+        all_parts=len(gpu_names)
+        for i_part in range(all_parts):
+            config.update(
+                {
+                    "i_part": str(i_part),
+                    "all_parts": str(all_parts),
+                    "_CUDA_VISIBLE_DEVICES": gpu_names[i_part],
+                }
+            )
+            os.environ.update(config)
+            cmd = '"%s" GPT_SoVITS/prepare_datasets/3-get-semantic.py'%python_exec
+            print(cmd)
+            p = Popen(cmd, shell=True)
+            ps1c.append(p)
+        yield "语义token提取进程执行中", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
+        for p in ps1c:
+            p.wait()
+        opt = ["item_name\tsemantic_audio"]
+        path_semantic = "%s/6-name2semantic.tsv" % opt_dir
+        for i_part in range(all_parts):
+            semantic_path = "%s/6-name2semantic-%s.tsv" % (opt_dir, i_part)
+            with open(semantic_path, "r", encoding="utf8") as f:
+                opt += f.read().strip("\n").split("\n")
+            os.remove(semantic_path)
+        with open(path_semantic, "w", encoding="utf8") as f:
+            f.write("\n".join(opt) + "\n")
+        ps1c=[]
+        yield "语义token提取进程结束",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
+    else:
+        yield "已有正在进行的语义token提取任务，需先终止才能开启下一次任务", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
+
+def close1c():
+    global ps1c
+    if (ps1c != []):
+        for p1c in ps1c:
+            try:
+                kill_process(p1c.pid)
+            except:
+                traceback.print_exc()
+        ps1c=[]
+    return "已终止所有语义token进程", {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False}
+#####inp_text,inp_wav_dir,exp_name,gpu_numbers1a,gpu_numbers1Ba,gpu_numbers1c,bert_pretrained_dir,cnhubert_base_dir,pretrained_s2G
+ps1abc=[]
+def open1abc(inp_text,inp_wav_dir,exp_name,gpu_numbers1a,gpu_numbers1Ba,gpu_numbers1c,bert_pretrained_dir,ssl_pretrained_dir,pretrained_s2G_path):
+    global ps1abc
+    inp_text = my_utils.clean_path(inp_text)
+    inp_wav_dir = my_utils.clean_path(inp_wav_dir)
+    if (ps1abc == []):
+        opt_dir="%s/%s"%(exp_root,exp_name)
+        try:
+            #############################1a
+            path_text="%s/2-name2text.txt" % opt_dir
+            if(os.path.exists(path_text)==False or (os.path.exists(path_text)==True and len(open(path_text,"r",encoding="utf8").read().strip("\n").split("\n"))<2)):
+                config={
+                    "inp_text":inp_text,
+                    "inp_wav_dir":inp_wav_dir,
+                    "exp_name":exp_name,
+                    "opt_dir":opt_dir,
+                    "bert_pretrained_dir":bert_pretrained_dir,
+                    "is_half": str(is_half)
+                }
+                gpu_names=gpu_numbers1a.split("-")
+                all_parts=len(gpu_names)
+                for i_part in range(all_parts):
+                    config.update(
+                        {
+                            "i_part": str(i_part),
+                            "all_parts": str(all_parts),
+                            "_CUDA_VISIBLE_DEVICES": gpu_names[i_part],
+                        }
+                    )
+                    os.environ.update(config)
+                    cmd = '"%s" GPT_SoVITS/prepare_datasets/1-get-text.py'%python_exec
+                    print(cmd)
+                    p = Popen(cmd, shell=True)
+                    ps1abc.append(p)
+                yield "进度：1a-ing", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
+                for p in ps1abc:p.wait()
+
+                opt = []
+                for i_part in range(all_parts):#txt_path="%s/2-name2text-%s.txt"%(opt_dir,i_part)
+                    txt_path = "%s/2-name2text-%s.txt" % (opt_dir, i_part)
+                    with open(txt_path, "r",encoding="utf8") as f:
+                        opt += f.read().strip("\n").split("\n")
