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| __version__ = "2.4.3 240414" | |
| import os, json | |
| import torch | |
| import logging | |
| from pydantic import BaseModel, Field | |
| logging.getLogger("markdown_it").setLevel(logging.ERROR) | |
| logging.getLogger("urllib3").setLevel(logging.ERROR) | |
| logging.getLogger("httpcore").setLevel(logging.ERROR) | |
| logging.getLogger("httpx").setLevel(logging.ERROR) | |
| logging.getLogger("asyncio").setLevel(logging.ERROR) | |
| logging.getLogger("charset_normalizer").setLevel(logging.ERROR) | |
| logging.getLogger("torchaudio._extension").setLevel(logging.ERROR) | |
| def test_fp16_computation(): | |
| # 检查CUDA是否可用 | |
| if not torch.cuda.is_available(): | |
| return False, "CUDA is not available. Please check your installation." | |
| try: | |
| # 创建一个简单的半精度张量计算任务 | |
| # 例如，执行一个半精度的矩阵乘法 | |
| a = torch.randn(3, 3, dtype=torch.float16).cuda() # 将张量a转换为半精度并移动到GPU | |
| b = torch.randn(3, 3, dtype=torch.float16).cuda() # 将张量b转换为半精度并移动到GPU | |
| c = torch.matmul(a, b) # 执行半精度的矩阵乘法 | |
| # 如果没有发生错误，我们认为GPU支持半精度运算 | |
| return True, "Your GPU supports FP16 computation." | |
| except Exception as e: | |
| # 如果执行过程中发生异常，我们认为GPU不支持半精度运算 | |
| return False, f"Your GPU does not support FP16 computation. Error: {e}" | |
| def get_device_info(device_config="auto", is_half_config="auto")-> tuple[str, bool]: | |
| global device, is_half | |
| try: | |
| return device, is_half | |
| except: | |
| if torch.cuda.is_available(): | |
| device = "cuda" | |
| is_half = True | |
| else: | |
| device = "cpu" | |
| is_half = False | |
| if device_config != "auto": | |
| device = device_config | |
| is_half = (device == "cpu") | |
| if is_half_config != "auto": | |
| is_half = str(is_half_config).lower() == "true" | |
| supports_fp16, message = test_fp16_computation() | |
| if not supports_fp16 and is_half: | |
| is_half = False | |
| print(message) | |
| return device, is_half | |
| def load_infer_config(character_path): | |
| config_path = os.path.join(character_path, "infer_config.json") | |
| """加载环境配置文件""" | |
| with open(config_path, 'r', encoding='utf-8') as f: | |
| config = json.load(f) | |
| return config | |
| def auto_generate_infer_config(character_path): | |
| ## TODO: Auto-generate wav-list and prompt-list from character_path | |
| ## | |
| # Initialize variables for file detection | |
| print(f"正在自动生成配置文件: {character_path}") | |
| ckpt_file_found = None | |
| pth_file_found = None | |
| wav_file_found = None | |
| # Iterate through files in character_path to find matching file types | |
| for dirpath, dirnames, filenames in os.walk(character_path): | |
| for file in filenames: | |
| # 构建文件的完整路径 | |
| full_path = os.path.join(dirpath, file) | |
| # 从full_path中移除character_path部分 | |
| relative_path = remove_character_path(full_path,character_path) | |
| # 根据文件扩展名和变量是否已赋值来更新变量 | |
| if file.lower().endswith(".ckpt") and ckpt_file_found is None: | |
| ckpt_file_found = relative_path | |
| elif file.lower().endswith(".pth") and pth_file_found is None: | |
| pth_file_found = relative_path | |
| elif file.lower().endswith(".wav") and wav_file_found is None: | |
| wav_file_found = relative_path | |
| elif file.lower().endswith(".mp3"): | |
| import pydub | |
| # Convert mp3 to wav | |
| wav_file_path = os.path.join(dirpath,os.path.splitext(file)[0] + ".wav") | |
| pydub.AudioSegment.from_mp3(full_path).export(wav_file_path, format="wav") | |
| if wav_file_found is None: | |
| wav_file_found = remove_character_path(os.path.join(dirpath,os.path.splitext(file)[0] + ".wav"),character_path) | |
| # Initialize infer_config with gpt_path and sovits_path regardless of wav_file_found | |
| infer_config = { | |
| "gpt_path": ckpt_file_found, | |
| "sovits_path": pth_file_found, | |
| "software_version": "1.1", | |
| r"简介": r"这是一个配置文件适用于https://github.com/X-T-E-R/TTS-for-GPT-soVITS，是一个简单好用的前后端项目" | |
| } | |
| # If wav file is also found, update infer_config to include ref_audio_path, prompt_text, and prompt_language | |
| if wav_file_found: | |
| wav_file_name = os.path.splitext(os.path.basename(wav_file_found))[0] # Extract the filename without extension | |
| infer_config["emotion_list"] = { | |
| "default": { | |
| "ref_audio_path": wav_file_found, | |
| "prompt_text": wav_file_name, | |
| "prompt_language": "多语种混合" | |
| } | |
| } | |
| else: | |
| raise Exception("找不到wav参考文件！请把有效wav文件放置在模型文件夹下。") | |
| pass | |
| # Check if the essential model files were found | |
| if ckpt_file_found and pth_file_found: | |
| infer_config_path = os.path.join(character_path, "infer_config.json") | |
| try: | |
| with open(infer_config_path , 'w', encoding='utf-8') as f: | |
| json.dump(infer_config, f, ensure_ascii=False, indent=4) | |
| except IOError as e: | |
| print(f"无法写入文件: {infer_config_path}. 错误: {e}") | |
| return infer_config_path | |
| else: | |
| return "Required model files (.ckpt or .pth) not found in character_path directory." | |
| def remove_character_path(full_path,character_path): | |
| # 从full_path中移除character_path部分 | |
| return os.path.relpath(full_path, character_path) | |