import os, re, logging import LangSegment from classic_text_cleaner import * 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) import json cnhubert_base_path = os.environ.get( "cnhubert_base_path", "GPT_SoVITS/pretrained_models/chinese-hubert-base" ) bert_path = os.environ.get( "bert_path", "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large" ) if "_CUDA_VISIBLE_DEVICES" in os.environ: os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"] is_half = eval(os.environ.get("is_half", "True")) from transformers import AutoModelForMaskedLM, AutoTokenizer import numpy as np import librosa, torch from feature_extractor import cnhubert cnhubert.cnhubert_base_path = cnhubert_base_path from module.models import SynthesizerTrn from AR.models.t2s_lightning_module import Text2SemanticLightningModule from time import time as ttime from module.mel_processing import spectrogram_torch from my_utils import load_audio from tools.i18n.i18n import I18nAuto i18n = I18nAuto() os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1' # 确保直接启动推理UI时也能够设置。 if torch.cuda.is_available(): device = "cuda" else: device = "cpu" is_half = False # 取得模型文件夹路径 config_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "config.json") if os.path.exists(config_path): with open(config_path, 'r', encoding='utf-8') as f: _config = json.load(f) if _config.get("device", "auto") != "auto": device = _config["device"] if device == "cpu": is_half = False if _config.get("half_precision", "auto") != "auto": is_half = _config["half_precision"] print(f"device: {device}, is_half: {is_half}") tokenizer = AutoTokenizer.from_pretrained(bert_path) bert_model = AutoModelForMaskedLM.from_pretrained(bert_path) if is_half == True: bert_model = bert_model.half().to(device) else: bert_model = bert_model.to(device) def get_bert_feature(text, word2ph): with torch.no_grad(): inputs = tokenizer(text, return_tensors="pt") for i in inputs: inputs[i] = inputs[i].to(device) res = bert_model(**inputs, output_hidden_states=True) res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1] assert len(word2ph) == len(text) phone_level_feature = [] for i in range(len(word2ph)): repeat_feature = res[i].repeat(word2ph[i], 1) phone_level_feature.append(repeat_feature) phone_level_feature = torch.cat(phone_level_feature, dim=0) return phone_level_feature.T class DictToAttrRecursive(dict): def __init__(self, input_dict): super().__init__(input_dict) for key, value in input_dict.items(): if isinstance(value, dict): value = DictToAttrRecursive(value) self[key] = value setattr(self, key, value) def __getattr__(self, item): try: return self[item] except KeyError: raise AttributeError(f"Attribute {item} not found") def __setattr__(self, key, value): if isinstance(value, dict): value = DictToAttrRecursive(value) super(DictToAttrRecursive, self).__setitem__(key, value) super().__setattr__(key, value) def __delattr__(self, item): try: del self[item] except KeyError: raise AttributeError(f"Attribute {item} not found") ssl_model = cnhubert.get_model() if is_half == True: ssl_model = ssl_model.half().to(device) else: ssl_model = ssl_model.to(device) def change_gpt_weights(gpt_path): global hz, max_sec, t2s_model, config hz = 50 dict_s1 = torch.load(gpt_path, map_location="cpu") config = dict_s1["config"] max_sec = config["data"]["max_sec"] t2s_model = Text2SemanticLightningModule(config, "****", is_train=False) t2s_model.load_state_dict(dict_s1["weight"]) if is_half == True: t2s_model = t2s_model.half() t2s_model = t2s_model.to(device) t2s_model.eval() total = sum([param.nelement() for param in t2s_model.parameters()]) print("Number of parameter: %.2fM" % (total / 1e6)) def change_sovits_weights(sovits_path): global vq_model, hps dict_s2 = torch.load(sovits_path, map_location="cpu") hps = dict_s2["config"] hps = DictToAttrRecursive(hps) hps.model.semantic_frame_rate = "25hz" vq_model = SynthesizerTrn( hps.data.filter_length // 2 + 1, hps.train.segment_size // hps.data.hop_length, n_speakers=hps.data.n_speakers, **hps.model ) if ("pretrained" not in sovits_path): del vq_model.enc_q if is_half == True: vq_model = vq_model.half().to(device) else: vq_model = vq_model.to(device) vq_model.eval() print(vq_model.load_state_dict(dict_s2["weight"], strict=False)) def get_spepc(hps, filename): audio = load_audio(filename, int(hps.data.sampling_rate)) audio = torch.FloatTensor(audio) audio_norm = audio audio_norm = audio_norm.unsqueeze(0) spec = spectrogram_torch( audio_norm, hps.data.filter_length, hps.data.sampling_rate, hps.data.hop_length, hps.data.win_length, center=False, ) return spec dict_language = { "中文": "all_zh",#全部按中文识别 "英文": "en",#全部按英文识别#######不变 "日文": "all_ja",#全部按日文识别 "中英混合": "zh",#按中英混合识别####不变 "日英混合": "ja",#按日英混合识别####不变 "多语种混合": "auto",#多语种启动切分识别语种 "auto": "auto", "zh": "zh", "en": "en", "ja": "ja", "all_zh": "all_zh", "all_ja": "all_ja", } dtype=torch.float16 if is_half == True else torch.float32 def get_bert_inf(phones, word2ph, norm_text, language): language=language.replace("all_","") if language == "zh": bert = get_bert_feature(norm_text, word2ph).to(device)#.to(dtype) else: bert = torch.zeros( (1024, len(phones)), dtype=torch.float16 if is_half == True else torch.float32, ).to(device) return bert splits = {"，", "。", "？", "！", ",", ".", "?", "!", "~", ":", "：", "—", "…", } def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut=i18n("不切"), top_k=20, top_p=0.6, temperature=0.6, ref_free=False, stream=False): if prompt_text is None or len(prompt_text) == 0: ref_free = True t0 = ttime() prompt_language = dict_language[prompt_language] text_language = dict_language[text_language] if not ref_free: prompt_text = prompt_text.strip("\n") if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_language != "en" else "." print(i18n("实际输入的参考文本:"), prompt_text) text = text.strip("\n") if (text[0] not in splits and len(get_first(text)) < 4): text = "。" + text if text_language != "en" else "." + text print(i18n("实际输入的目标文本:"), text) zero_wav = np.zeros( int(hps.data.sampling_rate * 0.3), dtype=np.float16 if is_half == True else np.float32, ) with torch.no_grad(): wav16k, sr = librosa.load(ref_wav_path, sr=16000) if (wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000): raise OSError(i18n("参考音频在3~10秒范围外，请更换！")) wav16k = torch.from_numpy(wav16k) zero_wav_torch = torch.from_numpy(zero_wav) if is_half == True: wav16k = wav16k.half().to(device) zero_wav_torch = zero_wav_torch.half().to(device) else: wav16k = wav16k.to(device) zero_wav_torch = zero_wav_torch.to(device) wav16k = torch.cat([wav16k, zero_wav_torch]) ssl_content = ssl_model.model(wav16k.unsqueeze(0))[ "last_hidden_state" ].transpose( 1, 2 ) # .float() codes = vq_model.extract_latent(ssl_content) prompt_semantic = codes[0, 0] t1 = ttime() text = auto_cut(text) while "\n\n" in text: text = text.replace("\n\n", "\n") print(i18n("实际输入的目标文本(切句后):"), text) texts = text.split("\n") texts = merge_short_text_in_array(texts, 5) audio_opt = [] if not ref_free: phones1, bert1, norm_text1 = get_phones_and_bert(prompt_text, prompt_language) else: phones1, bert1 = None, None for text in texts: # 解决输入目标文本的空行导致报错的问题 if (len(text.strip()) == 0): continue audio = get_tts_chunk(ref_wav_path, text, text_language, bert1, phones1, prompt_semantic, top_k, top_p, temperature, ref_free, t0, t1) audio_opt.append(audio) audio_opt.append(zero_wav) if (stream): # 流式模式下每句返回一次 yield (np.concatenate([audio, zero_wav], 0) * 32768).astype(np.int16).tobytes() if (not stream): # 非流式最终合并后返回 yield hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).astype( np.int16 ) def get_tts_chunk(ref_wav_path, text, text_language, bert1, phones1, prompt_semantic, top_k, top_p, temperature, ref_free, t0, t1): if (text[-1] not in splits): text += "。" if text_language != "en" else "." print(i18n("实际输入的目标文本(每句):"), text) phones2, bert2, norm_text2 = get_phones_and_bert(text, text_language) print(i18n("前端处理后的文本(每句):"), norm_text2) if not ref_free: bert = torch.cat([bert1, bert2], 1) all_phoneme_ids = torch.LongTensor(phones1+phones2).to(device).unsqueeze(0) else: bert = bert2 all_phoneme_ids = torch.LongTensor(phones2).to(device).unsqueeze(0) bert = bert.to(device).unsqueeze(0) all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device) prompt = prompt_semantic.unsqueeze(0).to(device) t2 = ttime() with torch.no_grad(): # pred_semantic = t2s_model.model.infer( pred_semantic, idx = t2s_model.model.infer_panel( all_phoneme_ids, all_phoneme_len, None if ref_free else prompt, bert, # prompt_phone_len=ph_offset, top_k=top_k, top_p=top_p, temperature=temperature, early_stop_num=hz * max_sec, ) t3 = ttime() # print(pred_semantic.shape,idx) if type(idx) == list: idx = idx[0] pred_semantic = pred_semantic[0][-idx:].unsqueeze(0).unsqueeze(0) print(f"pred_type:{type(pred_semantic)}") else: pred_semantic = pred_semantic[:, -idx:].unsqueeze( 0 ) # .unsqueeze(0)#mq要多unsqueeze一次 refer = get_spepc(hps, ref_wav_path) # .to(device) if is_half == True: refer = refer.half().to(device) else: refer = refer.to(device) # audio = vq_model.decode(pred_semantic, all_phoneme_ids, refer).detach().cpu().numpy()[0, 0] audio = ( vq_model.decode( pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refer ) .detach() .cpu() .numpy()[0, 0] ) ###试试重建不带上prompt部分 max_audio=np.abs(audio).max()#简单防止16bit爆音 if max_audio>1:audio/=max_audio t4 = ttime() print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3)) return audio def get_phones_and_bert(text,language): if language in {"en","all_zh","all_ja"}: language = language.replace("all_","") if language == "en": LangSegment.setfilters(["en"]) formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text)) else: # 因无法区别中日文汉字,以用户输入为准 formattext = text while " " in formattext: formattext = formattext.replace(" ", " ") phones, word2ph, norm_text = clean_text_inf(formattext, language) if language == "zh": bert = get_bert_feature(norm_text, word2ph).to(device) else: bert = torch.zeros( (1024, len(phones)), dtype=torch.float16 if is_half == True else torch.float32, ).to(device) elif language in {"zh", "ja","auto"}: textlist=[] langlist=[] LangSegment.setfilters(["zh","ja","en","ko"]) if language == "auto": for tmp in LangSegment.getTexts(text): if tmp["lang"] == "ko": langlist.append("zh") textlist.append(tmp["text"]) else: langlist.append(tmp["lang"]) textlist.append(tmp["text"]) else: for tmp in LangSegment.getTexts(text): if tmp["lang"] == "en": langlist.append(tmp["lang"]) else: # 因无法区别中日文汉字,以用户输入为准 langlist.append(language) textlist.append(tmp["text"]) print(textlist) print(langlist) phones_list = [] bert_list = [] norm_text_list = [] for i in range(len(textlist)): lang = langlist[i] phones, word2ph, norm_text = clean_text_inf(textlist[i], lang) bert = get_bert_inf(phones, word2ph, norm_text, lang) phones_list.append(phones) norm_text_list.append(norm_text) bert_list.append(bert) bert = torch.cat(bert_list, dim=1) phones = sum(phones_list, []) norm_text = ''.join(norm_text_list) return phones,bert.to(dtype),norm_text # from https://github.com/RVC-Boss/GPT-SoVITS/pull/448 import tempfile, io, wave from pydub import AudioSegment # from https://huggingface.co/spaces/coqui/voice-chat-with-mistral/blob/main/app.py def wave_header_chunk(frame_input=b"", channels=1, sample_width=2, sample_rate=32000): # This will create a wave header then append the frame input # It should be first on a streaming wav file # Other frames better should not have it (else you will hear some artifacts each chunk start) wav_buf = io.BytesIO() with wave.open(wav_buf, "wb") as vfout: vfout.setnchannels(channels) vfout.setsampwidth(sample_width) vfout.setframerate(sample_rate) vfout.writeframes(frame_input) wav_buf.seek(0) return wav_buf.read() def get_streaming_tts_wav( ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut=i18n("不切"), top_k=20, top_p=0.6, temperature=0.6, ref_free=False, byte_stream=True, ): chunks = get_tts_wav( ref_wav_path=ref_wav_path, prompt_text=prompt_text, prompt_language=prompt_language, text=text, text_language=text_language, how_to_cut=how_to_cut, top_k=top_k, top_p=top_p, temperature=temperature, ref_free=ref_free, stream=True, ) if byte_stream: yield wave_header_chunk() for chunk in chunks: assert isinstance(chunk, bytes), "Chunk must be bytes" yield chunk else: # Send chunk files i = 0 format = "wav" for chunk in chunks: i += 1 file = f"{tempfile.gettempdir()}/{i}.{format}" segment = AudioSegment(chunk, frame_rate=32000, sample_width=2, channels=1) segment.export(file, format=format) yield file