import gradio as gr import numpy as np import soundfile as sf from datetime import datetime from time import time as ttime from my_utils import load_audio from transformers import pipeline from text.cleaner import clean_text from polyglot.detect import Detector from feature_extractor import cnhubert from timeit import default_timer as timer from text import cleaned_text_to_sequence from module.models import SynthesizerTrn from module.mel_processing import spectrogram_torch from transformers.pipelines.audio_utils import ffmpeg_read import os,re,sys,LangSegment,librosa,pdb,torch,pytz,random from transformers import AutoModelForMaskedLM, AutoTokenizer from AR.models.t2s_lightning_module import Text2SemanticLightningModule from enum import Enum from env import * import logging 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) logging.getLogger("multipart").setLevel(logging.WARNING) # from download import * # download() if "_CUDA_VISIBLE_DEVICES" in os.environ: os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"] tz = pytz.timezone('Asia/Singapore') device = "cuda" if torch.cuda.is_available() else "cpu" def abs_path(dir): global_dir = os.path.dirname(os.path.abspath(sys.argv[0])) return(os.path.join(global_dir, dir)) gpt_path = abs_path("MODELS/22/22.ckpt") sovits_path=abs_path("MODELS/22/22.pth") cnhubert_base_path = os.environ.get("cnhubert_base_path", "pretrained_models/chinese-hubert-base") bert_path = os.environ.get("bert_path", "pretrained_models/chinese-roberta-wwm-ext-large") if not os.path.exists(cnhubert_base_path): cnhubert_base_path = "TencentGameMate/chinese-hubert-base" if not os.path.exists(bert_path): bert_path = "hfl/chinese-roberta-wwm-ext-large" cnhubert.cnhubert_base_path = cnhubert_base_path whisper_path = os.environ.get("whisper_path", "pretrained_models/whisper-tiny") if not os.path.exists(whisper_path): whisper_path = "openai/whisper-tiny" pipe = pipeline( task="automatic-speech-recognition", model=whisper_path, chunk_length_s=30, device=device,) is_half = eval( os.environ.get("is_half", "True" if torch.cuda.is_available() else "False") ) 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_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)) with open("./sweight.txt", "w", encoding="utf-8") as f: f.write(sovits_path) change_sovits_weights(sovits_path) 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)) with open("./gweight.txt", "w", encoding="utf-8") as f: f.write(gpt_path) change_gpt_weights(gpt_path) 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1"): "all_zh",#全部按中文识别 ("English"): "en",#全部按英文识别#######不变 ("日文1"): "all_ja",#全部按日文识别 ("中文"): "zh",#按中英混合识别####不变 ("日本語"): "ja",#按日英混合识别####不变 ("混合"): "auto",#多语种启动切分识别语种 } def splite_en_inf(sentence, language): pattern = re.compile(r'[a-zA-Z ]+') textlist = [] langlist = [] pos = 0 for match in pattern.finditer(sentence): start, end = match.span() if start > pos: textlist.append(sentence[pos:start]) langlist.append(language) textlist.append(sentence[start:end]) langlist.append("en") pos = end if pos < len(sentence): textlist.append(sentence[pos:]) langlist.append(language) # Merge punctuation into previous word for i in range(len(textlist)-1, 0, -1): if re.match(r'^[\W_]+$', textlist[i]): textlist[i-1] += textlist[i] del textlist[i] del langlist[i] # Merge consecutive words with the same language tag i = 0 while i < len(langlist) - 1: if langlist[i] == langlist[i+1]: textlist[i] += textlist[i+1] del textlist[i+1] del langlist[i+1] else: i += 1 return textlist, langlist def clean_text_inf(text, language): formattext = "" language = language.replace("all_","") for tmp in LangSegment.getTexts(text): if language == "ja": if tmp["lang"] == language or tmp["lang"] == "zh": formattext += tmp["text"] + " " continue if tmp["lang"] == language: formattext += tmp["text"] + " " while " " in formattext: formattext = formattext.replace(" ", " ") phones, word2ph, norm_text = clean_text(formattext, language) phones = cleaned_text_to_sequence(phones) return phones, word2ph, norm_text 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 def nonen_clean_text_inf(text, language): if(language!