Refactor decode.py to make it more readable and more modular. (k2-fsa#44

)

* Refactor decode.py to make it more readable and more modular.

* Fix an error.

Nbest.fsa should always have token IDs as labels and
word IDs as aux_labels.

* Add nbest decoding.

* Compute edit distance with k2.

* Refactor nbest-oracle.

* Add rescore with nbest lists.

* Add whole-lattice rescoring.

* Add rescoring with attention decoder.

* Refactoring.

* Fixes after refactoring.

* Fix a typo.

* Minor fixes.

* Replace [] with () for shapes.

* Use k2 v1.9

* Use Levenshtein graphs/alignment from k2 v1.9

* [doc] Require k2 >= v1.9

* Minor fixes.

.github/workflows/run-yesno-recipe.yml

@@ -34,7 +34,7 @@ jobs:
         os: [ubuntu-18.04]
         python-version: [3.8]
         torch: ["1.8.1"]
-        k2-version: ["1.8.dev20210917"]
+        k2-version: ["1.9.dev20210919"]
       fail-fast: false
 
     steps:

.github/workflows/test.yml

@@ -32,7 +32,7 @@ jobs:
         os: [ubuntu-18.04, macos-10.15]
         python-version: [3.6, 3.7, 3.8, 3.9]
         torch: ["1.8.1"]
-        k2-version: ["1.8.dev20210917"]
+        k2-version: ["1.9.dev20210919"]
 
       fail-fast: false
 

docs/source/installation/index.rst

@@ -21,7 +21,7 @@ Installation
 .. |torch_versions| image:: ./images/torch-1.6.0_1.7.0_1.7.1_1.8.0_1.8.1_1.9.0-green.svg
   :alt: Supported PyTorch versions
 
-.. |k2_versions| image:: ./images/k2-v-1.7.svg
+.. |k2_versions| image:: ./images/k2-v1.9-blueviolet.svg
   :alt: Supported k2 versions
 
 ``icefall`` depends on `k2 <https://github.com/k2-fsa/k2>`_ and
@@ -40,7 +40,7 @@ to install ``k2``.
 
 .. CAUTION::
 
-  You need to install ``k2`` with a version at least **v1.7**.
+  You need to install ``k2`` with a version at least **v1.9**.
 
 .. HINT::
 

egs/librispeech/ASR/conformer_ctc/conformer.py

@@ -98,7 +98,7 @@ def run_encoder(
         """
         Args:
           x:
-            The model input. Its shape is [N, T, C].
+            The model input. Its shape is (N, T, C).
           supervisions:
             Supervision in lhotse format.
             See https://github.com/lhotse-speech/lhotse/blob/master/lhotse/dataset/speech_recognition.py#L32  # noqa

egs/librispeech/ASR/conformer_ctc/decode.py

@@ -213,12 +213,12 @@ def decode_one_batch(
     feature = batch["inputs"]
     assert feature.ndim == 3
     feature = feature.to(device)
-    # at entry, feature is [N, T, C]
+    # at entry, feature is (N, T, C)
 
     supervisions = batch["supervisions"]
 
     nnet_output, memory, memory_key_padding_mask = model(feature, supervisions)
-    # nnet_output is [N, T, C]
+    # nnet_output is (N, T, C)
 
     supervision_segments = torch.stack(
         (
@@ -244,14 +244,19 @@ def decode_one_batch(
         # Note: You can also pass rescored lattices to it.
         # We choose the HLG decoded lattice for speed reasons
         # as HLG decoding is faster and the oracle WER
-        # is slightly worse than that of rescored lattices.
-        return nbest_oracle(
+        # is only slightly worse than that of rescored lattices.
+        best_path = nbest_oracle(
             lattice=lattice,
             num_paths=params.num_paths,
             ref_texts=supervisions["text"],
             word_table=word_table,
-            scale=params.lattice_score_scale,
+            lattice_score_scale=params.lattice_score_scale,
+            oov="<UNK>",
         )
+        hyps = get_texts(best_path)
+        hyps = [[word_table[i] for i in ids] for ids in hyps]
+        key = f"oracle_{params.num_paths}_lattice_score_scale_{params.lattice_score_scale}"  # noqa
+        return {key: hyps}
 
     if params.method in ["1best", "nbest"]:
         if params.method == "1best":
@@ -264,7 +269,7 @@ def decode_one_batch(
                 lattice=lattice,
                 num_paths=params.num_paths,
                 use_double_scores=params.use_double_scores,
-                scale=params.lattice_score_scale,
+                lattice_score_scale=params.lattice_score_scale,
             )
             key = f"no_rescore-scale-{params.lattice_score_scale}-{params.num_paths}"  # noqa
 
