import numpy as np import torch from modules.speaker import Speaker from modules.utils.SeedContext import SeedContext from modules import models, config import logging import gc from modules.devices import devices from typing import Union from modules.utils.cache import conditional_cache logger = logging.getLogger(__name__) def generate_audio( text: str, temperature: float = 0.3, top_P: float = 0.7, top_K: float = 20, spk: Union[int, Speaker] = -1, infer_seed: int = -1, use_decoder: bool = True, prompt1: str = "", prompt2: str = "", prefix: str = "", ): (sample_rate, wav) = generate_audio_batch( [text], temperature=temperature, top_P=top_P, top_K=top_K, spk=spk, infer_seed=infer_seed, use_decoder=use_decoder, prompt1=prompt1, prompt2=prompt2, prefix=prefix, )[0] return (sample_rate, wav) @torch.inference_mode() def generate_audio_batch( texts: list[str], temperature: float = 0.3, top_P: float = 0.7, top_K: float = 20, spk: Union[int, Speaker] = -1, infer_seed: int = -1, use_decoder: bool = True, prompt1: str = "", prompt2: str = "", prefix: str = "", ): chat_tts = models.load_chat_tts() params_infer_code = { "spk_emb": None, "temperature": temperature, "top_P": top_P, "top_K": top_K, "prompt1": prompt1 or "", "prompt2": prompt2 or "", "prefix": prefix or "", "repetition_penalty": 1.0, "disable_tqdm": config.runtime_env_vars.off_tqdm, } if isinstance(spk, int): with SeedContext(spk): params_infer_code["spk_emb"] = chat_tts.sample_random_speaker() logger.info(("spk", spk)) elif isinstance(spk, Speaker): params_infer_code["spk_emb"] = spk.emb logger.info(("spk", spk.name)) else: raise ValueError("spk must be int or Speaker") logger.info( { "text": texts, "infer_seed": infer_seed, "temperature": temperature, "top_P": top_P, "top_K": top_K, "prompt1": prompt1 or "", "prompt2": prompt2 or "", "prefix": prefix or "", } ) with SeedContext(infer_seed): wavs = chat_tts.generate_audio( texts, params_infer_code, use_decoder=use_decoder ) sample_rate = 24000 if config.auto_gc: devices.torch_gc() gc.collect() return [(sample_rate, np.array(wav).flatten().astype(np.float32)) for wav in wavs] lru_cache_enabled = False def setup_lru_cache(): global generate_audio_batch global lru_cache_enabled if lru_cache_enabled: return lru_cache_enabled = True def should_cache(*args, **kwargs): spk_seed = kwargs.get("spk", -1) infer_seed = kwargs.get("infer_seed", -1) return spk_seed != -1 and infer_seed != -1 lru_size = config.runtime_env_vars.lru_size if isinstance(lru_size, int): generate_audio_batch = conditional_cache(lru_size, should_cache)( generate_audio_batch ) logger.info(f"LRU cache enabled with size {lru_size}") else: logger.debug(f"LRU cache failed to enable, invalid size {lru_size}") if __name__ == "__main__": import soundfile as sf # 测试batch生成 inputs = ["你好[lbreak]", "再见[lbreak]", "长度不同的文本片段[lbreak]"] outputs = generate_audio_batch(inputs, spk=5, infer_seed=42) for i, (sample_rate, wav) in enumerate(outputs): print(i, sample_rate, wav.shape) sf.write(f"batch_{i}.wav", wav, sample_rate, format="wav") # 单独生成 for i, text in enumerate(inputs): sample_rate, wav = generate_audio(text, spk=5, infer_seed=42) print(i, sample_rate, wav.shape) sf.write(f"one_{i}.wav", wav, sample_rate, format="wav")