modify decode.py pretrained.py test_model.py train.py

egs/librispeech/ASR/lstm_transducer_stateless/decode.py

@@ -18,36 +18,36 @@
 """
 Usage:
 (1) greedy search
-./pruned_transducer_stateless2/decode.py \
+./lstm_transducer_stateless/decode.py \
     --epoch 28 \
     --avg 15 \
-    --exp-dir ./pruned_transducer_stateless2/exp \
+    --exp-dir ./lstm_transducer_stateless/exp \
     --max-duration 600 \
     --decoding-method greedy_search
 
 (2) beam search (not recommended)
-./pruned_transducer_stateless2/decode.py \
+./lstm_transducer_stateless/decode.py \
     --epoch 28 \
     --avg 15 \
-    --exp-dir ./pruned_transducer_stateless2/exp \
+    --exp-dir ./lstm_transducer_stateless/exp \
     --max-duration 600 \
     --decoding-method beam_search \
     --beam-size 4
 
 (3) modified beam search
-./pruned_transducer_stateless2/decode.py \
+./lstm_transducer_stateless/decode.py \
     --epoch 28 \
     --avg 15 \
-    --exp-dir ./pruned_transducer_stateless2/exp \
+    --exp-dir ./lstm_transducer_stateless/exp \
     --max-duration 600 \
     --decoding-method modified_beam_search \
     --beam-size 4
 
 (4) fast beam search (one best)
-./pruned_transducer_stateless2/decode.py \
+./lstm_transducer_stateless/decode.py \
     --epoch 28 \
     --avg 15 \
-    --exp-dir ./pruned_transducer_stateless2/exp \
+    --exp-dir ./lstm_transducer_stateless/exp \
     --max-duration 600 \
     --decoding-method fast_beam_search \
     --beam 20.0 \
@@ -55,10 +55,10 @@ Usage:
     --max-states 64
 
 (5) fast beam search (nbest)
-./pruned_transducer_stateless2/decode.py \
+./lstm_transducer_stateless/decode.py \
     --epoch 28 \
     --avg 15 \
-    --exp-dir ./pruned_transducer_stateless2/exp \
+    --exp-dir ./lstm_transducer_stateless/exp \
     --max-duration 600 \
     --decoding-method fast_beam_search_nbest \
     --beam 20.0 \
@@ -68,10 +68,10 @@ Usage:
     --nbest-scale 0.5
 
 (6) fast beam search (nbest oracle WER)
-./pruned_transducer_stateless2/decode.py \
+./lstm_transducer_stateless/decode.py \
     --epoch 28 \
     --avg 15 \
-    --exp-dir ./pruned_transducer_stateless2/exp \
+    --exp-dir ./lstm_transducer_stateless/exp \
     --max-duration 600 \
     --decoding-method fast_beam_search_nbest_oracle \
     --beam 20.0 \
@@ -81,30 +81,15 @@ Usage:
     --nbest-scale 0.5
 
 (7) fast beam search (with LG)
-./pruned_transducer_stateless2/decode.py \
+./lstm_transducer_stateless/decode.py \
     --epoch 28 \
     --avg 15 \
-    --exp-dir ./pruned_transducer_stateless2/exp \
+    --exp-dir ./lstm_transducer_stateless/exp \
     --max-duration 600 \
     --decoding-method fast_beam_search_nbest_LG \
     --beam 20.0 \
     --max-contexts 8 \
     --max-states 64
-
-(8) decode in streaming mode (take greedy search as an example)
-./pruned_transducer_stateless2/decode.py \
-    --epoch 28 \
-    --avg 15 \
-    --simulate-streaming 1 \
-    --causal-convolution 1 \
-    --decode-chunk-size 16 \
-    --left-context 64 \
-    --exp-dir ./pruned_transducer_stateless2/exp \
-    --max-duration 600 \
-    --decoding-method greedy_search
-    --beam 20.0 \
-    --max-contexts 8 \
-    --max-states 64
 """
 
 
@@ -130,7 +115,7 @@ from beam_search import (
     greedy_search_batch,
     modified_beam_search,
 )
-from train import add_model_arguments, get_params, get_transducer_model
+from train import get_params, get_transducer_model
 
 from icefall.checkpoint import (
     average_checkpoints,
@@ -142,7 +127,6 @@ from icefall.utils import (
     AttributeDict,
     setup_logger,
     store_transcripts,
-    str2bool,
     write_error_stats,
 )
 
@@ -286,29 +270,6 @@ def get_parser():
         Used only when --decoding_method is greedy_search""",
     )
 
