mirror of
https://github.com/modelscope/FunASR
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702ec03ad8
* add hotword for deploy_tools * Support wfst decoder and contextual biasing (#1039) * Support wfst decoder and contextual biasing * Turn on fstbin compilation --------- Co-authored-by: gongbo.gb <gongbo.gb@alibaba-inc.com> * mv funasr/runtime runtime * Fix crash caused by OOV in hotwords list * funasr infer * funasr infer * funasr infer * funasr infer * funasr infer * fix some bugs about fst hotword; support wfst for websocket server and clients; mv runtime out of funasr; modify relative docs * del onnxruntime/include/gflags * update tensor.h * update run_server.sh * update deploy tools * update deploy tools * update websocket-server * update funasr-wss-server * Remove self loop propagation * Update websocket_protocol_zh.md * Update websocket_protocol_zh.md * update hotword protocol * author zhaomingwork: change hotwords for h5 and java * update hotword protocol * catch exception for json_fst_hws * update hotword on message * update onnx benchmark for ngram&hotword * update docs * update funasr-wss-serve * add NONE for LM_DIR * update docs * update run_server.sh * add whats-new * modify whats-new * update whats-new * update whats-new * Support decoder option for beam searching * update benchmark_onnx_cpp * Support decoder option for websocket * fix bug of CompileHotwordEmbedding * update html client * update docs --------- Co-authored-by: gongbo.gb <35997837+aibulamusi@users.noreply.github.com> Co-authored-by: gongbo.gb <gongbo.gb@alibaba-inc.com> Co-authored-by: 游雁 <zhifu.gzf@alibaba-inc.com>
63 lines
2.9 KiB
Python
63 lines
2.9 KiB
Python
# -*- encoding: utf-8 -*-
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# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
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# MIT License (https://opensource.org/licenses/MIT)
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import numpy as np
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def time_stamp_lfr6_onnx(us_cif_peak, char_list, begin_time=0.0, total_offset=-1.5):
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if not len(char_list):
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return []
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START_END_THRESHOLD = 5
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MAX_TOKEN_DURATION = 30
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TIME_RATE = 10.0 * 6 / 1000 / 3 # 3 times upsampled
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cif_peak = us_cif_peak.reshape(-1)
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num_frames = cif_peak.shape[-1]
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if char_list[-1] == '</s>':
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char_list = char_list[:-1]
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# char_list = [i for i in text]
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timestamp_list = []
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new_char_list = []
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# for bicif model trained with large data, cif2 actually fires when a character starts
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# so treat the frames between two peaks as the duration of the former token
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fire_place = np.where(cif_peak>1.0-1e-4)[0] + total_offset # np format
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num_peak = len(fire_place)
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assert num_peak == len(char_list) + 1 # number of peaks is supposed to be number of tokens + 1
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# begin silence
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if fire_place[0] > START_END_THRESHOLD:
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# char_list.insert(0, '<sil>')
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timestamp_list.append([0.0, fire_place[0]*TIME_RATE])
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new_char_list.append('<sil>')
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# tokens timestamp
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for i in range(len(fire_place)-1):
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new_char_list.append(char_list[i])
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if i == len(fire_place)-2 or MAX_TOKEN_DURATION < 0 or fire_place[i+1] - fire_place[i] < MAX_TOKEN_DURATION:
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timestamp_list.append([fire_place[i]*TIME_RATE, fire_place[i+1]*TIME_RATE])
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else:
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# cut the duration to token and sil of the 0-weight frames last long
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_split = fire_place[i] + MAX_TOKEN_DURATION
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timestamp_list.append([fire_place[i]*TIME_RATE, _split*TIME_RATE])
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timestamp_list.append([_split*TIME_RATE, fire_place[i+1]*TIME_RATE])
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new_char_list.append('<sil>')
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# tail token and end silence
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if num_frames - fire_place[-1] > START_END_THRESHOLD:
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_end = (num_frames + fire_place[-1]) / 2
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timestamp_list[-1][1] = _end*TIME_RATE
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timestamp_list.append([_end*TIME_RATE, num_frames*TIME_RATE])
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new_char_list.append("<sil>")
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else:
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timestamp_list[-1][1] = num_frames*TIME_RATE
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if begin_time: # add offset time in model with vad
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for i in range(len(timestamp_list)):
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timestamp_list[i][0] = timestamp_list[i][0] + begin_time / 1000.0
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timestamp_list[i][1] = timestamp_list[i][1] + begin_time / 1000.0
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assert len(new_char_list) == len(timestamp_list)
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res_str = ""
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for char, timestamp in zip(new_char_list, timestamp_list):
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res_str += "{} {} {};".format(char, timestamp[0], timestamp[1])
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res = []
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for char, timestamp in zip(new_char_list, timestamp_list):
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if char != '<sil>':
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res.append([int(timestamp[0] * 1000), int(timestamp[1] * 1000)])
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return res_str, res
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