Merge branch 'k2-fsa:master' into dev/bilingual

egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/train.py

@@ -635,7 +635,6 @@ def train_one_epoch(
     tot_loss = MetricsTracker()
 
     for batch_idx, batch in enumerate(train_dl):
-
         params.batch_idx_train += 1
         batch_size = len(batch["supervisions"]["text"])
 
@@ -800,7 +799,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

egs/aishell/ASR/pruned_transducer_stateless2/train.py

@@ -872,7 +872,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

egs/aishell/ASR/pruned_transducer_stateless3/train.py

@@ -1045,7 +1045,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

egs/aishell/ASR/pruned_transducer_stateless7/train.py

@@ -1028,7 +1028,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

egs/aishell/ASR/pruned_transducer_stateless7/train2.py

@@ -1031,7 +1031,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

egs/aishell/ASR/pruned_transducer_stateless7_bbpe/train.py

@@ -1019,7 +1019,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

egs/aishell2/ASR/pruned_transducer_stateless5/train.py

@@ -730,7 +730,6 @@ def train_one_epoch(
     tot_loss = MetricsTracker()
 
     for batch_idx, batch in enumerate(train_dl):
-
         params.batch_idx_train += 1
         batch_size = len(batch["supervisions"]["text"])
 
@@ -919,7 +918,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

egs/aishell4/ASR/pruned_transducer_stateless5/train.py

@@ -908,7 +908,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

egs/alimeeting/ASR/pruned_transducer_stateless2/train.py

@@ -635,7 +635,6 @@ def train_one_epoch(
     tot_loss = MetricsTracker()
 
     for batch_idx, batch in enumerate(train_dl):
-
         params.batch_idx_train += 1
         batch_size = len(batch["supervisions"]["text"])
 
@@ -800,7 +799,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

egs/alimeeting/ASR_v2/pruned_transducer_stateless7/train.py

@@ -999,7 +999,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

egs/ami/ASR/pruned_transducer_stateless7/train.py

@@ -988,7 +988,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

egs/commonvoice/ASR/pruned_transducer_stateless7/train.py

@@ -1019,7 +1019,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

egs/csj/ASR/pruned_transducer_stateless7_streaming/train.py

@@ -1074,7 +1074,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

egs/csj/ASR/pruned_transducer_stateless7_streaming/train2.py

@@ -1075,7 +1075,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

egs/librispeech/ASR/conformer_ctc/train.py

@@ -557,7 +557,6 @@ def train_one_epoch(
             )
 
         if batch_idx % params.log_interval == 0:
-
             if tb_writer is not None:
                 loss_info.write_summary(
                     tb_writer, "train/current_", params.batch_idx_train

egs/librispeech/ASR/conv_emformer_transducer_stateless/train.py

@@ -953,7 +953,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

egs/librispeech/ASR/conv_emformer_transducer_stateless2/train.py

@@ -953,7 +953,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

egs/librispeech/ASR/conv_emformer_transducer_stateless2/train2.py

@@ -955,7 +955,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

egs/librispeech/ASR/local/download_lm.py

@@ -43,6 +43,7 @@ from pathlib import Path
 
 from tqdm.auto import tqdm
 
+
 # This function is copied from lhotse
 def tqdm_urlretrieve_hook(t):
     """Wraps tqdm instance.

egs/librispeech/ASR/long_file_recog/beam_search.py

@@ -236,7 +236,7 @@ def greedy_search_batch(
     encoder_out = model.joiner.encoder_proj(packed_encoder_out.data)
 
     offset = 0
-    for (t, batch_size) in enumerate(batch_size_list):
+    for t, batch_size in enumerate(batch_size_list):
         start = offset
         end = offset + batch_size
         current_encoder_out = encoder_out.data[start:end]
@@ -507,7 +507,7 @@ def modified_beam_search(
 
     offset = 0
     finalized_B = []
-    for (t, batch_size) in enumerate(batch_size_list):
+    for t, batch_size in enumerate(batch_size_list):
         start = offset
         end = offset + batch_size
         current_encoder_out = encoder_out.data[start:end]

egs/librispeech/ASR/long_file_recog/merge_chunks.py

@@ -162,7 +162,6 @@ def merge_chunks(
 
     futures = []
     with ThreadPoolExecutor(max_workers=1) as executor:
-
         for cut in cuts_chunk:
             cur_rec_id = cut.recording.id
             if len(cut_list) == 0:

egs/librispeech/ASR/long_file_recog/recognize.py

@@ -264,6 +264,7 @@ def decode_dataset(
         - timestamps of reference transcript
         - timestamps of predicted result
     """
+
     #  Background worker to add alignemnt and save cuts to disk.
     def _save_worker(
         cuts: List[Cut],

egs/librispeech/ASR/pruned2_knowledge/optim.py

@@ -66,7 +66,6 @@ class Eve(Optimizer):
         weight_decay=1e-3,
         target_rms=0.1,
     ):
-
         if not 0.0 <= lr:
             raise ValueError("Invalid learning rate: {}".format(lr))
         if not 0.0 <= eps:

egs/librispeech/ASR/pruned2_knowledge/train.py

@@ -811,7 +811,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

egs/librispeech/ASR/pruned_transducer_stateless2/beam_search.py

@@ -719,7 +719,7 @@ def greedy_search_batch(
     encoder_out = model.joiner.encoder_proj(packed_encoder_out.data)
 
     offset = 0
-    for (t, batch_size) in enumerate(batch_size_list):
+    for t, batch_size in enumerate(batch_size_list):
         start = offset
         end = offset + batch_size
         current_encoder_out = encoder_out.data[start:end]
@@ -1019,7 +1019,7 @@ def modified_beam_search(
 
     offset = 0
     finalized_B = []
-    for (t, batch_size) in enumerate(batch_size_list):
+    for t, batch_size in enumerate(batch_size_list):
         start = offset
         end = offset + batch_size
         current_encoder_out = encoder_out.data[start:end]
@@ -1227,7 +1227,7 @@ def modified_beam_search_lm_rescore(
 
     offset = 0
     finalized_B = []
-    for (t, batch_size) in enumerate(batch_size_list):
+    for t, batch_size in enumerate(batch_size_list):
         start = offset
         end = offset + batch_size
         current_encoder_out = encoder_out.data[start:end]
@@ -1427,7 +1427,7 @@ def modified_beam_search_lm_rescore_LODR(
 
     offset = 0
     finalized_B = []
-    for (t, batch_size) in enumerate(batch_size_list):
+    for t, batch_size in enumerate(batch_size_list):
         start = offset
         end = offset + batch_size
         current_encoder_out = encoder_out.data[start:end]
@@ -2608,7 +2608,6 @@ def modified_beam_search_LODR(
                 context_score = 0
                 new_context_state = None if context_graph is None else hyp.context_state
                 if new_token not in (blank_id, unk_id):
-
                     if context_graph is not None:
                         (
                             context_score,
@@ -2758,7 +2757,7 @@ def modified_beam_search_lm_shallow_fusion(
 
     offset = 0
     finalized_B = []
-    for (t, batch_size) in enumerate(batch_size_list):
+    for t, batch_size in enumerate(batch_size_list):
         start = offset
         end = offset + batch_size
         current_encoder_out = encoder_out.data[start:end]  # get batch
@@ -2900,7 +2899,6 @@ def modified_beam_search_lm_shallow_fusion(
                 new_token = topk_token_indexes[k]
                 new_timestamp = hyp.timestamp[:]
                 if new_token not in (blank_id, unk_id):
-
                     ys.append(new_token)
                     new_timestamp.append(t)
 

egs/librispeech/ASR/pruned_transducer_stateless2/optim.py

@@ -66,7 +66,6 @@ class Eve(Optimizer):
         weight_decay=1e-3,
         target_rms=0.1,
     ):
-
         if not 0.0 <= lr:
             raise ValueError("Invalid learning rate: {}".format(lr))
         if not 0.0 <= eps:

egs/librispeech/ASR/pruned_transducer_stateless2/scaling.py

@@ -528,7 +528,6 @@ class ScaledLSTM(nn.LSTM):
             return
 
         with torch.cuda.device_of(first_fw):
-
             # Note: no_grad() is necessary since _cudnn_rnn_flatten_weight is
             # an inplace operation on self._flat_weights
             with torch.no_grad():