@@ -288,17 +293,23 @@ def decode_one_batch(
             G=G,
             num_paths=params.num_paths,
             lm_scale_list=lm_scale_list,
-            scale=params.lattice_score_scale,
+            lattice_score_scale=params.lattice_score_scale,
         )
     elif params.method == "whole-lattice-rescoring":
         best_path_dict = rescore_with_whole_lattice(
-            lattice=lattice, G_with_epsilon_loops=G, lm_scale_list=lm_scale_list
+            lattice=lattice,
+            G_with_epsilon_loops=G,
+            lm_scale_list=lm_scale_list,
         )
     elif params.method == "attention-decoder":
         # lattice uses a 3-gram Lm. We rescore it with a 4-gram LM.
         rescored_lattice = rescore_with_whole_lattice(
-            lattice=lattice, G_with_epsilon_loops=G, lm_scale_list=None
+            lattice=lattice,
+            G_with_epsilon_loops=G,
+            lm_scale_list=None,
         )
+        # TODO: pass `lattice` instead of `rescored_lattice` to
+        # `rescore_with_attention_decoder`
 
         best_path_dict = rescore_with_attention_decoder(
             lattice=rescored_lattice,
@@ -308,16 +319,20 @@ def decode_one_batch(
             memory_key_padding_mask=memory_key_padding_mask,
             sos_id=sos_id,
             eos_id=eos_id,
-            scale=params.lattice_score_scale,
+            lattice_score_scale=params.lattice_score_scale,
         )
     else:
         assert False, f"Unsupported decoding method: {params.method}"
 
     ans = dict()
-    for lm_scale_str, best_path in best_path_dict.items():
-        hyps = get_texts(best_path)
-        hyps = [[word_table[i] for i in ids] for ids in hyps]
-        ans[lm_scale_str] = hyps
+    if best_path_dict is not None:
+        for lm_scale_str, best_path in best_path_dict.items():
+            hyps = get_texts(best_path)
+            hyps = [[word_table[i] for i in ids] for ids in hyps]
+            ans[lm_scale_str] = hyps
+    else:
+        for lm_scale in lm_scale_list:
+            ans[lm_scale_str] = [[] * lattice.shape[0]]
     return ans
 
 

egs/librispeech/ASR/conformer_ctc/pretrained.py

@@ -336,7 +336,7 @@ def main():
             memory_key_padding_mask=memory_key_padding_mask,
             sos_id=params.sos_id,
             eos_id=params.eos_id,
-            scale=params.lattice_score_scale,
+            lattice_score_scale=params.lattice_score_scale,
             ngram_lm_scale=params.ngram_lm_scale,
             attention_scale=params.attention_decoder_scale,
         )

egs/librispeech/ASR/conformer_ctc/subsampling.py

@@ -22,8 +22,8 @@
 class Conv2dSubsampling(nn.Module):
     """Convolutional 2D subsampling (to 1/4 length).
 
-    Convert an input of shape [N, T, idim] to an output
-    with shape [N, T', odim], where
+    Convert an input of shape (N, T, idim) to an output
+    with shape (N, T', odim), where
     T' = ((T-1)//2 - 1)//2, which approximates T' == T//4
 
     It is based on
@@ -34,10 +34,10 @@ def __init__(self, idim: int, odim: int) -> None:
         """
         Args:
           idim:
-            Input dim. The input shape is [N, T, idim].
+            Input dim. The input shape is (N, T, idim).
             Caution: It requires: T >=7, idim >=7
           odim:
-            Output dim. The output shape is [N, ((T-1)//2 - 1)//2, odim]
+            Output dim. The output shape is (N, ((T-1)//2 - 1)//2, odim)
         """
         assert idim >= 7
         super().__init__()
@@ -58,18 +58,18 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
         Args:
           x:
-            Its shape is [N, T, idim].
+            Its shape is (N, T, idim).
 
