Update README.md

zhifu gao

2023-06-18 29b9a0cf5e7deba14c145fe4e804136480da9c3d

 funasr/models/encoder/conformer_encoder.py

@@ -15,13 +15,13 @@
from typeguard import check_argument_types

from funasr.models.ctc import CTC
from funasr.models.encoder.abs_encoder import AbsEncoder
from funasr.modules.attention import (
    MultiHeadedAttention,  # noqa: H301
    RelPositionMultiHeadedAttention,  # noqa: H301
    RelPositionMultiHeadedAttentionChunk,
    LegacyRelPositionMultiHeadedAttention,  # noqa: H301
)
from funasr.models.encoder.abs_encoder import AbsEncoder
from funasr.modules.embedding import (
    PositionalEncoding,  # noqa: H301
    ScaledPositionalEncoding,  # noqa: H301
@@ -307,7 +307,7 @@
        feed_forward: torch.nn.Module,
        feed_forward_macaron: torch.nn.Module,
        conv_mod: torch.nn.Module,
        norm_class: torch.nn.Module = torch.nn.LayerNorm,
        norm_class: torch.nn.Module = LayerNorm,
        norm_args: Dict = {},
        dropout_rate: float = 0.0,
    ) -> None:
@@ -894,7 +894,7 @@

        return x, cache

class ConformerChunkEncoder(torch.nn.Module):
class ConformerChunkEncoder(AbsEncoder):
    """Encoder module definition.
    Args:
        input_size: Input size.
@@ -1007,7 +1007,7 @@
            output_size,
        )

        self.output_size = output_size
        self._output_size = output_size

        self.dynamic_chunk_training = dynamic_chunk_training
        self.short_chunk_threshold = short_chunk_threshold
@@ -1019,6 +1019,9 @@
        self.jitter_range = jitter_range

        self.time_reduction_factor = time_reduction_factor

    def output_size(self) -> int:
        return self._output_size

    def get_encoder_input_raw_size(self, size: int, hop_length: int) -> int:
        """Return the corresponding number of sample for a given chunk size, in frames.
@@ -1075,7 +1078,7 @@
                limit_size,
            )

        mask = make_source_mask(x_len)
        mask = make_source_mask(x_len).to(x.device)

        if self.unified_model_training:
            chunk_size = self.default_chunk_size + torch.randint(-self.jitter_range, self.jitter_range+1, (1,)).item()
@@ -1142,7 +1145,7 @@
            x = x[:,::self.time_reduction_factor,:]
            olens = torch.floor_divide(olens-1, self.time_reduction_factor) + 1

        return x, olens
        return x, olens, None

    def simu_chunk_forward(
        self,