From eaefc57d7184b93b7a19c0c2af923656d12ff074 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Stardust=C2=B7=E5=87=8F?= Date: Tue, 12 Dec 2023 19:19:50 +0800 Subject: [PATCH] Use clap to achieve prompt controlled generation (#223) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * 快速分类音频并把yml格式结果存在训练根目录里 (#190) * Add files via upload * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Update models.py * Update webui.py * Update infer.py * Create compress_model.py * 重新提交，更新Gradio推理UI (#193) * Update webui.py * Update webui.py * 更新 train_ms.py * 更新 models.py * 更新 models.py * 更新 models.py * 更新 train_ms.py * 更新 train_ms.py * 更新 models.py * Update preprocess_text.py * Update config.json * Update train_ms.py * Update webui.py (#206) * Add files via upload (#209) * Update train_ms.py * Update train_ms.py * Update preprocess_text.py * Update train_ms.py * fix (#211) * Update emotion_clustering.py * Add files via upload * Update emotion_clustering.py * add cluster center save * Add files via upload * Update config.py * Update default_config.yml * Update config.py * Update config.py * Update emotion_clustering.py * Update emotion_clustering.py * Update config.py * Update emotion_clustering.py * Update emotion_clustering.py * Update webui.py * Update emotion_clustering.py * Update commons.py * Update emotion_clustering.py * Update webui.py * Update webui.py * Add files via upload * Update train_ms.py * Update train_ms.py * Update train_ms.py * Update train_ms.py * Update train_ms.py * Update webui.py * Update emotion_clustering.py * Update emotion_clustering.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix default_config.yml. * Update infer.py * feat: support infer 2.1 models * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix: support infer 2.1 models 兼容bug修复 * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update train_ms.py * Add CLAP * Fix data loader * Fix infer.py * Fix webui.py * Add prompt template * Update clap_gen.py * Fix wrong environ value * Add g for dur disc * Update clap_gen.py * Fix multilang generation * Update config.json * Prompt mode * Improve slice segments performance * Add preprocess webui * Update webui_preprocess.py * Update webui_preprocess.py * Update config.py * Update default_config.yml * Update config.py * Update clap_gen.py * Delete emo_gen.py * Delete get_emo.py * Delete emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim directory * Update README.md * Update README * Split val per lang * Delete emotion_clustering.py * Update default_config.yml * Update default_config.yml * Update config.py * Update preprocess_text.py * Update webui_preprocess.py * Update defalut_config.yml * Update webui_preprocess.py * Update preprocess_text.py * Random augmentation for CLAP * Update data_utils.py * Update preprocess_text.py * Add vq for CLAP features to avoid overfitting * Random dummy inputs * Update webui.py * Update models.py * Update infer.py * Apply Code Formatter Change * Update config.json * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: YYuX-1145 <138500330+YYuX-1145@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Sora <654163754@qq.com> Co-authored-by: Sihan Wang Co-authored-by: Stardust-minus --- README.md | 5 + clap_gen.py | 64 + clap_wrapper.py | 49 + commons.py | 20 +- compress_model.py | 1 + config.py | 492 +- configs/config.json | 437 +- data_utils.py | 27 +- default_config.yml | 353 +- .../.gitattributes | 10 +- emotional/clap-htsat-fused/README.md | 107 + emotional/clap-htsat-fused/config.json | 207 + emotional/clap-htsat-fused/merges.txt | 50001 ++++++ .../clap-htsat-fused/preprocessor_config.json | 22 + .../clap-htsat-fused/special_tokens_map.json | 15 + emotional/clap-htsat-fused/tokenizer.json | 100362 +++++++++++ .../clap-htsat-fused/tokenizer_config.json | 16 + emotional/clap-htsat-fused/vocab.json | 1 + .../LICENSE | 437 - .../README.md | 127 - .../config.json | 122 - .../preprocessor_config.json | 9 - .../vocab.json | 1 - empty_emo.npy | Bin 0 -> 3238 bytes get_emo.py | 26 - infer.py | 722 +- models.py | 2115 +- oldVersion/V210/__init__.py | 221 + emo_gen.py => oldVersion/V210/emo_gen.py | 272 +- oldVersion/V210/models.py | 1040 + oldVersion/V210/text/__init__.py | 51 + oldVersion/V210/text/bert_utils.py | 23 + oldVersion/V210/text/chinese.py | 199 + oldVersion/V210/text/chinese_bert.py | 101 + oldVersion/V210/text/cleaner.py | 28 + oldVersion/V210/text/cmudict.rep | 129530 +++++++++++++++ oldVersion/V210/text/cmudict_cache.pickle | Bin 0 -> 6212655 bytes oldVersion/V210/text/english.py | 453 + oldVersion/V210/text/english_bert_mock.py | 42 + oldVersion/V210/text/japanese.py | 432 + oldVersion/V210/text/japanese_bert.py | 44 + oldVersion/V210/text/opencpop-strict.txt | 429 + oldVersion/V210/text/symbols.py | 187 + oldVersion/V210/text/tone_sandhi.py | 769 + preprocess_text.py | 13 +- resample.py | 16 +- train_ms.py | 38 +- webui.py | 1017 +- webui_preprocess.py | 177 + 49 files changed, 287404 insertions(+), 3426 deletions(-) create mode 100644 clap_gen.py create mode 100644 clap_wrapper.py rename emotional/{wav2vec2-large-robust-12-ft-emotion-msp-dim => clap-htsat-fused}/.gitattributes (76%) create mode 100644 emotional/clap-htsat-fused/README.md create mode 100644 emotional/clap-htsat-fused/config.json create mode 100644 emotional/clap-htsat-fused/merges.txt create mode 100644 emotional/clap-htsat-fused/preprocessor_config.json create mode 100644 emotional/clap-htsat-fused/special_tokens_map.json create mode 100644 emotional/clap-htsat-fused/tokenizer.json create mode 100644 emotional/clap-htsat-fused/tokenizer_config.json create mode 100644 emotional/clap-htsat-fused/vocab.json delete mode 100644 emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/LICENSE delete mode 100644 emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/README.md delete mode 100644 emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/config.json delete mode 100644 emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/preprocessor_config.json delete mode 100644 emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/vocab.json create mode 100644 empty_emo.npy delete mode 100644 get_emo.py create mode 100644 oldVersion/V210/__init__.py rename emo_gen.py => oldVersion/V210/emo_gen.py (55%) create mode 100644 oldVersion/V210/models.py create mode 100644 oldVersion/V210/text/__init__.py create mode 100644 oldVersion/V210/text/bert_utils.py create mode 100644 oldVersion/V210/text/chinese.py create mode 100644 oldVersion/V210/text/chinese_bert.py create mode 100644 oldVersion/V210/text/cleaner.py create mode 100644 oldVersion/V210/text/cmudict.rep create mode 100644 oldVersion/V210/text/cmudict_cache.pickle create mode 100644 oldVersion/V210/text/english.py create mode 100644 oldVersion/V210/text/english_bert_mock.py create mode 100644 oldVersion/V210/text/japanese.py create mode 100644 oldVersion/V210/text/japanese_bert.py create mode 100644 oldVersion/V210/text/opencpop-strict.txt create mode 100644 oldVersion/V210/text/symbols.py create mode 100644 oldVersion/V210/text/tone_sandhi.py create mode 100644 webui_preprocess.py diff --git a/README.md b/README.md index 6ff1bc0..4e00992 100644 --- a/README.md +++ b/README.md @@ -5,6 +5,11 @@ # Bert-VITS2 VITS2 Backbone with multilingual bert + +For quick guide, please refer to `webui_preprocess.py`. + +简易教程请参见 `webui_preprocess.py`。 + ## 请注意，本项目核心思路来源于[anyvoiceai/MassTTS](https://github.com/anyvoiceai/MassTTS) 一个非常好的tts项目 ## MassTTS的演示demo为[ai版峰哥锐评峰哥本人,并找回了在金三角失落的腰子](https://www.bilibili.com/video/BV1w24y1c7z9) diff --git a/clap_gen.py b/clap_gen.py new file mode 100644 index 0000000..20380ab --- /dev/null +++ b/clap_gen.py @@ -0,0 +1,64 @@ +import argparse +from multiprocessing import Pool, cpu_count + +import torch +import torch.multiprocessing as mp +from tqdm import tqdm + +import utils +from config import config +from clap_wrapper import get_clap_audio_feature +import librosa +import os + +os.environ["OMP_NUM_THREADS"] = "1" +os.environ["MKL_NUM_THREADS"] = "1" + + +def process_line(line): + device = config.emo_gen_config.device + if config.emo_gen_config.use_multi_device: + rank = mp.current_process()._identity + rank = rank[0] if len(rank) > 0 else 0 + if torch.cuda.is_available(): + gpu_id = rank % torch.cuda.device_count() + device = torch.device(f"cuda:{gpu_id}") + else: + device = torch.device("cpu") + wav_path, _, language_str, text, phones, tone, word2ph = line.strip().split("|") + + clap_path = wav_path.replace(".WAV", ".wav").replace(".wav", ".emo.npy") + if os.path.isfile(clap_path): + return + + audio = librosa.load(wav_path, 48000)[0] + # audio = librosa.resample(audio, 44100, 48000) + + clap = get_clap_audio_feature(audio, device) + torch.save(clap, clap_path) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "-c", "--config", type=str, default=config.emo_gen_config.config_path + ) + parser.add_argument( + "--num_processes", type=int, default=config.emo_gen_config.num_processes + ) + args, _ = parser.parse_known_args() + config_path = args.config + hps = utils.get_hparams_from_file(config_path) + lines = [] + with open(hps.data.training_files, encoding="utf-8") as f: + lines.extend(f.readlines()) + + with open(hps.data.validation_files, encoding="utf-8") as f: + lines.extend(f.readlines()) + if len(lines) != 0: + num_processes = min(args.num_processes, cpu_count()) + with Pool(processes=num_processes) as pool: + for _ in tqdm(pool.imap_unordered(process_line, lines), total=len(lines)): + pass + + print(f"clap生成完毕!, 共有{len(lines)}个emo.pt生成!") diff --git a/clap_wrapper.py b/clap_wrapper.py new file mode 100644 index 0000000..c1a99db --- /dev/null +++ b/clap_wrapper.py @@ -0,0 +1,49 @@ +import sys + +import torch +from transformers import ClapModel, ClapProcessor + +from config import config + +models = dict() +processor = ClapProcessor.from_pretrained("./emotional/clap-htsat-fused") + + +def get_clap_audio_feature(audio_data, device=config.bert_gen_config.device): + if ( + sys.platform == "darwin" + and torch.backends.mps.is_available() + and device == "cpu" + ): + device = "mps" + if not device: + device = "cuda" + if device not in models.keys(): + models[device] = ClapModel.from_pretrained("./emotional/clap-htsat-fused").to( + device + ) + with torch.no_grad(): + inputs = processor( + audios=audio_data, return_tensors="pt", sampling_rate=48000 + ).to(device) + emb = models[device].get_audio_features(**inputs) + return emb.T + + +def get_clap_text_feature(text, device=config.bert_gen_config.device): + if ( + sys.platform == "darwin" + and torch.backends.mps.is_available() + and device == "cpu" + ): + device = "mps" + if not device: + device = "cuda" + if device not in models.keys(): + models[device] = ClapModel.from_pretrained("./emotional/clap-htsat-fused").to( + device + ) + with torch.no_grad(): + inputs = processor(text=text, return_tensors="pt").to(device) + emb = models[device].get_text_features(**inputs) + return emb.T diff --git a/commons.py b/commons.py index 80d52bd..7af7f66 100644 --- a/commons.py +++ b/commons.py @@ -46,26 +46,18 @@ def rand_gumbel_like(x): def slice_segments(x, ids_str, segment_size=4): - ret = torch.zeros_like(x[:, :, :segment_size]) - for i in range(x.size(0)): - idx_str = ids_str[i] - idx_end = idx_str + segment_size - if idx_str < 0: - i1 = x.size(2) + idx_str - r1 = x[i, :, i1:] - r2 = x[i, :, :idx_end] - ret[i] = torch.cat([r1, r2], dim=1) - else: - ret[i] = x[i, :, idx_str:idx_end] - return ret + gather_indices = ids_str.view(x.size(0), 1, 1).repeat( + 1, x.size(1), 1 + ) + torch.arange(segment_size, device=x.device) + return torch.gather(x, 2, gather_indices) def rand_slice_segments(x, x_lengths=None, segment_size=4): b, d, t = x.size() if x_lengths is None: x_lengths = t - ids_str_max = x_lengths - segment_size + 1 - ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long) + ids_str_max = torch.clamp(x_lengths - segment_size + 1, min=0) + ids_str = (torch.rand([b], device=x.device) * ids_str_max).to(dtype=torch.long) ret = slice_segments(x, ids_str, segment_size) return ret, ids_str diff --git a/compress_model.py b/compress_model.py index 62a5a09..901490c 100644 --- a/compress_model.py +++ b/compress_model.py @@ -1,6 +1,7 @@ from collections import OrderedDict from text.symbols import symbols import torch + from tools.log import logger import utils from models import SynthesizerTrn diff --git a/config.py b/config.py index 01a5584..6cd4f5f 100644 --- a/config.py +++ b/config.py @@ -1,244 +1,248 @@ -""" -@Desc: 全局配置文件读取 -""" -import argparse -import yaml -from typing import Dict, List -import os -import shutil -import sys - - -class Resample_config: - """重采样配置""" - - def __init__(self, in_dir: str, out_dir: str, sampling_rate: int = 44100): - self.sampling_rate: int = sampling_rate # 目标采样率 - self.in_dir: str = in_dir # 待处理音频目录路径 - self.out_dir: str = out_dir # 重采样输出路径 - - @classmethod - def from_dict(cls, dataset_path: str, data: Dict[str, any]): - """从字典中生成实例""" - - # 不检查路径是否有效，此逻辑在resample.py中处理 - data["in_dir"] = os.path.join(dataset_path, data["in_dir"]) - data["out_dir"] = os.path.join(dataset_path, data["out_dir"]) - - return cls(**data) - - -class Preprocess_text_config: - """数据预处理配置""" - - def __init__( - self, - transcription_path: str, - cleaned_path: str, - train_path: str, - val_path: str, - config_path: str, - val_per_spk: int = 5, - max_val_total: int = 10000, - clean: bool = True, - ): - self.transcription_path: str = transcription_path # 原始文本文件路径，文本格式应为{wav_path}|{speaker_name}|{language}|{text}。 - self.cleaned_path: str = cleaned_path # 数据清洗后文本路径，可以不填。不填则将在原始文本目录生成 - self.train_path: str = train_path # 训练集路径，可以不填。不填则将在原始文本目录生成 - self.val_path: str = val_path # 验证集路径，可以不填。不填则将在原始文本目录生成 - self.config_path: str = config_path # 配置文件路径 - self.val_per_spk: int = val_per_spk # 每个speaker的验证集条数 - self.max_val_total: int = max_val_total # 验证集最大条数，多于的会被截断并放到训练集中 - self.clean: bool = clean # 是否进行数据清洗 - - @classmethod - def from_dict(cls, dataset_path: str, data: Dict[str, any]): - """从字典中生成实例""" - - data["transcription_path"] = os.path.join( - dataset_path, data["transcription_path"] - ) - if data["cleaned_path"] == "" or data["cleaned_path"] is None: - data["cleaned_path"] = None - else: - data["cleaned_path"] = os.path.join(dataset_path, data["cleaned_path"]) - data["train_path"] = os.path.join(dataset_path, data["train_path"]) - data["val_path"] = os.path.join(dataset_path, data["val_path"]) - data["config_path"] = os.path.join(dataset_path, data["config_path"]) - - return cls(**data) - - -class Bert_gen_config: - """bert_gen 配置""" - - def __init__( - self, - config_path: str, - num_processes: int = 2, - device: str = "cuda", - use_multi_device: bool = False, - ): - self.config_path = config_path - self.num_processes = num_processes - self.device = device - self.use_multi_device = use_multi_device - - @classmethod - def from_dict(cls, dataset_path: str, data: Dict[str, any]): - data["config_path"] = os.path.join(dataset_path, data["config_path"]) - - return cls(**data) - - -class Emo_gen_config: - """emo_gen 配置""" - - def __init__( - self, - config_path: str, - num_processes: int = 2, - device: str = "cuda", - ): - self.config_path = config_path - self.num_processes = num_processes - self.device = device - - @classmethod - def from_dict(cls, dataset_path: str, data: Dict[str, any]): - data["config_path"] = os.path.join(dataset_path, data["config_path"]) - - return cls(**data) - - -class Train_ms_config: - """训练配置""" - - def __init__( - self, - config_path: str, - env: Dict[str, any], - base: Dict[str, any], - model: str, - num_workers: int, - spec_cache: bool, - keep_ckpts: int, - ): - self.env = env # 需要加载的环境变量 - self.base = base # 底模配置 - self.model = model # 训练模型存储目录，该路径为相对于dataset_path的路径，而非项目根目录 - self.config_path = config_path # 配置文件路径 - self.num_workers = num_workers # worker数量 - self.spec_cache = spec_cache # 是否启用spec缓存 - self.keep_ckpts = keep_ckpts # ckpt数量 - - @classmethod - def from_dict(cls, dataset_path: str, data: Dict[str, any]): - # data["model"] = os.path.join(dataset_path, data["model"]) - data["config_path"] = os.path.join(dataset_path, data["config_path"]) - - return cls(**data) - - -class Webui_config: - """webui 配置""" - - def __init__( - self, - device: str, - model: str, - config_path: str, - language_identification_library: str, - port: int = 7860, - share: bool = False, - debug: bool = False, - ): - self.device: str = device - self.model: str = model # 端口号 - self.config_path: str = config_path # 是否公开部署，对外网开放 - self.port: int = port # 是否开启debug模式 - self.share: bool = share # 模型路径 - self.debug: bool = debug # 配置文件路径 - self.language_identification_library: str = ( - language_identification_library # 语种识别库 - ) - - @classmethod - def from_dict(cls, dataset_path: str, data: Dict[str, any]): - data["config_path"] = os.path.join(dataset_path, data["config_path"]) - data["model"] = os.path.join(dataset_path, data["model"]) - return cls(**data) - - -class Server_config: - def __init__( - self, models: List[Dict[str, any]], port: int = 5000, device: str = "cuda" - ): - self.models: List[Dict[str, any]] = models # 需要加载的所有模型的配置 - self.port: int = port # 端口号 - self.device: str = device # 模型默认使用设备 - - @classmethod - def from_dict(cls, data: Dict[str, any]): - return cls(**data) - - -class Translate_config: - """翻译api配置""" - - def __init__(self, app_key: str, secret_key: str): - self.app_key = app_key - self.secret_key = secret_key - - @classmethod - def from_dict(cls, data: Dict[str, any]): - return cls(**data) - - -class Config: - def __init__(self, config_path: str): - if not os.path.isfile(config_path) and os.path.isfile("default_config.yml"): - shutil.copy(src="default_config.yml", dst=config_path) - print( - f"已根据默认配置文件default_config.yml生成配置文件{config_path}。请按该配置文件的说明进行配置后重新运行。" - ) - print("如无特殊需求，请勿修改default_config.yml或备份该文件。") - sys.exit(0) - with open(file=config_path, mode="r", encoding="utf-8") as file: - yaml_config: Dict[str, any] = yaml.safe_load(file.read()) - dataset_path: str = yaml_config["dataset_path"] - openi_token: str = yaml_config["openi_token"] - self.dataset_path: str = dataset_path - self.mirror: str = yaml_config["mirror"] - self.openi_token: str = openi_token - self.resample_config: Resample_config = Resample_config.from_dict( - dataset_path, yaml_config["resample"] - ) - self.preprocess_text_config: Preprocess_text_config = ( - Preprocess_text_config.from_dict( - dataset_path, yaml_config["preprocess_text"] - ) - ) - self.bert_gen_config: Bert_gen_config = Bert_gen_config.from_dict( - dataset_path, yaml_config["bert_gen"] - ) - self.train_ms_config: Train_ms_config = Train_ms_config.from_dict( - dataset_path, yaml_config["train_ms"] - ) - self.webui_config: Webui_config = Webui_config.from_dict( - dataset_path, yaml_config["webui"] - ) - self.server_config: Server_config = Server_config.from_dict( - yaml_config["server"] - ) - self.translate_config: Translate_config = Translate_config.from_dict( - yaml_config["translate"] - ) - - -parser = argparse.ArgumentParser() -# 为避免与以前的config.json起冲突，将其更名如下 -parser.add_argument("-y", "--yml_config", type=str, default="config.yml") -args, _ = parser.parse_known_args() -config = Config(args.yml_config) -yml_config = args.yml_config +""" +@Desc: 全局配置文件读取 +""" +import argparse +import yaml +from typing import Dict, List +import os +import shutil +import sys + + +class Resample_config: + """重采样配置""" + + def __init__(self, in_dir: str, out_dir: str, sampling_rate: int = 44100): + self.sampling_rate: int = sampling_rate # 目标采样率 + self.in_dir: str = in_dir # 待处理音频目录路径 + self.out_dir: str = out_dir # 重采样输出路径 + + @classmethod + def from_dict(cls, dataset_path: str, data: Dict[str, any]): + """从字典中生成实例""" + + # 不检查路径是否有效，此逻辑在resample.py中处理 + data["in_dir"] = os.path.join(dataset_path, data["in_dir"]) + data["out_dir"] = os.path.join(dataset_path, data["out_dir"]) + + return cls(**data) + + +class Preprocess_text_config: + """数据预处理配置""" + + def __init__( + self, + transcription_path: str, + cleaned_path: str, + train_path: str, + val_path: str, + config_path: str, + val_per_lang: int = 5, + max_val_total: int = 10000, + clean: bool = True, + ): + self.transcription_path: str = transcription_path # 原始文本文件路径，文本格式应为{wav_path}|{speaker_name}|{language}|{text}。 + self.cleaned_path: str = cleaned_path # 数据清洗后文本路径，可以不填。不填则将在原始文本目录生成 + self.train_path: str = train_path # 训练集路径，可以不填。不填则将在原始文本目录生成 + self.val_path: str = val_path # 验证集路径，可以不填。不填则将在原始文本目录生成 + self.config_path: str = config_path # 配置文件路径 + self.val_per_lang: int = val_per_lang # 每个speaker的验证集条数 + self.max_val_total: int = max_val_total # 验证集最大条数，多于的会被截断并放到训练集中 + self.clean: bool = clean # 是否进行数据清洗 + + @classmethod + def from_dict(cls, dataset_path: str, data: Dict[str, any]): + """从字典中生成实例""" + + data["transcription_path"] = os.path.join( + dataset_path, data["transcription_path"] + ) + if data["cleaned_path"] == "" or data["cleaned_path"] is None: + data["cleaned_path"] = None + else: + data["cleaned_path"] = os.path.join(dataset_path, data["cleaned_path"]) + data["train_path"] = os.path.join(dataset_path, data["train_path"]) + data["val_path"] = os.path.join(dataset_path, data["val_path"]) + data["config_path"] = os.path.join(dataset_path, data["config_path"]) + + return cls(**data) + + +class Bert_gen_config: + """bert_gen 配置""" + + def __init__( + self, + config_path: str, + num_processes: int = 2, + device: str = "cuda", + use_multi_device: bool = False, + ): + self.config_path = config_path + self.num_processes = num_processes + self.device = device + self.use_multi_device = use_multi_device + + @classmethod + def from_dict(cls, dataset_path: str, data: Dict[str, any]): + data["config_path"] = os.path.join(dataset_path, data["config_path"]) + + return cls(**data) + + +class Emo_gen_config: + """emo_gen 配置""" + + def __init__( + self, + config_path: str, + num_processes: int = 2, + device: str = "cuda", + use_multi_device: bool = False, + ): + self.config_path = config_path + self.num_processes = num_processes + self.device = device + self.use_multi_device = use_multi_device + + @classmethod + def from_dict(cls, dataset_path: str, data: Dict[str, any]): + data["config_path"] = os.path.join(dataset_path, data["config_path"]) + + return cls(**data) + + +class Train_ms_config: + """训练配置""" + + def __init__( + self, + config_path: str, + env: Dict[str, any], + base: Dict[str, any], + model: str, + num_workers: int, + spec_cache: bool, + keep_ckpts: int, + ): + self.env = env # 需要加载的环境变量 + self.base = base # 底模配置 + self.model = model # 训练模型存储目录，该路径为相对于dataset_path的路径，而非项目根目录 + self.config_path = config_path # 配置文件路径 + self.num_workers = num_workers # worker数量 + self.spec_cache = spec_cache # 是否启用spec缓存 + self.keep_ckpts = keep_ckpts # ckpt数量 + + @classmethod + def from_dict(cls, dataset_path: str, data: Dict[str, any]): + # data["model"] = os.path.join(dataset_path, data["model"]) + data["config_path"] = os.path.join(dataset_path, data["config_path"]) + + return cls(**data) + + +class Webui_config: + """webui 配置""" + + def __init__( + self, + device: str, + model: str, + config_path: str, + language_identification_library: str, + port: int = 7860, + share: bool = False, + debug: bool = False, + ): + self.device: str = device + self.model: str = model # 端口号 + self.config_path: str = config_path # 是否公开部署，对外网开放 + self.port: int = port # 是否开启debug模式 + self.share: bool = share # 模型路径 + self.debug: bool = debug # 配置文件路径 + self.language_identification_library: str = ( + language_identification_library # 语种识别库 + ) + + @classmethod + def from_dict(cls, dataset_path: str, data: Dict[str, any]): + data["config_path"] = os.path.join(dataset_path, data["config_path"]) + data["model"] = os.path.join(dataset_path, data["model"]) + return cls(**data) + + +class Server_config: + def __init__( + self, models: List[Dict[str, any]], port: int = 5000, device: str = "cuda" + ): + self.models: List[Dict[str, any]] = models # 需要加载的所有模型的配置 + self.port: int = port # 端口号 + self.device: str = device # 模型默认使用设备 + + @classmethod + def from_dict(cls, data: Dict[str, any]): + return cls(**data) + + +class Translate_config: + """翻译api配置""" + + def __init__(self, app_key: str, secret_key: str): + self.app_key = app_key + self.secret_key = secret_key + + @classmethod + def from_dict(cls, data: Dict[str, any]): + return cls(**data) + + +class Config: + def __init__(self, config_path: str): + if not os.path.isfile(config_path) and os.path.isfile("default_config.yml"): + shutil.copy(src="default_config.yml", dst=config_path) + print( + f"已根据默认配置文件default_config.yml生成配置文件{config_path}。