diff --git a/.gitignore b/.gitignore index 563040e8da..22ec6e410a 100644 --- a/.gitignore +++ b/.gitignore @@ -169,3 +169,4 @@ wandb depot/* coqui_recipes/* local_scripts/* +coqui_demos/* \ No newline at end of file diff --git a/TTS/.models.json b/TTS/.models.json index 0c31874046..b33e4fd323 100644 --- a/TTS/.models.json +++ b/TTS/.models.json @@ -2,6 +2,20 @@ "tts_models": { "multilingual": { "multi-dataset": { + "xtts_v2": { + "description": "XTTS-v2 by Coqui with 16 languages.", + "hf_url": [ + "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v2/main/model.pth", + "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v2/main/config.json", + "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v2/main/vocab.json", + "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v2/main/hash.md5" + ], + "default_vocoder": null, + "commit": "480a6cdf7", + "license": "CPML", + "contact": "info@coqui.ai", + "tos_required": true + }, "xtts_v1": { "description": "XTTS-v1 by Coqui with 13 languages and cross-language voice cloning.", "hf_url": [ diff --git a/TTS/tts/configs/xtts_config.py b/TTS/tts/configs/xtts_config.py index 4e5031ba5a..1865a3fd69 100644 --- a/TTS/tts/configs/xtts_config.py +++ b/TTS/tts/configs/xtts_config.py @@ -59,6 +59,16 @@ class XttsConfig(BaseTTSConfig): decoder_sampler (str): Diffusion sampler to be used. `ddim` or `dpm++2m`. Defaults to `ddim`. + + gpt_cond_len (int): + Secs audio to be used as conditioning for the autoregressive model. Defaults to `3`. + + max_ref_len (int): + Maximum number of seconds of audio to be used as conditioning for the decoder. Defaults to `10`. + + sound_norm_refs (bool): + Whether to normalize the conditioning audio. Defaults to `False`. + Note: Check :class:`TTS.tts.configs.shared_configs.BaseTTSConfig` for the inherited parameters. @@ -74,7 +84,24 @@ class XttsConfig(BaseTTSConfig): audio: XttsAudioConfig = field(default_factory=XttsAudioConfig) model_dir: str = None languages: List[str] = field( - default_factory=lambda: ["en", "es", "fr", "de", "it", "pt", "pl", "tr", "ru", "nl", "cs", "ar", "zh-cn"] + default_factory=lambda: [ + "en", + "es", + "fr", + "de", + "it", + "pt", + "pl", + "tr", + "ru", + "nl", + "cs", + "ar", + "zh-cn", + "hu", + "ko", + "ja", + ] ) # inference params @@ -88,3 +115,8 @@ class XttsConfig(BaseTTSConfig): num_gpt_outputs: int = 1 decoder_iterations: int = 30 decoder_sampler: str = "ddim" + + # cloning + gpt_cond_len: int = 3 + max_ref_len: int = 10 + sound_norm_refs: bool = False diff --git a/TTS/tts/layers/tortoise/dpm_solver.py b/TTS/tts/layers/tortoise/dpm_solver.py index c70888df42..cb540577f8 100644 --- a/TTS/tts/layers/tortoise/dpm_solver.py +++ b/TTS/tts/layers/tortoise/dpm_solver.py @@ -562,21 +562,15 @@ def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type if order == 3: K = steps // 3 + 1 if steps % 3 == 0: - orders = [ - 3, - ] * ( + orders = [3,] * ( K - 2 ) + [2, 1] elif steps % 3 == 1: - orders = [ - 3, - ] * ( + orders = [3,] * ( K - 1 ) + [1] else: - orders = [ - 3, - ] * ( + orders = [3,] * ( K - 1 ) + [2] elif order == 2: @@ -587,9 +581,7 @@ def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type ] * K else: K = steps // 2 + 1 - orders = [ - 2, - ] * ( + orders = [2,] * ( K - 1 ) + [1] elif order == 1: @@ -1448,10 +1440,7 @@ def sample( model_prev_list[-1] = self.model_fn(x, t) elif method in ["singlestep", "singlestep_fixed"]: if method == "singlestep": - ( - timesteps_outer, - orders, - ) = self.get_orders_and_timesteps_for_singlestep_solver( + (timesteps_outer, orders,) = self.get_orders_and_timesteps_for_singlestep_solver( steps=steps, order=order, skip_type=skip_type, diff --git a/TTS/tts/layers/xtts/gpt.py b/TTS/tts/layers/xtts/gpt.py index dfd7774e36..683104d871 100644 --- a/TTS/tts/layers/xtts/gpt.py +++ b/TTS/tts/layers/xtts/gpt.py @@ -11,6 +11,7 @@ from TTS.tts.layers.xtts.gpt_inference import GPT2InferenceModel from TTS.tts.layers.xtts.latent_encoder import ConditioningEncoder +from TTS.tts.layers.xtts.perceiver_encoder import PerceiverResampler def null_position_embeddings(range, dim): @@ -105,6 +106,8 @@ def __init__( checkpointing=False, average_conditioning_embeddings=False, label_smoothing=0.0, + use_perceiver_resampler=False, + perceiver_cond_length_compression=256, ): """ Args: @@ -132,13 +135,12 @@ def __init__( self.conditioning_encoder = ConditioningEncoder(80, model_dim, num_attn_heads=heads) self.conditioning_dropout = nn.Dropout1d(0.1) self.average_conditioning_embeddings = average_conditioning_embeddings + self.use_perceiver_resampler = use_perceiver_resampler + self.perceiver_cond_length_compression = perceiver_cond_length_compression self.text_embedding = nn.Embedding(self.number_text_tokens, model_dim) self.mel_embedding = nn.Embedding(self.num_audio_tokens, model_dim) - self.prompt_embedding = nn.Embedding(self.num_audio_tokens, model_dim) - self.prompt_pos_embedding = LearnedPositionEmbeddings(24 * 9, model_dim) - ( self.gpt, self.mel_pos_embedding, @@ -165,9 +167,29 @@ def __init__( self.text_head = nn.Linear(model_dim, self.number_text_tokens) self.mel_head = nn.Linear(model_dim, self.num_audio_tokens) + if self.use_perceiver_resampler: + # XTTS v2 + self.conditioning_perceiver = PerceiverResampler( + dim=model_dim, + depth=2, + dim_context=model_dim, + num_latents=32, + dim_head=64, + heads=8, + ff_mult=4, + use_flash_attn=False, + ) + else: + # XTTS v1 + self.prompt_embedding = nn.Embedding(self.num_audio_tokens, model_dim) + self.prompt_pos_embedding = LearnedPositionEmbeddings(24 * 9, model_dim) + def get_grad_norm_parameter_groups(self): return { "conditioning_encoder": list(self.conditioning_encoder.parameters()), + "conditioning_perceiver": list(self.conditioning_perceiver.parameters()) + if self.use_perceiver_resampler + else None, "gpt": list(self.gpt.parameters()), "heads": list(self.text_head.parameters()) + list(self.mel_head.parameters()), } @@ -250,11 +272,8 @@ def get_logits( if attn_mask_text is not None: attn_mask = torch.cat([attn_mask_text, attn_mask_mel], dim=1) if prompt is not None: - if attn_mask_cond is not None: - attn_mask = torch.cat([attn_mask_cond, attn_mask], dim=1) - else: - attn_mask_cond = torch.ones(prompt.shape[0], offset, dtype=torch.bool, device=emb.device) - attn_mask = torch.cat([attn_mask_cond, attn_mask], dim=1) + attn_mask_cond = torch.ones(prompt.shape[0], offset, dtype=torch.bool, device=emb.device) + attn_mask = torch.cat([attn_mask_cond, attn_mask], dim=1) gpt_out = self.gpt( inputs_embeds=emb, @@ -318,7 +337,6 @@ def get_prompts(self, prompt_codes): prompt_len = 3 prompt_len = prompt_len * 24 # in frames if prompt_codes.shape[-1] >= prompt_len: - new_prompt = [] for i in range(prompt_codes.shape[0]): if lengths[i] < prompt_len: start = 0 @@ -340,7 +358,9 @@ def get_style_emb(self, cond_input, return_latent=False): if not return_latent: if cond_input.ndim == 4: cond_input = cond_input.squeeze(1) - conds = self.conditioning_encoder(cond_input) + conds = self.conditioning_encoder(cond_input) # (b, d, s) + if self.use_perceiver_resampler: + conds = self.conditioning_perceiver(conds.permute(0, 2, 1)).transpose(1, 2) # (b, d, 32) else: # already computed conds = cond_input.unsqueeze(1) @@ -354,6 +374,7 @@ def forward( wav_lengths, cond_mels=None, cond_idxs=None, + cond_lens=None, cond_latents=None, return_attentions=False, return_latent=False, @@ -379,10 +400,24 @@ def forward( max_text_len = text_lengths.max() code_lengths = torch.ceil(wav_lengths / self.code_stride_len).long() + 3 + if cond_lens is not None: + if self.use_perceiver_resampler: + cond_lens = cond_lens // self.perceiver_cond_length_compression + else: + cond_lens = cond_lens // self.code_stride_len + if cond_idxs is not None: # recompute cond idxs for mel lengths - for idx, l in enumerate(code_lengths): - cond_idxs[idx] = cond_idxs[idx] / self.code_stride_len + for idx in range(cond_idxs.size(0)): + if self.use_perceiver_resampler: + cond_idxs[idx] = cond_idxs[idx] // self.perceiver_cond_length_compression + else: + cond_idxs[idx] = cond_idxs[idx] // self.code_stride_len + + # ensure that the cond_mel does not have padding + # if cond_lens is not None and cond_idxs is None: + # min_cond_len = torch.min(cond_lens) + # cond_mels = cond_mels[:, :, :, :min_cond_len] # If len(codes) + 3 is larger than maxiumum allowed length, we truncate the codes. max_mel_len = code_lengths.max() @@ -450,9 +485,13 @@ def forward( ) if cond_idxs is not None: + # use masking approach for idx, r in enumerate(cond_idxs): l = r[1] - r[0] attn_mask_cond[idx, l:] = 0.0 + elif cond_lens is not None: + for idx, l in enumerate(cond_lens): + attn_mask_cond[idx, l:] = 0.0 for idx, l in enumerate(text_lengths): attn_mask_text[idx, l + 1 :] = 0.0 @@ -523,7 +562,7 @@ def forward( def inference(self, cond_latents, text_inputs, **hf_generate_kwargs): self.compute_embeddings(cond_latents, text_inputs) - return self.generate(cond_latents, text_inputs, input_tokens=None, **hf_generate_kwargs) + return self.generate(cond_latents, text_inputs, **hf_generate_kwargs) def compute_embeddings( self, diff --git a/TTS/tts/layers/xtts/perceiver_encoder.py b/TTS/tts/layers/xtts/perceiver_encoder.py new file mode 100644 index 0000000000..7b7ee79b50 --- /dev/null +++ b/TTS/tts/layers/xtts/perceiver_encoder.py @@ -0,0 +1,319 @@ +# Adapted from https://github.com/lucidrains/naturalspeech2-pytorch/blob/659bec7f7543e7747e809e950cc2f84242fbeec7/naturalspeech2_pytorch/naturalspeech2_pytorch.py#L532 + +from collections import namedtuple +from functools import wraps + +import torch +import torch.nn.functional as F +from einops import rearrange, repeat +from einops.layers.torch import Rearrange +from packaging import version +from torch import einsum, nn + + +def exists(val): + return val is not None + + +def once(fn): + called = False + + @wraps(fn) + def inner(x): + nonlocal called + if called: + return + called = True + return fn(x) + + return inner + + +print_once = once(print) + +# main class + + +class Attend(nn.Module): + def __init__(self, dropout=0.0, causal=False, use_flash=False): + super().__init__() + self.dropout = dropout + self.attn_dropout = nn.Dropout(dropout) + + self.causal = causal + self.register_buffer("mask", None, persistent=False) + + self.use_flash = use_flash + assert not ( + use_flash and version.parse(torch.__version__) < version.parse("2.0.0") + ), "in order to use flash attention, you must be using pytorch 2.0 or above" + + # determine efficient attention configs for cuda and cpu + self.config = namedtuple("EfficientAttentionConfig", ["enable_flash", "enable_math", "enable_mem_efficient"]) + self.cpu_config = self.config(True, True, True) + self.cuda_config = None + + if not torch.cuda.is_available() or not use_flash: + return + + device_properties = torch.cuda.get_device_properties(torch.device("cuda")) + + if device_properties.major == 8 and device_properties.minor == 0: + print_once("A100 GPU detected, using flash attention if input tensor is on cuda") + self.cuda_config = self.config(True, False, False) + else: + print_once("Non-A100 GPU detected, using math or mem efficient attention if input tensor is on cuda") + self.cuda_config = self.config(False, True, True) + + def get_mask(self, n, device): + if exists(self.mask) and self.mask.shape[-1] >= n: + return self.mask[:n, :n] + + mask = torch.ones((n, n), device=device, dtype=torch.bool).triu(1) + self.register_buffer("mask", mask, persistent=False) + return mask + + def flash_attn(self, q, k, v, mask=None): + _, heads, q_len, _, k_len, is_cuda = *q.shape, k.shape[-2], q.is_cuda + + # Recommended for multi-query single-key-value attention by Tri Dao + # kv shape torch.Size([1, 512, 64]) -> torch.Size([1, 8, 512, 64]) + + if k.ndim == 3: + k = rearrange(k, "b ... -> b 1 ...").expand_as(q) + + if v.ndim == 3: + v = rearrange(v, "b ... -> b 1 ...").expand_as(q) + + # Check if mask exists and expand to compatible shape + # The mask is B L, so it would have to be expanded to B H N L + + if exists(mask): + mask = rearrange(mask, "b j -> b 1 1 j") + mask = mask.expand(-1, heads, q_len, -1) + + # Check if there is a compatible device for flash attention + + config = self.cuda_config if is_cuda else self.cpu_config + + # pytorch 2.0 flash attn: q, k, v, mask, dropout, causal, softmax_scale + + with torch.backends.cuda.sdp_kernel(**config._asdict()): + out = F.scaled_dot_product_attention( + q, k, v, attn_mask=mask, dropout_p=self.dropout if self.training else 0.0, is_causal=self.causal + ) + + return out + + def forward(self, q, k, v, mask=None): + """ + einstein notation + b - batch + h - heads + n, i, j - sequence length (base sequence length, source, target) + d - feature dimension + """ + + n, device = q.shape[-2], q.device + + scale = q.shape[-1] ** -0.5 + + if self.use_flash: + return self.flash_attn(q, k, v, mask=mask) + + kv_einsum_eq = "b j d" if k.ndim == 3 else "b h j d" + + # similarity + + sim = einsum(f"b h i d, {kv_einsum_eq} -> b h i j", q, k) * scale + + # key padding mask + + if exists(mask): + mask = rearrange(mask, "b j -> b 1 1 j") + sim = sim.masked_fill(~mask, -torch.finfo(sim.dtype).max) + + # causal mask + + if self.causal: + causal_mask = self.get_mask(n, device) + sim = sim.masked_fill(causal_mask, -torch.finfo(sim.dtype).max) + + # attention + + attn = sim.softmax(dim=-1) + attn = self.attn_dropout(attn) + + # aggregate values + + out = einsum(f"b h i j, {kv_einsum_eq} -> b h i d", attn, v) + + return out + + +def Sequential(*mods): + return nn.Sequential(*filter(exists, mods)) + + +def exists(x): + return x is not None + + +def default(val, d): + if exists(val): + return val + return d() if callable(d) else d + + +class RMSNorm(nn.Module): + def __init__(self, dim, scale=True, dim_cond=None): + super().__init__() + self.cond = exists(dim_cond) + self.to_gamma_beta = nn.Linear(dim_cond, dim * 2) if self.cond else None + + self.scale = dim**0.5 + self.gamma = nn.Parameter(torch.ones(dim)) if scale else None + + def forward(self, x, cond=None): + gamma = default(self.gamma, 1) + out = F.normalize(x, dim=-1) * self.scale * gamma + + if not self.cond: + return out + + assert exists(cond) + gamma, beta = self.to_gamma_beta(cond).chunk(2, dim=-1) + gamma, beta = map(lambda t: rearrange(t, "b d -> b 1 d"), (gamma, beta)) + return out * gamma + beta + + +class CausalConv1d(nn.Conv1d): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + (kernel_size,) = self.kernel_size + (dilation,) = self.dilation + (stride,) = self.stride + + assert stride == 1 + self.causal_padding = dilation * (kernel_size - 1) + + def forward(self, x): + causal_padded_x = F.pad(x, (self.causal_padding, 0), value=0.0) + return super().forward(causal_padded_x) + + +class GEGLU(nn.Module): + def forward(self, x): + x, gate = x.chunk(2, dim=-1) + return F.gelu(gate) * x + + +def FeedForward(dim, mult=4, causal_conv=False): + dim_inner = int(dim * mult * 2 / 3) + + conv = None + if causal_conv: + conv = nn.Sequential( + Rearrange("b n d -> b d n"), + CausalConv1d(dim_inner, dim_inner, 3), + Rearrange("b d n -> b n d"), + ) + + return Sequential(nn.Linear(dim, dim_inner * 2), GEGLU(), conv, nn.Linear(dim_inner, dim)) + + +class PerceiverResampler(nn.Module): + def __init__( + self, + *, + dim, + depth=2, + dim_context=None, + num_latents=32, + dim_head=64, + heads=8, + ff_mult=4, + use_flash_attn=False, + ): + super().