+                    os.remove(txt_path)
+                with open(path_text, "w",encoding="utf8") as f:
+                    f.write("\n".join(opt) + "\n")
+
+            yield "进度：1a-done", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
+            ps1abc=[]
+            #############################1b
+            config={
+                "inp_text":inp_text,
+                "inp_wav_dir":inp_wav_dir,
+                "exp_name":exp_name,
+                "opt_dir":opt_dir,
+                "cnhubert_base_dir":ssl_pretrained_dir,
+            }
+            gpu_names=gpu_numbers1Ba.split("-")
+            all_parts=len(gpu_names)
+            for i_part in range(all_parts):
+                config.update(
+                    {
+                        "i_part": str(i_part),
+                        "all_parts": str(all_parts),
+                        "_CUDA_VISIBLE_DEVICES": gpu_names[i_part],
+                    }
+                )
+                os.environ.update(config)
+                cmd = '"%s" GPT_SoVITS/prepare_datasets/2-get-hubert-wav32k.py'%python_exec
+                print(cmd)
+                p = Popen(cmd, shell=True)
+                ps1abc.append(p)
+            yield "进度：1a-done, 1b-ing", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
+            for p in ps1abc:p.wait()
+            yield "进度：1a1b-done", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
+            ps1abc=[]
+            #############################1c
+            path_semantic = "%s/6-name2semantic.tsv" % opt_dir
+            if(os.path.exists(path_semantic)==False or (os.path.exists(path_semantic)==True and os.path.getsize(path_semantic)<31)):
+                config={
+                    "inp_text":inp_text,
+                    "exp_name":exp_name,
+                    "opt_dir":opt_dir,
+                    "pretrained_s2G":pretrained_s2G_path,
+                    "s2config_path":"GPT_SoVITS/configs/s2.json",
+                }
+                gpu_names=gpu_numbers1c.split("-")
+                all_parts=len(gpu_names)
+                for i_part in range(all_parts):
+                    config.update(
+                        {
+                            "i_part": str(i_part),
+                            "all_parts": str(all_parts),
+                            "_CUDA_VISIBLE_DEVICES": gpu_names[i_part],
+                        }
+                    )
+                    os.environ.update(config)
+                    cmd = '"%s" GPT_SoVITS/prepare_datasets/3-get-semantic.py'%python_exec
+                    print(cmd)
+                    p = Popen(cmd, shell=True)
+                    ps1abc.append(p)
+                yield "进度：1a1b-done, 1cing", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
+                for p in ps1abc:p.wait()
+
+                opt = ["item_name\tsemantic_audio"]
+                for i_part in range(all_parts):
+                    semantic_path = "%s/6-name2semantic-%s.tsv" % (opt_dir, i_part)
+                    with open(semantic_path, "r",encoding="utf8") as f:
+                        opt += f.read().strip("\n").split("\n")
+                    os.remove(semantic_path)
+                with open(path_semantic, "w",encoding="utf8") as f:
+                    f.write("\n".join(opt) + "\n")
+                yield "进度：all-done", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
+            ps1abc = []
+            yield "一键三连进程结束", {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False}
+        except:
+            traceback.print_exc()
+            close1abc()
+            yield "一键三连中途报错", {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False}
+    else:
+        yield "已有正在进行的一键三连任务，需先终止才能开启下一次任务", {"__type__": "update", "visible": False}, {"__type__": "update", "visible": True}
+
+def close1abc():
+    global ps1abc
+    if (ps1abc != []):