请按该配置文件的说明进行配置后重新运行。" + ) + print("如无特殊需求，请勿修改default_config.yml或备份该文件。") + sys.exit(0) + with open(file=config_path, mode="r", encoding="utf-8") as file: + yaml_config: Dict[str, any] = yaml.safe_load(file.read()) + dataset_path: str = yaml_config["dataset_path"] + openi_token: str = yaml_config["openi_token"] + self.dataset_path: str = dataset_path + self.mirror: str = yaml_config["mirror"] + self.openi_token: str = openi_token + self.resample_config: Resample_config = Resample_config.from_dict( + dataset_path, yaml_config["resample"] + ) + self.preprocess_text_config: Preprocess_text_config = ( + Preprocess_text_config.from_dict( + dataset_path, yaml_config["preprocess_text"] + ) + ) + self.bert_gen_config: Bert_gen_config = Bert_gen_config.from_dict( + dataset_path, yaml_config["bert_gen"] + ) + self.emo_gen_config: Emo_gen_config = Emo_gen_config.from_dict( + dataset_path, yaml_config["emo_gen"] + ) + self.train_ms_config: Train_ms_config = Train_ms_config.from_dict( + dataset_path, yaml_config["train_ms"] + ) + self.webui_config: Webui_config = Webui_config.from_dict( + dataset_path, yaml_config["webui"] + ) + self.server_config: Server_config = Server_config.from_dict( + yaml_config["server"] + ) + self.translate_config: Translate_config = Translate_config.from_dict( + yaml_config["translate"] + ) + + +parser = argparse.ArgumentParser() +# 为避免与以前的config.json起冲突，将其更名如下 +parser.add_argument("-y", "--yml_config", type=str, default="config.yml") +args, _ = parser.parse_known_args() +config = Config(args.yml_config) diff --git a/configs/config.json b/configs/config.json index 0cb0bf2..d1828ff 100644 --- a/configs/config.json +++ b/configs/config.json @@ -10,7 +10,7 @@ 0.99 ], "eps": 1e-09, - "batch_size": 24, + "batch_size": 12, "fp16_run": false, "lr_decay": 0.99995, "segment_size": 16384, @@ -18,7 +18,10 @@ "warmup_epochs": 0, "c_mel": 45, "c_kl": 1.0, - "skip_optimizer": true + "skip_optimizer": true, + "freeze_ZH_bert": false, + "freeze_JP_bert": false, + "freeze_EN_bert": false }, "data": { "training_files": "filelists/train.list", @@ -676,220 +679,220 @@ "埃舍尔_EN": 638, "萨齐因_EN": 639, "古田_EN": 640, - "陆景和": 641, - "莫弈": 642, - "左然": 643, - "夏彦": 644, - "三月七_ZH": 645, - "丹恒_ZH": 646, - "希儿_ZH": 647, - "娜塔莎_ZH": 648, - "希露瓦_ZH": 649, - "瓦尔特_ZH": 650, - "佩拉_ZH": 651, - "布洛妮娅_ZH": 652, - "虎克_ZH": 653, - "素裳_ZH": 654, - "克拉拉_ZH": 655, - "符玄_ZH": 656, - "白露_ZH": 657, - "杰帕德_ZH": 658, - "景元_ZH": 659, - "藿藿_ZH": 660, - "姬子_ZH": 661, - "穹_ZH": 662, - "星_ZH": 663, - "卡芙卡_ZH": 664, - "桂乃芬_ZH": 665, - "艾丝妲_ZH": 666, - "玲可_ZH": 667, - "彦卿_ZH": 668, - "托帕_ZH": 669, - "驭空_ZH": 670, - "浮烟_ZH": 671, - "停云_ZH": 672, - "镜流_ZH": 673, - "罗刹_ZH": 674, - "卢卡_ZH": 675, - "史瓦罗_ZH": 676, - "黑塔_ZH": 677, - "桑博_ZH": 678, - "伦纳德_ZH": 679, - "明曦_ZH": 680, - "银狼_ZH": 681, - "帕姆_ZH": 682, - "青雀_ZH": 683, - "乔瓦尼_ZH": 684, - "公输师傅_ZH": 685, - "晴霓_ZH": 686, - "螺丝咕姆_ZH": 687, - "阿兰_ZH": 688, - "奥列格_ZH": 689, - "丹枢_ZH": 690, - "尾巴_ZH": 691, - "寒鸦_ZH": 692, - "雪衣_ZH": 693, - "可可利亚_ZH": 694, - "青镞_ZH": 695, - "半夏_ZH": 696, - "银枝_ZH": 697, - "大毫_ZH": 698, - "霄翰_ZH": 699, - "信使_ZH": 700, - "费斯曼_ZH": 701, - "绿芙蓉_ZH": 702, - "dev_成男_ZH": 703, - "金人会长_ZH": 704, - "维利特_ZH": 705, - "维尔德_ZH": 706, - "斯科特_ZH": 707, - "卡波特_ZH": 708, - "刃_ZH": 709, - "岩明_ZH": 710, - "浣溪_ZH": 711, - "三月七_JP": 712, - "丹恒_JP": 713, - "希儿_JP": 714, - "娜塔莎_JP": 715, - "希露瓦_JP": 716, - "瓦尔特_JP": 717, - "佩拉_JP": 718, - "布洛妮娅_JP": 719, - "虎克_JP": 720, - "素裳_JP": 721, - "克拉拉_JP": 722, - "符玄_JP": 723, - "白露_JP": 724, - "杰帕德_JP": 725, - "景元_JP": 726, - "藿藿_JP": 727, - "姬子_JP": 728, - "卡芙卡_JP": 729, - "穹_JP": 730, - "星_JP": 731, - "桂乃芬_JP": 732, - "艾丝妲_JP": 733, - "彦卿_JP": 734, - "玲可_JP": 735, - "托帕_JP": 736, - "驭空_JP": 737, - "浮烟_JP": 738, - "停云_JP": 739, - "镜流_JP": 740, - "罗刹_JP": 741, - "卢卡_JP": 742, - "史瓦罗_JP": 743, - "黑塔_JP": 744, - "桑博_JP": 745, - "伦纳德_JP": 746, - "明曦_JP": 747, - "银狼_JP": 748, - "帕姆_JP": 749, - "青雀_JP": 750, - "乔瓦尼_JP": 751, - "公输师傅_JP": 752, - "晴霓_JP": 753, - "螺丝咕姆_JP": 754, - "阿兰_JP": 755, - "奥列格_JP": 756, - "丹枢_JP": 757, - "尾巴_JP": 758, - "寒鸦_JP": 759, - "雪衣_JP": 760, - "可可利亚_JP": 761, - "青镞_JP": 762, - "半夏_JP": 763, - "银枝_JP": 764, - "大毫_JP": 765, - "霄翰_JP": 766, - "信使_JP": 767, - "费斯曼_JP": 768, - "绿芙蓉_JP": 769, - "dev_成男_JP": 770, - "金人会长_JP": 771, - "维利特_JP": 772, - "维尔德_JP": 773, - "斯科特_JP": 774, - "刃_JP": 775, - "卡波特_JP": 776, - "岩明_JP": 777, - "浣溪_JP": 778, - "净砚_JP": 779, - "紫月季_JP": 780, - "歌蒂_JP": 781, - "奇怪的云骑_JP": 782, - "幻胧_JP": 783, - "斯薇塔_JP": 784, - "隐书_JP": 785, - "三月七_EN": 786, - "丹恒_EN": 787, - "希儿_EN": 788, - "娜塔莎_EN": 789, - "希露瓦_EN": 790, - "瓦尔特_EN": 791, - "佩拉_EN": 792, - "布洛妮娅_EN": 793, - "虎克_EN": 794, - "素裳_EN": 795, - "克拉拉_EN": 796, - "符玄_EN": 797, - "白露_EN": 798, - "杰帕德_EN": 799, - "景元_EN": 800, - "藿藿_EN": 801, - "姬子_EN": 802, - "卡芙卡_EN": 803, - "穹_EN": 804, - "星_EN": 805, - "桂乃芬_EN": 806, - "艾丝妲_EN": 807, - "彦卿_EN": 808, - "玲可_EN": 809, - "托帕_EN": 810, - "驭空_EN": 811, - "浮烟_EN": 812, - "停云_EN": 813, - "镜流_EN": 814, - "罗刹_EN": 815, - "卢卡_EN": 816, - "史瓦罗_EN": 817, - "黑塔_EN": 818, - "桑博_EN": 819, - "伦纳德_EN": 820, - "明曦_EN": 821, - "银狼_EN": 822, - "帕姆_EN": 823, - "青雀_EN": 824, - "乔瓦尼_EN": 825, - "公输师傅_EN": 826, - "晴霓_EN": 827, - "螺丝咕姆_EN": 828, - "阿兰_EN": 829, - "奥列格_EN": 830, - "丹枢_EN": 831, - "尾巴_EN": 832, - "寒鸦_EN": 833, - "雪衣_EN": 834, - "可可利亚_EN": 835, - "青镞_EN": 836, - "半夏_EN": 837, - "银枝_EN": 838, - "大毫_EN": 839, - "霄翰_EN": 840, - "信使_EN": 841, - "费斯曼_EN": 842, - "绿芙蓉_EN": 843, - "dev_成男_EN": 844, - "金人会长_EN": 845, - "维利特_EN": 846, - "维尔德_EN": 847, - "刃_EN": 848, - "卡波特_EN": 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getattr(hparams, "max_text_len", 384) + self.empty_emo = torch.squeeze( + torch.load("empty_emo.npy", map_location="cpu"), dim=1 + ) + random.seed(1234) random.shuffle(self.audiopaths_sid_text) self._filter() @@ -93,7 +97,14 @@ class TextAudioSpeakerLoader(torch.utils.data.Dataset): spec, wav = self.get_audio(audiopath) sid = torch.LongTensor([int(self.spk_map[sid])]) - emo = torch.FloatTensor(np.load(audiopath.replace(".wav", ".emo.npy"))) + + if np.random.rand() > 0.1: + emo = torch.squeeze( + torch.load(audiopath.replace(".wav", ".emo.npy"), map_location="cpu"), + dim=1, + ) + else: + emo = self.empty_emo return (phones, spec, wav, sid, tone, language, bert, ja_bert, en_bert, emo) def get_audio(self, filename): @@ -157,15 +168,15 @@ class TextAudioSpeakerLoader(torch.utils.data.Dataset): if language_str == "ZH": bert = bert_ori - ja_bert = torch.zeros(1024, len(phone)) - en_bert = torch.zeros(1024, len(phone)) + ja_bert = torch.rand(1024, len(phone)) + en_bert = torch.rand(1024, len(phone)) elif language_str == "JP": - bert = torch.zeros(1024, len(phone)) + bert = torch.rand(1024, len(phone)) ja_bert = bert_ori - en_bert = torch.zeros(1024, len(phone)) + en_bert = torch.rand(1024, len(phone)) elif language_str == "EN": - bert = torch.zeros(1024, len(phone)) - ja_bert = torch.zeros(1024, len(phone)) + bert = torch.rand(1024, len(phone)) + ja_bert = torch.rand(1024, len(phone)) en_bert = bert_ori phone = torch.LongTensor(phone) tone = torch.LongTensor(tone) @@ -215,7 +226,7 @@ class TextAudioSpeakerCollate: bert_padded = torch.FloatTensor(len(batch), 1024, max_text_len) ja_bert_padded = torch.FloatTensor(len(batch), 1024, max_text_len) en_bert_padded = torch.FloatTensor(len(batch), 1024, max_text_len) - emo = torch.FloatTensor(len(batch), 1024) + emo = torch.FloatTensor(len(batch), 512) spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len) wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len) diff --git a/default_config.yml b/default_config.yml index eb6872f..a1baec8 100644 --- a/default_config.yml +++ b/default_config.yml @@ -1,176 +1,177 @@ -# 全局配置 -# 对于希望在同一时间使用多个配置文件的情况，例如两个GPU同时跑两个训练集：通过环境变量指定配置文件，不指定则默认为./config.yml - -# 拟提供通用路径配置，统一存放数据，避免数据放得很乱 -# 每个数据集与其对应的模型存放至统一路径下，后续所有的路径配置均为相对于datasetPath的路径 -# 不填或者填空则路径为相对于项目根目录的路径 -dataset_path: "Data/" - -# 模型镜像源，默认huggingface，使用openi镜像源需指定openi_token -mirror: "" -openi_token: "" # openi token - -# resample 音频重采样配置 -# 注意， “:” 后需要加空格 -resample: - # 目标重采样率 - sampling_rate: 44100 - # 音频文件输入路径，重采样会将该路径下所有.wav音频文件重采样 - # 请填入相对于datasetPath的相对路径 - in_dir: "audios/raw" # 相对于根目录的路径为 /datasetPath/in_dir - # 音频文件重采样后输出路径 - out_dir: "audios/wavs" - - -# preprocess_text 数据集预处理相关配置 -# 注意， “:” 后需要加空格 -preprocess_text: - # 原始文本文件路径，文本格式应为{wav_path}|{speaker_name}|{language}|{text}。 - transcription_path: "filelists/你的数据集文本.list" - # 数据清洗后文本路径，可以不填。不填则将在原始文本目录生成 - cleaned_path: "" - # 训练集路径 - train_path: "filelists/train.list" - # 验证集路径 - val_path: "filelists/val.list" - # 配置文件路径 - config_path: "config.json" - # 每个speaker的验证集条数 - val_per_spk: 4 - # 验证集最大条数，多于的会被截断并放到训练集中 - max_val_total: 8 - # 是否进行数据清洗 - clean: true - - -# bert_gen 相关配置 -# 注意， “:” 后需要加空格 -bert_gen: - # 训练数据集配置文件路径 - config_path: "config.json" - # 并行数 - num_processes: 2 - # 使用设备：可选项 "cuda" 显卡推理，"cpu" cpu推理 - # 该选项同时决定了get_bert_feature的默认设备 - device: "cuda" - # 使用多卡推理 - use_multi_device: false - -# emo_gen 相关配置 -# 注意， “:” 后需要加空格 -emo_gen: - # 训练数据集配置文件路径 - config_path: "config.json" - # 并行数 - num_processes: 2 - # 使用设备：可选项 "cuda" 显卡推理，"cpu" cpu推理 - device: "cuda" - -# train 训练配置 -# 注意， “:” 后需要加空格 -train_ms: - env: - MASTER_ADDR: "localhost" - MASTER_PORT: 10086 - WORLD_SIZE: 1 - LOCAL_RANK: 0 - RANK: 0 - # 可以填写任意名的环境变量 - # THE_ENV_VAR_YOU_NEED_TO_USE: "1234567" - # 底模设置 - base: - use_base_model: false - repo_id: "Stardust_minus/Bert-VITS2" - model_image: "Bert-VITS2_2.1-Emo底模" # openi网页的模型名 - # 训练模型存储目录：与旧版本的区别，原先数据集是存放在logs/model_name下的，现在改为统一存放在Data/你的数据集/models下 - model: "models" - # 配置文件路径 - config_path: "config.json" - # 训练使用的worker，不建议超过CPU核心数 - num_workers: 16 - # 关闭此项可以节约接近50%的磁盘空间，但是可能导致实际训练速度变慢和更高的CPU使用率。 - spec_cache: True - # 保存的检查点数量，多于此数目的权重会被删除来节省空间。 - keep_ckpts: 8 - - -# webui webui配置 -# 注意， “:” 后需要加空格 -webui: - # 推理设备 - device: "cuda" - # 模型路径 - model: "genshin/models/G_8000.pth" - # 配置文件路径 - config_path: "config.json" - # 端口号 - port: 7860 - # 是否公开部署，对外网开放 - share: false - # 是否开启debug模式 - debug: false - # 语种识别库，可选langid, fastlid - language_identification_library: "langid" - - -# server api配置 -# 注意， “:” 后需要加空格 -# 注意，本配置下的所有配置均为相对于根目录的路径 -server: - # 端口号 - port: 5000 - # 模型默认使用设备：但是当前并没有实现这个配置。 - device: "cuda" - # 需要加载的所有模型的配置，可以填多个模型，也可以不填模型，等网页成功后手动加载模型 - # 不加载模型的配置格式：删除默认给的两个模型配置，给models赋值 [ ]，也就是空列表。参考模型2的speakers 即 models: [ ] - # 注意，所有模型都必须正确配置model与config的路径，空路径会导致加载错误。 - # 也可以不填模型，等网页加载成功后手动填写models。 - models: - - # 模型的路径 - model: "" - # 模型config.json的路径 - config: "" - # 模型使用设备，若填写则会覆盖默认配置 - device: "cuda" - # 模型默认使用的语言 - language: "ZH" - # 模型人物默认参数 - # 不必填写所有人物，不填的使用默认值 - # 暂时不用填写，当前尚未实现按人区分配置 - speakers: - - speaker: "科比" - sdp_ratio: 0.2 - noise_scale: 0.6 - noise_scale_w: 0.8 - length_scale: 1 - - speaker: "五条悟" - sdp_ratio: 0.3 - noise_scale: 0.7 - noise_scale_w: 0.8 - length_scale: 0.5 - - speaker: "安倍晋三" - sdp_ratio: 0.2 - noise_scale: 0.6 - noise_scale_w: 0.8 - length_scale: 1.2 - - # 模型的路径 - model: "" - # 模型config.json的路径 - config: "" - # 模型使用设备，若填写则会覆盖默认配置 - device: "cpu" - # 模型默认使用的语言 - language: "JP" - # 模型人物默认参数 - # 不必填写所有人物，不填的使用默认值 - speakers: [ ] # 也可以不填 - - -# 百度翻译开放平台 api配置 -# api接入文档 https://api.fanyi.baidu.com/doc/21 -# 请不要在github等网站公开分享你的app id 与 key -translate: - # 你的APPID - "app_key": "" - # 你的密钥 - "secret_key": "" +# 全局配置 +# 对于希望在同一时间使用多个配置文件的情况，例如两个GPU同时跑两个训练集：通过环境变量指定配置文件，不指定则默认为./config.yml + +# 拟提供通用路径配置，统一存放数据，避免数据放得很乱 +# 每个数据集与其对应的模型存放至统一路径下，后续所有的路径配置均为相对于datasetPath的路径 +# 不填或者填空则路径为相对于项目根目录的路径 +dataset_path: "Data/" + +# 模型镜像源，默认huggingface，使用openi镜像源需指定openi_token +mirror: "" +openi_token: "" # openi token + +# resample 音频重采样配置 +# 注意， “:” 后需要加空格 +resample: + # 目标重采样率 + sampling_rate: 44100 + # 音频文件输入路径，重采样会将该路径下所有.wav音频文件重采样 + # 请填入相对于datasetPath的相对路径 + in_dir: "audios/raw" # 相对于根目录的路径为 /datasetPath/in_dir + # 音频文件重采样后输出路径 + out_dir: "audios/wavs" + + +# preprocess_text 数据集预处理相关配置 +# 注意， “:” 后需要加空格 +preprocess_text: + # 原始文本文件路径，文本格式应为{wav_path}|{speaker_name}|{language}|{text}。 + transcription_path: "filelists/你的数据集文本.list" + # 数据清洗后文本路径，可以不填。不填则将在原始文本目录生成 + cleaned_path: "" + # 训练集路径 + train_path: "filelists/train.list" + # 验证集路径 + val_path: "filelists/val.list" + # 配置文件路径 + config_path: "config.json" + # 每个语言的验证集条数 + val_per_lang: 4 + # 验证集最大条数，多于的会被截断并放到训练集中 + max_val_total: 12 + # 是否进行数据清洗 + clean: true + + +# bert_gen 相关配置 +# 注意， “:” 后需要加空格 +bert_gen: + # 训练数据集配置文件路径 + config_path: "config.json" + # 并行数 + num_processes: 4 + # 使用设备：可选项 "cuda" 显卡推理，"cpu" cpu推理 + # 该选项同时决定了get_bert_feature的默认设备 + device: "cuda" + # 使用多卡推理 + use_multi_device: false + +# emo_gen 相关配置 +# 注意， “:” 后需要加空格 +emo_gen: + # 训练数据集配置文件路径 + config_path: "config.json" + # 并行数 + num_processes: 4 + # 使用设备：可选项 "cuda" 显卡推理，"cpu" cpu推理 + device: "cuda" + # 使用多卡推理 + use_multi_device: false + +# train 训练配置 +# 注意， “:” 后需要加空格 +train_ms: + env: + MASTER_ADDR: "localhost" + MASTER_PORT: 10086 + WORLD_SIZE: 1 + LOCAL_RANK: 0 + RANK: 0 + # 可以填写任意名的环境变量 + # THE_ENV_VAR_YOU_NEED_TO_USE: "1234567" + # 底模设置 + base: + use_base_model: false + repo_id: "Stardust_minus/Bert-VITS2" + model_image: "Bert-VITS2_2.1-Emo底模" # openi网页的模型名 + # 训练模型存储目录：与旧版本的区别，原先数据集是存放在logs/model_name下的，现在改为统一存放在Data/你的数据集/models下 + model: "models" + # 配置文件路径 + config_path: "configs/config.json" + # 训练使用的worker，不建议超过CPU核心数 + num_workers: 16 + # 关闭此项可以节约接近50%的磁盘空间，但是可能导致实际训练速度变慢和更高的CPU使用率。 + spec_cache: True + # 保存的检查点数量，多于此数目的权重会被删除来节省空间。 + keep_ckpts: 8 + + +# webui webui配置 +# 注意， “:” 后需要加空格 +webui: + # 推理设备 + device: "cuda" + # 模型路径 + model: "models/G_8000.pth" + # 配置文件路径 + config_path: "configs/config.json" + # 端口号 + port: 7860 + # 是否公开部署，对外网开放 + share: false + # 是否开启debug模式 + debug: false + # 语种识别库，可选langid, fastlid + language_identification_library: "langid" + + +# server-fastapi配置 +# 注意， “:” 后需要加空格 +# 注意，本配置下的所有配置均为相对于根目录的路径 +server: + # 端口号 + port: 5000 + # 模型默认使用设备：但是当前并没有实现这个配置。 + device: "cuda" + # 需要加载的所有模型的配置，可以填多个模型，也可以不填模型，等网页成功后手动加载模型 + # 不加载模型的配置格式：删除默认给的两个模型配置，给models赋值 [ ]，也就是空列表。参考模型2的speakers 即 models: [ ] + # 注意，所有模型都必须正确配置model与config的路径，空路径会导致加载错误。 + # 也可以不填模型，等网页加载成功后手动填写models。 + models: + - # 模型的路径 + model: "" + # 模型config.json的路径 + config: "" + # 模型使用设备，若填写则会覆盖默认配置 + device: "cuda" + # 模型默认使用的语言 + language: "ZH" + # 模型人物默认参数 + # 不必填写所有人物，不填的使用默认值 + # 暂时不用填写，当前尚未实现按人区分配置 + speakers: + - speaker: "科比" + sdp_ratio: 0.2 + noise_scale: 0.6 + noise_scale_w: 0.8 + length_scale: 1 + - speaker: "五条悟" + sdp_ratio: 0.3 + noise_scale: 0.7 + noise_scale_w: 0.8 + length_scale: 0.5 + - speaker: "安倍晋三" + sdp_ratio: 0.2 + noise_scale: 0.6 + noise_scale_w: 0.8 + length_scale: 1.2 + - # 模型的路径 + model: "" + # 模型config.json的路径 + config: "" + # 模型使用设备，若填写则会覆盖默认配置 + device: "cpu" + # 模型默认使用的语言 + language: "JP" + # 模型人物默认参数 + # 不必填写所有人物，不填的使用默认值 + speakers: [ ] # 也可以不填 + +# 百度翻译开放平台 api配置 +# api接入文档 https://api.fanyi.baidu.com/doc/21 +# 请不要在github等网站公开分享你的app id 与 key +translate: + # 你的APPID + "app_key": "" + # 你的密钥 + "secret_key": "" diff --git a/emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/.gitattributes b/emotional/clap-htsat-fused/.gitattributes similarity index 76% rename from emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/.gitattributes rename to emotional/clap-htsat-fused/.gitattributes index 64f23e0..c7d9f33 100644 --- a/emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/.gitattributes +++ b/emotional/clap-htsat-fused/.gitattributes @@ -1,22 +1,28 @@ *.7z filter=lfs diff=lfs merge=lfs -text *.arrow filter=lfs diff=lfs merge=lfs -text *.bin filter=lfs diff=lfs merge=lfs -text -*.bin.* filter=lfs diff=lfs merge=lfs -text *.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text *.ftz filter=lfs diff=lfs merge=lfs -text *.gz filter=lfs diff=lfs merge=lfs -text *.h5 filter=lfs diff=lfs merge=lfs -text *.joblib filter=lfs diff=lfs merge=lfs -text *.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text *.model filter=lfs diff=lfs merge=lfs -text *.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text *.onnx filter=lfs diff=lfs merge=lfs -text *.ot filter=lfs diff=lfs merge=lfs -text *.parquet filter=lfs diff=lfs merge=lfs -text *.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text *.pt filter=lfs diff=lfs merge=lfs -text *.pth filter=lfs diff=lfs merge=lfs -text *.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.tar.* filter=lfs diff=lfs merge=lfs -text *.tflite filter=lfs diff=lfs merge=lfs -text @@ -24,5 +30,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.wasm filter=lfs diff=lfs merge=lfs -text *.xz filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text -*.zstandard filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/emotional/clap-htsat-fused/README.md b/emotional/clap-htsat-fused/README.md new file mode 100644 index 0000000..9153cca --- /dev/null +++ b/emotional/clap-htsat-fused/README.md @@ -0,0 +1,107 @@ +--- +license: apache-2.0 +--- +# Model card for CLAP + +Model card for CLAP: Contrastive Language-Audio Pretraining + +![clap_image](https://s3.amazonaws.com/moonup/production/uploads/1678811100805-62441d1d9fdefb55a0b7d12c.png) + + +# Table of Contents + +0. [TL;DR](#TL;DR) +1. [Model Details](#model-details) +2. [Usage](#usage) +3. [Uses](#uses) +4. [Citation](#citation) + +# TL;DR + +The abstract of the paper states that: + +> Contrastive learning has shown remarkable success in the field of multimodal representation learning. In this paper, we propose a pipeline of contrastive language-audio pretraining to develop an audio representation by combining audio data with natural language descriptions. To accomplish this target, we first release LAION-Audio-630K, a large collection of 633,526 audio-text pairs from different data sources. Second, we construct a contrastive language-audio pretraining model by considering different audio encoders and text encoders. We incorporate the feature fusion mechanism and keyword-to-caption augmentation into the model design to further enable the model to process audio inputs of variable lengths and enhance the performance. Third, we perform comprehensive experiments to evaluate our model across three tasks: text-to-audio retrieval, zero-shot audio classification, and supervised audio classification. The results demonstrate that our model achieves superior performance in text-to-audio retrieval task. In audio classification tasks, the model achieves state-of-the-art performance in the zero-shot setting and is able to obtain performance comparable to models' results in the non-zero-shot setting. LAION-Audio-630K and the proposed model are both available to the public. + + +# Usage + +You can use this model for zero shot audio classification or extracting audio and/or textual features. + +# Uses + +## Perform zero-shot audio classification + +### Using `pipeline` + +```python +from datasets import load_dataset +from transformers import pipeline + +dataset = load_dataset("ashraq/esc50") +audio = dataset["train"]["audio"][-1]["array"] + +audio_classifier = pipeline(task="zero-shot-audio-classification", model="laion/clap-htsat-fused") +output = audio_classifier(audio, candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"]) +print(output) +>>> [{"score": 0.999, "label": "Sound of a dog"}, {"score": 0.001, "label": "Sound of vaccum cleaner"}] +``` + +## Run the model: + +You can also get the audio and text embeddings using `ClapModel` + +### Run the model on CPU: + +```python +from datasets import load_dataset +from transformers import ClapModel, ClapProcessor + +librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") +audio_sample = librispeech_dummy[0] + +model = ClapModel.from_pretrained("laion/clap-htsat-fused") +processor = ClapProcessor.from_pretrained("laion/clap-htsat-fused") + +inputs = processor(audios=audio_sample["audio"]["array"], return_tensors="pt") +audio_embed = model.get_audio_features(**inputs) +``` + +### Run the model on GPU: + +```python +from datasets import load_dataset +from transformers import ClapModel, ClapProcessor + +librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") +audio_sample = librispeech_dummy[0] + +model = ClapModel.from_pretrained("laion/clap-htsat-fused").to(0) +processor = ClapProcessor.from_pretrained("laion/clap-htsat-fused") + +inputs = processor(audios=audio_sample["audio"]["array"], return_tensors="pt").to(0) +audio_embed = model.get_audio_features(**inputs) +``` + + +# Citation + +If you are using this model for your work, please consider citing the original paper: +``` +@misc{https://doi.org/10.48550/arxiv.2211.06687, + doi = {10.48550/ARXIV.2211.06687}, + + url = {https://arxiv.org/abs/2211.06687}, + + author = {Wu, Yusong and Chen, Ke and Zhang, Tianyu and Hui, Yuchen and Berg-Kirkpatrick, Taylor and Dubnov, Shlomo}, + + keywords = {Sound (cs.SD), Audio and Speech Processing (eess.AS), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering}, + + title = {Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation}, + + publisher = {arXiv}, + + year = {2022}, + + copyright = {Creative Commons Attribution 4.0 International} +} +``` diff --git a/emotional/clap-htsat-fused/config.json b/emotional/clap-htsat-fused/config.json new file mode 100644 index 0000000..7907d3c --- /dev/null +++ b/emotional/clap-htsat-fused/config.json @@ -0,0 +1,207 @@ +{ + "_commit_hash": null, + "architectures": [ + "ClapModel" + ], + "audio_config": { + "_name_or_path": "", + "add_cross_attention": false, + "aff_block_r": 4, + "architectures": null, + "attention_probs_dropout_prob": 0.0, + "bad_words_ids": null, + "begin_suppress_tokens": null, + "bos_token_id": null, + "chunk_size_feed_forward": 0, + "cross_attention_hidden_size": null, + "decoder_start_token_id": null, + "depths": [ + 2, + 2, + 6, + 2 + ], + "diversity_penalty": 0.0, + "do_sample": false, + "drop_path_rate": 0.0, + "early_stopping": false, + "enable_fusion": true, + "enable_patch_fusion": true, + "enable_patch_layer_norm": true, + "encoder_no_repeat_ngram_size": 