#!/usr/bin/env python3
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# -*- encoding: utf-8 -*-
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# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
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# MIT License (https://opensource.org/licenses/MIT)
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import torch
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import types
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from funasr.register import tables
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from funasr.models.seaco_paraformer.export_meta import ContextualEmbedderExport
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class ContextualEmbedderExport2(ContextualEmbedderExport):
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def __init__(self,
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model,
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**kwargs):
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super().__init__(model)
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self.embedding = model.bias_embed
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model.bias_encoder.batch_first = False
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self.bias_encoder = model.bias_encoder
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def export_rebuild_model(model, **kwargs):
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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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model.encoder = encoder_class(model.encoder, onnx=is_onnx)
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predictor_class = tables.predictor_classes.get(kwargs["predictor"] + "Export")
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model.predictor = predictor_class(model.predictor, onnx=is_onnx)
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# little difference with bias encoder with seaco paraformer
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embedder_class = ContextualEmbedderExport2
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embedder_model = embedder_class(model, onnx=is_onnx)
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if kwargs["decoder"] == "ParaformerSANMDecoder":
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kwargs["decoder"] = "ParaformerSANMDecoderOnline"
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decoder_class = tables.decoder_classes.get(kwargs["decoder"] + "Export")
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model.decoder = decoder_class(model.decoder, onnx=is_onnx)
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from funasr.utils.torch_function import sequence_mask
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model.make_pad_mask = sequence_mask(kwargs["max_seq_len"], flip=False)
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model.feats_dim = 560
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import copy
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backbone_model = copy.copy(model)
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# backbone
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backbone_model.forward = types.MethodType(export_backbone_forward, backbone_model)
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backbone_model.export_dummy_inputs = types.MethodType(export_backbone_dummy_inputs, backbone_model)
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backbone_model.export_input_names = types.MethodType(export_backbone_input_names, backbone_model)
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backbone_model.export_output_names = types.MethodType(export_backbone_output_names, backbone_model)
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backbone_model.export_dynamic_axes = types.MethodType(export_backbone_dynamic_axes, backbone_model)
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backbone_model.export_name = types.MethodType(export_backbone_name, backbone_model)
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return backbone_model, embedder_model
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def export_backbone_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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bias_embed: torch.Tensor,
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):
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batch = {"speech": speech, "speech_lengths": speech_lengths}
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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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pre_acoustic_embeds, pre_token_length, _, _ = self.predictor(enc, mask)
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pre_token_length = pre_token_length.floor().type(torch.int32)
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decoder_out, _ = self.decoder(enc, enc_len, pre_acoustic_embeds, pre_token_length, bias_embed)
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decoder_out = torch.log_softmax(decoder_out, dim=-1)
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return decoder_out, pre_token_length
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def export_backbone_dummy_inputs(self):
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speech = torch.randn(2, 30, self.feats_dim)
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speech_lengths = torch.tensor([6, 30], dtype=torch.int32)
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bias_embed = torch.randn(2, 1, 512)
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return (speech, speech_lengths, bias_embed)
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def export_backbone_input_names(self):
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return ['speech', 'speech_lengths', 'bias_embed']
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def export_backbone_output_names(self):
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return ['logits', 'token_num']
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def export_backbone_dynamic_axes(self):
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return {
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'speech': {
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0: 'batch_size',
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1: 'feats_length'
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},
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'speech_lengths': {
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0: 'batch_size',
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},
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'bias_embed': {
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0: 'batch_size',
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1: 'num_hotwords'
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},
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'logits': {
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0: 'batch_size',
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1: 'logits_length'
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},
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}
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def export_backbone_name(self):
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return 'model.onnx'
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