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import gradio as gr |
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import numpy as np |
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import soundfile as sf |
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from datetime import datetime |
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from time import time as ttime |
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from my_utils import load_audio |
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from transformers import pipeline |
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from text.cleaner import clean_text |
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from polyglot.detect import Detector |
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from feature_extractor import cnhubert |
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from timeit import default_timer as timer |
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from text import cleaned_text_to_sequence |
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from module.models import SynthesizerTrn |
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from module.mel_processing import spectrogram_torch |
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from transformers.pipelines.audio_utils import ffmpeg_read |
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import os,re,sys,LangSegment,librosa,pdb,torch,pytz,random |
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from transformers import AutoModelForMaskedLM, AutoTokenizer |
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from AR.models.t2s_lightning_module import Text2SemanticLightningModule |
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import logging |
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logging.getLogger("markdown_it").setLevel(logging.ERROR) |
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logging.getLogger("urllib3").setLevel(logging.ERROR) |
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logging.getLogger("httpcore").setLevel(logging.ERROR) |
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logging.getLogger("httpx").setLevel(logging.ERROR) |
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logging.getLogger("asyncio").setLevel(logging.ERROR) |
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logging.getLogger("charset_normalizer").setLevel(logging.ERROR) |
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logging.getLogger("torchaudio._extension").setLevel(logging.ERROR) |
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logging.getLogger("multipart").setLevel(logging.WARNING) |
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from download import * |
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download() |
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from TTS_infer_pack.TTS import TTS, TTS_Config |
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from TTS_infer_pack.text_segmentation_method import get_method |
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if "_CUDA_VISIBLE_DEVICES" in os.environ: |
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os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"] |
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tz = pytz.timezone('Asia/Singapore') |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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|
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def abs_path(dir): |
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global_dir = os.path.dirname(os.path.abspath(sys.argv[0])) |
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return(os.path.join(global_dir, dir)) |
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gpt_path = abs_path("MODELS/22/22.ckpt") |
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sovits_path=abs_path("MODELS/22/22.pth") |
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cnhubert_base_path = os.environ.get("cnhubert_base_path", "pretrained_models/chinese-hubert-base") |
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bert_path = os.environ.get("bert_path", "pretrained_models/chinese-roberta-wwm-ext-large") |
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if not os.path.exists(cnhubert_base_path): |
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cnhubert_base_path = "TencentGameMate/chinese-hubert-base" |
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if not os.path.exists(bert_path): |
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bert_path = "hfl/chinese-roberta-wwm-ext-large" |
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cnhubert.cnhubert_base_path = cnhubert_base_path |
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whisper_path = os.environ.get("whisper_path", "pretrained_models/whisper-tiny") |
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if not os.path.exists(whisper_path): |
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whisper_path = "openai/whisper-tiny" |
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pipe = pipeline( |
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task="automatic-speech-recognition", |
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model=whisper_path, |
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chunk_length_s=30, |
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device=device,) |
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is_half = eval( |
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os.environ.get("is_half", "True" if torch.cuda.is_available() else "False") |
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) |
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dict_language = { |
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"中文1": "all_zh", |
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"English": "en", |
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"日文1": "all_ja", |
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"中文": "zh", |
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"日本語": "ja", |
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"混合": "auto", |
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} |
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cut_method = { |
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"Do not split/不切":"cut0", |
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"Split into groups of 4 sentences/四句一切": "cut1", |
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"Split every 50 characters/50字一切": "cut2", |
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"Split at CN/JP periods (。)/按中日文句号切": "cut3", |
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"Split at English periods (.)/按英文句号切": "cut4", |
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"Split at punctuation marks/按标点切": "cut5", |
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} |
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tts_config = TTS_Config("GPT_SoVITS/configs/tts_infer.yaml") |
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tts_config.device = device |
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tts_config.is_half = is_half |
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if gpt_path is not None: |
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tts_config.t2s_weights_path = gpt_path |
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if sovits_path is not None: |
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tts_config.vits_weights_path = sovits_path |
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if cnhubert_base_path is not None: |
