add cif_wo_hidden_v1

zhuangzhong

2024-06-07 c367f1b819bb40e70e379f8f80b1d9fd67e47c79

add cif_wo_hidden_v1

 funasr/models/paraformer/cif_predictor.py

@@ -661,14 +661,13 @@
    return torch.stack(list_ls, 0), fires





def cif_v1(hidden, alphas, threshold):

def cif_wo_hidden_v1(alphas, threshold, return_fire_idxs=False):

    batch_size, len_time = alphas.size()

    device = alphas.device

    dtype = alphas.dtype



    device = hidden.device

    dtype = hidden.dtype

    batch_size, len_time, hidden_size = hidden.size()

    threshold = torch.tensor([threshold], dtype=alphas.dtype).to(alphas.device)



    frames = torch.zeros(batch_size, len_time, hidden_size, dtype=dtype, device=device)

    fires = torch.zeros(batch_size, len_time, dtype=dtype, device=device)



    prefix_sum = torch.cumsum(alphas, dim=1)

@@ -682,7 +681,19 @@
    fire_idxs = dislocation_diff > 0

    fires[fire_idxs] = 1

    fires = fires + prefix_sum - prefix_sum_floor

    if return_fire_idxs:

        return fires, fire_idxs

    return fires





def cif_v1(hidden, alphas, threshold):

    fires, fire_idxs = cif_wo_hidden_v1(alphas, threshold, return_fire_idxs=True)



    device = hidden.device

    dtype = hidden.dtype

    batch_size, len_time, hidden_size = hidden.size()

    frames = torch.zeros(batch_size, len_time, hidden_size,

                         dtype=dtype, device=device)

    prefix_sum_hidden = torch.cumsum(

        alphas.unsqueeze(-1).tile((1, 1, hidden_size)) * hidden, dim=1

    )

@@ -698,7 +709,8 @@


    remains = fires - torch.floor(fires)

    remain_frames = (

        remains[fire_idxs].unsqueeze(-1).tile((1, hidden_size)) * hidden[fire_idxs]

        remains[fire_idxs].unsqueeze(-1).tile((1,

                                               hidden_size)) * hidden[fire_idxs]

    )



    shift_remain_frames = torch.roll(remain_frames, 1, dims=0)