| | |
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| | | from funasr.utils.timestamp_tools import ts_prediction_lfr6_standard |
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| | | from funasr.models.transformer.utils.nets_utils import make_pad_mask, pad_list |
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| | | from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank |
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| | | |
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| | | from funasr.train_utils.device_funcs import to_device |
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| | | |
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| | | if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"): |
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| | | from torch.cuda.amp import autocast |
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| | |
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| | | max_seq_len=512, |
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| | | **kwargs, |
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| | | ): |
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| | | self.device = kwargs.get("device") |
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| | | is_onnx = kwargs.get("type", "onnx") == "onnx" |
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| | | encoder_class = tables.encoder_classes.get(kwargs["encoder"] + "Export") |
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| | | self.encoder = encoder_class(self.encoder, onnx=is_onnx) |
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| | |
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| | | |
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| | | return self |
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| | | |
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| | | def _export_forward( |
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| | | def export_forward( |
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| | | self, |
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| | | speech: torch.Tensor, |
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| | | speech_lengths: torch.Tensor, |
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| | | ): |
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| | | # a. To device |
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| | | batch = {"speech": speech, "speech_lengths": speech_lengths} |
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| | | # batch = to_device(batch, device=self.device) |
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| | | batch = to_device(batch, device=self.device) |
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| | | |
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| | | enc, enc_len = self.encoder(**batch) |
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| | | mask = self.make_pad_mask(enc_len)[:, None, :] |
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