egs/librispeech/ASR/pruned_transducer_stateless5/train.py

@@ -1003,7 +1003,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

egs/librispeech/ASR/pruned_transducer_stateless6/vq_utils.py

@@ -56,7 +56,6 @@ class CodebookIndexExtractor:
     """
 
     def __init__(self, params: AttributeDict):
-
         self.params = params
         params.subsets = ["clean-100"]
         if self.params.full_libri:

egs/librispeech/ASR/pruned_transducer_stateless7/alignment.py

@@ -111,7 +111,7 @@ def batch_force_alignment(
 
     offset = 0
     finalized_B = []
-    for (t, batch_size) in enumerate(batch_size_list):
+    for t, batch_size in enumerate(batch_size_list):
         start = offset
         end = offset + batch_size
         current_encoder_out = encoder_out.data[start:end]

egs/librispeech/ASR/pruned_transducer_stateless7/finetune.py

@@ -1132,7 +1132,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

egs/librispeech/ASR/pruned_transducer_stateless7/optim.py

@@ -117,7 +117,7 @@ class BatchedOptimizer(Optimizer):
 
         yield tuples  # <-- calling code will do the actual optimization here!
 
-        for ((stacked_params, _state, _names), batch) in zip(tuples, batches):
+        for (stacked_params, _state, _names), batch in zip(tuples, batches):
             for i, p in enumerate(batch):  # batch is list of Parameter
                 p.copy_(stacked_params[i])
 
@@ -181,7 +181,6 @@ class ScaledAdam(BatchedOptimizer):
         parameters_names=None,
         show_dominant_parameters=True,
     ):
-
         assert parameters_names is not None, (
             "Please prepare parameters_names,"
             "which is a List[List[str]]. Each List[str] is for a group"
@@ -224,9 +223,7 @@ class ScaledAdam(BatchedOptimizer):
         batch = True
 
         for group, group_params_names in zip(self.param_groups, self.parameters_names):
-
             with self.batched_params(group["params"], group_params_names) as batches:
-
                 # batches is list of pairs (stacked_param, state).  stacked_param is like
                 # a regular parameter, and will have a .grad, but the 1st dim corresponds to
                 # a stacking dim, it is not a real dim.
@@ -325,7 +322,7 @@ class ScaledAdam(BatchedOptimizer):
         clipping_update_period = group["clipping_update_period"]
 
         tot_sumsq = torch.tensor(0.0, device=first_p.device)
-        for (p, state, param_names) in tuples:
+        for p, state, param_names in tuples:
             grad = p.grad
             if grad.is_sparse:
                 raise RuntimeError(
@@ -410,7 +407,7 @@ class ScaledAdam(BatchedOptimizer):
                 from tuples, we still pass it to save some time.
         """
         all_sumsq_orig = {}
-        for (p, state, batch_param_names) in tuples:
+        for p, state, batch_param_names in tuples:
             # p is a stacked batch parameters.
             batch_grad = p.grad
             if p.numel() == p.shape[0]:  # a batch of scalars
@@ -426,7 +423,6 @@ class ScaledAdam(BatchedOptimizer):
             for name, sumsq_orig, rms, grad in zip(
                 batch_param_names, batch_sumsq_orig, batch_rms_orig, batch_grad
             ):
-
                 proportion_orig = sumsq_orig / tot_sumsq
                 all_sumsq_orig[name] = (proportion_orig, sumsq_orig, rms, grad)
 
@@ -1039,7 +1035,7 @@ def _test_scaled_adam(hidden_dim: int):
 
             # if epoch == 130:
             #    opts = diagnostics.TensorDiagnosticOptions(
-            #        2 ** 22
+            #        512
             #    )  # allow 4 megabytes per sub-module
             #    diagnostic = diagnostics.attach_diagnostics(m, opts)
 

egs/librispeech/ASR/pruned_transducer_stateless7/train.py

@@ -1028,7 +1028,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

egs/librispeech/ASR/pruned_transducer_stateless7_ctc/train.py

@@ -1052,7 +1052,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/train.py

@@ -1042,7 +1042,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

egs/librispeech/ASR/pruned_transducer_stateless7_streaming/train.py

@@ -1029,7 +1029,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

egs/librispeech/ASR/pruned_transducer_stateless7_streaming/train2.py

@@ -1030,7 +1030,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/train.py

@@ -1141,7 +1141,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

egs/librispeech/ASR/pruned_transducer_stateless8/train.py

@@ -1154,7 +1154,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

egs/librispeech/ASR/streaming_conformer_ctc/conformer.py

@@ -230,7 +230,9 @@ class Conformer(Transformer):
                 x, pos_emb, mask=mask, src_key_padding_mask=src_key_padding_mask
             )  # (T, B, F)
         else:
-            x = self.encoder(x, pos_emb, src_key_padding_mask=src_key_padding_mask)  # (T, B, F)
+            x = self.encoder(
+                x, pos_emb, src_key_padding_mask=src_key_padding_mask
+            )  # (T, B, F)
 
         if self.normalize_before:
             x = self.after_norm(x)

egs/librispeech/ASR/streaming_conformer_ctc/train.py

@@ -543,7 +543,6 @@ def train_one_epoch(
             )
 
         if batch_idx % params.log_interval == 0:
-
             if tb_writer is not None:
                 loss_info.write_summary(
                     tb_writer, "train/current_", params.batch_idx_train

egs/librispeech/ASR/tdnn_lstm_ctc/train.py

@@ -463,7 +463,6 @@ def train_one_epoch(
                 f"tot_loss[{tot_loss}], batch size: {batch_size}"
             )
         if batch_idx % params.log_interval == 0:
-
             if tb_writer is not None:
                 loss_info.write_summary(
                     tb_writer, "train/current_", params.batch_idx_train

egs/librispeech/ASR/transducer/train.py

@@ -513,7 +513,6 @@ def train_one_epoch(
             )
 
         if batch_idx % params.log_interval == 0:
-
             if tb_writer is not None:
                 loss_info.write_summary(
                     tb_writer, "train/current_", params.batch_idx_train

egs/librispeech/ASR/transducer_lstm/train.py

@@ -517,7 +517,6 @@ def train_one_epoch(
             )
 
         if batch_idx % params.log_interval == 0:
-
             if tb_writer is not None:
                 loss_info.write_summary(
                     tb_writer, "train/current_", params.batch_idx_train

egs/librispeech/ASR/zipformer/decoder.py

@@ -61,10 +61,15 @@ class Decoder(nn.Module):
         )
         # the balancers are to avoid any drift in the magnitude of the
         # embeddings, which would interact badly with parameter averaging.
-        self.balancer = Balancer(decoder_dim, channel_dim=-1,
+        self.balancer = Balancer(
-                                 min_positive=0.0, max_positive=1.0,
+            decoder_dim,
-                                 min_abs=0.5, max_abs=1.0,
+            channel_dim=-1,
-                                 prob=0.05)
+            min_positive=0.0,
+            max_positive=1.0,
+            min_abs=0.5,
+            max_abs=1.0,
+            prob=0.05,
+        )
 
         self.blank_id = blank_id
 
@@ -81,10 +86,15 @@ class Decoder(nn.Module):
                 groups=decoder_dim // 4,  # group size == 4
                 bias=False,
             )
-            self.balancer2 = Balancer(decoder_dim, channel_dim=-1,
+            self.balancer2 = Balancer(
-                                      min_positive=0.0, max_positive=1.0,
+                decoder_dim,
-                                      min_abs=0.5, max_abs=1.0,
+                channel_dim=-1,
-                                      prob=0.05)
+                min_positive=0.0,
+                max_positive=1.0,
+                min_abs=0.5,
+                max_abs=1.0,
+                prob=0.05,
+            )
 
