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 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() from TTS_infer_pack.TTS import TTS, TTS_Config from TTS_infer_pack.text_segmentation_method import get_method 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") ) dict_language = { "中文1": "all_zh", "English": "en", "日文1": "all_ja", "中文": "zh", "日本語": "ja", "混合": "auto", } cut_method = { "Do not split/不切":"cut0", "Split into groups of 4 sentences/四句一切": "cut1", "Split every 50 characters/50字一切": "cut2", "Split at CN/JP periods (。)/按中日文句号切": "cut3", "Split at English periods (.)/按英文句号切": "cut4", "Split at punctuation marks/按标点切": "cut5", } tts_config = TTS_Config("GPT_SoVITS/configs/tts_infer.yaml") tts_config.device = device tts_config.is_half = is_half if gpt_path is not None: tts_config.t2s_weights_path = gpt_path if sovits_path is not None: tts_config.vits_weights_path = sovits_path if cnhubert_base_path is not None: tts_config.cnhuhbert_base_path = cnhubert_base_path if bert_path is not None: tts_config.bert_base_path = bert_path tts_pipline = TTS(tts_config) gpt_path = tts_config.t2s_weights_path sovits_path = tts_config.vits_weights_path def inference(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, volume ): if not duration(ref_audio_path): return None if text == '': wprint("Please input text to generate/请输入生成文字") return None text=trim_text(text,text_language) try: lang=dict_language[text_lang] inputs={ "text": text, "text_lang": lang, "ref_audio_path": ref_audio_path, "prompt_text": prompt_text if not ref_text_free else "", "prompt_lang": dict_language[prompt_lang], "top_k": top_k, "top_p": top_p, "temperature": temperature, "text_split_method": cut_method[text_split_method], "batch_size":int(batch_size), "speed_factor":float(speed_factor), "split_bucket":split_bucket, "volume":volume, "return_fragment":False, } yield next(tts_pipline.run(inputs)) except KeyError as e: wprint(f'Unsupported language type:{e}') return None #==========custom functions============ splits = {"，", "。", "？", "！", ",", ".", "?", "!", "~", ":", "：", "—", "…", } 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): limit_cj = 120 #character limit_en = 60 #words 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: 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: return False def update_model(choice): #global tts_config.vits_weights_path, tts_config.t2s_weights_path model_info = models[choice] gpt_path = abs_path(model_info["gpt_weight"]) sovits_path = abs_path(model_info["sovits_weight"]) tts_pipline.init_vits_weights(sovits_path) tts_pipline.init_t2s_weights(gpt_path) 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 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) 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}') try: prompt_text, prompt_lang = transcribe(user_voice) except UnboundLocalError as e: wprint(f"The language in the audio cannot be recognized ：{str(e)}") return None tts_pipline.init_vits_weights(sovits_path) tts_pipline.init_t2s_weights(gpt_path) inputs={ "text": user_text, "text_lang": dict_language[user_lang], "ref_audio_path": user_voice, "prompt_text": prompt_text, "prompt_lang": dict_language[prompt_lang], "top_k": 5, "top_p": 1, "temperature": 1, "text_split_method": "cut1", "batch_size":20, "speed_factor":1.0, "split_bucket":True, "volume":1.0, "return_fragment":False, } yield next(tts_pipline.run(inputs)) with open('dummy') as f: dummy_txt = f.read().strip().splitlines() def dice(): return random.choice(dummy_txt), '🎲' from info import models models_by_language = { "English": [], "中文": [], "日本語": [] } for model_name, model_info in models.items(): language = model_info["default_language"] models_by_language[language].append((model_name, model_info)) ##########GRADIO########### with gr.Blocks(theme='Kasien/ali_theme_custom') as app: gr.HTML('''

TEXT TO SPEECH

Support English/Chinese/Japanese

If you like this space, please click the ❤️ at the top of the page..如喜欢，请点一下页面顶部的❤️

