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| | | from funasr.register import tables |
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| | | from funasr.utils import postprocess_utils |
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| | | from funasr.metrics.compute_acc import th_accuracy |
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| | | from funasr.models.paraformer.model import Paraformer |
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| | | from funasr.utils.datadir_writer import DatadirWriter |
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| | | from funasr.models.paraformer.search import Hypothesis |
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| | | from funasr.models.paraformer.cif_predictor import mae_loss |
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| | | from funasr.train_utils.device_funcs import force_gatherable |
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| | | from funasr.models.bicif_paraformer.model import BiCifParaformer |
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| | | from funasr.losses.label_smoothing_loss import LabelSmoothingLoss |
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| | | from funasr.utils.timestamp_tools import ts_prediction_lfr6_standard |
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| | | from funasr.models.transformer.utils.add_sos_eos import add_sos_eos |
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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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| | | # bias encoder |
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| | | if self.bias_encoder_type == 'lstm': |
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| | | logging.warning("enable bias encoder sampling and contextual training") |
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| | | self.bias_encoder = torch.nn.LSTM(self.inner_dim, |
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| | | self.inner_dim, |
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| | | 2, |
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| | | self.lstm_proj = torch.nn.Linear(self.inner_dim*2, self.inner_dim) |
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| | | else: |
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| | | self.lstm_proj = None |
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| | | self.bias_embed = torch.nn.Embedding(self.vocab_size, self.inner_dim) |
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| | | # self.bias_embed = torch.nn.Embedding(self.vocab_size, self.inner_dim) |
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| | | elif self.bias_encoder_type == 'mean': |
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| | | logging.warning("enable bias encoder sampling and contextual training") |
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| | | self.bias_embed = torch.nn.Embedding(self.vocab_size, self.inner_dim) |
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| | | else: |
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| | | logging.error("Unsupport bias encoder type: {}".format(self.bias_encoder_type)) |
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| | | |
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| | | # ASF Core |
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| | | if nfilter > 0 and nfilter < num_hot_word: |
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| | | for dec in self.seaco_decoder.decoders: |
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| | | dec.reserve_attn = True |
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| | | # cif_attended, _ = self.decoder2(contextual_info, _contextual_length, sematic_embeds, ys_pad_lens) |
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| | | dec_attended, _ = self.seaco_decoder(contextual_info, _contextual_length, decoder_hidden, ys_pad_lens) |
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| | | # cif_filter = torch.topk(self.decoder2.decoders[-1].attn_mat[0][0].sum(0).sum(0)[:-1], min(nfilter, num_hot_word-1))[1].tolist() |
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| | | hotword_scores = self.seaco_decoder.decoders[-1].attn_mat[0][0].sum(0).sum(0)[:-1] |
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| | | hotword_scores = self.seaco_decoder.forward_asf6(contextual_info, _contextual_length, decoder_hidden, ys_pad_lens) |
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| | | hotword_scores = hotword_scores[0].sum(0).sum(0) |
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| | | # hotword_scores /= torch.sqrt(torch.tensor(hw_lengths)[:-1].float()).to(hotword_scores.device) |
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| | | dec_filter = torch.topk(hotword_scores, min(nfilter, num_hot_word-1))[1].tolist() |
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| | | add_filter = dec_filter |
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| | | contextual_info = selected.squeeze(0).repeat(encoder_out.shape[0], 1, 1).to(encoder_out.device) |
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| | | num_hot_word = contextual_info.shape[1] |
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| | | _contextual_length = torch.Tensor([num_hot_word]).int().repeat(encoder_out.shape[0]).to(encoder_out.device) |
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| | | for dec in self.seaco_decoder.decoders: |
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| | | dec.attn_mat = [] |
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| | | dec.reserve_attn = False |
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| | | |
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| | | # SeACo Core |
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| | | cif_attended, _ = self.seaco_decoder(contextual_info, _contextual_length, sematic_embeds, ys_pad_lens) |
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| | |
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| | | hotword_lengths): |
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| | | if self.bias_encoder_type != 'lstm': |
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| | | logging.error("Unsupported bias encoder type") |
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| | | |
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| | | ''' |
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| | | hw_embed = self.decoder.embed(hotword_pad) |
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| | | hw_embed, (_, _) = self.bias_encoder(hw_embed) |
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| | | if self.lstm_proj is not None: |
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| | | _ind = np.arange(0, hw_embed.shape[0]).tolist() |
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| | | selected = hw_embed[_ind, [i-1 for i in hotword_lengths.detach().cpu().tolist()]] |