     def forward(self, y: torch.Tensor, need_pad: bool = True) -> torch.Tensor:
         """
@@ -107,9 +117,7 @@ class Decoder(nn.Module):
         if self.context_size > 1:
             embedding_out = embedding_out.permute(0, 2, 1)
             if need_pad is True:
-                embedding_out = F.pad(
+                embedding_out = F.pad(embedding_out, pad=(self.context_size - 1, 0))
-                    embedding_out, pad=(self.context_size - 1, 0)
-                )
             else:
                 # During inference time, there is no need to do extra padding
                 # as we only need one output

egs/librispeech/ASR/zipformer/joiner.py

@@ -52,12 +52,13 @@ class Joiner(nn.Module):
         Returns:
           Return a tensor of shape (N, T, s_range, C).
         """
-        assert encoder_out.ndim == decoder_out.ndim, (encoder_out.shape, decoder_out.shape)
+        assert encoder_out.ndim == decoder_out.ndim, (
+            encoder_out.shape,
+            decoder_out.shape,
+        )
 
         if project_input:
-            logit = self.encoder_proj(encoder_out) + self.decoder_proj(
+            logit = self.encoder_proj(encoder_out) + self.decoder_proj(decoder_out)
-                decoder_out
-            )
         else:
             logit = encoder_out + decoder_out
 

egs/librispeech/ASR/zipformer/onnx_decode.py

@@ -303,7 +303,9 @@ def main():
 
     for test_set, test_dl in zip(test_sets, test_dl):
         start_time = time.time()
-        results, total_duration = decode_dataset(dl=test_dl, model=model, token_table=token_table)
+        results, total_duration = decode_dataset(
+            dl=test_dl, model=model, token_table=token_table
+        )
         end_time = time.time()
         elapsed_seconds = end_time - start_time
         rtf = elapsed_seconds / total_duration

egs/librispeech/ASR/zipformer/optim.py

@@ -116,7 +116,7 @@ class BatchedOptimizer(Optimizer):
 
         yield tuples  # <-- calling code will do the actual optimization here!
 
-        for ((stacked_params, _state, _names), batch) in zip(tuples, batches):
+        for (stacked_params, _state, _names), batch in zip(tuples, batches):
             for i, p in enumerate(batch):  # batch is list of Parameter
                 p.copy_(stacked_params[i])
 
@@ -181,7 +181,6 @@ class ScaledAdam(BatchedOptimizer):
         size_update_period=4,
         clipping_update_period=100,
     ):
-
         defaults = dict(
             lr=lr,
             clipping_scale=clipping_scale,
@@ -299,8 +298,8 @@ class ScaledAdam(BatchedOptimizer):
             # the input is groups of parameter or named parameter.
             for cur_group in iterable_or_groups:
                 assert "named_params" in cur_group
-                name_list = [ x[0] for x in cur_group["named_params"] ]
+                name_list = [x[0] for x in cur_group["named_params"]]
-                p_list = [ x[1] for x in cur_group["named_params"] ]
+                p_list = [x[1] for x in cur_group["named_params"]]
                 del cur_group["named_params"]
                 cur_group["params"] = p_list
                 param_groups.append(cur_group)
@@ -327,9 +326,7 @@ class ScaledAdam(BatchedOptimizer):
         batch = True
 
         for group, group_params_names in zip(self.param_groups, self.parameters_names):
-
             with self.batched_params(group["params"], group_params_names) as batches:
-
                 # batches is list of pairs (stacked_param, state).  stacked_param is like
                 # a regular parameter, and will have a .grad, but the 1st dim corresponds to
                 # a stacking dim, it is not a real dim.
@@ -428,7 +425,7 @@ class ScaledAdam(BatchedOptimizer):
         clipping_update_period = group["clipping_update_period"]
 
         tot_sumsq = torch.tensor(0.0, device=first_p.device)
-        for (p, state, param_names) in tuples:
+        for p, state, param_names in tuples:
             grad = p.grad
             if grad.is_sparse:
                 raise RuntimeError(
@@ -513,7 +510,7 @@ class ScaledAdam(BatchedOptimizer):
                 from tuples, we still pass it to save some time.
         """
         all_sumsq_orig = {}
-        for (p, state, batch_param_names) in tuples:
+        for p, state, batch_param_names in tuples:
             # p is a stacked batch parameters.
             batch_grad = p.grad
             if p.numel() == p.shape[0]:  # a batch of scalars
@@ -529,7 +526,6 @@ class ScaledAdam(BatchedOptimizer):
             for name, sumsq_orig, rms, grad in zip(
                 batch_param_names, batch_sumsq_orig, batch_rms_orig, batch_grad
             ):
-
                 proportion_orig = sumsq_orig / tot_sumsq
                 all_sumsq_orig[name] = (proportion_orig, sumsq_orig, rms, grad)
 
@@ -667,8 +663,7 @@ class ScaledAdam(BatchedOptimizer):
         # We have to look at the trained model for parameters at or around the
         # param_max_rms, because sometimes they can indicate a problem with the
         # topology or settings.
-        scale_step = torch.minimum(scale_step,
+        scale_step = torch.minimum(scale_step, (param_max_rms - param_rms) / param_rms)
-                                   (param_max_rms - param_rms) / param_rms)
 
         delta = state["delta"]
         # the factor of (1-beta1) relates to momentum.
@@ -879,7 +874,8 @@ class Eden(LRScheduler):
         warmup_factor = (
             1.0
             if self.batch >= self.warmup_batches
-            else self.warmup_start + (1.0 - self.warmup_start) * (self.batch / self.warmup_batches)
+            else self.warmup_start
+            + (1.0 - self.warmup_start) * (self.batch / self.warmup_batches)
             # else 0.5 + 0.5 * (self.batch / self.warmup_batches)
         )
 
@@ -1111,7 +1107,7 @@ def _test_scaled_adam(hidden_dim: int):
 
             # if epoch == 130:
             #    opts = diagnostics.TensorDiagnosticOptions(
-            #        2 ** 22
+            #        512
             #    )  # allow 4 megabytes per sub-module
             #    diagnostic = diagnostics.attach_diagnostics(m, opts)
 

egs/librispeech/ASR/zipformer/profile.py

@@ -100,17 +100,13 @@ class Model(nn.Module):
         self.encoder_embed = encoder_embed
         self.encoder_proj = encoder_proj
 
-    def forward(
+    def forward(self, feature: Tensor, feature_lens: Tensor) -> Tuple[Tensor, Tensor]:
-        self, feature: Tensor, feature_lens: Tensor
-    ) -> Tuple[Tensor, Tensor]:
         x, x_lens = self.encoder_embed(feature, feature_lens)
 
         src_key_padding_mask = make_pad_mask(x_lens)
         x = x.permute(1, 0, 2)  # (N, T, C) -> (T, N, C)
 
-        encoder_out, encoder_out_lens = self.encoder(
+        encoder_out, encoder_out_lens = self.encoder(x, x_lens, src_key_padding_mask)
-            x, x_lens, src_key_padding_mask
-        )
 
         encoder_out = encoder_out.permute(1, 0, 2)  # (N, T, C) -> (T, N, C)
         logits = self.encoder_proj(encoder_out)
@@ -168,9 +164,7 @@ def main():
 
 
 if __name__ == "__main__":
-    formatter = (
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
-        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
-    )
     logging.basicConfig(format=formatter, level=logging.INFO)
 
     main()

egs/librispeech/ASR/zipformer/streaming_decode.py

@@ -282,9 +282,7 @@ def stack_states(state_list: List[List[torch.Tensor]]) -> List[torch.Tensor]:
     )
     batch_states.append(cached_embed_left_pad)
 
-    processed_lens = torch.cat(
+    processed_lens = torch.cat([state_list[i][-1] for i in range(batch_size)], dim=0)
-        [state_list[i][-1] for i in range(batch_size)], dim=0
-    )
     batch_states.append(processed_lens)
 
     return batch_states
@@ -322,9 +320,7 @@ def unstack_states(batch_states: List[Tensor]) -> List[List[Tensor]]:
     for layer in range(tot_num_layers):
         layer_offset = layer * 6
         # cached_key: (left_context_len, batch_size, key_dim)
-        cached_key_list = batch_states[layer_offset].chunk(
+        cached_key_list = batch_states[layer_offset].chunk(chunks=batch_size, dim=1)
-            chunks=batch_size, dim=1
-        )
         # cached_nonlin_attn: (num_heads, batch_size, left_context_len, head_dim)
         cached_nonlin_attn_list = batch_states[layer_offset + 1].chunk(
             chunks=batch_size, dim=1
@@ -355,9 +351,7 @@ def unstack_states(batch_states: List[Tensor]) -> List[List[Tensor]]:
                 cached_conv2_list[i],
             ]
 
