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@@ -0,0 +1,52 @@
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+import torch
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+import torchaudio
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+from modelscope.pipelines import pipeline
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+from modelscope.utils.constant import Tasks
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+
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+inference_pipeline = pipeline(
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+ task=Tasks.auto_speech_recognition,
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+ model='damo/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online',
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+ model_revision='v1.0.2')
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+
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+waveform, sample_rate = torchaudio.load("asr_example_zh.wav")
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+speech_length = waveform.shape[1]
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+speech = waveform[0]
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+
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+cache_en = {"start_idx": 0, "pad_left": 0, "stride": 10, "pad_right": 5, "cif_hidden": None, "cif_alphas": None}
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+cache_de = {"decode_fsmn": None}
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+cache = {"encoder": cache_en, "decoder": cache_de}
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+param_dict = {}
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+param_dict["cache"] = cache
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+
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+first_chunk = True
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+speech_buffer = speech
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+speech_cache = []
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+final_result = ""
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+
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+while len(speech_buffer) > 0:
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+ if first_chunk:
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+ if len(speech_buffer) >= 14400:
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+ rec_result = inference_pipeline(audio_in=speech_buffer[0:14400], param_dict=param_dict)
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+ speech_buffer = speech_buffer[4800:]
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+ else:
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+ cache_en["stride"] = len(speech_buffer) // 960
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+ cache_en["pad_right"] = 0
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+ rec_result = inference_pipeline(audio_in=speech_buffer, param_dict=param_dict)
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+ speech_buffer = []
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+ cache_en["start_idx"] = -5
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+ first_chunk = False
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+ else:
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+ cache_en["start_idx"] += 10
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+ if len(speech_buffer) >= 4800:
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+ cache_en["pad_left"] = 5
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+ rec_result = inference_pipeline(audio_in=speech_buffer[:19200], param_dict=param_dict)
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+ speech_buffer = speech_buffer[9600:]
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+ else:
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+ cache_en["stride"] = len(speech_buffer) // 960
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+ cache_en["pad_right"] = 0
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+ rec_result = inference_pipeline(audio_in=speech_buffer, param_dict=param_dict)
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+ speech_buffer = []
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+ if rec_result['text'] != "sil":
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+ final_result += rec_result['text']
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+ print(rec_result)
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+print(final_result)
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