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| | | return selected |
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| | | ''' |
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| | | |
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| | | ''' |
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| | | def calc_predictor(self, encoder_out, encoder_out_lens): |
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| | | encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to( |
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| | | encoder_out.device) |
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| | | pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index, pre_token_length2 = self.predictor(encoder_out, |
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| | | None, |
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| | | encoder_out_mask, |
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| | | ignore_id=self.ignore_id) |
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| | | return pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index |
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| | | |
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| | | |
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| | | def calc_predictor_timestamp(self, encoder_out, encoder_out_lens, token_num): |
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| | | encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to( |
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| | | encoder_out.device) |
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| | | ds_alphas, ds_cif_peak, us_alphas, us_peaks = self.predictor.get_upsample_timestamp(encoder_out, |
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| | | encoder_out_mask, |
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| | | token_num) |
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| | | return ds_alphas, ds_cif_peak, us_alphas, us_peaks |
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| | | ''' |
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| | | # hw_embed = self.sac_embedding(hotword_pad) |
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| | | hw_embed = self.decoder.embed(hotword_pad) |
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| | | hw_embed = torch.nn.utils.rnn.pack_padded_sequence(hw_embed, hotword_lengths.cpu().type(torch.int64), batch_first=True, enforce_sorted=False) |
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| | | packed_rnn_output, _ = self.bias_encoder(hw_embed) |
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| | | rnn_output = torch.nn.utils.rnn.pad_packed_sequence(packed_rnn_output, batch_first=True)[0] |
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| | | if self.lstm_proj is not None: |
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| | | hw_hidden = self.lstm_proj(rnn_output) |
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| | | else: |
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| | | hw_hidden = rnn_output |
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| | | _ind = np.arange(0, hw_hidden.shape[0]).tolist() |
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| | | selected = hw_hidden[_ind, [i-1 for i in hotword_lengths.detach().cpu().tolist()]] |
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| | | return selected |
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| | | |
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| | | def inference(self, |
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| | | data_in, |
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| | | speech = speech.to(device=kwargs["device"]) |
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| | | speech_lengths = speech_lengths.to(device=kwargs["device"]) |
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| | | |
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| | | |
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| | | # hotword |
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| | | self.hotword_list = self.generate_hotwords_list(kwargs.get("hotword", None), tokenizer=tokenizer, frontend=frontend) |
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| | | |
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| | | nbest_hyps = [Hypothesis(yseq=yseq, score=score)] |
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| | | for nbest_idx, hyp in enumerate(nbest_hyps): |
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| | | ibest_writer = None |
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| | | if ibest_writer is None and kwargs.get("output_dir") is not None: |
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| | | writer = DatadirWriter(kwargs.get("output_dir")) |
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| | | ibest_writer = writer[f"{nbest_idx + 1}best_recog"] |
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| | | if kwargs.get("output_dir") is not None: |
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| | | if not hasattr(self, "writer"): |
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| | | self.writer = DatadirWriter(kwargs.get("output_dir")) |
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| | | ibest_writer = self.writer[f"{nbest_idx + 1}best_recog"] |
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| | | |
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| | | # remove sos/eos and get results |
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| | | last_pos = -1 |
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| | | if isinstance(hyp.yseq, list): |
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| | |
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| | | token, timestamp) |
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| | | |
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| | | result_i = {"key": key[i], "text": text_postprocessed, |
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| | | "timestamp": time_stamp_postprocessed, |
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| | | "timestamp": time_stamp_postprocessed |
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| | | } |
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| | | |
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| | | if ibest_writer is not None: |
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| | | ibest_writer["token"][key[i]] = " ".join(token) |
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| | | # ibest_writer["text"][key[i]] = text |
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| | | ibest_writer["timestamp"][key[i]] = time_stamp_postprocessed |
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| | | ibest_writer["text"][key[i]] = text_postprocessed |
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| | | else: |
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