+        for p1abc in ps1abc:
+            try:
+                kill_process(p1abc.pid)
+            except:
+                traceback.print_exc()
+        ps1abc=[]
+    return "已终止所有一键三连进程", {"__type__": "update", "visible": True}, {"__type__": "update", "visible": False}
+
+with gr.Blocks(title="GPT-SoVITS WebUI") as app:
+    gr.Markdown("# <center>🌊💕🎶 XTTS 微调：2分钟语音，开启中日英16种语言真实拟声</center>")
+    gr.Markdown("## <center>🌟 只需2分钟的语音，一键在线微调 最强多语种模型</center>")
+    gr.Markdown("### <center>🤗 更多精彩，尽在[滔滔AI](https://www.talktalkai.com/)；滔滔AI，��爱滔滔！💕</center>")
+
+    with gr.Tabs():
+        with gr.TabItem(i18n("1 - 制作数据集")):#提前随机切片防止uvr5爆内存->uvr5->slicer->asr->打标
+            #gr.Markdown(value=i18n("0a-UVR5人声伴奏分离&去混响去延迟工具"))
+            with gr.Row():
+                if_uvr5 = gr.Checkbox(label=i18n("是否开启UVR5-WebUI"),show_label=True, visible=False)
+                uvr5_info = gr.Textbox(label=i18n("UVR5进程输出信息"), visible=False)
+            gr.Markdown(value=i18n("1a-语音切分工具"))
+            with gr.Row():
+                with gr.Row():
+                    slice_inp_path=gr.Textbox(label=i18n("音频自动切分输入路径，可文件可文件夹"),info="您需要先在xtts-v2文件夹中上传训练音频，如jay.wav；音频时长建议大于2分钟",value="",placeholder="jay.wav")
+                    slice_opt_root=gr.Textbox(label=i18n("切分后的子音频的输出根目录"),value="output/slicer_opt")
+                    threshold=gr.Textbox(label=i18n("threshold:音量小于这个值视作静音的备选切割点"),value="-34")
+                    min_length=gr.Textbox(label=i18n("min_length:每段最小多长，如果第一段太短一直和后面段连起来直到超过这个值"),value="4000")
+                    min_interval=gr.Textbox(label=i18n("min_interval:最短切割间隔"),value="300")
+                    hop_size=gr.Textbox(label=i18n("hop_size:怎么算音量曲线，越小精度越大计算量越高（不是精度越大效果越好）"),value="10")
+                    max_sil_kept=gr.Textbox(label=i18n("max_sil_kept:切完后静音最多留多长"),value="500")
+                with gr.Row():
+                    open_slicer_button=gr.Button(i18n("1. 开启语音切割"), variant="primary",visible=True)
+                    close_slicer_button=gr.Button(i18n("终止语音切割"), variant="primary",visible=False)
+                    _max=gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("max:归一化后最大值多少"),value=0.9,interactive=True)
+                    alpha=gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("alpha_mix:混多少比例归一化后音频进来"),value=0.25,interactive=True)
+                    n_process=gr.Slider(minimum=1,maximum=n_cpu,step=1,label=i18n("切割使用的进程数"),value=4,interactive=True)
+                    slicer_info = gr.Textbox(label=i18n("语音切割进程输出信息"))
+            #gr.Markdown(value=i18n("0bb-语音降噪工具"))
+            with gr.Row():
+                open_denoise_button = gr.Button(i18n("开启语音降噪"), visible=False)
+                close_denoise_button = gr.Button(i18n("终止语音降噪进程"), variant="primary",visible=False)
+                denoise_input_dir=gr.Textbox(label=i18n("降噪音频文件输入文件夹"),value="", visible=False)
+                denoise_output_dir=gr.Textbox(label=i18n("降噪结果输出文件夹"),value="output/denoise_opt", visible=False)
+                denoise_info = gr.Textbox(label=i18n("语音降噪进程输出信息"), visible=False)
+            gr.Markdown(value=i18n("1b-批量语音识别"))
+            with gr.Row():
+                open_asr_button = gr.Button(i18n("2. 开启离线批量ASR"), variant="primary",visible=True)
+                close_asr_button = gr.Button(i18n("终止ASR进程"), variant="primary",visible=False)
+                with gr.Column():
+                    with gr.Row():
+                        asr_inp_dir = gr.Textbox(
+                            label=i18n("输入文件夹路径"),
+                            value="output/slicer_opt",
+                            interactive=True,
+                        )
+                        asr_opt_dir = gr.Textbox(
+                            label       = i18n("输出文件夹路径"),
+                            value       = "output/asr_opt",
+                            interactive = True,
+                        )
+                    with gr.Row():