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tts_config.cnhuhbert_base_path = cnhubert_base_path |
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if bert_path is not None: |
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tts_config.bert_base_path = bert_path |
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tts_pipline = TTS(tts_config) |
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gpt_path = tts_config.t2s_weights_path |
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sovits_path = tts_config.vits_weights_path |
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def inference(text, text_lang, |
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ref_audio_path, prompt_text, |
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prompt_lang, top_k, |
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top_p, temperature, |
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text_split_method, batch_size, |
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speed_factor, ref_text_free, |
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split_bucket, |
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volume |
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): |
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if not duration(ref_audio_path): |
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return None |
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if text == '': |
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wprint("Please input text to generate/请输入生成文字") |
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return None |
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text=trim_text(text,text_language) |
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tts_pipline.init_vits_weights(sovits_path) |
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tts_pipline.init_t2s_weights(gpt_path) |
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try: |
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lang=dict_language[text_lang] |
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inputs={ |
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"text": text, |
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"text_lang": lang, |
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"ref_audio_path": ref_audio_path, |
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"prompt_text": prompt_text if not ref_text_free else "", |
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"prompt_lang": dict_language[prompt_lang], |
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"top_k": top_k, |
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"top_p": top_p, |
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"temperature": temperature, |
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"text_split_method": cut_method[text_split_method], |
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"batch_size":int(batch_size), |
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"speed_factor":float(speed_factor), |
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"split_bucket":split_bucket, |
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"volume":volume, |
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"return_fragment":False, |
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} |
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yield next(tts_pipline.run(inputs)) |
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except KeyError as e: |
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wprint(f'Unsupported language type:{e}') |
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return None |
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splits = {"，", "。", "？", "！", ",", ".", "?", "!", "~", ":", "：", "—", "…", } |
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def tprint(text): |
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now=datetime.now(tz).strftime('%H:%M:%S') |
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print(f'UTC+8 - {now} - {text}') |
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def wprint(text): |
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tprint(text) |
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gr.Warning(text) |
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def lang_detector(text): |
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min_chars = 5 |
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if len(text) < min_chars: |
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return "Input text too short/输入文本太短" |
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try: |
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detector = Detector(text).language |
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lang_info = str(detector) |
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code = re.search(r"name: (\w+)", lang_info).group(1) |
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if code == 'Japanese': |
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return "日本語" |
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elif code == 'Chinese': |
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return "中文" |
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elif code == 'English': |
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return 'English' |
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else: |
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return code |
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except Exception as e: |
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return f"ERROR：{str(e)}" |
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def trim_text(text,language): |
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limit_cj = 120 |
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limit_en = 60 |
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search_limit_cj = limit_cj+30 |
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search_limit_en = limit_en +30 |
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text = text.replace('\n', '').strip() |
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if language =='English': |
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words = text.split() |
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if len(words) <= limit_en: |
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return text |
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for i in range(limit_en, -1, -1): |
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if any(punct in words[i] for punct in splits): |
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return ' '.join(words[:i+1]) |
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for i in range(limit_en, min(len(words), search_limit_en)): |
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if any(punct in words[i] for punct in splits): |
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return ' '.join(words[:i+1]) |
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return ' '.join(words[:limit_en]) |
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else: |
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if len(text) <= limit_cj: |
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return text |
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for i in range(limit_cj, -1, -1): |
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if text[i] in splits: |
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return text[:i+1] |
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for i in range(limit_cj, min(len(text), search_limit_cj)): |
