Easy-to-use Speech Toolkit including Self-Supervised Learning model, SOTA/Streaming ASR with punctuation, Streaming TTS with text frontend, Speaker Verification System, End-to-End Speech Translation and Keyword Spotting. Won NAACL2022 Best Demo Award.
paddlespeech_client asr --server_ip 127.0.0.1 --port 5000 --input cache/AxC9es3hCYFfiauh-0000.wav
shell1正确获得识别结果,shell2报错
服务端异常
[2023-07-20 23:59:38,693] [ INFO] - You need to initialize the beam_search_decoder firstly
Traceback (most recent call last):
File "/opt/conda/lib/python3.10/site-packages/paddlespeech/server/engine/asr/python/asr_engine.py", line 120, in run
self.infer(self.asr_engine.config.model)
File "/opt/conda/lib/python3.10/site-packages/decorator.py", line 232, in fun
return caller(func, *(extras + args), **kw)
File "/opt/conda/lib/python3.10/site-packages/paddle/fluid/dygraph/base.py", line 347, in _decorate_function
return func(*args, **kwargs)
File "/opt/conda/lib/python3.10/site-packages/paddlespeech/cli/asr/infer.py", line 306, in infer
result_transcripts = self.model.decode(audio, audio_len)
File "/opt/conda/lib/python3.10/site-packages/decorator.py", line 232, in fun
return caller(func, *(extras + args), **kw)
File "/opt/conda/lib/python3.10/site-packages/paddle/fluid/dygraph/base.py", line 347, in _decorate_function
return func(*args, **kwargs)
File "/opt/conda/lib/python3.10/site-packages/paddlespeech/s2t/models/ds2/deepspeech2.py", line 303, in decode
self.decoder.reset_decoder(batch_size=batch_size)
File "/opt/conda/lib/python3.10/site-packages/paddlespeech/s2t/modules/ctc.py", line 463, in reset_decoder
raise Exception(
Exception: You need to initialize the beam_search_decoder firstly
通过什么方式进行配置可以有效的进行多线程调用?是否与以下信息有关?
!!! Since PaddlePaddle support 0-D tensor from 2.5.0, PaddleSpeech Static model will not work for it, please re-export static model.
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重现步骤
启动服务端
asr_python: model: 'deepspeech2online_wenetspeech' lang: 'zh' sample_rate: 16000 cfg_path: # [optional] ckpt_path: # [optional] decode_method: 'attention_rescoring' num_decoding_left_chunks: -1 force_yes: True device: 'cpu'
text_python: task: punc model_type: 'ernie_linear_p3_wudao' lang: 'zh' sample_rate: 16000 cfg_path: # [optional] ckpt_path: # [optional] vocab_file: # [optional] device: 'cpu' # set 'gpu:id' or 'cpu'
通过什么方式进行配置可以有效的进行多线程调用?是否与以下信息有关?