mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
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Merge branch 'RVC-Boss:main' into main
This commit is contained in:
Cosmo Clara
GitHub
commit
787881a6ce
@ -1,5 +1,6 @@
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# modified from https://github.com/yangdongchao/SoundStorm/blob/master/soundstorm/s1/AR/models/t2s_model.py
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# reference: https://github.com/lifeiteng/vall-e
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import argparse
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from typing import Optional
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from my_utils import load_audio
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from text import cleaned_text_to_sequence
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@ -15,7 +16,7 @@ from feature_extractor import cnhubert
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from AR.models.t2s_lightning_module import Text2SemanticLightningModule
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from module.models_onnx import SynthesizerTrn
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from inference_webui import get_phones_and_bert
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import os
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import soundfile
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@ -329,11 +330,12 @@ class T2STransformer:
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for i in range(self.num_blocks):
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x, k_cache[i], v_cache[i] = self.blocks[i].decode_next_token(x, k_cache[i], v_cache[i])
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return x, k_cache, v_cache
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class VitsModel(nn.Module):
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def __init__(self, vits_path):
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super().__init__()
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dict_s2 = torch.load(vits_path,map_location="cpu")
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# dict_s2 = torch.load(vits_path,map_location="cpu")
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dict_s2 = torch.load(vits_path)
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self.hps = dict_s2["config"]
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if dict_s2['weight']['enc_p.text_embedding.weight'].shape[0] == 322:
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self.hps["model"]["version"] = "v1"
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@ -351,7 +353,7 @@ class VitsModel(nn.Module):
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self.vq_model.eval()
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self.vq_model.load_state_dict(dict_s2["weight"], strict=False)
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def forward(self, text_seq, pred_semantic, ref_audio):
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def forward(self, text_seq, pred_semantic, ref_audio, speed=1.0):
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refer = spectrogram_torch(
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ref_audio,
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self.hps.data.filter_length,
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@ -360,7 +362,7 @@ class VitsModel(nn.Module):
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self.hps.data.win_length,
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center=False
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)
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return self.vq_model(pred_semantic, text_seq, refer)[0, 0]
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return self.vq_model(pred_semantic, text_seq, refer, speed)[0, 0]
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class T2SModel(nn.Module):
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def __init__(self,raw_t2s:Text2SemanticLightningModule):
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@ -507,6 +509,8 @@ class T2SModel(nn.Module):
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y_emb = self.ar_audio_embedding(y[:, -1:])
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xy_pos = y_emb * self.ar_audio_position.x_scale + self.ar_audio_position.alpha * self.ar_audio_position.pe[:, y_len + idx].to(dtype=y_emb.dtype,device=y_emb.device)
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y[0,-1] = 0
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return y[:, -idx:].unsqueeze(0)
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bert_path = os.environ.get(
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@ -524,7 +528,7 @@ def build_phone_level_feature(res:Tensor, word2ph:IntTensor):
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phone_level_feature = torch.cat(phone_level_feature, dim=0)
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# [sum(word2ph), 1024]
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return phone_level_feature
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class MyBertModel(torch.nn.Module):
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def __init__(self, bert_model):
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super(MyBertModel, self).__init__()
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@ -532,7 +536,8 @@ class MyBertModel(torch.nn.Module):
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def forward(self, input_ids:torch.Tensor, attention_mask:torch.Tensor, token_type_ids:torch.Tensor, word2ph:IntTensor):
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outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)
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res = torch.cat(outputs["hidden_states"][-3:-2], -1)[0][1:-1]
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# res = torch.cat(outputs["hidden_states"][-3:-2], -1)[0][1:-1]
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res = torch.cat(outputs[1][-3:-2], -1)[0][1:-1]
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return build_phone_level_feature(res, word2ph)
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class SSLModel(torch.nn.Module):
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@ -557,66 +562,99 @@ class ExportSSLModel(torch.nn.Module):
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audio = resamplex(ref_audio,src_sr,dst_sr).float()
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return audio
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def export_bert(ref_bert_inputs):
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def export_bert(output_path):
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tokenizer = AutoTokenizer.from_pretrained(bert_path)
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text = "叹息声一声接着一声传出,木兰对着房门织布.听不见织布机织布的声音,只听见木兰在叹息.问木兰在想什么?问木兰在惦记什么?木兰答道,我也没有在想什么,也没有在惦记什么."