         Returns:
-          Return a tensor of shape [N, ((T-1)//2 - 1)//2, odim]
+          Return a tensor of shape (N, ((T-1)//2 - 1)//2, odim)
         """
-        # On entry, x is [N, T, idim]
-        x = x.unsqueeze(1)  # [N, T, idim] -> [N, 1, T, idim] i.e., [N, C, H, W]
+        # On entry, x is (N, T, idim)
+        x = x.unsqueeze(1)  # (N, T, idim) -> (N, 1, T, idim) i.e., (N, C, H, W)
         x = self.conv(x)
-        # Now x is of shape [N, odim, ((T-1)//2 - 1)//2, ((idim-1)//2 - 1)//2]
+        # Now x is of shape (N, odim, ((T-1)//2 - 1)//2, ((idim-1)//2 - 1)//2)
         b, c, t, f = x.size()
         x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f))
-        # Now x is of shape [N, ((T-1)//2 - 1))//2, odim]
+        # Now x is of shape (N, ((T-1)//2 - 1))//2, odim)
         return x
 
 
@@ -80,8 +80,8 @@ class VggSubsampling(nn.Module):
     This paper is not 100% explicit so I am guessing to some extent,
     and trying to compare with other VGG implementations.
 
-    Convert an input of shape [N, T, idim] to an output
-    with shape [N, T', odim], where
+    Convert an input of shape (N, T, idim) to an output
+    with shape (N, T', odim), where
     T' = ((T-1)//2 - 1)//2, which approximates T' = T//4
     """
 
@@ -93,10 +93,10 @@ def __init__(self, idim: int, odim: int) -> None:
 
         Args:
           idim:
-            Input dim. The input shape is [N, T, idim].
+            Input dim. The input shape is (N, T, idim).
             Caution: It requires: T >=7, idim >=7
           odim:
-            Output dim. The output shape is [N, ((T-1)//2 - 1)//2, odim]
+            Output dim. The output shape is (N, ((T-1)//2 - 1)//2, odim)
         """
         super().__init__()
 
@@ -149,10 +149,10 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
         Args:
           x:
-            Its shape is [N, T, idim].
+            Its shape is (N, T, idim).
 
         Returns:
-          Return a tensor of shape [N, ((T-1)//2 - 1)//2, odim]
+          Return a tensor of shape (N, ((T-1)//2 - 1)//2, odim)
         """
         x = x.unsqueeze(1)
         x = self.layers(x)

egs/librispeech/ASR/conformer_ctc/train.py

@@ -310,14 +310,14 @@ def compute_loss(
     """
     device = graph_compiler.device
     feature = batch["inputs"]
-    # at entry, feature is [N, T, C]
+    # at entry, feature is (N, T, C)
     assert feature.ndim == 3
     feature = feature.to(device)
 
     supervisions = batch["supervisions"]
     with torch.set_grad_enabled(is_training):
         nnet_output, encoder_memory, memory_mask = model(feature, supervisions)
-        # nnet_output is [N, T, C]
+        # nnet_output is (N, T, C)
 
     # NOTE: We need `encode_supervisions` to sort sequences with
     # different duration in decreasing order, required by

egs/librispeech/ASR/conformer_ctc/transformer.py

@@ -83,8 +83,8 @@ def __init__(
         if subsampling_factor != 4:
             raise NotImplementedError("Support only 'subsampling_factor=4'.")
 
-        # self.encoder_embed converts the input of shape [N, T, num_classes]
-        # to the shape [N, T//subsampling_factor, d_model].
+        # self.encoder_embed converts the input of shape (N, T, num_classes)
+        # to the shape (N, T//subsampling_factor, d_model).
         # That is, it does two things simultaneously:
         #   (1) subsampling: T -> T//subsampling_factor
         #   (2) embedding: num_classes -> d_model
@@ -162,7 +162,7 @@ def forward(
         """
         Args:
           x:
-            The input tensor. Its shape is [N, T, C].
+            The input tensor. Its shape is (N, T, C).
           supervision:
             Supervision in lhotse format.
             See https://github.com/lhotse-speech/lhotse/blob/master/lhotse/dataset/speech_recognition.py#L32  # noqa
@@ -171,17 +171,17 @@ def forward(
 