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if text[i] in splits: |
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return text[:i+1] |
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return text[:limit_cj] |
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def duration(audio_file_path): |
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if not audio_file_path: |
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wprint("Failed to obtain uploaded audio/未找到音频文件") |
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return False |
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try: |
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audio_duration = librosa.get_duration(filename=audio_file_path) |
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if not 3 < audio_duration < 10: |
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wprint("The audio length must be between 3~10 seconds/音频时长须在3~10秒之间") |
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return False |
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return True |
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except FileNotFoundError: |
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return False |
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def update_model(choice): |
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global gpt_path,sovits_path |
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model_info = models[choice] |
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gpt_path = abs_path(model_info["gpt_weight"]) |
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sovits_path = abs_path(model_info["sovits_weight"]) |
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model_name = choice |
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tone_info = model_info["tones"]["tone1"] |
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tone_sample_path = abs_path(tone_info["sample"]) |
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tprint(f'✅SELECT MODEL：{choice}') |
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return ( |
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tone_info["example_voice_wav"], |
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tone_info["example_voice_wav_words"], |
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model_info["default_language"], |
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model_info["default_language"], |
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model_name, |
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"tone1" , |
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tone_sample_path |
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) |
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def update_tone(model_choice, tone_choice): |
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model_info = models[model_choice] |
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tone_info = model_info["tones"][tone_choice] |
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example_voice_wav = abs_path(tone_info["example_voice_wav"]) |
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example_voice_wav_words = tone_info["example_voice_wav_words"] |
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tone_sample_path = abs_path(tone_info["sample"]) |
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return example_voice_wav, example_voice_wav_words,tone_sample_path |
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def transcribe(voice): |
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time1=timer() |
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tprint('⚡Start Clone - transcribe') |
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task="transcribe" |
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if voice is None: |
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wprint("No audio file submitted! Please upload or record an audio file before submitting your request.") |
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R = pipe(voice, batch_size=8, generate_kwargs={"task": task}, return_timestamps=True,return_language=True) |
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text=R['text'] |
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lang=R['chunks'][0]['language'] |
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if lang=='english': |
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language='English' |
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elif lang =='chinese': |
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language='中文' |
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elif lang=='japanese': |
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language = '日本語' |
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time2=timer() |
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tprint(f'transcribe COMPLETE,{round(time2-time1,4)}s') |
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tprint(f' \nTranscribe result：\n 🔣Language：{language} \n 🔣Text：{text}' ) |
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return text,language |
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def clone_voice(user_voice,user_text,user_lang): |
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if not duration(user_voice): |
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return None |
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if user_text == '': |
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wprint("Please enter text to generate/请输入生成文字") |
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return None |
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user_text=trim_text(user_text,user_lang) |
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gpt_path = abs_path("pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt") |
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sovits_path = abs_path("pretrained_models/s2G488k.pth") |
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try: |
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prompt_text, prompt_lang = transcribe(user_voice) |
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except UnboundLocalError as e: |
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wprint(f"The language in the audio cannot be recognized ：{str(e)}") |
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return None |
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tts_pipline.init_vits_weights(sovits_path) |
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tts_pipline.init_t2s_weights(gpt_path) |
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inputs={ |
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"text": user_text, |
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"text_lang": dict_language[user_lang], |
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"ref_audio_path": user_voice, |
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"prompt_text": prompt_text, |
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"prompt_lang": dict_language[prompt_lang], |
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"top_k": 5, |
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"top_p": 1, |
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"temperature": 1, |
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"text_split_method": "cut1", |
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"batch_size":20, |
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"speed_factor":1.0, |
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"split_bucket":True, |