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ref_bert_inputs = tokenizer(text, return_tensors="pt")
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word2ph = []
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for c in text:
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if c in ['，','。','：','？',",",".","?"]:
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word2ph.append(1)
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else:
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word2ph.append(2)
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ref_bert_inputs['word2ph'] = torch.Tensor(word2ph).int()
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bert_model = AutoModelForMaskedLM.from_pretrained(bert_path,output_hidden_states=True,torchscript=True)
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my_bert_model = MyBertModel(bert_model)
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ref_bert_inputs = {
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'input_ids': ref_bert_inputs['input_ids'],
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'attention_mask': ref_bert_inputs['attention_mask'],
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'token_type_ids': ref_bert_inputs['token_type_ids'],
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'word2ph': ref_bert_inputs['word2ph']
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}
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bert_model = AutoModelForMaskedLM.from_pretrained(bert_path,output_hidden_states=True)
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my_bert_model = MyBertModel(bert_model)
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torch._dynamo.mark_dynamic(ref_bert_inputs['input_ids'], 1)
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torch._dynamo.mark_dynamic(ref_bert_inputs['attention_mask'], 1)
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torch._dynamo.mark_dynamic(ref_bert_inputs['token_type_ids'], 1)
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torch._dynamo.mark_dynamic(ref_bert_inputs['word2ph'], 0)
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my_bert_model = torch.jit.trace(my_bert_model,example_kwarg_inputs=ref_bert_inputs)
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my_bert_model.save("onnx/bert_model.pt")
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output_path = os.path.join(output_path, "bert_model.pt")
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my_bert_model.save(output_path)
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print('#### exported bert ####')
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def export(gpt_path, vits_path, ref_audio_path, ref_text, output_path, export_bert_and_ssl=False, device='cpu'):
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if not os.path.exists(output_path):
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os.makedirs(output_path)
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print(f"目录已创建: {output_path}")
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else:
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print(f"目录已存在: {output_path}")
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def export(gpt_path, vits_path):
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tokenizer = AutoTokenizer.from_pretrained(bert_path)
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ref_bert_inputs = tokenizer("声音,是有温度的.夜晚的声音,会发光", return_tensors="pt")
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ref_seq = torch.LongTensor([cleaned_text_to_sequence(['sh','eng1','y','in1',',','sh','i4','y','ou3','w','en1','d','u4','d','e','.','y','e4','w','an3','d','e','sh','eng1','y','in1',',','h','ui4','f','a1','g','uang1'],version='v2')])
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ref_bert_inputs['word2ph'] = torch.Tensor([2,2,1,2,2,2,2,2,1,2,2,2,2,2,1,2,2,2]).int()
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text_berf_inputs = tokenizer("大家好,我有一个春晚问题.", return_tensors="pt")
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text_seq = torch.LongTensor([cleaned_text_to_sequence(["d", "a4", "j", "ia1", "h", "ao3",",","w","o3","y", "ou3","y","i2","g","e4","q","i2","g","uai4","w","en4","t","i2","."],version='v2')])
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text_berf_inputs['word2ph'] = torch.Tensor([2,2,2,1,2,2,2,2,2,2,2,2,1]).int()
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bert_model = AutoModelForMaskedLM.from_pretrained(bert_path,output_hidden_states=True)
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bert = MyBertModel(bert_model)
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# export_bert(ref_bert_inputs)
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ref_audio = torch.tensor([load_audio("output/denoise_opt/chen1.mp4_0000033600_0000192000.wav", 16000)]).float()
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ref_audio = torch.tensor([load_audio(ref_audio_path, 16000)]).float()
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ssl = SSLModel()
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s = ExportSSLModel(torch.jit.trace(ssl,example_inputs=(ref_audio)))
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torch.jit.script(s).save("onnx/xw/ssl_model.pt")
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print('#### exported ssl ####')
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if export_bert_and_ssl:
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s = ExportSSLModel(torch.jit.trace(ssl,example_inputs=(ref_audio)))
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ssl_path = os.path.join(output_path, "ssl_model.pt")
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torch.jit.script(s).save(ssl_path)
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print('#### exported ssl ####')
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export_bert(output_path)
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else:
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s = ExportSSLModel(ssl)
