Merge remote-tracking branch 'upstream/master'

README.md

@@ -53,6 +53,13 @@ It should print the path to `icefall`.
 At present, only LibriSpeech recipe is provided. Please
 follow [egs/librispeech/ASR/README.md][LibriSpeech] to run it.
 
+## Use Pre-trained models
+
+See [egs/librispeech/ASR/conformer_ctc/README.md](egs/librispeech/ASR/conformer_ctc/README.md)
+for how to use pre-trained models.
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1huyupXAcHsUrKaWfI83iMEJ6J0Nh0213?usp=sharing)
+
+
 [LibriSpeech]: egs/librispeech/ASR/README.md
 [k2-install]: https://k2.readthedocs.io/en/latest/installation/index.html#
 [k2]: https://github.com/k2-fsa/k2

egs/librispeech/ASR/RESULTS.md

@@ -0,0 +1,23 @@
+## Results
+
+### LibriSpeech BPE training results (Conformer-CTC)
+#### 2021-08-19
+(Wei Kang): Result of https://github.com/k2-fsa/icefall/pull/13
+
+TensorBoard log is available at https://tensorboard.dev/experiment/GnRzq8WWQW62dK4bklXBTg/#scalars
+
+Pretrained model is available at https://huggingface.co/pkufool/conformer_ctc
+
+The best decoding results (WER) are listed below, we got this results by averaging models from epoch 15 to 34, and using `attention-decoder` decoder with num_paths equals to 100.
+
+||test-clean|test-other|
+|--|--|--|
+|WER| 2.57% | 5.94% |
+
+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 WER above are also listed below.
+
+||lm_scale|attention_scale|
+|--|--|--|
+|test-clean|1.3|1.2|
+|test-other|1.2|1.1|
+