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"volume":1.0, |
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"return_fragment":False, |
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} |
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yield next(tts_pipline.run(inputs)) |
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|
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with open('dummy') as f: |
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dummy_txt = f.read().strip().splitlines() |
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|
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def dice(): |
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return random.choice(dummy_txt), '🎲' |
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from info import models |
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models_by_language = { |
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"English": [], |
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"中文": [], |
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"日本語": [] |
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} |
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for model_name, model_info in models.items(): |
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language = model_info["default_language"] |
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models_by_language[language].append((model_name, model_info)) |
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with gr.Blocks(theme='Kasien/ali_theme_custom') as app: |
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gr.HTML(''' |
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<h1 style="font-size: 25px;">TEXT TO SPEECH</h1> |
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<h1 style="font-size: 20px;">Support English/Chinese/Japanese</h1> |
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<p style="margin-bottom: 10px; font-size: 100%"> |
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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❤️<br> |
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</p>''') |
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gr.Markdown("""* This space is based on the text-to-speech generation solution [GPT-SoVITS](https://github.com/RVC-Boss/GPT-SoVITS) . |
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You can visit the repo's github homepage to learn training and inference.<br> |
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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。 |
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* ⚠️Generating voice is very slow due to using HuggingFace's free CPU in this space. |
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For faster generation, click the Colab icon below to use this space in Colab, |
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which will significantly improve the speed.<br> |
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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， |
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前往Colab使用已获得更快的生成速度。 |
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<br>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。 |
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* each model can speak three languages.<br>每个模型都能说三种语言<br>各モデルは3つの言語を話すことができます。""") |
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gr.HTML('''<a href="https://colab.research.google.com/drive/1fTuPZ4tZsAjS-TrhQWMCb7KRdnU8aF6j" target="_blank"><img src="https://camo.githubusercontent.com/dd83d4a334eab7ada034c13747d9e2237182826d32e3fda6629740b6e02f18d8/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f436f6c61622d4639414230303f7374796c653d666f722d7468652d6261646765266c6f676f3d676f6f676c65636f6c616226636f6c6f723d353235323532" alt="colab"></a> |
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''') |
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default_voice_wav, default_voice_wav_words, default_language, _, default_model_name, _, default_tone_sample_path = update_model("Trump") |
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english_models = [name for name, _ in models_by_language["English"]] |
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chinese_models = [name for name, _ in models_by_language["中文"]] |
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japanese_models = [name for name, _ in models_by_language["日本語"]] |
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with gr.Row(): |
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english_choice = gr.Radio(english_models, label="EN",value="Trump",scale=3) |
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chinese_choice = gr.Radio(chinese_models, label="ZH",scale=2) |
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japanese_choice = gr.Radio(japanese_models, label="JA",scale=4) |
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|
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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' |
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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' |
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gr.HTML(''' |
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<b>Input Text/输入文字</b>''') |
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with gr.Row(): |
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with gr.Column(scale=2): |
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model_name = gr.Textbox(label="Seleted Model/已选模型", value=default_model_name, scale=1) |
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text_language = gr.Textbox( |
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label="Language for input text/生成语言", |
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info='Automatic detection of input language type.',scale=1,interactive=False |
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) |
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text = gr.Textbox(label="INPUT TEXT", lines=5,placeholder=plsh,info=limit,scale=10,min_width=0) |
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ddice= gr.Button('🎲', variant='tool',min_width=0,scale=0) |
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ddice.click(dice, outputs=[text, ddice]) |
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text.change( lang_detector, text, text_language) |
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with gr.Row(): |
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with gr.Column(scale=2): |
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tone_select = gr.Radio( |
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label="Select Tone/选择语气", |
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choices=["tone1","tone2","tone3"], |
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value="tone1", |
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info='Tone influences the emotional expression ',scale=1) |
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tone_sample=gr.Audio(label="🔊Preview tone/试听语气 ", scale=8) |
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|
|
|
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with gr.Accordion(label="prpt voice", open=False,visible=False): |
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with gr.Row(visible=True): |
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inp_ref = gr.Audio(label="Reference audio", type="filepath", value=default_voice_wav, scale=3) |
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prompt_text = gr.Textbox(label="Reference text", value=default_voice_wav_words, scale=3) |
