#!/usr/bin/env python3 # -*- encoding: utf-8 -*- # Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. # MIT License (https://opensource.org/licenses/MIT) import torch from funasr.register import tables class ContextualEmbedderExport(torch.nn.Module): def __init__( self, model, max_seq_len=512, feats_dim=560, **kwargs, ): super().__init__() self.embedding = model.decoder.embed # model.bias_embed model.bias_encoder.batch_first = False self.bias_encoder = model.bias_encoder def forward(self, hotword): hotword = self.embedding(hotword).transpose(0, 1) # batch second hw_embed, (_, _) = self.bias_encoder(hotword) return hw_embed def export_dummy_inputs(self): hotword = torch.tensor( [ [10, 11, 12, 13, 14, 10, 11, 12, 13, 14], [100, 101, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [10, 11, 12, 13, 14, 10, 11, 12, 13, 14], [100, 101, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0], ], dtype=torch.int32, ) # hotword_length = torch.tensor([10, 2, 1], dtype=torch.int32) return hotword def export_input_names(self): return ["hotword"] def export_output_names(self): return ["hw_embed"] def export_dynamic_axes(self): return { "hotword": { 0: "num_hotwords", }, "hw_embed": { 1: "num_hotwords", }, } def export_name(self): return "model_eb.onnx" def export_rebuild_model(model, **kwargs): model.device = kwargs.get("device") is_onnx = kwargs.get("type", "onnx") == "onnx" encoder_class = tables.encoder_classes.get(kwargs["encoder"] + "Export") model.encoder = encoder_class(model.encoder, onnx=is_onnx) predictor_class = tables.predictor_classes.get(kwargs["predictor"] + "Export") model.predictor = predictor_class(model.predictor, onnx=is_onnx) # before decoder convert into export class embedder_class = ContextualEmbedderExport embedder_model = embedder_class(model, onnx=is_onnx) decoder_class = tables.decoder_classes.get(kwargs["decoder"] + "Export") model.decoder = decoder_class(model.decoder, onnx=is_onnx) seaco_decoder_class = tables.decoder_classes.get(kwargs["seaco_decoder"] + "Export") model.seaco_decoder = seaco_decoder_class(model.seaco_decoder, onnx=is_onnx) from funasr.utils.torch_function import sequence_mask model.make_pad_mask = sequence_mask(kwargs["max_seq_len"], flip=False) from funasr.utils.torch_function import sequence_mask model.make_pad_mask = sequence_mask(kwargs["max_seq_len"], flip=False) model.feats_dim = 560 model.NOBIAS = 8377 import copy import types backbone_model = copy.copy(model) # backbone backbone_model.forward = types.MethodType(export_backbone_forward, backbone_model) backbone_model.export_dummy_inputs = types.MethodType( export_backbone_dummy_inputs, backbone_model ) backbone_model.export_input_names = types.MethodType( export_backbone_input_names, backbone_model ) backbone_model.export_output_names = types.MethodType( export_backbone_output_names, backbone_model ) backbone_model.export_dynamic_axes = types.MethodType( export_backbone_dynamic_axes, backbone_model ) embedder_model.export_name = "model_eb" backbone_model.export_name = "model" return backbone_model, embedder_model def export_backbone_forward( self, speech: torch.Tensor, speech_lengths: torch.Tensor, bias_embed: torch.Tensor, # lmbd: float, ): # a. To device batch = {"speech": speech, "speech_lengths": speech_lengths} enc, enc_len = self.encoder(**batch) mask = self.make_pad_mask(enc_len)[:, None, :] pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = self.predictor(enc, mask) pre_token_length = pre_token_length.floor().type(torch.int32) decoder_out, decoder_hidden, _ = self.decoder( enc, enc_len, pre_acoustic_embeds, pre_token_length, return_hidden=True, return_both=True ) decoder_out = torch.log_softmax(decoder_out, dim=-1) # seaco forward B, N, D = bias_embed.shape _contextual_length = torch.ones(B) * N # ASF hotword_scores = self.seaco_decoder.forward_asf6( bias_embed, _contextual_length, decoder_hidden, pre_token_length ) hotword_scores = hotword_scores[0].sum(0).sum(0) # _ = self.decoder2(bias_embed, _contextual_length, decoder_hidden, pre_token_length) # hotword_scores = self.decoder2.model.decoders[-1].attn_mat[0][0].sum(0).sum(0) dec_filter = torch.sort(hotword_scores, descending=True)[1][:51] contextual_info = bias_embed[:, dec_filter] num_hot_word = contextual_info.shape[1] _contextual_length = torch.Tensor([num_hot_word]).int().repeat(B).to(enc.device) # again cif_attended, _ = self.seaco_decoder( contextual_info, _contextual_length, pre_acoustic_embeds, pre_token_length ) dec_attended, _ = self.seaco_decoder( contextual_info, _contextual_length, decoder_hidden, pre_token_length ) merged = cif_attended + dec_attended dha_output = self.hotword_output_layer(merged) dha_pred = torch.log_softmax(dha_output, dim=-1) # merging logits dha_ids = dha_pred.max(-1)[-1] dha_mask = (dha_ids == self.NOBIAS).int().unsqueeze(-1) decoder_out = decoder_out * dha_mask + dha_pred * (1 - dha_mask) # get predicted timestamps us_alphas, us_cif_peak = self.predictor.get_upsample_timestmap(enc, mask, pre_token_length) return decoder_out, pre_token_length, us_alphas, us_cif_peak def export_backbone_dummy_inputs(self): speech = torch.randn(2, 30, self.feats_dim) speech_lengths = torch.tensor([15, 30], dtype=torch.int32) bias_embed = torch.randn(2, 1, 512) return (speech, speech_lengths, bias_embed) def export_backbone_input_names(self): return ["speech", "speech_lengths", "bias_embed"] def export_backbone_output_names(self): return ["logits", "token_num", "us_alphas", "us_cif_peak"] def export_backbone_dynamic_axes(self): return { "speech": {0: "batch_size", 1: "feats_length"}, "speech_lengths": { 0: "batch_size", }, "bias_embed": {0: "batch_size", 1: "num_hotwords"}, "logits": {0: "batch_size", 1: "logits_length"}, "pre_acoustic_embeds": {1: "feats_length1"}, "us_alphas": {0: "batch_size", 1: "alphas_length"}, "us_cif_peak": {0: "batch_size", 1: "alphas_length"}, }