Update docs, pretrained.py & results

docs/source/recipes/aishell/conformer_ctc.rst

@@ -97,13 +97,17 @@ Configurable options
 shows you the training options that can be passed from the commandline.
 The following options are used quite often:
 
+  - ``--exp-dir``
+
+    The experiment folder to save logs and model checkpoints,
+    default ``./conformer_ctc/exp``.
 
   - ``--num-epochs``
 
     It is the number of epochs to train. For instance,
     ``./conformer_ctc/train.py --num-epochs 30`` trains for 30 epochs
     and generates ``epoch-0.pt``, ``epoch-1.pt``, ..., ``epoch-29.pt``
-    in the folder ``./conformer_ctc/exp``.
+    in the folder set with ``--exp-dir``.
 
   - ``--start-epoch``
 
@@ -174,7 +178,7 @@ Pre-configured options
 ~~~~~~~~~~~~~~~~~~~~~~
 
 There are some training options, e.g., weight decay,
-number of warmup steps, results dir, etc,
+number of warmup steps, etc,
 that are not passed from the commandline.
 They are pre-configured by the function ``get_params()`` in
 `conformer_ctc/train.py <https://github.com/k2-fsa/icefall/blob/master/egs/aishell/ASR/conformer_ctc/train.py>`_
@@ -192,8 +196,8 @@ them, please modify ``./conformer_ctc/train.py`` directly.
 Training logs
 ~~~~~~~~~~~~~
 
-Training logs and checkpoints are saved in ``conformer_ctc/exp``.
+Training logs and checkpoints are saved in the folder set by ``--exp-dir``
-You will find the following files in that directory:
+(default ``conformer_ctc/exp``). You will find the following files in that directory:
 
   - ``epoch-0.pt``, ``epoch-1.pt``, ...
 
@@ -223,10 +227,10 @@ You will find the following files in that directory:
 
         To stop uploading, press Ctrl-C.
 
-        New experiment created. View your TensorBoard at: https://tensorboard.dev/experiment/qvNrx6JIQAaN5Ly3uQotrg/
+        New experiment created. View your TensorBoard at: https://tensorboard.dev/experiment/WE1DocDqRRCOSAgmGyClhg/
 
-        [2021-09-12T16:41:16] Started scanning logdir.
+        [2021-11-16T10:51:46] Started scanning logdir.
-        [2021-09-12T16:42:17] Total uploaded: 125346 scalars, 0 tensors, 0 binary objects
+        [2021-11-16T10:52:32] Total uploaded: 111606 scalars, 0 tensors, 0 binary objects
         Listening for new data in logdir...
 
     Note there is a URL in the above output, click it and you will see
@@ -236,7 +240,7 @@ You will find the following files in that directory:
          :width: 600
          :alt: TensorBoard screenshot
          :align: center
-         :target: https://tensorboard.dev/experiment/qvNrx6JIQAaN5Ly3uQotrg/
+         :target: https://tensorboard.dev/experiment/WE1DocDqRRCOSAgmGyClhg/
 
          TensorBoard screenshot.
 
@@ -307,9 +311,9 @@ The commonly used options are:
     .. code-block::
 
       $ cd egs/aishell/ASR
-      $ ./conformer_ctc/decode.py --method attention-decoder --max-duration 30 --lattice-score-scale 0.5
+      $ ./conformer_ctc/decode.py --method attention-decoder --max-duration 30 --nbest-scale 0.5
 
-  - ``--lattice-score-scale``
+  - ``--nbest-scale``
 
     It is used to scale down lattice scores so that there are more unique
     paths for rescoring.
@@ -403,7 +407,7 @@ After downloading, you will have the following files:
   - ``exp/pretrained.pt``
 
       It contains pre-trained model parameters, obtained by averaging
-      checkpoints from ``epoch-18.pt`` to ``epoch-40.pt``.
+      checkpoints from ``epoch-25.pt`` to ``epoch-84.pt``.
       Note: We have removed optimizer ``state_dict`` to reduce file size.
 
   - ``test_waves/*.wav``

egs/aishell/ASR/RESULTS.md

@@ -1,16 +1,16 @@
 ## Results
 
 ### Aishell training results (Conformer-CTC)
-#### 2021-09-13
+#### 2021-11-16
 (Wei Kang): Result of https://github.com/k2-fsa/icefall/pull/30
 
 Pretrained model is available at https://huggingface.co/pkufool/icefall_asr_aishell_conformer_ctc
 
-The best decoding results (CER) are listed below, we got this results by averaging models from epoch 23 to 40, and using `attention-decoder` decoder with num_paths equals to 100.
+The best decoding results (CER) are listed below, we got this results by averaging models from epoch 25 to 84, and using `attention-decoder` decoder with num_paths equals to 100.
 
 ||test|
 |--|--|
-|CER| 4.74% |
+|CER| 4.26% |
 
 To get more unique paths, we scaled the lattice.scores with 0.5 (see https://github.com/k2-fsa/icefall/pull/10#discussion_r690951662 for more details), we searched the lm_score_scale and attention_score_scale for best results, the scales that produced the CER above are also listed below.
 