0, + "eos_token_id": null, + "exponential_decay_length_penalty": null, + "finetuning_task": null, + "flatten_patch_embeds": true, + "forced_bos_token_id": null, + "forced_eos_token_id": null, + "fusion_num_hidden_layers": 2, + 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"clap_text_model", + "no_repeat_ngram_size": 0, + "num_attention_heads": 12, + "num_beam_groups": 1, + "num_beams": 1, + "num_hidden_layers": 12, + "num_return_sequences": 1, + "output_attentions": false, + "output_hidden_states": false, + "output_scores": false, + "pad_token_id": 1, + "position_embedding_type": "absolute", + "prefix": null, + "problem_type": null, + "projection_dim": 512, + "projection_hidden_act": "relu", + "projection_hidden_size": 768, + "pruned_heads": {}, + "remove_invalid_values": false, + "repetition_penalty": 1.0, + "return_dict": true, + "return_dict_in_generate": false, + "sep_token_id": null, + "suppress_tokens": null, + "task_specific_params": null, + "temperature": 1.0, + "tf_legacy_loss": false, + "tie_encoder_decoder": false, + "tie_word_embeddings": true, + "tokenizer_class": null, + "top_k": 50, + "top_p": 1.0, + "torch_dtype": null, + "torchscript": false, + "transformers_version": "4.27.0.dev0", + "type_vocab_size": 1, + "typical_p": 1.0, + "use_bfloat16": false, + "use_cache": true, + "vocab_size": 50265 + }, + "torch_dtype": "float32", + "transformers_version": null +} diff --git a/emotional/clap-htsat-fused/merges.txt b/emotional/clap-htsat-fused/merges.txt new file mode 100644 index 0000000..6636bda --- /dev/null +++ b/emotional/clap-htsat-fused/merges.txt @@ -0,0 +1,50001 @@ +#version: 0.2 - Trained by `huggingface/tokenizers` +Ġ t +Ġ a +h e +i n +r e +o n +Ġt he +e r +Ġ s +a t +Ġ w +Ġ o +e n +Ġ c +i t +i s +a n +o r +e s +Ġ b +e d +Ġ f +in g +Ġ p +o u +Ġa n +a l +a r +Ġt o +Ġ m +Ġo f +Ġ in +Ġ d +Ġ h +Ġan d +i c +a s +l e +Ġt h +i on +o m +l l +en t +Ġ n +Ġ l +s t +Ġ re +v e +Ġ e +r o +l y +Ġb e +Ġ g +Ġ T +c t +Ġ S +i d +o t +Ġ I +u t +e t +Ġ A +Ġ is +Ġ on +i m +a m +o w +a y +a d +s e +Ġth at +Ġ C +i g +Ġf or +a c +Ġ y +v er +u r +Ġ u +l d +Ġs t +Ġ M +' s +Ġ he +Ġ it +at ion +it h +i r +c e +Ġy ou +i l +Ġ B +Ġw h +o l +Ġ P +Ġw ith +Ġ 1 +t er +c h +Ġa s +Ġw e +Ġ ( +n d +i ll +Ġ D +i f +Ġ 2 +a g +er s +k e +Ġ " +Ġ H 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"Ġlesser": 13514, + "Ġtit": 13515, + "Ġdat": 13516, + "Ġnail": 13517, + "ĠMarathon": 13518, + "ĠGren": 13519, + "ĠDol": 13520, + "Ġjointly": 13521, + "Ġamended": 13522, + "mine": 13523, + "ĠBashar": 13524, + "ĠHyundai": 13525, + "Ġuncovered": 13526, + "Ġeducated": 13527, + "atti": 13528, + "pres": 13529, + "ĠBRE": 13530, + "Ġya": 13531, + "Bank": 13532, + "odd": 13533, + "lit": 13534, + "ĠLinks": 13535, + "Ġswitching": 13536, + "itte": 13537, + "ĠSind": 13538, + "erved": 13539, + "Ġ**": 13540, + "Ġpositively": 13541, + "Ġfrankly": 13542, + "Ġrevenge": 13543, + "ĠTrinity": 13544, + "ĠCDC": 13545, + "Ġthreatens": 13546, + "Ġhammer": 13547, + "NET": 13548, + "ĠMut": 13549, + "Ġsy": 13550, + "Ġunidentified": 13551, + "icken": 13552, + "Ġdrills": 13553, + "Ġtense": 13554, + "Ġforeigners": 13555, + "OST": 13556, + "Ġethical": 13557, + "ĠDurham": 13558, + "ĠQual": 13559, + "Ġterritories": 13560, + "Ġid": 13561, + "hor": 13562, + "enders": 13563, + "Mc": 13564, + "OV": 13565, + "percent": 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"Ġsovereignty": 13617, + "Ġconvincing": 13618, + "ĠPrincipal": 13619, + "ĠHigher": 13620, + "ĠCut": 13621, + "ĠCarrie": 13622, + "ĠSpot": 13623, + "Sometimes": 13624, + "ĠJar": 13625, + "ĠConsider": 13626, + "ieu": 13627, + "Ġrefinery": 13628, + "Ġbloody": 13629, + "wheel": 13630, + "Ġcryptocurrencies": 13631, + "Fund": 13632, + "ĠSunderland": 13633, + "ĠEvents": 13634, + "âĢĭ": 13635, + "Ġaccidentally": 13636, + "deep": 13637, + "Ġfranc": 13638, + "bec": 13639, + "ĠHartford": 13640, + "Ġstellar": 13641, + "wright": 13642, + "kick": 13643, + "UG": 13644, + "ĠBeast": 13645, + "Ġrefusal": 13646, + "ĠRoberto": 13647, + "ĠDixon": 13648, + "ĠDiane": 13649, + "name": 13650, + "asts": 13651, + "ĠCharter": 13652, + "Ġfueled": 13653, + "Ġcontents": 13654, + "Ġaccessing": 13655, + "Ġtroubles": 13656, + "Ġtops": 13657, + "Ġdebuted": 13658, + "icating": 13659, + "Ġinvestigator": 13660, + "Ġsubscribing": 13661, + "Ġcoordinated": 13662, + "ĠFil": 13663, + "six": 13664, + "teen": 13665, + "Ġwithdrew": 13666, + "ĠGilbert": 13667, + "Ġ1983": 13668, + "arsity": 13669, + "Ġimagination": 13670, + "Ġhandgun": 13671, + "ĠAlibaba": 13672, + "Ġbug": 13673, + "Ġ107": 13674, + "ĠCOMP": 13675, + "ĠSomething": 13676, + "Ġreliability": 13677, + "ĠFCC": 13678, + "ĠFowler": 13679, + "Ġsingled": 13680, + "nom": 13681, + "Ġknocking": 13682, + "Ġmeddling": 13683, + "Ġdetermining": 13684, + "reports": 13685, + "Ġshade": 13686, + "ĠSN": 13687, + "anto": 13688, + "Ġcomplaining": 13689, + "ĠNan": 13690, + "WS": 13691, + "Ġyoungsters": 13692, + "Il": 13693, + "ĠKaw": 13694, + "ĠProp": 13695, + "ĠCell": 13696, + "ĠHurricanes": 13697, + "Ġpublicity": 13698, + "ĠXin": 13699, + "rial": 13700, + "ICO": 13701, + "Ġsupervision": 13702, + "ĠSpotify": 13703, + "ĠNewport": 13704, + "Ġprince": 13705, + "anche": 13706, + "Ġsubscriber": 13707, + "ĠVic": 13708, + "ACT": 13709, + "ĠRaf": 13710, + "ĠActing": 13711, + "Ġcollusion": 13712, + "pet": 13713, + "isl": 13714, + "Ġcommerce": 13715, + "Health": 13716, 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"ĠWings": 13770, + "pect": 13771, + "ĠRum": 13772, + "Ġanalyzed": 13773, + "Ġemploys": 13774, + "arte": 13775, + "ims": 13776, + "ĠEventually": 13777, + "Ġaffiliated": 13778, + "Ġhospitality": 13779, + "ĠSprint": 13780, + "Ġresolutions": 13781, + "Ġliquor": 13782, + "ĠNAFTA": 13783, + "ANY": 13784, + "Ġradiation": 13785, + "ĠProv": 13786, + "Ġpause": 13787, + "ĠTMZ": 13788, + "Ġelbow": 13789, + "Ġresilience": 13790, + "ĠParents": 13791, + "mus": 13792, + "ĠSafe": 13793, + "Ġinterpretation": 13794, + "Ġraced": 13795, + "IND": 13796, + "KR": 13797, + "Ġhinted": 13798, + "ĠErin": 13799, + "ĠBahrain": 13800, + "Ġcredentials": 13801, + "eless": 13802, + "Ġprocurement": 13803, + "ĠWebb": 13804, + "ĠLowe": 13805, + "ĠNak": 13806, + "ĠLearning": 13807, + "zh": 13808, + "Ġdipped": 13809, + "ĠSuite": 13810, + "Ġmisdemeanor": 13811, + "ALE": 13812, + "Ġstrengthened": 13813, + "ĠSophie": 13814, + "Ġconfirms": 13815, + "Ġrac": 13816, + "gey": 13817, + "Ġshootout": 13818, + "Ġble": 13819, + "Ġcircles": 13820, + "ĠChef": 13821, + "Ġcomprised": 13822, + "ĠSantiago": 13823, + "Ġfeud": 13824, + "beat": 13825, + "Ġstaffers": 13826, + "Ġacute": 13827, + "ski": 13828, + "Ġpolled": 13829, + "ĠKur": 13830, + "ĠJen": 13831, + "ĠUltimately": 13832, + "anded": 13833, + "ĠHoney": 13834, + "Ġannounces": 13835, + "Ġamateur": 13836, + "around": 13837, + "Ġfunctioning": 13838, + "group": 13839, + "ĠSqu": 13840, + "Where": 13841, + "Ġvoid": 13842, + "ĠSandra": 13843, + "isers": 13844, + "Ġhelicopters": 13845, + "ĠGym": 13846, + "ĠWol": 13847, + "mouth": 13848, + "Ġsubjected": 13849, + "ici": 13850, + "ually": 13851, + "ĠWash": 13852, + "ĠLindsay": 13853, + "ĠVers": 13854, + "Ġjumps": 13855, + "Ġneglect": 13856, + "ĠKuwait": 13857, + "fund": 13858, + "ĭ": 13859, + "ather": 13860, + "lly": 13861, + "ei": 13862, + "Although": 13863, + ".''": 13864, + "Ġunhappy": 13865, + "Ġpills": 13866, + "Ġmagical": 13867, + "Ġdro": 13868, + "Ġinviting": 13869, + "ĠJohnston": 13870, + "oving": 13871, + "450": 13872, + "ĠMerc": 13873, + "Ġadmitting": 13874, + "Ġinsisting": 13875, + "ĠCru": 13876, + "ĠResource": 13877, + "oir": 13878, + "Ġcomplexity": 13879, + "ĠRoth": 13880, + "ĠCher": 13881, + "July": 13882, + "raf": 13883, + "Ġaggregate": 13884, + "Ġhelm": 13885, + "uclear": 13886, + "olan": 13887, + "Ġoffenses": 13888, + "ĠWolves": 13889, + "ĠFu": 13890, + "ĠPierce": 13891, + "Ġemailed": 13892, + "ĠStra": 13893, + "Ġpedestrians": 13894, + "ĠER": 13895, + "ĠConway": 13896, + "Ġblowing": 13897, + "CLOSE": 13898, + "hab": 13899, + "ĠGreene": 13900, + "Ġconfessed": 13901, + "ĠTorres": 13902, + "ĠHolocaust": 13903, + "Ġrepay": 13904, + "Ġdemonstrates": 13905, + "ĠPool": 13906, + "gent": 13907, + "Ġdeleted": 13908, + "Ġ$$": 13909, + "ĠSO": 13910, + "Ġdri": 13911, + "ĠNeg": 13912, + "ĠVP": 13913, + "ĠPF": 13914, + "ĠPrep": 13915, + "Ġorganizing": 13916, + "icker": 13917, + "Ġmanufactured": 13918, + "enson": 13919, + "adas": 13920, + "Ġwines": 13921, + "Ġmachinery": 13922, + "Ġspecialists": 13923, + "ĠDetective": 13924, + "ĠDL": 13925, + "Op": 13926, + "Ġquicker": 13927, + "ĠPenguins": 13928, + "Engine": 13929, + "zone": 13930, + "Ġsequence": 13931, + "ĠLost": 13932, + "Ġwarmer": 13933, + "ĠEthiopia": 13934, + "Ġaffirmed": 13935, + "fest": 13936, + "resses": 13937, + "Ġsoap": 13938, + "Ġbooth": 13939, + "Ġnotorious": 13940, + "amin": 13941, + "Ġpursued": 13942, + "ĠCer": 13943, + "ĠSB": 13944, + "Ġlivestock": 13945, + "Ġtrace": 13946, + "Ġrespects": 13947, + "arden": 13948, + "April": 13949, + "Ġ128": 13950, + "ĠSaid": 13951, + "ennial": 13952, + "Ġnamely": 13953, + "ĠBot": 13954, + "Ġ108": 13955, + "ĠLem": 13956, + "nell": 13957, + "Ġconfirming": 13958, + "Ġlogged": 13959, + "Ġprofound": 13960, + "elo": 13961, + "ĠChambers": 13962, + "RT": 13963, + "Ġnewer": 13964, + "Ġsideline": 13965, + "ĠCardinal": 13966, + "este": 13967, + "Ġnarrowly": 13968, + "Ġcompromised": 13969, + "Ġpolicing": 13970, + "Ġporn": 13971, + "Ġarc": 13972, + "Ġlearnt": 13973, + "INE": 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15295, + "Ġimproves": 15296, + "ĠAMD": 15297, + "Ġexcluded": 15298, + "CON": 15299, + "ĠOscars": 15300, + "Ġfixtures": 15301, + "comb": 15302, + "ĠBerg": 15303, + "Ġbother": 15304, + "Ġboring": 15305, + "Ġobservation": 15306, + "ĠCad": 15307, + "Ġrecordings": 15308, + "ĠCultural": 15309, + "Ġweaken": 15310, + "Ġaccuse": 15311, + "ĠAbd": 15312, + "abor": 15313, + "115": 15314, + "uffle": 15315, + "Ġhighways": 15316, + "atham": 15317, + "empt": 15318, + "ĠDeer": 15319, + "ĠEDT": 15320, + "ĠWait": 15321, + "athan": 15322, + "Ġaccumulated": 15323, + "Ġguilt": 15324, + "Ġexempt": 15325, + "Ġdiluted": 15326, + "ĠJamal": 15327, + "Ġshit": 15328, + "cross": 15329, + "Ġeve": 15330, + "Ġshirts": 15331, + "Ġsatisfy": 15332, + "ĠPaulo": 15333, + "AH": 15334, + "sic": 15335, + "ĠChloe": 15336, + "ĠCities": 15337, + "ĠSwansea": 15338, + "Ġprecision": 15339, + "ĠTracy": 15340, + "ping": 15341, + "Ġcontinually": 15342, + "Ġdemographic": 15343, + "Ġcliff": 15344, + "Ġjaw": 15345, + "isted": 15346, + 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18038, + "Ġstaging": 18039, + "rolled": 18040, + "Ġspecially": 18041, + "oop": 18042, + "Ġbaseline": 18043, + "ĠRF": 18044, + "avis": 18045, + "Ġvocals": 18046, + "COL": 18047, + "LD": 18048, + "Ġimpending": 18049, + "ĠCaldwell": 18050, + "Ġaluminium": 18051, + "Ġstra": 18052, + "ĠTayyip": 18053, + "Ġadmissions": 18054, + "falls": 18055, + "Ġrealizing": 18056, + "oen": 18057, + "ĠRV": 18058, + "ĠMog": 18059, + "Ġadvocating": 18060, + "ĠPepper": 18061, + "lived": 18062, + "ĠWick": 18063, + "Facebook": 18064, + "ĠSpect": 18065, + "Ġshout": 18066, + "Ġfractured": 18067, + "vet": 18068, + "Ġ1966": 18069, + "Ġcompensate": 18070, + "ĠVolume": 18071, + "Ġcategor": 18072, + "ĠHuntington": 18073, + "Free": 18074, + "OUGH": 18075, + "local": 18076, + "Sch": 18077, + "uti": 18078, + "Ġburger": 18079, + "Ġbush": 18080, + "Ġimpacting": 18081, + "Ġfrost": 18082, + "tti": 18083, + "ĠFresno": 18084, + "onz": 18085, + "shaw": 18086, + "ĠLibyan": 18087, + "Ġassert": 18088, + "ĠLegacy": 18089, + "ĠIE": 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"Ġlosers": 19113, + "Ġscheduling": 19114, + "esar": 19115, + "Ġtransferring": 19116, + "Ġbankrupt": 19117, + "Ġmethamphetamine": 19118, + "ĠEsk": 19119, + "ĠTreatment": 19120, + "ĠResponse": 19121, + "Ġhomework": 19122, + "ĠBald": 19123, + "Ġembarrassment": 19124, + "Ġpoorest": 19125, + "ĠPlatinum": 19126, + "ĠFac": 19127, + "Ġunleashed": 19128, + "Ġbrighter": 19129, + "002": 19130, + "Ġdisl": 19131, + "ĠLowry": 19132, + "ived": 19133, + "ĠDemon": 19134, + "ĠNonetheless": 19135, + "arro": 19136, + "ĠCONT": 19137, + "ifted": 19138, + "ĠFreder": 19139, + "isson": 19140, + "Ġrout": 19141, + "ARA": 19142, + "Ġswinging": 19143, + "Oct": 19144, + "Ġliable": 19145, + "Ġleaning": 19146, + "Ġlungs": 19147, + "380": 19148, + "ĠProcess": 19149, + "ĠCov": 19150, + "terrorism": 19151, + "Ġresistant": 19152, + "Ġpumped": 19153, + "Ġtripled": 19154, + "Semitism": 19155, + "ĠMia": 19156, + "Ġpenetration": 19157, + "ĠLutheran": 19158, + "BU": 19159, + "odes": 19160, + "Ġspanning": 19161, + "utch": 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"aning": 19266, + "Ġaw": 19267, + "ĠMulti": 19268, + "Ġanimation": 19269, + "850": 19270, + "ĠCounter": 19271, + "ĠNit": 19272, + "alli": 19273, + "Ġcapitalize": 19274, + "Ġexecuting": 19275, + "Ġdescent": 19276, + "ovi": 19277, + "ĠKimberly": 19278, + "headed": 19279, + "Ġmentioning": 19280, + ")-": 19281, + "ĠSpecifically": 19282, + "ayette": 19283, + "ihad": 19284, + "ĠIss": 19285, + "Ġdisagreed": 19286, + "ĠKum": 19287, + "Ġurges": 19288, + "Ġpermitting": 19289, + "Ġpy": 19290, + "isp": 19291, + "Ġhygiene": 19292, + "Ġmourning": 19293, + "Ġcyclists": 19294, + "cats": 19295, + "FER": 19296, + "cycl": 19297, + "Ġnewcomers": 19298, + "Ġplead": 19299, + "Ġmend": 19300, + "secret": 19301, + "fan": 19302, + "Ġtranslates": 19303, + "unit": 19304, + "ĠTank": 19305, + "drive": 19306, + "ĠSite": 19307, + "Ġacceleration": 19308, + "ĠEnrique": 19309, + "ĠElaine": 19310, + "Ġstaring": 19311, + "Ġbackwards": 19312, + "Ġot": 19313, + "Ġvot": 19314, + "ĠHK": 19315, + "Ġfian": 19316, + "ĠLockheed": 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"Ġcongreg": 22880, + "RAL": 22881, + "Ġvolley": 22882, + "Ġestablishments": 22883, + "Ġcite": 22884, + "Miss": 22885, + "Int": 22886, + "iola": 22887, + "ĠBare": 22888, + "KING": 22889, + "ools": 22890, + "private": 22891, + "Ġflaw": 22892, + "Ġwires": 22893, + "Ġideals": 22894, + "oub": 22895, + "Ġ\"'": 22896, + "ĠCompet": 22897, + "ĠStatements": 22898, + "ĠHDR": 22899, + "rm": 22900, + "Ġbegging": 22901, + "uffs": 22902, + "Ġdispatch": 22903, + "Ġskipped": 22904, + "Ġlabs": 22905, + "hawks": 22906, + "Ġexpl": 22907, + "Ġpatriotic": 22908, + "ussions": 22909, + "Ġportrayal": 22910, + "ĠBudapest": 22911, + "ĠCod": 22912, + "Ġextingu": 22913, + "smart": 22914, + "Ġburdens": 22915, + "ĠDrama": 22916, + "Ġaltitude": 22917, + "Ġpursuant": 22918, + "à¥": 22919, + "atari": 22920, + "cot": 22921, + "Ġhotline": 22922, + "ooters": 22923, + "ĠRolls": 22924, + "Ġjeopardy": 22925, + "oids": 22926, + "Ġpageant": 22927, + "149": 22928, + "Ġdistinguish": 22929, + "support": 22930, + "ĠHighlands": 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"issue": 25268, + "Ġlearns": 25269, + "natural": 25270, + "Ġvinegar": 25271, + "ĠSUB": 25272, + "ĠRecon": 25273, + "blers": 25274, + "Ġsniff": 25275, + "Ġsuppression": 25276, + "Ġsaf": 25277, + "urger": 25278, + "Ġbunker": 25279, + "asaki": 25280, + "ĠSpartan": 25281, + "ĠTok": 25282, + "Ġrav": 25283, + "Ġfoc": 25284, + "Sean": 25285, + "etric": 25286, + "Ġballpark": 25287, + "ĠHerb": 25288, + "ĠBM": 25289, + "ĠPublishing": 25290, + "Ġroadmap": 25291, + "pered": 25292, + "Ġpredator": 25293, + "ĠBlockchain": 25294, + "Ġvalidity": 25295, + "ĠGlou": 25296, + "ĠYamaha": 25297, + "Ġadop": 25298, + "Ġswamp": 25299, + "Ġcomplied": 25300, + "Ky": 25301, + "Greg": 25302, + "casts": 25303, + "john": 25304, + "ĠBosnia": 25305, + "Ġcinematic": 25306, + "ĠTavern": 25307, + "Ġfrustrations": 25308, + "eryl": 25309, + "Ġfairy": 25310, + "UNCH": 25311, + "ĠTus": 25312, + "Corp": 25313, + "ĠNug": 25314, + "closed": 25315, + "Ġexercised": 25316, + "urden": 25317, + "Ġdigitally": 25318, + "137": 25319, + 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"bys": 25372, + "erving": 25373, + "antz": 25374, + "Ġwiden": 25375, + "vised": 25376, + "Ev": 25377, + "Ġdiagn": 25378, + "ĠMakes": 25379, + "Ġcer": 25380, + "ĠPats": 25381, + "single": 25382, + "sche": 25383, + "struct": 25384, + "Ġdissolved": 25385, + "Ġtimeout": 25386, + "Ġenhancement": 25387, + "CF": 25388, + "Ġindust": 25389, + "ĠDed": 25390, + "ĠZo": 25391, + "CB": 25392, + "Ġpesticides": 25393, + "ĠRubin": 25394, + "George": 25395, + "opal": 25396, + "Ġmotel": 25397, + "critical": 25398, + "Ġcollapsing": 25399, + "ĠShal": 25400, + "tex": 25401, + "Ġcomplementary": 25402, + "Ġoust": 25403, + "ĠFlu": 25404, + "Ġexporting": 25405, + "Ġdifferential": 25406, + "north": 25407, + "ĠFG": 25408, + "Ġspoon": 25409, + "sha": 25410, + "Ġdismantle": 25411, + "elta": 25412, + "Ġjar": 25413, + "space": 25414, + "Smart": 25415, + "mere": 25416, + "Ð": 25417, + "ĠGillespie": 25418, + "Lo": 25419, + "ĠMead": 25420, + "capacity": 25421, + "ĠIssue": 25422, + "050": 25423, + "ĠVall": 25424, + 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"Ġhydraulic": 29215, + "Ġpredatory": 29216, + "Charles": 29217, + "cond": 29218, + "Ġspawned": 29219, + "Fr": 29220, + "174": 29221, + "Ġtame": 29222, + "Ġaggrav": 29223, + "Ġchrist": 29224, + "true": 29225, + "ivable": 29226, + "Ġhen": 29227, + "ĠKut": 29228, + "Ġskyrocket": 29229, + "Ġeg": 29230, + "Ġveterinarian": 29231, + "ĠStats": 29232, + "Kit": 29233, + "Ġbiologist": 29234, + "Spe": 29235, + "Ġantenna": 29236, + "Ġsust": 29237, + "fill": 29238, + "Ġpayload": 29239, + "227": 29240, + "Ġlivestream": 29241, + "ORN": 29242, + "ĠAbel": 29243, + "Ġdeception": 29244, + "ussen": 29245, + "Britain": 29246, + "partisan": 29247, + "Ġbrowse": 29248, + "Ġmelan": 29249, + "172": 29250, + "ĠNumerous": 29251, + "ĠMansion": 29252, + "Ġassailants": 29253, + "£": 29254, + "olerance": 29255, + "Ġdirectives": 29256, + "ĠInteg": 29257, + "zers": 29258, + "Ġduct": 29259, + "ĠHonestly": 29260, + "ĠImmediately": 29261, + "ixty": 29262, + "Ġdiagnose": 29263, + "Ġimplication": 29264, + "ĠiPads": 29265, + 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"Ġtul": 30892, + "Ġfacilit": 30893, + "Ġgrandma": 30894, + "ĠFeeling": 30895, + "Ġobst": 30896, + "ĠFranz": 30897, + "ĠPalin": 30898, + "ĠIncrease": 30899, + "gets": 30900, + "ĠImam": 30901, + "âĢİ": 30902, + "Ġcoincides": 30903, + "urrence": 30904, + "Ġlifes": 30905, + "Lab": 30906, + "Ham": 30907, + "angelo": 30908, + "Wild": 30909, + "Ġvetoed": 30910, + "Ġventilation": 30911, + "olid": 30912, + "Summer": 30913, + "Ġfacade": 30914, + "neys": 30915, + "ĠWOM": 30916, + "ĠBenny": 30917, + "ĠMarried": 30918, + "squ": 30919, + "ĠReflect": 30920, + "return": 30921, + "elia": 30922, + "olding": 30923, + "Ġrefine": 30924, + "ĠMadness": 30925, + "innacle": 30926, + "posts": 30927, + "287": 30928, + "fruit": 30929, + "274": 30930, + "icator": 30931, + "ĠVoy": 30932, + "Ġunsett": 30933, + "Ġfant": 30934, + "Ġtreaties": 30935, + "Ġcrystals": 30936, + "Ġhijacked": 30937, + "words": 30938, + "ĠReleased": 30939, + "Save": 30940, + "Ġcannon": 30941, + "Ġanomaly": 30942, + "Ġbeacon": 30943, + "Ġcrippled": 30944, + "Ġbundles": 30945, + "Ġuntreated": 30946, + "Ġhappiest": 30947, + "Ġgalaxies": 30948, + "Ġoccupational": 30949, + "416": 30950, + "Dar": 30951, + "Ġcrank": 30952, + "Ġappropriation": 30953, + "asking": 30954, + "mens": 30955, + "Ġdetector": 30956, + "Ġskewed": 30957, + "Ġpoke": 30958, + "254": 30959, + "Ġhypertension": 30960, + "apolog": 30961, + "Ġevaluations": 30962, + "blocks": 30963, + "Ġpow": 30964, + "GEN": 30965, + "Ġscalp": 30966, + "Ġarrogant": 30967, + "AIDS": 30968, + "ority": 30969, + "Ġredirect": 30970, + "Ġderogatory": 30971, + "Ġlateral": 30972, + "495": 30973, + "rolley": 30974, + "brew": 30975, + "Ġbabys": 30976, + "Ġmuff": 30977, + "ĠRequ": 30978, + "Ġdime": 30979, + "Ġwonderfully": 30980, + "Ġtreasures": 30981, + "ĠNES": 30982, + "Ġponds": 30983, + "Ġimpulse": 30984, + "Ġdetecting": 30985, + "Ġgrin": 30986, + "Ġbrid": 30987, + "Ġshoved": 30988, + "Ġpurge": 30989, + "irteen": 30990, + "OTHER": 30991, + "ÙĦ": 30992, + "irsch": 30993, + "ĠOcc": 30994, + "193": 30995, + "Ġfodder": 30996, + "wrote": 30997, + "meric": 30998, + "posal": 30999, + "Ġwinters": 31000, + "ĠJuice": 31001, + "hub": 31002, + "Ġcontrasting": 31003, + "Brazil": 31004, + "Ġflashy": 31005, + "uffer": 31006, + "technology": 31007, + "Children": 31008, + "Ġcatapult": 31009, + "owsky": 31010, + "ĠEclipse": 31011, + "abeth": 31012, + "ĠParticip": 31013, + "Ġlaud": 31014, + "ĠQuiet": 31015, + "Ġsimulations": 31016, + "Ġsacrificing": 31017, + "Ġpreaching": 31018, + "Ġvoicing": 31019, + "itizen": 31020, + "Ġgn": 31021, + "Ġsans": 31022, + "Ġ285": 31023, + "ĠRobot": 31024, + "Ġ1936": 31025, + "Ġsham": 31026, + "ĠKislyak": 31027, + "ĠGCC": 31028, + "tale": 31029, + "ĠShades": 31030, + "Ġsediment": 31031, + "Ġconveniently": 31032, + "Give": 31033, + "mounted": 31034, + "Ġpeel": 31035, + "Jun": 31036, + "ĠEisenhower": 31037, + "Ġdiplom": 31038, + "ĠPreservation": 31039, + "Ġaffirm": 31040, + "Ġtaboo": 31041, + "ĠGarr": 31042, + "ĠApply": 31043, + "prim": 31044, + "Ġausp": 31045, + "Ġtextbook": 31046, + "Ġforfeit": 31047, + "icides": 31048, + "Ġundis": 31049, + "DJ": 31050, + "Ġ\"...": 31051, + "ĠXperia": 31052, + "Ġfurry": 31053, + "Australian": 31054, + "Ġpreach": 31055, + "Ġparamed": 31056, + "Ġ196": 31057, + "agos": 31058, + "ĠRIP": 31059, + "Ġ408": 31060, + "ĠQuarterly": 31061, + "ĠQuentin": 31062, + "Ġdeft": 31063, + "ĠVlad": 31064, + "massive": 31065, + "apore": 31066, + "Ġquestionnaire": 31067, + "secution": 31068, + "ĠTunnel": 31069, + "ĠAssist": 31070, + "BILITY": 31071, + "everything": 31072, + "vich": 31073, + "Ġcomparatively": 31074, + "heng": 31075, + "ETH": 31076, + "ĠiPod": 31077, + "Ġinsurgent": 31078, + "Ġtestosterone": 31079, + "191": 31080, + "Ġmoons": 31081, + "Ġgripped": 31082, + "Ġstrang": 31083, + "pects": 31084, + "ĠSERVICE": 31085, + "Ġnumb": 31086, + "Ġmeasurable": 31087, + "Ġdismantled": 31088, + "Ġdepict": 31089, + "Ġretake": 31090, + "Light": 31091, + "Ġaquatic": 31092, + "useum": 31093, + "judicial": 31094, + "Ġ****": 31095, + "Ġrosters": 31096, + "certain": 31097, + "Ġhypothesis": 31098, + "2002": 31099, + "Snow": 31100, + "Ġpounded": 31101, + "ĠZel": 31102, + "ĠTrem": 31103, + "iversity": 31104, + "219": 31105, + "Jen": 31106, + "ĠAdventures": 31107, + "Ġcylinder": 31108, + "Ġbanging": 31109, + "Ġbalk": 31110, + "analy": 31111, + "ĠHust": 31112, + "ookie": 31113, + "ĠReturning": 31114, + "Ġpods": 31115, + "analysis": 31116, + "ĠTruman": 31117, + "Ġorg": 31118, + "Ġsar": 31119, + "Ġdred": 31120, + "ĠTelecommunications": 31121, + "ĠSven": 31122, + "carry": 31123, + "ĠLOVE": 31124, + "Ġparting": 31125, + "asar": 31126, + "utations": 31127, + "itic": 31128, + "Ġactu": 31129, + "Ġbananas": 31130, + "ĠNights": 31131, + "410": 31132, + "Still": 31133, + "Ġtweaked": 31134, + "went": 31135, + "Ġtoddlers": 31136, + "irted": 31137, + "Ġpaed": 31138, + "ĠWink": 31139, + "Ġviewpoint": 31140, + "ĠHelic": 31141, + "Ġhandshake": 31142, + "Ġpoaching": 31143, + "Ġrounding": 31144, + "268": 31145, + "ĠNVIDIA": 31146, + "Ġsquat": 31147, + "Ġtowed": 31148, + "Ġhandler": 31149, + "Ġconspir": 31150, + "Ġadditionally": 31151, + "CENT": 31152, + "ĠÃľ": 31153, + "article": 31154, + "ĠTough": 31155, + "NM": 31156, + "Rem": 31157, + "Ġstunts": 31158, + "ILS": 31159, + "ĠLM": 31160, + "Connect": 31161, + "ĠParagu": 31162, + "Ġcomplexities": 31163, + "Ġhugging": 31164, + "Ġabolish": 31165, + "ricting": 31166, + "ĠItems": 31167, + "Ġtemples": 31168, + "ĠSeat": 31169, + "ĠRubber": 31170, + "Ġindic": 31171, + "ĠVitamin": 31172, + "Ġcitations": 31173, + "Ġarmored": 31174, + "---------------": 31175, + "ĠNeo": 31176, + "ippy": 31177, + "Que": 31178, + "Ġrag": 31179, + "Ġlov": 31180, + "630": 31181, + "Ġadept": 31182, + "orbit": 31183, + "253": 31184, + "412": 31185, + "Ġbutterflies": 31186, + "Ġoutl": 31187, + "ĠCycle": 31188, + "Ġaesthetics": 31189, + "ĠTwitch": 31190, + "405": 31191, + "factor": 31192, + "ðŁij": 31193, + "ĠCircus": 31194, + "Posted": 31195, + "Ġintroductory": 31196, + "ĠStack": 31197, + "atoes": 31198, + "Ġfurn": 31199, + "ĠHond": 31200, + "Ġbipolar": 31201, + "ĠAging": 31202, + "inches": 31203, + "Ġincompetence": 31204, + "Ġaloud": 31205, + "Imagine": 31206, + "Ġsepar": 31207, + "Ġmanip": 31208, + "ophobic": 31209, + "inion": 31210, + "bek": 31211, + "Ġquer": 31212, + "ĠArmen": 31213, + "Ġhumorous": 31214, + "Ġmundane": 31215, + "Ġapologizing": 31216, + "Ġpioneered": 31217, + "Ġ303": 31218, + "282": 31219, + "Ġcalming": 31220, + "orious": 31221, + "760": 31222, + "Ġstitches": 31223, + "Ġthrottle": 31224, + "Ġspinach": 31225, + "urities": 31226, + "ĠCologne": 31227, + "Ġripple": 31228, + "Cs": 31229, + "Cent": 31230, + "Should": 31231, + "Ġaffinity": 31232, + "amount": 31233, + "ĠMISS": 31234, + "Ġsage": 31235, + "Ġamusing": 31236, + "Ġsnatch": 31237, + "clair": 31238, + "ĠGuess": 31239, + "bench": 31240, + "ĠMoj": 31241, + "nuclear": 31242, + "Ġfid": 31243, + "ĠVM": 31244, + "ĠGN": 31245, + "brainer": 31246, + "Ġcurled": 31247, + "Ġbushes": 31248, + "icably": 31249, + "Ġcreeping": 31250, + "Ġveil": 31251, + "ĠALS": 31252, + "ESPN": 31253, + "ulsion": 31254, + "ĠGTX": 31255, + "ĠANN": 31256, + "Ġcomplicit": 31257, + "assault": 31258, + "IOR": 31259, + "Ġpolymer": 31260, + "Ġestimating": 31261, + "277": 31262, + "alog": 31263, + "Ġglimps": 31264, + "Ġreinforces": 31265, + "Ġtextbooks": 31266, + "Ġdictated": 31267, + "ĠReyn": 31268, + "latable": 31269, + "ĠOrth": 31270, + "520": 31271, + "Ġtrickle": 31272, + "ĠWrong": 31273, + ".