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| | | import json |
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| | | import time |
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| | | |
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| | | from modelscope.pipelines import pipeline |
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| | | from modelscope.utils.constant import Tasks |
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| | | import paraformer_pb2_grpc |
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| | | from paraformer_pb2 import Response |
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| | | from utils.frontend import load_bytes |
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| | | |
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| | | |
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| | | class ASRServicer(paraformer_pb2_grpc.ASRServicer): |
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| | | def __init__(self, user_allowed, model, sample_rate): |
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| | | def __init__(self, user_allowed, model, sample_rate, backend, onnx_dir): |
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| | | print("ASRServicer init") |
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| | | self.backend = backend |
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| | | self.init_flag = 0 |
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| | | self.client_buffers = {} |
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| | | self.client_transcription = {} |
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| | | self.auth_user = user_allowed.split("|") |
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| | | self.inference_16k_pipeline = pipeline(task=Tasks.auto_speech_recognition, model=model) |
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| | | if self.backend == "pipeline": |
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| | | try: |
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| | | from modelscope.pipelines import pipeline |
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| | | from modelscope.utils.constant import Tasks |
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| | | except ImportError: |
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| | | raise ImportError(f"Please install modelscope") |
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| | | self.inference_16k_pipeline = pipeline(task=Tasks.auto_speech_recognition, model=model) |
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| | | elif self.backend == "onnxruntime": |
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| | | try: |
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| | | from paraformer_onnx import Paraformer |
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| | | except ImportError: |
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| | | raise ImportError(f"Please install onnxruntime requirements, reference https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime/python/onnxruntime/rapid_paraformer") |
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| | | self.inference_16k_pipeline = Paraformer(model_dir=onnx_dir) |
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| | | self.sample_rate = sample_rate |
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| | | |
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| | | def clear_states(self, user): |
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| | |
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| | | result["text"] = "" |
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| | | print ("user: %s , delay(ms): %s, info: %s " % (req.user, delay_str, "waiting_for_more_voice")) |
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| | | yield Response(sentence=json.dumps(result), user=req.user, action="waiting", language=req.language) |
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| | | else: |
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| | | asr_result = self.inference_16k_pipeline(audio_in=tmp_data, audio_fs = self.sample_rate) |
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| | | if "text" in asr_result: |
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| | | asr_result = asr_result['text'] |
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| | | else: |
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| | | asr_result = "" |
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| | | else: |
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| | | if self.backend == "pipeline": |
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| | | asr_result = self.inference_16k_pipeline(audio_in=tmp_data, audio_fs = self.sample_rate) |
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| | | if "text" in asr_result: |
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| | | asr_result = asr_result['text'] |
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| | | else: |
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| | | asr_result = "" |
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| | | elif self.backend == "onnxruntime": |
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| | | array = load_bytes(tmp_data) |
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| | | asr_result = self.inference_16k_pipeline(array)[0] |
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| | | end_time = int(round(time.time() * 1000)) |
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| | | delay_str = str(end_time - begin_time) |
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| | | print ("user: %s , delay(ms): %s, text: %s " % (req.user, delay_str, asr_result)) |
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