Merge pull request #526 from alibaba-damo-academy/dev_infer

zhifu gao

2023-05-18 97a689d65da434345a641a909f13b78e5690c86b

 funasr/models/encoder/sanm_encoder.py

@@ -27,9 +27,10 @@
from funasr.modules.subsampling import Conv2dSubsampling8
from funasr.modules.subsampling import TooShortUttError
from funasr.modules.subsampling import check_short_utt
from funasr.modules.mask import subsequent_mask, vad_mask

from funasr.models.ctc import CTC
from funasr.models.encoder.abs_encoder import AbsEncoder
from funasr.modules.mask import subsequent_mask, vad_mask

class EncoderLayerSANM(nn.Module):
    def __init__(
@@ -354,18 +355,9 @@
    def _add_overlap_chunk(self, feats: np.ndarray, cache: dict = {}):
        if len(cache) == 0:
            return feats
        # process last chunk
        cache["feats"] = to_device(cache["feats"], device=feats.device)
        overlap_feats = torch.cat((cache["feats"], feats), dim=1)
        if cache["is_final"]:
            cache["feats"] = overlap_feats[:, -cache["chunk_size"][0]:, :]
            if not cache["last_chunk"]:
               padding_length = sum(cache["chunk_size"]) - overlap_feats.shape[1]
               overlap_feats = overlap_feats.transpose(1, 2)
               overlap_feats = F.pad(overlap_feats, (0, padding_length))
               overlap_feats = overlap_feats.transpose(1, 2)
        else:
            cache["feats"] = overlap_feats[:, -(cache["chunk_size"][0] + cache["chunk_size"][2]):, :]
        cache["feats"] = overlap_feats[:, -(cache["chunk_size"][0] + cache["chunk_size"][2]):, :]
        return overlap_feats

    def forward_chunk(self,