funasr1.0

游雁

2024-01-13 bdfd27b9e96bd55c449953bb577e1d4deeaf11c9

 funasr/models/paraformer/cif_predictor.py

@@ -8,9 +8,9 @@
from funasr.models.scama.utils import sequence_mask

from typing import Optional, Tuple



from funasr.utils.register import register_class, registry_tables

from funasr.register import tables



@register_class("predictor_classes", "CifPredictor")

@tables.register("predictor_classes", "CifPredictor")

class CifPredictor(nn.Module):

    def __init__(self, idim, l_order, r_order, threshold=1.0, dropout=0.1, smooth_factor=1.0, noise_threshold=0, tail_threshold=0.45):

        super().__init__()

@@ -136,7 +136,7 @@
        predictor_alignments_length = predictor_alignments.sum(-1).type(encoder_sequence_length.dtype)

        return predictor_alignments.detach(), predictor_alignments_length.detach()



@register_class("predictor_classes", "CifPredictorV2")

@tables.register("predictor_classes", "CifPredictorV2")

class CifPredictorV2(nn.Module):

    def __init__(self,

                 idim,

@@ -205,7 +205,8 @@


        return acoustic_embeds, token_num, alphas, cif_peak



    def forward_chunk(self, hidden, cache=None):

    def forward_chunk(self, hidden, cache=None, **kwargs):

        is_final = kwargs.get("is_final", False)

        batch_size, len_time, hidden_size = hidden.shape

        h = hidden

        context = h.transpose(1, 2)

@@ -226,14 +227,14 @@


        if cache is not None and "chunk_size" in cache:

            alphas[:, :cache["chunk_size"][0]] = 0.0

            if "is_final" in cache and not cache["is_final"]:

            if not is_final:

                alphas[:, sum(cache["chunk_size"][:2]):] = 0.0

        if cache is not None and "cif_alphas" in cache and "cif_hidden" in cache:

            cache["cif_hidden"] = to_device(cache["cif_hidden"], device=hidden.device)

            cache["cif_alphas"] = to_device(cache["cif_alphas"], device=alphas.device)

            hidden = torch.cat((cache["cif_hidden"], hidden), dim=1)

            alphas = torch.cat((cache["cif_alphas"], alphas), dim=1)

        if cache is not None and "is_final" in cache and cache["is_final"]:

        if cache is not None and is_final:

            tail_hidden = torch.zeros((batch_size, 1, hidden_size), device=hidden.device)

            tail_alphas = torch.tensor([[self.tail_threshold]], device=alphas.device)

            tail_alphas = torch.tile(tail_alphas, (batch_size, 1))

@@ -277,7 +278,7 @@


        max_token_len = max(token_length)

        if max_token_len == 0:

             return hidden, torch.stack(token_length, 0)

             return hidden, torch.stack(token_length, 0), None, None

        list_ls = []

        for b in range(batch_size):

            pad_frames = torch.zeros((max_token_len - token_length[b], hidden_size), device=alphas.device)

@@ -291,7 +292,7 @@
        cache["cif_alphas"] = torch.unsqueeze(cache["cif_alphas"], axis=0)

        cache["cif_hidden"] = torch.stack(cache_hiddens, axis=0)

        cache["cif_hidden"] = torch.unsqueeze(cache["cif_hidden"], axis=0)

        return torch.stack(list_ls, 0), torch.stack(token_length, 0)

        return torch.stack(list_ls, 0), torch.stack(token_length, 0), None, None





    def tail_process_fn(self, hidden, alphas, token_num=None, mask=None):