''') gr.Markdown("""* This space is based on the text-to-speech generation solution [GPT-SoVITS](https://github.com/RVC-Boss/GPT-SoVITS) . You can visit the repo's github homepage to learn training and inference.
本空间基于文字转语音生成方案 [GPT-SoVITS](https://github.com/RVC-Boss/GPT-SoVITS). 你可以前往项目的github主页学习如何推理和训练。 * ⚠️Generating voice is very slow due to using HuggingFace's free CPU in this space. For faster generation, click the Colab icon below to use this space in Colab, which will significantly improve the speed.
由于本空间使用huggingface的免费CPU进行推理，因此速度很慢，如想快速生成，请点击下方的Colab图标， 前往Colab使用已获得更快的生成速度。
Colabの使用を強くお勧めします。より速い生成速度が得られます。 * each model can speak three languages.
每个模型都能说三种语言
各モデルは3つの言語を話すことができます。""") gr.HTML('''colab ''') default_voice_wav, default_voice_wav_words, default_language, _, default_model_name, _, default_tone_sample_path = update_model("Trump") english_models = [name for name, _ in models_by_language["English"]] chinese_models = [name for name, _ in models_by_language["中文"]] japanese_models = [name for name, _ in models_by_language["日本語"]] with gr.Row(): english_choice = gr.Radio(english_models, label="EN",value="Trump",scale=3) chinese_choice = gr.Radio(chinese_models, label="ZH",scale=2) japanese_choice = gr.Radio(japanese_models, label="JA",scale=4) plsh='Support【English/中文/日本語】，Input text you like / 輸入文字 /テキストを入力する' limit='Max 70 words. Excess will be ignored./单次最多处理120字左右，多余的会被忽略' gr.HTML(''' Input Text/输入文字''') with gr.Row(): with gr.Column(scale=2): model_name = gr.Textbox(label="Seleted Model/已选模型", value=default_model_name, scale=1) text_language = gr.Textbox( label="Language for input text/生成语言", info='Automatic detection of input language type.',scale=1,interactive=False ) text = gr.Textbox(label="INPUT TEXT", lines=5,placeholder=plsh,info=limit,scale=10,min_width=0) ddice= gr.Button('🎲', variant='tool',min_width=0,scale=0) ddice.click(dice, outputs=[text, ddice]) text.change( lang_detector, text, text_language) with gr.Row(): with gr.Column(scale=2): tone_select = gr.Radio( label="Select Tone/选择语气", choices=["tone1","tone2","tone3"], value="tone1", info='Tone influences the emotional expression ',scale=1) tone_sample=gr.Audio(label="🔊Preview tone/试听语气 ", scale=8) with gr.Accordion(label="prpt voice", open=False,visible=False): with gr.Row(visible=True): inp_ref = gr.Audio(label="Reference audio", type="filepath", value=default_voice_wav, scale=3) prompt_text = gr.Textbox(label="Reference text", value=default_voice_wav_words, scale=3) prompt_language = gr.Dropdown(label="Language of the reference audio", choices=["中文", "English", "日本語"], value=default_language, scale=1,interactive=False) dummy = gr.Radio(choices=["中文","English","日本語"],visible=False) with gr.Accordion(label="Additional generation options/附加生成选项", open=False): with gr.Row(): how_to_cut = gr.Dropdown( label=("How to split input text?/如何对输入文字切片"), choices=[("Do not split/不切"), ("Split into groups of 4 sentences/四句一切"), ("Split every 50 characters/50字一切"), ("Split at CN/JP periods (。)/按中日文句号切"), ("Split at English periods (.)/按英文句号切"), ("Split at punctuation marks/按标点切"), ], value=("Split into groups of 4 sentences/四句一切"), interactive=True, info='A suitable splitting method can achieve better generation results/适合的切片方法会得到更好的效果' ) split_bucket = gr.Checkbox(label="Split bucket/数据分桶", value=True, info='Speed up the inference process/提升推理速度') with gr.Row(): volume = gr.Slider(minimum=0.5, maximum=5, value=1, step=0.1, label='Volume/音量',info='audio distortion due to excessive volume/大了要爆音') speed_factor = gr.Slider(minimum=0.25,maximum=4,step=0.05,label="Speed factor",value=1.0,info='Playback speed/播放速度') batch_size = gr.Slider(minimum=1,maximum=100,step=1,label="Batch size",value=20,info='The number of sentences for batch inference./并行推理的句子数量') with gr.Row(): top_k = gr.Slider(minimum=1,maximum=100,step=1,label="top_k",value=5) top_p = gr.Slider(minimum=0,maximum=1,step=0.05,label="top_p",value=1) temperature = gr.Slider(minimum=0,maximum=1,step=0.05,label="temperature",value=1) ref_text_free = gr.Checkbox(label="REF_TEXT_FREE", value=False, visible=False) gr.HTML(''' Generate Voice/生成''') with gr.Row(): main_button = gr.Button("✨Generate Voice", variant="primary", scale=2) output = gr.Audio(label="💾Download it by clicking ⬇️", scale=6) #info = gr.Textbox(label="INFO", visible=True, readonly=True, scale=1) gr.HTML(''' Generation is slower, please be patient and wait/合成比较慢，请耐心等待
If it generated silence, please try again./如果生成了空白声音，请重试



Clone custom Voice/克隆自定义声音

Need 3~10s audio.This involves voice-to-text conversion followed by text-to-voice conversion, so it takes longer time
需要3~10秒语音，这个会涉及语音转文字，之后再转语音，所以耗时比较久

''') with gr.Row(): user_voice = gr.Audio(type="filepath", label="（3~10s）Upload or Record audio/上传或录制声音",scale=3) with gr.Column(scale=7): user_lang = gr.Textbox(label="Language/生成语言",info='Automatic detection of input language type.',interactive=False) with gr.Row(): user_text= gr.Textbox(label="Text for generation/输入想要生成语音的文字", lines=5,placeholder=plsh,info=limit) dddice= gr.Button('🎲', variant='tool',min_width=0,scale=0) dddice.click(dice, outputs=[user_text, dddice]) user_text.change( lang_detector, user_text, user_lang) user_button = gr.Button("✨Clone Voice", variant="primary") user_output = gr.Audio(label="💾Download it by clicking ⬇️") gr.HTML('''
visitor badge
''') english_choice.change(update_model, inputs=[english_choice], outputs=[inp_ref, prompt_text, prompt_language,dummy,model_name, tone_select, tone_sample]) chinese_choice.change(update_model, inputs=[chinese_choice], outputs=[inp_ref, prompt_text, prompt_language, dummy,model_name, tone_select, tone_sample]) japanese_choice.change(update_model, inputs=[japanese_choice], outputs=[inp_ref, prompt_text, prompt_language,dummy,model_name, tone_select, tone_sample]) tone_select.change(update_tone, inputs=[model_name, tone_select], outputs=[inp_ref, prompt_text, tone_sample]) main_button.click( inference, inputs=[text, text_language, inp_ref, prompt_text, prompt_language, top_k, top_p, temperature, how_to_cut, batch_size, speed_factor, ref_text_free, split_bucket, volume], outputs=[output] ) user_button.click( clone_voice, inputs=[user_voice,user_text,user_lang], outputs=[user_output]) app.launch(share=True, show_api=False).queue(api_open=False)