test

语帆

2024-02-21 0a6eacc54c6b2564aaa048076c2b2a1202b9c6a2

test

 funasr/models/contextual_paraformer/model.py

@@ -29,7 +29,7 @@
from funasr.models.transformer.utils.add_sos_eos import add_sos_eos
from funasr.models.transformer.utils.nets_utils import make_pad_mask, pad_list
from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank

import pdb

if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
    from torch.cuda.amp import autocast
@@ -63,7 +63,7 @@
        crit_attn_smooth = kwargs.get("crit_attn_smooth", 0.0)
        bias_encoder_dropout_rate = kwargs.get("bias_encoder_dropout_rate", 0.0)


        pdb.set_trace()
        if bias_encoder_type == 'lstm':
            self.bias_encoder = torch.nn.LSTM(inner_dim, inner_dim, 1, batch_first=True, dropout=bias_encoder_dropout_rate)
            self.bias_embed = torch.nn.Embedding(self.vocab_size, inner_dim)
@@ -81,6 +81,7 @@
        if self.crit_attn_weight > 0:
            self.attn_loss = torch.nn.L1Loss()
        self.crit_attn_smooth = crit_attn_smooth
        pdb.set_trace()


    def forward(
@@ -103,17 +104,17 @@
            text_lengths = text_lengths[:, 0]
        if len(speech_lengths.size()) > 1:
            speech_lengths = speech_lengths[:, 0]
        
        pdb.set_trace()
        batch_size = speech.shape[0]

        hotword_pad = kwargs.get("hotword_pad")
        hotword_lengths = kwargs.get("hotword_lengths")
        dha_pad = kwargs.get("dha_pad")
        
        pdb.set_trace()
        # 1. Encoder
        encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)

        
        pdb.set_trace()
        loss_ctc, cer_ctc = None, None
        
        stats = dict()
@@ -128,12 +129,12 @@
            stats["loss_ctc"] = loss_ctc.detach() if loss_ctc is not None else None
            stats["cer_ctc"] = cer_ctc
        

        pdb.set_trace()
        # 2b. Attention decoder branch
        loss_att, acc_att, cer_att, wer_att, loss_pre, loss_ideal = self._calc_att_clas_loss(
            encoder_out, encoder_out_lens, text, text_lengths, hotword_pad, hotword_lengths
        )
        
        pdb.set_trace()
        # 3. CTC-Att loss definition
        if self.ctc_weight == 0.0:
            loss = loss_att + loss_pre * self.predictor_weight
@@ -171,22 +172,26 @@
    ):
        encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
            encoder_out.device)
        pdb.set_trace()
        if self.predictor_bias == 1:
            _, ys_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
            ys_pad_lens = ys_pad_lens + self.predictor_bias
        pdb.set_trace()
        pre_acoustic_embeds, pre_token_length, _, _ = self.predictor(encoder_out, ys_pad, encoder_out_mask,
                                                                     ignore_id=self.ignore_id)
        
        pdb.set_trace()
        # -1. bias encoder
        if self.use_decoder_embedding:
            hw_embed = self.decoder.embed(hotword_pad)
        else:
            hw_embed = self.bias_embed(hotword_pad)
        pdb.set_trace()
        hw_embed, (_, _) = self.bias_encoder(hw_embed)
        pdb.set_trace()
        _ind = np.arange(0, hotword_pad.shape[0]).tolist()
        selected = hw_embed[_ind, [i - 1 for i in hotword_lengths.detach().cpu().tolist()]]
        contextual_info = selected.squeeze(0).repeat(ys_pad.shape[0], 1, 1).to(ys_pad.device)
        
        pdb.set_trace()
        # 0. sampler
        decoder_out_1st = None
        if self.sampling_ratio > 0.0:
@@ -198,7 +203,7 @@
            if self.step_cur < 2:
                logging.info("disable sampler in paraformer, sampling_ratio: {}".format(self.sampling_ratio))
            sematic_embeds = pre_acoustic_embeds
        
        pdb.set_trace()
        # 1. Forward decoder
        decoder_outs = self.decoder(
            encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, contextual_info=contextual_info
@@ -214,7 +219,7 @@
            loss_ideal = None
        '''
        loss_ideal = None
        
        pdb.set_trace()
        if decoder_out_1st is None:
            decoder_out_1st = decoder_out
        # 2. Compute attention loss