replace some files with symlinks

egs/libricss/SURT/dprnn_zipformer/beam_search.py

@@ -120,7 +120,7 @@ def fast_beam_search_nbest_LG(
      - (5) The path with the largest score is used as the decoding output.
     Args:
       model:
-        An instance of `Transducer`.
+        An instance of `SURT`.
       decoding_graph:
         Decoding graph used for decoding, may be a TrivialGraph or a LG.
       encoder_out:
@@ -705,8 +705,8 @@ def modified_beam_search_LODR(
     external language model.
 
     Args:
-        model (Transducer):
+        model (SURT):
-            The transducer model
+            The SURT model
         encoder_out (torch.Tensor):
             Encoder output in (N,T,C)
         encoder_out_lens (torch.Tensor):

egs/libricss/SURT/dprnn_zipformer/scaling.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/pruned_transducer_stateless7/scaling.py

egs/libricss/SURT/dprnn_zipformer/scaling_converter.py

@@ -1,114 +0,0 @@
-# Copyright    2022  Xiaomi Corp.        (authors: Fangjun Kuang)
-#
-# See ../../../../LICENSE for clarification regarding multiple authors
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-This file replaces various modules in a model.
-Specifically, ActivationBalancer is replaced with an identity operator;
-Whiten is also replaced with an identity operator;
-BasicNorm is replaced by a module with `exp` removed.
-"""
-
-import copy
-from typing import List
-
-import torch
-import torch.nn as nn
-from scaling import ActivationBalancer, BasicNorm, Whiten
-
-
-class NonScaledNorm(nn.Module):
-    """See BasicNorm for doc"""
-
-    def __init__(
-        self,
-        num_channels: int,
-        eps_exp: float,
-        channel_dim: int = -1,  # CAUTION: see documentation.
-    ):
-        super().__init__()
-        self.num_channels = num_channels
-        self.channel_dim = channel_dim
-        self.eps_exp = eps_exp
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        if not torch.jit.is_tracing():
-            assert x.shape[self.channel_dim] == self.num_channels
-        scales = (
-            torch.mean(x * x, dim=self.channel_dim, keepdim=True) + self.eps_exp
-        ).pow(-0.5)
-        return x * scales
-
-
-def convert_basic_norm(basic_norm: BasicNorm) -> NonScaledNorm:
-    assert isinstance(basic_norm, BasicNorm), type(BasicNorm)
-    norm = NonScaledNorm(
-        num_channels=basic_norm.num_channels,
-        eps_exp=basic_norm.eps.data.exp().item(),
-        channel_dim=basic_norm.channel_dim,
-    )
-    return norm
-
-
-# Copied from https://pytorch.org/docs/1.9.0/_modules/torch/nn/modules/module.html#Module.get_submodule  # noqa
-# get_submodule was added to nn.Module at v1.9.0
-def get_submodule(model, target):
-    if target == "":
-        return model
-    atoms: List[str] = target.split(".")
-    mod: torch.nn.Module = model
-    for item in atoms:
-        if not hasattr(mod, item):
-            raise AttributeError(
-                mod._get_name() + " has no " "attribute `" + item + "`"
-            )
-        mod = getattr(mod, item)
-        if not isinstance(mod, torch.nn.Module):
-            raise AttributeError("`" + item + "` is not " "an nn.Module")
-    return mod
-
-
-def convert_scaled_to_non_scaled(
-    model: nn.Module,
-    inplace: bool = False,
-):
-    """
-    Args:
-      model:
-        The model to be converted.
-      inplace:
-        If True, the input model is modified inplace.
-        If False, the input model is copied and we modify the copied version.
-    Return:
-      Return a model without scaled layers.
-    """
-    if not inplace:
-        model = copy.deepcopy(model)
-
-    d = {}
-    for name, m in model.named_modules():
-        if isinstance(m, BasicNorm):
-            d[name] = convert_basic_norm(m)
-        elif isinstance(m, (ActivationBalancer, Whiten)):
-            d[name] = nn.Identity()
-
-    for k, v in d.items():
-        if "." in k:
-            parent, child = k.rsplit(".", maxsplit=1)
-            setattr(get_submodule(model, parent), child, v)
-        else:
-            setattr(model, k, v)
-
-    return model

egs/libricss/SURT/dprnn_zipformer/scaling_converter.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/pruned_transducer_stateless7/scaling_converter.py

egs/libricss/SURT/dprnn_zipformer/zipformer.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/pruned_transducer_stateless7/zipformer.py