+                        asr_model = gr.Dropdown(
+                            label       = i18n("ASR 模型"),
+                            choices     = list(asr_dict.keys()),
+                            interactive = True,
+                            value="达摩 ASR (中文)"
+                        )
+                        asr_size = gr.Dropdown(
+                            label       = i18n("ASR 模型尺寸"),
+                            choices     = ["large"],
+                            interactive = True,
+                            value="large"
+                        )
+                        asr_lang = gr.Dropdown(
+                            label       = i18n("ASR 语言设置"),
+                            choices     = ["zh"],
+                            interactive = True,
+                            value="zh"
+                        )
+                        lang = asr_lang
+                    with gr.Row():
+                        asr_info = gr.Textbox(label=i18n("ASR进程输出信息"))
+
+                def change_lang_choices(key): #根据选择的模型修改可选的语言
+                    # return gr.Dropdown(choices=asr_dict[key]['lang'])
+                    return {"__type__": "update", "choices": asr_dict[key]['lang'],"value":asr_dict[key]['lang'][0]}
+                def change_size_choices(key): # 根据选择的模型修改可选的模型尺寸
+                    # return gr.Dropdown(choices=asr_dict[key]['size'])
+                    return {"__type__": "update", "choices": asr_dict[key]['size']}
+                asr_model.change(change_lang_choices, [asr_model], [asr_lang])
+                asr_model.change(change_size_choices, [asr_model], [asr_size])
+                
+            gr.Markdown(value=i18n("1c-语音文本校对标注工具"))
+            with gr.Row():
+                if_label = gr.Checkbox(label=i18n("是否开启打标WebUI"),show_label=True)
+                path_list = gr.Textbox(
+                    label=i18n(".list标注文件的路径"),
+                    value="output/asr_opt/slicer_opt.list",
+                    interactive=True,
+                )
+                label_info = gr.Textbox(label=i18n("打标工具进程输出信息"))
+            if_label.change(change_label, [if_label,path_list], [label_info])
+            if_uvr5.change(change_uvr5, [if_uvr5], [uvr5_info])
+            open_asr_button.click(open_asr, [asr_inp_dir, asr_opt_dir, asr_model, asr_size, asr_lang], [asr_info,open_asr_button,close_asr_button])
+            close_asr_button.click(close_asr, [], [asr_info,open_asr_button,close_asr_button])
+            open_slicer_button.click(open_slice, [slice_inp_path,slice_opt_root,threshold,min_length,min_interval,hop_size,max_sil_kept,_max,alpha,n_process], [slicer_info,open_slicer_button,close_slicer_button])
+            close_slicer_button.click(close_slice, [], [slicer_info,open_slicer_button,close_slicer_button])
+            open_denoise_button.click(open_denoise, [denoise_input_dir,denoise_output_dir], [denoise_info,open_denoise_button,close_denoise_button])
+            close_denoise_button.click(close_denoise, [], [denoise_info,open_denoise_button,close_denoise_button])
+
+        with gr.Tab("2 - XTTS模型微调"):
+            inp_list_path_value = str(Path.cwd() / "output/asr_opt/slicer_opt.list")
+            out_csv_path_value = str(Path.cwd() / "output.csv")
+            inp_list_path = gr.Textbox(value=inp_list_path_value, label=".list文件地址")
+            out_csv_path = gr.Textbox(value=out_csv_path_value, label=".csv文件地址")
+            list_to_csv = gr.Button("3. 准备训练csv文件", variant="primary")
+            train_csv = gr.Textbox(
+                label="训练数据集csv文件",
+            )
+            eval_csv = gr.Textbox(
+                label="评价数据集csv文件",
+            )
+            list_to_csv.click(convert_list_to_csv, [inp_list_path, out_csv_path], [train_csv, eval_csv])
+            out_path_value = str(Path.cwd() / "finetune_models")