[": 31274, + "ĠDesigner": 31275, + "304": 31276, + "ĠInner": 31277, + "Ġrave": 31278, + "ppa": 31279, + "ĠGim": 31280, + "Ġswath": 31281, + "Ġcarts": 31282, + "atlantic": 31283, + "Ġpersists": 31284, + "ĠDeveloper": 31285, + "Ġgoodies": 31286, + "isive": 31287, + "Inf": 31288, + "ĠSaving": 31289, + "loop": 31290, + "tions": 31291, + "Ġabusers": 31292, + "Ġclot": 31293, + "Ġmesmer": 31294, + "Ġdeg": 31295, + "Ġskirts": 31296, + "257": 31297, + "Ġunreliable": 31298, + "ĠCOMM": 31299, + "Ġ194": 31300, + "Ġfledgling": 31301, + "administ": 31302, + "Israeli": 31303, + "ĠBarbie": 31304, + "ĠJeanne": 31305, + "Ġgenerously": 31306, + "ĠStruct": 31307, + "ĠZap": 31308, + "Ġvetted": 31309, + "ĠViolet": 31310, + "Ġ),": 31311, + "Ġembarrass": 31312, + "bang": 31313, + "ĠProvider": 31314, + "getting": 31315, + "alg": 31316, + "Ġunconditional": 31317, + "ĠHulk": 31318, + "ĠWad": 31319, + "utation": 31320, + "Ġpointless": 31321, + "Ġdeprivation": 31322, + "Ġstarving": 31323, + "ĠImpossible": 31324, + "ĠStir": 31325, + "Ġknack": 31326, + "anse": 31327, + "Ġsecurely": 31328, + "Ġply": 31329, + "395": 31330, + "Pack": 31331, + "liv": 31332, + "Ġridden": 31333, + "alks": 31334, + "308": 31335, + "male": 31336, + "Ġbitterly": 31337, + "Ġirrational": 31338, + "Members": 31339, + "ported": 31340, + "qq": 31341, + "ractor": 31342, + "Ġinflict": 31343, + "ĠBoehner": 31344, + "Ġthickness": 31345, + "Ġdome": 31346, + "ĠInflu": 31347, + "Ġheap": 31348, + "Ġmirrored": 31349, + "Ġconstituent": 31350, + "Ġfertile": 31351, + "Ġvaping": 31352, + "266": 31353, + "riages": 31354, + "Ġembassies": 31355, + "Ġpersu": 31356, + "ĠMacArthur": 31357, + "issions": 31358, + "Main": 31359, + "aths": 31360, + "onne": 31361, + "circ": 31362, + "Ġsweating": 31363, + "quartered": 31364, + "Ġsax": 31365, + "Ġ540": 31366, + "Ġreputable": 31367, + "Ġsatire": 31368, + "Ġpastors": 31369, + "ventional": 31370, + "Mic": 31371, + "female": 31372, + "Ġpity": 31373, + "appropri": 31374, + "voc": 31375, + "hei": 31376, + "Ġimperial": 31377, + "Ġcorrective": 31378, + "Ġresent": 31379, + "Ġtempered": 31380, + "Ġdiffers": 31381, + "Hamilton": 31382, + "Ġsaddle": 31383, + "Ġgrenades": 31384, + "ĠQuart": 31385, + "onymous": 31386, + "til": 31387, + "Ġdepiction": 31388, + "Ġdisreg": 31389, + "Ġpetitioner": 31390, + "Ġfret": 31391, + "ĠEns": 31392, + "Emer": 31393, + "540": 31394, + "opathy": 31395, + "vertisements": 31396, + "Ġsketches": 31397, + "venth": 31398, + "Ġautomate": 31399, + "Ġjihad": 31400, + "iping": 31401, + "Ġtert": 31402, + "ĠSop": 31403, + "ships": 31404, + "Ġdeceptive": 31405, + "ĠPryor": 31406, + "ĠGorge": 31407, + "ĠMeridian": 31408, + "rero": 31409, + "affected": 31410, + "Ġlame": 31411, + "660": 31412, + "rub": 31413, + "Hello": 31414, + "ĠNumbers": 31415, + "269": 31416, + "Ġmarg": 31417, + "Fran": 31418, + "640": 31419, + "Ġcath": 31420, + "winter": 31421, + "ĠMosque": 31422, + "Ġreckoning": 31423, + "ĠImaging": 31424, + "Ġmutation": 31425, + "ĠMild": 31426, + "Ġkidnap": 31427, + "Ġnav": 31428, + "Ġferocious": 31429, + "Ġdusty": 31430, + "Cele": 31431, + "ĠFoss": 31432, + "Ġregrett": 31433, + "lymp": 31434, + "Ġcoli": 31435, + "Ġstereo": 31436, + "Ġforesee": 31437, + "alties": 31438, + "Ġresusc": 31439, + "Full": 31440, + "wash": 31441, + "ĠINST": 31442, + "ĠPars": 31443, + "Ġcoated": 31444, + "ĠHT": 31445, + "Ġdiscord": 31446, + "Ġreforming": 31447, + "CAN": 31448, + "Ġblink": 31449, + "Ġlubric": 31450, + "Ġmishand": 31451, + "ensible": 31452, + "existent": 31453, + "secondary": 31454, + "ĠDoesn": 31455, + "terrorist": 31456, + "Ġriff": 31457, + "custom": 31458, + "ĠDET": 31459, + "Ġreusable": 31460, + "ĠCRA": 31461, + "ĠScalia": 31462, + "Ġaccelerator": 31463, + "Ġpropag": 31464, + "ĠMID": 31465, + "ework": 31466, + "Ġlooted": 31467, + "oscope": 31468, + "eners": 31469, + "ruction": 31470, + "Ġbarr": 31471, + "Ġviewership": 31472, + "Ġlends": 31473, + "obil": 31474, + "ĠRoots": 31475, + "ĠCame": 31476, + "ibel": 31477, + "Ġglobalization": 31478, + "lab": 31479, + "information": 31480, + "Ġcoordin": 31481, + "Ġglitch": 31482, + "Ġworms": 31483, + "Ġslurs": 31484, + "Ġcontemplated": 31485, + "ĠPenal": 31486, + "Ġ191": 31487, + "Ġ221": 31488, + "Ġexposes": 31489, + "Ġ248": 31490, + "ĠASP": 31491, + "Ġdependency": 31492, + "urga": 31493, + "pdf": 31494, + "Ġvibr": 31495, + "clone": 31496, + "ossible": 31497, + "ĠUtt": 31498, + "serv": 31499, + "ĠLevant": 31500, + "maybe": 31501, + "MU": 31502, + "ĠLunar": 31503, + "Ġbystanders": 31504, + "Ġcapitals": 31505, + "Ġpreacher": 31506, + "thin": 31507, + "Ġunderscore": 31508, + "Ġ('": 31509, + "Ġmedd": 31510, + "Ġautobiography": 31511, + "Ġpersistence": 31512, + "Ġarming": 31513, + "Ġappalled": 31514, + "Ġcontradictory": 31515, + "Ġreciproc": 31516, + "Ġtakedown": 31517, + "tan": 31518, + "Ġnecessities": 31519, + "itans": 31520, + "ĠAlas": 31521, + "Ġsegregated": 31522, + "ĠResponsibility": 31523, + "ĠSHOW": 31524, + "ISIS": 31525, + "Ġpengu": 31526, + "Ġumb": 31527, + "ĠHO": 31528, + "HB": 31529, + "ĠChou": 31530, + "Ġalluded": 31531, + "Ġharms": 31532, + "bara": 31533, + "ĠWOR": 31534, + "Sorry": 31535, + "Ġstarvation": 31536, + "Ġspilling": 31537, + "Ġcarb": 31538, + "annis": 31539, + "ĠGarrison": 31540, + "Ġmillionaire": 31541, + "ifling": 31542, + "ĠCancel": 31543, + "Ġimprint": 31544, + "Ġborrower": 31545, + "455": 31546, + "ĠCic": 31547, + "Ġexposures": 31548, + "dest": 31549, + "Ġunn": 31550, + "Ġ802": 31551, + "Ġadherence": 31552, + "prints": 31553, + "Ġweary": 31554, + 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34723, + "ĠWhale": 34724, + "Ġshorten": 34725, + "Ġcorrectness": 34726, + "evil": 34727, + "network": 34728, + "Ġreactive": 34729, + "assuming": 34730, + "ĠLaksh": 34731, + "games": 34732, + "Ġruining": 34733, + "excluding": 34734, + "annels": 34735, + "º": 34736, + "Ġrubbed": 34737, + "aleb": 34738, + "flex": 34739, + "iped": 34740, + "ĠLimit": 34741, + "allowed": 34742, + "ĠDMV": 34743, + "ĠLD": 34744, + "Ġstamina": 34745, + "conduct": 34746, + "Ġmislead": 34747, + "lib": 34748, + "ĠEminem": 34749, + "Ġpayoff": 34750, + "Ġkernel": 34751, + "Ġsweeps": 34752, + "Ġsonic": 34753, + "ĠKodi": 34754, + "unique": 34755, + "Ġsurrog": 34756, + "Michigan": 34757, + "Ġattest": 34758, + "Ġdummy": 34759, + "ĠStellar": 34760, + "ĠSquadron": 34761, + "ĠHait": 34762, + "ĠSpirits": 34763, + "605": 34764, + "ĠHemisphere": 34765, + "legram": 34766, + "ĠRack": 34767, + "opol": 34768, + "Ġfreshwater": 34769, + "cession": 34770, + "Ġabort": 34771, + "ĠLOG": 34772, + "Ġfuzzy": 34773, + "Ġcrystall": 34774, + 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34930, + "bands": 34931, + "Ġavatar": 34932, + "ĠBarrier": 34933, + "Ġcharacterization": 34934, + "student": 34935, + "Ġgays": 34936, + "Ġspecialize": 34937, + "ĠJudging": 34938, + "Ġinitiation": 34939, + "Ġshove": 34940, + "Ġpirates": 34941, + "Ġfictitious": 34942, + "ĠPoker": 34943, + "ĠElsa": 34944, + "ĠTECH": 34945, + "handedly": 34946, + "Ġglued": 34947, + "Ġclinically": 34948, + "Ġinaccessible": 34949, + "Ġderegulation": 34950, + "Ġprohib": 34951, + "Ġdangling": 34952, + "Ġnoses": 34953, + "Ġstash": 34954, + "اØ": 34955, + "ESH": 34956, + "Ġmonstrous": 34957, + "Ġcrept": 34958, + "ĠCharm": 34959, + "Ġbeh": 34960, + "Ġshuts": 34961, + "Ġ236": 34962, + "imedia": 34963, + "445": 34964, + "Du": 34965, + "Ġafar": 34966, + "ĠRout": 34967, + "Ġflares": 34968, + "Utah": 34969, + "Ġ808": 34970, + "Ġjewels": 34971, + "2004": 34972, + "Ġrecal": 34973, + "Gas": 34974, + "ĠExcellent": 34975, + "Ġpitfalls": 34976, + "ĠDrawing": 34977, + "viously": 34978, + "angered": 34979, + "changes": 34980, 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"ĠVIEW": 35086, + "ovation": 35087, + "Ġneedless": 35088, + "ĠIU": 35089, + "ignant": 35090, + "ĠConfeder": 35091, + "316": 35092, + "fine": 35093, + "Ġjars": 35094, + "gotten": 35095, + "Bron": 35096, + "Ġmindfulness": 35097, + "imating": 35098, + "Ġhysteria": 35099, + "Ġhurried": 35100, + "Ġinfantry": 35101, + "ĠNYU": 35102, + "tags": 35103, + "Penn": 35104, + "Ġtracing": 35105, + "ĠSwing": 35106, + "ĠIo": 35107, + "Ġreckoned": 35108, + "ĠRecall": 35109, + "ĠVersion": 35110, + "314": 35111, + "Ġecology": 35112, + "Ġarmoured": 35113, + "Ġresonance": 35114, + "970": 35115, + "Ġvigilance": 35116, + "Ġrede": 35117, + "ĠBohem": 35118, + "Ġchau": 35119, + "ĠDevi": 35120, + "Ġtru": 35121, + "))": 35122, + "Put": 35123, + "Ġflavored": 35124, + "ĠClown": 35125, + "Senate": 35126, + "ĠScandinavian": 35127, + "mable": 35128, + "Residents": 35129, + "ĠFranchise": 35130, + "Ġprecincts": 35131, + "Prem": 35132, + "ĠNeutral": 35133, + "coal": 35134, + "Ġdelinqu": 35135, + "Mus": 35136, + "UME": 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35188, + "ĠVerd": 35189, + "cook": 35190, + "Ġsimulator": 35191, + "players": 35192, + "Early": 35193, + "infeld": 35194, + "Ġmaximizing": 35195, + "Philipp": 35196, + "ĠPhotoshop": 35197, + "Ġdestroys": 35198, + "Ġbefriend": 35199, + "Ġfilthy": 35200, + "ĠIncident": 35201, + "gha": 35202, + "Ġcomplicity": 35203, + "Ġmessing": 35204, + "YA": 35205, + "ĠNegro": 35206, + "adows": 35207, + "374": 35208, + "Ġpip": 35209, + "cean": 35210, + "Ġ1924": 35211, + "Sent": 35212, + "represent": 35213, + "Ġdeems": 35214, + "ĠRue": 35215, + "Ġtitanium": 35216, + "Ġmanners": 35217, + "âĢ¦âĢ¦": 35218, + "bare": 35219, + "Ġusur": 35220, + "mma": 35221, + "ĠPanda": 35222, + "ulus": 35223, + "ĠSlav": 35224, + "324": 35225, + "ĠMole": 35226, + "^": 35227, + "micro": 35228, + "foreign": 35229, + "lest": 35230, + "ocular": 35231, + "ĠUniv": 35232, + "ĠFrag": 35233, + "Ġshepherd": 35234, + "Ġelectron": 35235, + "ĠFSA": 35236, + "Ġunl": 35237, + "dose": 35238, + "Ġimmersion": 35239, + "ĠDeL": 35240, + 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"Ġkitten": 35292, + "Ġconstit": 35293, + "iration": 35294, + "Speed": 35295, + "ecycle": 35296, + "ĠFalse": 35297, + "ĠDealer": 35298, + "Could": 35299, + "655": 35300, + "outside": 35301, + "Ġworldview": 35302, + "Ġ246": 35303, + "Ġspitting": 35304, + "595": 35305, + "MN": 35306, + "ĠComes": 35307, + "ingu": 35308, + "Ġenzymes": 35309, + "Ġcompass": 35310, + "Ġexclaimed": 35311, + "ĠMalays": 35312, + "Ġ1916": 35313, + "Ġcoloring": 35314, + "Ġrepeats": 35315, + "Ġsoils": 35316, + "Ġtrivia": 35317, + "ĠIsles": 35318, + "Const": 35319, + "ĠFiction": 35320, + "665": 35321, + "Ġcriminality": 35322, + "ĠZi": 35323, + "384": 35324, + "ĠWilderness": 35325, + "ĠCanary": 35326, + "ĠVs": 35327, + "и": 35328, + "ĠAPIs": 35329, + "Ġbehest": 35330, + "Ġeb": 35331, + "ĠHipp": 35332, + "Ġpreempt": 35333, + "Ġevoke": 35334, + "Ġinept": 35335, + "tele": 35336, + "447": 35337, + "ĠGarmin": 35338, + "Ġpursuits": 35339, + "351": 35340, + "Ġcliché": 35341, + "ĠJihad": 35342, + "Ġ308": 35343, + "ĠSnake": 35344, + "ĠAnnounce": 35345, + "Nearly": 35346, + "!'\"": 35347, + "Ġ1927": 35348, + "saw": 35349, + "Ġabhor": 35350, + "Plan": 35351, + "rawled": 35352, + "ĠRiy": 35353, + "ensor": 35354, + "Fal": 35355, + "quick": 35356, + "odynamic": 35357, + "Ġsubstitution": 35358, + "Ġprovoking": 35359, + "Operation": 35360, + "rupulous": 35361, + "Ġsweetness": 35362, + "folk": 35363, + "ĠDefault": 35364, + "Ġstarved": 35365, + "ĠPrinting": 35366, + "urious": 35367, + "ĠTracker": 35368, + "them": 35369, + "Ġleth": 35370, + "Ġemptied": 35371, + "Ġfootprints": 35372, + "ilian": 35373, + "Ġbattalion": 35374, + "Ġprophet": 35375, + "Ġrailing": 35376, + "Ġhect": 35377, + "rouch": 35378, + "lees": 35379, + "Ġideologies": 35380, + "Ġ254": 35381, + "ĠGods": 35382, + "ĠAvalon": 35383, + "Ġfrontrunner": 35384, + "ĠPork": 35385, + "ĠPipe": 35386, + "Ġscaven": 35387, + "Ġming": 35388, + "Ġerg": 35389, + "Ġ520": 35390, + "Ġhatched": 35391, + "asant": 35392, + "ĠHI": 35393, + "Ġpend": 35394, + "Ġ288": 35395, + 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40924, + "apsed": 40925, + "Fortunately": 40926, + "gered": 40927, + "Pict": 40928, + "Ġmastering": 40929, + "ĠHIM": 40930, + "ĠObesity": 40931, + "Ġornament": 40932, + "advant": 40933, + "ĠCous": 40934, + "032": 40935, + "cells": 40936, + "Ġpreclude": 40937, + "Ġanecdote": 40938, + "Ġpatriarchy": 40939, + "ĠSending": 40940, + "Pie": 40941, + "Ġdepressive": 40942, + "ĠEnds": 40943, + "712": 40944, + "zos": 40945, + "icka": 40946, + "Ġ1906": 40947, + "Anti": 40948, + "vana": 40949, + "ĠRestrict": 40950, + "Ġprotr": 40951, + "Ġusername": 40952, + "Ġparach": 40953, + "1997": 40954, + "imental": 40955, + "rower": 40956, + "carb": 40957, + "033": 40958, + "Ġobligatory": 40959, + "Ġwillful": 40960, + "Ġsnail": 40961, + "json": 40962, + "izarre": 40963, + "Ġmiscar": 40964, + "Ġdopamine": 40965, + "л": 40966, + "Ġapplic": 40967, + "Ġnervously": 40968, + "YY": 40969, + "alez": 40970, + "ĠSoviets": 40971, + "ĠMister": 40972, + "Ġcrates": 40973, + "Ġheavenly": 40974, + "Ġdoct": 40975, + "048": 40976, + "Ġ2400": 40977, + "ivia": 40978, + "adies": 40979, + "Phone": 40980, + "asks": 40981, + "Ġperenn": 40982, + "Ġcomposing": 40983, + "Ġraiding": 40984, + "requent": 40985, + "ibli": 40986, + "ĠFeedback": 40987, + "cellaneous": 40988, + "ĠContracts": 40989, + "ĠCasting": 40990, + "vim": 40991, + "Cut": 40992, + "Ġabbrevi": 40993, + "Ġintest": 40994, + "ricted": 40995, + "969": 40996, + "nostic": 40997, + "Ġinverted": 40998, + "ĠEG": 40999, + "aiden": 41000, + "ĠClaud": 41001, + "ĠiP": 41002, + "urized": 41003, + "Emily": 41004, + "Ġ353": 41005, + "Ġ((": 41006, + "ammad": 41007, + "Reb": 41008, + "plom": 41009, + "YES": 41010, + "connection": 41011, + "ĠWra": 41012, + "ĠMerch": 41013, + "Ġether": 41014, + "Elizabeth": 41015, + "Chip": 41016, + "relevant": 41017, + "URA": 41018, + "Ġantioxidant": 41019, + "ĠChron": 41020, + "Ġtheological": 41021, + "HCR": 41022, + "ruits": 41023, + "Body": 41024, + "enezuel": 41025, + "Few": 41026, + "adder": 41027, + "Ġinducing": 41028, + "ĠDarth": 41029, + "Ġimplicitly": 41030, + "Ġoverfl": 41031, + "Ġrelics": 41032, + "Must": 41033, + "ĠAnswers": 41034, + "Ġretina": 41035, + "ĠSlowly": 41036, + "ĠShib": 41037, + "software": 41038, + "Ġ\"\"": 41039, + "hack": 41040, + "Apart": 41041, + "told": 41042, + "Ger": 41043, + "Civil": 41044, + "problem": 41045, + "Ġslang": 41046, + "Ġtactile": 41047, + "Ġtabl": 41048, + "ĠAscension": 41049, + "Ġhumankind": 41050, + "Howard": 41051, + "rescent": 41052, + "ĠReleases": 41053, + "arijuana": 41054, + "Christopher": 41055, + "ĠWarden": 41056, + "blogspot": 41057, + "ĠVari": 41058, + "idency": 41059, + "ĠHandler": 41060, + "Round": 41061, + "MJ": 41062, + "Ġrhyth": 41063, + "Tai": 41064, + "terson": 41065, + "Ġ,\"": 41066, + "portation": 41067, + "ĠOrbital": 41068, + "Ġfantas": 41069, + "Ġattribut": 41070, + "Ġdiagram": 41071, + "atech": 41072, + "1992": 41073, + "ibl": 41074, + "Woman": 41075, + "ternally": 41076, + "Days": 41077, + "Ġdebunk": 41078, + "ĠPhant": 41079, + "ĠOath": 41080, + "sharp": 41081, + "Ġclaws": 41082, + "Lots": 41083, + "Incre": 41084, + "Aff": 41085, + "hooting": 41086, + "rect": 41087, + "Ġaltru": 41088, + "Ġwors": 41089, + "Ġtho": 41090, + "Ġ349": 41091, + "clusions": 41092, + "Ġpseudonym": 41093, + "Bec": 41094, + "Ġphosphorus": 41095, + "ivic": 41096, + "Ġ348": 41097, + "otent": 41098, + "Ġub": 41099, + "Ġcoales": 41100, + "regate": 41101, + "Ġ1870": 41102, + "Ġglide": 41103, + "treated": 41104, + "ĠSymb": 41105, + "Ġenchant": 41106, + "Besides": 41107, + "stocks": 41108, + "Ġ388": 41109, + "--------------": 41110, + "interpret": 41111, + "ouple": 41112, + "Ġdrawback": 41113, + "ĠRevised": 41114, + "Ġanat": 41115, + "Ġpsychosis": 41116, + "ب": 41117, + "Ġdiffuse": 41118, + "Ġaffidav": 41119, + "elve": 41120, + "amination": 41121, + "ĠTackle": 41122, + "hunter": 41123, + "env": 41124, + "Ġchests": 41125, + "Ġsubter": 41126, + "Ġconquest": 41127, + "Ġfidelity": 41128, + "Ġinfringing": 41129, + "opathic": 41130, + "ĠGrip": 41131, + "ĠKeyboard": 41132, + "Ġobjectionable": 41133, + "Ġmetabol": 41134, + "ĠGö": 41135, + "Room": 41136, + "...)": 41137, + "KEN": 41138, + "assic": 41139, + "Ġgeop": 41140, + "Tro": 41141, + "Ġcursing": 41142, + "Ġdile": 41143, + "Ġultraviolet": 41144, + "inarily": 41145, + "Ġdistilled": 41146, + "sect": 41147, + "ĠShooter": 41148, + "uckles": 41149, + "Ġdistortions": 41150, + "Map": 41151, + "Doctor": 41152, + "Ġinstalls": 41153, + "oire": 41154, + "Ġstarch": 41155, + "ociation": 41156, + "Lev": 41157, + "Ġscripture": 41158, + "Ġsalient": 41159, + "ilitating": 41160, + "wb": 41161, + "ĠSov": 41162, + "ĠDamn": 41163, + "Grey": 41164, + "Ġ980": 41165, + "Ġjung": 41166, + "Ġlicking": 41167, + "029": 41168, + "ĠDian": 41169, + "ĠBabylon": 41170, + "к": 41171, + "ĠRomantic": 41172, + "Ġguesses": 41173, + "ĠFren": 41174, + "Generally": 41175, + "ultural": 41176, + "istence": 41177, + "Ġiniti": 41178, + "Ġ341": 41179, + "ĠSlave": 41180, + "ultan": 41181, + "ĠTrash": 41182, + "ĠEmpty": 41183, + "ĠHundred": 41184, + "ĠDirective": 41185, + "Anderson": 41186, + "Advertisement": 41187, + "RH": 41188, + "ĠOo": 41189, + "ĠHik": 41190, + "peg": 41191, + "Sup": 41192, + "ĠXT": 41193, + "Ġencrypt": 41194, + "selage": 41195, + "ĠThrone": 41196, + "Ġconsecut": 41197, + "Li": 41198, + "ĠVirus": 41199, + "ĠCookies": 41200, + "SHIP": 41201, + "Ġflavorful": 41202, + "odynamics": 41203, + "animal": 41204, + "spread": 41205, + "ĠIPCC": 41206, + "jobs": 41207, + "ernand": 41208, + "ĠHaunted": 41209, + "Ġintolerable": 41210, + "ĠLAR": 41211, + "ixtape": 41212, + "Ġneur": 41213, + "Ġcausal": 41214, + "ĠPsychiatry": 41215, + "ĠVim": 41216, + "Ġgenomic": 41217, + "duration": 41218, + "ĠUsername": 41219, + "ategy": 41220, + "Ġunic": 41221, + "ĠKILL": 41222, + "blooded": 41223, + "Ġcaucuses": 41224, + "ĠPOLITICO": 41225, + "Spanish": 41226, + "Ġobedience": 41227, + "Ġinconven": 41228, + "MAT": 41229, + "Ġbends": 41230, + "ĠImprovements": 41231, + "Ġrelig": 41232, + "ĠForth": 41233, + "ĠLumia": 41234, + "uces": 41235, + "Ġunim": 41236, + "ĠStatistical": 41237, + "kb": 41238, + "auntlet": 41239, + "ĠDisco": 41240, + "ĠInstruction": 41241, + "ooo": 41242, + "ĠDictionary": 41243, + "culated": 41244, + "Adv": 41245, + "ĠAvatar": 41246, + "ictional": 41247, + "Ġcentr": 41248, + "ifles": 41249, + "orks": 41250, + "skill": 41251, + "Ġlatex": 41252, + "ĠPagan": 41253, + "Ġdevast": 41254, + "Ġprol": 41255, + "896": 41256, + "Product": 41257, + "968": 41258, + "Ġfrench": 41259, + "083": 41260, + "ĠCluster": 41261, + "cloth": 41262, + "ĠFilter": 41263, + "ĠDisorders": 41264, + "etimes": 41265, + "Ġinstinctively": 41266, + "ĠBritann": 41267, + "Ġaft": 41268, + "ĠVict": 41269, + "Ġâĺħ": 41270, + "Ġperverse": 41271, + "Ġcontraceptives": 41272, + "ĠHannibal": 41273, + "escap": 41274, + "ĠApostle": 41275, + "ĠXiao": 41276, + "ĠMagnum": 41277, + "Ġphosphate": 41278, + "Ġ399": 41279, + "utable": 41280, + "Ġsten": 41281, + "Ġwearer": 41282, + "Ġsmug": 41283, + "ĠInfluence": 41284, + "Ġ384": 41285, + "Truth": 41286, + "struction": 41287, + "Ġmaniac": 41288, + "ĠMagnetic": 41289, + "ousands": 41290, + "Ġsemen": 41291, + "dir": 41292, + "ĠTornado": 41293, + "Ġexplos": 41294, + "1995": 41295, + "Xi": 41296, + "Steel": 41297, + "057": 41298, + "Barn": 41299, + "Fan": 41300, + "ĠChatt": 41301, + "Chem": 41302, + "ĠFold": 41303, + "bees": 41304, + "1080": 41305, + "ĠMaze": 41306, + "ierre": 41307, + "oeuv": 41308, + "Cand": 41309, + "odium": 41310, + "mmm": 41311, + "ereo": 41312, + "Ġreactionary": 41313, + "Ġacidic": 41314, + "ĠRemoval": 41315, + "Ġnont": 41316, + "031": 41317, + "ĠTerminator": 41318, + "ĠVendor": 41319, + "enemy": 41320, + "Ġreconstructed": 41321, + "ĠGalileo": 41322, + "Ġtesters": 41323, + "albeit": 41324, + "uminium": 41325, + "Ġrite": 41326, + "ĠInput": 41327, + "committee": 41328, + "Ġjour": 41329, + "gements": 41330, + "Ġgerm": 41331, + "Dick": 41332, + "ĠRequirements": 41333, + "omsday": 41334, + "Î": 41335, + "ISSION": 41336, + "Ġmolded": 41337, + "Ġrye": 41338, + "Attorney": 41339, + "population": 41340, + "Ġrepet": 41341, + "Sync": 41342, + "breaks": 41343, + "Ġbanished": 41344, + "Ġraspberry": 41345, + "Ġammo": 41346, + "Ġorthodox": 41347, + "Ġwebcam": 41348, + "ĠAsc": 41349, + "vl": 41350, + "1989": 41351, + "Ġdiscipl": 41352, + "Ġmoreover": 41353, + "Ġexplodes": 41354, + "1960": 41355, + "Ġpropositions": 41356, + "Protect": 41357, + "Ġsexes": 41358, + "physical": 41359, + "ĠAthena": 41360, + "ocent": 41361, + "ĠGothic": 41362, + "ĠRacial": 41363, + "istani": 41364, + "Ġhelium": 41365, + "ĠPresumably": 41366, + "Ġperman": 41367, + "becue": 41368, + "ĠHW": 41369, + "rued": 41370, + "ĠCNS": 41371, + "DEP": 41372, + "ĠManifest": 41373, + "2500": 41374, + "ĠMyst": 41375, + "Economic": 41376, + "Prot": 41377, + "Ġledge": 41378, + "Ġimitate": 41379, + "ĠTotally": 41380, + "ĠBeaut": 41381, + "OIL": 41382, + "Ġ1440": 41383, + "Moscow": 41384, + "ĠSets": 41385, + "merga": 41386, + "Ġlesbians": 41387, + "Walker": 41388, + "Move": 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41441, + "Ġvec": 41442, + "ĠActions": 41443, + "Ġdistinguishing": 41444, + "ĠTsarnaev": 41445, + "ĠEndurance": 41446, + "ederation": 41447, + "itant": 41448, + "Ġstreetcar": 41449, + "041": 41450, + "ĠAval": 41451, + "ĠCompanion": 41452, + "ĠCartoon": 41453, + "Ġcalculus": 41454, + "993": 41455, + "eq": 41456, + "ĠVanilla": 41457, + "MAC": 41458, + "wolves": 41459, + "fg": 41460, + "Ġfermentation": 41461, + "Ġinformants": 41462, + "Ġsudo": 41463, + "Ġperipher": 41464, + "Ġindign": 41465, + "parts": 41466, + "detail": 41467, + "femin": 41468, + "blade": 