@@ -27,17 +27,18 @@ cd icefall
 cd egs/aishell/ASR
 ./prepare.sh
 
-export CUDA_VISIBLE_DEVICES="0,1"
+export CUDA_VISIBLE_DEVICES="0,1,2,3"
-python conformer_ctc/train.py --bucketing-sampler False \
+python conformer_ctc/train.py --bucketing-sampler True \
-                              --concatenate-cuts False \
                               --max-duration 200 \
-                              --world-size 2
+                              --start-epoch 0 \
+                              --num-epoch 90 \
+                              --world-size 4
 
-python conformer_ctc/decode.py --lattice-score-scale 0.5 \
+python conformer_ctc/decode.py --nbest-scale 0.5 \
-                               --epoch 40 \
+                               --epoch 84 \
-                               --avg 18 \
+                               --avg 25 \
                                --method attention-decoder \
-                               --max-duration 50 \
+                               --max-duration 20 \
                                --num-paths 100
 ```
 
@@ -53,4 +54,3 @@ The best decoding results (CER) are listed below, we got this results by averagi
 ||test|
 |--|--|
 |CER| 10.16% |
-

egs/aishell/ASR/conformer_ctc/decode.py

@@ -77,6 +77,8 @@ def get_parser():
         default="attention-decoder",
         help="""Decoding method.
         Supported values are:
+            - (0) ctc-decoding. Use CTC decoding. It maps the tokens ids to
+              tokens using token symbol tabel directly.
             - (1) 1best. Extract the best path from the decoding lattice as the
               decoding result.
             - (2) nbest. Extract n paths from the decoding lattice; the path

egs/aishell/ASR/conformer_ctc/pretrained.py

@@ -34,7 +34,7 @@ from icefall.decode import (
     one_best_decoding,
     rescore_with_attention_decoder,
 )
-from icefall.utils import AttributeDict, get_texts
+from icefall.utils import AttributeDict, get_env_info, get_texts
 
 
 def get_parser():
@@ -52,14 +52,21 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--words-file",
+        "--tokens-file",
         type=str,
-        required=True,
+        help="Path to tokens.txt" "Used only when method is ctc-decoding",
-        help="Path to words.txt",
     )
 
     parser.add_argument(
-        "--HLG", type=str, required=True, help="Path to HLG.pt."
+        "--words-file",
+        type=str,
+        help="Path to words.txt" "Used when method is NOT ctc-decoding",
+    )
+
+    parser.add_argument(
+        "--HLG",
+        type=str,
+        help="Path to HLG.pt." "Used when method is NOT ctc-decoding",
     )
 
     parser.add_argument(
@@ -68,6 +75,8 @@ def get_parser():
         default="1best",
         help="""Decoding method.
         Possible values are:
+        (0) ctc-decoding - Use ctc decoding. It maps the tokens ids to tokens
+            using the token symbol table directly.
         (1) 1best - Use the best path as decoding output. Only
             the transformer encoder output is used for decoding.
             We call it HLG decoding.
@@ -111,7 +120,7 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--lattice-score-scale",
+        "--nbest-scale",
         type=float,
         default=0.5,
         help="""
@@ -125,7 +134,7 @@ def get_parser():
 
     parser.add_argument(
         "--sos-id",
-        type=float,
+        type=int,
         default=1,
         help="""
         Used only when method is attention-decoder.
@@ -135,7 +144,7 @@ def get_parser():
 
     parser.add_argument(
         "--eos-id",
-        type=float,
+        type=int,
         default=1,
         help="""
         Used only when method is attention-decoder.
@@ -143,6 +152,13 @@ def get_parser():
         """,
     )
 
+    parser.add_argument(
+        "--num_classes",
+        type=int,
+        default=4336,
+        help="The Vocab size.",
+    )
+
     parser.add_argument(
         "sound_files",
         type=str,
@@ -160,7 +176,6 @@ def get_params() -> AttributeDict:
     params = AttributeDict(
         {
             "sample_rate": 16000,
-            "num_classes": 4336,
             # parameters for conformer
             "subsampling_factor": 4,
             "feature_dim": 80,
@@ -175,6 +190,7 @@ def get_params() -> AttributeDict:
             "min_active_states": 30,
             "max_active_states": 10000,
             "use_double_scores": True,
+            "env_info": get_env_info(),
         }
     )
     return params
@@ -212,6 +228,11 @@ def main():
     params.update(vars(args))
     logging.info(f"{params}")
 
+    if args.method != "attention-decoder":
+        # to save memory as the attention decoder
+        # will not be used
+        params.num_decoder_layers = 0
+