         Returns:
           Return a tuple containing 3 tensors:
-            - CTC output for ctc decoding. Its shape is [N, T, C]
-            - Encoder output with shape [T, N, C]. It can be used as key and
+            - CTC output for ctc decoding. Its shape is (N, T, C)
+            - Encoder output with shape (T, N, C). It can be used as key and
               value for the decoder.
             - Encoder output padding mask. It can be used as
-              memory_key_padding_mask for the decoder. Its shape is [N, T].
+              memory_key_padding_mask for the decoder. Its shape is (N, T).
               It is None if `supervision` is None.
         """
         if self.use_feat_batchnorm:
-            x = x.permute(0, 2, 1)  # [N, T, C] -> [N, C, T]
+            x = x.permute(0, 2, 1)  # (N, T, C) -> (N, C, T)
             x = self.feat_batchnorm(x)
-            x = x.permute(0, 2, 1)  # [N, C, T] -> [N, T, C]
+            x = x.permute(0, 2, 1)  # (N, C, T) -> (N, T, C)
         encoder_memory, memory_key_padding_mask = self.run_encoder(
             x, supervision
         )
@@ -195,7 +195,7 @@ def run_encoder(
 
         Args:
           x:
-            The model input. Its shape is [N, T, C].
+            The model input. Its shape is (N, T, C).
           supervisions:
             Supervision in lhotse format.
             See https://github.com/lhotse-speech/lhotse/blob/master/lhotse/dataset/speech_recognition.py#L32  # noqa
@@ -206,8 +206,8 @@ def run_encoder(
             padding mask for the decoder.
         Returns:
           Return a tuple with two tensors:
-            - The encoder output, with shape [T, N, C]
-            - encoder padding mask, with shape [N, T].
+            - The encoder output, with shape (T, N, C)
+            - encoder padding mask, with shape (N, T).
               The mask is None if `supervisions` is None.
               It is used as memory key padding mask in the decoder.
         """
@@ -225,11 +225,11 @@ def ctc_output(self, x: torch.Tensor) -> torch.Tensor:
         Args:
           x:
             The output tensor from the transformer encoder.
-            Its shape is [T, N, C]
+            Its shape is (T, N, C)
 
         Returns:
           Return a tensor that can be used for CTC decoding.
-          Its shape is [N, T, C]
+          Its shape is (N, T, C)
         """
         x = self.encoder_output_layer(x)
         x = x.permute(1, 0, 2)  # (T, N, C) ->(N, T, C)
@@ -247,7 +247,7 @@ def decoder_forward(
         """
         Args:
           memory:
-            It's the output of the encoder with shape [T, N, C]
+            It's the output of the encoder with shape (T, N, C)
           memory_key_padding_mask:
             The padding mask from the encoder.
           token_ids:
@@ -312,7 +312,7 @@ def decoder_nll(
         """
         Args:
           memory:
-            It's the output of the encoder with shape [T, N, C]
+            It's the output of the encoder with shape (T, N, C)
           memory_key_padding_mask:
             The padding mask from the encoder.
           token_ids:
@@ -654,13 +654,13 @@ def __init__(self, d_model: int, dropout: float = 0.1) -> None:
     def extend_pe(self, x: torch.Tensor) -> None:
         """Extend the time t in the positional encoding if required.
 
-        The shape of `self.pe` is [1, T1, d_model]. The shape of the input x
-        is [N, T, d_model]. If T > T1, then we change the shape of self.pe
-        to [N, T, d_model]. Otherwise, nothing is done.
+        The shape of `self.pe` is (1, T1, d_model). The shape of the input x
+        is (N, T, d_model). If T > T1, then we change the shape of self.pe
+        to (N, T, d_model). Otherwise, nothing is done.
 
         Args:
           x:
-            It is a tensor of shape [N, T, C].
+            It is a tensor of shape (N, T, C).
         Returns:
           Return None.
         """
@@ -678,7 +678,7 @@ def extend_pe(self, x: torch.Tensor) -> None:
         pe[:, 0::2] = torch.sin(position * div_term)
         pe[:, 1::2] = torch.cos(position * div_term)
         pe = pe.unsqueeze(0)
-        # Now pe is of shape [1, T, d_model], where T is x.size(1)
+        # Now pe is of shape (1, T, d_model), where T is x.size(1)
         self.pe = pe.to(device=x.device, dtype=x.dtype)
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
@@ -687,10 +687,10 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
         Args:
           x:
-            Its shape is [N, T, C]
+            Its shape is (N, T, C)
 
         Returns:
-          Return a tensor of shape [N, T, C]
+          Return a tensor of shape (N, T, C)
         """
         self.extend_pe(x)
         x = x * self.xscale + self.pe[:, : x.size(1), :]