+            out_path = gr.Textbox(value=out_path_value, label="XTTS微调模型的文件夹")
+            num_epochs =  gr.Slider(
+                label="训练步数 Number of epochs:",
+                minimum=1,
+                maximum=100,
+                step=1,
+                value=6,
+            )
+            batch_size = gr.Slider(
+                label="Batch size:",
+                minimum=2,
+                maximum=512,
+                step=1,
+                value=2,
+            )
+            grad_acumm = gr.Slider(
+                label="Grad accumulation steps:",
+                minimum=1,
+                maximum=128,
+                step=1,
+                value=1,
+            )
+            max_audio_length = gr.Slider(
+                label="Max permitted audio size in seconds:",
+                minimum=2,
+                maximum=20,
+                step=1,
+                value=11,
+                visible=False,
+            )
+            progress_train = gr.Label(
+                label="训练进程"
+            )
+            logs_tts_train = gr.Textbox(
+                label="训练详细信息",
+                interactive=False,
+            )
+            app.load(read_logs, None, logs_tts_train, every=1)
+            train_btn = gr.Button(value="4. 开始模型训练", variant="primary")
+
+            def train_model(language, train_csv, eval_csv, num_epochs, batch_size, grad_acumm, output_path, max_audio_length):
+                print(f"开始训练，训练素材的语种为：{language}")
+                clear_gpu_cache()
+                if not train_csv or not eval_csv:
+                    return "You need to run the data processing step or manually set `Train CSV` and `Eval CSV` fields !", "", "", "", ""
+                try:
+                    # convert seconds to waveform frames
+                    max_audio_length = int(max_audio_length * 22050)
+                    config_path, original_xtts_checkpoint, vocab_file, exp_path, speaker_wav = train_gpt(language, num_epochs, batch_size, grad_acumm, train_csv, eval_csv, output_path=output_path, max_audio_length=max_audio_length)
+                except:
+                    traceback.print_exc()
+                    error = traceback.format_exc()
+                    return f"The training was interrupted due an error !! Please check the console to check the full error message! \n Error summary: {error}", "", "", "", ""
+
+                # copy original files to avoid parameters changes issues
+                os.system(f"cp {config_path} {exp_path}")
+                os.system(f"cp {vocab_file} {exp_path}")
+
+                ft_xtts_checkpoint = os.path.join(exp_path, "best_model.pth")
+                print("模型已成功微调！")
+                clear_gpu_cache()
+                ref_audio_names = os.listdir("dingzhen/dingzhen")
+                ref_audio_list = [os.path.join("dingzhen/dingzhen", ref_audio_name) for ref_audio_name in ref_audio_names]
+                first_five_ref_audio = "\n".join(ref_audio_list[0:8])
+                return "模型已成功微调！", config_path, vocab_file, ft_xtts_checkpoint, first_five_ref_audio, speaker_wav
+
+        with gr.Tab("3 - XTTS语音合成"):
+            with gr.Row():
+                with gr.Column() as col1:
+                    xtts_checkpoint = gr.Textbox(
+                        label="XTTS checkpoint 路径",
+                        value="",
+                    )
+                    xtts_config = gr.Textbox(
+                        label="XTTS config 路径",
+                        value="",
+                    )
+
+                    xtts_vocab = gr.Textbox(
+                        label="XTTS vocab 路径",
+                        value="",
+                    )
+                    progress_load = gr.Label(
+                        label="模型加载进程"
+                    )
+                    load_btn = gr.Button(value="5. 加载已训练好的模型", variant="primary")
+
+                with gr.Column() as col2:
+                    first_five_speaker_reference_audio = gr.Textbox(label="您可以选用的参考音频", visible=True, interactive=True)
+                    speaker_reference_audio = gr.Textbox(
+                        label="您正在使用的参考音频",
+                        info="不同参考音频对应的合成效果不同。您可以尝试多次，每次填写一条音频路径",
+                        value="",
+                    )
+                    tts_text = gr.Textbox(
+                        label="请填写语音合成的文本🍻",
+                        placeholder="想说却还没说的，还很多",
+                    )
+                    tts_language = gr.Dropdown(
+                        label="请选择文本对应的语言",
+                        value="zh",
+                        choices=[
+                            "en",
+                            "es",
+                            "fr",
+                            "de",
+                            "it",
+                            "pt",
+                            "pl",
+                            "tr",
+                            "ru",
+                            "nl",
+                            "cs",
+                            "ar",
+                            "zh",
+                            "hu",
+                            "ko",
+                            "ja",
+                        ]
+                    )
+
+                    tts_btn = gr.Button(value="6. 开启AI语音之旅吧💕", variant="primary")
+
+                with gr.Column() as col3:
+                    progress_gen = gr.Label(
+                        label="语音合成进程"
+                    )
+                    tts_output_audio = gr.Audio(label="为您合成的专属音频🎶")
+                    reference_audio = gr.Audio(label="您使用的参考音频")
+
+            train_btn.click(
+                fn=train_model,
+                inputs=[
+                    lang,
+                    train_csv,
+                    eval_csv,
+                    num_epochs,
+                    batch_size,
+                    grad_acumm,
+                    out_path,
+                    max_audio_length,
+                ],
+                outputs=[progress_train, xtts_config, xtts_vocab, xtts_checkpoint, first_five_speaker_reference_audio, speaker_reference_audio],
+            )
+            
+            load_btn.click(
+                fn=load_model,
+                inputs=[
+                    xtts_checkpoint,
+                    xtts_config,
+                    xtts_vocab
+                ],
+                outputs=[progress_load],
+            )
+
+            tts_btn.click(
+                fn=run_tts,
+                inputs=[
+                    tts_language,
+                    tts_text,
+                    speaker_reference_audio,
+                ],
+                outputs=[progress_gen, tts_output_audio, reference_audio],
+            )
+
+    gr.Markdown("### <center>注意❗：请不要生成会对个人以及���织造成侵害的内容，此程序仅供科研、学习及个人娱乐使用。请自觉合规使用此程序，程序开发者不负有任何责任。</center>")
+    gr.HTML('''
+        <div class="footer">
+                    <p>🌊🏞️🎶 - 江水东流急，滔滔无尽声。 明·顾璘
+                    </p>
+        </div>
+    ''')
+    app.queue().launch(
+        share=True,
+        show_error=True,
+    )

finetune_models/run/training/XTTS_v2.0_original_model_files/config.json

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+{
+    "output_path": "output",
+    "logger_uri": null,
+    "run_name": "run",
+    "project_name": null,
+    "run_description": "\ud83d\udc38Coqui trainer run.",
+    "print_step": 25,
+    "plot_step": 100,
+    "model_param_stats": false,
+    "wandb_entity": null,
+    "dashboard_logger": "tensorboard",
+    "save_on_interrupt": true,
+    "log_model_step": null,
+    "save_step": 10000,
+    "save_n_checkpoints": 5,
+    "save_checkpoints": true,
+    "save_all_best": false,
+    "save_best_after": 10000,
+    "target_loss": null,
+    "print_eval": false,
+    "test_delay_epochs": 0,
+    "run_eval": true,
+    "run_eval_steps": null,