-    cached_embed_left_pad_list = batch_states[-2].chunk(
+    cached_embed_left_pad_list = batch_states[-2].chunk(chunks=batch_size, dim=0)
-        chunks=batch_size, dim=0
-    )
     for i in range(batch_size):
         state_list[i].append(cached_embed_left_pad_list[i])
 
@@ -380,11 +374,7 @@ def streaming_forward(
     Returns encoder outputs, output lengths, and updated states.
     """
     cached_embed_left_pad = states[-2]
-    (
+    (x, x_lens, new_cached_embed_left_pad,) = model.encoder_embed.streaming_forward(
-        x,
-        x_lens,
-        new_cached_embed_left_pad,
-    ) = model.encoder_embed.streaming_forward(
         x=features,
         x_lens=feature_lens,
         cached_left_pad=cached_embed_left_pad,
@@ -404,9 +394,7 @@ def streaming_forward(
     new_processed_lens = processed_lens + x_lens
 
     # (batch, left_context_size + chunk_size)
-    src_key_padding_mask = torch.cat(
+    src_key_padding_mask = torch.cat([processed_mask, src_key_padding_mask], dim=1)
-        [processed_mask, src_key_padding_mask], dim=1
-    )
 
     x = x.permute(1, 0, 2)  # (N, T, C) -> (T, N, C)
     encoder_states = states[:-2]
@@ -494,9 +482,7 @@ def decode_one_chunk(
     encoder_out = model.joiner.encoder_proj(encoder_out)
 
     if params.decoding_method == "greedy_search":
-        greedy_search(
+        greedy_search(model=model, encoder_out=encoder_out, streams=decode_streams)
-            model=model, encoder_out=encoder_out, streams=decode_streams
-        )
     elif params.decoding_method == "fast_beam_search":
         processed_lens = torch.tensor(processed_lens, device=device)
         processed_lens = processed_lens + encoder_out_lens
@@ -517,9 +503,7 @@ def decode_one_chunk(
             num_active_paths=params.num_active_paths,
         )
     else:
-        raise ValueError(
+        raise ValueError(f"Unsupported decoding method: {params.decoding_method}")
-            f"Unsupported decoding method: {params.decoding_method}"
-        )
 
     states = unstack_states(new_states)
 
@@ -577,9 +561,7 @@ def decode_dataset(
     decode_streams = []
     for num, cut in enumerate(cuts):
         # each utterance has a DecodeStream.
-        initial_states = get_init_states(
+        initial_states = get_init_states(model=model, batch_size=1, device=device)
-            model=model, batch_size=1, device=device
-        )
         decode_stream = DecodeStream(
             params=params,
             cut_id=cut.id,
@@ -649,9 +631,7 @@ def decode_dataset(
     elif params.decoding_method == "modified_beam_search":
         key = f"num_active_paths_{params.num_active_paths}"
     else:
-        raise ValueError(
+        raise ValueError(f"Unsupported decoding method: {params.decoding_method}")
-            f"Unsupported decoding method: {params.decoding_method}"
-        )
     return {key: decode_results}
 
 
@@ -684,8 +664,7 @@ def save_results(
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
     errs_info = (
-        params.res_dir
+        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
-        / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
     )
     with open(errs_info, "w") as f:
         print("settings\tWER", file=f)
@@ -718,9 +697,7 @@ def main():
         params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
 
     assert params.causal, params.causal
-    assert (
+    assert "," not in params.chunk_size, "chunk_size should be one value in decoding."
-        "," not in params.chunk_size
-    ), "chunk_size should be one value in decoding."
     assert (
         "," not in params.left_context_frames
     ), "left_context_frames should be one value in decoding."
@@ -760,9 +737,9 @@ def main():
 
     if not params.use_averaged_model:
         if params.iter > 0:
-            filenames = find_checkpoints(
+            filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
-                params.exp_dir, iteration=-params.iter
+                : params.avg
-            )[: params.avg]
+            ]
             if len(filenames) == 0:
                 raise ValueError(
                     f"No checkpoints found for"
@@ -789,9 +766,9 @@ def main():
             model.load_state_dict(average_checkpoints(filenames, device=device))
     else:
         if params.iter > 0:
-            filenames = find_checkpoints(
+            filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
-                params.exp_dir, iteration=-params.iter
+                : params.avg + 1
-            )[: params.avg + 1]
+            ]
             if len(filenames) == 0:
                 raise ValueError(
                     f"No checkpoints found for"

egs/librispeech/ASR/zipformer/subsampling.py

@@ -107,9 +107,7 @@ class ConvNeXt(nn.Module):
         if layerdrop_rate != 0.0:
             batch_size = x.shape[0]
             mask = (
-                torch.rand(
+                torch.rand((batch_size, 1, 1, 1), dtype=x.dtype, device=x.device)
-                    (batch_size, 1, 1, 1), dtype=x.dtype, device=x.device
-                )
                 > layerdrop_rate
             )
         else:
@@ -278,9 +276,7 @@ class Conv2dSubsampling(nn.Module):
         # many copies of this extra gradient term.
         self.out_whiten = Whiten(
             num_groups=1,
-            whitening_limit=ScheduledFloat(
+            whitening_limit=ScheduledFloat((0.0, 4.0), (20000.0, 8.0), default=4.0),
-                (0.0, 4.0), (20000.0, 8.0), default=4.0
-            ),
             prob=(0.025, 0.25),
             grad_scale=0.02,
         )
@@ -331,7 +327,7 @@ class Conv2dSubsampling(nn.Module):
             with warnings.catch_warnings():
                 warnings.simplefilter("ignore")
                 x_lens = (x_lens - 7) // 2
-        assert x.size(1) == x_lens.max().item() , (x.size(1), x_lens.max())
+        assert x.size(1) == x_lens.max().item(), (x.size(1), x_lens.max())
 
         return x, x_lens
 
@@ -403,8 +399,8 @@ class Conv2dSubsampling(nn.Module):
         left_pad = self.convnext.padding[0]
         freq = self.out_width
         channels = self.layer3_channels
-        cached_embed_left_pad = torch.zeros(
+        cached_embed_left_pad = torch.zeros(batch_size, channels, left_pad, freq).to(
-            batch_size, channels, left_pad, freq
+            device
-        ).to(device)
+        )
 
         return cached_embed_left_pad

egs/librispeech/ASR/zipformer/train.py

@@ -604,11 +604,11 @@ def get_joiner_model(params: AttributeDict) -> nn.Module:
 
 
 def get_model(params: AttributeDict) -> nn.Module:
-    assert (
+    assert params.use_transducer or params.use_ctc, (
-        params.use_transducer or params.use_ctc
+        f"At least one of them should be True, "
-    ), (f"At least one of them should be True, "
         f"but got params.use_transducer={params.use_transducer}, "
-        f"params.use_ctc={params.use_ctc}")
+        f"params.use_ctc={params.use_ctc}"
+    )
 
     encoder_embed = get_encoder_embed(params)
     encoder = get_encoder_model(params)
@@ -808,17 +808,16 @@ def compute_loss(
             # take down the scale on the simple loss from 1.0 at the start
             # to params.simple_loss scale by warm_step.
             simple_loss_scale = (
-                s if batch_idx_train >= warm_step
+                s
+                if batch_idx_train >= warm_step
                 else 1.0 - (batch_idx_train / warm_step) * (1.0 - s)
             )
             pruned_loss_scale = (
-                1.0 if batch_idx_train >= warm_step
+                1.0
+                if batch_idx_train >= warm_step
                 else 0.1 + 0.9 * (batch_idx_train / warm_step)
             )
-            loss += (
+            loss += simple_loss_scale * simple_loss + pruned_loss_scale * pruned_loss
-                simple_loss_scale * simple_loss
-                + pruned_loss_scale * pruned_loss
-            )
 