-    parser.add_argument(
-        "--simulate-streaming",
-        type=str2bool,
-        default=False,
-        help="""Whether to simulate streaming in decoding, this is a good way to
-        test a streaming model.
-        """,
-    )
-
-    parser.add_argument(
-        "--decode-chunk-size",
-        type=int,
-        default=16,
-        help="The chunk size for decoding (in frames after subsampling)",
-    )
-
-    parser.add_argument(
-        "--left-context",
-        type=int,
-        default=64,
-        help="left context can be seen during decoding (in frames after subsampling)",
-    )
-
     parser.add_argument(
         "--num-paths",
         type=int,
@@ -327,7 +288,6 @@ def get_parser():
         fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""",
     )
 
-    add_model_arguments(parser)
     return parser
 
 
@@ -387,18 +347,9 @@ def decode_one_batch(
         value=LOG_EPS,
     )
 
-    if params.simulate_streaming:
-        encoder_out, encoder_out_lens, _ = model.encoder.streaming_forward(
-            x=feature,
-            x_lens=feature_lens,
-            chunk_size=params.decode_chunk_size,
-            left_context=params.left_context,
-            simulate_streaming=True,
-        )
-    else:
-        encoder_out, encoder_out_lens = model.encoder(
-            x=feature, x_lens=feature_lens
-        )
+    encoder_out, encoder_out_lens = model.encoder(
+        x=feature, x_lens=feature_lens
+    )
 
     hyps = []
 
@@ -674,10 +625,6 @@ def main():
     else:
         params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
 
-    if params.simulate_streaming:
-        params.suffix += f"-streaming-chunk-size-{params.decode_chunk_size}"
-        params.suffix += f"-left-context-{params.left_context}"
-
     if "fast_beam_search" in params.decoding_method:
         params.suffix += f"-beam-{params.beam}"
         params.suffix += f"-max-contexts-{params.max_contexts}"
@@ -712,11 +659,6 @@ def main():
     params.unk_id = sp.piece_to_id("<unk>")
     params.vocab_size = sp.get_piece_size()
 
-    if params.simulate_streaming:
-        assert (
-            params.causal_convolution
-        ), "Decoding in streaming requires causal convolution"
-
     logging.info(params)
 
     logging.info("About to create model")

egs/librispeech/ASR/lstm_transducer_stateless/pretrained.py

@@ -77,9 +77,7 @@ from beam_search import (
     modified_beam_search,
 )
 from torch.nn.utils.rnn import pad_sequence
-from train import add_model_arguments, get_params, get_transducer_model
-
-from icefall.utils import str2bool
+from train import get_params, get_transducer_model
 
 
 def get_parser():
@@ -180,30 +178,6 @@ def get_parser():
         """,
     )
 
-    parser.add_argument(
-        "--simulate-streaming",
-        type=str2bool,
-        default=False,
-        help="""Whether to simulate streaming in decoding, this is a good way to
-        test a streaming model.
-        """,
-    )
-
-    parser.add_argument(
-        "--decode-chunk-size",
-        type=int,
-        default=16,
-        help="The chunk size for decoding (in frames after subsampling)",
-    )
-    parser.add_argument(
-        "--left-context",
-        type=int,
-        default=64,
-        help="left context can be seen during decoding (in frames after subsampling)",
-    )
-
-    add_model_arguments(parser)
-
     return parser
 
 
@@ -248,11 +222,6 @@ def main():
     params.unk_id = sp.piece_to_id("<unk>")
     params.vocab_size = sp.get_piece_size()
 
-    if params.simulate_streaming:
-        assert (
-            params.causal_convolution
-        ), "Decoding in streaming requires causal convolution"
-
     logging.info(f"{params}")
 
     device = torch.device("cpu")
@@ -299,18 +268,9 @@ def main():
 
     feature_lengths = torch.tensor(feature_lengths, device=device)
 
-    if params.simulate_streaming:
-        encoder_out, encoder_out_lens, _ = model.encoder.streaming_forward(
-            x=features,
-            x_lens=feature_lengths,
-            chunk_size=params.decode_chunk_size,
-            left_context=params.left_context,
-            simulate_streaming=True,
-        )
-    else:
-        encoder_out, encoder_out_lens = model.encoder(
-            x=features, x_lens=feature_lengths
-        )
+    encoder_out, encoder_out_lens = model.encoder(
+        x=features, x_lens=feature_lengths
+    )
 
     num_waves = encoder_out.size(0)
     hyps = []

egs/librispeech/ASR/lstm_transducer_stateless/test_model.py

@@ -34,31 +34,6 @@ def test_model():
     params.context_size = 2
     params.unk_id = 2
 
