198 lines
6.5 KiB
Python
198 lines
6.5 KiB
Python
#!/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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from funasr.register import tables
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class ContextualEmbedderExport(torch.nn.Module):
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def __init__(
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self,
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model,
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max_seq_len=512,
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feats_dim=560,
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**kwargs,
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):
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super().__init__()
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self.embedding = model.decoder.embed # 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 forward(self, hotword):
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hotword = self.embedding(hotword).transpose(0, 1) # batch second
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hw_embed, (_, _) = self.bias_encoder(hotword)
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return hw_embed
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def export_dummy_inputs(self):
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hotword = torch.tensor(
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[
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[10, 11, 12, 13, 14, 10, 11, 12, 13, 14],
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[100, 101, 0, 0, 0, 0, 0, 0, 0, 0],
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[1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[10, 11, 12, 13, 14, 10, 11, 12, 13, 14],
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[100, 101, 0, 0, 0, 0, 0, 0, 0, 0],
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[1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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],
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dtype=torch.int32,
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)
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# hotword_length = torch.tensor([10, 2, 1], dtype=torch.int32)
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return hotword
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def export_input_names(self):
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return ["hotword"]
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def export_output_names(self):
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return ["hw_embed"]
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def export_dynamic_axes(self):
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return {
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"hotword": {
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0: "num_hotwords",
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},
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"hw_embed": {
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0: "num_hotwords",
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},
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}
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def export_name(self):
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return "model_eb.onnx"
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def export_rebuild_model(model, **kwargs):
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model.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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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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# before decoder convert into export class
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embedder_class = ContextualEmbedderExport
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embedder_model = embedder_class(model, onnx=is_onnx)
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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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seaco_decoder_class = tables.decoder_classes.get(kwargs["seaco_decoder"] + "Export")
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model.seaco_decoder = seaco_decoder_class(model.seaco_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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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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model.NOBIAS = 8377
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import copy
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import types
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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(
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export_backbone_dummy_inputs, backbone_model
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)
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backbone_model.export_input_names = types.MethodType(
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export_backbone_input_names, backbone_model
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)
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backbone_model.export_output_names = types.MethodType(
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export_backbone_output_names, backbone_model
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)
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backbone_model.export_dynamic_axes = types.MethodType(
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export_backbone_dynamic_axes, backbone_model
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)
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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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# lmbd: float,
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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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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, alphas, pre_peak_index = self.predictor(enc, mask)
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pre_token_length = pre_token_length.floor().type(torch.int32)
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decoder_out, decoder_hidden, _ = self.decoder(
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enc, enc_len, pre_acoustic_embeds, pre_token_length, return_hidden=True, return_both=True
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)
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decoder_out = torch.log_softmax(decoder_out, dim=-1)
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# seaco forward
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B, N, D = bias_embed.shape
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_contextual_length = torch.ones(B) * N
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# ASF
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hotword_scores = self.seaco_decoder.forward_asf6(
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bias_embed, _contextual_length, decoder_hidden, pre_token_length
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)
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hotword_scores = hotword_scores[0].sum(0).sum(0)
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# _ = self.decoder2(bias_embed, _contextual_length, decoder_hidden, pre_token_length)
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# hotword_scores = self.decoder2.model.decoders[-1].attn_mat[0][0].sum(0).sum(0)
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dec_filter = torch.sort(hotword_scores, descending=True)[1][:51]
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contextual_info = bias_embed[:, dec_filter]
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num_hot_word = contextual_info.shape[1]
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_contextual_length = torch.Tensor([num_hot_word]).int().repeat(B).to(enc.device)
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# again
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cif_attended, _ = self.seaco_decoder(
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contextual_info, _contextual_length, pre_acoustic_embeds, pre_token_length
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)
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dec_attended, _ = self.seaco_decoder(
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contextual_info, _contextual_length, decoder_hidden, pre_token_length
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)
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merged = cif_attended + dec_attended
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dha_output = self.hotword_output_layer(merged)
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dha_pred = torch.log_softmax(dha_output, dim=-1)
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# merging logits
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dha_ids = dha_pred.max(-1)[-1]
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dha_mask = (dha_ids == self.NOBIAS).int().unsqueeze(-1)
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decoder_out = decoder_out * dha_mask + dha_pred * (1 - dha_mask)
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return decoder_out, pre_token_length, alphas
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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([15, 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", "alphas"]
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def export_backbone_dynamic_axes(self):
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return {
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"speech": {0: "batch_size", 1: "feats_length"},
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"speech_lengths": {
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0: "batch_size",
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},
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"bias_embed": {0: "batch_size", 1: "num_hotwords"},
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"logits": {0: "batch_size", 1: "logits_length"},
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"pre_acoustic_embeds": {1: "feats_length1"},
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}
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def export_backbone_name(self):
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return "model.onnx"
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