         if params.use_ctc:
             loss += params.ctc_loss_scale * ctc_loss
@@ -1166,7 +1165,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

egs/librispeech/ASR/zipformer_mmi/train.py

@@ -981,7 +981,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

egs/mgb2/ASR/pruned_transducer_stateless5/train.py

@@ -746,7 +746,6 @@ def train_one_epoch(
     tot_loss = MetricsTracker()
 
     for batch_idx, batch in enumerate(train_dl):
-
         if batch["inputs"].shape[0] == len(batch["supervisions"]["text"]):
             params.batch_idx_train += 1
             batch_size = len(batch["supervisions"]["text"])
@@ -966,7 +965,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 
@@ -1019,7 +1018,6 @@ def run(rank, world_size, args):
         scaler.load_state_dict(checkpoints["grad_scaler"])
 
     for epoch in range(params.start_epoch, params.num_epochs + 1):
-
         scheduler.step_epoch(epoch - 1)
         fix_random_seed(params.seed + epoch - 1)
         train_dl.sampler.set_epoch(epoch - 1)
@@ -1118,7 +1116,6 @@ def scan_pessimistic_batches_for_oom(
             # (i.e. are not remembered by the decaying-average in adam), because
             # we want to avoid these params being subject to shrinkage in adam.
             with torch.cuda.amp.autocast(enabled=params.use_fp16):
-
                 loss, _, _ = compute_loss(
                     params=params,
                     model=model,

egs/multi_zh-hans/ASR/zipformer/train.py

@@ -1164,7 +1164,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

egs/tal_csasr/ASR/pruned_transducer_stateless5/train.py

@@ -915,7 +915,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

egs/tal_csasr/ASR/pruned_transducer_stateless7_bbpe/train.py

@@ -69,7 +69,7 @@ from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.utils.tensorboard import SummaryWriter
 from zipformer import Zipformer
 
-from icefall import diagnostics, byte_encode, tokenize_by_CJK_char
+from icefall import byte_encode, diagnostics, tokenize_by_CJK_char
 from icefall.checkpoint import load_checkpoint, remove_checkpoints
 from icefall.checkpoint import save_checkpoint as save_checkpoint_impl
 from icefall.checkpoint import (
@@ -1018,7 +1018,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

egs/tedlium3/ASR/conformer_ctc2/train.py

@@ -905,7 +905,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

egs/tedlium3/ASR/zipformer/train.py

@@ -1126,7 +1126,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

egs/wenetspeech/ASR/pruned_transducer_stateless2/finetune.py

@@ -886,7 +886,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

egs/wenetspeech/ASR/pruned_transducer_stateless2/train.py

@@ -851,7 +851,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

egs/wenetspeech/ASR/pruned_transducer_stateless5/train.py

@@ -985,7 +985,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

egs/wenetspeech/ASR/zipformer/train.py

@@ -1128,7 +1128,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

egs/xbmu_amdo31/ASR/pruned_transducer_stateless5/train.py

@@ -1001,7 +1001,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

egs/xbmu_amdo31/ASR/pruned_transducer_stateless7/train.py

@@ -993,7 +993,7 @@ def run(rank, world_size, args):
 
     if params.print_diagnostics:
         opts = diagnostics.TensorDiagnosticOptions(
-            2**22
+            512
         )  # allow 4 megabytes per sub-module
         diagnostic = diagnostics.attach_diagnostics(model, opts)
 

icefall/__init__.py

@@ -1,12 +1,6 @@
 # isort:skip_file
 
-from . import (
+from . import checkpoint, decode, dist, env, utils
-    checkpoint,
-    decode,
-    dist,
-    env,
-    utils
-)
 
 from .byte_utils import (
     byte_decode,

icefall/context_graph.py

@@ -227,7 +227,6 @@ class ContextGraph:
         filename: Optional[str] = "",
         symbol_table: Optional[Dict[int, str]] = None,
     ) -> "Digraph":  # noqa
-
         """Visualize a ContextGraph via graphviz.
 
         Render ContextGraph as an image via graphviz, and return the Digraph object;

icefall/diagnostics.py

@@ -23,6 +23,7 @@ from typing import Optional, Tuple, List
 import torch
 from torch import Tensor, nn
 
+
 class TensorDiagnosticOptions(object):
     """Options object for tensor diagnostics:
 
@@ -77,11 +78,11 @@ def get_tensor_stats(
     elif stats_type == "abs":
         x = x.abs()
     elif stats_type == "rms":
-        x = x ** 2
+        x = x**2
     elif stats_type == "positive":
         x = (x > 0).to(dtype=torch.float)
     else:
-        assert stats_type in [ "value", "max", "min" ]
+        assert stats_type in ["value", "max", "min"]
 
     sum_dims = [d for d in range(x.ndim) if d != dim]
     if len(sum_dims) > 0:
@@ -121,10 +122,10 @@ class TensorDiagnostic(object):
         self.class_name = None  # will assign in accumulate()
 
         self.stats = None  # we'll later assign a list to self.stats.
-                           # It's a list of dicts, indexed by dim (i.e. by the
+        # It's a list of dicts, indexed by dim (i.e. by the
-                           # axis of the tensor).  The dicts, in turn, are
+        # axis of the tensor).  The dicts, in turn, are
-                           # indexed by `stats-type` which are strings in
+        # indexed by `stats-type` which are strings in
-                           # ["abs", "max", "min", "positive", "value", "rms"].
+        # ["abs", "max", "min", "positive", "value", "rms"].
 
         # scalar_stats contains some analysis of the activations and gradients,
         self.scalar_stats = None
@@ -139,7 +140,6 @@ class TensorDiagnostic(object):
         # only adding a new element to the list if there was a different dim.
         # if the string in the key is "eigs", if we detect a length mismatch we put None as the value.
 
-
     def accumulate(self, x, class_name: Optional[str] = None):
         """
         Accumulate tensors.
@@ -193,17 +193,12 @@ class TensorDiagnostic(object):
                         done = True
                         break
                 if not done:
-                    if (
+                    if this_dim_stats[stats_type] != [] and stats_type == "eigs":
-                        this_dim_stats[stats_type] != []
-                        and stats_type == "eigs"
-                    ):
                         # >1 size encountered on this dim, e.g. it's a batch or time dimension,
                         # don't accumulat "eigs" stats type, it uses too much memory
                         this_dim_stats[stats_type] = None
                     else:
-                        this_dim_stats[stats_type].append(
+                        this_dim_stats[stats_type].append(TensorAndCount(stats, count))
-                            TensorAndCount(stats, count)
-                        )
 
     def print_diagnostics(self):
         """Print diagnostics for each dimension of the tensor."""
@@ -220,8 +215,11 @@ class TensorDiagnostic(object):
                     for r, v in zip(rms_stats_list, value_stats_list):
                         stddev_stats_list.append(
                             # r.count and v.count should be the same, but we don't check this.
-                            TensorAndCount(r.tensor - v.tensor * v.tensor / (v.count + 1.0e-20),
+                            TensorAndCount(
-                                           r.count))
+                                r.tensor - v.tensor * v.tensor / (v.count + 1.0e-20),
+                                r.count,
+                            )
+                        )
                     this_dim_stats["stddev"] = stddev_stats_list
 
             for stats_type, stats_list in this_dim_stats.items():
@@ -232,7 +230,6 @@ class TensorDiagnostic(object):
                     assert stats_type == "eigs"
                     continue
 
-
                 def get_count(count):
                     return 1 if stats_type in ["max", "min"] else count
 
@@ -250,22 +247,20 @@ class TensorDiagnostic(object):
                         eigs, _ = torch.symeig(stats)
                         stats = eigs.abs().sqrt()
                     except:  # noqa
-                        print(
+                        print("Error getting eigenvalues, trying another method.")
-                            "Error getting eigenvalues, trying another method."
-                        )
                         eigs, _ = torch.eig(stats)
                         stats = eigs.norm(dim=1).sqrt()
                         # sqrt so it reflects data magnitude, like stddev- not variance
 