__init__() + dim_context = default(dim_context, dim) + + self.proj_context = nn.Linear(dim_context, dim) if dim_context != dim else nn.Identity() + + self.latents = nn.Parameter(torch.randn(num_latents, dim)) + nn.init.normal_(self.latents, std=0.02) + + self.layers = nn.ModuleList([]) + for _ in range(depth): + self.layers.append( + nn.ModuleList( + [ + Attention( + dim=dim, + dim_head=dim_head, + heads=heads, + use_flash=use_flash_attn, + cross_attn_include_queries=True, + ), + FeedForward(dim=dim, mult=ff_mult), + ] + ) + ) + + self.norm = RMSNorm(dim) + + def forward(self, x, mask=None): + batch = x.shape[0] + + x = self.proj_context(x) + + latents = repeat(self.latents, "n d -> b n d", b=batch) + + for attn, ff in self.layers: + latents = attn(latents, x, mask=mask) + latents + latents = ff(latents) + latents + + return self.norm(latents) + + +class Attention(nn.Module): + def __init__( + self, + dim, + *, + dim_context=None, + causal=False, + dim_head=64, + heads=8, + dropout=0.0, + use_flash=False, + cross_attn_include_queries=False, + ): + super().__init__() + self.scale = dim_head**-0.5 + self.heads = heads + self.cross_attn_include_queries = cross_attn_include_queries + + dim_inner = dim_head * heads + dim_context = default(dim_context, dim) + + self.attend = Attend(causal=causal, dropout=dropout, use_flash=use_flash) + self.to_q = nn.Linear(dim, dim_inner, bias=False) + self.to_kv = nn.Linear(dim_context, dim_inner * 2, bias=False) + self.to_out = nn.Linear(dim_inner, dim, bias=False) + + def forward(self, x, context=None, mask=None): + h, has_context = self.heads, exists(context) + + context = default(context, x) + + if has_context and self.cross_attn_include_queries: + context = torch.cat((x, context), dim=-2) + + q, k, v = (self.to_q(x), *self.to_kv(context).chunk(2, dim=-1)) + q, k, v = map(lambda t: rearrange(t, "b n (h d) -> b h n d", h=h), (q, k, v)) + + out = self.attend(q, k, v, mask=mask) + + out = rearrange(out, "b h n d -> b n (h d)") + return self.to_out(out) diff --git a/TTS/tts/layers/xtts/tokenizer.py b/TTS/tts/layers/xtts/tokenizer.py index 456f8081be..4f2da02d63 100644 --- a/TTS/tts/layers/xtts/tokenizer.py +++ b/TTS/tts/layers/xtts/tokenizer.py @@ -4,6 +4,8 @@ import pypinyin import torch +from hangul_romanize import Transliter +from hangul_romanize.rule import academic from num2words import num2words from tokenizers import Tokenizer @@ -112,7 +114,7 @@ # There are not many common abbreviations in Arabic as in English. ] ], - "zh-cn": [ + "zh": [ (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1]) for x in [ # Chinese doesn't typically use abbreviations in the same way as Latin-based scripts. @@ -155,6 +157,21 @@ # Add other Turkish abbreviations here if needed. ] ], + "hu": [ + (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1]) + for x in [ + ("dr", "doktor"), # doctor + ("b", "bácsi"), # Mr. + ("nőv", "nővér"), # nurse + # Add other Hungarian abbreviations here if needed. + ] + ], + "ko": [ + (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1]) + for x in [ + # Korean doesn't typically use abbreviations in the same way as Latin-based scripts. + ] + ], } @@ -262,7 +279,7 @@ def expand_abbreviations_multilingual(text, lang="en"): ("°", " درجة "), ] ], - "zh-cn": [ + "zh": [ # Chinese (re.compile(r"%s" % re.escape(x[0]), re.IGNORECASE), x[1]) for x in [ @@ -326,6 +343,31 @@ def expand_abbreviations_multilingual(text, lang="en"): ("°", " derece "), ] ], + "hu": [ + (re.compile(r"%s" % re.escape(x[0]), re.IGNORECASE), x[1]) + for x in [ + ("&", " és "), + ("@", " kukac "), + ("%", " százalék "), + ("#", " kettőskereszt "), + ("$", " dollár "), + ("£", " font "), + ("°", " fok "), + ] + ], + "ko": [ + # Korean + (re.compile(r"%s" % re.escape(x[0]), re.IGNORECASE), x[1]) + for x in [ + ("&", " 그리고 "), + ("@", " 에 "), + ("%", " 퍼센트 "), + ("#", " 번호 "), + ("$", " 달러 "), + ("£", " 파운드 "), + ("°", " 도 "), + ] + ], } @@ -349,6 +391,8 @@ def expand_symbols_multilingual(text, lang="en"): "ru": re.compile(r"([0-9]+)(-й|-я|-е|-ое|-ье|-го)"), "nl": re.compile(r"([0-9]+)(de|ste|e)"), "tr": re.compile(r"([0-9]+)(\.|inci|nci|uncu|üncü|\.)"), + "hu": re.compile(r"([0-9]+)(\.|adik|edik|odik|edik|ödik|ödike|ik)"), + "ko": re.compile(r"([0-9]+)(번째|번|차|째)"), } _number_re = re.compile(r"[0-9]+") _currency_re = { @@ -398,6 +442,8 @@ def _expand_currency(m, lang="en", currency="USD"): "nl": ", ", "ar": ", ", "tr": ", ", + "hu": ", ", + "ko": ", ", } if amount.is_integer(): @@ -417,7 +463,7 @@ def _expand_number(m, lang="en"): def expand_numbers_multilingual(text, lang="en"): - if lang == "zh-cn": + if lang == "zh" or lang == "zh-cn": text = zh_num2words()(text) else: if lang in ["en", "ru"]: @@ -468,7 +514,7 @@ def basic_cleaners(text): def chinese_transliterate(text): return "".join( - p[0] for p in pypinyin.pinyin(text, style=pypinyin.Style.TONE3, heteronym=False, neutral_tone_with_five=True) + [p[0] for p in pypinyin.pinyin(text, style=pypinyin.Style.TONE3, heteronym=False, neutral_tone_with_five=True)] ) @@ -478,42 +524,23 @@ def japanese_cleaners(text, katsu): return text -class VoiceBpeTokenizer: - def __init__(self, vocab_file=None, preprocess=None): - self.tokenizer = None - self.katsu = None +def korean_cleaners(text): + r = Transliter(academic) + return r.translit(text) - if vocab_file is not None: - with open(vocab_file, "r", encoding="utf-8") as f: - vocab = json.load(f) - self.language = vocab["model"]["language"] if "language" in vocab["model"] else None +DEFAULT_VOCAB_FILE = os.path.join(os.path.dirname(os.path.realpath(__file__)), "../data/tokenizer.json") - if preprocess is None: - self.preprocess = "pre_tokenizer" in vocab and vocab["pre_tokenizer"] - else: - self.preprocess = preprocess +class VoiceBpeTokenizer: + def __init__(self, vocab_file=None): + self.tokenizer = None + self.katsu = None + if vocab_file is not None: self.tokenizer = Tokenizer.from_file(vocab_file) - def preprocess_text(self, txt, lang): - if lang in ["en", "es", "fr", "de", "pt", "it", "pl", "ar", "cs", "ru", "nl", "tr", "zh-cn"]: - txt = multilingual_cleaners(txt, lang) - if lang == "zh-cn": - txt = chinese_transliterate(txt) - elif lang == "ja": - if self.katsu is None: - import cutlet - - self.katsu = cutlet.Cutlet() - txt = japanese_cleaners(txt, self.katsu) - else: - raise NotImplementedError() - return txt - def encode(self, txt, lang): - if self.preprocess: - txt = self.preprocess_text(txt, lang) + txt = self.preprocess_text(txt, lang) txt = f"[{lang}]{txt}" txt = txt.replace(" ", "[SPACE]") return self.tokenizer.encode(txt).ids @@ -527,8 +554,200 @@ def decode(self, seq): txt = txt.replace("[UNK]", "") return txt + def preprocess_text(self, txt, lang): + if lang in ["en", "es", "fr", "de", "pt", "it", "pl", "zh", "ar", "cs", "ru", "nl", "tr", "hu"]: + txt = multilingual_cleaners(txt, lang) + elif lang == "ja": + if self.katsu is None: + import cutlet + + self.katsu = cutlet.Cutlet() + txt = japanese_cleaners(txt, self.katsu) + elif lang == "zh-cn" or lang == "zh": + txt = chinese_transliterate(txt) + elif lang == "ko": + txt = korean_cleaners(txt) + else: + raise NotImplementedError() + return txt + def __len__(self): return self.tokenizer.get_vocab_size() def get_number_tokens(self): return max(self.tokenizer.get_vocab().values()) + 1 + + +def test_expand_numbers_multilingual(): + test_cases = [ + # English + ("In 12.5 seconds.", "In twelve point five seconds.", "en"), + ("There were 50 soldiers.", "There were fifty soldiers.", "en"), + ("This is a 1st test", "This is a first test", "en"), + ("That will be $20 sir.", "That will be twenty dollars sir.", "en"), + ("That will be 20€ sir.", "That will be twenty euro sir.", "en"), + ("That will be 20.15€ sir.", "That will be twenty euro, fifteen cents sir.", "en"), + ("That's 100,000.5.", "That's one hundred thousand point five.", "en"), + # French + ("En 12,5 secondes.", "En douze virgule cinq secondes.", "fr"), + ("Il y avait 50 soldats.", "Il y avait cinquante soldats.", "fr"), + ("Ceci est un 1er test", "Ceci est un premier test", "fr"), + ("Cela vous fera $20 monsieur.", "Cela vous fera vingt dollars monsieur.", "fr"), + ("Cela vous fera 20€ monsieur.", "Cela vous fera vingt euros monsieur.", "fr"), + ("Cela vous fera 20,15€ monsieur.", "Cela vous fera vingt euros et quinze centimes monsieur.", "fr"), + ("Ce sera 100.000,5.", "Ce sera cent mille virgule cinq.", "fr"), + # German + ("In 12,5 Sekunden.", "In zwölf Komma fünf Sekunden.", "de"), + ("Es gab 50 Soldaten.", "Es gab fünfzig Soldaten.", "de"), + ("Dies ist ein 1. Test", "Dies ist ein erste Test", "de"), # Issue with gender + ("Das macht $20 Herr.", "Das macht zwanzig Dollar Herr.", "de"), + ("Das macht 20€ Herr.", "Das macht zwanzig Euro Herr.", "de"), + ("Das macht 20,15€ Herr.", "Das macht zwanzig Euro und fünfzehn Cent Herr.", "de"), + # Spanish + ("En 12,5 segundos.", "En doce punto cinco segundos.", "es"), + ("Había 50 soldados.", "Había cincuenta soldados.", "es"), + ("Este es un 1er test", "Este es un primero test", "es"), + ("Eso le costará $20 señor.", "Eso le costará veinte dólares señor.", "es"), + ("Eso le costará 20€ señor.", "Eso le costará veinte euros señor.", "es"), + ("Eso le costará 20,15€ señor.", "Eso le costará veinte euros con quince céntimos señor.", "es"), + # Italian + ("In 12,5 secondi.", "In dodici virgola cinque secondi.", "it"), + ("C'erano 50 soldati.", "C'erano cinquanta soldati.", "it"), + ("Questo è un 1° test", "Questo è un primo test", "it"), + ("Ti costerà $20 signore.", "Ti costerà venti dollari signore.", "it"), + ("Ti costerà 20€ signore.", "Ti costerà venti euro signore.", "it"), + ("Ti costerà 20,15€ signore.", "Ti costerà venti euro e quindici centesimi signore.", "it"), + # Portuguese + ("Em 12,5 segundos.", "Em doze vírgula cinco segundos.", "pt"), + ("Havia 50 soldados.", "Havia cinquenta soldados.", "pt"), + ("Este é um 1º teste", "Este é um primeiro teste", "pt"), + ("Isso custará $20 senhor.", "Isso custará vinte dólares senhor.", "pt"), + ("Isso custará 20€ senhor.", "Isso custará vinte euros senhor.", "pt"), + ( + "Isso custará 20,15€ senhor.", + "Isso custará vinte euros e quinze cêntimos senhor.", + "pt", + ), # "cêntimos" should be "centavos" num2words issue + # Polish + ("W 12,5 sekundy.", "W dwanaście przecinek pięć sekundy.", "pl"), + ("Było 50 żołnierzy.", "Było pięćdziesiąt żołnierzy.", "pl"), + ("To będzie kosztować 20€ panie.", "To będzie kosztować dwadzieścia euro panie.", "pl"), + ("To będzie kosztować 20,15€ panie.", "To będzie kosztować dwadzieścia euro, piętnaście centów panie.", "pl"), + # Arabic + ("في الـ 12,5 ثانية.", "في الـ اثنا عشر , خمسون ثانية.", "ar"), + ("كان هناك 50 جنديًا.", "كان هناك خمسون جنديًا.", "ar"), + # ("ستكون النتيجة $20 يا سيد.", 'ستكون النتيجة عشرون دولار يا سيد.', 'ar'), # $ and € are mising from num2words + # ("ستكون النتيجة 20€ يا سيد.", 'ستكون النتيجة عشرون يورو يا سيد.', 'ar'), + # Czech + ("Za 12,5 vteřiny.", "Za dvanáct celá pět vteřiny.", "cs"), + ("Bylo tam 50 vojáků.", "Bylo tam padesát vojáků.", "cs"), + ("To bude stát 20€ pane.", "To bude stát dvacet euro pane.", "cs"), + ("To bude 20.15€ pane.", "To bude dvacet euro, patnáct centů pane.", "cs"), + # Russian + ("Через 12.5 секунды.", "Через двенадцать запятая пять секунды.", "ru"), + ("Там было 50 солдат.", "Там было пятьдесят солдат.", "ru"), + ("Это будет 20.15€ сэр.", "Это будет двадцать евро, пятнадцать центов сэр.", "ru"), + ("Это будет стоить 20€ господин.", "Это будет стоить двадцать евро господин.", "ru"), + # Dutch + ("In 12,5 seconden.", "In twaalf komma vijf seconden.", "nl"), + ("Er waren 50 soldaten.", "Er waren vijftig soldaten.", "nl"), + ("Dat wordt dan $20 meneer.", "Dat wordt dan twintig dollar meneer.", "nl"), + ("Dat wordt dan 20€ meneer.", "Dat wordt dan twintig euro meneer.", "nl"), + # Chinese (Simplified) + ("在12.5秒内", "在十二点五秒内", "zh"), + ("有50名士兵", "有五十名士兵", "zh"), + # ("那将是$20先生", '那将是二十美元先生', 'zh'), currency doesn't work + # ("那将是20€先生", '那将是二十欧元先生', 'zh'), + # Turkish + # ("12,5 saniye içinde.", 'On iki virgül beş saniye içinde.', 'tr'), # decimal doesn't work for TR + ("50 asker vardı.", "elli asker vardı.", "tr"), + ("Bu 1. test", "Bu birinci test", "tr"), + # ("Bu 100.000,5.", 'Bu yüz bin virgül beş.', 'tr'), + # Hungarian + ("12,5 másodperc alatt.", "tizenkettő egész öt tized másodperc alatt.", "hu"), + ("50 katona volt.", "ötven katona volt.", "hu"), + ("Ez az 1. teszt", "Ez az első teszt", "hu"), + # Korean + ("12.5 초 안에.", "십이 점 다섯 초 안에.", "ko"), + ("50 명의 병사가 있었다.", "오십 명의 병사가 있었다.", "ko"), + ("이것은 1 번째 테스트입니다", "이것은 첫 번째 테스트입니다", "ko"), + ] + for a, b, lang in test_cases: + out = expand_numbers_multilingual(a, lang=lang) + assert out == b, f"'{out}' vs '{b}'" + + +def test_abbreviations_multilingual(): + test_cases = [ + # English + ("Hello Mr. Smith.", "Hello mister Smith.", "en"), + ("Dr. Jones is here.", "doctor Jones is here.", "en"), + # Spanish + ("Hola Sr. Garcia.", "Hola señor Garcia.", "es"), + ("La Dra. Martinez es muy buena.", "La doctora Martinez es muy buena.", "es"), + # French + ("Bonjour Mr. Dupond.", "Bonjour monsieur Dupond.", "fr"), + ("Mme. Moreau est absente aujourd'hui.", "madame Moreau est absente aujourd'hui.", "fr"), + # German + ("Frau Dr. Müller ist sehr klug.", "Frau doktor Müller ist sehr klug.", "de"), + # Portuguese + ("Olá Sr. Silva.", "Olá senhor Silva.", "pt"), + ("Dra. Costa, você está disponível?", "doutora Costa, você está disponível?", "pt"), + # Italian + ("Buongiorno, Sig. Rossi.", "Buongiorno, signore Rossi.", "it"), + # ("Sig.ra Bianchi, posso aiutarti?", 'signora Bianchi, posso aiutarti?', 'it'), # Issue with matching that pattern + # Polish + ("Dzień dobry, P. Kowalski.", "Dzień dobry, pani Kowalski.", "pl"), + ("M. Nowak, czy mogę zadać pytanie?", "pan Nowak, czy mogę zadać pytanie?", "pl"), + # Czech + ("P. Novák", "pan Novák", "cs"), + ("Dr. Vojtěch", "doktor Vojtěch", "cs"), + # Dutch + ("Dhr. Jansen", "de heer Jansen", "nl"), + ("Mevr. de Vries", "mevrouw de Vries", "nl"), + # Russian + ("Здравствуйте Г-н Иванов.", "Здравствуйте господин Иванов.", "ru"), + ("Д-р Смирнов здесь, чтобы увидеть вас.", "доктор Смирнов здесь, чтобы увидеть вас.", "ru"), + # Turkish + ("Merhaba B. Yılmaz.", "Merhaba bay Yılmaz.", "tr"), + ("Dr. Ayşe burada.", "doktor Ayşe burada.", "tr"), + # Hungarian + ("Dr. Szabó itt van.", "doktor Szabó itt van.", "hu"), + ] + + for a, b, lang in test_cases: + out = expand_abbreviations_multilingual(a, lang=lang) + assert out == b, f"'{out}' vs '{b}'" + + +def test_symbols_multilingual(): + test_cases = [ + ("I have 14% battery", "I have 14 percent battery", "en"), + ("Te veo @ la fiesta", "Te veo arroba la fiesta", "es"), + ("J'ai 14° de fièvre", "J'ai 14 degrés de fièvre", "fr"), + ("Die Rechnung beträgt £ 20", "Die Rechnung beträgt pfund 20", "de"), + ("O meu email é ana&joao@gmail.com", "O meu email é ana e joao arroba gmail.com", "pt"), + ("linguaggio di programmazione C#", "linguaggio di programmazione