41469, + "Ġinserts": 41470, + "Ġoffsets": 41471, + "Ġantidepressants": 41472, + "Ġphr": 41473, + "Ġresultant": 41474, + "biology": 41475, + "Ġacquies": 41476, + "UFF": 41477, + "****************": 41478, + "ĠPenalty": 41479, + "Ġrever": 41480, + "heric": 41481, + "ĠShadows": 41482, + "command": 41483, + "Ġreprint": 41484, + "089": 41485, + "empty": 41486, + "ĠTAG": 41487, + "stim": 41488, + "FK": 41489, + "Ġkins": 41490, + "uggle": 41491, + "imura": 41492, + "wit": 41493, + "Kill": 41494, + "Beck": 41495, + "Ocean": 41496, + "Ġlabyrinth": 41497, + "ĠNorse": 41498, + "IENCE": 41499, + "Ġ+++": 41500, + "DoS": 41501, + "gm": 41502, + "Ġbarbar": 41503, + "ĠCeres": 41504, + "Ġhashing": 41505, + "eworthy": 41506, + "Ġrecite": 41507, + "Ġelectrodes": 41508, + "Ġconformity": 41509, + "response": 41510, + "olate": 41511, + "Ġ357": 41512, + "Snap": 41513, + "Crime": 41514, + "Ġpointer": 41515, + "ĠTIT": 41516, + "Ġdistinctions": 41517, + "Ġ427": 41518, + "ĠÙĪ": 41519, + "abases": 41520, + "Mars": 41521, + "ĠSpiritual": 41522, + "Ġimpuls": 41523, + "Philadelphia": 41524, + "1994": 41525, + "Ġcunning": 41526, + "Ġfram": 41527, + "Ġinco": 41528, + "Ġomnip": 41529, + "imize": 41530, + "ervative": 41531, + "Gy": 41532, + "Drug": 41533, + "Ġcarniv": 41534, + "ĠSailor": 41535, + "download": 41536, + "ĠBeetle": 41537, + "ĠEarthqu": 41538, + "izontal": 41539, + "Alan": 41540, + "Nice": 41541, + "Prior": 41542, + "MAG": 41543, + "Ġautobi": 41544, + "ĠBrill": 41545, + "Ġpredominant": 41546, + "ĠMessiah": 41547, + "REM": 41548, + "ĠSlip": 41549, + "ĠWebs": 41550, + "ademic": 41551, + "<": 41552, + "ĠVessel": 41553, + "vari": 41554, + "Code": 41555, + "Ġbeetle": 41556, + "projects": 41557, + "BAT": 41558, + "Ġpsychotic": 41559, + "Ġunderside": 41560, + "Ġrefute": 41561, + "Considering": 41562, + "kees": 41563, + "wd": 41564, + "priority": 41565, + "Ġtwentieth": 41566, + "Ġatheist": 41567, + "amina": 41568, + "Ġeuphem": 41569, + "Ġtripod": 41570, + "ĠTrayvon": 41571, + "ĠNON": 41572, + "2200": 41573, + "ĠNPC": 41574, + "ependence": 41575, + "ĠMHz": 41576, + "ĠBung": 41577, + "Ġpane": 41578, + "Ġaboriginal": 41579, + "ĠPLUS": 41580, + "igers": 41581, + "ĠSexy": 41582, + "MF": 41583, + "Chall": 41584, + "Ay": 41585, + "ilingual": 41586, + "adj": 41587, + "Ġfrown": 41588, + "successful": 41589, + "stack": 41590, + "Ġic": 41591, + "ĠSeah": 41592, + "Ġconsequ": 41593, + "bugs": 41594, + "ĠScand": 41595, + "ĠCurve": 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42163, + "ĠPHP": 42164, + "Ġclaimant": 42165, + "uitive": 42166, + "Ġpussy": 42167, + "Ġcorpus": 42168, + "ĠAo": 42169, + "Qual": 42170, + "ĠXVI": 42171, + "requ": 42172, + "Ġsympt": 42173, + "mination": 42174, + "Ġhairy": 42175, + "ĠBattles": 42176, + "owntown": 42177, + "Roberts": 42178, + "Ġnec": 42179, + "ablo": 42180, + "AMD": 42181, + "internet": 42182, + "Tar": 42183, + "direction": 42184, + "ouston": 42185, + "ĠGlock": 42186, + "ĠYanukovych": 42187, + "ogens": 42188, + "rogram": 42189, + "otype": 42190, + "ĠPt": 42191, + "tenance": 42192, + "Ġaromatic": 42193, + "oxin": 42194, + "Vert": 42195, + "Ġsociop": 42196, + "cible": 42197, + "Db": 42198, + "________________": 42199, + "Third": 42200, + "ĠShips": 42201, + "!.": 42202, + "expensive": 42203, + "WOR": 42204, + "primary": 42205, + "Ġ666": 42206, + "Ġdecaying": 42207, + "Ġclustered": 42208, + "Ġbeetles": 42209, + "ĠHogwarts": 42210, + "Ġheaders": 42211, + "ĠJudah": 42212, + "Ġscen": 42213, + "Ġcosmos": 42214, + "ĠGenetic": 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42633, + "LIST": 42634, + "IDES": 42635, + "ĠThick": 42636, + "Ġdisks": 42637, + "Kin": 42638, + "Ġghetto": 42639, + "ĠObjects": 42640, + "Ġprism": 42641, + "ĠNether": 42642, + "Ġvul": 42643, + "iky": 42644, + "]:": 42645, + "ĠDetail": 42646, + "Ġfucked": 42647, + "!?": 42648, + "anium": 42649, + "Ġlords": 42650, + "ilities": 42651, + "ĠEthnic": 42652, + "static": 42653, + "$$": 42654, + "evidence": 42655, + "Ġmainline": 42656, + "Ġpeasant": 42657, + "ĠEnhance": 42658, + "ĠForced": 42659, + "virt": 42660, + "Ġii": 42661, + "Ġsymm": 42662, + "Ġconverter": 42663, + "ularity": 42664, + "Ġrepent": 42665, + "num": 42666, + "ĠScrew": 42667, + "ĠFTA": 42668, + "Ġmarines": 42669, + "hetto": 42670, + "blow": 42671, + "Ġado": 42672, + "ĠTypical": 42673, + "Ġoverw": 42674, + "ĠBerm": 42675, + "keley": 42676, + "Song": 42677, + "hao": 42678, + "valid": 42679, + "EXT": 42680, + "ĠProvides": 42681, + "âĺħâĺħ": 42682, + "ĠOdin": 42683, + "Shot": 42684, + "Ġgamma": 42685, + "Princ": 42686, + "asonry": 42687, + "ĠAccuracy": 42688, + "Ġcriterion": 42689, + "Ġdescriptive": 42690, + "Gall": 42691, + "gray": 42692, + "ĠCalcul": 42693, + "Ġaxes": 42694, + "ĠCommunists": 42695, + "ĠRebellion": 42696, + "Success": 42697, + "tg": 42698, + "Ġâĺ": 42699, + "Ġmultiplier": 42700, + "ravity": 42701, + "Thus": 42702, + "URL": 42703, + "Ġalternatively": 42704, + "duction": 42705, + "Ġsarcast": 42706, + "ĠCarth": 42707, + "ĠUSL": 42708, + "ĠInvisible": 42709, + "larg": 42710, + "pleted": 42711, + "pathic": 42712, + "Additionally": 42713, + "ĠCao": 42714, + "Ġlatent": 42715, + "ĠSurge": 42716, + "MEN": 42717, + "communications": 42718, + "ĠArray": 42719, + "Pink": 42720, + "commit": 42721, + "isodes": 42722, + "earcher": 42723, + "Ukraine": 42724, + "ĠAnthrop": 42725, + "incial": 42726, + "Ġquotations": 42727, + "adena": 42728, + "Ġwhining": 42729, + "Ġretri": 42730, + "ĠAssass": 42731, + "elligent": 42732, + "ĠPERSON": 42733, + "Py": 42734, + "Send": 42735, + "ĠâĪĴ": 42736, + "DON": 42737, + "Ġwatt": 42738, + "description": 42739, + "POS": 42740, + "Ġrepro": 42741, + "destroy": 42742, + "icidal": 42743, + "Ġmidrange": 42744, + "Ġinfographic": 42745, + "interesting": 42746, + "category": 42747, + "Flash": 42748, + "ĠInvasion": 42749, + "ĠExodus": 42750, + "restricted": 42751, + "Ġinference": 42752, + "dding": 42753, + "mingham": 42754, + "Ġcircumst": 42755, + "Wi": 42756, + "ĠHast": 42757, + "Ġsubjug": 42758, + "Ġwhispering": 42759, + "-.": 42760, + "Ġadren": 42761, + "ĠPattern": 42762, + "BOX": 42763, + "ĠEnhancement": 42764, + "Exc": 42765, + "ĠBucket": 42766, + "ĠGUN": 42767, + "deen": 42768, + "ĠHomo": 42769, + "1985": 42770, + "Ġclo": 42771, + "Ġsnippet": 42772, + "Ġ1896": 42773, + "TPP": 42774, + "Seg": 42775, + "success": 42776, + ";\"": 42777, + "ĠMUCH": 42778, + "Author": 42779, + "Ġreplication": 42780, + "Ġhallucinations": 42781, + "Inv": 42782, + "ĠAware": 42783, + "ĠViper": 42784, + "kai": 42785, + "frames": 42786, + "ĠTHANK": 42787, + "ĠSHA": 42788, + "wordpress": 42789, + "Ġbc": 42790, + "CIA": 42791, + "arrison": 42792, + "Ġalloc": 42793, + "ĠAlz": 42794, + "letcher": 42795, + "ĠDaredevil": 42796, + "iversary": 42797, + "Ġmanuals": 42798, + "Catholic": 42799, + "feat": 42800, + "Ġkinetic": 42801, + "JB": 42802, + "yeah": 42803, + "ĠLDS": 42804, + "Ġppm": 42805, + "ĠADC": 42806, + "pring": 42807, + "cence": 42808, + "Ġclasp": 42809, + "Ġsetups": 42810, + "Ġdeity": 42811, + "ĠIndra": 42812, + "ĠWander": 42813, + "Ġantib": 42814, + "Otherwise": 42815, + "ombie": 42816, + "Bitcoin": 42817, + "ipop": 42818, + "expression": 42819, + "Animal": 42820, + "ĠResurrection": 42821, + "ĠMoral": 42822, + "ĠSDK": 42823, + "Ġwretched": 42824, + "ogenous": 42825, + "species": 42826, + "Ġchuckled": 42827, + "Thor": 42828, + "Ġ428": 42829, + "avery": 42830, + "ĠPry": 42831, + "asures": 42832, + "ĠErn": 42833, + "apor": 42834, + "Ġinnumerable": 42835, + "Ġbaptized": 42836, + "ĠExplosive": 42837, + "Ġelves": 42838, + "idges": 42839, + "ĠParadox": 42840, + "Close": 42841, + "aldehyde": 42842, + "construct": 42843, + "Ġvirginity": 42844, + "Poll": 42845, + "assin": 42846, + "Doctors": 42847, + "Pos": 42848, + "NECT": 42849, + "Moreover": 42850, + "Commercial": 42851, + "cknowled": 42852, + "1988": 42853, + "Ġquotation": 42854, + "marriage": 42855, + "ĠBapt": 42856, + "ĠSina": 42857, + "ĠGloves": 42858, + "gian": 42859, + "Ġconfounding": 42860, + "URRENT": 42861, + "Dean": 42862, + "Brew": 42863, + "thur": 42864, + "pty": 42865, + "immune": 42866, + "ĠSQU": 42867, + "Ġcounterfe": 42868, + "rider": 42869, + "Ġinferred": 42870, + "ĠDimension": 42871, + "ĠToad": 42872, + "Ġafterlife": 42873, + "ĠHERO": 42874, + "Indiana": 42875, + "seek": 42876, + "Ġdistinguishes": 42877, + "ĠQur": 42878, + "ĠMethods": 42879, + "combat": 42880, + "Ġcateg": 42881, + "ĠStruggle": 42882, + "teness": 42883, + "liquid": 42884, + "Ġblinking": 42885, + "ĠCONTIN": 42886, + "iae": 42887, + "Ġaerobic": 42888, + "Ġstrugg": 42889, + "Ġegalitarian": 42890, + "hello": 42891, + "orrect": 42892, + "ĠAbandon": 42893, + "Ġferment": 42894, + "Area": 42895, + "idem": 42896, + "ĠMania": 42897, + "Ġjs": 42898, + "ĠBALL": 42899, + "Running": 42900, + "Ġregenerate": 42901, + "iquid": 42902, + "Uh": 42903, + "Crystal": 42904, + "ĠItal": 42905, + "ĠHeavenly": 42906, + "в": 42907, + "CRIPTION": 42908, + "Consumer": 42909, + "dust": 42910, + "amiliar": 42911, + "ĠRhino": 42912, + "Rocket": 42913, + "Ġreversible": 42914, + "kok": 42915, + "ĠSketch": 42916, + "Ġshotguns": 42917, + "apses": 42918, + "Ġdetach": 42919, + "ĠCells": 42920, + "artist": 42921, + "rily": 42922, + "ĠRestore": 42923, + "Scar": 42924, + "Ġevid": 42925, + "Ġspaced": 42926, + "ĠContributions": 42927, + "Ġ418": 42928, + "ĠMystic": 42929, + "Ġobfusc": 42930, + "Russ": 42931, + "wings": 42932, + "Pear": 42933, + "osite": 42934, + "Nusra": 42935, + "urations": 42936, + "ovie": 42937, + "icago": 42938, + "ĠConcepts": 42939, + "Ġstimuli": 42940, + "Ġaroused": 42941, + "aughty": 42942, + 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42993, + "Ġconquering": 42994, + "ĠFinder": 42995, + "window": 42996, + "Gra": 42997, + "Ġfonts": 42998, + "laughter": 42999, + "Ġcolonization": 43000, + "ĠDOD": 43001, + ")!": 43002, + ",)": 43003, + "ĠGeral": 43004, + "ĠSpoiler": 43005, + "ĠComponent": 43006, + "Ġgist": 43007, + "hiro": 43008, + "Ġlicens": 43009, + "nesses": 43010, + "Ġkarma": 43011, + "?\".": 43012, + "OPA": 43013, + "Ġsquats": 43014, + "ĠRAND": 43015, + "Ġorally": 43016, + "document": 43017, + "olars": 43018, + "Ġpresumptive": 43019, + "Pers": 43020, + "OAD": 43021, + "ufficient": 43022, + "LESS": 43023, + "Hidden": 43024, + "ORK": 43025, + "xs": 43026, + "Ġmathematician": 43027, + "ĠGloss": 43028, + "Ġannihilation": 43029, + "Ġmanifold": 43030, + "Ry": 43031, + "Thunder": 43032, + "Yan": 43033, + "Activ": 43034, + "Ġworldly": 43035, + "TED": 43036, + "marg": 43037, + "ĠStun": 43038, + "ryce": 43039, + "ĠVG": 43040, + "Isn": 43041, + "ĠCyn": 43042, + "Expl": 43043, + "IRED": 43044, + "Ġcompr": 43045, + "Ġindisc": 43046, + "Boss": 43047, + "()": 43048, + "berman": 43049, + "ĠBegins": 43050, + "ujah": 43051, + "ornia": 43052, + "hetical": 43053, + "Ġcivilizations": 43054, + "Ġfundamentalist": 43055, + "strap": 43056, + "Forward": 43057, + "ettlement": 43058, + "Ġprophetic": 43059, + "glers": 43060, + "bending": 43061, + "Terry": 43062, + "Ġidi": 43063, + "Ġtrunc": 43064, + "Ġcreeps": 43065, + "intel": 43066, + "switch": 43067, + "ailand": 43068, + "Ġinstaller": 43069, + "GOP": 43070, + "Ġ499": 43071, + "ĠParallel": 43072, + "Cru": 43073, + "Ġ\"@": 43074, + "Ġ396": 43075, + "ĠUnlock": 43076, + "Raven": 43077, + "Corn": 43078, + "Ġcircadian": 43079, + "Ġ********************************": 43080, + "iliate": 43081, + "ĠFunctional": 43082, + "Ġpronouns": 43083, + "ĠSatoshi": 43084, + "Ġstim": 43085, + "Gay": 43086, + "Iss": 43087, + "ĠThief": 43088, + "atellite": 43089, + "Ġshards": 43090, + "Ġphil": 43091, + "protein": 43092, + "Ġalters": 43093, + "Poor": 43094, + "Typically": 43095, + "KER": 43096, + "ociate": 43097, + "Ġemits": 43098, + "recy": 43099, + "Ġmechanically": 43100, + "Ġ...\"": 43101, + "nature": 43102, + "sys": 43103, + "ysc": 43104, + "Ġwavelengths": 43105, + "pattern": 43106, + "insured": 43107, + "Ġparasitic": 43108, + "ĠLCS": 43109, + "ĠPACs": 43110, + "Ġheals": 43111, + "ĠCCP": 43112, + "ĠHacker": 43113, + "Ġpsy": 43114, + "ĠBeans": 43115, + "Ġdemonic": 43116, + "JV": 43117, + "Ġatmosp": 43118, + "equality": 43119, + "Ġairst": 43120, + "Ġincarn": 43121, + "ynthesis": 43122, + "Ġequations": 43123, + "tch": 43124, + "ĠHUGE": 43125, + "ĠChanged": 43126, + "itatively": 43127, + "Job": 43128, + "gaming": 43129, + "Ġ1899": 43130, + "ĠMorsi": 43131, + "Ġconjecture": 43132, + "riad": 43133, + "Ġprimates": 43134, + "ĠArtemis": 43135, + "ĠThro": 43136, + "Ġbiologically": 43137, + "Church": 43138, + "topia": 43139, + "recomm": 43140, + "Ġgradient": 43141, + "Ġful": 43142, + "Ġbastard": 43143, + "CHO": 43144, + "IUM": 43145, + "sleep": 43146, + "Construction": 43147, + "raints": 43148, + "vable": 43149, + "ionage": 43150, + "Ġcomrade": 43151, + "Ġpopulate": 43152, + "Ġnerds": 43153, + "ĠXie": 43154, + "result": 43155, + "ĠImper": 43156, + "Ġpamphlet": 43157, + "Ku": 43158, + "Ġbackend": 43159, + "ificent": 43160, + "etus": 43161, + "Ġdisson": 43162, + "config": 43163, + "Ġsuc": 43164, + "Ġwavelength": 43165, + "external": 43166, + "owder": 43167, + "Ġpredis": 43168, + "eenth": 43169, + "Det": 43170, + "andem": 43171, + "Ġ1865": 43172, + "ĠDefeat": 43173, + "Individual": 43174, + "Ġretrieving": 43175, + "stories": 43176, + "Ġdesolate": 43177, + "Ġlett": 43178, + "Ġunpublished": 43179, + "Ġpassively": 43180, + "Ġdissertation": 43181, + "raits": 43182, + "abee": 43183, + "ĠResist": 43184, + "Robin": 43185, + "Ġbenevolent": 43186, + "blast": 43187, + "Offic": 43188, + "snap": 43189, + "vernment": 43190, + "Ġextermin": 43191, + "wt": 43192, + "bitious": 43193, + "hibited": 43194, + "Insp": 43195, + "posted": 43196, + "ĠYugoslav": 43197, + "rational": 43198, + "adapt": 43199, + "ĠAtari": 43200, + "Ġplugin": 43201, + "oglobin": 43202, + "efeated": 43203, + "ĠHRC": 43204, + "cko": 43205, + "ilver": 43206, + "ĠDestruction": 43207, + "gewater": 43208, + "ĠRadiation": 43209, + "Ġimprison": 43210, + "origin": 43211, + "antine": 43212, + "ĠPublication": 43213, + "Ġhealer": 43214, + "istered": 43215, + "ĠTHEIR": 43216, + "hazard": 43217, + "Contract": 43218, + "Ġmediated": 43219, + "Ġindexed": 43220, + "ĠSYSTEM": 43221, + "Labor": 43222, + "Blade": 43223, + "Ġyog": 43224, + "Champ": 43225, + "Gordon": 43226, + "IAS": 43227, + "Ġnineteenth": 43228, + "animous": 43229, + "begin": 43230, + "ĠHolo": 43231, + "Planet": 43232, + "udding": 43233, + "default": 43234, + "ĠOMG": 43235, + "Ġwond": 43236, + "wm": 43237, + "pend": 43238, + "Extreme": 43239, + "Ġinterstellar": 43240, + "ASED": 43241, + "ĠBerks": 43242, + "Ġprimal": 43243, + "Foot": 43244, + "Ġinadvert": 43245, + "amboo": 43246, + "ĠLeica": 43247, + "Events": 43248, + "ĠPigs": 43249, + "RAFT": 43250, + "ï": 43251, + "ĠGentleman": 43252, + "Multiple": 43253, + "ĠPsychiatric": 43254, + "Ġdespise": 43255, + "ĠZionism": 43256, + "ĠSSL": 43257, + "shit": 43258, + "Ġthreaded": 43259, + "Ġartifact": 43260, + "Ġmitochondrial": 43261, + "ĠLayer": 43262, + "inus": 43263, + "podcast": 43264, + "Ġawaken": 43265, + "Management": 43266, + "Ġdelusions": 43267, + "grey": 43268, + "Ġpseud": 43269, + "agonal": 43270, + "ĠHirosh": 43271, + "Georg": 43272, + "Dragon": 43273, + "Stack": 43274, + "ohm": 43275, + "Ġvener": 43276, + "Row": 43277, + "Ġsandbox": 43278, + "Ġblinding": 43279, + "razen": 43280, + "Ġ389": 43281, + "Ġcrappy": 43282, + "Ġlith": 43283, + "antha": 43284, + "Ġplurality": 43285, + "ĠDAC": 43286, + "inently": 43287, + "intage": 43288, + "Ġ1902": 43289, + "ĠDepend": 43290, + "Ġelapsed": 43291, + "==": 43292, + "ĠGenie": 43293, + "Bush": 43294, + "ĠPlanetary": 43295, + "Bah": 43296, + "ĠKira": 43297, + "emn": 43298, + "Month": 43299, + "allic": 43300, + "coded": 43301, + 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os.path.dirname(__file__) +CMU_DICT_PATH = os.path.join(current_file_path, "cmudict.rep") +CACHE_PATH = os.path.join(current_file_path, "cmudict_cache.pickle") +_g2p = G2p() +LOCAL_PATH = "./bert/deberta-v3-large" +tokenizer = DebertaV2Tokenizer.from_pretrained(LOCAL_PATH) + +arpa = { + "AH0", + "S", + "AH1", + "EY2", + "AE2", + "EH0", + "OW2", + "UH0", + "NG", + "B", + "G", + "AY0", + "M", + "AA0", + "F", + "AO0", + "ER2", + "UH1", + "IY1", + "AH2", + "DH", + "IY0", + "EY1", + "IH0", + "K", + "N", + "W", + "IY2", + "T", + "AA1", + "ER1", + "EH2", + "OY0", + "UH2", + "UW1", + "Z", + "AW2", + "AW1", + "V", + "UW2", + "AA2", + "ER", + "AW0", + "UW0", + "R", + "OW1", + "EH1", + "ZH", + "AE0", + "IH2", + "IH", + "Y", + "JH", + "P", + "AY1", + "EY0", + "OY2", + "TH", + "HH", + "D", + "ER0", + "CH", + "AO1", + "AE1", + "AO2", + "OY1", + "AY2", + "IH1", + "OW0", + "L", + "SH", +} + + +def post_replace_ph(ph): + rep_map = { + "：": ",", + "；": ",", + "，": ",", + "。": ".", + "！": "!", + "？": "?", + "\n": ".", + "·": ",", + "、": ",", + "…": "...", + "···": "...", + "・・・": "...", + "v": "V", + } + if ph in rep_map.keys(): + ph = rep_map[ph] + if ph in symbols: + return ph + if ph not in symbols: + ph = "UNK" + return ph + + +rep_map = { + "：": ",", + "；": ",", + "，": ",", + "。": ".", + "！": "!", + "？": "?", + "\n": ".", + "．": ".", + "…": "...", + "···": "...", + "・・・": "...", + "·": ",", + "・": ",", + "、": ",", + "$": ".", + "“": "'", + "”": "'", + '"': "'", + "‘": "'", + "’": "'", + "（": "'", + "）": "'", + "(": "'", + ")": "'", + "《": "'", + "》": "'", + "【": "'", + "】": "'", + "[": "'", + "]": "'", + "—": "-", + "−": "-", + "～": "-", + "~": "-", + "「": "'", + "」": "'", +} + + +def replace_punctuation(text): + pattern = re.compile("|".join(re.escape(p) for p in rep_map.keys())) + + replaced_text = pattern.sub(lambda x: rep_map[x.group()], text) + + # replaced_text = re.sub( + # r"[^\u3040-\u309F\u30A0-\u30FF\u4E00-\u9FFF\u3400-\u4DBF\u3005" + # + "".join(punctuation) + # + r"]+", + # "", + # replaced_text, + # ) + + return replaced_text + + +def read_dict(): + g2p_dict = {} + start_line = 49 + with open(CMU_DICT_PATH) as f: + line = f.readline() + line_index = 1 + while line: + if line_index >= start_line: + line = line.strip() + word_split = line.split(" ") + word = word_split[0] + + syllable_split = word_split[1].split(" - ") + g2p_dict[word] = [] + for syllable in syllable_split: + phone_split = syllable.split(" ") + g2p_dict[word].append(phone_split) + + line_index = line_index + 1 + line = f.readline() + + return g2p_dict + + +def cache_dict(g2p_dict, file_path): + with open(file_path, "wb") as pickle_file: + pickle.dump(g2p_dict, pickle_file) + + +def get_dict(): + if os.path.exists(CACHE_PATH): + with open(CACHE_PATH, "rb") as pickle_file: + g2p_dict = pickle.load(pickle_file) + else: + g2p_dict = read_dict() + cache_dict(g2p_dict, CACHE_PATH) + + return g2p_dict + + +eng_dict = get_dict() + + +def refine_ph(phn): + tone = 0 + if re.search(r"\d$", phn): + tone = int(phn[-1]) + 1 + phn = phn[:-1] + return phn.lower(), tone + + +def refine_syllables(syllables): + tones = [] + phonemes = [] + for phn_list in syllables: + for i in range(len(phn_list)): + phn = phn_list[i] + phn, tone = refine_ph(phn) + phonemes.append(phn) + tones.append(tone) + return phonemes, tones + + +import re +import inflect + +_inflect = inflect.engine() +_comma_number_re = re.compile(r"([0-9][0-9\,]+[0-9])") +_decimal_number_re = re.compile(r"([0-9]+\.[0-9]+)") +_pounds_re = re.compile(r"£([0-9\,]*[0-9]+)") +_dollars_re = re.compile(r"\$([0-9\.\,]*[0-9]+)") +_ordinal_re = re.compile(r"[0-9]+(st|nd|rd|th)") +_number_re = re.compile(r"[0-9]+") + +# List of (regular expression, replacement) pairs for abbreviations: +_abbreviations = [ + (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1]) + for x in [ + ("mrs", "misess"), + ("mr", "mister"), + ("dr", "doctor"), + ("st", "saint"), + ("co", "company"), + ("jr", "junior"), + ("maj", "major"), + ("gen", "general"), + ("drs", "doctors"), + ("rev", "reverend"), + ("lt", "lieutenant"), + ("hon", "honorable"), + ("sgt", "sergeant"), + ("capt", "captain"), + ("esq", "esquire"), + ("ltd", "limited"), + ("col", "colonel"), + ("ft", "fort"), + ] +] + + +# List of (ipa, lazy ipa) pairs: +_lazy_ipa = [ + (re.compile("%s" % x[0]), x[1]) + for x in [ + ("r", "ɹ"), + ("æ", "e"), + ("ɑ", "a"), + ("ɔ", "o"), + ("ð", "z"), + ("θ", "s"), + ("ɛ", "e"), + ("ɪ", "i"), + ("ʊ", "u"), + ("ʒ", "ʥ"), + ("ʤ", "ʥ"), + ("ˈ", "↓"), + ] +] + +# List of (ipa, lazy ipa2) pairs: +_lazy_ipa2 = [ + (re.compile("%s" % x[0]), x[1]) + for x in [ + ("r", "ɹ"), + ("ð", "z"), + ("θ", "s"), + ("ʒ", "ʑ"), + ("ʤ", "dʑ"), + ("ˈ", "↓"), + ] +] + +# List of (ipa, ipa2) pairs +_ipa_to_ipa2 = [ + (re.compile("%s" % x[0]), x[1]) for x in [("r", "ɹ"), ("ʤ", "dʒ"), ("ʧ", "tʃ")] +] + + +def _expand_dollars(m): + match = m.group(1) + parts = match.split(".") + if len(parts) > 2: + return match + " dollars" # Unexpected format + dollars = int(parts[0]) if parts[0] else 0 + cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0 + if dollars and cents: + dollar_unit = "dollar" if dollars == 1 else "dollars" + cent_unit = "cent" if cents == 1 else "cents" + return "%s %s, %s %s" % (dollars, dollar_unit, cents, cent_unit) + elif dollars: + dollar_unit = "dollar" if dollars == 1 else "dollars" + return "%s %s" % (dollars, dollar_unit) + elif cents: + cent_unit = "cent" if cents == 1 else "cents" + return "%s %s" % (cents, cent_unit) + else: + return "zero dollars" + + +def _remove_commas(m): + return m.group(1).replace(",", "") + + +def _expand_ordinal(m): + return _inflect.number_to_words(m.group(0)) + + +def _expand_number(m): + num = int(m.group(0)) + if num > 1000 and num < 3000: + if num == 2000: + return "two thousand" + elif num > 2000 and num < 2010: + return "two thousand " + _inflect.number_to_words(num % 100) + elif num % 100 == 0: + return _inflect.number_to_words(num // 100) + " hundred" + else: + return _inflect.number_to_words( + num, andword="", zero="oh", group=2 + ).replace(", ", " ") + else: + return _inflect.number_to_words(num, andword="") + + +def _expand_decimal_point(m): + return m.group(1).replace(".", " point ") + + +def normalize_numbers(text): + text = re.sub(_comma_number_re, _remove_commas, text) + text = re.sub(_pounds_re, r"\1 pounds", text) + text = re.sub(_dollars_re, _expand_dollars, text) + text = re.sub(_decimal_number_re, _expand_decimal_point, text) + text = re.sub(_ordinal_re, _expand_ordinal, text) + text = re.sub(_number_re, _expand_number, text) + return text + + +def text_normalize(text): + text = normalize_numbers(text) + text = replace_punctuation(text) + text = re.sub(r"([,;.\?\!])([\w])", r"\1 \2", text) + return text + + +def distribute_phone(n_phone, n_word): + phones_per_word = [0] * n_word + for task in range(n_phone): + min_tasks = min(phones_per_word) + min_index = phones_per_word.index(min_tasks) + phones_per_word[min_index] += 1 + return phones_per_word + + +def sep_text(text): + words = re.split(r"([,;.\?\!