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ref_bert = bert(**ref_bert_inputs)
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text_bert = bert(**text_berf_inputs)
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ssl_content = ssl(ref_audio)
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print(f"device: {device}")
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ref_seq_id,ref_bert_T,ref_norm_text = get_phones_and_bert(ref_text,"all_zh",'v2')
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ref_seq = torch.LongTensor([ref_seq_id]).to(device)
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ref_bert = ref_bert_T.T.to(ref_seq.device)
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text_seq_id,text_bert_T,norm_text = get_phones_and_bert("这是一条测试语音，说什么无所谓，只是给它一个例子","all_zh",'v2')
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text_seq = torch.LongTensor([text_seq_id]).to(device)
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text_bert = text_bert_T.T.to(text_seq.device)
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ssl_content = ssl(ref_audio).to(device)
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# vits_path = "SoVITS_weights_v2/xw_e8_s216.pth"
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vits = VitsModel(vits_path)
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vits = VitsModel(vits_path).to(device)
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vits.eval()
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# gpt_path = "GPT_weights_v2/xw-e15.ckpt"
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dict_s1 = torch.load(gpt_path, map_location="cpu")
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raw_t2s = get_raw_t2s_model(dict_s1)
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# dict_s1 = torch.load(gpt_path, map_location=device)
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dict_s1 = torch.load(gpt_path)
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raw_t2s = get_raw_t2s_model(dict_s1).to(device)
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print('#### get_raw_t2s_model ####')
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print(raw_t2s.config)
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t2s_m = T2SModel(raw_t2s)
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t2s_m.eval()
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t2s = torch.jit.script(t2s_m)
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t2s = torch.jit.script(t2s_m).to(device)
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print('#### script t2s_m ####')
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print("vits.hps.data.sampling_rate:",vits.hps.data.sampling_rate)
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gpt_sovits = GPT_SoVITS(t2s,vits)
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gpt_sovits = GPT_SoVITS(t2s,vits).to(device)
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gpt_sovits.eval()
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ref_audio_sr = s.resample(ref_audio,16000,32000)
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print('ref_audio_sr:',ref_audio_sr.shape)
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gpt_sovits_export = torch.jit.trace(
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ref_audio_sr = s.resample(ref_audio,16000,32000).to(device)
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torch._dynamo.mark_dynamic(ssl_content, 2)
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torch._dynamo.mark_dynamic(ref_audio_sr, 1)
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torch._dynamo.mark_dynamic(ref_seq, 1)
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torch._dynamo.mark_dynamic(text_seq, 1)
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torch._dynamo.mark_dynamic(ref_bert, 0)
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torch._dynamo.mark_dynamic(text_bert, 0)
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with torch.no_grad():
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gpt_sovits_export = torch.jit.trace(
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gpt_sovits,
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example_inputs=(
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ssl_content,
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@ -624,11 +662,11 @@ def export(gpt_path, vits_path):
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ref_seq,
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text_seq,
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ref_bert,
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text_bert),
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check_trace=False) # 默认是True 但是 check 的时候可能是随机生成的一个奇怪维度的值，导致报错
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text_bert))
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gpt_sovits_export.save("onnx/xw/gpt_sovits_model.pt")
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print('#### exported gpt_sovits ####')
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gpt_sovits_path = os.path.join(output_path, "gpt_sovits_model.pt")
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gpt_sovits_export.save(gpt_sovits_path)
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print('#### exported gpt_sovits ####')
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@torch.jit.script
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def parse_audio(ref_audio):
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@ -646,63 +684,88 @@ class GPT_SoVITS(nn.Module):
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self.t2s = t2s
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self.vits = vits
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def forward(self, ssl_content:torch.Tensor, ref_audio_sr:torch.Tensor, ref_seq:Tensor, text_seq:Tensor, ref_bert:Tensor, text_bert:Tensor):
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codes = self.vits.vq_model.extract_latent(ssl_content.float())
