import librosa import re import numpy as np import torch from torch import no_grad, LongTensor, inference_mode, FloatTensor import utils from contants import ModelType from utils import get_hparams_from_file, lang_dict from utils.sentence import sentence_split_and_markup from vits import commons from vits.mel_processing import spectrogram_torch from vits.text import text_to_sequence from vits.models import SynthesizerTrn class VITS: def __init__(self, model, config, additional_model=None, model_type=None, device=torch.device("cpu"), **kwargs): self.model_type = model_type self.hps_ms = get_hparams_from_file(config) if isinstance(config, str) else config self.n_speakers = getattr(self.hps_ms.data, 'n_speakers', 0) self.n_symbols = len(getattr(self.hps_ms, 'symbols', [])) self.speakers = getattr(self.hps_ms, 'speakers', ['0']) if not isinstance(self.speakers, list): self.speakers = [item[0] for item in sorted(list(self.speakers.items()), key=lambda x: x[1])] self.use_f0 = getattr(self.hps_ms.data, 'use_f0', False) self.emotion_embedding = getattr(self.hps_ms.data, 'emotion_embedding', getattr(self.hps_ms.model, 'emotion_embedding', False)) self.bert_embedding = getattr(self.hps_ms.data, 'bert_embedding', getattr(self.hps_ms.model, 'bert_embedding', False)) self.hps_ms.model.emotion_embedding = self.emotion_embedding self.hps_ms.model.bert_embedding = self.bert_embedding self.net_g_ms = SynthesizerTrn( self.n_symbols, self.hps_ms.data.filter_length // 2 + 1, self.hps_ms.train.segment_size // self.hps_ms.data.hop_length, n_speakers=self.n_speakers, **self.hps_ms.model) _ = self.net_g_ms.eval() self.device = device key = getattr(self.hps_ms.data, "text_cleaners", ["none"])[0] self.lang = lang_dict.get(key, ["unknown"]) # load model self.load_model(model, additional_model) def load_model(self, model, additional_model=None): utils.load_checkpoint(model, self.net_g_ms) self.net_g_ms.to(self.device) if self.model_type == ModelType.HUBERT_VITS: self.hubert = additional_model elif self.model_type == ModelType.W2V2_VITS: self.emotion_reference = additional_model def get_cleaned_text(self, text, hps, cleaned=False): if cleaned: text_norm = text_to_sequence(text, hps.symbols, []) else: if self.bert_embedding: text_norm, char_embed = text_to_sequence(text, hps.symbols, hps.data.text_cleaners, bert_embedding=self.bert_embedding) text_norm = LongTensor(text_norm) return text_norm, char_embed else: text_norm = text_to_sequence(text, hps.symbols, hps.data.text_cleaners) if hps.data.add_blank: text_norm = commons.intersperse(text_norm, 0) text_norm = LongTensor(text_norm) return text_norm def get_cleaner(self): return getattr(self.hps_ms.data, 'text_cleaners', [None])[0] def get_speakers(self, escape=False): return self.speakers @property def sampling_rate(self): return self.hps_ms.data.sampling_rate def infer(self, params): with no_grad(): x_tst = params.get("stn_tst").unsqueeze(0).to(self.device) x_tst_lengths = LongTensor([params.get("stn_tst").size(0)]).to(self.device) x_tst_prosody = torch.FloatTensor(params.get("char_embeds")).unsqueeze(0).to( self.device) if self.bert_embedding else None sid = params.get("sid").to(self.device) emotion = params.get("emotion").to(self.device) if self.emotion_embedding else None audio = self.net_g_ms.infer(x=x_tst, x_lengths=x_tst_lengths, sid=sid, noise_scale=params.get("noise_scale"), noise_scale_w=params.get("noise_scale_w"), length_scale=params.get("length_scale"), emotion_embedding=emotion, bert=x_tst_prosody)[0][0, 0].data.float().cpu().numpy() torch.cuda.empty_cache() return audio def get_infer_param(self, length_scale, noise_scale, noise_scale_w, text=None, speaker_id=None, audio_path=None, emotion=None, cleaned=False, f0_scale=1): emo = None char_embeds = None if self.model_type != ModelType.HUBERT_VITS: if self.bert_embedding: stn_tst, char_embeds = self.get_cleaned_text(text, self.hps_ms, cleaned=cleaned) else: stn_tst = self.get_cleaned_text(text, self.hps_ms, cleaned=cleaned) sid = LongTensor([speaker_id]) if self.model_type == ModelType.W2V2_VITS: # if emotion_reference.endswith('.npy'): # emotion = np.load(emotion_reference) # emotion = FloatTensor(emotion).unsqueeze(0) # else: # audio16000, sampling_rate = librosa.load( # emotion_reference, sr=16000, mono=True) # emotion = self.w2v2(audio16000, sampling_rate)[ # 'hidden_states'] # emotion_reference = re.sub( # r'\..