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prompt_language = gr.Dropdown(label="Language of the reference audio", choices=["中文", "English", "日本語"], value=default_language, scale=1,interactive=False) |
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dummy = gr.Radio(choices=["中文","English","日本語"],visible=False) |
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|
|
|
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with gr.Accordion(label="Additional generation options/附加生成选项", open=False): |
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with gr.Row(): |
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how_to_cut = gr.Dropdown( |
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label=("How to split input text?/如何对输入文字切片"), |
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choices=[("Do not split/不切"), ("Split into groups of 4 sentences/四句一切"), ("Split every 50 characters/50字一切"), |
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("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"), ], |
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value=("Split into groups of 4 sentences/四句一切"), |
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interactive=True, |
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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' |
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) |
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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') |
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with gr.Row(): |
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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') |
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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') |
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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') |
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with gr.Row(): |
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top_k = gr.Slider(minimum=1,maximum=100,step=1,label="top_k",value=5) |
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top_p = gr.Slider(minimum=0,maximum=1,step=0.05,label="top_p",value=1) |
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temperature = gr.Slider(minimum=0,maximum=1,step=0.05,label="temperature",value=1) |
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ref_text_free = gr.Checkbox(label="REF_TEXT_FREE", value=False, visible=False) |
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|
|
|
|
|
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gr.HTML(''' |
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<b>Generate Voice/生成</b>''') |
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with gr.Row(): |
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main_button = gr.Button("✨Generate Voice", variant="primary", scale=2) |
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output = gr.Audio(label="💾Download it by clicking ⬇️", scale=6) |
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|
|
|
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gr.HTML(''' |
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Generation is slower, please be patient and wait/合成比较慢，请耐心等待<br> |
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If it generated silence, please try again./如果生成了空白声音，请重试 |
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<br><br><br><br> |
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<h1 style="font-size: 25px;">Clone custom Voice/克隆自定义声音</h1> |
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<p style="margin-bottom: 10px; font-size: 100%">Need 3~10s audio.This involves voice-to-text conversion followed by text-to-voice conversion, so it takes longer time<br> |
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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 |
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</p>''') |
|
|
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with gr.Row(): |
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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) |
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with gr.Row(): |
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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) |
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dddice= gr.Button('🎲', variant='tool',min_width=0,scale=0) |
|
|
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dddice.click(dice, outputs=[user_text, dddice]) |
|
|
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user_text.change( lang_detector, user_text, user_lang) |
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|
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user_button = gr.Button("✨Clone Voice", variant="primary") |
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user_output = gr.Audio(label="💾Download it by clicking ⬇️") |
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|
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gr.HTML('''<div align=center><img id="visitor-badge" alt="visitor badge" src="https://visitor-badge.laobi.icu/badge?page_id=Ailyth/DLMP9" /></div>''') |
|
|
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english_choice.change(update_model, inputs=[english_choice], outputs=[inp_ref, prompt_text, prompt_language,dummy,model_name, tone_select, tone_sample]) |
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chinese_choice.change(update_model, inputs=[chinese_choice], outputs=[inp_ref, prompt_text, prompt_language, dummy,model_name, tone_select, tone_sample]) |
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japanese_choice.change(update_model, inputs=[japanese_choice], outputs=[inp_ref, prompt_text, prompt_language,dummy,model_name, tone_select, tone_sample]) |
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tone_select.change(update_tone, inputs=[model_name, tone_select], outputs=[inp_ref, prompt_text, tone_sample]) |
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|
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main_button.click( |
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inference, |
|
inputs=[text, |
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text_language, |
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inp_ref, |
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prompt_text, |
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prompt_language, |
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top_k, |
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top_p, |
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temperature, |
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how_to_cut, |
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batch_size, |
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speed_factor, |
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ref_text_free, |
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split_bucket, |
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volume], |
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outputs=[output] |
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) |
|
|
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user_button.click( |
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clone_voice, |
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inputs=[user_voice,user_text,user_lang], |
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outputs=[user_output]) |
|
|
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app.launch(share=True, show_api=False).queue(api_open=False) |