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def forward(self, ssl_content:torch.Tensor, ref_audio_sr:torch.Tensor, ref_seq:Tensor, text_seq:Tensor, ref_bert:Tensor, text_bert:Tensor, speed=1.0):
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codes = self.vits.vq_model.extract_latent(ssl_content)
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prompt_semantic = codes[0, 0]
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prompts = prompt_semantic.unsqueeze(0)
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pred_semantic = self.t2s(prompts, ref_seq, text_seq, ref_bert, text_bert)
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audio = self.vits(text_seq, pred_semantic, ref_audio_sr)
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audio = self.vits(text_seq, pred_semantic, ref_audio_sr, speed)
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return audio
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def test(gpt_path, vits_path):
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tokenizer = AutoTokenizer.from_pretrained(bert_path)
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bert_model = AutoModelForMaskedLM.from_pretrained(bert_path,output_hidden_states=True)
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bert = MyBertModel(bert_model)
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# bert = torch.jit.load("onnx/bert_model.pt",map_location='cuda')
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def test():
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parser = argparse.ArgumentParser(description="GPT-SoVITS Command Line Tool")
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parser.add_argument('--gpt_model', required=True, help="Path to the GPT model file")
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parser.add_argument('--sovits_model', required=True, help="Path to the SoVITS model file")
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parser.add_argument('--ref_audio', required=True, help="Path to the reference audio file")
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parser.add_argument('--ref_text', required=True, help="Path to the reference text file")
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parser.add_argument('--output_path', required=True, help="Path to the output directory")
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# gpt_path = "GPT_weights_v2/xw-e15.ckpt"
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dict_s1 = torch.load(gpt_path, map_location="cpu")
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raw_t2s = get_raw_t2s_model(dict_s1)
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t2s = T2SModel(raw_t2s)
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t2s.eval()
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args = parser.parse_args()
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gpt_path = args.gpt_model
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vits_path = args.sovits_model
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ref_audio_path = args.ref_audio
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ref_text = args.ref_text
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tokenizer = AutoTokenizer.from_pretrained(bert_path)
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# bert_model = AutoModelForMaskedLM.from_pretrained(bert_path,output_hidden_states=True,torchscript=True)
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# bert = MyBertModel(bert_model)
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my_bert = torch.jit.load("onnx/bert_model.pt",map_location='cuda')
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# dict_s1 = torch.load(gpt_path, map_location="cuda")
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# raw_t2s = get_raw_t2s_model(dict_s1)
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# t2s = T2SModel(raw_t2s)
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# t2s.eval()
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# t2s = torch.jit.load("onnx/xw/t2s_model.pt",map_location='cuda')
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# vits_path = "SoVITS_weights_v2/xw_e8_s216.pth"
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vits = VitsModel(vits_path)
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vits.eval()
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# vits = VitsModel(vits_path)
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# vits.eval()
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ssl = ExportSSLModel(SSLModel())
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ssl.eval()
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# ssl = ExportSSLModel(SSLModel()).to('cuda')
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# ssl.eval()
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ssl = torch.jit.load("onnx/by/ssl_model.pt",map_location='cuda')
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gpt_sovits = GPT_SoVITS(t2s,vits)
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# gpt_sovits = GPT_SoVITS(t2s,vits)
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gpt_sovits = torch.jit.load("onnx/by/gpt_sovits_model.pt",map_location='cuda')
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# vits = torch.jit.load("onnx/xw/vits_model.pt",map_location='cuda')
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# ssl = torch.jit.load("onnx/xw/ssl_model.pt",map_location='cuda')
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ref_seq_id,ref_bert_T,ref_norm_text = get_phones_and_bert(ref_text,"all_zh",'v2')
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ref_seq = torch.LongTensor([ref_seq_id])
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ref_bert = ref_bert_T.T.to(ref_seq.device)
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# text_seq_id,text_bert_T,norm_text = get_phones_and_bert("昨天晚上看见征兵文书,知道君主在大规模征兵,那么多卷征兵文册,每一卷上都有父亲的名字.","all_zh",'v2')
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text = "昨天晚上看见征兵文书,知道君主在大规模征兵,那么多卷征兵文册,每一卷上都有父亲的名字."