="auto"): textlist, langlist = splite_en_inf(text, language) else: textlist=[] langlist=[] for tmp in LangSegment.getTexts(text): langlist.append(tmp["lang"]) textlist.append(tmp["text"]) print(textlist) print(langlist) phones_list = [] word2ph_list = [] norm_text_list = [] for i in range(len(textlist)): lang = langlist[i] phones, word2ph, norm_text = clean_text_inf(textlist[i], lang) phones_list.append(phones) if lang == "zh": word2ph_list.append(word2ph) norm_text_list.append(norm_text) print(word2ph_list) phones = sum(phones_list, []) word2ph = sum(word2ph_list, []) norm_text = ' '.join(norm_text_list) return phones, word2ph, norm_text def nonen_get_bert_inf(text, language): if(language!="auto"): textlist, langlist = splite_en_inf(text, language) else: textlist=[] langlist=[] for tmp in LangSegment.getTexts(text): langlist.append(tmp["lang"]) textlist.append(tmp["text"]) print(textlist) print(langlist) bert_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) bert_list.append(bert) bert = torch.cat(bert_list, dim=1) return bert splits = {"，", "。", "？", "！", ",", ".", "?", "!", "~", ":", "：", "—", "…", } def get_first(text): pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]" text = re.split(pattern, text)[0].strip() return text def get_cleaned_text_final(text,language): if language in {"en","all_zh","all_ja"}: phones, word2ph, norm_text = clean_text_inf(text, language) elif language in {"zh", "ja","auto"}: phones, word2ph, norm_text = nonen_clean_text_inf(text, language) return phones, word2ph, norm_text def get_bert_final(phones, word2ph, text,language,device): if language == "en": bert = get_bert_inf(phones, word2ph, text, language) elif language in {"zh", "ja","auto"}: bert = nonen_get_bert_inf(text, language) elif language == "all_zh": bert = get_bert_feature(text, word2ph).to(device) else: bert = torch.zeros((1024, len(phones))).to(device) return bert def merge_short_text_in_array(texts, threshold): if (len(texts)) < 2: return texts result = [] text = "" for ele in texts: text += ele if len(text) >= threshold: result.append(text) text = "" if (len(text) > 0): if len(result) == 0: result.append(text) else: result[len(result) - 1] += text return result def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut=("Do not split"), volume_scale=1.0): if not duration(ref_wav_path): return None if text == '': wprint("Please enter text to generate/请输入生成文字") return None t0 = ttime() startTime=timer() text=trim_text(text,text_language) prompt_language = dict_language[prompt_language] try: text_language = dict_language[text_language] except KeyError as e: wprint(f"Unsupported language type: {e}") return None prompt_text = prompt_text.strip("\n") if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_language != "en" else "." 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(("实际输入的参考文本:"), prompt_text) #print(("📝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): errinfo='参考音频在3~10秒范围外，请更换！' raise OSError((errinfo)) 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() phones1, word2ph1, norm_text1=get_cleaned_text_final(prompt_text, prompt_language) if (how_to_cut == ("Split into groups of 4 sentences")): text = cut1(text) elif (how_to_cut == ("Split every 50 characters")): text = cut2(text) elif (how_to_cut == ("Split at CN/JP periods (。)")): text = cut3(text) elif (how_to_cut == ("Split at English periods (.)")): text = cut4(text) elif (how_to_cut == ("Split at punctuation marks")): text = cut5(text) while "\n\n" in text: text = text.replace("\n\n", "\n") print(f"🧨实际输入的目标文本(切句后):{text}\n") texts = text.split("\n") texts = merge_short_text_in_array(texts, 5) audio_opt = [] bert1=get_bert_final(phones1, word2ph1, norm_text1,prompt_language,device).to(dtype) for text in texts: if (len(text.strip()) == 0): continue if (text[-1] not in splits): text += "。" if text_language != "en" else "." print(("\n🎈实际输入的目标文本(每句):"), text) phones2, word2ph2, norm_text2 = get_cleaned_text_final(text, text_language) try: bert2 = get_bert_final(phones2, word2ph2, norm_text2, text_language, device).to(dtype) except RuntimeError as e: wprint(f"The input text does not match the language/输入文本与语言不匹配: {e}") return None bert = torch.cat([bert1, bert2], 1) all_phoneme_ids = torch.LongTensor(phones1 + 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, prompt, bert, # prompt_phone_len=ph_offset, top_k=config["inference"]["top_k"], early_stop_num=hz * max_sec, ) t3 = ttime() # print(pred_semantic.shape,idx) 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] try: audio = ( vq_model.decode( pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refer ) .detach() .cpu() .numpy()[0, 0] ) except RuntimeError as e: wprint(f"The input text does not match the language/输入文本与语言不匹配: {e}") return None max_audio=np.abs(audio).max() if max_audio>1:audio/=max_audio audio_opt.append(audio) audio_opt.append(zero_wav) t4 = ttime() print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3)) #yield hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).astype(np.int16) audio_data = (np.concatenate(audio_opt, 0) * 32768).astype(np.int16) audio_data = (audio_data.astype(np.float32) * volume_scale).astype(np.int16) output_wav = f"{str(int(ttime()))}-{random.randint(1000, 9999)}.wav" sf.write(output_wav, audio_data, hps.data.sampling_rate) endTime=timer() tprint(f'🆗TTS COMPLETE,{round(endTime-startTime,4)}s') return output_wav def split(todo_text): todo_text = todo_text.replace("……", "。").replace("——", "，") if todo_text[-1] not in splits: todo_text += "。" i_split_head = i_split_tail = 0 len_text = len(todo_text) todo_texts = [] while 1: if i_split_head >= len_text: break if todo_text[i_split_head] in splits: i_split_head += 1 todo_texts.append(todo_text[i_split_tail:i_split_head]) i_split_tail = i_split_head else: i_split_head += 1 return todo_texts def cut1(inp): inp = inp.strip("\n") inps = split(inp) split_idx = list(range(0, len(inps), 4)) split_idx[-1] = None if len(split_idx) > 1: opts = [] for idx in range(len(split_idx) - 1): opts.append("".join(inps[split_idx[idx]: split_idx[idx + 1]])) else: opts = [inp] return "\n".join(opts) def cut2(inp): inp = inp.strip("\n") inps = split(inp) if len(inps) < 2: return inp opts = [] summ = 0 tmp_str = "" for i in range(len(inps)): summ += len(inps[i]) tmp_str += inps[i] if summ > 50: summ = 0 opts.append(tmp_str) tmp_str = "" if tmp_str != "": opts.append(tmp_str) # print(opts) if len(opts) > 1 and len(opts[-1]) < 50: opts[-2] = opts[-2] + opts[-1] opts = opts[:-1] return "\n".join(opts) def cut3(inp): inp = inp.strip("\n") return "\n".join(["%s" % item for item in inp.strip("。").split("。")]) def cut4(inp): inp = inp.strip("\n") return "\n".join(["%s" % item for item in inp.strip(".").split(".")]) # contributed by https://github.com/AI-Hobbyist/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py def cut5(inp): # if not re.search(r'[^\w\s]', inp[-1]): # inp += '。' inp = inp.strip("\n") punds = r'[,.;?!、，。？！;：…]' items = re.split(f'({punds})', inp) mergeitems = ["".join(group) for group in zip(items[::2], items[1::2])] if len(items)%2 == 1: mergeitems.append(items[-1]) opt = "\n".join(mergeitems) return opt def custom_sort_key(s): # 使用正则表达式提取字符串中的数字部分和非数字部分 parts = re.split('(\d+)', s) # 将数字部分转换为整数，非数字部分保持不变 parts = [int(part) if part.isdigit() else part for part in parts] return parts #==========custom functions============ def tprint(text): now=datetime.now(tz).strftime('%H:%M:%S') print(f'UTC+8 - {now} - {text}') def wprint(text): tprint(text) gr.Warning(text) def lang_detector(text): min_chars = 5 if len(text) < min_chars: return "Input text too short/输入文本太短" try: detector = Detector(text).language lang_info = str(detector) code = re.search(r"name: (\w+)", lang_info).group(1) if code == 'Japanese': return "日本語" elif code == 'Chinese': return "中文" elif code == 'English': return 'English' else: return code except Exception as