import os import random import typing from pathlib import Path from .TTS import TTS, TTS_Config # from pydub import AudioSegment # from pydub.playback import play # from setting import settings current_dir = Path(__file__).parent parent_dir = current_dir.parent class TTSModule: def __init__(self, tts_config: TTS_Config): self.tts_pipeline = TTS(tts_config) def setup_inference_params( self, text_lang: typing.Literal["en", "all_zh", "all_ja"] = 'en', ref_audio: str = '', prompt_text: str = '', prompt_lang: str = '', top_k: int = 60, top_p: float = 1.0, temperature: float = 1.0, text_split_method: typing.Literal['cut0', 'cut1', 'cut2', 'cut3', 'cut4', 'cut5'] = 'cut5', batch_size: int = 100, speed_factor: float = 1.1, split_bucket: bool = True, return_fragment: bool = False, fragment_interval: float = 0.1, seed: int = 233333, parallel_infer: bool = True, ): """ Set up inference parameters for the model. :param text_lang: Language of the input text. Choose from "en" (English), "all_zh" (Chinese), or "all_ja" (Japanese). :param ref_audio: Path to the reference audio file. This audio is used as a voice reference for the output. :param prompt_text: Text of the reference speech. This can be used to guide the model's output style or content. :param prompt_lang: Language of the reference speech. Should correspond to the language of prompt_text. :param top_k: Top-K sampling parameter for GPT. Limits the next token selection to the K most likely tokens. :param top_p: Top-P (nucleus) sampling parameter for GPT. Sets a cumulative probability cutoff for token selection. :param temperature: Temperature for GPT sampling. Higher values increase randomness, lower values make output more deterministic. :param text_split_method: Method to split the input text: - "cut0": No splitting (process whole text at once) - "cut1": Split every 4 sentences - "cut2": Split every 50 words - "cut3": Split at Chinese full stops '。' - "cut4": Split at English periods '.' - "cut5": Automatic splitting (recommended) :param batch_size: Batch size for inference. Larger values may speed up processing but require more memory. :param speed_factor: Factor to control the speed of the output audio. Values > 1 speed up, < 1 slow down. :param split_bucket: Whether to use bucket splitting for more efficient processing. Recommended to keep on. :param return_fragment: If True, return individual fragments of the generated audio. :param fragment_interval: Time interval (in seconds) between each sentence or fragment in the output audio. :param seed: Random seed for reproducibility. Set a fixed value for consistent results across runs. :param parallel_infer: whether to use parallel inference. """ self.text_lang = text_lang self.ref_audio = str(ref_audio) self.prompt_text = prompt_text self.prompt_lang = prompt_lang self.top_k = max(1, int(top_k)) # 确保top_k至少为1 self.top_p = max(0.0, min(1.0, float(top_p))) # 确保top_p在0到1之间 self.temperature = max(0.0, float(temperature)) # 确保temperature非负 self.text_split_method = text_split_method self.batch_size = max(1, int(batch_size)) # 确保batch_size至少为1 self.speed_factor = max(0.1, float(speed_factor)) # 确保speed_factor至少为0.1 self.split_bucket = bool(split_bucket) self.return_fragment = bool(return_fragment) self.fragment_interval = max(0.0, float(fragment_interval)) # 确保fragment_interval非负 self.seed = int(seed) self.parallel_infer = parallel_infer # Validate that the reference audio file exists if self.ref_audio and not os.path.exists(self.ref_audio): raise FileNotFoundError(f"Reference audio file not found: {self.ref_audio}") def inference(self, text, text_lang, ref_audio_path, prompt_text, prompt_lang, top_k, top_p, temperature, text_split_method, batch_size, speed_factor, ref_text_free, split_bucket, fragment_interval, seed, parallel_infer: bool): actual_seed = seed if seed not in [-1, "", None] else random.randrange(1 << 32) inputs = { "text": text, "text_lang": text_lang, "ref_audio_path": ref_audio_path, "prompt_text": prompt_text if not ref_text_free else "", "prompt_lang": prompt_lang, "top_k": top_k, "top_p": top_p, "temperature": temperature, "text_split_method": text_split_method, "batch_size": int(batch_size), "speed_factor": float(speed_factor), "split_bucket": split_bucket, "return_fragment": False, "fragment_interval": fragment_interval, "seed": actual_seed, "parallel_infer": parallel_infer } # print(inputs) for item in self.tts_pipeline.run(inputs): yield item, actual_seed def generate_audio(self, text, warmup: bool = False): [output] = self.inference( text, self.text_lang, self.ref_audio, self.prompt_text, self.prompt_lang, self.top_k, self.top_p, self.temperature, self.text_split_method, self.batch_size, self.speed_factor, self.return_fragment, self.split_bucket, self.fragment_interval, self.seed, self.parallel_infer, ) if warmup: return None return output # solution v1 # print(f"Audio generated path : '{parent_dir}'") # # output_folder = "./output" # 指定输出目录 # output_folder = str(settings.AUDIO_OUTPUT_FOLDER) # if not os.path.exists(output_folder): # os.makedirs(output_folder) # 如果目录不存在，则创建它 # now = datetime.datetime.now().strftime("%Y%m%d_%H%M%S%f") # 获取当前时间的时间戳 # file_name = os.path.join(output_folder, f'output_{now}_{count}.wav') # sf.write(file_name, output[0][1], samplerate=output[0][0], subtype='PCM_16') # # print(f"Audio file saved to: {file_name}") # return file_name # 使用示例 # tts_module = TTSModule("./moyoyo_tts/configs/tts_infer.yaml") # text = "Well, you know what the say, Families are like fudge, mostly sweet, but sometimes nuts. My family is doing great, thanks for asking! My son is growing up to be a smart and handsome young man, just like his mom. He's currently working on his own talker show, which I'm sure will be even more hilarious than mine." # tts_module.generate_audio(text)