|
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|
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ref_bert_inputs = tokenizer("声音,是有温度的.夜晚的声音,会发光", return_tensors="pt")
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ref_seq = torch.LongTensor([cleaned_text_to_sequence(['sh','eng1','y','in1',',','sh','i4','y','ou3','w','en1','d','u4','d','e','.','y','e4','w','an3','d','e','sh','eng1','y','in1',',','h','ui4','f','a1','g','uang1'],version='v2')])
|
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ref_bert_inputs['word2ph'] = torch.Tensor([2,2,1,2,2,2,2,2,1,2,2,2,2,2,1,2,2,2]).int()
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text_seq_id,text_bert_T,norm_text = get_phones_and_bert(text,"all_zh",'v2')
|
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test_bert = tokenizer(text, return_tensors="pt")
|
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word2ph = []
|
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for c in text:
|
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if c in ['，','。','：','？',"?",",","."]:
|
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word2ph.append(1)
|
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else:
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word2ph.append(2)
|
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test_bert['word2ph'] = torch.Tensor(word2ph).int()
|
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|
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text_berf_inputs = tokenizer("大家好,我有一个春晚问题.", return_tensors="pt")
|
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text_seq = torch.LongTensor([cleaned_text_to_sequence(["d", "a4", "j", "ia1", "h", "ao3",",","w","o3","y", "ou3","y","i2","g","e4","q","i2","g","uai4","w","en4","t","i2","."],version='v2')])
|
||||
text_berf_inputs['word2ph'] = torch.Tensor([2,2,2,1,2,2,2,2,2,2,2,2,1]).int()
|
||||
|
||||
ref_bert = bert(
|
||||
ref_bert_inputs['input_ids'],
|
||||
ref_bert_inputs['attention_mask'],
|
||||
ref_bert_inputs['token_type_ids'],
|
||||
ref_bert_inputs['word2ph']
|
||||
test_bert = my_bert(
|
||||
test_bert['input_ids'].to('cuda'),
|
||||
test_bert['attention_mask'].to('cuda'),
|
||||
test_bert['token_type_ids'].to('cuda'),
|
||||
test_bert['word2ph'].to('cuda')
|
||||
)
|
||||
|
||||
text_bert = bert(text_berf_inputs['input_ids'],
|
||||
text_berf_inputs['attention_mask'],
|
||||
text_berf_inputs['token_type_ids'],
|
||||
text_berf_inputs['word2ph'])
|
||||
text_seq = torch.LongTensor([text_seq_id])
|
||||
text_bert = text_bert_T.T.to(text_seq.device)
|
||||
|
||||
print('text_bert:',text_bert.shape,text_bert)
|
||||
print('test_bert:',test_bert.shape,test_bert)
|
||||
print(torch.allclose(text_bert.to('cuda'),test_bert))
|
||||
|
||||
print('text_seq:',text_seq.shape)
|
||||
print('text_bert:',text_bert.shape,text_bert.type())
|
||||
|
||||
#[1,N]
|
||||
ref_audio = torch.tensor([load_audio("output/denoise_opt/chen1.mp4_0000033600_0000192000.wav", 16000)]).float()
|
||||
ref_audio = torch.tensor([load_audio(ref_audio_path, 16000)]).float().to('cuda')
|
||||
print('ref_audio:',ref_audio.shape)
|
||||
|
||||
ref_audio_sr = ssl.resample(ref_audio,16000,32000)
|
||||
@ -710,13 +773,22 @@ def test(gpt_path, vits_path):
|
||||
ssl_content = ssl(ref_audio)
|
||||
|
||||
print('start gpt_sovits:')
|
||||
print('ssl_content:',ssl_content.shape)
|
||||
print('ref_audio_sr:',ref_audio_sr.shape)
|
||||
print('ref_seq:',ref_seq.shape)
|
||||
ref_seq=ref_seq.to('cuda')
|
||||
print('text_seq:',text_seq.shape)
|
||||
text_seq=text_seq.to('cuda')
|
||||
print('ref_bert:',ref_bert.shape)
|
||||
ref_bert=ref_bert.to('cuda')
|
||||
print('text_bert:',text_bert.shape)
|
||||
text_bert=text_bert.to('cuda')
|
||||
|
||||
with torch.no_grad():
|
||||
audio = gpt_sovits(ssl_content, ref_audio_sr, ref_seq, text_seq, ref_bert, text_bert)
|
||||
audio = gpt_sovits(ssl_content, ref_audio_sr, ref_seq, text_seq, ref_bert, test_bert)
|
||||
print('start write wav')
|
||||
soundfile.write("out.wav", audio.detach().cpu().numpy(), 32000)
|
||||
|
||||
# audio = vits(text_seq, pred_semantic1, ref_audio)
|
||||