-                if stats_type in [ "rms", "stddev" ]:
+                if stats_type in ["rms", "stddev"]:
                     # we stored the square; after aggregation we need to take sqrt.
                     stats = stats.sqrt()
 
                 # if `summarize` we print percentiles of the stats; else,
                 # we print out individual elements.
-                summarize = (
+                summarize = (len(stats_list) > 1) or self.opts.dim_is_summarized(
-                    len(stats_list) > 1
+                    stats.numel()
-                ) or self.opts.dim_is_summarized(stats.numel())
+                )
                 if summarize:  # usually `summarize` will be true
                     # print out percentiles.
                     stats = stats.sort()[0]
@@ -282,15 +277,15 @@ class TensorDiagnostic(object):
                     ans = stats.tolist()
                     ans = ["%.2g" % x for x in ans]
                     ans = "[" + " ".join(ans) + "]"
-                if stats_type in [ "value", "rms", "stddev", "eigs" ]:
+                if stats_type in ["value", "rms", "stddev", "eigs"]:
                     # This norm is useful because it is strictly less than the largest
                     # sqrt(eigenvalue) of the variance, which we print out, and shows,
                     # speaking in an approximate way, how much of that largest eigenvalue
                     # can be attributed to the mean of the distribution.
-                    norm = (stats ** 2).sum().sqrt().item()
+                    norm = (stats**2).sum().sqrt().item()
                     ans += f", norm={norm:.2g}"
                 mean = stats.mean().item()
-                rms = (stats ** 2).mean().sqrt().item()
+                rms = (stats**2).mean().sqrt().item()
                 ans += f", mean={mean:.3g}, rms={rms:.3g}"
 
                 # OK, "ans" contains the actual stats, e.g.
@@ -298,11 +293,11 @@ class TensorDiagnostic(object):
 
                 sizes = [x.tensor.shape[0] for x in stats_list]
                 size_str = (
-                    f"{sizes[0]}"
+                    f"{sizes[0]}" if len(sizes) == 1 else f"{min(sizes)}..{max(sizes)}"
-                    if len(sizes) == 1
+                )
-                    else f"{min(sizes)}..{max(sizes)}"
+                maybe_class_name = (
+                    f" type={self.class_name}," if self.class_name is not None else ""
                 )
-                maybe_class_name = f" type={self.class_name}," if self.class_name is not None else ""
                 print(
                     f"module={self.name},{maybe_class_name} dim={dim}, size={size_str}, {stats_type} {ans}"
                 )
@@ -330,7 +325,6 @@ class ScalarDiagnostic(object):
         self.sum_gradsq = None
         self.sum_abs_grad = None
 
-
     def accumulate_input(self, x: Tensor, class_name: Optional[str] = None):
         """
         Called in forward pass.
@@ -347,8 +341,10 @@ class ScalarDiagnostic(object):
 
         limit = 10
         if len(self.saved_inputs) > limit:
-            print(f"ERROR: forward pass called for this module over {limit} times with no backward pass. "
+            print(
-                  f" Will not accumulate scalar stats.")
+                f"ERROR: forward pass called for this module over {limit} times with no backward pass. "
+                f" Will not accumulate scalar stats."
+            )
             self.is_ok = False
             return
         self.saved_inputs.append(x)
@@ -359,11 +355,15 @@ class ScalarDiagnostic(object):
         if self.is_forward_pass:
             self.is_forward_pass = False
 
-        last_shape = 'n/a' if len(self.saved_inputs) == 0 else self.saved_inputs[-1].shape
+        last_shape = (
+            "n/a" if len(self.saved_inputs) == 0 else self.saved_inputs[-1].shape
+        )
         if len(self.saved_inputs) == 0 or grad.shape != last_shape:
-            print(f"ERROR: shape mismatch or no forward activation present when backward "
+            print(
-                  f"pass called: grad shape ={tuple(grad.shape)}, num-saved-inputs={len(self.saved_inputs)}"
+                f"ERROR: shape mismatch or no forward activation present when backward "
-                  f", shape-of-last-saved-input={last_shape}")
+                f"pass called: grad shape ={tuple(grad.shape)}, num-saved-inputs={len(self.saved_inputs)}"
+                f", shape-of-last-saved-input={last_shape}"
+            )
             self.is_ok = False
             return
 
@@ -384,11 +384,19 @@ class ScalarDiagnostic(object):
             self.tick_scale = float(x_abs_sorted[index] / num_ticks_per_side)
 
             # integerize from tick * (-num ticks_per_side ..  num_ticks_per_side - 1]
-            self.counts = torch.zeros(2 * num_ticks_per_side, dtype=torch.long, device=x.device)
+            self.counts = torch.zeros(
-            self.sum_grad = torch.zeros(2 * num_ticks_per_side, dtype=torch.double, device=x.device)
+                2 * num_ticks_per_side, dtype=torch.long, device=x.device
+            )
+            self.sum_grad = torch.zeros(
+                2 * num_ticks_per_side, dtype=torch.double, device=x.device
+            )
             # sum_gradsq is for getting error bars.
-            self.sum_gradsq = torch.zeros(2 * num_ticks_per_side, dtype=torch.double, device=x.device)
+            self.sum_gradsq = torch.zeros(
-            self.sum_abs_grad = torch.zeros(2 * num_ticks_per_side, dtype=torch.double, device=x.device)
+                2 * num_ticks_per_side, dtype=torch.double, device=x.device
+            )
+            self.sum_abs_grad = torch.zeros(
+                2 * num_ticks_per_side, dtype=torch.double, device=x.device
+            )
 
         # this will round down.
         x = (x / self.tick_scale).to(torch.long)
@@ -397,20 +405,21 @@ class ScalarDiagnostic(object):
 
         self.counts.index_add_(dim=0, index=x, source=torch.ones_like(x))
         self.sum_grad.index_add_(dim=0, index=x, source=grad.to(torch.double))
-        self.sum_gradsq.index_add_(dim=0, index=x, source=(grad*grad).to(torch.double))
+        self.sum_gradsq.index_add_(
+            dim=0, index=x, source=(grad * grad).to(torch.double)
+        )
         self.sum_abs_grad.index_add_(dim=0, index=x, source=grad.abs().to(torch.double))
 
-
     def print_diagnostics(self):
         """Print diagnostics."""
         if self.is_ok is False or self.counts is None:
             print(f"Warning: no stats accumulated for {self.name}, is_ok={self.is_ok}")
             return
 
-        counts = self.counts.to('cpu')
+        counts = self.counts.to("cpu")
-        sum_grad = self.sum_grad.to(device='cpu', dtype=torch.float32)
+        sum_grad = self.sum_grad.to(device="cpu", dtype=torch.float32)
-        sum_gradsq = self.sum_gradsq.to(device='cpu', dtype=torch.float32)
+        sum_gradsq = self.sum_gradsq.to(device="cpu", dtype=torch.float32)
-        sum_abs_grad = self.sum_abs_grad.to(device='cpu', dtype=torch.float32)
+        sum_abs_grad = self.sum_abs_grad.to(device="cpu", dtype=torch.float32)
 
         counts_cumsum = counts.cumsum(dim=0)
         counts_tot = counts_cumsum[-1]
@@ -433,19 +442,22 @@ class ScalarDiagnostic(object):
         bin_abs_grad = torch.zeros(num_bins)
         bin_abs_grad.index_add_(dim=0, index=bin_indexes, source=sum_abs_grad)
 
-        avg_grad = (bin_grad / bin_counts)
+        avg_grad = bin_grad / bin_counts
         avg_grad_stddev = (bin_gradsq / bin_counts).sqrt()
 
-        bin_boundary_counts = torch.arange(num_bins + 1, dtype=torch.long) * counts_per_bin
+        bin_boundary_counts = (
+            torch.arange(num_bins + 1, dtype=torch.long) * counts_per_bin
+        )
         bin_tick_indexes = torch.searchsorted(counts_cumsum, bin_boundary_counts)
         # boundaries are the "x" values between the bins, e.g. corresponding to the
         # locations of percentiles of the distribution.
         num_ticks_per_side = counts.numel() // 2
         bin_boundaries = (bin_tick_indexes - num_ticks_per_side) * self.tick_scale
 
-
         bin_grad = bin_grad / (bin_counts + 1)
-        bin_conf_interval = bin_gradsq.sqrt() / (bin_counts + 1) # consider this a standard deviation.
+        bin_conf_interval = bin_gradsq.sqrt() / (
+            bin_counts + 1
+        )  # consider this a standard deviation.
         # bin_grad / bin_abs_grad will give us a sense for how important in a practical sense,
         # the gradients are.
         bin_abs_grad = bin_abs_grad / (bin_counts + 1)
@@ -458,8 +470,9 @@ class ScalarDiagnostic(object):
             x = "[" + " ".join(x) + "]"
             return x
 
-
+        maybe_class_name = (
-        maybe_class_name = f" type={self.class_name}," if self.class_name is not None else ""
+            f" type={self.class_name}," if self.class_name is not None else ""
+        )
 
         print(
             f"module={self.name},{maybe_class_name} bin-boundaries={tensor_to_str(bin_boundaries)}, "
@@ -467,7 +480,6 @@ class ScalarDiagnostic(object):
         )
 
 
-
 class ModelDiagnostic(object):
     """This class stores diagnostics for all tensors in the torch.nn.Module.
 