+    "distributed_backend": "nccl",
+    "distributed_url": "tcp://localhost:54321",
+    "mixed_precision": false,
+    "precision": "fp16",
+    "epochs": 1000,
+    "batch_size": 32,
+    "eval_batch_size": 16,
+    "grad_clip": 0.0,
+    "scheduler_after_epoch": true,
+    "lr": 0.001,
+    "optimizer": "radam",
+    "optimizer_params": null,
+    "lr_scheduler": null,
+    "lr_scheduler_params": {},
+    "use_grad_scaler": false,
+    "allow_tf32": false,
+    "cudnn_enable": true,
+    "cudnn_deterministic": false,
+    "cudnn_benchmark": false,
+    "training_seed": 54321,
+    "model": "xtts",
+    "num_loader_workers": 0,
+    "num_eval_loader_workers": 0,
+    "use_noise_augment": false,
+    "audio": {
+        "sample_rate": 22050,
+        "output_sample_rate": 24000
+    },
+    "use_phonemes": false,
+    "phonemizer": null,
+    "phoneme_language": null,
+    "compute_input_seq_cache": false,
+    "text_cleaner": null,
+    "enable_eos_bos_chars": false,
+    "test_sentences_file": "",
+    "phoneme_cache_path": null,
+    "characters": null,
+    "add_blank": false,
+    "batch_group_size": 0,
+    "loss_masking": null,
+    "min_audio_len": 1,
+    "max_audio_len": Infinity,
+    "min_text_len": 1,
+    "max_text_len": Infinity,
+    "compute_f0": false,
+    "compute_energy": false,
+    "compute_linear_spec": false,
+    "precompute_num_workers": 0,
+    "start_by_longest": false,
+    "shuffle": false,
+    "drop_last": false,
+    "datasets": [
+        {
+            "formatter": "",
+            "dataset_name": "",
+            "path": "",
+            "meta_file_train": "",
+            "ignored_speakers": null,
+            "language": "",
+            "phonemizer": "",
+            "meta_file_val": "",
+            "meta_file_attn_mask": ""
+        }
+    ],
+    "test_sentences": [],
+    "eval_split_max_size": null,
+    "eval_split_size": 0.01,
+    "use_speaker_weighted_sampler": false,
+    "speaker_weighted_sampler_alpha": 1.0,
+    "use_language_weighted_sampler": false,
+    "language_weighted_sampler_alpha": 1.0,
+    "use_length_weighted_sampler": false,
+    "length_weighted_sampler_alpha": 1.0,
+    "model_args": {
+        "gpt_batch_size": 1,
+        "enable_redaction": false,
+        "kv_cache": true,
+        "gpt_checkpoint": null,
+        "clvp_checkpoint": null,
+        "decoder_checkpoint": null,
+        "num_chars": 255,
+        "tokenizer_file": "",
+        "gpt_max_audio_tokens": 605,
+        "gpt_max_text_tokens": 402,
+        "gpt_max_prompt_tokens": 70,
+        "gpt_layers": 30,
+        "gpt_n_model_channels": 1024,
+        "gpt_n_heads": 16,
+        "gpt_number_text_tokens": 6681,
+        "gpt_start_text_token": null,
+        "gpt_stop_text_token": null,
+        "gpt_num_audio_tokens": 1026,
+        "gpt_start_audio_token": 1024,
+        "gpt_stop_audio_token": 1025,
+        "gpt_code_stride_len": 1024,
+        "gpt_use_masking_gt_prompt_approach": true,
+        "gpt_use_perceiver_resampler": true,
+        "input_sample_rate": 22050,
+        "output_sample_rate": 24000,
+        "output_hop_length": 256,
+        "decoder_input_dim": 1024,
+        "d_vector_dim": 512,
+        "cond_d_vector_in_each_upsampling_layer": true,
+        "duration_const": 102400
+    },
+    "model_dir": null,
+    "languages": [
+        "en",
+        "es",
+        "fr",
+        "de",
+        "it",
+        "pt",
+        "pl",
+        "tr",
+        "ru",
+        "nl",
+        "cs",
+        "ar",
+        "zh-cn",
+        "hu",
+        "ko",
+        "ja",
+        "hi"
+    ],
+    "temperature": 0.75,
+    "length_penalty": 1.0,
+    "repetition_penalty": 5.0,
+    "top_k": 50,
+    "top_p": 0.85,
+    "num_gpt_outputs": 1,
+    "gpt_cond_len": 30,
+    "gpt_cond_chunk_len": 4,
+    "max_ref_len": 30,
+    "sound_norm_refs": false
+}

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