\s+])", text) + words = [word for word in words if word.strip() != ""] + return words + + +def g2p(text): + phones = [] + tones = [] + # word2ph = [] + words = sep_text(text) + tokens = [tokenizer.tokenize(i) for i in words] + for word in words: + if word.upper() in eng_dict: + phns, tns = refine_syllables(eng_dict[word.upper()]) + phones.append([post_replace_ph(i) for i in phns]) + tones.append(tns) + # word2ph.append(len(phns)) + else: + phone_list = list(filter(lambda p: p != " ", _g2p(word))) + phns = [] + tns = [] + for ph in phone_list: + if ph in arpa: + ph, tn = refine_ph(ph) + phns.append(ph) + tns.append(tn) + else: + phns.append(ph) + tns.append(0) + phones.append([post_replace_ph(i) for i in phns]) + tones.append(tns) + # word2ph.append(len(phns)) + # phones = [post_replace_ph(i) for i in phones] + + word2ph = [] + for token, phoneme in zip(tokens, phones): + phone_len = len(phoneme) + word_len = len(token) + + aaa = distribute_phone(phone_len, word_len) + word2ph += aaa + + phones = ["_"] + [j for i in phones for j in i] + ["_"] + tones = [0] + [j for i in tones for j in i] + [0] + word2ph = [1] + word2ph + [1] + assert len(phones) == len(tones), text + assert len(phones) == sum(word2ph), text + + return phones, tones, word2ph + + +def get_bert_feature(text, word2ph): + from text import english_bert_mock + + return english_bert_mock.get_bert_feature(text, word2ph) + + +if __name__ == "__main__": + # print(get_dict()) + # print(eng_word_to_phoneme("hello")) + print(g2p("In this paper, we propose 1 DSPGAN, a GAN-based universal vocoder.")) + # all_phones = set() + # for k, syllables in eng_dict.items(): + # for group in syllables: + # for ph in group: + # all_phones.add(ph) + # print(all_phones) diff --git a/oldVersion/V210/text/english_bert_mock.py b/oldVersion/V210/text/english_bert_mock.py new file mode 100644 index 0000000..85b241c --- /dev/null +++ b/oldVersion/V210/text/english_bert_mock.py @@ -0,0 +1,42 @@ +import sys + +import torch +from transformers import DebertaV2Model, DebertaV2Tokenizer + +from config import config + + +LOCAL_PATH = "./bert/deberta-v3-large" + +tokenizer = DebertaV2Tokenizer.from_pretrained(LOCAL_PATH) + +models = dict() + + +def get_bert_feature(text, word2ph, device=config.bert_gen_config.device): + if ( + sys.platform == "darwin" + and torch.backends.mps.is_available() + and device == "cpu" + ): + device = "mps" + if not device: + device = "cuda" + if device not in models.keys(): + models[device] = DebertaV2Model.from_pretrained(LOCAL_PATH).to(device) + with torch.no_grad(): + inputs = tokenizer(text, return_tensors="pt") + for i in inputs: + inputs[i] = inputs[i].to(device) + res = models[device](**inputs, output_hidden_states=True) + res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu() + assert len(word2ph) == res.shape[0], (text, res.shape[0], len(word2ph)) + word2phone = word2ph + phone_level_feature = [] + for i in range(len(word2phone)): + repeat_feature = res[i].repeat(word2phone[i], 1) + phone_level_feature.append(repeat_feature) + + phone_level_feature = torch.cat(phone_level_feature, dim=0) + + return phone_level_feature.T diff --git a/oldVersion/V210/text/japanese.py b/oldVersion/V210/text/japanese.py new file mode 100644 index 0000000..5f6eac5 --- /dev/null +++ b/oldVersion/V210/text/japanese.py @@ -0,0 +1,432 @@ +# Convert Japanese text to phonemes which is +# compatible with Julius https://github.com/julius-speech/segmentation-kit +import re +import unicodedata + +from transformers import AutoTokenizer + +from . import punctuation, symbols + +from num2words import num2words + +import pyopenjtalk +import jaconv + + +def kata2phoneme(text: str) -> str: + """Convert katakana text to phonemes.""" + text = text.strip() + if text == "ー": + return ["ー"] + elif text.startswith("ー"): + return ["ー"] + kata2phoneme(text[1:]) + res = [] + prev = None + while text: + if re.match(_MARKS, text): + res.append(text) + text = text[1:] + continue + if text.startswith("ー"): + if prev: + res.append(prev[-1]) + text = text[1:] + continue + res += pyopenjtalk.g2p(text).lower().replace("cl", "q").split(" ") + break + # res = _COLON_RX.sub(":", res) + return res + + +def hira2kata(text: str) -> str: + return jaconv.hira2kata(text) + + +_SYMBOL_TOKENS = set(list("・、。？！")) +_NO_YOMI_TOKENS = set(list("「」『』―（）［］[]")) +_MARKS = re.compile( + r"[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]" +) + + +def text2kata(text: str) -> str: + parsed = pyopenjtalk.run_frontend(text) + + res = [] + for parts in parsed: + word, yomi = replace_punctuation(parts["string"]), parts["pron"].replace( + "’", "" + ) + if yomi: + if re.match(_MARKS, yomi): + if len(word) > 1: + word = [replace_punctuation(i) for i in list(word)] + yomi = word + res += yomi + sep += word + continue + elif word not in rep_map.keys() and word not in rep_map.values(): + word = "," + yomi = word + res.append(yomi) + else: + if word in _SYMBOL_TOKENS: + res.append(word) + elif word in ("っ", "ッ"): + res.append("ッ") + elif word in _NO_YOMI_TOKENS: + pass + else: + res.append(word) + return hira2kata("".join(res)) + + +def text2sep_kata(text: str) -> (list, list): + parsed = pyopenjtalk.run_frontend(text) + + res = [] + sep = [] + for parts in parsed: + word, yomi = replace_punctuation(parts["string"]), parts["pron"].replace( + "’", "" + ) + if yomi: + if re.match(_MARKS, yomi): + if len(word) > 1: + word = [replace_punctuation(i) for i in list(word)] + yomi = word + res += yomi + sep += word + continue + elif word not in rep_map.keys() and word not in rep_map.values(): + word = "," + yomi = word + res.append(yomi) + else: + if word in _SYMBOL_TOKENS: + res.append(word) + elif word in ("っ", "ッ"): + res.append("ッ") + elif word in _NO_YOMI_TOKENS: + pass + else: + res.append(word) + sep.append(word) + return sep, [hira2kata(i) for i in res], get_accent(parsed) + + +def get_accent(parsed): + labels = pyopenjtalk.make_label(parsed) + + phonemes = [] + accents = [] + for n, label in enumerate(labels): + phoneme = re.search(r"\-([^\+]*)\+", label).group(1) + if phoneme not in ["sil", "pau"]: + phonemes.append(phoneme.replace("cl", "q").lower()) + else: + continue + a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1)) + a2 = int(re.search(r"\+(\d+)\+", label).group(1)) + if re.search(r"\-([^\+]*)\+", labels[n + 1]).group(1) in ["sil", "pau"]: + a2_next = -1 + else: + a2_next = int(re.search(r"\+(\d+)\+", labels[n + 1]).group(1)) + # Falling + if a1 == 0 and a2_next == a2 + 1: + accents.append(-1) + # Rising + elif a2 == 1 and a2_next == 2: + accents.append(1) + else: + accents.append(0) + return list(zip(phonemes, accents)) + + +_ALPHASYMBOL_YOMI = { + "#": "シャープ", + "%": "パーセント", + "&": "アンド", + "+": "プラス", + "-": "マイナス", + ":": "コロン", + ";": "セミコロン", + "<": "小なり", + "=": "イコール", + ">": "大なり", + "@": "アット", + "a": "エー", + "b": "ビー", + "c": "シー", + "d": "ディー", + "e": "イー", + "f": "エフ", + "g": "ジー", + "h": "エイチ", + "i": "アイ", + "j": "ジェー", + "k": "ケー", + "l": "エル", + "m": "エム", + "n": "エヌ", + "o": "オー", + "p": "ピー", + "q": "キュー", + "r": "アール", + "s": "エス", + "t": "ティー", + "u": "ユー", + "v": "ブイ", + "w": "ダブリュー", + "x": "エックス", + "y": "ワイ", + "z": "ゼット", + "α": "アルファ", + "β": "ベータ", + "γ": "ガンマ", + "δ": "デルタ", + "ε": "イプシロン", + "ζ": "ゼータ", + "η": "イータ", + "θ": "シータ", + "ι": "イオタ", + "κ": "カッパ", + "λ": "ラムダ", + "μ": "ミュー", + "ν": "ニュー", + "ξ": "クサイ", + "ο": "オミクロン", + "π": "パイ", + "ρ": "ロー", + "σ": "シグマ", + "τ": "タウ", + "υ": "ウプシロン", + "φ": "ファイ", + "χ": "カイ", + "ψ": "プサイ", + "ω": "オメガ", +} + + +_NUMBER_WITH_SEPARATOR_RX = re.compile("[0-9]{1,3}(,[0-9]{3})+") +_CURRENCY_MAP = {"$": "ドル", "¥": "円", "£": "ポンド", "€": "ユーロ"} +_CURRENCY_RX = re.compile(r"([$¥£€])([0-9.]*[0-9])") +_NUMBER_RX = re.compile(r"[0-9]+(\.[0-9]+)?") + + +def japanese_convert_numbers_to_words(text: str) -> str: + res = _NUMBER_WITH_SEPARATOR_RX.sub(lambda m: m[0].replace(",", ""), text) + res = _CURRENCY_RX.sub(lambda m: m[2] + _CURRENCY_MAP.get(m[1], m[1]), res) + res = _NUMBER_RX.sub(lambda m: num2words(m[0], lang="ja"), res) + return res + + +def japanese_convert_alpha_symbols_to_words(text: str) -> str: + return "".join([_ALPHASYMBOL_YOMI.get(ch, ch) for ch in text.lower()]) + + +def japanese_text_to_phonemes(text: str) -> str: + """Convert Japanese text to phonemes.""" + res = unicodedata.normalize("NFKC", text) + res = japanese_convert_numbers_to_words(res) + # res = japanese_convert_alpha_symbols_to_words(res) + res = text2kata(res) + res = kata2phoneme(res) + return res + + +def is_japanese_character(char): + # 定义日语文字系统的 Unicode 范围 + japanese_ranges = [ + (0x3040, 0x309F), # 平假名 + (0x30A0, 0x30FF), # 片假名 + (0x4E00, 0x9FFF), # 汉字 (CJK Unified Ideographs) + (0x3400, 0x4DBF), # 汉字扩展 A + (0x20000, 0x2A6DF), # 汉字扩展 B + # 可以根据需要添加其他汉字扩展范围 + ] + + # 将字符的 Unicode 编码转换为整数 + char_code = ord(char) + + # 检查字符是否在任何一个日语范围内 + for start, end in japanese_ranges: + if start <= char_code <= end: + return True + + return False + + +rep_map = { + "：": ",", + "；": ",", + "，": ",", + "。": ".", + "！": "!", + "？": "?", + "\n": ".", + "．": ".", + "…": "...", + "···": "...", + "・・・": "...", + "·": ",", + "・": ",", + "、": ",", + "$": ".", + "“": "'", + "”": "'", + '"': "'", + "‘": "'", + "’": "'", + "（": "'", + "）": "'", + "(": "'", + ")": "'", + "《": "'", + "》": "'", + "【": "'", + "】": "'", + "[": "'", + "]": "'", + "—": "-", + "−": "-", + "～": "-", + "~": "-", + "「": "'", + "」": "'", +} + + +def replace_punctuation(text): + pattern = re.compile("|".join(re.escape(p) for p in rep_map.keys())) + + replaced_text = pattern.sub(lambda x: rep_map[x.group()], text) + + replaced_text = re.sub( + r"[^\u3040-\u309F\u30A0-\u30FF\u4E00-\u9FFF\u3400-\u4DBF\u3005" + + "".join(punctuation) + + r"]+", + "", + replaced_text, + ) + + return replaced_text + + +def text_normalize(text): + res = unicodedata.normalize("NFKC", text) + res = japanese_convert_numbers_to_words(res) + # res = "".join([i for i in res if is_japanese_character(i)]) + res = replace_punctuation(res) + res = res.replace("゙", "") + return res + + +def distribute_phone(n_phone, n_word): + phones_per_word = [0] * n_word + for task in range(n_phone): + min_tasks = min(phones_per_word) + min_index = phones_per_word.index(min_tasks) + phones_per_word[min_index] += 1 + return phones_per_word + + +def handle_long(sep_phonemes): + for i in range(len(sep_phonemes)): + if sep_phonemes[i][0] == "ー": + sep_phonemes[i][0] = sep_phonemes[i - 1][-1] + if "ー" in sep_phonemes[i]: + for j in range(len(sep_phonemes[i])): + if sep_phonemes[i][j] == "ー": + sep_phonemes[i][j] = sep_phonemes[i][j - 1][-1] + return sep_phonemes + + +tokenizer = AutoTokenizer.from_pretrained("./bert/deberta-v2-large-japanese-char-wwm") + + +def align_tones(phones, tones): + res = [] + for pho in phones: + temp = [0] * len(pho) + for idx, p in enumerate(pho): + if len(tones) == 0: + break + if p == tones[0][0]: + temp[idx] = tones[0][1] + if idx > 0: + temp[idx] += temp[idx - 1] + tones.pop(0) + temp = [0] + temp + temp = temp[:-1] + if -1 in temp: + temp = [i + 1 for i in temp] + res.append(temp) + res = [i for j in res for i in j] + assert not any([i < 0 for i in res]) and not any([i > 1 for i in res]) + return res + + +def rearrange_tones(tones, phones): + res = [0] * len(tones) + for i in range(len(tones)): + if i == 0: + if tones[i] not in punctuation: + res[i] = 1 + elif tones[i] == prev: + if phones[i] in punctuation: + res[i] = 0 + else: + res[i] = 1 + elif tones[i] > prev: + res[i] = 2 + elif tones[i] < prev: + res[i - 1] = 3 + res[i] = 1 + prev = tones[i] + return res + + +def g2p(norm_text): + sep_text, sep_kata, acc = text2sep_kata(norm_text) + sep_tokenized = [] + for i in sep_text: + if i not in punctuation: + sep_tokenized.append(tokenizer.tokenize(i)) + else: + sep_tokenized.append([i]) + + sep_phonemes = handle_long([kata2phoneme(i) for i in sep_kata]) + # 异常处理，MeCab不认识的词的话会一路传到这里来，然后炸掉。目前来看只有那些超级稀有的生僻词会出现这种情况 + for i in sep_phonemes: + for j in i: + assert j in symbols, (sep_text, sep_kata, sep_phonemes) + tones = align_tones(sep_phonemes, acc) + + word2ph = [] + for token, phoneme in zip(sep_tokenized, sep_phonemes): + phone_len = len(phoneme) + word_len = len(token) + + aaa = distribute_phone(phone_len, word_len) + word2ph += aaa + phones = ["_"] + [j for i in sep_phonemes for j in i] + ["_"] + # tones = [0] + rearrange_tones(tones, phones[1:-1]) + [0] + tones = [0] + tones + [0] + word2ph = [1] + word2ph + [1] + assert len(phones) == len(tones) + return phones, tones, word2ph + + +if __name__ == "__main__": + tokenizer = AutoTokenizer.from_pretrained("./bert/deberta-v2-large-japanese") + text = "hello,こんにちは、世界ー！……" + from text.japanese_bert import get_bert_feature + + text = text_normalize(text) + print(text) + + phones, tones, word2ph = g2p(text) + bert = get_bert_feature(text, word2ph) + + print(phones, tones, word2ph, bert.shape) diff --git a/oldVersion/V210/text/japanese_bert.py b/oldVersion/V210/text/japanese_bert.py new file mode 100644 index 0000000..7dbe284 --- /dev/null +++ b/oldVersion/V210/text/japanese_bert.py @@ -0,0 +1,44 @@ +import sys + +import torch +from transformers import AutoModelForMaskedLM, AutoTokenizer + +from config import config +from .japanese import text2sep_kata + +LOCAL_PATH = "./bert/deberta-v2-large-japanese-char-wwm" + +tokenizer = AutoTokenizer.from_pretrained(LOCAL_PATH) + +models = dict() + + +def get_bert_feature(text, word2ph, device=config.bert_gen_config.device): + text = "".join(text2sep_kata(text)[0]) + if ( + sys.platform == "darwin" + and torch.backends.mps.is_available() + and device == "cpu" + ): + device = "mps" + if not device: + device = "cuda" + if device not in models.keys(): + models[device] = AutoModelForMaskedLM.from_pretrained(LOCAL_PATH).to(device) + with torch.no_grad(): + inputs = tokenizer(text, return_tensors="pt") + for i in inputs: + inputs[i] = inputs[i].to(device) + res = models[device](**inputs, output_hidden_states=True) + res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu() + + assert len(word2ph) == len(text) + 2 + word2phone = word2ph + phone_level_feature = [] + for i in range(len(word2phone)): + repeat_feature = res[i].repeat(word2phone[i], 1) + phone_level_feature.append(repeat_feature) + + phone_level_feature = torch.cat(phone_level_feature, dim=0) + + return phone_level_feature.T diff --git a/oldVersion/V210/text/opencpop-strict.txt b/oldVersion/V210/text/opencpop-strict.txt new file mode 100644 index 0000000..1bede89 --- /dev/null +++ b/oldVersion/V210/text/opencpop-strict.txt @@ -0,0 +1,429 @@ +a AA a +ai AA ai +an AA an +ang AA ang +ao AA ao +ba b a +bai b ai +ban b an +bang b ang +bao b ao +bei b ei +ben b en +beng b eng +bi b i +bian b ian +biao b iao +bie b ie +bin b in +bing b ing +bo b o +bu b u +ca c a +cai c ai +can c an +cang c ang +cao c ao +ce c e +cei c ei +cen c en +ceng c eng +cha ch a +chai ch ai +chan ch an +chang ch ang +chao ch ao +che ch e +chen ch en +cheng ch eng +chi ch ir +chong ch ong +chou ch ou +chu ch u +chua ch ua +chuai ch uai +chuan ch uan +chuang ch uang +chui ch ui +chun ch un +chuo ch uo +ci c i0 +cong c ong +cou c ou +cu c u +cuan c uan +cui c ui +cun c un +cuo c uo +da d a +dai d ai +dan d an +dang d ang +dao d ao +de d e +dei d ei +den d en +deng d eng +di d i +dia d ia +dian d ian +diao d iao +die d ie +ding d ing +diu d iu +dong d ong +dou d ou +du d u +duan d uan +dui d ui +dun d un +duo d uo +e EE e +ei EE ei +en EE en +eng EE eng +er EE er +fa f a +fan f an +fang f ang +fei f ei +fen f en +feng f eng +fo f o +fou f ou +fu f u +ga g a +gai g ai +gan g an +gang g ang +gao g ao +ge g e +gei g ei +gen g en +geng g eng +gong g ong +gou g ou +gu g u +gua g ua +guai g uai +guan g uan +guang g uang +gui g ui +gun g un +guo g uo +ha h a +hai h ai +han h an +hang h ang +hao h ao +he h e +hei h ei +hen h en +heng h eng +hong h ong +hou h ou +hu h u +hua h ua +huai h uai +huan h uan +huang h uang +hui h ui +hun h un +huo h uo +ji j i +jia j ia +jian j ian +jiang j iang +jiao j iao +jie j ie +jin j in +jing j ing +jiong j iong +jiu j iu +ju j v +jv j v +juan j van +jvan j van +jue j ve +jve j ve +jun j vn +jvn j vn +ka k a +kai k ai +kan k an +kang k ang +kao k ao +ke k e +kei k ei +ken k en +keng k eng +kong k ong +kou k ou +ku k u +kua k ua +kuai k uai +kuan k uan +kuang k uang +kui k ui +kun k un +kuo k uo +la l a +lai l ai +lan l an +lang l ang +lao l ao +le l e +lei l ei +leng l eng +li l i +lia l ia +lian l ian +liang l iang +liao l iao +lie l ie +lin l in +ling l ing +liu l iu +lo l o +long l ong +lou l ou +lu l u +luan l uan +lun l un +luo l uo +lv l v +lve l ve +ma m a +mai m ai +man m an +mang m ang +mao m ao +me m e +mei m ei +men m en +meng m eng +mi m i +mian m ian +miao m iao +mie m ie +min m in +ming m ing +miu m iu +mo m o +mou m ou +mu m u +na n a +nai n ai +nan n an +nang n ang +nao n ao +ne n e +nei n ei +nen n en +neng n eng +ni n i +nian n ian +niang n iang +niao n iao +nie n ie +nin n in +ning n ing +niu n iu +nong n ong +nou n ou +nu n u +nuan n uan +nun n un +nuo n uo +nv n v +nve n ve +o OO o +ou OO ou +pa p a +pai p ai +pan p an +pang p ang +pao p ao +pei p ei +pen p en +peng p eng +pi p i +pian p ian +piao p iao +pie p ie +pin p in +ping p ing +po p o +pou p ou +pu p u +qi q i +qia q ia +qian q ian +qiang q iang +qiao q iao +qie q ie +qin q in +qing q ing +qiong q iong +qiu q iu +qu q v +qv q v +quan q van +qvan q van +que q ve +qve q ve +qun q vn +qvn q vn +ran r an +rang r ang +rao r ao +re r e +ren r en +reng r eng +ri r ir +rong r ong +rou r ou +ru r u +rua r ua +ruan r uan +rui r ui +run r un +ruo r uo +sa s a +sai s ai +san s an +sang s ang +sao s ao +se s e +sen s en +seng s eng +sha sh a +shai sh ai +shan sh an +shang sh ang +shao sh ao +she sh e +shei sh ei +shen sh en +sheng sh eng +shi sh ir +shou sh ou +shu sh u +shua sh ua +shuai sh uai +shuan sh uan +shuang sh uang +shui sh ui +shun sh un +shuo sh uo +si s i0 +song s ong +sou s ou +su s u +suan s uan +sui s ui +sun s un +suo s uo +ta t a +tai t ai +tan t an +tang t ang +tao t ao +te t e +tei t ei +teng t eng +ti t i +tian t ian +tiao t iao +tie t ie +ting t ing +tong t ong +tou t ou +tu t u +tuan t uan +tui t ui +tun t un +tuo t uo +wa w a +wai w ai +wan w an +wang w ang +wei w ei +wen w en +weng w eng +wo w o +wu w u +xi x i +xia x ia +xian x ian +xiang x iang +xiao x iao +xie x ie +xin x in +xing x ing +xiong x iong +xiu x iu +xu x v +xv x v +xuan x van +xvan x van +xue x ve +xve x ve +xun x vn +xvn x vn +ya y a +yan y En +yang y ang +yao y ao +ye y E +yi y i +yin y in +ying y ing +yo y o +yong y ong +you y ou +yu y v +yv y v +yuan y van +yvan y van +yue y ve +yve y ve +yun y vn +yvn y vn +za z a +zai z ai +zan z an +zang z ang +zao z ao +ze z e +zei z ei +zen z en +zeng z eng +zha zh a +zhai zh ai +zhan zh an +zhang zh ang +zhao zh ao +zhe zh e +zhei zh ei +zhen zh en +zheng zh eng +zhi zh ir +zhong zh ong +zhou zh ou +zhu zh u +zhua zh ua +zhuai zh uai +zhuan zh uan +zhuang zh uang +zhui zh ui +zhun zh un +zhuo zh uo +zi z i0 +zong z ong +zou z ou +zu z u +zuan z uan +zui z ui +zun z un +zuo z uo diff --git a/oldVersion/V210/text/symbols.py b/oldVersion/V210/text/symbols.py new file mode 100644 index 0000000..846de64 --- /dev/null +++ b/oldVersion/V210/text/symbols.py @@ -0,0 +1,187 @@ +punctuation = ["!", "?", "…", ",", ".", "'", "-"] +pu_symbols = punctuation + ["SP", "UNK"] +pad = "_" + +# chinese +zh_symbols = [ + "E", + "En", + "a", + "ai", + "an", + "ang", + "ao", + "b", + "c", + "ch", + "d", + "e", + "ei", + "en", + "eng", + "er", + "f", + "g", + "h", + "i", + "i0", + "ia", + "ian", + "iang", + "iao", + "ie", + "in", + "ing", + "iong", + "ir", + "iu", + "j", + "k", + "l", + "m", + "n", + "o", + "ong", + "ou", + "p", + "q", + "r", + "s", + "sh", + "t", + "u", + "ua", + "uai", + "uan", + "uang", + "ui", + "un", + "uo", + "v", + "van", + "ve", + "vn", + "w", + "x", + "y", + "z", + "zh", + "AA", + "EE", + "OO", +] +num_zh_tones = 6 + +# japanese +ja_symbols = [ + "N", + "a", + "a:", + "b", + "by", + "ch", + "d", + "dy", + "e", + "e:", + "f", + "g", + "gy", + "h", + "hy", + "i", + "i:", + "j", + "k", + "ky", + "m", + "my", + "n", + "ny", + "o", + "o:", + "p", + "py", + "q", + "r", + "ry", + "s", + "sh", + "t", + "ts", + "ty", + "u", + "u:", + "w", + "y", + "z", + "zy", +] +num_ja_tones = 2 + +# English +en_symbols = [ + "aa", + "ae", + "ah", + "ao", + "aw", + "ay", + "b", + "ch", + "d", + "dh", + "eh", + "er", + "ey", + "f", + "g", + "hh", + "ih", + "iy", + "jh", + "k", + "l", + "m", + "n", + "ng", + "ow", + "oy", + "p", + "r", + "s", + "sh", + "t", + "th", + "uh", + "uw", + "V", + "w", + "y", + "z", + "zh", +] +num_en_tones = 4 + +# combine all symbols +normal_symbols = sorted(set(zh_symbols + ja_symbols + en_symbols)) +symbols = [pad] + normal_symbols + pu_symbols +sil_phonemes_ids = [symbols.index(i) for i in pu_symbols] + +# combine all tones +num_tones = num_zh_tones + num_ja_tones + num_en_tones + +# language maps +language_id_map = {"ZH": 0, "JP": 1, "EN": 2} +num_languages = len(language_id_map.keys()) + +language_tone_start_map = { + "ZH": 0, + "JP": num_zh_tones, + "EN": num_zh_tones + num_ja_tones, +} + +if __name__ == "__main__": + a = set(zh_symbols) + b = set(en_symbols) + print(sorted(a & b)) diff --git a/oldVersion/V210/text/tone_sandhi.py b/oldVersion/V210/text/tone_sandhi.py new file mode 100644 index 0000000..6a6e4c3 --- /dev/null +++ b/oldVersion/V210/text/tone_sandhi.py @@ -0,0 +1,769 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import List +from typing import Tuple + +import jieba +from pypinyin import lazy_pinyin +from pypinyin import Style + + +class ToneSandhi: + def __init__(self): + self.must_neural_tone_words = { + "麻烦", + "麻利", + "鸳鸯", + "高粱", + "骨头", + "骆驼", + "马虎", + "首饰", + "馒头", + "馄饨", + "风筝", + "难为", + "队伍", + "阔气", + "闺女", + "门道", + "锄头", + "铺盖", + "铃铛", + "铁匠", + "钥匙", + "里脊", + "里头", + "部分", + "那么", + "道士", + "造化", + "迷糊", + "连累", + "这么", + "这个", + "运气", + "过去", + "软和", + "转悠", + "踏实", + "跳蚤", + "跟头", + "趔趄", + "财主", + "豆腐", + "讲究", + "记性", + "记号", + "认识", + "规矩", + "见识", + "裁缝", + "补丁", + "衣裳", + "衣服", + "衙门", + "街坊", + "行李", + "行当", + "蛤蟆", + "蘑菇", + "薄荷", + "葫芦", + "葡萄", + "萝卜", + "荸荠", + "苗条", + "苗头", + "苍蝇", + "芝麻", + "舒服", + "舒坦", + "舌头", + "自在", + "膏药", + "脾气", + "脑袋", + "脊梁", + "能耐", + "胳膊", + "胭脂", + "胡萝", + "胡琴", + "胡同", + "聪明", + "耽误", + "耽搁", + "耷拉", + "耳朵", + "老爷", + "老实", + "老婆", + "老头", + "老太", + "翻腾", + "罗嗦", + "罐头", + "编辑", + "结实", + "红火", + "累赘", + "糨糊", + "糊涂", + "精神", + "粮食", + "簸箕", + "篱笆", + "算计", + "算盘", + "答应", + "笤帚", + "笑语", + "笑话", + "窟窿", + "窝囊", + "窗户", + "稳当", + "稀罕", + "称呼", + "秧歌", + "秀气", + "秀才", + "福气", + "祖宗", + "砚台", + "码头", + "石榴", + "石头", + "石匠", + "知识", + "眼睛", + "眯缝", + "眨巴", + "眉毛", + "相声", + "盘算", + "白净", + "痢疾", + "痛快", + "疟疾", + "疙瘩", + "疏忽", + "畜生", + "生意", + "甘蔗", + "琵琶", + "琢磨", + "琉璃", + "玻璃", + "玫瑰", + "玄乎", + "狐狸", + "状元", + "特务", + "牲口", + "牙碜", + "牌楼", + "爽快", + "爱人", + "热闹", + "烧饼", + "烟筒", + "烂糊", + "点心", + "炊帚", + "灯笼", + "火候", + "漂亮", + "滑溜", + "溜达", + "温和", + "清楚", + "消息", + "浪头", + "活泼", + "比方", + "正经", + "欺负", + "模糊", + "槟榔", + "棺材", + "棒槌", + "棉花", + "核桃", + "栅栏", + "柴火", + "架势", + "枕头", + "枇杷", + "机灵", + "本事", + "木头", + "木匠", + "朋友", + "月饼", + "月亮", + "暖和", + "明白", + "时候", + "新鲜", + "故事", + "收拾", + "收成", + "提防", + "挖苦", + "挑剔", + "指甲", + "指头", + "拾掇", + "拳头", + "拨弄", + "招牌", + "招呼", + "抬举", + "护士", + "折腾", + "扫帚", + "打量", + "打算", + "打点", + "打扮", + "打听", + "打发", + "扎实", + "扁担", + "戒指", + "懒得", + "意识", + "意思", + "情形", + "悟性", + "怪物", + "思量", + "怎么", + "念头", + "念叨", + "快活", + "忙活", + "志气", + "心思", + "得罪", + "张罗", + "弟兄", + "开通", + "应酬", + "庄稼", + "干事", + "帮手", + "帐篷", + "希罕", + "师父", + "师傅", + "巴结", + "巴掌", + "差事", + "工夫", + "岁数", + "屁股", + "尾巴", + "少爷", + "小气", + "小伙", + "将就", + "对头", + "对付", + "寡妇", + "家伙", + "客气", + "实在", + "官司", + "学问", + "学生", + "字号", + "嫁妆", + "媳妇", + "媒人", + "婆家", + "娘家", + "委屈", + "姑娘", + "姐夫", + "妯娌", + "妥当", + "妖精", + "奴才", + "女婿", + "头发", + "太阳", + "大爷", + "大方", + "大意", + "大夫", + "多少", + "多么", + "外甥", + "壮实", + "地道", + "地方", + "在乎", + "困难", + "嘴巴", + "嘱咐", + "嘟囔", + "嘀咕", + "喜欢", + "喇嘛", + "喇叭", + "商量", + "唾沫", + "哑巴", + "哈欠", + "哆嗦", + "咳嗽", + "和尚", + "告诉", + "告示", + "含糊", + "吓唬", + "后头", + "名字", + "名堂", + "合同", + "吆喝", + "叫唤", + "口袋", + "厚道", + "厉害", + "千斤", + "包袱", + "包涵", + "匀称", + "勤快", + "动静", + "动弹", + "功夫", + "力气", + "前头", + "刺猬", + "刺激", + "别扭", + "利落", + "利索", + "利害", + "分析", + "出息", + "凑合", + "凉快", + "冷战", + "冤枉", + "冒失", + "养活", + "关系", + "先生", + "兄弟", + "便宜", + "使唤", + "佩服", + "作坊", + "体面", + "位置", + "似的", + "伙计", + "休息", + "什么", + "人家", + "亲戚", + "亲家", + "交情", + "云彩", + "事情", + "买卖", + "主意", + "丫头", + "丧气", + "两口", + "东西", + "东家", + "世故", + "不由", + "不在", + "下水", + "下巴", + "上头", + "上司", + "丈夫", + "丈人", + "一辈", + "那个", + "菩萨", + "父亲", + "母亲", + "咕噜", + "邋遢", + "费用", + "冤家", + "甜头", + "介绍", + "荒唐", + "大人", + "泥鳅", + "幸福", + "熟悉", + "计划", + "扑腾", + "蜡烛", + "姥爷", + "照顾", + "喉咙", + "吉他", + "弄堂", + "蚂蚱", + "凤凰", + "拖沓", + "寒碜", + "糟蹋", + "倒腾", + "报复", + "逻辑", + "盘缠", + "喽啰", + "牢骚", + "咖喱", + "扫把", + "惦记", + } + self.must_not_neural_tone_words = { + "男子", + "女子", + "分子", + "原子", + "量子", + "莲子", + "石子", + "瓜子", + "电子", + "人人", + "虎虎", + } + self.punc = "：，；。？！