@@ -485,9 +497,8 @@ class ModelDiagnostic(object):
             self.opts = opts
         self.diagnostics = dict()
 
-
     def __getitem__(self, name: str):
-        T = ScalarDiagnostic if name[-7:] == '.scalar' else TensorDiagnostic
+        T = ScalarDiagnostic if name[-7:] == ".scalar" else TensorDiagnostic
         if name not in self.diagnostics:
             self.diagnostics[name] = T(self.opts, name)
         return self.diagnostics[name]
@@ -502,18 +513,19 @@ def get_class_name(module: nn.Module):
     ans = type(module).__name__
     # we put the below in try blocks in case anyone is using a different version of these modules that
     # might have different member names.
-    if ans == 'Balancer' or ans == 'ActivationBalancer':
+    if ans == "Balancer" or ans == "ActivationBalancer":
         try:
-            ans += f'[{float(module.min_positive)},{float(module.max_positive)},{float(module.min_abs)},{float(module.max_abs)}]'
+            ans += f"[{float(module.min_positive)},{float(module.max_positive)},{float(module.min_abs)},{float(module.max_abs)}]"
         except:
             pass
-    elif ans == 'AbsValuePenalizer':
+    elif ans == "AbsValuePenalizer":
         try:
-            ans += f'[{module.limit}]'
+            ans += f"[{module.limit}]"
         except:
             pass
     return ans
 
+
 def attach_diagnostics(
     model: nn.Module, opts: Optional[TensorDiagnosticOptions] = None
 ) -> ModelDiagnostic:
@@ -538,73 +550,85 @@ def attach_diagnostics(
         if name == "":
             name = "<top-level>"
 
-
-
         # Setting model_diagnostic=ans and n=name below, instead of trying to
         # capture the variables, ensures that we use the current values.
         # (this matters for `name`, since the variable gets overwritten).
         # These closures don't really capture by value, only by
         # "the final value the variable got in the function" :-(
-        def forward_hook(
+        def forward_hook(_module, _input, _output, _model_diagnostic=ans, _name=name):
-            _module, _input, _output, _model_diagnostic=ans, _name=name
-        ):
             if isinstance(_output, tuple) and len(_output) == 1:
                 _output = _output[0]
 
-            if isinstance(_output, Tensor) and _output.dtype in ( torch.float32, torch.float16, torch.float64 ):
+            if isinstance(_output, Tensor) and _output.dtype in (
-                _model_diagnostic[f"{_name}.output"].accumulate(_output,
+                torch.float32,
-                                                                class_name=get_class_name(_module))
+                torch.float16,
+                torch.float64,
+            ):
+                _model_diagnostic[f"{_name}.output"].accumulate(
+                    _output, class_name=get_class_name(_module)
+                )
             elif isinstance(_output, tuple):
                 for i, o in enumerate(_output):
-                    if o.dtype in ( torch.float32, torch.float16, torch.float64 ):
+                    if o.dtype in (torch.float32, torch.float16, torch.float64):
-                        _model_diagnostic[f"{_name}.output[{i}]"].accumulate(o,
+                        _model_diagnostic[f"{_name}.output[{i}]"].accumulate(
-                                                                             class_name=get_class_name(_module))
+                            o, class_name=get_class_name(_module)
+                        )
 
-        def backward_hook(
+        def backward_hook(_module, _input, _output, _model_diagnostic=ans, _name=name):
-            _module, _input, _output, _model_diagnostic=ans, _name=name
-        ):
             if isinstance(_output, tuple) and len(_output) == 1:
                 _output = _output[0]
-            if isinstance(_output, Tensor) and _output.dtype in ( torch.float32, torch.float16, torch.float64 ):
+            if isinstance(_output, Tensor) and _output.dtype in (
-                _model_diagnostic[f"{_name}.grad"].accumulate(_output,
+                torch.float32,
-                                                              class_name=get_class_name(_module))
+                torch.float16,
+                torch.float64,
+            ):
+                _model_diagnostic[f"{_name}.grad"].accumulate(
+                    _output, class_name=get_class_name(_module)
+                )
             elif isinstance(_output, tuple):
                 for i, o in enumerate(_output):
-                    if o.dtype in ( torch.float32, torch.float16, torch.float64 ):
+                    if o.dtype in (torch.float32, torch.float16, torch.float64):
-                        _model_diagnostic[f"{_name}.grad[{i}]"].accumulate(o,
+                        _model_diagnostic[f"{_name}.grad[{i}]"].accumulate(
-                                                                           class_name=get_class_name(_module))
+                            o, class_name=get_class_name(_module)
-
+                        )
 
         module.register_forward_hook(forward_hook)
         module.register_backward_hook(backward_hook)
 
-        if type(module).__name__ in  ["Sigmoid", "Tanh", "ReLU", "TanSwish", "Swish", "DoubleSwish", "Swoosh"]:
+        if type(module).__name__ in [
+            "Sigmoid",
+            "Tanh",
+            "ReLU",
+            "TanSwish",
+            "Swish",
+            "DoubleSwish",
+            "Swoosh",
+        ]:
             # For these specific module types, accumulate some additional diagnostics
             # that can help us improve the activation function.  These require a lot of memory,
             # to save the forward activations, so limit this to some select classes.
             # Note: this will not work correctly for all model types.
             def scalar_forward_hook(
-                    _module, _input, _output, _model_diagnostic=ans, _name=name
+                _module, _input, _output, _model_diagnostic=ans, _name=name
             ):
                 if isinstance(_input, tuple):
-                    _input, = _input
+                    (_input,) = _input
                 assert isinstance(_input, Tensor)
-                _model_diagnostic[f"{_name}.scalar"].accumulate_input(_input,
+                _model_diagnostic[f"{_name}.scalar"].accumulate_input(
-                                                                      class_name=get_class_name(_module))
+                    _input, class_name=get_class_name(_module)
+                )
 
             def scalar_backward_hook(
-                    _module, _input, _output, _model_diagnostic=ans, _name=name
+                _module, _input, _output, _model_diagnostic=ans, _name=name
             ):
                 if isinstance(_output, tuple):
-                    _output, = _output
+                    (_output,) = _output
                 assert isinstance(_output, Tensor)
                 _model_diagnostic[f"{_name}.scalar"].accumulate_output_grad(_output)
 
             module.register_forward_hook(scalar_forward_hook)
             module.register_backward_hook(scalar_backward_hook)
 
-
-
     for name, parameter in model.named_parameters():
 
         def param_backward_hook(

icefall/profiler.py

@@ -70,25 +70,17 @@ class FlopsProfiler(object):
                 module_flop_count.append([])
 
             if not hasattr(module, "__pre_hook_handle__"):
-                module.__pre_hook_handle__ = module.register_forward_pre_hook(
+                module.__pre_hook_handle__ = module.register_forward_pre_hook(pre_hook)
-                    pre_hook
-                )
 
             def post_hook(module, input, output):
                 if module_flop_count:
-                    module.__flops__ += sum(
+                    module.__flops__ += sum([elem[1] for elem in module_flop_count[-1]])
-                        [elem[1] for elem in module_flop_count[-1]]
-                    )
                     module_flop_count.pop()
 
             if not hasattr(module, "__post_hook_handle__"):
-                module.__post_hook_handle__ = module.register_forward_hook(
+                module.__post_hook_handle__ = module.register_forward_hook(post_hook)
-                    post_hook
-                )
 