-    params.dynamic_chunk_training = False
-    params.short_chunk_size = 25
-    params.num_left_chunks = 4
-    params.causal_convolution = False
-
-    model = get_transducer_model(params)
-
-    num_param = sum([p.numel() for p in model.parameters()])
-    print(f"Number of model parameters: {num_param}")
-    model.__class__.forward = torch.jit.ignore(model.__class__.forward)
-    torch.jit.script(model)
-
-
-def test_model_streaming():
-    params = get_params()
-    params.vocab_size = 500
-    params.blank_id = 0
-    params.context_size = 2
-    params.unk_id = 2
-
-    params.dynamic_chunk_training = True
-    params.short_chunk_size = 25
-    params.num_left_chunks = 4
-    params.causal_convolution = True
-
     model = get_transducer_model(params)
 
     num_param = sum([p.numel() for p in model.parameters()])
@@ -69,7 +44,6 @@ def test_model_streaming():
 
 def main():
     test_model()
-    test_model_streaming()
 
 
 if __name__ == "__main__":

egs/librispeech/ASR/lstm_transducer_stateless/train.py

@@ -21,37 +21,24 @@ Usage:
 
 export CUDA_VISIBLE_DEVICES="0,1,2,3"
 
-./pruned_transducer_stateless2/train.py \
+./lstm_transducer_stateless/train.py \
   --world-size 4 \
   --num-epochs 30 \
   --start-epoch 0 \
-  --exp-dir pruned_transducer_stateless2/exp \
+  --exp-dir lstm_transducer_stateless/exp \
   --full-libri 1 \
   --max-duration 300
 
 # For mix precision training:
 
-./pruned_transducer_stateless2/train.py \
+./lstm_transducer_stateless/train.py \
   --world-size 4 \
   --num-epochs 30 \
   --start-epoch 0 \
   --use-fp16 1 \
-  --exp-dir pruned_transducer_stateless2/exp \
+  --exp-dir lstm_transducer_stateless/exp \
   --full-libri 1 \
   --max-duration 550
-
-# train a streaming model
-./pruned_transducer_stateless2/train.py \
-  --world-size 4 \
-  --num-epochs 30 \
-  --start-epoch 0 \
-  --exp-dir pruned_transducer_stateless/exp \
-  --full-libri 1 \
-  --dynamic-chunk-training 1 \
-  --causal-convolution 1 \
-  --short-chunk-size 25 \
-  --num-left-chunks 4 \
-  --max-duration 300
 """
 
 
@@ -69,7 +56,7 @@ import torch
 import torch.multiprocessing as mp
 import torch.nn as nn
 from asr_datamodule import LibriSpeechAsrDataModule
-from conformer import Conformer
+from lstm import RNN
 from decoder import Decoder
 from joiner import Joiner
 from lhotse.cut import Cut
@@ -95,42 +82,6 @@ LRSchedulerType = Union[
 ]
 
 
-def add_model_arguments(parser: argparse.ArgumentParser):
-    parser.add_argument(
-        "--dynamic-chunk-training",
-        type=str2bool,
-        default=False,
-        help="""Whether to use dynamic_chunk_training, if you want a streaming
-        model, this requires to be True.
-        """,
-    )
-
-    parser.add_argument(
-        "--causal-convolution",
-        type=str2bool,
-        default=False,
-        help="""Whether to use causal convolution, this requires to be True when
-        using dynamic_chunk_training.
-        """,
-    )
-
-    parser.add_argument(
-        "--short-chunk-size",
-        type=int,
-        default=25,
-        help="""Chunk length of dynamic training, the chunk size would be either
-        max sequence length of current batch or uniformly sampled from (1, short_chunk_size).
-        """,
-    )
-
-    parser.add_argument(
-        "--num-left-chunks",
-        type=int,
-        default=4,
-        help="How many left context can be seen in chunks when calculating attention.",
-    )
-
-
 def get_parser():
     parser = argparse.ArgumentParser(
         formatter_class=argparse.ArgumentDefaultsHelpFormatter
@@ -311,8 +262,6 @@ def get_parser():
         help="Whether to use half precision training.",
     )
 
-    add_model_arguments(parser)
-
     return parser
 
 
@@ -374,7 +323,6 @@ def get_params() -> AttributeDict:
             "feature_dim": 80,
             "subsampling_factor": 4,
             "encoder_dim": 512,
-            "nhead": 8,
             "dim_feedforward": 2048,
             "num_encoder_layers": 12,
             # parameters for decoder
@@ -392,17 +340,12 @@ def get_params() -> AttributeDict:
 
 def get_encoder_model(params: AttributeDict) -> nn.Module:
     # TODO: We can add an option to switch between Conformer and Transformer
-    encoder = Conformer(
+    encoder = RNN(
         num_features=params.feature_dim,
         subsampling_factor=params.subsampling_factor,
         d_model=params.encoder_dim,
-        nhead=params.nhead,
         dim_feedforward=params.dim_feedforward,
         num_encoder_layers=params.num_encoder_layers,
-        dynamic_chunk_training=params.dynamic_chunk_training,
-        short_chunk_size=params.short_chunk_size,
-        num_left_chunks=params.num_left_chunks,
-        causal=params.causal_convolution,
     )
     return encoder