     device = torch.device("cpu")
     if torch.cuda.is_available():
         device = torch.device("cuda", 0)
@@ -231,17 +252,10 @@ def main():
     )
 
     checkpoint = torch.load(args.checkpoint, map_location="cpu")
-    model.load_state_dict(checkpoint["model"])
+    model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()
 
-    logging.info(f"Loading HLG from {params.HLG}")
-    HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu"))
-    HLG = HLG.to(device)
-    if not hasattr(HLG, "lm_scores"):
-        # For whole-lattice-rescoring and attention-decoder
-        HLG.lm_scores = HLG.scores.clone()
-
     logging.info("Constructing Fbank computer")
     opts = kaldifeat.FbankOptions()
     opts.device = device
@@ -275,6 +289,42 @@ def main():
         dtype=torch.int32,
     )
 
+    if params.method == "ctc-decoding":
+        logging.info("Use CTC decoding")
+        token_sym_table = k2.SymbolTable.from_file(params.tokens_file)
+        max_token_id = params.num_classes - 1
+
+        H = k2.ctc_topo(
+            max_token=max_token_id,
+            modified=False,
+            device=device,
+        )
+
+        lattice = get_lattice(
+            nnet_output=nnet_output,
+            decoding_graph=H,
+            supervision_segments=supervision_segments,
+            search_beam=params.search_beam,
+            output_beam=params.output_beam,
+            min_active_states=params.min_active_states,
+            max_active_states=params.max_active_states,
+            subsampling_factor=params.subsampling_factor,
+        )
+
+        best_path = one_best_decoding(
+            lattice=lattice, use_double_scores=params.use_double_scores
+        )
+        token_ids = get_texts(best_path)
+        hyps = [[token_sym_table[i] for i in ids] for ids in token_ids]
+        hyps = [s.split() for s in hyps]
+    elif params.method in ["1best", "attention-decoder"]:
+        logging.info(f"Loading HLG from {params.HLG}")
+        HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu"))
+        HLG = HLG.to(device)
+        if not hasattr(HLG, "lm_scores"):
+            # For whole-lattice-rescoring and attention-decoder
+            HLG.lm_scores = HLG.scores.clone()
+
         lattice = get_lattice(
             nnet_output=nnet_output,
             HLG=HLG,
@@ -310,6 +360,8 @@ def main():
         hyps = get_texts(best_path)
         word_sym_table = k2.SymbolTable.from_file(params.words_file)
         hyps = [[word_sym_table[i] for i in ids] for ids in hyps]
+    else:
+        raise ValueError(f"Unsupported decoding method: {params.method}")
 
     s = "\n"
     for filename, hyp in zip(params.sound_files, hyps):

egs/aishell/ASR/local/compute_fbank_aishell.py

@@ -23,6 +23,7 @@ It looks for manifests in the directory data/manifests.
 The generated fbank features are saved in data/fbank.
 """
 
+import argparse
 import logging
 import os
 from pathlib import Path
@@ -43,7 +44,7 @@ torch.set_num_interop_threads(1)
 
 def compute_fbank_aishell(num_mel_bins: int = 80):
     src_dir = Path("data/manifests")
-    output_dir = Path("data/fbank40")
+    output_dir = Path("data/fbank")
     num_jobs = min(15, os.cpu_count())
 
     dataset_parts = (
@@ -106,4 +107,3 @@ if __name__ == "__main__":
 
     args = get_args()
     compute_fbank_aishell(num_mel_bins=args.num_mel_bins)
-

egs/aishell/ASR/local/compute_fbank_musan.py

@@ -23,6 +23,7 @@ It looks for manifests in the directory data/manifests.
 The generated fbank features are saved in data/fbank.
 """
 
+import argparse
 import logging
 import os
 from pathlib import Path
@@ -43,7 +44,7 @@ torch.set_num_interop_threads(1)
 
 def compute_fbank_musan(num_mel_bins: int = 80):
     src_dir = Path("data/manifests")
-    output_dir = Path("data/fbank40")
+    output_dir = Path("data/fbank")
     num_jobs = min(15, os.cpu_count())
 
     dataset_parts = (
@@ -86,6 +87,7 @@ def compute_fbank_musan(num_mel_bins: int = 80):
         )
         musan_cuts.to_json(musan_cuts_path)
 
+
 def get_args():
     parser = argparse.ArgumentParser()
     parser.add_argument(
@@ -106,4 +108,3 @@ if __name__ == "__main__":
     logging.basicConfig(format=formatter, level=logging.INFO)
     args = get_args()
     compute_fbank_musan(num_mel_bins=args.num_mel_bins)
-

egs/aishell/ASR/prepare.sh

@@ -69,7 +69,7 @@ if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
   #     |-- lexicon.txt
   #     `-- speaker.info
 
-  if [ ! -d $dl_dir/aishell/wav ]; then
+  if [ ! -d $dl_dir/aishell/data_aishell/wav ]; then
     lhotse download aishell $dl_dir
   fi
 
@@ -160,4 +160,3 @@ if [ $stage -le 8 ] && [ $stop_stage -ge 8 ]; then
   ./local/compile_hlg.py --lang-dir data/lang_phone
   ./local/compile_hlg.py --lang-dir data/lang_char
 fi
-