C cancelletto", "it"), + ("Moja temperatura to 36.6°", "Moja temperatura to 36.6 stopnie", "pl"), + ("Mám 14% baterie", "Mám 14 procento baterie", "cs"), + ("Těším se na tebe @ party", "Těším se na tebe na party", "cs"), + ("У меня 14% заряда", "У меня 14 процентов заряда", "ru"), + ("Я буду @ дома", "Я буду собака дома", "ru"), + ("Ik heb 14% batterij", "Ik heb 14 procent batterij", "nl"), + ("Ik zie je @ het feest", "Ik zie je bij het feest", "nl"), + ("لدي 14% في البطارية", "لدي 14 في المئة في البطارية", "ar"), + ("我的电量为 14%", "我的电量为 14 百分之", "zh"), + ("Pilim %14 dolu.", "Pilim yüzde 14 dolu.", "tr"), + ("Az akkumulátorom töltöttsége 14%", "Az akkumulátorom töltöttsége 14 százalék", "hu"), + ("배터리 잔량이 14%입니다.", "배터리 잔량이 14 퍼센트입니다.", "ko"), + ] + + for a, b, lang in test_cases: + out = expand_symbols_multilingual(a, lang=lang) + assert out == b, f"'{out}' vs '{b}'" + + +if __name__ == "__main__": + test_expand_numbers_multilingual() + test_abbreviations_multilingual() + test_symbols_multilingual() diff --git a/TTS/tts/layers/xtts/trainer/dataset.py b/TTS/tts/layers/xtts/trainer/dataset.py index 41401fd6f8..5d8b2ae66b 100644 --- a/TTS/tts/layers/xtts/trainer/dataset.py +++ b/TTS/tts/layers/xtts/trainer/dataset.py @@ -88,6 +88,7 @@ def __init__(self, config, samples, tokenizer, sample_rate, is_eval=False): self.sample_rate = sample_rate self.max_wav_len = model_args.max_wav_length self.max_text_len = model_args.max_text_length + self.use_masking_gt_prompt_approach = model_args.gpt_use_masking_gt_prompt_approach assert self.max_wav_len is not None and self.max_text_len is not None self.samples = samples @@ -109,7 +110,7 @@ def check_eval_samples(self): try: tseq, _, wav, _, _, _ = self.load_item(sample) except: - pass + continue # Basically, this audio file is nonexistent or too long to be supported by the dataset. if ( wav is None @@ -140,10 +141,24 @@ def load_item(self, sample): # Ultra short clips are also useless (and can cause problems within some models). raise ValueError - # get a slice from GT to condition the model - cond, cond_len, cond_idxs = get_prompt_slice( - audiopath, self.max_conditioning_length, self.min_conditioning_length, self.sample_rate, self.is_eval - ) + if self.use_masking_gt_prompt_approach: + # get a slice from GT to condition the model + cond, _, cond_idxs = get_prompt_slice( + audiopath, self.max_conditioning_length, self.min_conditioning_length, self.sample_rate, self.is_eval + ) + # if use masking do not use cond_len + cond_len = torch.nan + else: + ref_sample = ( + sample["reference_path"] + if "reference_path" in sample and sample["reference_path"] is not None + else audiopath + ) + cond, cond_len, _ = get_prompt_slice( + ref_sample, self.max_conditioning_length, self.min_conditioning_length, self.sample_rate, self.is_eval + ) + # if do not use masking use cond_len + cond_idxs = torch.nan return tseq, audiopath, wav, cond, cond_len, cond_idxs @@ -199,8 +214,10 @@ def __getitem__(self, index): "wav_lengths": torch.tensor(wav.shape[-1], dtype=torch.long), "filenames": audiopath, "conditioning": cond.unsqueeze(1), - "cond_lens": torch.tensor(cond_len, dtype=torch.long), - "cond_idxs": torch.tensor(cond_idxs), + "cond_lens": torch.tensor(cond_len, dtype=torch.long) + if cond_len is not torch.nan + else torch.tensor([cond_len]), + "cond_idxs": torch.tensor(cond_idxs) if cond_idxs is not torch.nan else torch.tensor([cond_idxs]), } return res @@ -221,6 +238,13 @@ def collate_fn(self, batch): batch["conditioning"] = torch.stack(batch["conditioning"]) batch["cond_lens"] = torch.stack(batch["cond_lens"]) batch["cond_idxs"] = torch.stack(batch["cond_idxs"]) + + if torch.any(batch["cond_idxs"].isnan()): + batch["cond_idxs"] = None + + if torch.any(batch["cond_lens"].isnan()): + batch["cond_lens"] = None + max_text_len = batch["text_lengths"].max() max_wav_len = batch["wav_lengths"].max() diff --git a/TTS/tts/layers/xtts/trainer/gpt_trainer.py b/TTS/tts/layers/xtts/trainer/gpt_trainer.py index e93063faf6..ef32a4abd0 100644 --- a/TTS/tts/layers/xtts/trainer/gpt_trainer.py +++ b/TTS/tts/layers/xtts/trainer/gpt_trainer.py @@ -141,17 +141,30 @@ def __init__(self, config: Coqpit): print(">> GPT weights restored from:", self.args.gpt_checkpoint) # Mel spectrogram extractor for conditioning - self.torch_mel_spectrogram_style_encoder = TorchMelSpectrogram( - filter_length=4096, - hop_length=1024, - win_length=4096, - normalize=False, - sampling_rate=config.audio.sample_rate, - mel_fmin=0, - mel_fmax=8000, - n_mel_channels=80, - mel_norm_file=self.args.mel_norm_file, - ) + if self.args.gpt_use_perceiver_resampler: + self.torch_mel_spectrogram_style_encoder = TorchMelSpectrogram( + filter_length=2048, + hop_length=256, + win_length=1024, + normalize=False, + sampling_rate=config.audio.sample_rate, + mel_fmin=0, + mel_fmax=8000, + n_mel_channels=80, + mel_norm_file=self.args.mel_norm_file, + ) + else: + self.torch_mel_spectrogram_style_encoder = TorchMelSpectrogram( + filter_length=4096, + hop_length=1024, + win_length=4096, + normalize=False, + sampling_rate=config.audio.sample_rate, + mel_fmin=0, + mel_fmax=8000, + n_mel_channels=80, + mel_norm_file=self.args.mel_norm_file, + ) # Load DVAE self.dvae = DiscreteVAE( @@ -186,7 +199,7 @@ def __init__(self, config: Coqpit): def device(self): return next(self.parameters()).device - def forward(self, text_inputs, text_lengths, audio_codes, wav_lengths, cond_mels, cond_idxs): + def forward(self, text_inputs, text_lengths, audio_codes, wav_lengths, cond_mels, cond_idxs, cond_lens): """ Forward pass that uses both text and voice in either text conditioning mode or voice conditioning mode (actuated by `text_first`). @@ -197,9 +210,16 @@ def forward(self, text_inputs, text_lengths, audio_codes, wav_lengths, cond_mels wav_lengths: long tensor, (b,) cond_mels: MEL float tensor, (b, num_samples, 80,t_m) cond_idxs: cond start and end indexs, (b, 2) + cond_lens: long tensor, (b,) """ losses = self.xtts.gpt( - text_inputs, text_lengths, audio_codes, wav_lengths, cond_mels=cond_mels, cond_idxs=cond_idxs + text_inputs, + text_lengths, + audio_codes, + wav_lengths, + cond_mels=cond_mels, + cond_idxs=cond_idxs, + cond_lens=cond_lens, ) return losses @@ -213,7 +233,12 @@ def test_run(self, assets) -> Tuple[Dict, Dict]: # pylint: disable=W0613 print(" | > Synthesizing test sentences.") for idx, s_info in enumerate(self.config.test_sentences): wav = self.xtts.synthesize( - s_info["text"], self.config, s_info["speaker_wav"], s_info["language"], gpt_cond_len=3 + s_info["text"], + self.config, + s_info["speaker_wav"], + s_info["language"], + gpt_cond_len=3, + decoder="ne_hifigan", )["wav"] test_audios["{}-audio".format(idx)] = wav @@ -269,7 +294,6 @@ def format_batch_on_device(self, batch): del batch["padded_text"] del batch["wav"] del batch["conditioning"] - del batch["cond_lens"] return batch def train_step(self, batch, criterion): @@ -280,8 +304,11 @@ def train_step(self, batch, criterion): audio_codes = batch["audio_codes"] wav_lengths = batch["wav_lengths"] cond_idxs = batch["cond_idxs"] + cond_lens = batch["cond_lens"] - loss_text, loss_mel, _ = self.forward(text_inputs, text_lengths, audio_codes, wav_lengths, cond_mels, cond_idxs) + loss_text, loss_mel, _ = self.forward( + text_inputs, text_lengths, audio_codes, wav_lengths, cond_mels, cond_idxs, cond_lens + ) loss_dict["loss_text_ce"] = loss_text * self.args.gpt_loss_text_ce_weight loss_dict["loss_mel_ce"] = loss_mel * self.args.gpt_loss_mel_ce_weight loss_dict["loss"] = loss_dict["loss_text_ce"] + loss_dict["loss_mel_ce"] diff --git a/TTS/tts/layers/xtts/zh_num2words.py b/TTS/tts/layers/xtts/zh_num2words.py index 2c56e3bbeb..ea6d98d3da 100644 --- a/TTS/tts/layers/xtts/zh_num2words.py +++ b/TTS/tts/layers/xtts/zh_num2words.py @@ -14,375 +14,379 @@ # ================================================================================ # # basic constant # ================================================================================ # -CHINESE_DIGIS = u'零一二三四五六七八九' -BIG_CHINESE_DIGIS_SIMPLIFIED = u'零壹贰叁肆伍陆柒捌玖' -BIG_CHINESE_DIGIS_TRADITIONAL = u'零壹貳參肆伍陸柒捌玖' -SMALLER_BIG_CHINESE_UNITS_SIMPLIFIED = u'十百千万' -SMALLER_BIG_CHINESE_UNITS_TRADITIONAL = u'拾佰仟萬' -LARGER_CHINESE_NUMERING_UNITS_SIMPLIFIED = u'亿兆京垓秭穰沟涧正载' -LARGER_CHINESE_NUMERING_UNITS_TRADITIONAL = u'億兆京垓秭穰溝澗正載' -SMALLER_CHINESE_NUMERING_UNITS_SIMPLIFIED = u'十百千万' -SMALLER_CHINESE_NUMERING_UNITS_TRADITIONAL = u'拾佰仟萬' - -ZERO_ALT = u'〇' -ONE_ALT = u'幺' -TWO_ALTS = [u'两', u'兩'] - -POSITIVE = [u'正', u'正'] -NEGATIVE = [u'负', u'負'] -POINT = [u'点', u'點'] +CHINESE_DIGIS = "零一二三四五六七八九" +BIG_CHINESE_DIGIS_SIMPLIFIED = "零壹贰叁肆伍陆柒捌玖" +BIG_CHINESE_DIGIS_TRADITIONAL = "零壹貳參肆伍陸柒捌玖" +SMALLER_BIG_CHINESE_UNITS_SIMPLIFIED = "十百千万" +SMALLER_BIG_CHINESE_UNITS_TRADITIONAL = "拾佰仟萬" +LARGER_CHINESE_NUMERING_UNITS_SIMPLIFIED = "亿兆京垓秭穰沟涧正载" +LARGER_CHINESE_NUMERING_UNITS_TRADITIONAL = "億兆京垓秭穰溝澗正載" +SMALLER_CHINESE_NUMERING_UNITS_SIMPLIFIED = "十百千万" +SMALLER_CHINESE_NUMERING_UNITS_TRADITIONAL = "拾佰仟萬" + +ZERO_ALT = "〇" +ONE_ALT = "幺" +TWO_ALTS = ["两", "兩"] + +POSITIVE = ["正", "正"] +NEGATIVE = ["负", "負"] +POINT = ["点", "點"] # PLUS = [u'加', u'加'] # SIL = [u'杠', u'槓'] -FILLER_CHARS = ['呃', '啊'] +FILLER_CHARS = ["呃", "啊"] -ER_WHITELIST = '(儿女|儿子|儿孙|女儿|儿媳|妻儿|' \ - '胎儿|婴儿|新生儿|婴幼儿|幼儿|少儿|小儿|儿歌|儿童|儿科|托儿所|孤儿|' \ - '儿戏|儿化|台儿庄|鹿儿岛|正儿八经|吊儿郎当|生儿育女|托儿带女|养儿防老|痴儿呆女|' \ - '佳儿佳妇|儿怜兽扰|儿无常父|儿不嫌母丑|儿行千里母担忧|儿大不由爷|苏乞儿)' +ER_WHITELIST = ( + "(儿女|儿子|儿孙|女儿|儿媳|妻儿|" + "胎儿|婴儿|新生儿|婴幼儿|幼儿|少儿|小儿|儿歌|儿童|儿科|托儿所|孤儿|" + "儿戏|儿化|台儿庄|鹿儿岛|正儿八经|吊儿郎当|生儿育女|托儿带女|养儿防老|痴儿呆女|" + "佳儿佳妇|儿怜兽扰|儿无常父|儿不嫌母丑|儿行千里母担忧|儿大不由爷|苏乞儿)" +) ER_WHITELIST_PATTERN = re.compile(ER_WHITELIST) # 中文数字系统类型 -NUMBERING_TYPES = ['low', 'mid', 'high'] - -CURRENCY_NAMES = '(人民币|美元|日元|英镑|欧元|马克|法郎|加拿大元|澳元|港币|先令|芬兰马克|爱尔兰镑|' \ - '里拉|荷兰盾|埃斯库多|比塞塔|印尼盾|林吉特|新西兰元|比索|卢布|新加坡元|韩元|泰铢)' -CURRENCY_UNITS = '((亿|千万|百万|万|千|百)|(亿|千万|百万|万|千|百|)元|(亿|千万|百万|万|千|百|)块|角|毛|分)' -COM_QUANTIFIERS = '(匹|张|座|回|场|尾|条|个|首|阙|阵|网|炮|顶|丘|棵|只|支|袭|辆|挑|担|颗|壳|窠|曲|墙|群|腔|' \ - '砣|座|客|贯|扎|捆|刀|令|打|手|罗|坡|山|岭|江|溪|钟|队|单|双|对|出|口|头|脚|板|跳|枝|件|贴|' \ - '针|线|管|名|位|身|堂|课|本|页|家|户|层|丝|毫|厘|分|钱|两|斤|担|铢|石|钧|锱|忽|(千|毫|微)克|' \ - '毫|厘|分|寸|尺|丈|里|寻|常|铺|程|(千|分|厘|毫|微)米|撮|勺|合|升|斗|石|盘|碗|碟|叠|桶|笼|盆|' \ - '盒|杯|钟|斛|锅|簋|篮|盘|桶|罐|瓶|壶|卮|盏|箩|箱|煲|啖|袋|钵|年|月|日|季|刻|时|周|天|秒|分|旬|' \ - '纪|岁|世|更|夜|春|夏|秋|冬|代|伏|辈|丸|泡|粒|颗|幢|堆|条|根|支|道|面|片|张|颗|块)' +NUMBERING_TYPES = ["low", "mid", "high"] + +CURRENCY_NAMES = "(人民币|美元|日元|英镑|欧元|马克|法郎|加拿大元|澳元|港币|先令|芬兰马克|爱尔兰镑|" "里拉|荷兰盾|埃斯库多|比塞塔|印尼盾|林吉特|新西兰元|比索|卢布|新加坡元|韩元|泰铢)" +CURRENCY_UNITS = "((亿|千万|百万|万|千|百)|(亿|千万|百万|万|千|百|)元|(亿|千万|百万|万|千|百|)块|角|毛|分)" +COM_QUANTIFIERS = ( + "(匹|张|座|回|场|尾|条|个|首|阙|阵|网|炮|顶|丘|棵|只|支|袭|辆|挑|担|颗|壳|窠|曲|墙|群|腔|" + "砣|座|客|贯|扎|捆|刀|令|打|手|罗|坡|山|岭|江|溪|钟|队|单|双|对|出|口|头|脚|板|跳|枝|件|贴|" + "针|线|管|名|位|身|堂|课|本|页|家|户|层|丝|毫|厘|分|钱|两|斤|担|铢|石|钧|锱|忽|(千|毫|微)克|" + "毫|厘|分|寸|尺|丈|里|寻|常|铺|程|(千|分|厘|毫|微)米|撮|勺|合|升|斗|石|盘|碗|碟|叠|桶|笼|盆|" + "盒|杯|钟|斛|锅|簋|篮|盘|桶|罐|瓶|壶|卮|盏|箩|箱|煲|啖|袋|钵|年|月|日|季|刻|时|周|天|秒|分|旬|" + "纪|岁|世|更|夜|春|夏|秋|冬|代|伏|辈|丸|泡|粒|颗|幢|堆|条|根|支|道|面|片|张|颗|块)" +) # Punctuation information are based on Zhon project (https://github.com/tsroten/zhon.git) -CN_PUNCS_STOP = '！？｡。' -CN_PUNCS_NONSTOP = '＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､、〃《》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏·〈〉-' +CN_PUNCS_STOP = "！？｡。" +CN_PUNCS_NONSTOP = "＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､、〃《》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏·〈〉-" CN_PUNCS = CN_PUNCS_STOP + CN_PUNCS_NONSTOP PUNCS = CN_PUNCS + string.punctuation -PUNCS_TRANSFORM = str.maketrans(PUNCS, ' ' * len(PUNCS), '') # replace puncs with space +PUNCS_TRANSFORM = str.maketrans(PUNCS, " " * len(PUNCS), "") # replace puncs with space # https://zh.wikipedia.org/wiki/全行和半行 QJ2BJ = { - '　': ' ', - '！': '!', - '＂': '"', - '＃': '#', - '＄': '$', - '％': '%', - '＆': '&', - '＇': "'", - '（': '(', - '）': ')', - '＊': '*', - '＋': '+', - '，': ',', - '－': '-', - '．': '.', - '／': '/', - '０': '0', - '１': '1', - '２': '2', - '３': '3', - '４': '4', - '５': '5', - '６': '6', - '７': '7', - '８': '8', - '９': '9', - '：': ':', - '；': ';', - '＜': '<', - '＝': '=', - '＞': '>', - '？': '?', - '＠': '@', - 'Ａ': 'A', - 'Ｂ': 'B', - 'Ｃ': 'C', - 'Ｄ': 'D', - 'Ｅ': 'E', - 'Ｆ': 'F', - 'Ｇ': 'G', - 'Ｈ': 'H', - 'Ｉ': 'I', - 'Ｊ': 'J', - 'Ｋ': 'K', - 'Ｌ': 'L', - 'Ｍ': 'M', - 'Ｎ': 'N', - 'Ｏ': 'O', - 'Ｐ': 'P', - 'Ｑ': 'Q', - 'Ｒ': 'R', - 'Ｓ': 'S', - 'Ｔ': 'T', - 'Ｕ': 'U', - 'Ｖ': 'V', - 'Ｗ': 'W', - 'Ｘ': 'X', - 'Ｙ': 'Y', - 'Ｚ': 'Z', - '［': '[', - '＼': '\\', - '］': ']', - '＾': '^', - '＿': '_', - '｀': '`', - 'ａ': 'a', - 'ｂ': 'b', - 'ｃ': 'c', - 'ｄ': 'd', - 'ｅ': 'e', - 'ｆ': 'f', - 'ｇ': 'g', - 'ｈ': 'h', - 'ｉ': 'i', - 'ｊ': 'j', - 'ｋ': 'k', - 'ｌ': 'l', - 'ｍ': 'm', - 'ｎ': 'n', - 'ｏ': 'o', - 'ｐ': 'p', - 'ｑ': 'q', - 'ｒ': 'r', - 'ｓ': 's', - 'ｔ': 't', - 'ｕ': 'u', - 'ｖ': 'v', - 'ｗ': 'w', - 'ｘ': 'x', - 'ｙ': 'y', - 'ｚ': 'z', - '｛': '{', - '｜': '|', - '｝': '}', - '～': '~', + "　": " ", + "！": "!", + "＂": '"', + "＃": "#", + "＄": "$", + "％": "%", + "＆": "&", + "＇": "'", + "（": "(", + "）": ")", + "＊": "*", + "＋": "+", + "，": ",", + "－": "-", + "．": ".", + "／": "/", + "０": "0", + "１": "1", + "２": "2", + "３": "3", + "４": "4", + "５": "5", + "６": "6", + "７": "7", + "８": "8", + "９": "9", + "：": ":", + "；": ";", + "＜": "<", + "＝": "=", + "＞": ">", + "？": "?", + "＠": "@", + "Ａ": "A", + "Ｂ": "B", + "Ｃ": "C", + "Ｄ": "D", + "Ｅ": "E", + "Ｆ": "F", + "Ｇ": "G", + "Ｈ": "H", + "Ｉ": "I", + "Ｊ": "J", + "Ｋ": "K", + "Ｌ": "L", + "Ｍ": "M", + "Ｎ": "N", + "Ｏ": "O", + "Ｐ": "P", + "Ｑ": "Q", + "Ｒ": "R", + "Ｓ": "S", + "Ｔ": "T", + "Ｕ": "U", + "Ｖ": "V", + "Ｗ": "W", + "Ｘ": "X", + "Ｙ": "Y", + "Ｚ": "Z", + "［": "[", + "＼": "\\", + "］": "]", + "＾": "^", + "＿": "_", + "｀": "`", + "ａ": "a", + "ｂ": "b", + "ｃ": "c", + "ｄ": "d", + "ｅ": "e", + "ｆ": "f", + "ｇ": "g", + "ｈ": "h", + "ｉ": "i", + "ｊ": "j", + "ｋ": "k", + "ｌ": "l", + "ｍ": "m", + "ｎ": "n", + "ｏ": "o", + "ｐ": "p", + "ｑ": "q", + "ｒ": "r", + "ｓ": "s", + "ｔ": "t", + "ｕ": "u", + "ｖ": "v", + "ｗ": "w", + "ｘ": "x", + "ｙ": "y", + "ｚ": "z", + "｛": "{", + "｜": "|", + "｝": "}", + "～": "~", } -QJ2BJ_TRANSFORM = str.maketrans(''.join(QJ2BJ.keys()), ''.join(QJ2BJ.values()), '') +QJ2BJ_TRANSFORM = str.maketrans("".join(QJ2BJ.keys()), "".join(QJ2BJ.values()), "") # 2013 China National Standard: https://zh.wikipedia.org/wiki/通用规范汉字表, raw resources: # https://github.com/mozillazg/pinyin-data/blob/master/kMandarin_8105.txt with 8105 chinese chars in total CN_CHARS_COMMON = ( - '一丁七万丈三上下不与丏丐丑专且丕世丘丙业丛东丝丞丢两严丧个丫中丰串临丸丹为主丽举' - '乂乃久么义之乌乍乎乏乐乒乓乔乖乘乙乜九乞也习乡书乩买乱乳乸乾了予争事二亍于亏云互' - '亓五井亘亚些亟亡亢交亥亦产亨亩享京亭亮亲亳亵亶亸亹人亿什仁仂仃仄仅仆仇仉今介仍从' - '仑仓仔仕他仗付仙仝仞仟仡代令以仨仪仫们仰仲仳仵件价任份仿企伈伉伊伋伍伎伏伐休众优' - '伙会伛伞伟传伢伣伤伥伦伧伪伫伭伯估伲伴伶伸伺似伽伾佁佃但位低住佐佑体何佖佗佘余佚' - '佛作佝佞佟你佣佤佥佩佬佯佰佳佴佶佸佺佻佼佽佾使侁侂侃侄侈侉例侍侏侑侔侗侘供依侠侣' - '侥侦侧侨侩侪侬侮侯侴侵侹便促俄俅俊俍俎俏俐俑俗俘俙俚俜保俞俟信俣俦俨俩俪俫俭修俯' - '俱俳俵俶俸俺俾倌倍倏倒倓倔倕倘候倚倜倞借倡倥倦倧倨倩倪倬倭倮倴债倻值倾偁偃假偈偌' - '偎偏偓偕做停偡健偬偭偰偲偶偷偻偾偿傀傃傅傈傉傍傒傕傣傥傧储傩催傲傺傻僇僎像僔僖僚' - '僦僧僬僭僮僰僳僵僻儆儇儋儒儡儦儳儴儿兀允元兄充兆先光克免兑兔兕兖党兜兢入全八公六' - '兮兰共关兴兵其具典兹养兼兽冀冁内冈冉册再冏冒冔冕冗写军农冠冢冤冥冬冮冯冰冱冲决况' - '冶冷冻冼冽净凄准凇凉凋凌减凑凓凘凛凝几凡凤凫凭凯凰凳凶凸凹出击凼函凿刀刁刃分切刈' - '刊刍刎刑划刖列刘则刚创初删判刨利别刬刭刮到刳制刷券刹刺刻刽刿剀剁剂剃剅削剋剌前剐' - '剑剔剕剖剜剞剟剡剥剧剩剪副割剽剿劁劂劄劈劐劓力劝办功加务劢劣动助努劫劬劭励劲劳劼' - '劾势勃勇勉勋勍勐勒勔勖勘勚募勠勤勰勺勾勿匀包匆匈匍匏匐匕化北匙匜匝匠匡匣匦匪匮匹' - '区医匼匾匿十千卅升午卉半华协卑卒卓单卖南博卜卞卟占卡卢卣卤卦卧卫卬卮卯印危即却卵' - '卷卸卺卿厂厄厅历厉压厌厍厕厖厘厚厝原厢厣厥厦厨厩厮去厾县叁参叆叇又叉及友双反发叔' - '叕取受变叙叚叛叟叠口古句另叨叩只叫召叭叮可台叱史右叵叶号司叹叻叼叽吁吃各吆合吉吊' - '同名后吏吐向吒吓吕吖吗君吝吞吟吠吡吣否吧吨吩含听吭吮启吱吲吴吵吸吹吻吼吽吾呀呃呆' - '呇呈告呋呐呒呓呔呕呖呗员呙呛呜呢呣呤呦周呱呲味呵呶呷呸呻呼命咀咂咄咆咇咉咋和咍咎' - '咏咐咒咔咕咖咙咚咛咝咡咣咤咥咦咧咨咩咪咫咬咯咱咳咴咸咺咻咽咿哀品哂哃哄哆哇哈哉哌' - '响哎哏哐哑哒哓哔哕哗哙哚哝哞哟哢哥哦哧哨哩哪哭哮哱哲哳哺哼哽哿唁唆唇唉唏唐唑唔唛' - '唝唠唢唣唤唧唪唬售唯唰唱唳唵唷唼唾唿啁啃啄商啉啊啐啕啖啜啡啤啥啦啧啪啫啬啭啮啰啴' - '啵啶啷啸啻啼啾喀喁喂喃善喆喇喈喉喊喋喏喑喔喘喙喜喝喟喤喧喱喳喵喷喹喻喽喾嗄嗅嗉嗌' - '嗍嗐嗑嗒嗓嗔嗖嗜嗝嗞嗟嗡嗣嗤嗥嗦嗨嗪嗫嗬嗯嗲嗳嗵嗷嗽嗾嘀嘁嘈嘉嘌嘎嘏嘘嘚嘛嘞嘟嘡' - '嘣嘤嘧嘬嘭嘱嘲嘴嘶嘹嘻嘿噀噂噇噌噍噎噔噗噘噙噜噢噤器噩噪噫噬噱噶噻噼嚄嚅嚆嚎嚏嚓' - '嚚嚣嚭嚯嚷嚼囊囔囚四回囟因囡团囤囫园困囱围囵囷囹固国图囿圃圄圆圈圉圊圌圐圙圜土圢' - '圣在圩圪圫圬圭圮圯地圲圳圹场圻圾址坂均坉坊坋坌坍坎坏坐坑坒块坚坛坜坝坞坟坠坡坤坥' - '坦坨坩坪坫坬坭坯坰坳坷坻坼坽垂垃垄垆垈型垌垍垎垏垒垓垕垙垚垛垞垟垠垡垢垣垤垦垧垩' - '垫垭垮垯垱垲垴垵垸垺垾垿埂埃埆埇埋埌城埏埒埔埕埗埘埙埚埝域埠埤埪埫埭埯埴埵埸培基' - '埼埽堂堃堆堇堉堋堌堍堎堐堑堕堙堞堠堡堤堧堨堪堰堲堵堼堽堾塄塅塆塌塍塑塔塘塝塞塥填' - '塬塱塾墀墁境墅墈墉墐墒墓墕墘墙墚增墟墡墣墦墨墩墼壁壅壑壕壤士壬壮声壳壶壸壹处备复' - '夏夐夔夕外夙多夜够夤夥大天太夫夬夭央夯失头夷夸夹夺夼奁奂奄奇奈奉奋奎奏契奓奔奕奖' - '套奘奚奠奡奢奥奭女奴奶奸她好妁如妃妄妆妇妈妊妍妒妓妖妗妘妙妞妣妤妥妧妨妩妪妫妭妮' - '妯妲妹妻妾姆姈姊始姐姑姒姓委姗姘姚姜姝姞姣姤姥姨姬姮姱姶姹姻姽姿娀威娃娄娅娆娇娈' - '娉娌娑娓娘娜娟娠娣娥娩娱娲娴娵娶娼婀婆婉婊婌婍婕婘婚婞婠婢婤婧婪婫婳婴婵婶婷婺婻' - '婼婿媂媄媆媒媓媖媚媛媞媪媭媱媲媳媵媸媾嫁嫂嫄嫉嫌嫒嫔嫕嫖嫘嫚嫜嫠嫡嫣嫦嫩嫪嫫嫭嫱' - '嫽嬉嬖嬗嬛嬥嬬嬴嬷嬿孀孅子孑孓孔孕孖字存孙孚孛孜孝孟孢季孤孥学孩孪孬孰孱孳孵孺孽' - '宁它宄宅宇守安宋完宏宓宕宗官宙定宛宜宝实宠审客宣室宥宦宧宪宫宬宰害宴宵家宸容宽宾' - '宿寁寂寄寅密寇富寐寒寓寝寞察寡寤寥寨寮寰寸对寺寻导寿封射将尉尊小少尔尕尖尘尚尜尝' - '尢尤尥尧尨尪尬就尴尸尹尺尻尼尽尾尿局屁层屃居屈屉届屋屎屏屐屑展屙属屠屡屣履屦屯山' - '屹屺屼屾屿岁岂岈岊岌岍岐岑岔岖岗岘岙岚岛岜岞岠岢岣岨岩岫岬岭岱岳岵岷岸岽岿峁峂峃' - '峄峋峒峗峘峙峛峡峣峤峥峦峧峨峪峭峰峱峻峿崀崁崂崃崄崆崇崌崎崒崔崖崚崛崞崟崡崤崦崧' - '崩崭崮崴崶崽崾崿嵁嵅嵇嵊嵋嵌嵎嵖嵘嵚嵛嵝嵩嵫嵬嵯嵲嵴嶂嶅嶍嶒嶓嶙嶝嶟嶦嶲嶷巅巇巉' - '巍川州巡巢工左巧巨巩巫差巯己已巳巴巷巽巾币市布帅帆师希帏帐帑帔帕帖帘帙帚帛帜帝帡' - '带帧帨席帮帱帷常帻帼帽幂幄幅幌幔幕幖幛幞幡幢幪干平年并幸幺幻幼幽广庄庆庇床庋序庐' - '庑库应底庖店庙庚府庞废庠庤庥度座庭庱庳庵庶康庸庹庼庾廆廉廊廋廑廒廓廖廙廛廨廪延廷' - '建廿开弁异弃弄弆弇弈弊弋式弑弓引弗弘弛弟张弢弥弦弧弨弩弭弯弱弶弸弹强弼彀归当录彖' - '彗彘彝彟形彤彦彧彩彪彬彭彰影彳彷役彻彼往征徂径待徇很徉徊律徐徒徕得徘徙徛徜御徨循' - '徭微徵德徼徽心必忆忉忌忍忏忐忑忒忖志忘忙忝忞忠忡忤忧忪快忭忮忱忳念忸忺忻忽忾忿怀' - '态怂怃怄怅怆怊怍怎怏怒怔怕怖怙怛怜思怠怡急怦性怨怩怪怫怯怵总怼怿恁恂恃恋恍恐恒恓' - '恔恕恙恚恝恢恣恤恧恨恩恪恫恬恭息恰恳恶恸恹恺恻恼恽恿悃悄悆悈悉悌悍悒悔悖悚悛悝悟' - '悠悢患悦您悫悬悭悯悰悱悲悴悸悻悼情惆惇惊惋惎惑惔惕惘惙惚惛惜惝惟惠惦惧惨惩惫惬惭' - '惮惯惰想惴惶惹惺愀愁愃愆愈愉愍愎意愐愔愕愚感愠愣愤愦愧愫愭愿慆慈慊慌慎慑慕慝慢慥' - '慧慨慬慭慰慵慷憋憎憔憕憙憧憨憩憬憭憷憺憾懂懈懊懋懑懒懔懦懵懿戆戈戊戋戌戍戎戏成我' - '戒戕或戗战戚戛戟戡戢戣戤戥截戬戭戮戳戴户戽戾房所扁扂扃扅扆扇扈扉扊手才扎扑扒打扔' - '托扛扞扣扦执扩扪扫扬扭扮扯扰扳扶批扺扼扽找承技抃抄抉把抑抒抓抔投抖抗折抚抛抟抠抡' - '抢护报抨披抬抱抵抹抻押抽抿拂拃拄担拆拇拈拉拊拌拍拎拐拒拓拔拖拗拘拙招拜拟拢拣拤拥' - '拦拧拨择括拭拮拯拱拳拴拶拷拼拽拾拿持挂指挈按挎挑挓挖挚挛挝挞挟挠挡挣挤挥挦挨挪挫' - '振挲挹挺挽捂捃捅捆捉捋捌捍捎捏捐捕捞损捡换捣捧捩捭据捯捶捷捺捻捽掀掂掇授掉掊掌掎' - '掏掐排掖掘掞掠探掣接控推掩措掬掭掮掰掳掴掷掸掺掼掾揄揆揉揍描提插揕揖揠握揣揩揪揭' - '揳援揶揸揽揿搀搁搂搅搋搌搏搐搒搓搔搛搜搞搠搡搦搪搬搭搴携搽摁摄摅摆摇摈摊摏摒摔摘' - '摛摞摧摩摭摴摸摹摽撂撄撅撇撑撒撕撖撙撞撤撩撬播撮撰撵撷撸撺撼擀擂擅操擎擐擒擘擞擢' - '擤擦擿攀攉攒攘攥攫攮支收攸改攻攽放政故效敉敌敏救敔敕敖教敛敝敞敢散敦敩敫敬数敲整' - '敷文斋斌斐斑斓斗料斛斜斝斟斠斡斤斥斧斩斫断斯新斶方於施旁旃旄旅旆旋旌旎族旐旒旖旗' - '旞无既日旦旧旨早旬旭旮旯旰旱旴旵时旷旸旺旻旿昀昂昃昄昆昇昈昉昊昌明昏昒易昔昕昙昝' - '星映昡昣昤春昧昨昪昫昭是昱昳昴昵昶昺昼昽显晁晃晅晊晋晌晏晐晒晓晔晕晖晗晙晚晞晟晡' - '晢晤晦晨晪晫普景晰晱晴晶晷智晾暂暄暅暇暌暑暕暖暗暝暧暨暮暲暴暵暶暹暾暿曈曌曙曛曜' - '曝曦曩曰曲曳更曷曹曼曾替最月有朋服朏朐朓朔朕朗望朝期朦木未末本札术朱朳朴朵朸机朽' - '杀杂权杄杆杈杉杌李杏材村杓杕杖杙杜杞束杠条来杧杨杩杪杭杯杰杲杳杵杷杻杼松板极构枅' - '枇枉枋枍析枕林枘枚果枝枞枢枣枥枧枨枪枫枭枯枰枲枳枵架枷枸枹柁柃柄柈柊柏某柑柒染柔' - '柖柘柙柚柜柝柞柠柢查柩柬柯柰柱柳柴柷柽柿栀栅标栈栉栊栋栌栎栏栐树栒栓栖栗栝栟校栩' - '株栲栳栴样核根栻格栽栾桀桁桂桃桄桅框案桉桊桌桎桐桑桓桔桕桠桡桢档桤桥桦桧桨桩桫桯' - '桲桴桶桷桹梁梃梅梆梌梏梓梗梠梢梣梦梧梨梭梯械梳梴梵梼梽梾梿检棁棂棉棋棍棐棒棓棕棘' - '棚棠棣棤棨棪棫棬森棰棱棵棹棺棻棼棽椀椁椅椆椋植椎椐椑椒椓椟椠椤椪椭椰椴椸椹椽椿楂' - '楒楔楗楙楚楝楞楠楣楦楩楪楫楮楯楷楸楹楼概榃榄榅榆榇榈榉榍榑榔榕榖榛榜榧榨榫榭榰榱' - '榴榷榻槁槃槊槌槎槐槔槚槛槜槟槠槭槱槲槽槿樊樗樘樟模樨横樯樱樵樽樾橄橇橐橑橘橙橛橞' - '橡橥橦橱橹橼檀檄檎檐檑檗檞檠檩檫檬櫆欂欠次欢欣欤欧欲欸欹欺欻款歃歅歆歇歉歌歙止正' - '此步武歧歪歹死歼殁殂殃殄殆殇殉殊残殍殒殓殖殚殛殡殣殪殳殴段殷殿毁毂毅毋毌母每毐毒' - '毓比毕毖毗毙毛毡毪毫毯毳毵毹毽氅氆氇氍氏氐民氓气氕氖氘氙氚氛氟氡氢氤氦氧氨氩氪氮' - '氯氰氲水永氾氿汀汁求汆汇汈汉汊汋汐汔汕汗汛汜汝汞江池污汤汧汨汩汪汫汭汰汲汴汶汹汽' - '汾沁沂沃沄沅沆沇沈沉沌沏沐沓沔沘沙沚沛沟没沣沤沥沦沧沨沩沪沫沭沮沱河沸油沺治沼沽' - '沾沿泂泃泄泅泇泉泊泌泐泓泔法泖泗泙泚泛泜泞泠泡波泣泥注泪泫泮泯泰泱泳泵泷泸泺泻泼' - '泽泾洁洄洇洈洋洌洎洑洒洓洗洘洙洚洛洞洢洣津洧洨洪洫洭洮洱洲洳洴洵洸洹洺活洼洽派洿' - '流浃浅浆浇浈浉浊测浍济浏浐浑浒浓浔浕浙浚浛浜浞浟浠浡浣浥浦浩浪浬浭浮浯浰浲浴海浸' - '浼涂涄涅消涉涌涍涎涐涑涓涔涕涘涛涝涞涟涠涡涢涣涤润涧涨涩涪涫涮涯液涴涵涸涿淀淄淅' - '淆淇淋淌淏淑淖淘淙淜淝淞淟淠淡淤淦淫淬淮淯深淳淴混淹添淼清渊渌渍渎渐渑渔渗渚渝渟' - '渠渡渣渤渥温渫渭港渰渲渴游渺渼湃湄湉湍湎湑湓湔湖湘湛湜湝湟湣湫湮湲湴湾湿溁溃溅溆' - '溇溉溍溏源溘溚溜溞溟溠溢溥溦溧溪溯溱溲溴溵溶溷溹溺溻溽滁滂滃滆滇滉滋滍滏滑滓滔滕' - '滗滘滚滞滟滠满滢滤滥滦滧滨滩滪滫滴滹漂漆漈漉漋漏漓演漕漖漠漤漦漩漪漫漭漯漱漳漴漶' - '漷漹漻漼漾潆潇潋潍潏潖潘潜潞潟潢潦潩潭潮潲潴潵潸潺潼潽潾澂澄澈澉澌澍澎澛澜澡澥澧' - '澪澭澳澴澶澹澼澽激濂濉濋濑濒濞濠濡濩濮濯瀌瀍瀑瀔瀚瀛瀣瀱瀵瀹瀼灈灌灏灞火灭灯灰灵' - '灶灸灼灾灿炀炅炆炉炊炌炎炒炔炕炖炘炙炜炝炟炣炫炬炭炮炯炱炳炷炸点炻炼炽烀烁烂烃烈' - '烊烔烘烙烛烜烝烟烠烤烦烧烨烩烫烬热烯烶烷烹烺烻烽焆焉焊焌焐焓焕焖焗焘焙焚焜焞焦焯' - '焰焱然煁煃煅煊煋煌煎煓煜煞煟煤煦照煨煮煲煳煴煸煺煽熄熇熊熏熔熘熙熛熜熟熠熥熨熬熵' - '熹熻燃燊燋燎燏燔燕燚燠燥燧燮燹爆爇爔爚爝爟爨爪爬爰爱爵父爷爸爹爻爽爿牁牂片版牌牍' - '牒牖牙牚牛牝牟牡牢牤牥牦牧物牮牯牲牵特牺牻牾牿犀犁犄犇犊犋犍犏犒犟犨犬犯犰犴状犷' - '犸犹狁狂狃狄狈狉狍狎狐狒狗狙狝狞狠狡狨狩独狭狮狯狰狱狲狳狴狷狸狺狻狼猁猃猄猇猊猎' - '猕猖猗猛猜猝猞猡猢猥猩猪猫猬献猯猰猱猴猷猹猺猾猿獍獐獒獗獠獬獭獯獴獾玃玄率玉王玎' - '玑玒玓玕玖玘玙玚玛玞玟玠玡玢玤玥玦玩玫玭玮环现玱玲玳玶玷玹玺玻玼玿珀珂珅珇珈珉珊' - '珋珌珍珏珐珑珒珕珖珙珛珝珞珠珢珣珥珦珧珩珪珫班珰珲珵珷珸珹珺珽琀球琄琅理琇琈琉琊' - '琎琏琐琔琚琛琟琡琢琤琥琦琨琪琫琬琭琮琯琰琲琳琴琵琶琼瑀瑁瑂瑃瑄瑅瑆瑑瑓瑔瑕瑖瑗瑙' - '瑚瑛瑜瑝瑞瑟瑢瑧瑨瑬瑭瑰瑱瑳瑶瑷瑾璀璁璃璆璇璈璋璎璐璒璘璜璞璟璠璥璧璨璩璪璬璮璱' - '璲璺瓀瓒瓖瓘瓜瓞瓠瓢瓣瓤瓦瓮瓯瓴瓶瓷瓻瓿甄甍甏甑甓甗甘甚甜生甡甥甦用甩甪甫甬甭甯' - '田由甲申电男甸町画甾畀畅畈畋界畎畏畔畖留畚畛畜畤略畦番畬畯畲畴畸畹畿疁疃疆疍疏疐' - '疑疔疖疗疙疚疝疟疠疡疢疣疤疥疫疬疭疮疯疰疱疲疳疴疵疸疹疼疽疾痂痃痄病症痈痉痊痍痒' - '痓痔痕痘痛痞痢痣痤痦痧痨痪痫痰痱痴痹痼痿瘀瘁瘃瘅瘆瘊瘌瘐瘕瘗瘘瘙瘛瘟瘠瘢瘤瘥瘦瘩' - '瘪瘫瘭瘰瘳瘴瘵瘸瘼瘾瘿癀癃癌癍癔癖癗癜癞癣癫癯癸登白百癿皂的皆皇皈皋皎皑皓皕皖皙' - '皛皞皤皦皭皮皱皲皴皿盂盅盆盈盉益盍盎盏盐监盒盔盖盗盘盛盟盥盦目盯盱盲直盷相盹盼盾' - '省眄眇眈眉眊看眍眙眚真眠眢眦眨眩眬眭眯眵眶眷眸眺眼着睁睃睄睇睎睐睑睚睛睡睢督睥睦' - '睨睫睬睹睽睾睿瞀瞄瞅瞋瞌瞍瞎瞑瞒瞟瞠瞢瞥瞧瞩瞪瞫瞬瞭瞰瞳瞵瞻瞽瞿矍矗矛矜矞矢矣知' - '矧矩矫矬短矮矰石矶矸矻矼矾矿砀码砂砄砆砉砌砍砑砒研砖砗砘砚砜砝砟砠砣砥砧砫砬砭砮' - '砰破砵砷砸砹砺砻砼砾础硁硅硇硊硌硍硎硐硒硔硕硖硗硙硚硝硪硫硬硭确硼硿碃碇碈碉碌碍' - '碎碏碑碓碗碘碚碛碜碟碡碣碥碧碨碰碱碲碳碴碶碹碾磁磅磉磊磋磏磐磔磕磙磜磡磨磬磲磴磷' - '磹磻礁礅礌礓礞礴礵示礼社祀祁祃祆祇祈祉祊祋祎祏祐祓祕祖祗祚祛祜祝神祟祠祢祥祧票祭' - '祯祲祷祸祺祼祾禀禁禄禅禊禋福禒禔禘禚禛禤禧禳禹禺离禽禾秀私秃秆秉秋种科秒秕秘租秣' - '秤秦秧秩秫秬秭积称秸移秽秾稀稂稃稆程稌稍税稑稔稗稙稚稞稠稣稳稷稹稻稼稽稿穄穆穑穗' - '穙穜穟穰穴究穷穸穹空穿窀突窃窄窅窈窊窍窎窑窒窕窖窗窘窜窝窟窠窣窥窦窨窬窭窳窸窿立' - '竑竖竘站竞竟章竣童竦竫竭端竹竺竽竿笃笄笆笈笊笋笏笑笔笕笙笛笞笠笤笥符笨笪笫第笮笯' - '笱笳笸笺笼笾筀筅筇等筋筌筏筐筑筒答策筘筚筛筜筝筠筢筤筥筦筮筱筲筵筶筷筹筻筼签简箅' - '箍箐箓箔箕箖算箜管箢箦箧箨箩箪箫箬箭箱箴箸篁篆篇篌篑篓篙篚篝篡篥篦篪篮篯篱篷篼篾' - '簃簇簉簋簌簏簕簖簝簟簠簧簪簰簸簿籀籁籍籥米籴类籼籽粉粑粒粕粗粘粜粝粞粟粢粤粥粪粮' - '粱粲粳粹粼粽精粿糁糅糇糈糊糌糍糒糕糖糗糙糜糟糠糨糯糵系紊素索紧紫累絜絮絷綦綮縠縢' - '縻繁繄繇纂纛纠纡红纣纤纥约级纨纩纪纫纬纭纮纯纰纱纲纳纴纵纶纷纸纹纺纻纼纽纾线绀绁' - '绂练组绅细织终绉绊绋绌绍绎经绐绑绒结绔绕绖绗绘给绚绛络绝绞统绠绡绢绣绤绥绦继绨绩' - '绪绫续绮绯绰绱绲绳维绵绶绷绸绹绺绻综绽绾绿缀缁缂缃缄缅缆缇缈缉缊缌缎缐缑缒缓缔缕' - '编缗缘缙缚缛缜缝缞缟缠缡缢缣缤缥缦缧缨缩缪缫缬缭缮缯缰缱缲缳缴缵缶缸缺罂罄罅罍罐' - '网罔罕罗罘罚罟罡罢罨罩罪置罱署罴罶罹罽罾羁羊羌美羑羓羔羕羖羚羝羞羟羡群羧羯羰羱羲' - '羸羹羼羽羿翀翁翂翃翅翈翊翌翎翔翕翘翙翚翛翟翠翡翥翦翩翮翯翰翱翳翷翻翼翾耀老考耄者' - '耆耇耋而耍耏耐耑耒耔耕耖耗耘耙耜耠耢耤耥耦耧耨耩耪耰耱耳耵耶耷耸耻耽耿聂聃聆聊聋' - '职聍聒联聘聚聩聪聱聿肃肄肆肇肉肋肌肓肖肘肚肛肝肟肠股肢肤肥肩肪肫肭肮肯肱育肴肷肸' - '肺肼肽肾肿胀胁胂胃胄胆胈背胍胎胖胗胙胚胛胜胝胞胠胡胣胤胥胧胨胩胪胫胬胭胯胰胱胲胳' - '胴胶胸胺胼能脂脆脉脊脍脎脏脐脑脒脓脔脖脘脚脞脟脩脬脯脱脲脶脸脾脿腆腈腊腋腌腐腑腒' - '腓腔腕腘腙腚腠腥腧腨腩腭腮腯腰腱腴腹腺腻腼腽腾腿膀膂膈膊膏膑膘膙膛膜膝膦膨膳膺膻' - '臀臂臃臆臊臌臑臜臣臧自臬臭至致臻臼臾舀舁舂舄舅舆舌舍舐舒舔舛舜舞舟舠舢舣舥航舫般' - '舭舯舰舱舲舳舴舵舶舷舸船舻舾艄艅艇艉艋艎艏艘艚艟艨艮良艰色艳艴艺艽艾艿节芃芄芈芊' - '芋芍芎芏芑芒芗芘芙芜芝芟芠芡芣芤芥芦芨芩芪芫芬芭芮芯芰花芳芴芷芸芹芼芽芾苁苄苇苈' - '苉苊苋苌苍苎苏苑苒苓苔苕苗苘苛苜苞苟苠苡苣苤若苦苧苫苯英苴苷苹苻苾茀茁茂范茄茅茆' - '茈茉茋茌茎茏茑茓茔茕茗茚茛茜茝茧茨茫茬茭茯茱茳茴茵茶茸茹茺茼茽荀荁荃荄荆荇草荏荐' - '荑荒荓荔荖荙荚荛荜荞荟荠荡荣荤荥荦荧荨荩荪荫荬荭荮药荷荸荻荼荽莅莆莉莎莒莓莘莙莛' - '莜莝莞莠莨莩莪莫莰莱莲莳莴莶获莸莹莺莼莽莿菀菁菂菅菇菉菊菌菍菏菔菖菘菜菝菟菠菡菥' - '菩菪菰菱菲菹菼菽萁萃萄萆萋萌萍萎萏萑萘萚萜萝萣萤营萦萧萨萩萱萳萸萹萼落葆葎葑葖著' - '葙葚葛葜葡董葩葫葬葭葰葱葳葴葵葶葸葺蒂蒄蒇蒈蒉蒋蒌蒎蒐蒗蒙蒜蒟蒡蒨蒯蒱蒲蒴蒸蒹蒺' - '蒻蒽蒿蓁蓂蓄蓇蓉蓊蓍蓏蓐蓑蓓蓖蓝蓟蓠蓢蓣蓥蓦蓬蓰蓼蓿蔀蔃蔈蔊蔌蔑蔓蔗蔚蔟蔡蔫蔬蔷' - '蔸蔹蔺蔻蔼蔽蕃蕈蕉蕊蕖蕗蕙蕞蕤蕨蕰蕲蕴蕹蕺蕻蕾薁薄薅薇薏薛薜薢薤薨薪薮薯薰薳薷薸' - '薹薿藁藉藏藐藓藕藜藟藠藤藦藨藩藻藿蘅蘑蘖蘘蘧蘩蘸蘼虎虏虐虑虒虓虔虚虞虢虤虫虬虮虱' - '虷虸虹虺虻虼虽虾虿蚀蚁蚂蚄蚆蚊蚋蚌蚍蚓蚕蚜蚝蚣蚤蚧蚨蚩蚪蚬蚯蚰蚱蚲蚴蚶蚺蛀蛃蛄蛆' - '蛇蛉蛊蛋蛎蛏蛐蛑蛔蛘蛙蛛蛞蛟蛤蛩蛭蛮蛰蛱蛲蛳蛴蛸蛹蛾蜀蜂蜃蜇蜈蜉蜊蜍蜎蜐蜒蜓蜕蜗' - '蜘蜚蜜蜞蜡蜢蜣蜥蜩蜮蜱蜴蜷蜻蜾蜿蝇蝈蝉蝌蝎蝓蝗蝘蝙蝠蝣蝤蝥蝮蝰蝲蝴蝶蝻蝼蝽蝾螂螃' - '螅螈螋融螗螟螠螣螨螫螬螭螯螱螳螵螺螽蟀蟆蟊蟋蟏蟑蟒蟛蟠蟥蟪蟫蟮蟹蟾蠃蠊蠋蠓蠕蠖蠡' - '蠢蠲蠹蠼血衃衄衅行衍衎衒衔街衙衠衡衢衣补表衩衫衬衮衰衲衷衽衾衿袁袂袄袅袆袈袋袍袒' - '袖袗袜袢袤袪被袭袯袱袷袼裁裂装裆裈裉裎裒裔裕裘裙裛裟裢裣裤裥裨裰裱裳裴裸裹裼裾褂' - '褊褐褒褓褕褙褚褛褟褡褥褪褫褯褰褴褶襁襄襕襚襜襞襟襦襫襻西要覃覆见观觃规觅视觇览觉' - '觊觋觌觎觏觐觑角觖觚觜觞觟解觥触觫觭觯觱觳觿言訄訇訚訾詈詟詹誉誊誓謇警譬计订讣认' - '讥讦讧讨让讪讫训议讯记讱讲讳讴讵讶讷许讹论讻讼讽设访诀证诂诃评诅识诇诈诉诊诋诌词' - '诎诏诐译诒诓诔试诖诗诘诙诚诛诜话诞诟诠诡询诣诤该详诧诨诩诫诬语诮误诰诱诲诳说诵请' - '诸诹诺读诼诽课诿谀谁谂调谄谅谆谇谈谊谋谌谍谎谏谐谑谒谓谔谕谖谗谙谚谛谜谝谞谟谠谡' - '谢谣谤谥谦谧谨谩谪谫谬谭谮谯谰谱谲谳谴谵谶谷谼谿豁豆豇豉豌豕豚象豢豨豪豫豮豳豸豹' - '豺貂貅貆貉貊貌貔貘贝贞负贡财责贤败账货质贩贪贫贬购贮贯贰贱贲贳贴贵贶贷贸费贺贻贼' - '贽贾贿赀赁赂赃资赅赆赇赈赉赊赋赌赍赎赏赐赑赒赓赔赕赖赗赘赙赚赛赜赝赞赟赠赡赢赣赤' - '赦赧赪赫赭走赳赴赵赶起趁趄超越趋趑趔趟趣趯趱足趴趵趸趺趼趾趿跂跃跄跆跋跌跎跏跐跑' - '跖跗跚跛距跞跟跣跤跨跪跬路跱跳践跶跷跸跹跺跻跽踅踉踊踌踏踒踔踝踞踟踢踣踦踩踪踬踮' - '踯踱踵踶踹踺踽蹀蹁蹂蹄蹅蹇蹈蹉蹊蹋蹐蹑蹒蹙蹚蹜蹢蹦蹩蹬蹭蹯蹰蹲蹴蹶蹼蹽蹾蹿躁躅躇' - '躏躐躔躜躞身躬躯躲躺车轧轨轩轪轫转轭轮软轰轱轲轳轴轵轶轷轸轹轺轻轼载轾轿辀辁辂较' - '辄辅辆辇辈辉辊辋辌辍辎辏辐辑辒输辔辕辖辗辘辙辚辛辜辞辟辣辨辩辫辰辱边辽达辿迁迂迄' - '迅过迈迎运近迓返迕还这进远违连迟迢迤迥迦迨迩迪迫迭迮述迳迷迸迹迺追退送适逃逄逅逆' - '选逊逋逍透逐逑递途逖逗通逛逝逞速造逡逢逦逭逮逯逴逵逶逸逻逼逾遁遂遄遆遇遍遏遐遑遒' - '道遗遘遛遢遣遥遨遭遮遴遵遹遽避邀邂邃邈邋邑邓邕邗邘邙邛邝邠邡邢那邦邨邪邬邮邯邰邱' - '邲邳邴邵邶邸邹邺邻邽邾邿郁郃郄郅郇郈郊郎郏郐郑郓郗郚郛郜郝郡郢郤郦郧部郪郫郭郯郴' - '郸都郾郿鄀鄂鄃鄄鄅鄌鄑鄗鄘鄙鄚鄜鄞鄠鄢鄣鄫鄯鄱鄹酂酃酅酆酉酊酋酌配酎酏酐酒酗酚酝' - '酞酡酢酣酤酥酦酩酪酬酮酯酰酱酲酴酵酶酷酸酹酺酽酾酿醅醇醉醋醌醍醐醑醒醚醛醢醨醪醭' - '醮醯醴醵醺醾采釉释里重野量釐金釜鉴銎銮鋆鋈錾鍪鎏鏊鏖鐾鑫钆钇针钉钊钋钌钍钎钏钐钒' - '钓钔钕钖钗钘钙钚钛钜钝钞钟钠钡钢钣钤钥钦钧钨钩钪钫钬钭钮钯钰钱钲钳钴钵钷钹钺钻钼' - '钽钾钿铀铁铂铃铄铅铆铈铉铊铋铌铍铎铏铐铑铒铕铖铗铘铙铚铛铜铝铞铟铠铡铢铣铤铥铧铨' - '铩铪铫铬铭铮铯铰铱铲铳铴铵银铷铸铹铺铻铼铽链铿销锁锂锃锄锅锆锇锈锉锊锋锌锍锎锏锐' - '锑锒锓锔锕锖锗锘错锚锛锜锝锞锟锡锢锣锤锥锦锧锨锩锪锫锬锭键锯锰锱锲锳锴锵锶锷锸锹' - '锺锻锼锽锾锿镀镁镂镃镄镅镆镇镈镉镊镋镌镍镎镏镐镑镒镓镔镕镖镗镘镚镛镜镝镞镠镡镢镣' - '镤镥镦镧镨镩镪镫镬镭镮镯镰镱镲镳镴镵镶长门闩闪闫闭问闯闰闱闲闳间闵闶闷闸闹闺闻闼' - '闽闾闿阀阁阂阃阄阅阆阇阈阉阊阋阌阍阎阏阐阑阒阔阕阖阗阘阙阚阜队阡阪阮阱防阳阴阵阶' - '阻阼阽阿陀陂附际陆陇陈陉陋陌降陎限陑陔陕陛陞陟陡院除陧陨险陪陬陲陴陵陶陷隃隅隆隈' - '隋隍随隐隔隗隘隙障隧隩隰隳隶隹隺隼隽难雀雁雄雅集雇雉雊雌雍雎雏雒雕雠雨雩雪雯雱雳' - '零雷雹雾需霁霄霅霆震霈霉霍霎霏霓霖霜霞霨霪霭霰露霸霹霾青靓靖静靛非靠靡面靥革靬靰' - '靳靴靶靸靺靼靽靿鞁鞅鞋鞍鞑鞒鞔鞘鞠鞡鞣鞧鞨鞫鞬鞭鞮鞯鞲鞳鞴韂韦韧韨韩韪韫韬韭音韵' - '韶页顶顷顸项顺须顼顽顾顿颀颁颂颃预颅领颇颈颉颊颋颌颍颎颏颐频颓颔颖颗题颙颚颛颜额' - '颞颟颠颡颢颤颥颦颧风飏飐飑飒飓飔飕飗飘飙飞食飧飨餍餐餮饔饕饥饧饨饩饪饫饬饭饮饯饰' - '饱饲饳饴饵饶饷饸饹饺饻饼饽饿馁馃馄馅馆馇馈馉馊馋馌馍馏馐馑馒馓馔馕首馗馘香馝馞馥' - '馧馨马驭驮驯驰驱驲驳驴驵驶驷驸驹驺驻驼驽驾驿骀骁骂骃骄骅骆骇骈骉骊骋验骍骎骏骐骑' - '骒骓骕骖骗骘骙骚骛骜骝骞骟骠骡骢骣骤骥骦骧骨骰骱骶骷骸骺骼髀髁髂髃髅髋髌髎髑髓高' - '髡髢髦髫髭髯髹髻髽鬃鬈鬏鬒鬓鬘鬟鬣鬯鬲鬶鬷鬻鬼魁魂魃魄魅魆魇魈魉魋魍魏魑魔鱼鱽鱾' - '鱿鲀鲁鲂鲃鲅鲆鲇鲈鲉鲊鲋鲌鲍鲎鲏鲐鲑鲒鲔鲕鲖鲗鲘鲙鲚鲛鲜鲝鲞鲟鲠鲡鲢鲣鲤鲥鲦鲧鲨' - '鲩鲪鲫鲬鲭鲮鲯鲰鲱鲲鲳鲴鲵鲷鲸鲹鲺鲻鲼鲽鲾鲿鳀鳁鳂鳃鳄鳅鳇鳈鳉鳊鳌鳍鳎鳏鳐鳑鳒鳓' - '鳔鳕鳖鳗鳘鳙鳚鳛鳜鳝鳞鳟鳠鳡鳢鳣鳤鸟鸠鸡鸢鸣鸤鸥鸦鸧鸨鸩鸪鸫鸬鸭鸮鸯鸰鸱鸲鸳鸵鸶' - '鸷鸸鸹鸺鸻鸼鸽鸾鸿鹀鹁鹂鹃鹄鹅鹆鹇鹈鹉鹊鹋鹌鹍鹎鹏鹐鹑鹒鹔鹕鹖鹗鹘鹙鹚鹛鹜鹝鹞鹟' - '鹠鹡鹢鹣鹤鹦鹧鹨鹩鹪鹫鹬鹭鹮鹯鹰鹱鹲鹳鹴鹾鹿麀麂麇麈麋麑麒麓麖麝麟麦麸麹麻麽麾黄' - '黇黉黍黎黏黑黔默黛黜黝黟黠黡黢黥黧黩黪黯黹黻黼黾鼋鼍鼎鼐鼒鼓鼗鼙鼠鼢鼩鼫鼬鼯鼱鼷' - '鼹鼻鼽鼾齁齇齉齐齑齿龀龁龂龃龄龅龆龇龈龉龊龋龌龙龚龛龟龠龢鿍鿎鿏㑇㑊㕮㘎㙍㙘㙦㛃' - '㛚㛹㟃㠇㠓㤘㥄㧐㧑㧟㫰㬊㬎㬚㭎㭕㮾㰀㳇㳘㳚㴔㵐㶲㸆㸌㺄㻬㽏㿠䁖䂮䃅䃎䅟䌹䎃䎖䏝䏡' - '䏲䐃䓖䓛䓨䓫䓬䗖䗛䗪䗴䜣䝙䢺䢼䣘䥽䦃䲟䲠䲢䴓䴔䴕䴖䴗䴘䴙䶮𠅤𠙶𠳐𡎚𡐓𣗋𣲗𣲘𣸣𤧛𤩽' - '𤫉𥔲𥕢𥖨𥻗𦈡𦒍𦙶𦝼𦭜𦰡𧿹𨐈𨙸𨚕𨟠𨭉𨱇𨱏𨱑𨱔𨺙𩽾𩾃𩾌𪟝𪣻𪤗𪨰𪨶𪩘𪾢𫄧𫄨𫄷𫄸𫇭𫌀𫍣𫍯' - '𫍲𫍽𫐄𫐐𫐓𫑡𫓧𫓯𫓶𫓹𫔍𫔎𫔶𫖮𫖯𫖳𫗧𫗴𫘜𫘝𫘦𫘧𫘨𫘪𫘬𫚕𫚖𫚭𫛭𫞩𫟅𫟦𫟹𫟼𫠆𫠊𫠜𫢸𫫇𫭟' - '𫭢𫭼𫮃𫰛𫵷𫶇𫷷𫸩𬀩𬀪𬂩𬃊𬇕𬇙𬇹𬉼𬊈𬊤𬌗𬍛𬍡𬍤𬒈𬒔𬒗𬕂𬘓𬘘𬘡𬘩𬘫𬘬𬘭𬘯𬙂𬙊𬙋𬜬𬜯𬞟' - '𬟁𬟽𬣙𬣞𬣡𬣳𬤇𬤊𬤝𬨂𬨎𬩽𬪩𬬩𬬭𬬮𬬱𬬸𬬹𬬻𬬿𬭁𬭊𬭎𬭚𬭛𬭤𬭩𬭬𬭯𬭳𬭶𬭸𬭼𬮱𬮿𬯀𬯎𬱖𬱟' - '𬳵𬳶𬳽𬳿𬴂𬴃𬴊𬶋𬶍𬶏𬶐𬶟𬶠𬶨𬶭𬶮𬷕𬸘𬸚𬸣𬸦𬸪𬹼𬺈𬺓' + "一丁七万丈三上下不与丏丐丑专且丕世丘丙业丛东丝丞丢两严丧个丫中丰串临丸丹为主丽举" + "乂乃久么义之乌乍乎乏乐乒乓乔乖乘乙乜九乞也习乡书乩买乱乳乸乾了予争事二亍于亏云互" + "亓五井亘亚些亟亡亢交亥亦产亨亩享京亭亮亲亳亵亶亸亹人亿什仁仂仃仄仅仆仇仉今介仍从" + "仑仓仔仕他仗付仙仝仞仟仡代令以仨仪仫们仰仲仳仵件价任份仿企伈伉伊伋伍伎伏伐休众优" + "伙会伛伞伟传伢伣伤伥伦伧伪伫伭伯估伲伴伶伸伺似伽伾佁佃但位低住佐佑体何佖佗佘余佚" + "佛作佝佞佟你佣佤佥佩佬佯佰佳佴佶佸佺佻佼佽佾使侁侂侃侄侈侉例侍侏侑侔侗侘供依侠侣" + "侥侦侧侨侩侪侬侮侯侴侵侹便促俄俅俊俍俎俏俐俑俗俘俙俚俜保俞俟信俣俦俨俩俪俫俭修俯" + "俱俳俵俶俸俺俾倌倍倏倒倓倔倕倘候倚倜倞借倡倥倦倧倨倩倪倬倭倮倴债倻值倾偁偃假偈偌" + "偎偏偓偕做停偡健偬偭偰偲偶偷偻偾偿傀傃傅傈傉傍傒傕傣傥傧储傩催傲傺傻僇僎像僔僖僚" + "僦僧僬僭僮僰僳僵僻儆儇儋儒儡儦儳儴儿兀允元兄充兆先光克免兑兔兕兖党兜兢入全八公六" + "兮兰共关兴兵其具典兹养兼兽冀冁内冈冉册再冏冒冔冕冗写军农冠冢冤冥冬冮冯冰冱冲决况" + "冶冷冻冼冽净凄准凇凉凋凌减凑凓凘凛凝几凡凤凫凭凯凰凳凶凸凹出击凼函凿刀刁刃分切刈" + "刊刍刎刑划刖列刘则刚创初删判刨利别刬刭刮到刳制刷券刹刺刻刽刿剀剁剂剃剅削剋剌前剐" + "剑剔剕剖剜剞剟剡剥剧剩剪副割剽剿劁劂劄劈劐劓力劝办功加务劢劣动助努劫劬劭励劲劳劼" + "劾势勃勇勉勋勍勐勒勔勖勘勚募勠勤勰勺勾勿匀包匆匈匍匏匐匕化北匙匜匝匠匡匣匦匪匮匹" + "区医匼匾匿十千卅升午卉半华协卑卒卓单卖南博卜卞卟占卡卢卣卤卦卧卫卬卮卯印危即却卵" + "卷卸卺卿厂厄厅历厉压厌厍厕厖厘厚厝原厢厣厥厦厨厩厮去厾县叁参叆叇又叉及友双反发叔" + "叕取受变叙叚叛叟叠口古句另叨叩只叫召叭叮可台叱史右叵叶号司叹叻叼叽吁吃各吆合吉吊" + "同名后吏吐向吒吓吕吖吗君吝吞吟吠吡吣否吧吨吩含听吭吮启吱吲吴吵吸吹吻吼吽吾呀呃呆" + "呇呈告呋呐呒呓呔呕呖呗员呙呛呜呢呣呤呦周呱呲味呵呶呷呸呻呼命咀咂咄咆咇咉咋和咍咎" + "咏咐咒咔咕咖咙咚咛咝咡咣咤咥咦咧咨咩咪咫咬咯咱咳咴咸咺咻咽咿哀品哂哃哄哆哇哈哉哌" + "响哎哏哐哑哒哓哔哕哗哙哚哝哞哟哢哥哦哧哨哩哪哭哮哱哲哳哺哼哽哿唁唆唇唉唏唐唑唔唛" + "唝唠唢唣唤唧唪唬售唯唰唱唳唵唷唼唾唿啁啃啄商啉啊啐啕啖啜啡啤啥啦啧啪啫啬啭啮啰啴" + "啵啶啷啸啻啼啾喀喁喂喃善喆喇喈喉喊喋喏喑喔喘喙喜喝喟喤喧喱喳喵喷喹喻喽喾嗄嗅嗉嗌" + "嗍嗐嗑嗒嗓嗔嗖嗜嗝嗞嗟嗡嗣嗤嗥嗦嗨嗪嗫嗬嗯嗲嗳嗵嗷嗽嗾嘀嘁嘈嘉嘌嘎嘏嘘嘚嘛嘞嘟嘡" + "嘣嘤嘧嘬嘭嘱嘲嘴嘶嘹嘻嘿噀噂噇噌噍噎噔噗噘噙噜噢噤器噩噪噫噬噱噶噻噼嚄嚅嚆嚎嚏嚓" + "嚚嚣嚭嚯嚷嚼囊囔囚四回囟因囡团囤囫园困囱围囵囷囹固国图囿圃圄圆圈圉圊圌圐圙圜土圢" + "圣在圩圪圫圬圭圮圯地圲圳圹场圻圾址坂均坉坊坋坌坍坎坏坐坑坒块坚坛坜坝坞坟坠坡坤坥" + "坦坨坩坪坫坬坭坯坰坳坷坻坼坽垂垃垄垆垈型垌垍垎垏垒垓垕垙垚垛垞垟垠垡垢垣垤垦垧垩" + "垫垭垮垯垱垲垴垵垸垺垾垿埂埃埆埇埋埌城埏埒埔埕埗埘埙埚埝域埠埤埪埫埭埯埴埵埸培基" + "埼埽堂堃堆堇堉堋堌堍堎堐堑堕堙堞堠堡堤堧堨堪堰堲堵堼堽堾塄塅塆塌塍塑塔塘塝塞塥填" + "塬塱塾墀墁境墅墈墉墐墒墓墕墘墙墚增墟墡墣墦墨墩墼壁壅壑壕壤士壬壮声壳壶壸壹处备复" + "夏夐夔夕外夙多夜够夤夥大天太夫夬夭央夯失头夷夸夹夺夼奁奂奄奇奈奉奋奎奏契奓奔奕奖" + "套奘奚奠奡奢奥奭女奴奶奸她好妁如妃妄妆妇妈妊妍妒妓妖妗妘妙妞妣妤妥妧妨妩妪妫妭妮" + "妯妲妹妻妾姆姈姊始姐姑姒姓委姗姘姚姜姝姞姣姤姥姨姬姮姱姶姹姻姽姿娀威娃娄娅娆娇娈" + "娉娌娑娓娘娜娟娠娣娥娩娱娲娴娵娶娼婀婆婉婊婌婍婕婘婚婞婠婢婤婧婪婫婳婴婵婶婷婺婻" + "婼婿媂媄媆媒媓媖媚媛媞媪媭媱媲媳媵媸媾嫁嫂嫄嫉嫌嫒嫔嫕嫖嫘嫚嫜嫠嫡嫣嫦嫩嫪嫫嫭嫱" + "嫽嬉嬖嬗嬛嬥嬬嬴嬷嬿孀孅子孑孓孔孕孖字存孙孚孛孜孝孟孢季孤孥学孩孪孬孰孱孳孵孺孽" + "宁它宄宅宇守安宋完宏宓宕宗官宙定宛宜宝实宠审客宣室宥宦宧宪宫宬宰害宴宵家宸容宽宾" + "宿寁寂寄寅密寇富寐寒寓寝寞察寡寤寥寨寮寰寸对寺寻导寿封射将尉尊小少尔尕尖尘尚尜尝" + "尢尤尥尧尨尪尬就尴尸尹尺尻尼尽尾尿局屁层屃居屈屉届屋屎屏屐屑展屙属屠屡屣履屦屯山" + "屹屺屼屾屿岁岂岈岊岌岍岐岑岔岖岗岘岙岚岛岜岞岠岢岣岨岩岫岬岭岱岳岵岷岸岽岿峁峂峃" + "峄峋峒峗峘峙峛峡峣峤峥峦峧峨峪峭峰峱峻峿崀崁崂崃崄崆崇崌崎崒崔崖崚崛崞崟崡崤崦崧" + "崩崭崮崴崶崽崾崿嵁嵅嵇嵊嵋嵌嵎嵖嵘嵚嵛嵝嵩嵫嵬嵯嵲嵴嶂嶅嶍嶒嶓嶙嶝嶟嶦嶲嶷巅巇巉" + "巍川州巡巢工左巧巨巩巫差巯己已巳巴巷巽巾币市布帅帆师希帏帐帑帔帕帖帘帙帚帛帜帝帡" + "带帧帨席帮帱帷常帻帼帽幂幄幅幌幔幕幖幛幞幡幢幪干平年并幸幺幻幼幽广庄庆庇床庋序庐" + "庑库应底庖店庙庚府庞废庠庤庥度座庭庱庳庵庶康庸庹庼庾廆廉廊廋廑廒廓廖廙廛廨廪延廷" + "建廿开弁异弃弄弆弇弈弊弋式弑弓引弗弘弛弟张弢弥弦弧弨弩弭弯弱弶弸弹强弼彀归当录彖" + "彗彘彝彟形彤彦彧彩彪彬彭彰影彳彷役彻彼往征徂径待徇很徉徊律徐徒徕得徘徙徛徜御徨循" + "徭微徵德徼徽心必忆忉忌忍忏忐忑忒忖志忘忙忝忞忠忡忤忧忪快忭忮忱忳念忸忺忻忽忾忿怀" + "态怂怃怄怅怆怊怍怎怏怒怔怕怖怙怛怜思怠怡急怦性怨怩怪怫怯怵总怼怿恁恂恃恋恍恐恒恓" + "恔恕恙恚恝恢恣恤恧恨恩恪恫恬恭息恰恳恶恸恹恺恻恼恽恿悃悄悆悈悉悌悍悒悔悖悚悛悝悟" + "悠悢患悦您悫悬悭悯悰悱悲悴悸悻悼情惆惇惊惋惎惑惔惕惘惙惚惛惜惝惟惠惦惧惨惩惫惬惭" + "惮惯惰想惴惶惹惺愀愁愃愆愈愉愍愎意愐愔愕愚感愠愣愤愦愧愫愭愿慆慈慊慌慎慑慕慝慢慥" + "慧慨慬慭慰慵慷憋憎憔憕憙憧憨憩憬憭憷憺憾懂懈懊懋懑懒懔懦懵懿戆戈戊戋戌戍戎戏成我" + "戒戕或戗战戚戛戟戡戢戣戤戥截戬戭戮戳戴户戽戾房所扁扂扃扅扆扇扈扉扊手才扎扑扒打扔" + "托扛扞扣扦执扩扪扫扬扭扮扯扰扳扶批扺扼扽找承技抃抄抉把抑抒抓抔投抖抗折抚抛抟抠抡" + "抢护报抨披抬抱抵抹抻押抽抿拂拃拄担拆拇拈拉拊拌拍拎拐拒拓拔拖拗拘拙招拜拟拢拣拤拥" + "拦拧拨择括拭拮拯拱拳拴拶拷拼拽拾拿持挂指挈按挎挑挓挖挚挛挝挞挟挠挡挣挤挥挦挨挪挫" + "振挲挹挺挽捂捃捅捆捉捋捌捍捎捏捐捕捞损捡换捣捧捩捭据捯捶捷捺捻捽掀掂掇授掉掊掌掎" + "掏掐排掖掘掞掠探掣接控推掩措掬掭掮掰掳掴掷掸掺掼掾揄揆揉揍描提插揕揖揠握揣揩揪揭" + "揳援揶揸揽揿搀搁搂搅搋搌搏搐搒搓搔搛搜搞搠搡搦搪搬搭搴携搽摁摄摅摆摇摈摊摏摒摔摘" + "摛摞摧摩摭摴摸摹摽撂撄撅撇撑撒撕撖撙撞撤撩撬播撮撰撵撷撸撺撼擀擂擅操擎擐擒擘擞擢" + "擤擦擿攀攉攒攘攥攫攮支收攸改攻攽放政故效敉敌敏救敔敕敖教敛敝敞敢散敦敩敫敬数敲整" + "敷文斋斌斐斑斓斗料斛斜斝斟斠斡斤斥斧斩斫断斯新斶方於施旁旃旄旅旆旋旌旎族旐旒旖旗" + "旞无既日旦旧旨早旬旭旮旯旰旱旴旵时旷旸旺旻旿昀昂昃昄昆昇昈昉昊昌明昏昒易昔昕昙昝" + "星映昡昣昤春昧昨昪昫昭是昱昳昴昵昶昺昼昽显晁晃晅晊晋晌晏晐晒晓晔晕晖晗晙晚晞晟晡" + "晢晤晦晨晪晫普景晰晱晴晶晷智晾暂暄暅暇暌暑暕暖暗暝暧暨暮暲暴暵暶暹暾暿曈曌曙曛曜" + "曝曦曩曰曲曳更曷曹曼曾替最月有朋服朏朐朓朔朕朗望朝期朦木未末本札术朱朳朴朵朸机朽" + "杀杂权杄杆杈杉杌李杏材村杓杕杖杙杜杞束杠条来杧杨杩杪杭杯杰杲杳杵杷杻杼松板极构枅" + "枇枉枋枍析枕林枘枚果枝枞枢枣枥枧枨枪枫枭枯枰枲枳枵架枷枸枹柁柃柄柈柊柏某柑柒染柔" + "柖柘柙柚柜柝柞柠柢查柩柬柯柰柱柳柴柷柽柿栀栅标栈栉栊栋栌栎栏栐树栒栓栖栗栝栟校栩" + "株栲栳栴样核根栻格栽栾桀桁桂桃桄桅框案桉桊桌桎桐桑桓桔桕桠桡桢档桤桥桦桧桨桩桫桯" + "桲桴桶桷桹梁梃梅梆梌梏梓梗梠梢梣梦梧梨梭梯械梳梴梵梼梽梾梿检棁棂棉棋棍棐棒棓棕棘" + "棚棠棣棤棨棪棫棬森棰棱棵棹棺棻棼棽椀椁椅椆椋植椎椐椑椒椓椟椠椤椪椭椰椴椸椹椽椿楂" + "楒楔楗楙楚楝楞楠楣楦楩楪楫楮楯楷楸楹楼概榃榄榅榆榇榈榉榍榑榔榕榖榛榜榧榨榫榭榰榱" + "榴榷榻槁槃槊槌槎槐槔槚槛槜槟槠槭槱槲槽槿樊樗樘樟模樨横樯樱樵樽樾橄橇橐橑橘橙橛橞" + "橡橥橦橱橹橼檀檄檎檐檑檗檞檠檩檫檬櫆欂欠次欢欣欤欧欲欸欹欺欻款歃歅歆歇歉歌歙止正" + "此步武歧歪歹死歼殁殂殃殄殆殇殉殊残殍殒殓殖殚殛殡殣殪殳殴段殷殿毁毂毅毋毌母每毐毒" + "毓比毕毖毗毙毛毡毪毫毯毳毵毹毽氅氆氇氍氏氐民氓气氕氖氘氙氚氛氟氡氢氤氦氧氨氩氪氮" + "氯氰氲水永氾氿汀汁求汆汇汈汉汊汋汐汔汕汗汛汜汝汞江池污汤汧汨汩汪汫汭汰汲汴汶汹汽" + "汾沁沂沃沄沅沆沇沈沉沌沏沐沓沔沘沙沚沛沟没沣沤沥沦沧沨沩沪沫沭沮沱河沸油沺治沼沽" + "沾沿泂泃泄泅泇泉泊泌泐泓泔法泖泗泙泚泛泜泞泠泡波泣泥注泪泫泮泯泰泱泳泵泷泸泺泻泼" + "泽泾洁洄洇洈洋洌洎洑洒洓洗洘洙洚洛洞洢洣津洧洨洪洫洭洮洱洲洳洴洵洸洹洺活洼洽派洿" + "流浃浅浆浇浈浉浊测浍济浏浐浑浒浓浔浕浙浚浛浜浞浟浠浡浣浥浦浩浪浬浭浮浯浰浲浴海浸" + "浼涂涄涅消涉涌涍涎涐涑涓涔涕涘涛涝涞涟涠涡涢涣涤润涧涨涩涪涫涮涯液涴涵涸涿淀淄淅" + "淆淇淋淌淏淑淖淘淙淜淝淞淟淠淡淤淦淫淬淮淯深淳淴混淹添淼清渊渌渍渎渐渑渔渗渚渝渟" + "渠渡渣渤渥温渫渭港渰渲渴游渺渼湃湄湉湍湎湑湓湔湖湘湛湜湝湟湣湫湮湲湴湾湿溁溃溅溆" + "溇溉溍溏源溘溚溜溞溟溠溢溥溦溧溪溯溱溲溴溵溶溷溹溺溻溽滁滂滃滆滇滉滋滍滏滑滓滔滕" + "滗滘滚滞滟滠满滢滤滥滦滧滨滩滪滫滴滹漂漆漈漉漋漏漓演漕漖漠漤漦漩漪漫漭漯漱漳漴漶" + "漷漹漻漼漾潆潇潋潍潏潖潘潜潞潟潢潦潩潭潮潲潴潵潸潺潼潽潾澂澄澈澉澌澍澎澛澜澡澥澧" + "澪澭澳澴澶澹澼澽激濂濉濋濑濒濞濠濡濩濮濯瀌瀍瀑瀔瀚瀛瀣瀱瀵瀹瀼灈灌灏灞火灭灯灰灵" + "灶灸灼灾灿炀炅炆炉炊炌炎炒炔炕炖炘炙炜炝炟炣炫炬炭炮炯炱炳炷炸点炻炼炽烀烁烂烃烈" + "烊烔烘烙烛烜烝烟烠烤烦烧烨烩烫烬热烯烶烷烹烺烻烽焆焉焊焌焐焓焕焖焗焘焙焚焜焞焦焯" + "焰焱然煁煃煅煊煋煌煎煓煜煞煟煤煦照煨煮煲煳煴煸煺煽熄熇熊熏熔熘熙熛熜熟熠熥熨熬熵" + "熹熻燃燊燋燎燏燔燕燚燠燥燧燮燹爆爇爔爚爝爟爨爪爬爰爱爵父爷爸爹爻爽爿牁牂片版牌牍" + "牒牖牙牚牛牝牟牡牢牤牥牦牧物牮牯牲牵特牺牻牾牿犀犁犄犇犊犋犍犏犒犟犨犬犯犰犴状犷" + "犸犹狁狂狃狄狈狉狍狎狐狒狗狙狝狞狠狡狨狩独狭狮狯狰狱狲狳狴狷狸狺狻狼猁猃猄猇猊猎" + "猕猖猗猛猜猝猞猡猢猥猩猪猫猬献猯猰猱猴猷猹猺猾猿獍獐獒獗獠獬獭獯獴獾玃玄率玉王玎" + "玑玒玓玕玖玘玙玚玛玞玟玠玡玢玤玥玦玩玫玭玮环现玱玲玳玶玷玹玺玻玼玿珀珂珅珇珈珉珊" + "珋珌珍珏珐珑珒珕珖珙珛珝珞珠珢珣珥珦珧珩珪珫班珰珲珵珷珸珹珺珽琀球琄琅理琇琈琉琊" + "琎琏琐琔琚琛琟琡琢琤琥琦琨琪琫琬琭琮琯琰琲琳琴琵琶琼瑀瑁瑂瑃瑄瑅瑆瑑瑓瑔瑕瑖瑗瑙" + "瑚瑛瑜瑝瑞瑟瑢瑧瑨瑬瑭瑰瑱瑳瑶瑷瑾璀璁璃璆璇璈璋璎璐璒璘璜璞璟璠璥璧璨璩璪璬璮璱" + "璲璺瓀瓒瓖瓘瓜瓞瓠瓢瓣瓤瓦瓮瓯瓴瓶瓷瓻瓿甄甍甏甑甓甗甘甚甜生甡甥甦用甩甪甫甬甭甯" + "田由甲申电男甸町画甾畀畅畈畋界畎畏畔畖留畚畛畜畤略畦番畬畯畲畴畸畹畿疁疃疆疍疏疐" + "疑疔疖疗疙疚疝疟疠疡疢疣疤疥疫疬疭疮疯疰疱疲疳疴疵疸疹疼疽疾痂痃痄病症痈痉痊痍痒" + "痓痔痕痘痛痞痢痣痤痦痧痨痪痫痰痱痴痹痼痿瘀瘁瘃瘅瘆瘊瘌瘐瘕瘗瘘瘙瘛瘟瘠瘢瘤瘥瘦瘩" + "瘪瘫瘭瘰瘳瘴瘵瘸瘼瘾瘿癀癃癌癍癔癖癗癜癞癣癫癯癸登白百癿皂的皆皇皈皋皎皑皓皕皖皙" + "皛皞皤皦皭皮皱皲皴皿盂盅盆盈盉益盍盎盏盐监盒盔盖盗盘盛盟盥盦目盯盱盲直盷相盹盼盾" + "省眄眇眈眉眊看眍眙眚真眠眢眦眨眩眬眭眯眵眶眷眸眺眼着睁睃睄睇睎睐睑睚睛睡睢督睥睦" + "睨睫睬睹睽睾睿瞀瞄瞅瞋瞌瞍瞎瞑瞒瞟瞠瞢瞥瞧瞩瞪瞫瞬瞭瞰瞳瞵瞻瞽瞿矍矗矛矜矞矢矣知" + "矧矩矫矬短矮矰石矶矸矻矼矾矿砀码砂砄砆砉砌砍砑砒研砖砗砘砚砜砝砟砠砣砥砧砫砬砭砮" + "砰破砵砷砸砹砺砻砼砾础硁硅硇硊硌硍硎硐硒硔硕硖硗硙硚硝硪硫硬硭确硼硿碃碇碈碉碌碍" + "碎碏碑碓碗碘碚碛碜碟碡碣碥碧碨碰碱碲碳碴碶碹碾磁磅磉磊磋磏磐磔磕磙磜磡磨磬磲磴磷" + "磹磻礁礅礌礓礞礴礵示礼社祀祁祃祆祇祈祉祊祋祎祏祐祓祕祖祗祚祛祜祝神祟祠祢祥祧票祭" + "祯祲祷祸祺祼祾禀禁禄禅禊禋福禒禔禘禚禛禤禧禳禹禺离禽禾秀私秃秆秉秋种科秒秕秘租秣" + "秤秦秧秩秫秬秭积称秸移秽秾稀稂稃稆程稌稍税稑稔稗稙稚稞稠稣稳稷稹稻稼稽稿穄穆穑穗" + "穙穜穟穰穴究穷穸穹空穿窀突窃窄窅窈窊窍窎窑窒窕窖窗窘窜窝窟窠窣窥窦窨窬窭窳窸窿立" + "竑竖竘站竞竟章竣童竦竫竭端竹竺竽竿笃笄笆笈笊笋笏笑笔笕笙笛笞笠笤笥符笨笪笫第笮笯" + "笱笳笸笺笼笾筀筅筇等筋筌筏筐筑筒答策筘筚筛筜筝筠筢筤筥筦筮筱筲筵筶筷筹筻筼签简箅" + "箍箐箓箔箕箖算箜管箢箦箧箨箩箪箫箬箭箱箴箸篁篆篇篌篑篓篙篚篝篡篥篦篪篮篯篱篷篼篾" + "簃簇簉簋簌簏簕簖簝簟簠簧簪簰簸簿籀籁籍籥米籴类籼籽粉粑粒粕粗粘粜粝粞粟粢粤粥粪粮" + "粱粲粳粹粼粽精粿糁糅糇糈糊糌糍糒糕糖糗糙糜糟糠糨糯糵系紊素索紧紫累絜絮絷綦綮縠縢" + "縻繁繄繇纂纛纠纡红纣纤纥约级纨纩纪纫纬纭纮纯纰纱纲纳纴纵纶纷纸纹纺纻纼纽纾线绀绁" + "绂练组绅细织终绉绊绋绌绍绎经绐绑绒结绔绕绖绗绘给绚绛络绝绞统绠绡绢绣绤绥绦继绨绩" + "绪绫续绮绯绰绱绲绳维绵绶绷绸绹绺绻综绽绾绿缀缁缂缃缄缅缆缇缈缉缊缌缎缐缑缒缓缔缕" + "编缗缘缙缚缛缜缝缞缟缠缡缢缣缤缥缦缧缨缩缪缫缬缭缮缯缰缱缲缳缴缵缶缸缺罂罄罅罍罐" + "网罔罕罗罘罚罟罡罢罨罩罪置罱署罴罶罹罽罾羁羊羌美羑羓羔羕羖羚羝羞羟羡群羧羯羰羱羲" + "羸羹羼羽羿翀翁翂翃翅翈翊翌翎翔翕翘翙翚翛翟翠翡翥翦翩翮翯翰翱翳翷翻翼翾耀老考耄者" + "耆耇耋而耍耏耐耑耒耔耕耖耗耘耙耜耠耢耤耥耦耧耨耩耪耰耱耳耵耶耷耸耻耽耿聂聃聆聊聋" + "职聍聒联聘聚聩聪聱聿肃肄肆肇肉肋肌肓肖肘肚肛肝肟肠股肢肤肥肩肪肫肭肮肯肱育肴肷肸" + "肺肼肽肾肿胀胁胂胃胄胆胈背胍胎胖胗胙胚胛胜胝胞胠胡胣胤胥胧胨胩胪胫胬胭胯胰胱胲胳" + "胴胶胸胺胼能脂脆脉脊脍脎脏脐脑脒脓脔脖脘脚脞脟脩脬脯脱脲脶脸脾脿腆腈腊腋腌腐腑腒" + "腓腔腕腘腙腚腠腥腧腨腩腭腮腯腰腱腴腹腺腻腼腽腾腿膀膂膈膊膏膑膘膙膛膜膝膦膨膳膺膻" + "臀臂臃臆臊臌臑臜臣臧自臬臭至致臻臼臾舀舁舂舄舅舆舌舍舐舒舔舛舜舞舟舠舢舣舥航舫般" + "舭舯舰舱舲舳舴舵舶舷舸船舻舾艄艅艇艉艋艎艏艘艚艟艨艮良艰色艳艴艺艽艾艿节芃芄芈芊" + "芋芍芎芏芑芒芗芘芙芜芝芟芠芡芣芤芥芦芨芩芪芫芬芭芮芯芰花芳芴芷芸芹芼芽芾苁苄苇苈" + "苉苊苋苌苍苎苏苑苒苓苔苕苗苘苛苜苞苟苠苡苣苤若苦苧苫苯英苴苷苹苻苾茀茁茂范茄茅茆" + "茈茉茋茌茎茏茑茓茔茕茗茚茛茜茝茧茨茫茬茭茯茱茳茴茵茶茸茹茺茼茽荀荁荃荄荆荇草荏荐" + "荑荒荓荔荖荙荚荛荜荞荟荠荡荣荤荥荦荧荨荩荪荫荬荭荮药荷荸荻荼荽莅莆莉莎莒莓莘莙莛" + "莜莝莞莠莨莩莪莫莰莱莲莳莴莶获莸莹莺莼莽莿菀菁菂菅菇菉菊菌菍菏菔菖菘菜菝菟菠菡菥" + "菩菪菰菱菲菹菼菽萁萃萄萆萋萌萍萎萏萑萘萚萜萝萣萤营萦萧萨萩萱萳萸萹萼落葆葎葑葖著" + "葙葚葛葜葡董葩葫葬葭葰葱葳葴葵葶葸葺蒂蒄蒇蒈蒉蒋蒌蒎蒐蒗蒙蒜蒟蒡蒨蒯蒱蒲蒴蒸蒹蒺" + "蒻蒽蒿蓁蓂蓄蓇蓉蓊蓍蓏蓐蓑蓓蓖蓝蓟蓠蓢蓣蓥蓦蓬蓰蓼蓿蔀蔃蔈蔊蔌蔑蔓蔗蔚蔟蔡蔫蔬蔷" + "蔸蔹蔺蔻蔼蔽蕃蕈蕉蕊蕖蕗蕙蕞蕤蕨蕰蕲蕴蕹蕺蕻蕾薁薄薅薇薏薛薜薢薤薨薪薮薯薰薳薷薸" + "薹薿藁藉藏藐藓藕藜藟藠藤藦藨藩藻藿蘅蘑蘖蘘蘧蘩蘸蘼虎虏虐虑虒虓虔虚虞虢虤虫虬虮虱" + "虷虸虹虺虻虼虽虾虿蚀蚁蚂蚄蚆蚊蚋蚌蚍蚓蚕蚜蚝蚣蚤蚧蚨蚩蚪蚬蚯蚰蚱蚲蚴蚶蚺蛀蛃蛄蛆" + "蛇蛉蛊蛋蛎蛏蛐蛑蛔蛘蛙蛛蛞蛟蛤蛩蛭蛮蛰蛱蛲蛳蛴蛸蛹蛾蜀蜂蜃蜇蜈蜉蜊蜍蜎蜐蜒蜓蜕蜗" + "蜘蜚蜜蜞蜡蜢蜣蜥蜩蜮蜱蜴蜷蜻蜾蜿蝇蝈蝉蝌蝎蝓蝗蝘蝙蝠蝣蝤蝥蝮蝰蝲蝴蝶蝻蝼蝽蝾螂螃" + "螅螈螋融螗螟螠螣螨螫螬螭螯螱螳螵螺螽蟀蟆蟊蟋蟏蟑蟒蟛蟠蟥蟪蟫蟮蟹蟾蠃蠊蠋蠓蠕蠖蠡" + "蠢蠲蠹蠼血衃衄衅行衍衎衒衔街衙衠衡衢衣补表衩衫衬衮衰衲衷衽衾衿袁袂袄袅袆袈袋袍袒" + "袖袗袜袢袤袪被袭袯袱袷袼裁裂装裆裈裉裎裒裔裕裘裙裛裟裢裣裤裥裨裰裱裳裴裸裹裼裾褂" + "褊褐褒褓褕褙褚褛褟褡褥褪褫褯褰褴褶襁襄襕襚襜襞襟襦襫襻西要覃覆见观觃规觅视觇览觉" + "觊觋觌觎觏觐觑角觖觚觜觞觟解觥触觫觭觯觱觳觿言訄訇訚訾詈詟詹誉誊誓謇警譬计订讣认" + "讥讦讧讨让讪讫训议讯记讱讲讳讴讵讶讷许讹论讻讼讽设访诀证诂诃评诅识诇诈诉诊诋诌词" + "诎诏诐译诒诓诔试诖诗诘诙诚诛诜话诞诟诠诡询诣诤该详诧诨诩诫诬语诮误诰诱诲诳说诵请" + "诸诹诺读诼诽课诿谀谁谂调谄谅谆谇谈谊谋谌谍谎谏谐谑谒谓谔谕谖谗谙谚谛谜谝谞谟谠谡" + "谢谣谤谥谦谧谨谩谪谫谬谭谮谯谰谱谲谳谴谵谶谷谼谿豁豆豇豉豌豕豚象豢豨豪豫豮豳豸豹" + "豺貂貅貆貉貊貌貔貘贝贞负贡财责贤败账货质贩贪贫贬购贮贯贰贱贲贳贴贵贶贷贸费贺贻贼" + "贽贾贿赀赁赂赃资赅赆赇赈赉赊赋赌赍赎赏赐赑赒赓赔赕赖赗赘赙赚赛赜赝赞赟赠赡赢赣赤" + "赦赧赪赫赭走赳赴赵赶起趁趄超越趋趑趔趟趣趯趱足趴趵趸趺趼趾趿跂跃跄跆跋跌跎跏跐跑" + "跖跗跚跛距跞跟跣跤跨跪跬路跱跳践跶跷跸跹跺跻跽踅踉踊踌踏踒踔踝踞踟踢踣踦踩踪踬踮" + "踯踱踵踶踹踺踽蹀蹁蹂蹄蹅蹇蹈蹉蹊蹋蹐蹑蹒蹙蹚蹜蹢蹦蹩蹬蹭蹯蹰蹲蹴蹶蹼蹽蹾蹿躁躅躇" + "躏躐躔躜躞身躬躯躲躺车轧轨轩轪轫转轭轮软轰轱轲轳轴轵轶轷轸轹轺轻轼载轾轿辀辁辂较" + "辄辅辆辇辈辉辊辋辌辍辎辏辐辑辒输辔辕辖辗辘辙辚辛辜辞辟辣辨辩辫辰辱边辽达辿迁迂迄" + "迅过迈迎运近迓返迕还这进远违连迟迢迤迥迦迨迩迪迫迭迮述迳迷迸迹迺追退送适逃逄逅逆" + "选逊逋逍透逐逑递途逖逗通逛逝逞速造逡逢逦逭逮逯逴逵逶逸逻逼逾遁遂遄遆遇遍遏遐遑遒" + "道遗遘遛遢遣遥遨遭遮遴遵遹遽避邀邂邃邈邋邑邓邕邗邘邙邛邝邠邡邢那邦邨邪邬邮邯邰邱" + "邲邳邴邵邶邸邹邺邻邽邾邿郁郃郄郅郇郈郊郎郏郐郑郓郗郚郛郜郝郡郢郤郦郧部郪郫郭郯郴" + "郸都郾郿鄀鄂鄃鄄鄅鄌鄑鄗鄘鄙鄚鄜鄞鄠鄢鄣鄫鄯鄱鄹酂酃酅酆酉酊酋酌配酎酏酐酒酗酚酝" + "酞酡酢酣酤酥酦酩酪酬酮酯酰酱酲酴酵酶酷酸酹酺酽酾酿醅醇醉醋醌醍醐醑醒醚醛醢醨醪醭" + "醮醯醴醵醺醾采釉释里重野量釐金釜鉴銎銮鋆鋈錾鍪鎏鏊鏖鐾鑫钆钇针钉钊钋钌钍钎钏钐钒" + "钓钔钕钖钗钘钙钚钛钜钝钞钟钠钡钢钣钤钥钦钧钨钩钪钫钬钭钮钯钰钱钲钳钴钵钷钹钺钻钼" + "钽钾钿铀铁铂铃铄铅铆铈铉铊铋铌铍铎铏铐铑铒铕铖铗铘铙铚铛铜铝铞铟铠铡铢铣铤铥铧铨" + "铩铪铫铬铭铮铯铰铱铲铳铴铵银铷铸铹铺铻铼铽链铿销锁锂锃锄锅锆锇锈锉锊锋锌锍锎锏锐" + "锑锒锓锔锕锖锗锘错锚锛锜锝锞锟锡锢锣锤锥锦锧锨锩锪锫锬锭键锯锰锱锲锳锴锵锶锷锸锹" + "锺锻锼锽锾锿镀镁镂镃镄镅镆镇镈镉镊镋镌镍镎镏镐镑镒镓镔镕镖镗镘镚镛镜镝镞镠镡镢镣" + "镤镥镦镧镨镩镪镫镬镭镮镯镰镱镲镳镴镵镶长门闩闪闫闭问闯闰闱闲闳间闵闶闷闸闹闺闻闼" + "闽闾闿阀阁阂阃阄阅阆阇阈阉阊阋阌阍阎阏阐阑阒阔阕阖阗阘阙阚阜队阡阪阮阱防阳阴阵阶" + "阻阼阽阿陀陂附际陆陇陈陉陋陌降陎限陑陔陕陛陞陟陡院除陧陨险陪陬陲陴陵陶陷隃隅隆隈" + "隋隍随隐隔隗隘隙障隧隩隰隳隶隹隺隼隽难雀雁雄雅集雇雉雊雌雍雎雏雒雕雠雨雩雪雯雱雳" + "零雷雹雾需霁霄霅霆震霈霉霍霎霏霓霖霜霞霨霪霭霰露霸霹霾青靓靖静靛非靠靡面靥革靬靰" + "靳靴靶靸靺靼靽靿鞁鞅鞋鞍鞑鞒鞔鞘鞠鞡鞣鞧鞨鞫鞬鞭鞮鞯鞲鞳鞴韂韦韧韨韩韪韫韬韭音韵" + "韶页顶顷顸项顺须顼顽顾顿颀颁颂颃预颅领颇颈颉颊颋颌颍颎颏颐频颓颔颖颗题颙颚颛颜额" + "颞颟颠颡颢颤颥颦颧风飏飐飑飒飓飔飕飗飘飙飞食飧飨餍餐餮饔饕饥饧饨饩饪饫饬饭饮饯饰" + "饱饲饳饴饵饶饷饸饹饺饻饼饽饿馁馃馄馅馆馇馈馉馊馋馌馍馏馐馑馒馓馔馕首馗馘香馝馞馥" + "馧馨马驭驮驯驰驱驲驳驴驵驶驷驸驹驺驻驼驽驾驿骀骁骂骃骄骅骆骇骈骉骊骋验骍骎骏骐骑" + "骒骓骕骖骗骘骙骚骛骜骝骞骟骠骡骢骣骤骥骦骧骨骰骱骶骷骸骺骼髀髁髂髃髅髋髌髎髑髓高" + "髡髢髦髫髭髯髹髻髽鬃鬈鬏鬒鬓鬘鬟鬣鬯鬲鬶鬷鬻鬼魁魂魃魄魅魆魇魈魉魋魍魏魑魔鱼鱽鱾" + "鱿鲀鲁鲂鲃鲅鲆鲇鲈鲉鲊鲋鲌鲍鲎鲏鲐鲑鲒鲔鲕鲖鲗鲘鲙鲚鲛鲜鲝鲞鲟鲠鲡鲢鲣鲤鲥鲦鲧鲨" + "鲩鲪鲫鲬鲭鲮鲯鲰鲱鲲鲳鲴鲵鲷鲸鲹鲺鲻鲼鲽鲾鲿鳀鳁鳂鳃鳄鳅鳇鳈鳉鳊鳌鳍鳎鳏鳐鳑鳒鳓" + "鳔鳕鳖鳗鳘鳙鳚鳛鳜鳝鳞鳟鳠鳡鳢鳣鳤鸟鸠鸡鸢鸣鸤鸥鸦鸧鸨鸩鸪鸫鸬鸭鸮鸯鸰鸱鸲鸳鸵鸶" + "鸷鸸鸹鸺鸻鸼鸽鸾鸿鹀鹁鹂鹃鹄鹅鹆鹇鹈鹉鹊鹋鹌鹍鹎鹏鹐鹑鹒鹔鹕鹖鹗鹘鹙鹚鹛鹜鹝鹞鹟" + "鹠鹡鹢鹣鹤鹦鹧鹨鹩鹪鹫鹬鹭鹮鹯鹰鹱鹲鹳鹴鹾鹿麀麂麇麈麋麑麒麓麖麝麟麦麸麹麻麽麾黄" + "黇黉黍黎黏黑黔默黛黜黝黟黠黡黢黥黧黩黪黯黹黻黼黾鼋鼍鼎鼐鼒鼓鼗鼙鼠鼢鼩鼫鼬鼯鼱鼷" + "鼹鼻鼽鼾齁齇齉齐齑齿龀龁龂龃龄龅龆龇龈龉龊龋龌龙龚龛龟龠龢鿍鿎鿏㑇㑊㕮㘎㙍㙘㙦㛃" + "㛚㛹㟃㠇㠓㤘㥄㧐㧑㧟㫰㬊㬎㬚㭎㭕㮾㰀㳇㳘㳚㴔㵐㶲㸆㸌㺄㻬㽏㿠䁖䂮䃅䃎䅟䌹䎃䎖䏝䏡" + "䏲䐃䓖䓛䓨䓫䓬䗖䗛䗪䗴䜣䝙䢺䢼䣘䥽䦃䲟䲠䲢䴓䴔䴕䴖䴗䴘䴙䶮𠅤𠙶𠳐𡎚𡐓𣗋𣲗𣲘𣸣𤧛𤩽" + "𤫉𥔲𥕢𥖨𥻗𦈡𦒍𦙶𦝼𦭜𦰡𧿹𨐈𨙸𨚕𨟠𨭉𨱇𨱏𨱑𨱔𨺙𩽾𩾃𩾌𪟝𪣻𪤗𪨰𪨶𪩘𪾢𫄧𫄨𫄷𫄸𫇭𫌀𫍣𫍯" + "𫍲𫍽𫐄𫐐𫐓𫑡𫓧𫓯𫓶𫓹𫔍𫔎𫔶𫖮𫖯𫖳𫗧𫗴𫘜𫘝𫘦𫘧𫘨𫘪𫘬𫚕𫚖𫚭𫛭𫞩𫟅𫟦𫟹𫟼𫠆𫠊𫠜𫢸𫫇𫭟" + "𫭢𫭼𫮃𫰛𫵷𫶇𫷷𫸩𬀩𬀪𬂩𬃊𬇕𬇙𬇹𬉼𬊈𬊤𬌗𬍛𬍡𬍤𬒈𬒔𬒗𬕂𬘓𬘘𬘡𬘩𬘫𬘬𬘭𬘯𬙂𬙊𬙋𬜬𬜯𬞟" + "𬟁𬟽𬣙𬣞𬣡𬣳𬤇𬤊𬤝𬨂𬨎𬩽𬪩𬬩𬬭𬬮𬬱𬬸𬬹𬬻𬬿𬭁𬭊𬭎𬭚𬭛𬭤𬭩𬭬𬭯𬭳𬭶𬭸𬭼𬮱𬮿𬯀𬯎𬱖𬱟" + "𬳵𬳶𬳽𬳿𬴂𬴃𬴊𬶋𬶍𬶏𬶐𬶟𬶠𬶨𬶭𬶮𬷕𬸘𬸚𬸣𬸦𬸪𬹼𬺈𬺓" ) -CN_CHARS_EXT = '吶诶屌囧飚屄' +CN_CHARS_EXT = "吶诶屌囧飚屄" CN_CHARS = CN_CHARS_COMMON + CN_CHARS_EXT -IN_CH_CHARS = { c : True for c in CN_CHARS } +IN_CH_CHARS = {c: True for c in CN_CHARS} EN_CHARS = string.ascii_letters + string.digits -IN_EN_CHARS = { c : True for c in EN_CHARS } +IN_EN_CHARS = {c: True for c in EN_CHARS} + +VALID_CHARS = CN_CHARS + EN_CHARS + " " +IN_VALID_CHARS = {c: True for c in VALID_CHARS} -VALID_CHARS = CN_CHARS + EN_CHARS + ' ' -IN_VALID_CHARS = { c : True for c in VALID_CHARS } # ================================================================================ # # basic class @@ -398,7 +402,7 @@ class ChineseChar(object): def __init__(self, simplified, traditional): self.simplified = simplified self.traditional = traditional - #self.__repr__ = self.__str__ + # self.__repr__ = self.__str__ def __str__(self): return self.simplified or self.traditional or None @@ -421,26 +425,28 @@ def __init__(self, power, simplified, traditional, big_s, big_t): self.big_t = big_t def __str__(self): - return '10^{}'.format(self.power) + return "10^{}".format(self.power) @classmethod def create(cls, index, value, numbering_type=NUMBERING_TYPES[1], small_unit=False): - if small_unit: - return ChineseNumberUnit(power=index + 1, - simplified=value[0], traditional=value[1], big_s=value[1], big_t=value[1]) + return ChineseNumberUnit( + power=index + 1, simplified=value[0], traditional=value[1], big_s=value[1], big_t=value[1] + ) elif numbering_type == NUMBERING_TYPES[0]: - return ChineseNumberUnit(power=index + 8, - simplified=value[0], traditional=value[1], big_s=value[0], big_t=value[1]) + return ChineseNumberUnit( + power=index + 8, simplified=value[0], traditional=value[1], big_s=value[0], big_t=value[1] + ) elif numbering_type == NUMBERING_TYPES[1]: - return ChineseNumberUnit(power=(index + 2) * 4, - simplified=value[0], traditional=value[1], big_s=value[0], big_t=value[1]) + return ChineseNumberUnit( + power=(index + 2) * 4, simplified=value[0], traditional=value[1], big_s=value[0], big_t=value[1] + ) elif numbering_type == NUMBERING_TYPES[2]: - return ChineseNumberUnit(power=pow(2, index + 3), - simplified=value[0], traditional=value[1], big_s=value[0], big_t=value[1]) + return ChineseNumberUnit( + power=pow(2, index + 3), simplified=value[0], traditional=value[1], big_s=value[0], big_t=value[1] + ) else: - raise ValueError( - 'Counting type should be in {0} ({1} provided).'.format(NUMBERING_TYPES, numbering_type)) + raise ValueError("Counting type should be in {0} ({1} provided).".format(NUMBERING_TYPES, numbering_type)) class ChineseNumberDigit(ChineseChar): @@ -484,6 +490,7 @@ class NumberSystem(object): """ 中文数字系统 """ + pass @@ -532,28 +539,22 @@ def create_system(numbering_type=NUMBERING_TYPES[1]): """ # chinese number units of '亿' and larger - all_larger_units = zip( - LARGER_CHINESE_NUMERING_UNITS_SIMPLIFIED, LARGER_CHINESE_NUMERING_UNITS_TRADITIONAL) - larger_units = [CNU.create(i, v, numbering_type, False) - for i, v in enumerate(all_larger_units)] + all_larger_units = zip(LARGER_CHINESE_NUMERING_UNITS_SIMPLIFIED, LARGER_CHINESE_NUMERING_UNITS_TRADITIONAL) + larger_units = [CNU.create(i, v, numbering_type, False) for i, v in enumerate(all_larger_units)] # chinese number units of '十, 百, 千, 万' - all_smaller_units = zip( - SMALLER_CHINESE_NUMERING_UNITS_SIMPLIFIED, SMALLER_CHINESE_NUMERING_UNITS_TRADITIONAL) - smaller_units = [CNU.create(i, v, small_unit=True) - for i, v in enumerate(all_smaller_units)] + all_smaller_units = zip(SMALLER_CHINESE_NUMERING_UNITS_SIMPLIFIED, SMALLER_CHINESE_NUMERING_UNITS_TRADITIONAL) + smaller_units = [CNU.create(i, v, small_unit=True) for i, v in enumerate(all_smaller_units)] # digis - chinese_digis = zip(CHINESE_DIGIS, CHINESE_DIGIS, - BIG_CHINESE_DIGIS_SIMPLIFIED, BIG_CHINESE_DIGIS_TRADITIONAL) + chinese_digis = zip(CHINESE_DIGIS, CHINESE_DIGIS, BIG_CHINESE_DIGIS_SIMPLIFIED, BIG_CHINESE_DIGIS_TRADITIONAL) digits = [CND.create(i, v) for i, v in enumerate(chinese_digis)] digits[0].alt_s, digits[0].alt_t = ZERO_ALT, ZERO_ALT digits[1].alt_s, digits[1].alt_t = ONE_ALT, ONE_ALT digits[2].alt_s, digits[2].alt_t = TWO_ALTS[0], TWO_ALTS[1] # symbols - positive_cn = CM(POSITIVE[0], POSITIVE[1], '+', lambda x: x) - negative_cn = CM(NEGATIVE[0], NEGATIVE[1], '-', lambda x: -x) - point_cn = CM(POINT[0], POINT[1], '.', lambda x, - y: float(str(x) + '.' + str(y))) + positive_cn = CM(POSITIVE[0], POSITIVE[1], "+", lambda x: x) + negative_cn = CM(NEGATIVE[0], NEGATIVE[1], "-", lambda x: -x) + point_cn = CM(POINT[0], POINT[1], ".", lambda x, y: float(str(x) + "." + str(y))) # sil_cn = CM(SIL[0], SIL[1], '-', lambda x, y: float(str(x) + '-' + str(y))) system = NumberSystem() system.units = smaller_units + larger_units @@ -564,7 +565,6 @@ def create_system(numbering_type=NUMBERING_TYPES[1]): def chn2num(chinese_string, numbering_type=NUMBERING_TYPES[1]): - def get_symbol(char, system): for u in system.units: if char in [u.traditional, u.simplified, u.big_s, u.big_t]: @@ -577,13 +577,12 @@ def get_symbol(char, system): return m def string2symbols(chinese_string, system): - int_string, dec_string = chinese_string, '' + int_string, dec_string = chinese_string, "" for p in [system.math.point.simplified, system.math.point.traditional]: if p in chinese_string: int_string, dec_string = chinese_string.split(p) break - return [get_symbol(c, system) for c in int_string], \ - [get_symbol(c, system) for c in dec_string] + return [get_symbol(c, system) for c in int_string], [get_symbol(c, system) for c in dec_string] def correct_symbols(integer_symbols, system): """ @@ -597,8 +596,7 @@ def correct_symbols(integer_symbols, system): if len(integer_symbols) > 1: if isinstance(integer_symbols[-1], CND) and isinstance(integer_symbols[-2], CNU): - integer_symbols.append( - CNU(integer_symbols[-2].power - 1, None, None, None, None)) + integer_symbols.append(CNU(integer_symbols[-2].power - 1, None, None, None, None)) result = [] unit_count = 0 @@ -615,8 +613,7 @@ def correct_symbols(integer_symbols, system): elif unit_count > 1: for i in range(len(result)): if isinstance(result[-i - 1], CNU) and result[-i - 1].power < current_unit.power: - result[-i - 1] = CNU(result[-i - 1].power + - current_unit.power, None, None, None, None) + result[-i - 1] = CNU(result[-i - 1].power + current_unit.power, None, None, None, None) return result def compute_value(integer_symbols): @@ -633,8 +630,7 @@ def compute_value(integer_symbols): elif isinstance(s, CNU): value[-1] *= pow(10, s.power) if s.power > last_power: - value[:-1] = list(map(lambda v: v * - pow(10, s.power), value[:-1])) + value[:-1] = list(map(lambda v: v * pow(10, s.power), value[:-1])) last_power = s.power value.append(0) return sum(value) @@ -643,20 +639,26 @@ def compute_value(integer_symbols): int_part, dec_part = string2symbols(chinese_string, system) int_part = correct_symbols(int_part, system) int_str = str(compute_value(int_part)) - dec_str = ''.join([str(d.value) for d in dec_part]) + dec_str = "".join([str(d.value) for d in dec_part]) if dec_part: - return '{0}.{1}'.format(int_str, dec_str) + return "{0}.{1}".format(int_str, dec_str) else: return int_str -def num2chn(number_string, numbering_type=NUMBERING_TYPES[1], big=False, - traditional=False, alt_zero=False, alt_one=False, alt_two=True, - use_zeros=True, use_units=True): - +def num2chn( + number_string, + numbering_type=NUMBERING_TYPES[1], + big=False, + traditional=False, + alt_zero=False, + alt_one=False, + alt_two=True, + use_zeros=True, + use_units=True, +): def get_value(value_string, use_zeros=True): - - striped_string = value_string.lstrip('0') + striped_string = value_string.lstrip("0") # record nothing if all zeros if not striped_string: @@ -671,14 +673,13 @@ def get_value(value_string, use_zeros=True): # recursively record multiple digits else: - result_unit = next(u for u in reversed( - system.units) if u.power < len(striped_string)) - result_string = value_string[:-result_unit.power] - return get_value(result_string) + [result_unit] + get_value(striped_string[-result_unit.power:]) + result_unit = next(u for u in reversed(system.units) if u.power < len(striped_string)) + result_string = value_string[: -result_unit.power] + return get_value(result_string) + [result_unit] + get_value(striped_string[-result_unit.power :]) system = create_system(numbering_type) - int_dec = number_string.split('.') + int_dec = number_string.split(".") if len(int_dec) == 1: int_string = int_dec[0] dec_string = "" @@ -686,8 +687,7 @@ def get_value(value_string, use_zeros=True): int_string = int_dec[0] dec_string = int_dec[1] else: - raise ValueError( - "invalid input num string with more than one dot: {}".format(number_string)) + raise ValueError("invalid input num string with more than one dot: {}".format(number_string)) if use_units and len(int_string) > 1: result_symbols = get_value(int_string) @@ -698,12 +698,10 @@ def get_value(value_string, use_zeros=True): result_symbols += [system.math.point] + dec_symbols if alt_two: - liang = CND(2, system.digits[2].alt_s, system.digits[2].alt_t, - system.digits[2].big_s, system.digits[2].big_t) + liang = CND(2, system.digits[2].alt_s, system.digits[2].alt_t, system.digits[2].big_s, system.digits[2].big_t) for i, v in enumerate(result_symbols): if isinstance(v, CND) and v.value == 2: - next_symbol = result_symbols[i + - 1] if i < len(result_symbols) - 1 else None + next_symbol = result_symbols[i + 1] if i < len(result_symbols) - 1 else None previous_symbol = result_symbols[i - 1] if i > 0 else None if isinstance(next_symbol, CNU) and isinstance(previous_symbol, (CNU, type(None))): if next_symbol.power != 1 and ((previous_symbol is None) or (previous_symbol.power != 1)): @@ -711,38 +709,38 @@ def get_value(value_string, use_zeros=True): # if big is True, '两' will not be used and `alt_two` has no impact on output if big: - attr_name = 'big_' + attr_name = "big_" if traditional: - attr_name += 't' + attr_name += "t" else: - attr_name += 's' + attr_name += "s" else: if traditional: - attr_name = 'traditional' + attr_name = "traditional" else: - attr_name = 'simplified' + attr_name = "simplified" - result = ''.join([getattr(s, attr_name) for s in result_symbols]) + result = "".join([getattr(s, attr_name) for s in result_symbols]) # if not use_zeros: # result = result.strip(getattr(system.digits[0], attr_name)) if alt_zero: - result = result.replace( - getattr(system.digits[0], attr_name), system.digits[0].alt_s) + result = result.replace(getattr(system.digits[0], attr_name), system.digits[0].alt_s) if alt_one: - result = result.replace( - getattr(system.digits[1], attr_name), system.digits[1].alt_s) + result = result.replace(getattr(system.digits[1], attr_name), system.digits[1].alt_s) for i, p in enumerate(POINT): if result.startswith(p): return CHINESE_DIGIS[0] + result # ^10, 11, .., 19 - if len(result) >= 2 and result[1] in [SMALLER_CHINESE_NUMERING_UNITS_SIMPLIFIED[0], - SMALLER_CHINESE_NUMERING_UNITS_TRADITIONAL[0]] and \ - result[0] in [CHINESE_DIGIS[1], BIG_CHINESE_DIGIS_SIMPLIFIED[1], BIG_CHINESE_DIGIS_TRADITIONAL[1]]: + if ( + len(result) >= 2 + and result[1] in [SMALLER_CHINESE_NUMERING_UNITS_SIMPLIFIED[0], SMALLER_CHINESE_NUMERING_UNITS_TRADITIONAL[0]] + and result[0] in [CHINESE_DIGIS[1], BIG_CHINESE_DIGIS_SIMPLIFIED[1], BIG_CHINESE_DIGIS_TRADITIONAL[1]] + ): result = result[1:] return result @@ -766,6 +764,7 @@ def chntext2cardinal(self): def cardinal2chntext(self): return num2chn(self.cardinal) + class Digit: """ DIGIT类 @@ -800,19 +799,14 @@ def __init__(self, telephone=None, raw_chntext=None, chntext=None): # return self.telephone def telephone2chntext(self, fixed=False): - if fixed: - sil_parts = self.telephone.split('-') - self.raw_chntext = ''.join([ - num2chn(part, alt_two=False, use_units=False) for part in sil_parts - ]) - self.chntext = self.raw_chntext.replace('', '') + sil_parts = self.telephone.split("-") + self.raw_chntext = "".join([num2chn(part, alt_two=False, use_units=False) for part in sil_parts]) + self.chntext = self.raw_chntext.replace("", "") else: - sp_parts = self.telephone.strip('+').split() - self.raw_chntext = ''.join([ - num2chn(part, alt_two=False, use_units=False) for part in sp_parts - ]) - self.chntext = self.raw_chntext.replace('', '') + sp_parts = self.telephone.strip("+").split() + self.raw_chntext = "".join([num2chn(part, alt_two=False, use_units=False) for part in sp_parts]) + self.chntext = self.raw_chntext.replace("", "") return self.chntext @@ -826,12 +820,12 @@ def __init__(self, fraction=None, chntext=None): self.chntext = chntext def chntext2fraction(self): - denominator, numerator = self.chntext.split('分之') - return chn2num(numerator) + '/' + chn2num(denominator) + denominator, numerator = self.chntext.split("分之") + return chn2num(numerator) + "/" + chn2num(denominator) def fraction2chntext(self): - numerator, denominator = self.fraction.split('/') - return num2chn(denominator) + '分之' + num2chn(numerator) + numerator, denominator = self.fraction.split("/") + return num2chn(denominator) + "分之" + num2chn(numerator) class Date: @@ -870,23 +864,23 @@ def __init__(self, date=None, chntext=None): def date2chntext(self): date = self.date try: - year, other = date.strip().split('年', 1) - year = Digit(digit=year).digit2chntext() + '年' + year, other = date.strip().split("年", 1) + year = Digit(digit=year).digit2chntext() + "年" except ValueError: other = date - year = '' + year = "" if other: try: - month, day = other.strip().split('月', 1) - month = Cardinal(cardinal=month).cardinal2chntext() + '月' + month, day = other.strip().split("月", 1) + month = Cardinal(cardinal=month).cardinal2chntext() + "月" except ValueError: day = date - month = '' + month = "" if day: day = Cardinal(cardinal=day[:-1]).cardinal2chntext() + day[-1] else: - month = '' - day = '' + month = "" + day = "" chntext = year + month + day self.chntext = chntext return self.chntext @@ -906,7 +900,7 @@ def __init__(self, money=None, chntext=None): def money2chntext(self): money = self.money - pattern = re.compile(r'(\d+(\.