import argparse import os import sys import tempfile import gradio as gr import librosa.display import numpy as np import os import torch import torchaudio import traceback from TTS.tts.configs.xtts_config import XttsConfig from TTS.tts.models.xtts import Xtts import spaces def clear_gpu_cache(): # clear the GPU cache if torch.cuda.is_available(): torch.cuda.empty_cache() XTTS_MODEL = None def load_model(choice): global XTTS_MODEL clear_gpu_cache() if choice == "dingzhen": xtts_checkpoint="./finetune_models/run/training/GPT_XTTS_FT-July-04-2024_01+29PM-44c61c9/best_model.pth" xtts_config="./finetune_models/run/training/XTTS_v2.0_original_model_files/config.json" xtts_vocab="./finetune_models/run/training/XTTS_v2.0_original_model_files/vocab.json" elif choice == "kobe": xtts_checkpoint="./finetune_models_kobe/run/training/GPT_XTTS_FT-July-05-2024_09+09AM-44c61c9/best_model.pth" xtts_config="./finetune_models_kobe/run/training/XTTS_v2.0_original_model_files/config.json" xtts_vocab="./finetune_models_kobe/run/training/XTTS_v2.0_original_model_files/vocab.json" 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, speaker_file_path="./speakers_xtts.pth", 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!" @spaces.GPU def run_tts(lang, tts_text, speaker_audio_file): #print(XTTS_MODEL) #print(speaker_audio_file) if XTTS_MODEL is None or not speaker_audio_file: return "您需要先执行第1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() with gr.Blocks(title="GPT-SoVITS WebUI") as app: gr.Markdown("#