*$', '', emotion_reference) # np.save(emotion_reference, emotion.squeeze(0)) # emotion = FloatTensor(emotion) emo = torch.FloatTensor(self.emotion_reference[emotion]).unsqueeze(0) elif self.model_type == ModelType.HUBERT_VITS: if self.use_f0: audio, sampling_rate = librosa.load(audio_path, sr=self.hps_ms.data.sampling_rate, mono=True) audio16000 = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000) else: audio16000, sampling_rate = librosa.load(audio_path, sr=16000, mono=True) with inference_mode(): units = self.hubert.units(FloatTensor(audio16000).unsqueeze(0).unsqueeze(0)).squeeze(0).numpy() if self.use_f0: f0 = librosa.pyin(audio, sr=sampling_rate, fmin=librosa.note_to_hz('C0'), fmax=librosa.note_to_hz('C7'), frame_length=1780)[0] target_length = len(units[:, 0]) f0 = np.nan_to_num(np.interp(np.arange(0, len(f0) * target_length, len(f0)) / target_length, np.arange(0, len(f0)), f0)) * f0_scale units[:, 0] = f0 / 10 stn_tst = FloatTensor(units) sid = LongTensor([speaker_id]) params = {"length_scale": length_scale, "noise_scale": noise_scale, "noise_scale_w": noise_scale_w, "stn_tst": stn_tst, "sid": sid, "emotion": emo, "char_embeds": char_embeds} return params def get_tasks(self, voice): text = voice.get("text", None) speaker_id = voice.get("id", 0) length = voice.get("length", 1) noise = voice.get("noise", 0.667) noisew = voice.get("noisew", 0.8) max = voice.get("max", 50) lang = voice.get("lang", "auto") speaker_lang = voice.get("speaker_lang", None) audio_path = voice.get("audio_path", None) emotion = voice.get("emotion", 0) # 去除所有多余的空白字符 if text is not None: text = re.sub(r'\s+', ' ', text).strip() tasks = [] if self.model_type == ModelType.VITS: sentence_list = sentence_split_and_markup(text, max, lang, speaker_lang) for sentence in sentence_list: params = self.get_infer_param(text=sentence, speaker_id=speaker_id, length_scale=length, noise_scale=noise, noise_scale_w=noisew) tasks.append(params) elif self.model_type == ModelType.HUBERT_VITS: params = self.get_infer_param(speaker_id=speaker_id, length_scale=length, noise_scale=noise, noise_scale_w=noisew, audio_path=audio_path) tasks.append(params) elif self.model_type == ModelType.W2V2_VITS: sentence_list = sentence_split_and_markup(text, max, lang, speaker_lang) for sentence in sentence_list: params = self.get_infer_param(text=sentence, speaker_id=speaker_id, length_scale=length, noise_scale=noise, noise_scale_w=noisew, emotion=emotion) tasks.append(params) else: raise ValueError(f"Unsupported model type: {self.model_type}") return tasks def get_audio(self, voice, auto_break=False): tasks = self.get_tasks(voice) # 停顿0.75s，避免语音分段合成再拼接后的连接突兀 brk = np.zeros(int(0.75 * self.sampling_rate), dtype=np.int16) audios = [] num_tasks = len(tasks) for i, task in enumerate(tasks): if auto_break and i < num_tasks - 1: chunk = np.concatenate((self.infer(task), brk), axis=0) else: chunk = self.infer(task) audios.append(chunk) audio = np.concatenate(audios, axis=0) return audio def get_stream_audio(self, voice, auto_break=False): tasks = self.get_tasks(voice) brk = np.zeros(int(0.75 * 22050), dtype=np.int16) for task in tasks: if auto_break: chunk = np.concatenate((self.infer(task), brk), axis=0) else: chunk = self.infer(task) yield chunk def voice_conversion(self, voice): audio_path = voice.get("audio_path") original_id = voice.get("original_id") target_id = voice.get("target_id") audio = utils.load_audio_to_torch( audio_path, self.hps_ms.data.sampling_rate) y = audio.unsqueeze(0) spec = spectrogram_torch(y, self.hps_ms.data.filter_length, self.hps_ms.data.sampling_rate, self.hps_ms.data.hop_length, self.hps_ms.data.win_length, center=False) spec_lengths = LongTensor([spec.size(-1)]) sid_src = LongTensor([original_id]) with no_grad(): sid_tgt = LongTensor([target_id]) audio = self.net_g_ms.voice_conversion(spec.to(self.device), spec_lengths.to(self.device), sid_src=sid_src.to(self.device), sid_tgt=sid_tgt.to(self.device))[0][0, 0].data.cpu().float().numpy() torch.cuda.empty_cache() return audio