提交

kongdeqiang

2026-03-13 28ccfbfc51068a663a80764e14074df5edf2b5ba

 funasr/models/paraformer/cif_predictor.py

@@ -80,7 +80,7 @@
                    hidden, alphas, token_num, mask=mask

                )



            acoustic_embeds, cif_peak = cif_v1(hidden, alphas, self.threshold)

            acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold)



            if target_length is None and self.tail_threshold > 0.0:

                token_num_int = torch.max(token_num).type(torch.int32).item()

@@ -494,6 +494,8 @@
        token_num_floor = torch.floor(token_num)



        return hidden, alphas, token_num_floor





@torch.jit.script

def cif_v1_export(hidden, alphas, threshold: float):

    device = hidden.device

@@ -504,7 +506,10 @@
    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)

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

    prefix_sum = torch.cumsum(alphas, dim=1, dtype=torch.float64).to(

        torch.float32

    )  # cumsum precision degradation cause wrong result in extreme

    prefix_sum_floor = torch.floor(prefix_sum)

    dislocation_prefix_sum = torch.roll(prefix_sum, 1, dims=1)

    dislocation_prefix_sum_floor = torch.floor(dislocation_prefix_sum)

@@ -516,10 +521,8 @@
    fires[fire_idxs] = 1

    fires = fires + prefix_sum - prefix_sum_floor



    prefix_sum_hidden = torch.cumsum(

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

    )



    # prefix_sum_hidden = torch.cumsum(alphas.unsqueeze(-1).tile((1, 1, hidden_size)) * hidden, dim=1)

    prefix_sum_hidden = torch.cumsum(alphas.unsqueeze(-1).repeat((1, 1, hidden_size)) * hidden, dim=1)

    frames = prefix_sum_hidden[fire_idxs]

    shift_frames = torch.roll(frames, 1, dims=0)



@@ -530,24 +533,25 @@
    shift_frames[shift_batch_idxs] = 0



    remains = fires - torch.floor(fires)

    remain_frames = (

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

    )

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

    remain_frames = remains[fire_idxs].unsqueeze(-1).repeat((1, hidden_size)) * hidden[fire_idxs]



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

    shift_remain_frames[shift_batch_idxs] = 0



    frames = frames - shift_frames + shift_remain_frames - remain_frames



    max_label_len = batch_len.max()

    # max_label_len = batch_len.max()

    max_label_len = alphas.sum(dim=-1)

    max_label_len = torch.floor(max_label_len).max().to(dtype=torch.int64)



    frame_fires = torch.zeros(

        batch_size, max_label_len, hidden_size, dtype=dtype, device=device

    )

    # frame_fires = torch.zeros(batch_size, max_label_len, hidden_size, dtype=dtype, device=device)

    frame_fires = torch.zeros(batch_size, max_label_len, hidden_size, dtype=dtype, device=device)

    indices = torch.arange(max_label_len, device=device).expand(batch_size, -1)

    frame_fires_idxs = indices < batch_len.unsqueeze(1)

    frame_fires[frame_fires_idxs] = frames

    return frame_fires, fires





@torch.jit.script

def cif_export(hidden, alphas, threshold: float):

@@ -670,7 +674,10 @@


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



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

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

    prefix_sum = torch.cumsum(alphas, dim=1, dtype=torch.float64).to(

        torch.float32

    )  # cumsum precision degradation cause wrong result in extreme

    prefix_sum_floor = torch.floor(prefix_sum)

    dislocation_prefix_sum = torch.roll(prefix_sum, 1, dims=1)

    dislocation_prefix_sum_floor = torch.floor(dislocation_prefix_sum)

@@ -692,11 +699,10 @@
    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

    )

    # 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)

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

    prefix_sum_hidden = torch.cumsum(alphas.unsqueeze(-1).repeat((1, 1, hidden_size)) * hidden, dim=1)



    frames = prefix_sum_hidden[fire_idxs]

    shift_frames = torch.roll(frames, 1, dims=0)

@@ -708,21 +714,21 @@
    shift_frames[shift_batch_idxs] = 0



    remains = fires - torch.floor(fires)

    remain_frames = (

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

                                               hidden_size)) * hidden[fire_idxs]

    )

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

    remain_frames = remains[fire_idxs].unsqueeze(-1).repeat((1, hidden_size)) * hidden[fire_idxs]



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

    shift_remain_frames[shift_batch_idxs] = 0



    frames = frames - shift_frames + shift_remain_frames - remain_frames



    max_label_len = batch_len.max()

    # max_label_len = batch_len.max()

    max_label_len = (

        torch.round(alphas.sum(-1)).int().max()

    )  # torch.round to calculate the max length



    frame_fires = torch.zeros(

        batch_size, max_label_len, hidden_size, dtype=dtype, device=device

    )

    # frame_fires = torch.zeros(batch_size, max_label_len, hidden_size, dtype=dtype, device=device)

    frame_fires = torch.zeros(batch_size, max_label_len, hidden_size, dtype=dtype, device=device)

    indices = torch.arange(max_label_len, device=device).expand(batch_size, -1)

    frame_fires_idxs = indices < batch_len.unsqueeze(1)

    frame_fires[frame_fires_idxs] = frames