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https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-10-07 15:19:59 +08:00
82 lines
2.5 KiB
Python
82 lines
2.5 KiB
Python
PUNC_LIST = ['，', '。', '！', '？', '、']
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def pre_proc(text):
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res = ''
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for i in range(len(text)):
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if text[i] in PUNC_LIST:
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continue
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if '\u4e00' <= text[i] <= '\u9fff':
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if len(res) and res[-1] != " ":
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res += ' ' + text[i]+' '
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else:
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res += text[i]+' '
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else:
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res += text[i]
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if res[-1] == ' ':
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res = res[:-1]
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return res
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def proc(raw_text, timestamp, dest_text):
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# simple matching
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ld = len(dest_text.split())
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mi, ts = [], []
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offset = 0
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while True:
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fi = raw_text.find(dest_text, offset, len(raw_text))
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# import pdb; pdb.set_trace()
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ti = raw_text[:fi].count(' ')
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if fi == -1:
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break
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offset = fi + ld
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mi.append(fi)
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ts.append([timestamp[ti][0]*16, timestamp[ti+ld-1][1]*16])
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# import pdb; pdb.set_trace()
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return ts
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def proc_spk(dest_spk, sd_sentences):
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ts = []
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for d in sd_sentences:
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d_start = d['ts_list'][0][0]
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d_end = d['ts_list'][-1][1]
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spkid=dest_spk[3:]
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if str(d['spk']) == spkid and d_end-d_start>999:
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ts.append([d['start']*16, d['end']*16])
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return ts
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def generate_vad_data(data, sd_sentences, sr=16000):
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assert len(data.shape) == 1
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vad_data = []
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for d in sd_sentences:
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d_start = round(d['ts_list'][0][0]/1000, 2)
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d_end = round(d['ts_list'][-1][1]/1000, 2)
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vad_data.append([d_start, d_end, data[int(d_start * sr):int(d_end * sr)]])
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return vad_data
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def write_state(output_dir, state):
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for key in ['/recog_res_raw', '/timestamp', '/sentences', '/sd_sentences']:
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with open(output_dir+key, 'w') as fout:
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fout.write(str(state[key[1:]]))
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if 'sd_sentences' in state:
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with open(output_dir+'/sd_sentences', 'w') as fout:
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fout.write(str(state['sd_sentences']))
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import os
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def load_state(output_dir):
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state = {}
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with open(output_dir+'/recog_res_raw') as fin:
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line = fin.read()
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state['recog_res_raw'] = line
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with open(output_dir+'/timestamp') as fin:
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line = fin.read()
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state['timestamp'] = eval(line)
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with open(output_dir+'/sentences') as fin:
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line = fin.read()
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state['sentences'] = eval(line)
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if os.path.exists(output_dir+'/sd_sentences'):
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with open(output_dir+'/sd_sentences') as fin:
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line = fin.read()
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state['sd_sentences'] = eval(line)
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return state
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