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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>
31 lines
1.2 KiB
Python
31 lines
1.2 KiB
Python
import soundfile
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from funasr_onnx.paraformer_online_bin import Paraformer
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from pathlib import Path
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model_dir = "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online"
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wav_path = '{}/.cache/modelscope/hub/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/example/asr_example.wav'.format(Path.home())
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chunk_size = [5, 10, 5]
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model = Paraformer(model_dir, batch_size=1, quantize=True, chunk_size=chunk_size, intra_op_num_threads=4) # only support batch_size = 1
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##online asr
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speech, sample_rate = soundfile.read(wav_path)
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speech_length = speech.shape[0]
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sample_offset = 0
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step = chunk_size[1] * 960
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param_dict = {'cache': dict()}
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final_result = ""
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for sample_offset in range(0, speech_length, min(step, speech_length - sample_offset)):
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if sample_offset + step >= speech_length - 1:
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step = speech_length - sample_offset
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is_final = True
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else:
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is_final = False
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param_dict['is_final'] = is_final
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rec_result = model(audio_in=speech[sample_offset: sample_offset + step],
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param_dict=param_dict)
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if len(rec_result) > 0:
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final_result += rec_result[0]["preds"][0]
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print(rec_result)
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print(final_result)
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