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import numpy as np |
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import torch |
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from bert_vits2 import commons |
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from bert_vits2 import utils as bert_vits2_utils |
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from bert_vits2.models import SynthesizerTrn |
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from bert_vits2.text import * |
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from bert_vits2.text.cleaner import clean_text |
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from utils import classify_language, get_hparams_from_file, lang_dict |
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from utils.sentence import sentence_split_and_markup, cut |
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class Bert_VITS2: |
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def __init__(self, model, config, device=torch.device("cpu"), **kwargs): |
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self.hps_ms = get_hparams_from_file(config) |
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self.n_speakers = getattr(self.hps_ms.data, 'n_speakers', 0) |
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self.speakers = [item[0] for item in |
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sorted(list(getattr(self.hps_ms.data, 'spk2id', {'0': 0}).items()), key=lambda x: x[1])] |
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self.legacy = getattr(self.hps_ms.data, 'legacy', False) |
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self.symbols = symbols_legacy if self.legacy else symbols |
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self._symbol_to_id = {s: i for i, s in enumerate(self.symbols)} |
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self.net_g = SynthesizerTrn( |
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len(self.symbols), |
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self.hps_ms.data.filter_length // 2 + 1, |
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self.hps_ms.train.segment_size // self.hps_ms.data.hop_length, |
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n_speakers=self.hps_ms.data.n_speakers, |
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symbols=self.symbols, |
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**self.hps_ms.model).to(device) |
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_ = self.net_g.eval() |
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self.device = device |
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self.load_model(model) |
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def load_model(self, model): |
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bert_vits2_utils.load_checkpoint(model, self.net_g, None, skip_optimizer=True) |
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def get_speakers(self): |
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return self.speakers |
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def get_text(self, text, language_str, hps): |
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norm_text, phone, tone, word2ph = clean_text(text, language_str) |
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phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str, self._symbol_to_id) |
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if hps.data.add_blank: |
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phone = commons.intersperse(phone, 0) |
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tone = commons.intersperse(tone, 0) |
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language = commons.intersperse(language, 0) |
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for i in range(len(word2ph)): |
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word2ph[i] = word2ph[i] * 2 |
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word2ph[0] += 1 |
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bert = get_bert(norm_text, word2ph, language_str) |
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del word2ph |
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assert bert.shape[-1] == len(phone), phone |
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if language_str == "zh": |
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bert = bert |
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ja_bert = torch.zeros(768, len(phone)) |
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elif language_str == "ja": |
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ja_bert = bert |
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bert = torch.zeros(1024, len(phone)) |
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else: |
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bert = torch.zeros(1024, len(phone)) |
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ja_bert = torch.zeros(768, len(phone)) |
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assert bert.shape[-1] == len( |
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phone |
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), f"Bert seq len {bert.shape[-1]} != {len(phone)}" |
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phone = torch.LongTensor(phone) |
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tone = torch.LongTensor(tone) |
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language = torch.LongTensor(language) |
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return bert, ja_bert, phone, tone, language |
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def infer(self, text, lang, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid): |
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bert, ja_bert, phones, tones, lang_ids = self.get_text(text, lang, self.hps_ms) |
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with torch.no_grad(): |
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x_tst = phones.to(self.device).unsqueeze(0) |
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tones = tones.to(self.device).unsqueeze(0) |
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lang_ids = lang_ids.to(self.device).unsqueeze(0) |
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bert = bert.to(self.device).unsqueeze(0) |
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ja_bert = ja_bert.to(self.device).unsqueeze(0) |
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x_tst_lengths = torch.LongTensor([phones.size(0)]).to(self.device) |
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speakers = torch.LongTensor([int(sid)]).to(self.device) |
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audio = self.net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids, bert, ja_bert, sdp_ratio=sdp_ratio |
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, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[ |
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0][0, 0].data.cpu().float().numpy() |
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torch.cuda.empty_cache() |
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return audio |
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def get_audio(self, voice, auto_break=False): |
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text = voice.get("text", None) |
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lang = voice.get("lang", "auto") |
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sdp_ratio = voice.get("sdp_ratio", 0.2) |
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noise_scale = voice.get("noise", 0.5) |
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noise_scale_w = voice.get("noisew", 0.6) |
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length_scale = voice.get("length", 1) |
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sid = voice.get("id", 0) |
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max = voice.get("max", 50) |
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if lang == "auto": |
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lang = classify_language(text, target_languages=lang_dict["bert_vits2"]) |
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sentence_list = cut(text, max) |
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audios = [] |
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for sentence in sentence_list: |
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audio = self.infer(sentence, lang, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid) |
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audios.append(audio) |
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audio = np.concatenate(audios) |
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return audio |
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