-        self.model.apply(
+        self.model.apply(partial(register_module_hooks, ignore_list=ignore_list))
-            partial(register_module_hooks, ignore_list=ignore_list)
-        )
         self.started = True
         self.func_patched = True
 
@@ -194,9 +186,7 @@ def _prelu_flops_compute(input: Tensor, weight: Tensor):
     return input.numel()
 
 
-def _elu_flops_compute(
+def _elu_flops_compute(input: Tensor, alpha: float = 1.0, inplace: bool = False):
-    input: Tensor, alpha: float = 1.0, inplace: bool = False
-):
     return input.numel()
 
 
@@ -259,9 +249,7 @@ def _conv_flops_compute(
         output_dims.append(output_dim)
 
     filters_per_channel = out_channels // groups
-    conv_per_position_macs = (
+    conv_per_position_macs = int(_prod(kernel_dims)) * in_channels * filters_per_channel
-        int(_prod(kernel_dims)) * in_channels * filters_per_channel
-    )
     active_elements_count = batch_size * int(_prod(output_dims))
     overall_conv_macs = conv_per_position_macs * active_elements_count
     overall_conv_flops = 2 * overall_conv_macs
@@ -297,7 +285,6 @@ def _conv_trans_flops_compute(
 
     output_dims = []
     for idx, input_dim in enumerate(input_dims):
-
         output_dim = (
             input_dim
             + 2 * paddings[idx]
@@ -310,9 +297,7 @@ def _conv_trans_flops_compute(
     dilations = dilation if type(dilation) is tuple else (dilation, dilation)
 
     filters_per_channel = out_channels // groups
-    conv_per_position_macs = (
+    conv_per_position_macs = int(_prod(kernel_dims)) * in_channels * filters_per_channel
-        int(_prod(kernel_dims)) * in_channels * filters_per_channel
-    )
     active_elements_count = batch_size * int(_prod(input_dims))
     overall_conv_macs = conv_per_position_macs * active_elements_count
     overall_conv_flops = 2 * overall_conv_macs
@@ -389,9 +374,7 @@ def _upsample_flops_compute(input, **kwargs):
         else:
             return int(size), 0
     scale_factor = kwargs.get("scale_factor", None)
-    assert (
+    assert scale_factor is not None, "either size or scale_factor should be defined"
-        scale_factor is not None
-    ), "either size or scale_factor should be defined"
     flops = input.numel()
     if isinstance(scale_factor, tuple) and len(scale_factor) == len(input):
         flops * int(_prod(scale_factor))
@@ -593,12 +576,8 @@ def _patch_functionals():
     F.embedding = wrapFunc(F.embedding, _embedding_flops_compute)
 
     # swoosh functions in k2
-    k2.swoosh_l_forward = wrapFunc(
+    k2.swoosh_l_forward = wrapFunc(k2.swoosh_l_forward, _k2_swoosh_flops_compute)
-        k2.swoosh_l_forward, _k2_swoosh_flops_compute
+    k2.swoosh_r_forward = wrapFunc(k2.swoosh_r_forward, _k2_swoosh_flops_compute)
-    )
-    k2.swoosh_r_forward = wrapFunc(
-        k2.swoosh_r_forward, _k2_swoosh_flops_compute
-    )
     k2.swoosh_l = wrapFunc(k2.swoosh_l, _k2_swoosh_flops_compute)
     k2.swoosh_r = wrapFunc(k2.swoosh_r, _k2_swoosh_flops_compute)
 
@@ -612,9 +591,7 @@ def _patch_tensor_methods():
     torch.Tensor.bmm = wrapFunc(torch.Tensor.bmm, _matmul_flops_compute)
 
     torch.addmm = wrapFunc(torch.addmm, _addmm_flops_compute)
-    torch.Tensor.addmm = wrapFunc(
+    torch.Tensor.addmm = wrapFunc(torch.Tensor.addmm, _tensor_addmm_flops_compute)
-        torch.Tensor.addmm, _tensor_addmm_flops_compute
-    )
 
     torch.mul = wrapFunc(torch.mul, _mul_flops_compute)
     torch.Tensor.mul = wrapFunc(torch.Tensor.mul, _mul_flops_compute)
@@ -631,14 +608,10 @@ def _patch_tensor_methods():
 
     torch.tanh = wrapFunc(torch.tanh, _tanh_flops_compute)
 
-    torch.Tensor.softmax = wrapFunc(
+    torch.Tensor.softmax = wrapFunc(torch.Tensor.softmax, _softmax_flops_compute)
-        torch.Tensor.softmax, _softmax_flops_compute
-    )
 
     torch.sigmoid = wrapFunc(torch.sigmoid, _sigmoid_flops_compute)
-    torch.Tensor.sigmoid = wrapFunc(
+    torch.Tensor.sigmoid = wrapFunc(torch.Tensor.sigmoid, _sigmoid_flops_compute)
-        torch.Tensor.sigmoid, _sigmoid_flops_compute
-    )
 
 
 def _reload_functionals():
@@ -732,15 +705,11 @@ def _rnn_flops(flops, rnn_module, w_ih, w_hh, input_size):
         flops += rnn_module.hidden_size * 4
         # two hadamard _product and add for C state
         flops += (
-            rnn_module.hidden_size
+            rnn_module.hidden_size + rnn_module.hidden_size + rnn_module.hidden_size
-            + rnn_module.hidden_size
-            + rnn_module.hidden_size
         )
         # final hadamard
         flops += (
-            rnn_module.hidden_size
+            rnn_module.hidden_size + rnn_module.hidden_size + rnn_module.hidden_size
-            + rnn_module.hidden_size
-            + rnn_module.hidden_size
         )
     return flops
 

icefall/rnn_lm/check-onnx-streaming.py

@@ -112,7 +112,6 @@ def main():
         for torch_v, onnx_v in zip(
             (torch_log_prob, torch_h0, torch_c0), (onnx_log_prob, onnx_h0, onnx_c0)
         ):
-
             assert torch.allclose(torch_v, onnx_v, atol=1e-5), (
                 torch_v.shape,
                 onnx_v.shape,

icefall/rnn_lm/train.py

@@ -463,7 +463,6 @@ def train_one_epoch(
     cur_batch_idx = params.get("cur_batch_idx", 0)
 
     for batch_idx, batch in enumerate(train_dl):
-
         if batch_idx < cur_batch_idx:
             continue
         cur_batch_idx = batch_idx

icefall/shared/make_kn_lm.py

@@ -225,7 +225,6 @@ class NgramCounts:
         for n in range(0, self.ngram_order - 1):
             this_order_counts = self.counts[n]
             for hist, counts_for_hist in this_order_counts.items():
-
                 n_star_star = 0
                 for w in counts_for_hist.word_to_count.keys():
                     n_star_star += len(counts_for_hist.word_to_context[w])
@@ -424,7 +423,6 @@ class NgramCounts:
 
 
 if __name__ == "__main__":
-
     ngram_counts = NgramCounts(args.ngram_order)
 
     if args.text is None:

icefall/transformer_lm/model.py

@@ -103,7 +103,6 @@ class TransformerLM(torch.nn.Module):
         return nll_loss
 
     def score_token(self, x: torch.Tensor, x_lens: torch.Tensor, state=None):
-
         bs = x.size(0)
 
         state = None

requirements-ci.txt

@@ -20,6 +20,7 @@ kaldialign==0.7.1
 sentencepiece==0.1.96
 tensorboard==2.8.0
 typeguard==2.13.3
+black==22.3.0
 multi_quantization
 
 onnx

requirements.txt

@@ -5,3 +5,4 @@ sentencepiece>=0.1.96
 tensorboard
 typeguard
 dill
+black==22.3.0