\d+)?)') + pattern = re.compile(r"(\d+(\.\d+)?)") matchers = pattern.findall(money) if matchers: for matcher in matchers: @@ -925,20 +919,20 @@ def __init__(self, percentage=None, chntext=None): self.chntext = chntext def chntext2percentage(self): - return chn2num(self.chntext.strip().strip('百分之')) + '%' + return chn2num(self.chntext.strip().strip("百分之")) + "%" def percentage2chntext(self): - return '百分之' + num2chn(self.percentage.strip().strip('%')) + return "百分之" + num2chn(self.percentage.strip().strip("%")) def normalize_nsw(raw_text): - text = '^' + raw_text + '$' + text = "^" + raw_text + "$" # 规范化日期 pattern = re.compile(r"\D+((([089]\d|(19|20)\d{2})年)?(\d{1,2}月(\d{1,2}[日号])?)?)") matchers = pattern.findall(text) if matchers: - #print('date') + # print('date') for matcher in matchers: text = text.replace(matcher[0], Date(date=matcher[0]).date2chntext(), 1) @@ -946,7 +940,7 @@ def normalize_nsw(raw_text): pattern = re.compile(r"\D+((\d+(\.\d+)?)[多余几]?" + CURRENCY_UNITS + r"(\d" + CURRENCY_UNITS + r"?)?)") matchers = pattern.findall(text) if matchers: - #print('money') + # print('money') for matcher in matchers: text = text.replace(matcher[0], Money(money=matcher[0]).money2chntext(), 1) @@ -959,7 +953,7 @@ def normalize_nsw(raw_text): pattern = re.compile(r"\D((\+?86 ?)?1([38]\d|5[0-35-9]|7[678]|9[89])\d{8})\D") matchers = pattern.findall(text) if matchers: - #print('telephone') + # print('telephone') for matcher in matchers: text = text.replace(matcher[0], TelePhone(telephone=matcher[0]).telephone2chntext(), 1) # 固话 @@ -974,16 +968,16 @@ def normalize_nsw(raw_text): pattern = re.compile(r"(\d+/\d+)") matchers = pattern.findall(text) if matchers: - #print('fraction') + # print('fraction') for matcher in matchers: text = text.replace(matcher, Fraction(fraction=matcher).fraction2chntext(), 1) # 规范化百分数 - text = text.replace('％', '%') + text = text.replace("％", "%") pattern = re.compile(r"(\d+(\.\d+)?%)") matchers = pattern.findall(text) if matchers: - #print('percentage') + # print('percentage') for matcher in matchers: text = text.replace(matcher[0], Percentage(percentage=matcher[0]).percentage2chntext(), 1) @@ -991,7 +985,7 @@ def normalize_nsw(raw_text): pattern = re.compile(r"(\d+(\.\d+)?)[多余几]?" + COM_QUANTIFIERS) matchers = pattern.findall(text) if matchers: - #print('cardinal+quantifier') + # print('cardinal+quantifier') for matcher in matchers: text = text.replace(matcher[0], Cardinal(cardinal=matcher[0]).cardinal2chntext(), 1) @@ -999,7 +993,7 @@ def normalize_nsw(raw_text): pattern = re.compile(r"(\d{4,32})") matchers = pattern.findall(text) if matchers: - #print('digit') + # print('digit') for matcher in matchers: text = text.replace(matcher, Digit(digit=matcher).digit2chntext(), 1) @@ -1007,20 +1001,19 @@ def normalize_nsw(raw_text): pattern = re.compile(r"(\d+(\.\d+)?)") matchers = pattern.findall(text) if matchers: - #print('cardinal') + # print('cardinal') for matcher in matchers: text = text.replace(matcher[0], Cardinal(cardinal=matcher[0]).cardinal2chntext(), 1) - # restore P2P, O2O, B2C, B2B etc pattern = re.compile(r"(([a-zA-Z]+)二([a-zA-Z]+))") matchers = pattern.findall(text) if matchers: # print('particular') for matcher in matchers: - text = text.replace(matcher[0], matcher[1]+'2'+matcher[2], 1) + text = text.replace(matcher[0], matcher[1] + "2" + matcher[2], 1) - return text.lstrip('^').rstrip('$') + return text.lstrip("^").rstrip("$") def remove_erhua(text): @@ -1029,9 +1022,9 @@ def remove_erhua(text): 他女儿在那边儿 -> 他女儿在那边 """ - new_str='' - while re.search('儿',text): - a = re.search('儿',text).span() + new_str = "" + while re.search("儿", text): + a = re.search("儿", text).span() remove_er_flag = 0 if ER_WHITELIST_PATTERN.search(text): @@ -1039,12 +1032,12 @@ def remove_erhua(text): if b[0] <= a[0]: remove_er_flag = 1 - if remove_er_flag == 0 : - new_str = new_str + text[0:a[0]] - text = text[a[1]:] + if remove_er_flag == 0: + new_str = new_str + text[0 : a[0]] + text = text[a[1] :] else: - new_str = new_str + text[0:b[1]] - text = text[b[1]:] + new_str = new_str + text[0 : b[1]] + text = text[b[1] :] text = new_str + text return text @@ -1053,25 +1046,26 @@ def remove_erhua(text): def remove_space(text): tokens = text.split() new = [] - for k,t in enumerate(tokens): + for k, t in enumerate(tokens): if k != 0: - if IN_EN_CHARS.get(tokens[k-1][-1]) and IN_EN_CHARS.get(t[0]): - new.append(' ') + if IN_EN_CHARS.get(tokens[k - 1][-1]) and IN_EN_CHARS.get(t[0]): + new.append(" ") new.append(t) - return ''.join(new) + return "".join(new) class TextNorm: - def __init__(self, - to_banjiao:bool = False, - to_upper:bool = False, - to_lower:bool = False, - remove_fillers:bool = False, - remove_erhua:bool = False, - check_chars:bool = False, - remove_space:bool = False, - cc_mode:str = '', - ) : + def __init__( + self, + to_banjiao: bool = False, + to_upper: bool = False, + to_lower: bool = False, + remove_fillers: bool = False, + remove_erhua: bool = False, + check_chars: bool = False, + remove_space: bool = False, + cc_mode: str = "", + ): self.to_banjiao = to_banjiao self.to_upper = to_upper self.to_lower = to_lower @@ -1083,6 +1077,7 @@ def __init__(self, self.cc = None if cc_mode: from opencc import OpenCC # Open Chinese Convert: pip install opencc + self.cc = OpenCC(cc_mode) def __call__(self, text): @@ -1100,7 +1095,7 @@ def __call__(self, text): if self.remove_fillers: for c in FILLER_CHARS: - text = text.replace(c, '') + text = text.replace(c, "") if self.remove_erhua: text = remove_erhua(text) @@ -1112,8 +1107,8 @@ def __call__(self, text): if self.check_chars: for c in text: if not IN_VALID_CHARS.get(c): - print(f'WARNING: illegal char {c} in: {text}', file=sys.stderr) - return '' + print(f"WARNING: illegal char {c} in: {text}", file=sys.stderr) + return "" if self.remove_space: text = remove_space(text) @@ -1121,79 +1116,81 @@ def __call__(self, text): return text -if __name__ == '__main__': +if __name__ == "__main__": p = argparse.ArgumentParser() # normalizer options - p.add_argument('--to_banjiao', action='store_true', help='convert quanjiao chars to banjiao') - p.add_argument('--to_upper', action='store_true', help='convert to upper case') - p.add_argument('--to_lower', action='store_true', help='convert to lower case') - p.add_argument('--remove_fillers', action='store_true', help='remove filler chars such as "呃, 啊"') - p.add_argument('--remove_erhua', action='store_true', help='remove erhua chars such as "他女儿在那边儿 -> 他女儿在那边"') - p.add_argument('--check_chars', action='store_true' , help='skip sentences containing illegal chars') - p.add_argument('--remove_space', action='store_true' , help='remove whitespace') - p.add_argument('--cc_mode', choices=['', 't2s', 's2t'], default='', help='convert between traditional to simplified') + p.add_argument("--to_banjiao", action="store_true", help="convert quanjiao chars to banjiao") + p.add_argument("--to_upper", action="store_true", help="convert to upper case") + p.add_argument("--to_lower", action="store_true", help="convert to lower case") + p.add_argument("--remove_fillers", action="store_true", help='remove filler chars such as "呃, 啊"') + p.add_argument("--remove_erhua", action="store_true", help='remove erhua chars such as "他女儿在那边儿 -> 他女儿在那边"') + p.add_argument("--check_chars", action="store_true", help="skip sentences containing illegal chars") + p.add_argument("--remove_space", action="store_true", help="remove whitespace") + p.add_argument( + "--cc_mode", choices=["", "t2s", "s2t"], default="", help="convert between traditional to simplified" + ) # I/O options - p.add_argument('--log_interval', type=int, default=10000, help='log interval in number of processed lines') - p.add_argument('--has_key', action='store_true', help="will be deprecated, set --format ark instead") - p.add_argument('--format', type=str, choices=['txt', 'ark', 'tsv'], default='txt', help='input format') - p.add_argument('ifile', help='input filename, assume utf-8 encoding') - p.add_argument('ofile', help='output filename') + p.add_argument("--log_interval", type=int, default=10000, help="log interval in number of processed lines") + p.add_argument("--has_key", action="store_true", help="will be deprecated, set --format ark instead") + p.add_argument("--format", type=str, choices=["txt", "ark", "tsv"], default="txt", help="input format") + p.add_argument("ifile", help="input filename, assume utf-8 encoding") + p.add_argument("ofile", help="output filename") args = p.parse_args() if args.has_key: - args.format = 'ark' + args.format = "ark" normalizer = TextNorm( - to_banjiao = args.to_banjiao, - to_upper = args.to_upper, - to_lower = args.to_lower, - remove_fillers = args.remove_fillers, - remove_erhua = args.remove_erhua, - check_chars = args.check_chars, - remove_space = args.remove_space, - cc_mode = args.cc_mode, + to_banjiao=args.to_banjiao, + to_upper=args.to_upper, + to_lower=args.to_lower, + remove_fillers=args.remove_fillers, + remove_erhua=args.remove_erhua, + check_chars=args.check_chars, + remove_space=args.remove_space, + cc_mode=args.cc_mode, ) normalizer = TextNorm( - to_banjiao = args.to_banjiao, - to_upper = args.to_upper, - to_lower = args.to_lower, - remove_fillers = args.remove_fillers, - remove_erhua = args.remove_erhua, - check_chars = args.check_chars, - remove_space = args.remove_space, - cc_mode = args.cc_mode, + to_banjiao=args.to_banjiao, + to_upper=args.to_upper, + to_lower=args.to_lower, + remove_fillers=args.remove_fillers, + remove_erhua=args.remove_erhua, + check_chars=args.check_chars, + remove_space=args.remove_space, + cc_mode=args.cc_mode, ) ndone = 0 - with open(args.ifile, 'r', encoding = 'utf8') as istream, open(args.ofile, 'w+', encoding = 'utf8') as ostream: - if args.format == 'tsv': - reader = csv.DictReader(istream, delimiter = '\t') - assert('TEXT' in reader.fieldnames) - print('\t'.join(reader.fieldnames), file=ostream) + with open(args.ifile, "r", encoding="utf8") as istream, open(args.ofile, "w+", encoding="utf8") as ostream: + if args.format == "tsv": + reader = csv.DictReader(istream, delimiter="\t") + assert "TEXT" in reader.fieldnames + print("\t".join(reader.fieldnames), file=ostream) for item in reader: - text = item['TEXT'] + text = item["TEXT"] if text: text = normalizer(text) if text: - item['TEXT'] = text - print('\t'.join([ item[f] for f in reader.fieldnames ]), file = ostream) + item["TEXT"] = text + print("\t".join([item[f] for f in reader.fieldnames]), file=ostream) ndone += 1 if ndone % args.log_interval == 0: - print(f'text norm: {ndone} lines done.', file = sys.stderr, flush = True) + print(f"text norm: {ndone} lines done.", file=sys.stderr, flush=True) else: for l in istream: - key, text = '', '' - if args.format == 'ark': # KALDI archive, line format: "key text" + key, text = "", "" + if args.format == "ark": # KALDI archive, line format: "key text" cols = l.strip().split(maxsplit=1) - key, text = cols[0], cols[1] if len(cols) == 2 else '' + key, text = cols[0], cols[1] if len(cols) == 2 else "" else: text = l.strip() @@ -1201,12 +1198,12 @@ def __call__(self, text): text = normalizer(text) if text: - if args.format == 'ark': - print(key + '\t' + text, file = ostream) + if args.format == "ark": + print(key + "\t" + text, file=ostream) else: - print(text, file = ostream) + print(text, file=ostream) ndone += 1 if ndone % args.log_interval == 0: - print(f'text norm: {ndone} lines done.', file = sys.stderr, flush = True) - print(f'text norm: {ndone} lines done in total.', file = sys.stderr, flush = True) + print(f"text norm: {ndone} lines done.", file=sys.stderr, flush=True) + print(f"text norm: {ndone} lines done in total.", file=sys.stderr, flush=True) diff --git a/TTS/tts/models/base_tacotron.py b/TTS/tts/models/base_tacotron.py index f38dace235..4aaf526111 100644 --- a/TTS/tts/models/base_tacotron.py +++ b/TTS/tts/models/base_tacotron.py @@ -252,12 +252,7 @@ def compute_gst(self, inputs, style_input, speaker_embedding=None): def compute_capacitron_VAE_embedding(self, inputs, reference_mel_info, text_info=None, speaker_embedding=None): """Capacitron Variational Autoencoder""" - ( - VAE_outputs, - posterior_distribution, - prior_distribution, - capacitron_beta, - ) = self.capacitron_vae_layer( + (VAE_outputs, posterior_distribution, prior_distribution, capacitron_beta,) = self.capacitron_vae_layer( reference_mel_info, text_info, speaker_embedding, # pylint: disable=not-callable diff --git a/TTS/tts/models/tortoise.py b/TTS/tts/models/tortoise.py index 16644ff95e..c8cfcfdd04 100644 --- a/TTS/tts/models/tortoise.py +++ b/TTS/tts/models/tortoise.py @@ -676,12 +676,7 @@ def inference( ), "Too much text provided. Break the text up into separate segments and re-try inference." if voice_samples is not None: - ( - auto_conditioning, - diffusion_conditioning, - _, - _, - ) = self.get_conditioning_latents( + (auto_conditioning, diffusion_conditioning, _, _,) = self.get_conditioning_latents( voice_samples, return_mels=True, latent_averaging_mode=latent_averaging_mode, diff --git a/TTS/tts/models/xtts.py b/TTS/tts/models/xtts.py index c0532b36b1..58f8542bdf 100644 --- a/TTS/tts/models/xtts.py +++ b/TTS/tts/models/xtts.py @@ -23,7 +23,19 @@ def wav_to_mel_cloning( - wav, mel_norms_file="../experiments/clips_mel_norms.pth", mel_norms=None, device=torch.device("cpu") + wav, + mel_norms_file="../experiments/clips_mel_norms.pth", + mel_norms=None, + device=torch.device("cpu"), + n_fft=4096, + hop_length=1024, + win_length=4096, + power=2, + normalized=False, + sample_rate=22050, + f_min=0, + f_max=8000, + n_mels=80, ): """ Convert waveform to mel-spectrogram with hard-coded parameters for cloning. @@ -38,15 +50,15 @@ def wav_to_mel_cloning( torch.Tensor: Mel-spectrogram tensor. """ mel_stft = torchaudio.transforms.MelSpectrogram( - n_fft=4096, - hop_length=1024, - win_length=4096, - power=2, - normalized=False, - sample_rate=22050, - f_min=0, - f_max=8000, - n_mels=80, + n_fft=n_fft, + hop_length=hop_length, + win_length=win_length, + power=power, + normalized=normalized, + sample_rate=sample_rate, + f_min=f_min, + f_max=f_max, + n_mels=n_mels, norm="slaney", ).to(device) wav = wav.to(device) @@ -177,19 +189,23 @@ class XttsArgs(Coqpit): clvp_checkpoint (str, optional): The checkpoint for the ConditionalLatentVariablePerseq model. Defaults to None. decoder_checkpoint (str, optional): The checkpoint for the DiffTTS model. Defaults to None. num_chars (int, optional): The maximum number of characters to generate. Defaults to 255. - use_hifigan (bool, optional): Whether to use hifigan or diffusion + univnet as a decoder. Defaults to True. + use_hifigan (bool, optional): Whether to use hifigan with implicit enhancement or diffusion + univnet as a decoder. Defaults to True. + use_ne_hifigan (bool, optional): Whether to use regular hifigan or diffusion + univnet as a decoder. Defaults to False. For GPT model: - ar_max_audio_tokens (int, optional): The maximum mel tokens for the autoregressive model. Defaults to 604. - ar_max_text_tokens (int, optional): The maximum text tokens for the autoregressive model. Defaults to 402. - ar_max_prompt_tokens (int, optional): The maximum prompt tokens or the autoregressive model. Defaults to 70. - ar_layers (int, optional): The number of layers for the autoregressive model. Defaults to 30. - ar_n_model_channels (int, optional): The model dimension for the autoregressive model. Defaults to 1024. - ar_n_heads (int, optional): The number of heads for the autoregressive model. Defaults to 16. - ar_number_text_tokens (int, optional): The number of text tokens for the autoregressive model. Defaults to 255. - ar_start_text_token (int, optional): The start text token for the autoregressive model. Defaults to 255. + gpt_max_audio_tokens (int, optional): The maximum mel tokens for the autoregressive model. Defaults to 604. + gpt_max_text_tokens (int, optional): The maximum text tokens for the autoregressive model. Defaults to 402. + gpt_max_prompt_tokens (int, optional): The maximum prompt tokens or the autoregressive model. Defaults to 70. + gpt_layers (int, optional): The number of layers for the autoregressive model. Defaults to 30. + gpt_n_model_channels (int, optional): The model dimension for the autoregressive model. Defaults to 1024. + gpt_n_heads (int, optional): The number of heads for the autoregressive model. Defaults to 16. + gpt_number_text_tokens (int, optional): The number of text tokens for the autoregressive model. Defaults to 255. + gpt_start_text_token (int, optional): The start text token for the autoregressive model. Defaults to 255. gpt_checkpointing (bool, optional): Whether to use checkpointing for the autoregressive model. Defaults to False. - ar_train_solo_embeddings (bool, optional): Whether to train embeddings for the autoregressive model. Defaults to False. + gpt_train_solo_embeddings (bool, optional): Whether to train embeddings for the autoregressive model. Defaults to False. + gpt_code_stride_len (int, optional): The hop_size of dvae and consequently of the gpt output. Defaults to 1024. + gpt_use_masking_gt_prompt_approach (bool, optional): If True, it will use ground truth as prompt and it will mask the loss to avoid repetition. Defaults to True. + gpt_use_perceiver_resampler (bool, optional): If True, it will use perceiver resampler from flamingo paper - https://arxiv.org/abs/2204.14198. Defaults to False. For DiffTTS model: diff_model_channels (int, optional): The number of channels for the DiffTTS model. Defaults to 1024. @@ -229,6 +245,9 @@ class XttsArgs(Coqpit): gpt_num_audio_tokens: int = 8194 gpt_start_audio_token: int = 8192 gpt_stop_audio_token: int = 8193 + gpt_code_stride_len: int = 1024 + gpt_use_masking_gt_prompt_approach: bool = True + gpt_use_perceiver_resampler: bool = False # Diffusion Decoder params diff_model_channels: int = 1024 @@ -247,7 +266,6 @@ class XttsArgs(Coqpit): input_sample_rate: int = 22050 output_sample_rate: int = 24000 output_hop_length: int = 256 - ar_mel_length_compression: int = 1024 decoder_input_dim: int = 1024 d_vector_dim: int = 512 cond_d_vector_in_each_upsampling_layer: bool = True @@ -304,6 +322,8 @@ def init_models(self): num_audio_tokens=self.args.gpt_num_audio_tokens, start_audio_token=self.args.gpt_start_audio_token, stop_audio_token=self.args.gpt_stop_audio_token, + use_perceiver_resampler=self.args.gpt_use_perceiver_resampler, + code_stride_len=self.args.gpt_code_stride_len, ) if self.args.use_hifigan: @@ -311,7 +331,7 @@ def init_models(self): input_sample_rate=self.args.input_sample_rate, output_sample_rate=self.args.output_sample_rate, output_hop_length=self.args.output_hop_length, - ar_mel_length_compression=self.args.ar_mel_length_compression, + ar_mel_length_compression=self.args.gpt_code_stride_len, decoder_input_dim=self.args.decoder_input_dim, d_vector_dim=self.args.d_vector_dim, cond_d_vector_in_each_upsampling_layer=self.args.cond_d_vector_in_each_upsampling_layer, @@ -322,7 +342,7 @@ def init_models(self): input_sample_rate=self.args.input_sample_rate, output_sample_rate=self.args.output_sample_rate, output_hop_length=self.args.output_hop_length, - ar_mel_length_compression=self.args.ar_mel_length_compression, + ar_mel_length_compression=self.args.gpt_code_stride_len, decoder_input_dim=self.args.decoder_input_dim, d_vector_dim=self.args.d_vector_dim, cond_d_vector_in_each_upsampling_layer=self.args.cond_d_vector_in_each_upsampling_layer, @@ -354,12 +374,33 @@ def get_gpt_cond_latents(self, audio, sr, length: int = 3): Args: audio_path (str): Path to the audio file. + sr (int): Sample rate of the audio. length (int): Length of the audio in seconds. Defaults to 3. """ - - audio_22k = torchaudio.functional.resample(audio, sr, 22050) - audio_22k = audio_22k[:, : 22050 * length] - mel = wav_to_mel_cloning(audio_22k, mel_norms=self.mel_stats.cpu()) + if sr != 22050: + audio = torchaudio.functional.resample(audio, sr, 22050) + audio = audio[:, : 22050 * length] + if self.args.gpt_use_perceiver_resampler: + n_fft = 2048 + hop_length = 256 + win_length = 1024 + else: + n_fft = 4096 + hop_length = 1024 + win_length = 4096 + mel = wav_to_mel_cloning( + audio, + mel_norms=self.mel_stats.cpu(), + n_fft=n_fft, + hop_length=hop_length, + win_length=win_length, + power=2, + normalized=False, + sample_rate=22050, + f_min=0, + f_max=8000, + n_mels=80, + ) cond_latent = self.gpt.get_style_emb(mel.to(self.device)) return cond_latent.transpose(1, 2) @@ -461,6 +502,9 @@ def inference_with_config(self, text, config, ref_audio_path, language, **kwargs "diffusion_temperature": config.diffusion_temperature, "decoder_iterations": config.decoder_iterations, "decoder_sampler": config.decoder_sampler, + "gpt_cond_len": config.gpt_cond_len, + "max_ref_len": config.max_ref_len, + "sound_norm_refs": config.sound_norm_refs, } settings.update(kwargs) # allow overriding of preset settings with kwargs return self.full_inference(text, ref_audio_path, language, **settings) @@ -477,8 +521,11 @@ def full_inference( repetition_penalty=2.0, top_k=50, top_p=0.85, - gpt_cond_len=6, do_sample=True, + # Cloning + gpt_cond_len=6, + max_ref_len=10, + sound_norm_refs=False, # Decoder inference decoder_iterations=100, cond_free=True, @@ -546,8 +593,12 @@ def full_inference( Sample rate is 24kHz. """ (gpt_cond_latent, diffusion_conditioning, speaker_embedding) = self.get_conditioning_latents( - audio_path=ref_audio_path, gpt_cond_len=gpt_cond_len + audio_path=ref_audio_path, + gpt_cond_len=gpt_cond_len, + max_ref_length=max_ref_len, + sound_norm_refs=sound_norm_refs, ) + return self.inference( text, language, @@ -591,11 +642,16 @@ def inference( diffusion_temperature=1.0, decoder_sampler="ddim", decoder="hifigan", + num_beams=1, **hf_generate_kwargs, ): text = text.strip().lower() text_tokens = torch.IntTensor(self.tokenizer.encode(text, lang=language)).unsqueeze(0).to(self.device) + # print(" > Input text: ", text) + # print(" > Input text preprocessed: ",self.tokenizer.preprocess_text(text, language)) + # print(" > Input tokens: ", text_tokens) + # print(" > Decoded text: ", self.tokenizer.decode(text_tokens[0].cpu().numpy())) assert ( text_tokens.shape[-1] < self.args.gpt_max_text_tokens ), " ❗ XTTS can only generate text with a maximum of 400 tokens." @@ -618,6 +674,7 @@ def inference( top_k=top_k, temperature=temperature, num_return_sequences=self.gpt_batch_size, + num_beams=num_beams, length_penalty=length_penalty, repetition_penalty=repetition_penalty, output_attentions=False, @@ -671,7 +728,12 @@ def inference( ) wav = self.vocoder.inference(mel) - return {"wav": wav.cpu().numpy().squeeze()} + return { + "wav": wav.cpu().numpy().squeeze(), + "gpt_latents": gpt_latents, + "speaker_embedding": speaker_embedding, + "diffusion_conditioning": diffusion_conditioning, + } def handle_chunks(self, wav_gen, wav_gen_prev, wav_overlap, overlap_len): """Handle chunk formatting in streaming mode""" diff --git a/TTS/utils/manage.py b/TTS/utils/manage.py index eef987efd4..c732e1f511 100644 --- a/TTS/utils/manage.py +++ b/TTS/utils/manage.py @@ -392,7 +392,7 @@ def download_model(self, model_name): self.create_dir_and_download_model(model_name, model_item, output_path) # if the configs are different, redownload it # ToDo: we need a better way to handle it - if "xtts_v1" in model_name: + if "xtts" in model_name: try: self.check_if_configs_are_equal(model_name, model_item, output_path) except: @@ -406,7 +406,7 @@ def download_model(self, model_name): output_model_path = output_path output_config_path = None if ( - model not in ["tortoise-v2", "bark", "xtts_v1", "xtts_v1.1"] and "fairseq" not in model_name + model not in ["tortoise-v2", "bark"] and "fairseq" not in model_name and "xtts" not in model_name ): # TODO:This is stupid but don't care for now. output_model_path, output_config_path = self._find_files(output_path) # update paths in the config.json diff --git a/docs/source/models/xtts.md b/docs/source/models/xtts.md index 09373b4adf..1d034aeadf 100644 --- a/docs/source/models/xtts.md +++ b/docs/source/models/xtts.md @@ -7,17 +7,25 @@ This is the same model that powers [Coqui Studio](https://coqui.ai/), and [Coqui a few tricks to make it faster and support streaming inference. ### Features -- Voice cloning with just a 3-second audio clip. +- Voice cloning. - Cross-language voice cloning. - Multi-lingual speech generation. - 24khz sampling rate. +- Streaming inference with < 200ms latency. (See [Streaming inference](#streaming-inference)) +- Fine-tuning support. (See [Training](#training)) + +### Updates with v2 +- Improved voice cloning. +- Voices can be cloned with a single audio file or multiple audio files, without any effect on the runtime. +- 2 new languages: Hungarian and Korean. +- Across the board quality improvements. ### Code Current implementation only supports inference. ### Languages -As of now, XTTS-v1.1 supports 14 languages: English, Spanish, French, German, Italian, Portuguese, -Polish, Turkish, Russian, Dutch, Czech, Arabic, Chinese (Simplified) and Japanese. +As of now, XTTS-v2 supports 16 languages: English, Spanish, French, German, Italian, Portuguese, +Polish, Turkish, Russian, Dutch, Czech, Arabic, Chinese (Simplified), Japanese, Hungarian, Korean Stay tuned as we continue to add support for more languages. If you have any language requests, please feel free to reach out. @@ -33,7 +41,7 @@ You can also mail us at info@coqui.ai. ```python from TTS.api import TTS -tts = TTS("tts_models/multilingual/multi-dataset/xtts_v1.1", gpu=True) +tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2", gpu=True) # generate speech by cloning a voice using default settings tts.tts_to_file(text="It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.", @@ -45,7 +53,7 @@ tts.tts_to_file(text="It took me quite a long time to develop a voice, and now t #### 🐸TTS Command line ```console - tts --model_name tts_models/multilingual/multi-dataset/xtts_v1.1 \ + tts --model_name tts_models/multilingual/multi-dataset/xtts_v2 \ --text "Bugün okula gitmek istemiyorum." \ --speaker_wav /path/to/target/speaker.wav \ --language_idx tr \ @@ -73,7 +81,7 @@ config.load_json("/path/to/xtts/config.json") model = Xtts.init_from_config(config) model.load_checkpoint(config, checkpoint_dir="/path/to/xtts/", use_deepspeed=True) model.cuda() - + print("Computing speaker latents...") gpt_cond_latent, diffusion_conditioning, speaker_embedding = model.get_conditioning_latents(audio_path="reference.wav") @@ -122,7 +130,7 @@ chunks = model.inference_stream( gpt_cond_latent, speaker_embedding ) - + wav_chuncks = [] for i, chunk in enumerate(chunks): if i == 0: @@ -136,7 +144,7 @@ torchaudio.save("xtts_streaming.wav", wav.squeeze().unsqueeze(0).cpu(), 24000) ### Training -A recipe for `XTTS_v1.1` GPT encoder training using `LJSpeech` dataset is available at https://github.com/coqui-ai/TTS/tree/dev/recipes/ljspeech/xtts_v1/train_gpt_xtts.py +A recipe for `XTTS_v2` GPT encoder training using `LJSpeech` dataset is available at https://github.com/coqui-ai/TTS/tree/dev/recipes/ljspeech/xtts_v1/train_gpt_xtts.py You need to change the fields of the `BaseDatasetConfig` to match your dataset and then update `GPTArgs` and `GPTTrainerConfig` fields as you need. By default, it will use the same parameters that XTTS v1.1 model was trained with. To speed up the model convergence, as default, it will also download the XTTS v1.1 checkpoint and load it. @@ -152,7 +160,7 @@ from TTS.tts.models.xtts import Xtts # Add here the xtts_config path CONFIG_PATH = "recipes/ljspeech/xtts_v1/run/training/GPT_XTTS_LJSpeech_FT-October-23-2023_10+36AM-653f2e75/config.json" # Add here the vocab file that you have used to train the model -TOKENIZER_PATH = "recipes/ljspeech/xtts_v1/run/training/XTTS_v1.1_original_model_files/vocab.json" +TOKENIZER_PATH = "recipes/ljspeech/xtts_v1/run/training/XTTS_v2_original_model_files/vocab.json" # Add here the checkpoint that you want to do inference with XTTS_CHECKPOINT = "recipes/ljspeech/xtts_v1/run/training/GPT_XTTS_LJSpeech_FT/best_model.pth" # Add here the speaker reference @@ -184,13 +192,14 @@ torchaudio.save(OUTPUT_WAV_PATH, torch.tensor(out["wav"]).unsqueeze(0), 24000) ``` -## Important resources & papers +## References and Acknowledgements - VallE: https://arxiv.org/abs/2301.02111 - Tortoise Repo: https://github.com/neonbjb/tortoise-tts - Faster implementation: https://github.com/152334H/tortoise-tts-fast - Univnet: https://arxiv.org/abs/2106.07889 - Latent Diffusion:https://arxiv.org/abs/2112.10752 - DALL-E: https://arxiv.org/abs/2102.12092 +- Perceiver: https://arxiv.org/abs/2103.03206 ## XttsConfig diff --git a/recipes/ljspeech/xtts_v2/train_gpt_xtts.py b/recipes/ljspeech/xtts_v2/train_gpt_xtts.py new file mode 100644 index 0000000000..ee6b22becd --- /dev/null +++ b/recipes/ljspeech/xtts_v2/train_gpt_xtts.py @@ -0,0 +1,180 @@ +import os + +from trainer import Trainer, TrainerArgs + +from TTS.config.shared_configs import BaseDatasetConfig +from TTS.tts.datasets import load_tts_samples +from TTS.tts.layers.xtts.trainer.gpt_trainer import GPTArgs, GPTTrainer, GPTTrainerConfig, XttsAudioConfig +from TTS.utils.manage import ModelManager + +# Logging parameters +RUN_NAME = "GPT_XTTS_v2.0_LJSpeech_FT" +PROJECT_NAME = "XTTS_trainer" +DASHBOARD_LOGGER = "tensorboard" +LOGGER_URI = None + +# Set here the path that the checkpoints will be saved. Default: ./run/training/ +OUT_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), "run", "training") + +# Training Parameters +OPTIMIZER_WD_ONLY_ON_WEIGHTS = True # for multi-gpu training please make it False +START_WITH_EVAL = True # if True it will star with evaluation +BATCH_SIZE = 3 # set here the batch size +GRAD_ACUMM_STEPS = 84 # set here the grad accumulation steps +# Note: we recommend that BATCH_SIZE * GRAD_ACUMM_STEPS need to be at least 252 for more efficient training. You can increase/decrease BATCH_SIZE but then set GRAD_ACUMM_STEPS accordingly. + +# Define here the dataset that you want to use for the fine-tuning on. +config_dataset = BaseDatasetConfig( + formatter="ljspeech", + dataset_name="ljspeech", + path="/raid/datasets/LJSpeech-1.1_24khz/", + meta_file_train="/raid/datasets/LJSpeech-1.1_24khz/metadata.csv", + language="en", +) + +# Add here the configs of the datasets +DATASETS_CONFIG_LIST = [config_dataset] + +# Define the path where XTTS v2.0.1 files will be downloaded +CHECKPOINTS_OUT_PATH = os.path.join(OUT_PATH, "XTTS_v2.0_original_model_files/") +os.makedirs(CHECKPOINTS_OUT_PATH, exist_ok=True) + + +# DVAE files +DVAE_CHECKPOINT_LINK = "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v1/v1.1.1/dvae.pth" +MEL_NORM_LINK = "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v1/v1.1.1/mel_stats.pth" + +# Set the path to the downloaded files +DVAE_CHECKPOINT = os.path.join(CHECKPOINTS_OUT_PATH, DVAE_CHECKPOINT_LINK.split("/")[-1]) +MEL_NORM_FILE = os.path.join(CHECKPOINTS_OUT_PATH, MEL_NORM_LINK.split("/")[-1]) + +# download DVAE files if needed +if not os.path.isfile(DVAE_CHECKPOINT) or not os.path.isfile(MEL_NORM_FILE): + print(" > Downloading DVAE files!") + ModelManager._download_model_files([MEL_NORM_LINK, DVAE_CHECKPOINT_LINK], CHECKPOINTS_OUT_PATH, progress_bar=True) + +# ToDo: Update links for XTTS v2.0 + +# Download XTTS v2.0 checkpoint if needed +TOKENIZER_FILE_LINK = "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v1/v2.0/vocab.json" +XTTS_CHECKPOINT_LINK = "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v1/v2.0/model.pth" + +# XTTS transfer learning parameters: You we need to provide the paths of XTTS model checkpoint that you want to do the fine tuning. +TOKENIZER_FILE = os.path.join(CHECKPOINTS_OUT_PATH, TOKENIZER_FILE_LINK.split("/")[-1]) # vocab.json file +XTTS_CHECKPOINT = os.path.join(CHECKPOINTS_OUT_PATH, XTTS_CHECKPOINT_LINK.split("/")[-1]) # model.pth file + +# download XTTS v2.0 files if needed +if not os.path.isfile(TOKENIZER_FILE) or not os.path.isfile(XTTS_CHECKPOINT): + print(" > Downloading XTTS v2.0 files!") + ModelManager._download_model_files( + [TOKENIZER_FILE_LINK, XTTS_CHECKPOINT_LINK], CHECKPOINTS_OUT_PATH, progress_bar=True + ) + + +# Training sentences generations +SPEAKER_REFERENCE = ( + "./tests/data/ljspeech/wavs/LJ001-0002.wav" # speaker reference to be used in training test sentences +) +LANGUAGE = config_dataset.language + + +def main(): + # init args and config + model_args = GPTArgs( + max_conditioning_length=132300, # 6 secs + min_conditioning_length=66150, # 3 secs + debug_loading_failures=False, + max_wav_length=255995, # ~11.6 seconds + max_text_length=200, + mel_norm_file=MEL_NORM_FILE, + dvae_checkpoint=DVAE_CHECKPOINT, + xtts_checkpoint=XTTS_CHECKPOINT, # checkpoint path of the model that you want to fine-tune + tokenizer_file=TOKENIZER_FILE, + gpt_num_audio_tokens=8194, + gpt_start_audio_token=8192, + gpt_stop_audio_token=8193, + use_ne_hifigan=True, # if it is true it will keep the non-enhanced keys on the output checkpoint + gpt_use_masking_gt_prompt_approach=True, + gpt_use_perceiver_resampler=True, + ) + # define audio config + audio_config = XttsAudioConfig( + sample_rate=22050, dvae_sample_rate=22050, diffusion_sample_rate=24000, output_sample_rate=24000 + ) + # training parameters config + config = GPTTrainerConfig( + output_path=OUT_PATH, + model_args=model_args, + run_name=RUN_NAME, + project_name=PROJECT_NAME, + run_description=""" + GPT XTTS training + """, + dashboard_logger=DASHBOARD_LOGGER, + logger_uri=LOGGER_URI, + audio=audio_config, + batch_size=BATCH_SIZE, + batch_group_size=48, + eval_batch_size=BATCH_SIZE, + num_loader_workers=8, + eval_split_max_size=256, + print_step=50, + plot_step=100, + log_model_step=1000, + save_step=10000, + save_n_checkpoints=1, + save_checkpoints=True, + # target_loss="loss", + print_eval=False, + # Optimizer values like tortoise, pytorch implementation with modifications to not apply WD to non-weight parameters. + optimizer="AdamW", + optimizer_wd_only_on_weights=OPTIMIZER_WD_ONLY_ON_WEIGHTS, + optimizer_params={"betas": [0.9, 0.96], "eps": 1e-8, "weight_decay": 1e-2}, + lr=5e-06, # learning rate + lr_scheduler="MultiStepLR", + # it was adjusted accordly for the new step scheme + lr_scheduler_params={"milestones": [50000 * 18, 150000 * 18, 300000 * 18], "gamma": 0.5, "last_epoch": -1}, + test_sentences=[ + { + "text": "It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.", + "speaker_wav": SPEAKER_REFERENCE, + "language": LANGUAGE, + }, + { + "text": "This cake is great. It's so delicious and moist.", + "speaker_wav": SPEAKER_REFERENCE, + "language": LANGUAGE, + }, + ], + ) + + # init the model from config + model = GPTTrainer.init_from_config(config) + + # load training samples + train_samples, eval_samples = load_tts_samples( + DATASETS_CONFIG_LIST, + eval_split=True, + eval_split_max_size=config.eval_split_max_size, + eval_split_size=config.eval_split_size, + ) + + # init the trainer and 🚀 + trainer = Trainer( + TrainerArgs( + restore_path=None, # xtts checkpoint is restored via xtts_checkpoint key so no need of restore it using Trainer restore_path parameter + skip_train_epoch=False, + start_with_eval=START_WITH_EVAL, + grad_accum_steps=GRAD_ACUMM_STEPS, + ), + config, + output_path=OUT_PATH, + model=model, + train_samples=train_samples, + eval_samples=eval_samples, + ) + trainer.fit() + + +if __name__ == "__main__": + main() diff --git a/requirements.txt b/requirements.txt index 2837c36e66..04343c848d 100644 --- a/requirements.txt +++ b/requirements.txt @@ -33,6 +33,8 @@ coqpit>=0.0.16 # chinese g2p deps jieba pypinyin +# korean +hangul_romanize # gruut+supported langs gruut[de,es,fr]==2.2.3 # deps for korean @@ -51,3 +53,4 @@ transformers==4.33.* encodec==0.1.* # deps for XTTS unidecode==1.3.* +num2words diff --git a/tests/xtts_tests/test_xtts_gpt_train.py b/tests/xtts_tests/test_xtts_gpt_train.py index 5e3bc22648..47b1dd7d27 100644 --- a/tests/xtts_tests/test_xtts_gpt_train.py +++ b/tests/xtts_tests/test_xtts_gpt_train.py @@ -86,6 +86,7 @@ gpt_num_audio_tokens=8194, gpt_start_audio_token=8192, gpt_stop_audio_token=8193, + use_ne_hifigan=True, ) audio_config = XttsAudioConfig( sample_rate=22050, dvae_sample_rate=22050, diffusion_sample_rate=24000, output_sample_rate=24000 diff --git a/tests/xtts_tests/test_xtts_v2-0_gpt_train.py b/tests/xtts_tests/test_xtts_v2-0_gpt_train.py new file mode 100644 index 0000000000..6b6f1330dc --- /dev/null +++ b/tests/xtts_tests/test_xtts_v2-0_gpt_train.py @@ -0,0 +1,162 @@ +import os +import shutil + +import torch +from trainer import Trainer, TrainerArgs + +from tests import get_tests_output_path +from TTS.config.shared_configs import BaseDatasetConfig +from TTS.tts.datasets import load_tts_samples +from TTS.tts.layers.xtts.dvae import DiscreteVAE +from TTS.tts.layers.xtts.trainer.gpt_trainer import GPTArgs, GPTTrainer, GPTTrainerConfig, XttsAudioConfig + +config_dataset = BaseDatasetConfig( + formatter="ljspeech", + dataset_name="ljspeech", + path="tests/data/ljspeech/", + meta_file_train="metadata.csv", + meta_file_val="metadata.csv", + language="en", +) + +DATASETS_CONFIG_LIST = [config_dataset] + +# Logging parameters +RUN_NAME = "GPT_XTTS_LJSpeech_FT" +PROJECT_NAME = "XTTS_trainer" +DASHBOARD_LOGGER = "tensorboard" +LOGGER_URI = None + +OUT_PATH = os.path.join(get_tests_output_path(), "train_outputs", "xtts_tests") +os.makedirs(OUT_PATH, exist_ok=True) + +# Create DVAE checkpoint and mel_norms on test time +# DVAE parameters: For the training we need the dvae to extract the dvae tokens, given that you must provide the paths for this model +DVAE_CHECKPOINT = os.path.join(OUT_PATH, "dvae.pth") # DVAE checkpoint +# Mel spectrogram norms, required for dvae mel spectrogram extraction +MEL_NORM_FILE = os.path.join(OUT_PATH, "mel_stats.pth") +dvae = DiscreteVAE( + channels=80, + normalization=None, + positional_dims=1, + num_tokens=8192, + codebook_dim=512, + hidden_dim=512, + num_resnet_blocks=3, + kernel_size=3, + num_layers=2, + use_transposed_convs=False, +) +torch.save(dvae.state_dict(), DVAE_CHECKPOINT) +mel_stats = torch.ones(80) +torch.save(mel_stats, MEL_NORM_FILE) + + +# XTTS transfer learning parameters: You we need to provide the paths of XTTS model checkpoint that you want to do the fine tuning. +TOKENIZER_FILE = "tests/inputs/xtts_vocab.json" # vocab.json file +XTTS_CHECKPOINT = None # "/raid/edresson/dev/Checkpoints/XTTS_evaluation/xtts_style_emb_repetition_fix_gt/132500_gpt_ema_coqui_tts_with_enhanced_hifigan.pth" # model.pth file + + +# Training sentences generations +SPEAKER_REFERENCE = "tests/data/ljspeech/wavs/LJ001-0002.wav" # speaker reference to be used in training test sentences +LANGUAGE = config_dataset.language + + +# Training Parameters +OPTIMIZER_WD_ONLY_ON_WEIGHTS = True # for multi-gpu training please make it False +START_WITH_EVAL = False # if True it will star with evaluation +BATCH_SIZE = 2 # set here the batch size +GRAD_ACUMM_STEPS = 1 # set here the grad accumulation steps +# Note: we recommend that BATCH_SIZE * GRAD_ACUMM_STEPS need to be at least 252 for more efficient training. You can increase/decrease BATCH_SIZE but then set GRAD_ACUMM_STEPS accordingly. + + +# init args and config +model_args = GPTArgs( + max_conditioning_length=132300, # 6 secs + min_conditioning_length=66150, # 3 secs + debug_loading_failures=False, + max_wav_length=255995, # ~11.6 seconds + max_text_length=200, + mel_norm_file=MEL_NORM_FILE, + dvae_checkpoint=DVAE_CHECKPOINT, + xtts_checkpoint=XTTS_CHECKPOINT, # checkpoint path of the model that you want to fine-tune + tokenizer_file=TOKENIZER_FILE, + gpt_num_audio_tokens=8194, + gpt_start_audio_token=8192, + gpt_stop_audio_token=8193, + gpt_use_masking_gt_prompt_approach=True, + gpt_use_perceiver_resampler=True, + use_ne_hifigan=True, +) +audio_config = XttsAudioConfig( + sample_rate=22050, dvae_sample_rate=22050, diffusion_sample_rate=24000, output_sample_rate=24000 +) +config = GPTTrainerConfig( + epochs=1, + output_path=OUT_PATH, + model_args=model_args, + run_name=RUN_NAME, + project_name=PROJECT_NAME, + run_description="GPT XTTS training", + dashboard_logger=DASHBOARD_LOGGER, + logger_uri=LOGGER_URI, + audio=audio_config, + batch_size=BATCH_SIZE, + batch_group_size=48, + eval_batch_size=BATCH_SIZE, + num_loader_workers=8, + eval_split_max_size=256, + print_step=50, + plot_step=100, + log_model_step=1000, + save_step=10000, + save_n_checkpoints=1, + save_checkpoints=True, + # target_loss="loss", + print_eval=False, + # Optimizer values like tortoise, pytorch implementation with modifications to not apply WD to non-weight parameters. + optimizer="AdamW", + optimizer_wd_only_on_weights=OPTIMIZER_WD_ONLY_ON_WEIGHTS, + optimizer_params={"betas": [0.9, 0.96], "eps": 1e-8, "weight_decay": 1e-2}, + lr=5e-06, # learning rate + lr_scheduler="MultiStepLR", + # it was adjusted accordly for the new step scheme + lr_scheduler_params={"milestones": [50000 * 18, 150000 * 18, 300000 * 18], "gamma": 0.5, "last_epoch": -1}, + test_sentences=[ + { + "text": "This cake is great. It's so delicious and moist.", + "speaker_wav": SPEAKER_REFERENCE, + "language": LANGUAGE, + }, + ], +) + +# init the model from config +model = GPTTrainer.init_from_config(config) + +# load training samples +train_samples, eval_samples = load_tts_samples( + DATASETS_CONFIG_LIST, + eval_split=True, + eval_split_max_size=config.eval_split_max_size, + eval_split_size=config.eval_split_size, +) + +# init the trainer and 🚀 +trainer = Trainer( + TrainerArgs( + restore_path=None, # xtts checkpoint is restored via xtts_checkpoint key so no need of restore it using Trainer restore_path parameter + skip_train_epoch=False, + start_with_eval=True, + grad_accum_steps=GRAD_ACUMM_STEPS, + ), + config, + output_path=OUT_PATH, + model=model, + train_samples=train_samples, + eval_samples=eval_samples, +) +trainer.fit() + +# remove output path +shutil.rmtree(OUT_PATH) diff --git a/tests/zoo_tests/test_models.py b/tests/zoo_tests/test_models.py index 7194ed5c33..2f9399add8 100644 --- a/tests/zoo_tests/test_models.py +++ b/tests/zoo_tests/test_models.py @@ -16,6 +16,7 @@ "tts_models/en/multi-dataset/tortoise-v2", "tts_models/multilingual/multi-dataset/xtts_v1", "tts_models/multilingual/multi-dataset/xtts_v1.1", + "tts_models/multilingual/multi-dataset/xtts_v2", ] @@ -126,6 +127,58 @@ def test_xtts_streaming(): assert len(wav_chuncks) > 1 +def test_xtts_v2(): + """XTTS is too big to run on github actions. We need to test it locally""" + output_path = os.path.join(get_tests_output_path(), "output.wav") + speaker_wav = os.path.join(get_tests_data_path(), "ljspeech", "wavs", "LJ001-0001.wav") + use_gpu = torch.cuda.is_available() + if use_gpu: + run_cli( + "yes | " + f"tts --model_name tts_models/multilingual/multi-dataset/xtts_v2 " + f'--text "This is an example." --out_path "{output_path}" --progress_bar False --use_cuda True ' + f'--speaker_wav "{speaker_wav}" --language_idx "en"' + ) + else: + run_cli( + "yes | " + f"tts --model_name tts_models/multilingual/multi-dataset/xtts_v2 " + f'--text "This is an example." --out_path "{output_path}" --progress_bar False ' + f'--speaker_wav "{speaker_wav}" --language_idx "en"' + ) + + +def test_xtts_v2_streaming(): + """Testing the new inference_stream method""" + from TTS.tts.configs.xtts_config import XttsConfig + from TTS.tts.models.xtts import Xtts + + speaker_wav = os.path.join(get_tests_data_path(), "ljspeech", "wavs", "LJ001-0001.wav") + model_path = os.path.join(get_user_data_dir("tts"), "tts_models--multilingual--multi-dataset--xtts_v2") + config = XttsConfig() + config.load_json(os.path.join(model_path, "config.json")) + model = Xtts.init_from_config(config) + model.load_checkpoint(config, checkpoint_dir=model_path) + model.to(torch.device("cuda" if torch.cuda.is_available() else "cpu")) + + print("Computing speaker latents...") + gpt_cond_latent, _, speaker_embedding = model.get_conditioning_latents(audio_path=speaker_wav) + + print("Inference...") + chunks = model.inference_stream( + "It took me quite a long time to develop a voice and now that I have it I am not going to be silent.", + "en", + gpt_cond_latent, + speaker_embedding, + ) + wav_chuncks = [] + for i, chunk in enumerate(chunks): + if i == 0: + assert chunk.shape[-1] > 5000 + wav_chuncks.append(chunk) + assert len(wav_chuncks) > 1 + + def test_tortoise(): output_path = os.path.join(get_tests_output_path(), "output.wav") use_gpu = torch.cuda.is_available()