“”‘’':,;.?!" + + # the meaning of jieba pos tag: https://blog.csdn.net/weixin_44174352/article/details/113731041 + # e.g. + # word: "家里" + # pos: "s" + # finals: ['ia1', 'i3'] + def _neural_sandhi(self, word: str, pos: str, finals: List[str]) -> List[str]: + # reduplication words for n. and v. e.g. 奶奶, 试试, 旺旺 + for j, item in enumerate(word): + if ( + j - 1 >= 0 + and item == word[j - 1] + and pos[0] in {"n", "v", "a"} + and word not in self.must_not_neural_tone_words + ): + finals[j] = finals[j][:-1] + "5" + ge_idx = word.find("个") + if len(word) >= 1 and word[-1] in "吧呢啊呐噻嘛吖嗨呐哦哒额滴哩哟喽啰耶喔诶": + finals[-1] = finals[-1][:-1] + "5" + elif len(word) >= 1 and word[-1] in "的地得": + finals[-1] = finals[-1][:-1] + "5" + # e.g. 走了, 看着, 去过 + # elif len(word) == 1 and word in "了着过" and pos in {"ul", "uz", "ug"}: + # finals[-1] = finals[-1][:-1] + "5" + elif ( + len(word) > 1 + and word[-1] in "们子" + and pos in {"r", "n"} + and word not in self.must_not_neural_tone_words + ): + finals[-1] = finals[-1][:-1] + "5" + # e.g. 桌上, 地下, 家里 + elif len(word) > 1 and word[-1] in "上下里" and pos in {"s", "l", "f"}: + finals[-1] = finals[-1][:-1] + "5" + # e.g. 上来, 下去 + elif len(word) > 1 and word[-1] in "来去" and word[-2] in "上下进出回过起开": + finals[-1] = finals[-1][:-1] + "5" + # 个做量词 + elif ( + ge_idx >= 1 + and (word[ge_idx - 1].isnumeric() or word[ge_idx - 1] in "几有两半多各整每做是") + ) or word == "个": + finals[ge_idx] = finals[ge_idx][:-1] + "5" + else: + if ( + word in self.must_neural_tone_words + or word[-2:] in self.must_neural_tone_words + ): + finals[-1] = finals[-1][:-1] + "5" + + word_list = self._split_word(word) + finals_list = [finals[: len(word_list[0])], finals[len(word_list[0]) :]] + for i, word in enumerate(word_list): + # conventional neural in Chinese + if ( + word in self.must_neural_tone_words + or word[-2:] in self.must_neural_tone_words + ): + finals_list[i][-1] = finals_list[i][-1][:-1] + "5" + finals = sum(finals_list, []) + return finals + + def _bu_sandhi(self, word: str, finals: List[str]) -> List[str]: + # e.g. 看不懂 + if len(word) == 3 and word[1] == "不": + finals[1] = finals[1][:-1] + "5" + else: + for i, char in enumerate(word): + # "不" before tone4 should be bu2, e.g. 不怕 + if char == "不" and i + 1 < len(word) and finals[i + 1][-1] == "4": + finals[i] = finals[i][:-1] + "2" + return finals + + def _yi_sandhi(self, word: str, finals: List[str]) -> List[str]: + # "一" in number sequences, e.g. 一零零, 二一零 + if word.find("一") != -1 and all( + [item.isnumeric() for item in word if item != "一"] + ): + return finals + # "一" between reduplication words should be yi5, e.g. 看一看 + elif len(word) == 3 and word[1] == "一" and word[0] == word[-1]: + finals[1] = finals[1][:-1] + "5" + # when "一" is ordinal word, it should be yi1 + elif word.startswith("第一"): + finals[1] = finals[1][:-1] + "1" + else: + for i, char in enumerate(word): + if char == "一" and i + 1 < len(word): + # "一" before tone4 should be yi2, e.g. 一段 + if finals[i + 1][-1] == "4": + finals[i] = finals[i][:-1] + "2" + # "一" before non-tone4 should be yi4, e.g. 一天 + else: + # "一" 后面如果是标点，还读一声 + if word[i + 1] not in self.punc: + finals[i] = finals[i][:-1] + "4" + return finals + + def _split_word(self, word: str) -> List[str]: + word_list = jieba.cut_for_search(word) + word_list = sorted(word_list, key=lambda i: len(i), reverse=False) + first_subword = word_list[0] + first_begin_idx = word.find(first_subword) + if first_begin_idx == 0: + second_subword = word[len(first_subword) :] + new_word_list = [first_subword, second_subword] + else: + second_subword = word[: -len(first_subword)] + new_word_list = [second_subword, first_subword] + return new_word_list + + def _three_sandhi(self, word: str, finals: List[str]) -> List[str]: + if len(word) == 2 and self._all_tone_three(finals): + finals[0] = finals[0][:-1] + "2" + elif len(word) == 3: + word_list = self._split_word(word) + if self._all_tone_three(finals): + # disyllabic + monosyllabic, e.g. 蒙古/包 + if len(word_list[0]) == 2: + finals[0] = finals[0][:-1] + "2" + finals[1] = finals[1][:-1] + "2" + # monosyllabic + disyllabic, e.g. 纸/老虎 + elif len(word_list[0]) == 1: + finals[1] = finals[1][:-1] + "2" + else: + finals_list = [finals[: len(word_list[0])], finals[len(word_list[0]) :]] + if len(finals_list) == 2: + for i, sub in enumerate(finals_list): + # e.g. 所有/人 + if self._all_tone_three(sub) and len(sub) == 2: + finals_list[i][0] = finals_list[i][0][:-1] + "2" + # e.g. 好/喜欢 + elif ( + i == 1 + and not self._all_tone_three(sub) + and finals_list[i][0][-1] == "3" + and finals_list[0][-1][-1] == "3" + ): + finals_list[0][-1] = finals_list[0][-1][:-1] + "2" + finals = sum(finals_list, []) + # split idiom into two words who's length is 2 + elif len(word) == 4: + finals_list = [finals[:2], finals[2:]] + finals = [] + for sub in finals_list: + if self._all_tone_three(sub): + sub[0] = sub[0][:-1] + "2" + finals += sub + + return finals + + def _all_tone_three(self, finals: List[str]) -> bool: + return all(x[-1] == "3" for x in finals) + + # merge "不" and the word behind it + # if don't merge, "不" sometimes appears alone according to jieba, which may occur sandhi error + def _merge_bu(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]: + new_seg = [] + last_word = "" + for word, pos in seg: + if last_word == "不": + word = last_word + word + if word != "不": + new_seg.append((word, pos)) + last_word = word[:] + if last_word == "不": + new_seg.append((last_word, "d")) + last_word = "" + return new_seg + + # function 1: merge "一" and reduplication words in it's left and right, e.g. "听","一","听" ->"听一听" + # function 2: merge single "一" and the word behind it + # if don't merge, "一" sometimes appears alone according to jieba, which may occur sandhi error + # e.g. + # input seg: [('听', 'v'), ('一', 'm'), ('听', 'v')] + # output seg: [['听一听', 'v']] + def _merge_yi(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]: + new_seg = [] + # function 1 + for i, (word, pos) in enumerate(seg): + if ( + i - 1 >= 0 + and word == "一" + and i + 1 < len(seg) + and seg[i - 1][0] == seg[i + 1][0] + and seg[i - 1][1] == "v" + ): + new_seg[i - 1][0] = new_seg[i - 1][0] + "一" + new_seg[i - 1][0] + else: + if ( + i - 2 >= 0 + and seg[i - 1][0] == "一" + and seg[i - 2][0] == word + and pos == "v" + ): + continue + else: + new_seg.append([word, pos]) + seg = new_seg + new_seg = [] + # function 2 + for i, (word, pos) in enumerate(seg): + if new_seg and new_seg[-1][0] == "一": + new_seg[-1][0] = new_seg[-1][0] + word + else: + new_seg.append([word, pos]) + return new_seg + + # the first and the second words are all_tone_three + def _merge_continuous_three_tones( + self, seg: List[Tuple[str, str]] + ) -> List[Tuple[str, str]]: + new_seg = [] + sub_finals_list = [ + lazy_pinyin(word, neutral_tone_with_five=True, style=Style.FINALS_TONE3) + for (word, pos) in seg + ] + assert len(sub_finals_list) == len(seg) + merge_last = [False] * len(seg) + for i, (word, pos) in enumerate(seg): + if ( + i - 1 >= 0 + and self._all_tone_three(sub_finals_list[i - 1]) + and self._all_tone_three(sub_finals_list[i]) + and not merge_last[i - 1] + ): + # if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi + if ( + not self._is_reduplication(seg[i - 1][0]) + and len(seg[i - 1][0]) + len(seg[i][0]) <= 3 + ): + new_seg[-1][0] = new_seg[-1][0] + seg[i][0] + merge_last[i] = True + else: + new_seg.append([word, pos]) + else: + new_seg.append([word, pos]) + + return new_seg + + def _is_reduplication(self, word: str) -> bool: + return len(word) == 2 and word[0] == word[1] + + # the last char of first word and the first char of second word is tone_three + def _merge_continuous_three_tones_2( + self, seg: List[Tuple[str, str]] + ) -> List[Tuple[str, str]]: + new_seg = [] + sub_finals_list = [ + lazy_pinyin(word, neutral_tone_with_five=True, style=Style.FINALS_TONE3) + for (word, pos) in seg + ] + assert len(sub_finals_list) == len(seg) + merge_last = [False] * len(seg) + for i, (word, pos) in enumerate(seg): + if ( + i - 1 >= 0 + and sub_finals_list[i - 1][-1][-1] == "3" + and sub_finals_list[i][0][-1] == "3" + and not merge_last[i - 1] + ): + # if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi + if ( + not self._is_reduplication(seg[i - 1][0]) + and len(seg[i - 1][0]) + len(seg[i][0]) <= 3 + ): + new_seg[-1][0] = new_seg[-1][0] + seg[i][0] + merge_last[i] = True + else: + new_seg.append([word, pos]) + else: + new_seg.append([word, pos]) + return new_seg + + def _merge_er(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]: + new_seg = [] + for i, (word, pos) in enumerate(seg): + if i - 1 >= 0 and word == "儿" and seg[i - 1][0] != "#": + new_seg[-1][0] = new_seg[-1][0] + seg[i][0] + else: + new_seg.append([word, pos]) + return new_seg + + def _merge_reduplication(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]: + new_seg = [] + for i, (word, pos) in enumerate(seg): + if new_seg and word == new_seg[-1][0]: + new_seg[-1][0] = new_seg[-1][0] + seg[i][0] + else: + new_seg.append([word, pos]) + return new_seg + + def pre_merge_for_modify(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]: + seg = self._merge_bu(seg) + try: + seg = self._merge_yi(seg) + except: + print("_merge_yi failed") + seg = self._merge_reduplication(seg) + seg = self._merge_continuous_three_tones(seg) + seg = self._merge_continuous_three_tones_2(seg) + seg = self._merge_er(seg) + return seg + + def modified_tone(self, word: str, pos: str, finals: List[str]) -> List[str]: + finals = self._bu_sandhi(word, finals) + finals = self._yi_sandhi(word, finals) + finals = self._neural_sandhi(word, pos, finals) + finals = self._three_sandhi(word, finals) + return finals diff --git a/preprocess_text.py b/preprocess_text.py index 1167277..8a21bf3 100644 --- a/preprocess_text.py +++ b/preprocess_text.py @@ -27,7 +27,7 @@ preprocess_text_config = config.preprocess_text_config default=preprocess_text_config.config_path, type=click.Path(exists=True, file_okay=True, dir_okay=False), ) -@click.option("--val-per-spk", default=preprocess_text_config.val_per_spk) +@click.option("--val-per-lang", default=preprocess_text_config.val_per_lang) @click.option("--max-val-total", default=preprocess_text_config.max_val_total) @click.option("--clean/--no-clean", default=preprocess_text_config.clean) @click.option("-y", "--yml_config") @@ -37,7 +37,7 @@ def preprocess( train_path: str, val_path: str, config_path: str, - val_per_spk: int, + val_per_lang: int, max_val_total: int, clean: bool, yml_config: str, # 这个不要删 @@ -94,8 +94,7 @@ def preprocess( countNotFound += 1 continue audioPaths.add(utt) - spk_utt_map[spk].append(line) - + spk_utt_map[language].append(line) if spk not in spk_id_map.keys(): spk_id_map[spk] = current_sid current_sid += 1 @@ -106,9 +105,10 @@ def preprocess( for spk, utts in spk_utt_map.items(): shuffle(utts) - val_list += utts[:val_per_spk] - train_list += utts[val_per_spk:] + val_list += utts[:val_per_lang] + train_list += utts[val_per_lang:] + shuffle(val_list) if len(val_list) > max_val_total: train_list += val_list[max_val_total:] val_list = val_list[:max_val_total] @@ -123,6 +123,7 @@ def preprocess( json_config = json.load(open(config_path, encoding="utf-8")) json_config["data"]["spk2id"] = spk_id_map + json_config["data"]["n_speakers"] = len(spk_id_map) # 新增写入：写入训练版本、数据集路径 json_config["version"] = latest_version json_config["data"]["training_files"] = os.path.normpath(train_path).replace( diff --git a/resample.py b/resample.py index 678c352..a0d6175 100644 --- a/resample.py +++ b/resample.py @@ -10,11 +10,11 @@ from config import config def process(item): - spkdir, wav_name, args = item - wav_path = os.path.join(args.in_dir, spkdir, wav_name) + wav_name, args = item + wav_path = os.path.join(args.in_dir, wav_name) if os.path.exists(wav_path) and wav_path.lower().endswith(".wav"): wav, sr = librosa.load(wav_path, sr=args.sr) - soundfile.write(os.path.join(args.out_dir, spkdir, wav_name), wav, sr) + soundfile.write(os.path.join(args.out_dir, wav_name), wav, sr) if __name__ == "__main__": @@ -54,15 +54,11 @@ if __name__ == "__main__": tasks = [] for dirpath, _, filenames in os.walk(args.in_dir): - # 子级目录 - spk_dir = os.path.relpath(dirpath, args.in_dir) - spk_dir_out = os.path.join(args.out_dir, spk_dir) - if not os.path.isdir(spk_dir_out): - os.makedirs(spk_dir_out, exist_ok=True) + if not os.path.isdir(args.out_dir): + os.makedirs(args.out_dir, exist_ok=True) for filename in filenames: if filename.lower().endswith(".wav"): - twople = (spk_dir, filename, args) - tasks.append(twople) + tasks.append((filename, args)) for _ in tqdm( pool.imap_unordered(process, tasks), diff --git a/train_ms.py b/train_ms.py index 30fd315..d02b42d 100644 --- a/train_ms.py +++ b/train_ms.py @@ -194,6 +194,21 @@ def run(): **hps.model, ).cuda(local_rank) + if getattr(hps.train, "freeze_ZH_bert", False): + print("Freezing ZH bert encoder !!!") + for param in net_g.enc_p.bert_proj.parameters(): + param.requires_grad = False + + if getattr(hps.train, "freeze_EN_bert", False): + print("Freezing EN bert encoder !!!") + for param in net_g.enc_p.en_bert_proj.parameters(): + param.requires_grad = False + + if getattr(hps.train, "freeze_JP_bert", False): + print("Freezing JP bert encoder !!!") + for param in net_g.enc_p.ja_bert_proj.parameters(): + param.requires_grad = False + net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(local_rank) optim_g = torch.optim.AdamW( filter(lambda p: p.requires_grad, net_g.parameters()), @@ -216,12 +231,15 @@ def run(): ) else: optim_dur_disc = None - net_g = DDP(net_g, device_ids=[local_rank]) - net_d = DDP(net_d, device_ids=[local_rank]) + net_g = DDP(net_g, device_ids=[local_rank], bucket_cap_mb=512) + net_d = DDP(net_d, device_ids=[local_rank], bucket_cap_mb=512) dur_resume_lr = None if net_dur_disc is not None: net_dur_disc = DDP( - net_dur_disc, device_ids=[local_rank], find_unused_parameters=True + net_dur_disc, + device_ids=[local_rank], + find_unused_parameters=True, + bucket_cap_mb=512, ) # 下载底模 @@ -371,7 +389,7 @@ def train_and_evaluate( ja_bert, en_bert, emo, - ) in tqdm(enumerate(train_loader)): + ) in enumerate(tqdm(train_loader)): if net_g.module.use_noise_scaled_mas: current_mas_noise_scale = ( net_g.module.mas_noise_scale_initial @@ -405,6 +423,7 @@ def train_and_evaluate( z_mask, (z, z_p, m_p, logs_p, m_q, logs_q), (hidden_x, logw, logw_), + g, loss_commit, ) = net_g( x, @@ -454,7 +473,11 @@ def train_and_evaluate( loss_disc_all = loss_disc if net_dur_disc is not None: y_dur_hat_r, y_dur_hat_g = net_dur_disc( - hidden_x.detach(), x_mask.detach(), logw.detach(), logw_.detach() + hidden_x.detach(), + x_mask.detach(), + logw.detach(), + logw_.detach(), + g.detach(), ) with autocast(enabled=False): # TODO: I think need to mean using the mask, but for now, just mean all @@ -480,7 +503,9 @@ def train_and_evaluate( # Generator y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat) if net_dur_disc is not None: - y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x, x_mask, logw, logw_) + y_dur_hat_r, y_dur_hat_g = net_dur_disc( + hidden_x, x_mask, logw, logw_, g + ) with autocast(enabled=False): loss_dur = torch.sum(l_length.float()) loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel @@ -591,6 +616,7 @@ def train_and_evaluate( ) global_step += 1 + gc.collect() torch.cuda.empty_cache() if rank == 0: diff --git a/webui.py b/webui.py index 6ad6db2..dfec239 100644 --- a/webui.py +++ b/webui.py @@ -1,485 +1,532 @@ -# flake8: noqa: E402 -import os -import logging -import re_matching -from tools.sentence import split_by_language - -logging.getLogger("numba").setLevel(logging.WARNING) -logging.getLogger("markdown_it").setLevel(logging.WARNING) -logging.getLogger("urllib3").setLevel(logging.WARNING) -logging.getLogger("matplotlib").setLevel(logging.WARNING) - -logging.basicConfig( - level=logging.INFO, format="| %(name)s | %(levelname)s | %(message)s" -) - -logger = logging.getLogger(__name__) - -import torch -import utils -from infer import infer, latest_version, get_net_g, infer_multilang -import gradio as gr -import webbrowser -import numpy as np -from config import config -from tools.translate import translate -import librosa - -net_g = None - -device = config.webui_config.device -if device == "mps": - os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" - - -def generate_audio( - slices, - sdp_ratio, - noise_scale, - noise_scale_w, - length_scale, - speaker, - language, - reference_audio, - emotion, - skip_start=False, - skip_end=False, -): - audio_list = [] - # silence = np.zeros(hps.data.sampling_rate // 2, dtype=np.int16) - with torch.no_grad(): - for idx, piece in enumerate(slices): - skip_start = (idx != 0) and skip_start - skip_end = (idx != len(slices) - 1) and skip_end - audio = infer( - piece, - reference_audio=reference_audio, - emotion=emotion, - sdp_ratio=sdp_ratio, - noise_scale=noise_scale, - noise_scale_w=noise_scale_w, - length_scale=length_scale, - sid=speaker, - language=language, - hps=hps, - net_g=net_g, - device=device, - skip_start=skip_start, - skip_end=skip_end, - ) - audio16bit = gr.processing_utils.convert_to_16_bit_wav(audio) - audio_list.append(audio16bit) - # audio_list.append(silence) # 将静音添加到列表中 - return audio_list - - -def generate_audio_multilang( - slices, - sdp_ratio, - noise_scale, - noise_scale_w, - length_scale, - speaker, - language, - reference_audio, - emotion, - skip_start=False, - skip_end=False, -): - audio_list = [] - # silence = np.zeros(hps.data.sampling_rate // 2, dtype=np.int16) - with torch.no_grad(): - for idx, piece in enumerate(slices): - skip_start = (idx != 0) and skip_start - skip_end = (idx != len(slices) - 1) and skip_end - audio = infer_multilang( - piece, - reference_audio=reference_audio, - emotion=emotion, - sdp_ratio=sdp_ratio, - noise_scale=noise_scale, - noise_scale_w=noise_scale_w, - length_scale=length_scale, - sid=speaker, - language=language[idx], - hps=hps, - net_g=net_g, - device=device, - skip_start=skip_start, - skip_end=skip_end, - ) - audio16bit = gr.processing_utils.convert_to_16_bit_wav(audio) - audio_list.append(audio16bit) - # audio_list.append(silence) # 将静音添加到列表中 - return audio_list - - -def tts_split( - text: str, - speaker, - sdp_ratio, - noise_scale, - noise_scale_w, - length_scale, - language, - cut_by_sent, - interval_between_para, - interval_between_sent, - reference_audio, - emotion, -): - if language == "mix": - return ("invalid", None) - while text.find("\n\n") != -1: - text = text.replace("\n\n", "\n") - para_list = re_matching.cut_para(text) - audio_list = [] - if not cut_by_sent: - for idx, p in enumerate(para_list): - skip_start = idx != 0 - skip_end = idx != len(para_list) - 1 - audio = infer( - p, - reference_audio=reference_audio, - emotion=emotion, - sdp_ratio=sdp_ratio, - noise_scale=noise_scale, - noise_scale_w=noise_scale_w, - length_scale=length_scale, - sid=speaker, - language=language, - hps=hps, - net_g=net_g, - device=device, - skip_start=skip_start, - skip_end=skip_end, - ) - audio16bit = gr.processing_utils.convert_to_16_bit_wav(audio) - audio_list.append(audio16bit) - silence = np.zeros((int)(44100 * interval_between_para), dtype=np.int16) - audio_list.append(silence) - else: - for idx, p in enumerate(para_list): - skip_start = idx != 0 - skip_end = idx != len(para_list) - 1 - audio_list_sent = [] - sent_list = re_matching.cut_sent(p) - for idx, s in enumerate(sent_list): - skip_start = (idx != 0) and skip_start - skip_end = (idx != len(sent_list) - 1) and skip_end - audio = infer( - s, - reference_audio=reference_audio, - emotion=emotion, - sdp_ratio=sdp_ratio, - noise_scale=noise_scale, - noise_scale_w=noise_scale_w, - length_scale=length_scale, - sid=speaker, - language=language, - hps=hps, - net_g=net_g, - device=device, - skip_start=skip_start, - skip_end=skip_end, - ) - audio_list_sent.append(audio) - silence = np.zeros((int)(44100 * interval_between_sent)) - audio_list_sent.append(silence) - if (interval_between_para - interval_between_sent) > 0: - silence = np.zeros( - (int)(44100 * (interval_between_para - interval_between_sent)) - ) - audio_list_sent.append(silence) - audio16bit = gr.processing_utils.convert_to_16_bit_wav( - np.concatenate(audio_list_sent) - ) # 对完整句子做音量归一 - audio_list.append(audio16bit) - audio_concat = np.concatenate(audio_list) - return ("Success", (44100, audio_concat)) - - -def tts_fn( - text: str, - speaker, - sdp_ratio, - noise_scale, - noise_scale_w, - length_scale, - language, - reference_audio, - emotion, -): - audio_list = [] - if language == "mix": - bool_valid, str_valid = re_matching.validate_text(text) - if not bool_valid: - return str_valid, ( - hps.data.sampling_rate, - np.concatenate([np.zeros(hps.data.sampling_rate // 2)]), - ) - result = [] - for slice in re_matching.text_matching(text): - _speaker = slice.pop() - temp_contant = [] - temp_lang = [] - for lang, content in slice: - if "|" in content: - temp = [] - temp_ = [] - for i in content.split("|"): - if i != "": - temp.append([i]) - temp_.append([lang]) - else: - temp.append([]) - temp_.append([]) - temp_contant += temp - temp_lang += temp_ - else: - if len(temp_contant) == 0: - temp_contant.append([]) - temp_lang.append([]) - temp_contant[-1].append(content) - temp_lang[-1].append(lang) - for i, j in zip(temp_lang, temp_contant): - result.append([*zip(i, j), _speaker]) - for i, one in enumerate(result): - skip_start = i != 0 - skip_end = i != len(result) - 1 - _speaker = one.pop() - idx = 0 - while idx < len(one): - text_to_generate = [] - lang_to_generate = [] - while True: - lang, content = one[idx] - temp_text = [content] - if len(text_to_generate) > 0: - text_to_generate[-1] += [temp_text.pop(0)] - lang_to_generate[-1] += [lang] - if len(temp_text) > 0: - text_to_generate += [[i] for i in temp_text] - lang_to_generate += [[lang]] * len(temp_text) - if idx + 1 < len(one): - idx += 1 - else: - break - skip_start = (idx != 0) and skip_start - skip_end = (idx != len(one) - 1) and skip_end - print(text_to_generate, lang_to_generate) - audio_list.extend( - generate_audio_multilang( - text_to_generate, - sdp_ratio, - noise_scale, - noise_scale_w, - length_scale, - _speaker, - lang_to_generate, - reference_audio, - emotion, - skip_start, - skip_end, - ) - ) - idx += 1 - elif language.lower() == "auto": - for idx, slice in enumerate(text.split("|")): - if slice == "": - continue - skip_start = idx != 0 - skip_end = idx != len(text.split("|")) - 1 - sentences_list = split_by_language( - slice, target_languages=["zh", "ja", "en"] - ) - idx = 0 - while idx < len(sentences_list): - text_to_generate = [] - lang_to_generate = [] - while True: - content, lang = sentences_list[idx] - temp_text = [content] - lang = lang.upper() - if lang == "JA": - lang = "JP" - if len(text_to_generate) > 0: - text_to_generate[-1] += [temp_text.pop(0)] - lang_to_generate[-1] += [lang] - if len(temp_text) > 0: - text_to_generate += [[i] for i in temp_text] - lang_to_generate += [[lang]] * len(temp_text) - if idx + 1 < len(sentences_list): - idx += 1 - else: - break - skip_start = (idx != 0) and skip_start - skip_end = (idx != len(sentences_list) - 1) and skip_end - print(text_to_generate, lang_to_generate) - audio_list.extend( - generate_audio_multilang( - text_to_generate, - sdp_ratio, - noise_scale, - noise_scale_w, - length_scale, - speaker, - lang_to_generate, - reference_audio, - emotion, - skip_start, - skip_end, - ) - ) - idx += 1 - else: - audio_list.extend( - generate_audio( - text.split("|"), - sdp_ratio, - noise_scale, - noise_scale_w, - length_scale, - speaker, - language, - reference_audio, - emotion, - ) - ) - - audio_concat = np.concatenate(audio_list) - return "Success", (hps.data.sampling_rate, audio_concat) - - -if __name__ == "__main__": - if config.webui_config.debug: - logger.info("Enable DEBUG-LEVEL log") - logging.basicConfig(level=logging.DEBUG) - hps = utils.get_hparams_from_file(config.webui_config.config_path) - # 若config.json中未指定版本则默认为最新版本 - version = hps.version if hasattr(hps, "version") else latest_version - net_g = get_net_g( - model_path=config.webui_config.model, version=version, device=device, hps=hps - ) - speaker_ids = hps.data.spk2id - speakers = list(speaker_ids.keys()) - languages = ["ZH", "JP", "EN", "mix", "auto"] - with gr.Blocks() as app: - with gr.Row(): - with gr.Column(): - text = gr.TextArea( - label="输入文本内容", - placeholder=""" - 如果你选择语言为\'mix\'，必须按照格式输入，否则报错: - 格式举例(zh是中文，jp是日语，不区分大小写；说话人举例:gongzi): - [说话人1]你好，こんにちは！ こんにちは，世界。 - [说话人2]你好吗？元気ですか？ - [说话人3]谢谢。どういたしまして。 - ... - 另外，所有的语言选项都可以用'|'分割长段实现分句生成。 - """, - ) - trans = gr.Button("中翻日", variant="primary") - slicer = gr.Button("快速切分", variant="primary") - speaker = gr.Dropdown( - choices=speakers, value=speakers[0], label="Speaker" - ) - emotion = gr.Slider( - minimum=0, maximum=9, value=0, step=1, label="Emotion" - ) - sdp_ratio = gr.Slider( - minimum=0, maximum=1, value=0.2, step=0.1, label="SDP Ratio" - ) - noise_scale = gr.Slider( - minimum=0.1, maximum=2, value=0.6, step=0.1, label="Noise" - ) - noise_scale_w = gr.Slider( - minimum=0.1, maximum=2, value=0.8, step=0.1, label="Noise_W" - ) - length_scale = gr.Slider( - minimum=0.1, maximum=2, value=1.0, step=0.1, label="Length" - ) - language = gr.Dropdown( - choices=languages, value=languages[0], label="Language" - ) - btn = gr.Button("生成音频！", variant="primary") - with gr.Column(): - with gr.Row(): - with gr.Column(): - interval_between_sent = gr.Slider( - minimum=0, - maximum=5, - value=0.2, - step=0.1, - label="句间停顿(秒)，勾选按句切分才生效", - ) - interval_between_para = gr.Slider( - minimum=0, - maximum=10, - value=1, - step=0.1, - label="段间停顿(秒)，需要大于句间停顿才有效", - ) - opt_cut_by_sent = gr.Checkbox( - label="按句切分 在按段落切分的基础上再按句子切分文本" - ) - slicer = gr.Button("切分生成", variant="primary") - text_output = gr.Textbox(label="状态信息") - audio_output = gr.Audio(label="输出音频") - # explain_image = gr.Image( - # label="参数解释信息", - # show_label=True, - # show_share_button=False, - # show_download_button=False, - # value=os.path.abspath("./img/参数说明.png"), - # ) - reference_text = gr.Markdown(value="## 情感参考音频（WAV 格式）：用于生成语音的情感参考。") - reference_audio = gr.Audio(label="情感参考音频（WAV 格式）", type="filepath") - btn.click( - tts_fn, - inputs=[ - text, - speaker, - sdp_ratio, - noise_scale, - noise_scale_w, - length_scale, - language, - reference_audio, - emotion, - ], - outputs=[text_output, audio_output], - ) - - trans.click( - translate, - inputs=[text], - outputs=[text], - ) - slicer.click( - tts_split, - inputs=[ - text, - speaker, - sdp_ratio, - noise_scale, - noise_scale_w, - length_scale, - language, - opt_cut_by_sent, - interval_between_para, - interval_between_sent, - reference_audio, - emotion, - ], - outputs=[text_output, audio_output], - ) - - reference_audio.upload( - lambda x: librosa.load(x, 16000)[::-1], - inputs=[reference_audio], - outputs=[reference_audio], - ) - print("推理页面已开启!") - webbrowser.open(f"http://127.0.0.1:{config.webui_config.port}") - app.launch(share=config.webui_config.share, server_port=config.webui_config.port) +# flake8: noqa: E402 +import os +import logging +import re_matching +from tools.sentence import split_by_language + +logging.getLogger("numba").setLevel(logging.WARNING) +logging.getLogger("markdown_it").setLevel(logging.WARNING) +logging.getLogger("urllib3").setLevel(logging.WARNING) +logging.getLogger("matplotlib").setLevel(logging.WARNING) + +logging.basicConfig( + level=logging.INFO, format="| %(name)s | %(levelname)s | %(message)s" +) + +logger = logging.getLogger(__name__) + +import torch +import utils +from infer import infer, latest_version, get_net_g, infer_multilang +import gradio as gr +import webbrowser +import numpy as np +from config import config +from tools.translate import translate +import librosa + +net_g = None + +device = config.webui_config.device +if device == "mps": + os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" + + +def generate_audio( + slices, + sdp_ratio, + noise_scale, + noise_scale_w, + length_scale, + speaker, + language, + reference_audio, + emotion, + skip_start=False, + skip_end=False, +): + audio_list = [] + # silence = np.zeros(hps.data.sampling_rate // 2, dtype=np.int16) + with torch.no_grad(): + for idx, piece in enumerate(slices): + skip_start = (idx != 0) and skip_start + skip_end = (idx != len(slices) - 1) and skip_end + audio = infer( + piece, + reference_audio=reference_audio, + emotion=emotion, + sdp_ratio=sdp_ratio, + noise_scale=noise_scale, + noise_scale_w=noise_scale_w, + length_scale=length_scale, + sid=speaker, + language=language, + hps=hps, + net_g=net_g, + device=device, + skip_start=skip_start, + skip_end=skip_end, + ) + audio16bit = gr.processing_utils.convert_to_16_bit_wav(audio) + audio_list.append(audio16bit) + # audio_list.append(silence) # 将静音添加到列表中 + return audio_list + + +def generate_audio_multilang( + slices, + sdp_ratio, + noise_scale, + noise_scale_w, + length_scale, + speaker, + language, + reference_audio, + emotion, + skip_start=False, + skip_end=False, +): + audio_list = [] + # silence = np.zeros(hps.data.sampling_rate // 2, dtype=np.int16) + with torch.no_grad(): + for idx, piece in enumerate(slices): + skip_start = (idx != 0) and skip_start + skip_end = (idx != len(slices) - 1) and skip_end + audio = infer_multilang( + piece, + reference_audio=reference_audio, + emotion=emotion, + sdp_ratio=sdp_ratio, + noise_scale=noise_scale, + noise_scale_w=noise_scale_w, + length_scale=length_scale, + sid=speaker, + language=language[idx], + hps=hps, + net_g=net_g, + device=device, + skip_start=skip_start, + skip_end=skip_end, + ) + audio16bit = gr.processing_utils.convert_to_16_bit_wav(audio) + audio_list.append(audio16bit) + # audio_list.append(silence) # 将静音添加到列表中 + return audio_list + + +def tts_split( + text: str, + speaker, + sdp_ratio, + noise_scale, + noise_scale_w, + length_scale, + language, + cut_by_sent, + interval_between_para, + interval_between_sent, + reference_audio, + emotion, +): + if language == "mix": + return ("invalid", None) + while text.find("\n\n") != -1: + text = text.replace("\n\n", "\n") + para_list = re_matching.cut_para(text) + audio_list = [] + if not cut_by_sent: + for idx, p in enumerate(para_list): + skip_start = idx != 0 + skip_end = idx != len(para_list) - 1 + audio = infer( + p, + reference_audio=reference_audio, + emotion=emotion, + sdp_ratio=sdp_ratio, + noise_scale=noise_scale, + noise_scale_w=noise_scale_w, + length_scale=length_scale, + sid=speaker, + language=language, + hps=hps, + net_g=net_g, + device=device, + skip_start=skip_start, + skip_end=skip_end, + ) + audio16bit = gr.processing_utils.convert_to_16_bit_wav(audio) + audio_list.append(audio16bit) + silence = np.zeros((int)(44100 * interval_between_para), dtype=np.int16) + audio_list.append(silence) + else: + for idx, p in enumerate(para_list): + skip_start = idx != 0 + skip_end = idx != len(para_list) - 1 + audio_list_sent = [] + sent_list = re_matching.cut_sent(p) + for idx, s in enumerate(sent_list): + skip_start = (idx != 0) and skip_start + skip_end = (idx != len(sent_list) - 1) and skip_end + audio = infer( + s, + reference_audio=reference_audio, + emotion=emotion, + sdp_ratio=sdp_ratio, + noise_scale=noise_scale, + noise_scale_w=noise_scale_w, + length_scale=length_scale, + sid=speaker, + language=language, + hps=hps, + net_g=net_g, + device=device, + skip_start=skip_start, + skip_end=skip_end, + ) + audio_list_sent.append(audio) + silence = np.zeros((int)(44100 * interval_between_sent)) + audio_list_sent.append(silence) + if (interval_between_para - interval_between_sent) > 0: + silence = np.zeros( + (int)(44100 * (interval_between_para - interval_between_sent)) + ) + audio_list_sent.append(silence) + audio16bit = gr.processing_utils.convert_to_16_bit_wav( + np.concatenate(audio_list_sent) + ) # 对完整句子做音量归一 + audio_list.append(audio16bit) + audio_concat = np.concatenate(audio_list) + return ("Success", (44100, audio_concat)) + + +def tts_fn( + text: str, + speaker, + sdp_ratio, + noise_scale, + noise_scale_w, + length_scale, + language, + reference_audio, + emotion, + prompt_mode, +): + if prompt_mode == "Audio prompt": + if reference_audio == None: + return ("Invalid audio prompt", None) + else: + reference_audio = load_audio(reference_audio)[1] + else: + reference_audio = None + audio_list = [] + if language == "mix": + bool_valid, str_valid = re_matching.validate_text(text) + if not bool_valid: + return str_valid, ( + hps.data.sampling_rate, + np.concatenate([np.zeros(hps.data.sampling_rate // 2)]), + ) + result = [] + for slice in re_matching.text_matching(text): + _speaker = slice.pop() + temp_contant = [] + temp_lang = [] + for lang, content in slice: + if "|" in content: + temp = [] + temp_ = [] + for i in content.split("|"): + if i != "": + temp.append([i]) + temp_.append([lang]) + else: + temp.append([]) + temp_.append([]) + temp_contant += temp + temp_lang += temp_ + else: + if len(temp_contant) == 0: + temp_contant.append([]) + temp_lang.append([]) + temp_contant[-1].append(content) + temp_lang[-1].append(lang) + for i, j in zip(temp_lang, temp_contant): + result.append([*zip(i, j), _speaker]) + for i, one in enumerate(result): + skip_start = i != 0 + skip_end = i != len(result) - 1 + _speaker = one.pop() + idx = 0 + while idx < len(one): + text_to_generate = [] + lang_to_generate = [] + while True: + lang, content = one[idx] + temp_text = [content] + if len(text_to_generate) > 0: + text_to_generate[-1] += [temp_text.pop(0)] + lang_to_generate[-1] += [lang] + if len(temp_text) > 0: + text_to_generate += [[i] for i in temp_text] + lang_to_generate += [[lang]] * len(temp_text) + if idx + 1 < len(one): + idx += 1 + else: + break + skip_start = (idx != 0) and skip_start + skip_end = (idx != len(one) - 1) and skip_end + print(text_to_generate, lang_to_generate) + audio_list.extend( + generate_audio_multilang( + text_to_generate, + sdp_ratio, + noise_scale, + noise_scale_w, + length_scale, + _speaker, + lang_to_generate, + reference_audio, + emotion, + skip_start, + skip_end, + ) + ) + idx += 1 + elif language.lower() == "auto": + for idx, slice in enumerate(text.split("|")): + if slice == "": + continue + skip_start = idx != 0 + skip_end = idx != len(text.split("|")) - 1 + sentences_list = split_by_language( + slice, target_languages=["zh", "ja", "en"] + ) + idx = 0 + while idx < len(sentences_list): + text_to_generate = [] + lang_to_generate = [] + while True: + content, lang = sentences_list[idx] + temp_text = [content] + lang = lang.upper() + if lang == "JA": + lang = "JP" + if len(text_to_generate) > 0: + text_to_generate[-1] += [temp_text.pop(0)] + lang_to_generate[-1] += [lang] + if len(temp_text) > 0: + text_to_generate += [[i] for i in temp_text] + lang_to_generate += [[lang]] * len(temp_text) + if idx + 1 < len(sentences_list): + idx += 1 + else: + break + skip_start = (idx != 0) and skip_start + skip_end = (idx != len(sentences_list) - 1) and skip_end + print(text_to_generate, lang_to_generate) + audio_list.extend( + generate_audio_multilang( + text_to_generate, + sdp_ratio, + noise_scale, + noise_scale_w, + length_scale, + speaker, + lang_to_generate, + reference_audio, + emotion, + skip_start, + skip_end, + ) + ) + idx += 1 + else: + audio_list.extend( + generate_audio( + text.split("|"), + sdp_ratio, + noise_scale, + noise_scale_w, + length_scale, + speaker, + language, + reference_audio, + emotion, + ) + ) + + audio_concat = np.concatenate(audio_list) + return "Success", (hps.data.sampling_rate, audio_concat) + + +def load_audio(path): + audio, sr = librosa.load(path, 48000) + # audio = librosa.resample(audio, 44100, 48000) + return sr, audio + + +def gr_util(item): + if item == "Text prompt": + return {"visible": True, "__type__": "update"}, { + "visible": False, + "__type__": "update", + } + else: + return {"visible": False, "__type__": "update"}, { + "visible": True, + "__type__": "update", + } + + +if __name__ == "__main__": + if config.webui_config.debug: + logger.info("Enable DEBUG-LEVEL log") + logging.basicConfig(level=logging.DEBUG) + hps = utils.get_hparams_from_file(config.webui_config.config_path) + # 若config.json中未指定版本则默认为最新版本 + version = hps.version if hasattr(hps, "version") else latest_version + net_g = get_net_g( + model_path=config.webui_config.model, version=version, device=device, hps=hps + ) + speaker_ids = hps.data.spk2id + speakers = list(speaker_ids.keys()) + languages = ["ZH", "JP", "EN", "mix", "auto"] + with gr.Blocks() as app: + with gr.Row(): + with gr.Column(): + text = gr.TextArea( + label="输入文本内容", + placeholder=""" + 如果你选择语言为\'mix\'，必须按照格式输入，否则报错: + 格式举例(zh是中文，jp是日语，不区分大小写；说话人举例:gongzi): + [说话人1]你好，こんにちは！ こんにちは，世界。 + [说话人2]你好吗？元気ですか？ + [说话人3]谢谢。どういたしまして。 + ... + 另外，所有的语言选项都可以用'|'分割长段实现分句生成。 + """, + ) + trans = gr.Button("中翻日", variant="primary") + slicer = gr.Button("快速切分", variant="primary") + speaker = gr.Dropdown( + choices=speakers, value=speakers[0], label="Speaker" + ) + _ = gr.Markdown( + value="提示模式（Prompt mode）：可选文字提示或音频提示，用于生成文字或音频指定风格的声音。\n" + ) + prompt_mode = gr.Radio( + ["Text prompt", "Audio prompt"], + label="Prompt Mode", + value="Text prompt", + ) + text_prompt = gr.Textbox( + label="Text prompt", + placeholder="用文字描述生成风格。如：Happy", + value="Happy", + visible=True, + ) + audio_prompt = gr.Audio( + label="Audio prompt", type="filepath", visible=False + ) + sdp_ratio = gr.Slider( + minimum=0, maximum=1, value=0.2, step=0.1, label="SDP Ratio" + ) + noise_scale = gr.Slider( + minimum=0.1, maximum=2, value=0.6, step=0.1, label="Noise" + ) + noise_scale_w = gr.Slider( + minimum=0.1, maximum=2, value=0.8, step=0.1, label="Noise_W" + ) + length_scale = gr.Slider( + minimum=0.1, maximum=2, value=1.0, step=0.1, label="Length" + ) + language = gr.Dropdown( + choices=languages, value=languages[0], label="Language" + ) + btn = gr.Button("生成音频！", variant="primary") + with gr.Column(): + with gr.Row(): + with gr.Column(): + interval_between_sent = gr.Slider( + minimum=0, + maximum=5, + value=0.2, + step=0.1, + label="句间停顿(秒)，勾选按句切分才生效", + ) + interval_between_para = gr.Slider( + minimum=0, + maximum=10, + value=1, + step=0.1, + label="段间停顿(秒)，需要大于句间停顿才有效", + ) + opt_cut_by_sent = gr.Checkbox( + label="按句切分 在按段落切分的基础上再按句子切分文本" + ) + slicer = gr.Button("切分生成", variant="primary") + text_output = gr.Textbox(label="状态信息") + audio_output = gr.Audio(label="输出音频") + # explain_image = gr.Image( + # label="参数解释信息", + # show_label=True, + # show_share_button=False, + # show_download_button=False, + # value=os.path.abspath("./img/参数说明.png"), + # ) + btn.click( + tts_fn, + inputs=[ + text, + speaker, + sdp_ratio, + noise_scale, + noise_scale_w, + length_scale, + language, + audio_prompt, + text_prompt, + prompt_mode, + ], + outputs=[text_output, audio_output], + ) + + trans.click( + translate, + inputs=[text], + outputs=[text], + ) + slicer.click( + tts_split, + inputs=[ + text, + speaker, + sdp_ratio, + noise_scale, + noise_scale_w, + length_scale, + language, + opt_cut_by_sent, + interval_between_para, + interval_between_sent, + audio_prompt, + text_prompt, + ], + outputs=[text_output, audio_output], + ) + + prompt_mode.change( + lambda x: gr_util(x), + inputs=[prompt_mode], + outputs=[text_prompt, audio_prompt], + ) + + audio_prompt.upload( + lambda x: load_audio(x), + inputs=[audio_prompt], + outputs=[audio_prompt], + ) + + print("推理页面已开启!") + webbrowser.open(f"http://127.0.0.1:{config.webui_config.port}") + app.launch(share=config.webui_config.share, server_port=config.webui_config.port) diff --git a/webui_preprocess.py b/webui_preprocess.py new file mode 100644 index 0000000..af2ddee --- /dev/null +++ b/webui_preprocess.py @@ -0,0 +1,177 @@ +import gradio as gr +import webbrowser +import os +import json +import subprocess +import shutil + + +def get_path(data_dir): + start_path = os.path.join("./data", data_dir) + lbl_path = os.path.join(start_path, "esd.list") + train_path = os.path.join(start_path, "train.list") + val_path = os.path.join(start_path, "val.list") + config_path = os.path.join(start_path, "configs", "config.json") + return start_path, lbl_path, train_path, val_path, config_path + + +def generate_config(data_dir, batch_size): + assert data_dir != "", "数据集名称不能为空" + start_path, _, train_path, val_path, config_path = get_path(data_dir) + if os.path.isfile(config_path): + config = json.load(open(config_path)) + else: + config = json.load(open("configs/config.json")) + config["data"]["training_files"] = train_path + config["data"]["validation_files"] = val_path + config["train"]["batch_size"] = batch_size + out_path = os.path.join(start_path, "configs") + if not os.path.isdir(out_path): + os.mkdir(out_path) + model_path = os.path.join(start_path, "models") + if not os.path.isdir(model_path): + os.mkdir(model_path) + with open(config_path, "w", encoding="utf-8") as f: + json.dump(config, f, indent=4) + if not os.path.exists("config.yml"): + shutil.copy(src="default_config.yml", dst="config.yml") + return "配置文件生成完成" + + +def resample(data_dir): + assert data_dir != "", "数据集名称不能为空" + start_path, _, _, _, config_path = get_path(data_dir) + in_dir = os.path.join(start_path, "raw") + out_dir = os.path.join(start_path, "wavs") + subprocess.run( + f"python resample.py " + f"--sr 44100 " + f"--in_dir {in_dir} " + f"--out_dir {out_dir} ", + shell=True, + ) + return "音频文件预处理完成" + + +def preprocess_text(data_dir): + assert data_dir != "", "数据集名称不能为空" + start_path, lbl_path, train_path, val_path, config_path = get_path(data_dir) + lines = open(lbl_path, "r", encoding="utf-8").readlines() + with open(lbl_path, "w", encoding="utf-8") as f: + for line in lines: + path, spk, language, text = line.strip().split("|") + path = os.path.join(start_path, "wavs", os.path.basename(path)) + f.writelines(f"{path}|{spk}|{language}|{text}\n") + subprocess.run( + f"python preprocess_text.py " + f"--transcription-path {lbl_path} " + f"--train-path {train_path} " + f"--val-path {val_path} " + f"--config-path {config_path}", + shell=True, + ) + return "标签文件预处理完成" + + +def bert_gen(data_dir): + assert data_dir != "", "数据集名称不能为空" + _, _, _, _, config_path = get_path(data_dir) + subprocess.run( + f"python bert_gen.py " f"--config {config_path}", + shell=True, + ) + return "BERT 特征文件生成完成" + + +def clap_gen(data_dir): + assert data_dir != "", "数据集名称不能为空" + _, _, _, _, config_path = get_path(data_dir) + subprocess.run( + f"python clap_gen.py " f"--config {config_path}", + shell=True, + ) + return "CLAP 特征文件生成完成" + + +if __name__ == "__main__": + with gr.Blocks() as app: + with gr.Row(): + with gr.Column(): + _ = gr.Markdown( + value="# Bert-VITS2 数据预处理\n" + "## 预先准备：\n" + "下载 BERT 和 CLAP 模型：\n" + "- [中文 RoBERTa](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large)\n" + "- [日文 DeBERTa](https://huggingface.co/ku-nlp/deberta-v2-large-japanese-char-wwm)\n" + "- [英文 DeBERTa](https://huggingface.co/microsoft/deberta-v3-large)\n" + "- [CLAP](https://huggingface.co/laion/clap-htsat-fused)\n" + "\n" + "将 BERT 模型放置到 `bert` 文件夹下，CLAP 模型放置到 `emotional` 文件夹下，覆盖同名文件夹。\n" + "\n" + "数据准备：\n" + "将数据放置在 data 文件夹下，按照如下结构组织：\n" + "\n" + "```\n" + "├── data\n" + "│ ├── {你的数据集名称}\n" + "│ │ ├── esd.list\n" + "│ │ ├── raw\n" + "│ │ │ ├── ****.wav\n" + "│ │ │ ├── ****.wav\n" + "│ │ │ ├── ...\n" + "```\n" + "\n" + "其中，`raw` 文件夹下保存所有的音频文件，`esd.list` 文件为标签文本，格式为\n" + "\n" + "```\n" + "****.wav|{说话人名}|{语言 ID}|{标签文本}\n" + "```\n" + "\n" + "例如：\n" + "```\n" + "vo_ABDLQ001_1_paimon_02.wav|派蒙|ZH|没什么没什么，只是平时他总是站在这里，有点奇怪而已。\n" + "noa_501_0001.wav|NOA|JP|そうだね、油断しないのはとても大事なことだと思う\n" + "Albedo_vo_ABDLQ002_4_albedo_01.wav|Albedo|EN|Who are you? Why did you alarm them?\n" + "...\n" + "```\n" + ) + data_dir = gr.Textbox( + label="数据集名称", + placeholder="你放置在 data 文件夹下的数据集所在文件夹的名称，如 data/genshin 则填 genshin", + ) + info = gr.Textbox(label="状态信息") + _ = gr.Markdown(value="## 第一步：生成配置文件") + with gr.Row(): + batch_size = gr.Slider( + label="批大小（Batch size）：24 GB 显存可用 12", + value=8, + minimum=1, + maximum=64, + step=1, + ) + generate_config_btn = gr.Button(value="执行", variant="primary") + _ = gr.Markdown(value="## 第二步：预处理音频文件") + resample_btn = gr.Button(value="执行", variant="primary") + _ = gr.Markdown(value="## 第三步：预处理标签文件") + preprocess_text_btn = gr.Button(value="执行", variant="primary") + _ = gr.Markdown(value="## 第四步：生成 BERT 特征文件") + bert_gen_btn = gr.Button(value="执行", variant="primary") + _ = gr.Markdown(value="## 第五步：生成 CLAP 特征文件") + clap_gen_btn = gr.Button(value="执行", variant="primary") + _ = gr.Markdown( + value="## 训练模型及部署：\n" + "修改根目录下的 `config.yml` 中 `dataset_path` 一项为 `data/{你的数据集名称}`\n" + "- 训练：将[预训练模型文件](https://openi.pcl.ac.cn/Stardust_minus/Bert-VITS2/modelmanage/show_model)（`D_0.pth`、`DUR_0.pth` 和 `G_0.pth`）放到 `data/{你的数据集名称}/models` 文件夹下，执行 `torchrun --nproc_per_node=1 train_ms.py` 命令（多卡运行可参考 `run_MnodesAndMgpus.sh` 中的命令。\n" + "- 部署：修改根目录下的 `config.yml` 中 `webui` 下 `model` 一项为 `models/{权重文件名}.pth` （如 G_10000.pth），然后执行 `python webui.py`" + ) + + generate_config_btn.click( + generate_config, inputs=[data_dir, batch_size], outputs=[info] + ) + resample_btn.click(resample, inputs=[data_dir], outputs=[info]) + preprocess_text_btn.click(preprocess_text, inputs=[data_dir], outputs=[info]) + bert_gen_btn.click(bert_gen, inputs=[data_dir], outputs=[info]) + clap_gen_btn.click(clap_gen, inputs=[data_dir], outputs=[info]) + + webbrowser.open("http://127.0.0.1:7860") + app.launch(share=False, server_port=7860)

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