Merge pull request #367 from alibaba-damo-academy/dev_lhn2

hnluo

2023-04-17 24f73665e2d8ea8e4de2fe4f900bc539d7f7b989

 funasr/modules/embedding.py

@@ -407,7 +407,24 @@

        return x + position_encoding

    def forward_chunk(self, x, cache=None):
class StreamSinusoidalPositionEncoder(torch.nn.Module):
    '''

    '''
    def __int__(self, d_model=80, dropout_rate=0.1):
        pass

    def encode(self, positions: torch.Tensor = None, depth: int = None, dtype: torch.dtype = torch.float32):
        batch_size = positions.size(0)
        positions = positions.type(dtype)
        log_timescale_increment = torch.log(torch.tensor([10000], dtype=dtype)) / (depth / 2 - 1)
        inv_timescales = torch.exp(torch.arange(depth / 2).type(dtype) * (-log_timescale_increment))
        inv_timescales = torch.reshape(inv_timescales, [batch_size, -1])
        scaled_time = torch.reshape(positions, [1, -1, 1]) * torch.reshape(inv_timescales, [1, 1, -1])
        encoding = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=2)
        return encoding.type(dtype)

    def forward(self, x, cache=None):
        start_idx = 0
        pad_left = 0
        pad_right = 0
@@ -419,8 +436,8 @@
        positions = torch.arange(1, timesteps+start_idx+1)[None, :]
        position_encoding = self.encode(positions, input_dim, x.dtype).to(x.device)
        outputs = x + position_encoding[:, start_idx: start_idx + timesteps]
        outputs = outputs.transpose(1,2)
        outputs = outputs.transpose(1, 2)
        outputs = F.pad(outputs, (pad_left, pad_right))
        outputs = outputs.transpose(1,2)
        outputs = outputs.transpose(1, 2)
        return outputs