update with main (#1800)

zhifu gao

2024-06-11 20aa07268a7fafaaab7762b488615af32a0e82b4

 funasr/models/paraformer/cif_predictor.py

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

                )



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

            acoustic_embeds, cif_peak = cif_v1(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()

@@ -245,7 +245,7 @@
                        hidden, alphas, token_num, mask=None

                    )



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

            acoustic_embeds, cif_peak = cif_v1(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()

                acoustic_embeds = acoustic_embeds[:, :token_num_int, :]

@@ -449,7 +449,7 @@
        mask = mask.transpose(-1, -2).float()

        mask = mask.squeeze(-1)

        hidden, alphas, token_num = self.tail_process_fn(hidden, alphas, mask=mask)

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

        acoustic_embeds, cif_peak = cif_v1_export(hidden, alphas, self.threshold)



        return acoustic_embeds, token_num, alphas, cif_peak



@@ -494,7 +494,60 @@
        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

    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)

    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)



    dislocation_prefix_sum_floor[:, 0] = 0

    dislocation_diff = prefix_sum_floor - dislocation_prefix_sum_floor



    fire_idxs = dislocation_diff > 0

    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

    )



    frames = prefix_sum_hidden[fire_idxs]

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



    batch_len = fire_idxs.sum(1)

    batch_idxs = torch.cumsum(batch_len, dim=0)

    shift_batch_idxs = torch.roll(batch_idxs, 1, dims=0)

    shift_batch_idxs[0] = 0

    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]

    )



    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()



    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):

@@ -608,6 +661,74 @@
    return torch.stack(list_ls, 0), fires





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

    batch_size, len_time = alphas.size()

    device = alphas.device

    dtype = alphas.dtype



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



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



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

    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)



    dislocation_prefix_sum_floor[:, 0] = 0

    dislocation_diff = prefix_sum_floor - dislocation_prefix_sum_floor



    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

    )



    frames = prefix_sum_hidden[fire_idxs]

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



    batch_len = fire_idxs.sum(1)

    batch_idxs = torch.cumsum(batch_len, dim=0)

    shift_batch_idxs = torch.roll(batch_idxs, 1, dims=0)

    shift_batch_idxs[0] = 0

    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]

    )



    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()



    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





def cif_wo_hidden(alphas, threshold):

    batch_size, len_time = alphas.size()