# soundfile.write("out.wav", audio, 32000)
|
||||
|
||||
import text
|
||||
import json
|
||||
@ -731,7 +803,30 @@ def export_symbel(version='v2'):
|
||||
with open(f"onnx/symbols_v2.json", "w") as file:
|
||||
json.dump(symbols, file, indent=4)
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="GPT-SoVITS Command Line Tool")
|
||||
parser.add_argument('--gpt_model', required=True, help="Path to the GPT model file")
|
||||
parser.add_argument('--sovits_model', required=True, help="Path to the SoVITS model file")
|
||||
parser.add_argument('--ref_audio', required=True, help="Path to the reference audio file")
|
||||
parser.add_argument('--ref_text', required=True, help="Path to the reference text file")
|
||||
parser.add_argument('--output_path', required=True, help="Path to the output directory")
|
||||
parser.add_argument('--export_common_model', action='store_true', help="Export Bert and SSL model")
|
||||
parser.add_argument('--device', help="Device to use")
|
||||
|
||||
args = parser.parse_args()
|
||||
export(
|
||||
gpt_path=args.gpt_model,
|
||||
vits_path=args.sovits_model,
|
||||
ref_audio_path=args.ref_audio,
|
||||
ref_text=args.ref_text,
|
||||
output_path=args.output_path,
|
||||
device=args.device,
|
||||
export_bert_and_ssl=args.export_common_model,
|
||||
)
|
||||
|
||||
import inference_webui
|
||||
if __name__ == "__main__":
|
||||
export(gpt_path="GPT_weights_v2/chen1-e15.ckpt", vits_path="SoVITS_weights_v2/chen1_e8_s208.pth")
|
||||
# test(gpt_path="GPT_weights_v2/chen1-e15.ckpt", vits_path="SoVITS_weights_v2/chen1_e8_s208.pth")
|
||||
# export_symbel()
|
||||
inference_webui.is_half=False
|
||||
inference_webui.dtype=torch.float32
|
||||
main()
|
||||
# test()
|
||||
|
||||
@ -231,7 +231,7 @@ class TextEncoder(nn.Module):
|
||||
|
||||
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
|
||||
|
||||
def forward(self, y, text, ge):
|
||||
def forward(self, y, text, ge, speed=1):
|
||||
y_mask = torch.ones_like(y[:1,:1,:])
|
||||
|
||||
y = self.ssl_proj(y * y_mask) * y_mask
|
||||
@ -244,6 +244,9 @@ class TextEncoder(nn.Module):
|
||||
y = self.mrte(y, y_mask, text, text_mask, ge)
|
||||
|
||||
y = self.encoder2(y * y_mask, y_mask)
|
||||
if(speed!=1):
|
||||
y = F.interpolate(y, size=int(y.shape[-1] / speed)+1, mode="linear")
|
||||
y_mask = F.interpolate(y_mask, size=y.shape[-1], mode="nearest")
|
||||
|
||||
stats = self.proj(y) * y_mask
|
||||
m, logs = torch.split(stats, self.out_channels, dim=1)
|
||||
@ -887,7 +890,7 @@ class SynthesizerTrn(nn.Module):
|
||||
# self.enc_p.encoder_text.requires_grad_(False)
|
||||
# self.enc_p.mrte.requires_grad_(False)
|
||||
|
||||
def forward(self, codes, text, refer):
|
||||
def forward(self, codes, text, refer,noise_scale=0.5, speed=1):
|
||||
refer_mask = torch.ones_like(refer[:1,:1,:])
|
||||
if (self.version == "v1"):
|
||||
ge = self.ref_enc(refer * refer_mask, refer_mask)
|
||||
@ -900,10 +903,10 @@ class SynthesizerTrn(nn.Module):
|
||||
quantized = dquantized.contiguous().view(1, self.ssl_dim, -1)
|
||||
|
||||
x, m_p, logs_p, y_mask = self.enc_p(
|
||||
quantized, text, ge
|
||||
quantized, text, ge, speed
|
||||
)
|
||||
|
||||
z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p)
|
||||
z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
|
||||
|
||||
z = self.flow(z_p, y_mask, g=ge, reverse=True)
|
||||
|
||||
|
||||
@ -127,14 +127,14 @@ def get_dict():
|
||||
|
||||
def read_dict():
|
||||
polyphonic_dict = {}
|
||||
with open(PP_DICT_PATH) as f:
|
||||
with open(PP_DICT_PATH,encoding="utf-8") as f:
|
||||
line = f.readline()
|
||||
while line:
|
||||
key, value_str = line.split(':')
|
||||
value = eval(value_str.strip())
|
||||
polyphonic_dict[key.strip()] = value
|
||||
line = f.readline()
|
||||
with open(PP_FIX_DICT_PATH) as f:
|
||||
with open(PP_FIX_DICT_PATH,encoding="utf-8") as f:
|
||||
line = f.readline()
|
||||
while line:
|
||||
key, value_str = line.split(':')
|
||||
@ -151,4 +151,4 @@ def correct_pronunciation(word,word_pinyins):
|
||||
return word_pinyins
|
||||
|
||||
|
||||
pp_dict = get_dict()
|
||||
pp_dict = get_dict()
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user