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@ -181,7 +181,7 @@ class ASRExecutor(BaseExecutor):
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lm_url,
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lm_url,
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os.path.dirname(self.config.decode.lang_model_path), lm_md5)
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os.path.dirname(self.config.decode.lang_model_path), lm_md5)
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elif "conformer" in model_type or "transformer" in model_type or "wenetspeech" in model_type:
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elif "conformer" in model_type or "transformer" in model_type:
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self.config.spm_model_prefix = os.path.join(
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self.config.spm_model_prefix = os.path.join(
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self.res_path, self.config.spm_model_prefix)
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self.res_path, self.config.spm_model_prefix)
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self.text_feature = TextFeaturizer(
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self.text_feature = TextFeaturizer(
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@ -205,7 +205,7 @@ class ASRExecutor(BaseExecutor):
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self.model.set_state_dict(model_dict)
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self.model.set_state_dict(model_dict)
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# compute the max len limit
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# compute the max len limit
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if "conformer" in model_type or "transformer" in model_type or "wenetspeech" in model_type:
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if "conformer" in model_type or "transformer" in model_type:
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# in transformer like model, we may use the subsample rate cnn network
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# in transformer like model, we may use the subsample rate cnn network
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subsample_rate = self.model.subsampling_rate()
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subsample_rate = self.model.subsampling_rate()
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frame_shift_ms = self.config.preprocess_config.process[0][
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frame_shift_ms = self.config.preprocess_config.process[0][
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@ -242,7 +242,7 @@ class ASRExecutor(BaseExecutor):
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self._inputs["audio_len"] = audio_len
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self._inputs["audio_len"] = audio_len
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logger.info(f"audio feat shape: {audio.shape}")
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logger.info(f"audio feat shape: {audio.shape}")
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elif "conformer" in model_type or "transformer" in model_type or "wenetspeech" in model_type:
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elif "conformer" in model_type or "transformer" in model_type:
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logger.info("get the preprocess conf")
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logger.info("get the preprocess conf")
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preprocess_conf = self.config.preprocess_config
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preprocess_conf = self.config.preprocess_config
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preprocess_args = {"train": False}
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preprocess_args = {"train": False}
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