e: return f"ERROR：{str(e)}" def trim_text(text,language): search_limit_cj = limit_cj+30 search_limit_en = limit_en +30 text = text.replace('\n', '').strip() if language =='English': words = text.split() if len(words) <= limit_en: return text # English for i in range(limit_en, -1, -1): if any(punct in words[i] for punct in splits): return ' '.join(words[:i+1]) for i in range(limit_en, min(len(words), search_limit_en)): if any(punct in words[i] for punct in splits): return ' '.join(words[:i+1]) return ' '.join(words[:limit_en]) else:#中文日文 if len(text) <= limit_cj: return text for i in range(limit_cj, -1, -1): if text[i] in splits: return text[:i+1] for i in range(limit_cj, min(len(text), search_limit_cj)): if text[i] in splits: return text[:i+1] return text[:limit_cj] def duration(audio_file_path): if not audio_file_path: wprint("Failed to obtain uploaded audio/未找到音频文件") return False try: audio_duration = librosa.get_duration(filename=audio_file_path) if not 3 < audio_duration < 10: wprint("The audio length must be between 3~10 seconds/音频时长须在3~10秒之间") return False return True except FileNotFoundError as e: wprint(f"文件沒有找到: {audio_file_path}") return False def update_model(choice): global gpt_path, sovits_path model_info = models[choice] gpt_path = abs_path(model_info["gpt_weight"]) sovits_path = abs_path(model_info["sovits_weight"]) model_name = choice tone_info = model_info["tones"]["tone1"] tone_sample_path = abs_path(tone_info["sample"]) tprint(f'✅SELECT MODEL：{choice}') # 返回默认tone“tone1” return ( tone_info["example_voice_wav"], tone_info["example_voice_wav_words"], model_info["default_language"], model_info["default_language"], model_name, "tone1" , tone_sample_path ) def update_tone(model_choice, tone_choice): model_info = models[model_choice] tone_info = model_info["tones"][tone_choice] example_voice_wav = abs_path(tone_info["example_voice_wav"]) example_voice_wav_words = tone_info["example_voice_wav_words"] tone_sample_path = abs_path(tone_info["sample"]) return example_voice_wav, example_voice_wav_words,tone_sample_path def transcribe(voice): time1=timer() tprint('⚡Start Clone - transcribe') task="transcribe" if voice is None: wprint("No audio file submitted! Please upload or record an audio file before submitting your request.") R = pipe(voice, batch_size=8, generate_kwargs={"task": task}, return_timestamps=True,return_language=True) text=R['text'] lang=R['chunks'][0]['language'] if lang=='english': language='English' elif lang =='chinese': language='中文' elif lang=='japanese': language = '日本語' time2=timer() tprint(f'transcribe COMPLETE,{round(time2-time1,4)}s') tprint(f'\nTRANSCRIBE RESULT：\n 🔣Language：{language} \n 🔣Text：{text}' ) return text,language def init(): global gpt_path, sovits_path gpt_path = abs_path("pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt") #tprint(f'Model loaded:{gpt_path}') sovits_path = abs_path("pretrained_models/s2G488k.pth") #tprint(f'Model loaded:{sovits_path}') change_sovits_weights(sovits_path) tprint(f'🏕️LOADED SoVITS Model: {sovits_path}') change_gpt_weights(gpt_path) tprint(f'🏕️LOADED GPT Model: {gpt_path}') init() def clone_voice(user_voice,user_text,user_lang): if not duration(user_voice): return None if user_text == '': wprint("Please enter text to generate/请输入生成文字") return None user_text=trim_text(user_text,user_lang) time1=timer() try: prompt_text, prompt_language = transcribe(user_voice) except UnboundLocalError as e: wprint(f"The language in the audio cannot be recognized ：{str(e)}") return None output_wav = get_tts_wav( user_voice, prompt_text, prompt_language, user_text, user_lang, how_to_cut=how_to_cut, volume_scale=1.0) time2=timer() tprint(f'🆗CLONE COMPLETE,{round(time2-time1,4)}s') return output_wav def text_to_speech(voice, text): language = lang_detector(text) if language == "English": voice = f"static/en/{voice}.mp3" else: voice = f"static/zh/{voice}.mp3" wav_path = clone_voice( user_voice=voice, user_text=text, user_lang=language) return wav_path speech_to_text = transcribe