egs/librispeech/ASR/conformer_ctc/README.md

@@ -0,0 +1,351 @@
+
+# How to use a pre-trained model to transcribe a sound file or multiple sound files
+
+(See the bottom of this document for the link to a colab notebook.)
+
+You need to prepare 4 files:
+
+  - a model checkpoint file, e.g., epoch-20.pt
+  - HLG.pt, the decoding graph
+  - words.txt, the word symbol table
+  - a sound file, whose sampling rate has to be 16 kHz.
+    Supported formats are those supported by `torchaudio.load()`,
+    e.g., wav and flac.
+
+Also, you need to install `kaldifeat`. Please refer to
+<https://github.com/csukuangfj/kaldifeat> for installation.
+
+```bash
+./conformer_ctc/pretrained.py --help
+```
+
+displays the help information.
+
+## HLG decoding
+
+Once you have the above files ready and have `kaldifeat` installed,
+you can run:
+
+```bash
+./conformer_ctc/pretrained.py \
+  --checkpoint /path/to/your/checkpoint.pt \
+  --words-file /path/to/words.txt \
+  --HLG /path/to/HLG.pt \
+  /path/to/your/sound.wav
+```
+
+and you will see the transcribed result.
+
+If you want to transcribe multiple files at the same time, you can use:
+
+```bash
+./conformer_ctc/pretrained.py \
+  --checkpoint /path/to/your/checkpoint.pt \
+  --words-file /path/to/words.txt \
+  --HLG /path/to/HLG.pt \
+  /path/to/your/sound1.wav \
+  /path/to/your/sound2.wav \
+  /path/to/your/sound3.wav
+```
+
+**Note**: This is the fastest decoding method.
+
+## HLG decoding + LM rescoring
+
+`./conformer_ctc/pretrained.py` also supports `whole lattice LM rescoring`
+and `attention decoder rescoring`.
+
+To use whole lattice LM rescoring, you also need the following files:
+
+  - G.pt, e.g., `data/lm/G_4_gram.pt` if you have run `./prepare.sh`
+
+The command to run decoding with LM rescoring is:
+
+```bash
+./conformer_ctc/pretrained.py \
+  --checkpoint /path/to/your/checkpoint.pt \
+  --words-file /path/to/words.txt \
+  --HLG /path/to/HLG.pt \
+  --method whole-lattice-rescoring \
+  --G data/lm/G_4_gram.pt \
+  --ngram-lm-scale 0.8 \
+  /path/to/your/sound1.wav \
+  /path/to/your/sound2.wav \
+  /path/to/your/sound3.wav
+```
+
+## HLG Decoding + LM rescoring + attention decoder rescoring
+
+To use attention decoder for rescoring, you need the following extra information:
+
+  - sos token ID
+  - eos token ID
+
+The command to run decoding with attention decoder rescoring is:
+
+```bash
+./conformer_ctc/pretrained.py \
+  --checkpoint /path/to/your/checkpoint.pt \
+  --words-file /path/to/words.txt \
+  --HLG /path/to/HLG.pt \
+  --method attention-decoder \
+  --G data/lm/G_4_gram.pt \
+  --ngram-lm-scale 1.3 \
+  --attention-decoder-scale 1.2 \
+  --lattice-score-scale 0.5 \
+  --num-paths 100 \
+  --sos-id 1 \
+  --eos-id 1 \
+  /path/to/your/sound1.wav \
+  /path/to/your/sound2.wav \
+  /path/to/your/sound3.wav
+```
+
+# Decoding with a pre-trained model in action
+
+We have uploaded a pre-trained model to <https://huggingface.co/pkufool/conformer_ctc>
+
+The following shows the steps about the usage of the provided pre-trained model.
+
+### (1) Download the pre-trained model
+
+```bash
+sudo apt-get install git-lfs
+cd /path/to/icefall/egs/librispeech/ASR
+git lfs install
+mkdir tmp
+cd tmp
+git clone https://huggingface.co/pkufool/conformer_ctc
+```
+
+**CAUTION**: You have to install `git-lfst` to download the pre-trained model.
+
+You will find the following files:
+
+```
+tmp
+`-- conformer_ctc
+    |-- README.md
+    |-- data
+    |   |-- lang_bpe
+    |   |   |-- HLG.pt
+    |   |   |-- bpe.model
+    |   |   |-- tokens.txt
+    |   |   `-- words.txt
+    |   `-- lm
+    |       `-- G_4_gram.pt
+    |-- exp
+    |   `-- pretraind.pt
+    `-- test_wavs
+        |-- 1089-134686-0001.flac
+        |-- 1221-135766-0001.flac
+        |-- 1221-135766-0002.flac
+        `-- trans.txt
+
+6 directories, 11 files
+```
+
+**File descriptions**:
+
+  - `data/lang_bpe/HLG.pt`
+
+      It is the decoding graph.
+
+  - `data/lang_bpe/bpe.model`
+
+      It is a sentencepiece model. You can use it to reproduce our results.
+
+  - `data/lang_bpe/tokens.txt`
+
+      It contains tokens and their IDs, generated from `bpe.model`.
+      Provided only for convienice so that you can look up the SOS/EOS ID easily.
+
+  - `data/lang_bpe/words.txt`
+
+      It contains words and their IDs.
+
+  - `data/lm/G_4_gram.pt`
+
+      It is a 4-gram LM, useful for LM rescoring.
+
+  - `exp/pretrained.pt`
+
+      It contains pre-trained model parameters, obtained by averaging
+      checkpoints from `epoch-15.pt` to `epoch-34.pt`.
+      Note: We have removed optimizer `state_dict` to reduce file size.
+
+  - `test_waves/*.flac`
+
+      It contains some test sound files from LibriSpeech `test-clean` dataset.
+
+  - `test_waves/trans.txt`
+
+      It contains the reference transcripts for the sound files in `test_waves/`.
+
+The information of the test sound files is listed below:
+
+```
+$ soxi tmp/conformer_ctc/test_wavs/*.flac
+
+Input File     : 'tmp/conformer_ctc/test_wavs/1089-134686-0001.flac'
+Channels       : 1
+Sample Rate    : 16000
+Precision      : 16-bit
+Duration       : 00:00:06.62 = 106000 samples ~ 496.875 CDDA sectors
+File Size      : 116k
+Bit Rate       : 140k
+Sample Encoding: 16-bit FLAC
+
+Input File     : 'tmp/conformer_ctc/test_wavs/1221-135766-0001.flac'
+Channels       : 1
+Sample Rate    : 16000
+Precision      : 16-bit
+Duration       : 00:00:16.71 = 267440 samples ~ 1253.62 CDDA sectors
+File Size      : 343k
+Bit Rate       : 164k
+Sample Encoding: 16-bit FLAC
+
+Input File     : 'tmp/conformer_ctc/test_wavs/1221-135766-0002.flac'
+Channels       : 1
+Sample Rate    : 16000
+Precision      : 16-bit
+Duration       : 00:00:04.83 = 77200 samples ~ 361.875 CDDA sectors
+File Size      : 105k
+Bit Rate       : 174k
+Sample Encoding: 16-bit FLAC
+
+Total Duration of 3 files: 00:00:28.16
+```
+
+### (2) Use HLG decoding
+
+```bash
+cd /path/to/icefall/egs/librispeech/ASR
+
+./conformer_ctc/pretrained.py \
+  --checkpoint ./tmp/conformer_ctc/exp/pretraind.pt \
+  --words-file ./tmp/conformer_ctc/data/lang_bpe/words.txt \
+  --HLG ./tmp/conformer_ctc/data/lang_bpe/HLG.pt \
+  ./tmp/conformer_ctc/test_wavs/1089-134686-0001.flac \
+  ./tmp/conformer_ctc/test_wavs/1221-135766-0001.flac \
+  ./tmp/conformer_ctc/test_wavs/1221-135766-0002.flac
+```
+
+The output is given below:
+
+```
+2021-08-20 11:03:05,712 INFO [pretrained.py:217] device: cuda:0
+2021-08-20 11:03:05,712 INFO [pretrained.py:219] Creating model
+2021-08-20 11:03:11,345 INFO [pretrained.py:238] Loading HLG from ./tmp/conformer_ctc/data/lang_bpe/HLG.pt
+2021-08-20 11:03:18,442 INFO [pretrained.py:255] Constructing Fbank computer
+2021-08-20 11:03:18,444 INFO [pretrained.py:265] Reading sound files: ['./tmp/conformer_ctc/test_wavs/1089-134686-0001.flac', './tmp/conformer_ctc/test_wavs/1221-135766-0001.flac', './tmp/conformer_ctc/test_wavs/1221-135766-0002.flac']
+2021-08-20 11:03:18,507 INFO [pretrained.py:271] Decoding started
+2021-08-20 11:03:18,795 INFO [pretrained.py:300] Use HLG decoding
+2021-08-20 11:03:19,149 INFO [pretrained.py:339]
+./tmp/conformer_ctc/test_wavs/1089-134686-0001.flac:
+AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD LIGHT UP HERE AND THERE THE SQUALID QUARTER OF THE BROTHELS
+
+./tmp/conformer_ctc/test_wavs/1221-135766-0001.flac:
+GOD AS A DIRECT CONSEQUENCE OF THE SIN WHICH MAN THUS PUNISHED HAD GIVEN HER A LOVELY CHILD WHOSE PLACE WAS ON THAT SAME DISHONOURED
+BOSOM TO CONNECT HER PARENT FOR EVER WITH THE RACE AND DESCENT OF MORTALS AND TO BE FINALLY A BLESSED SOUL IN HEAVEN
+
+./tmp/conformer_ctc/test_wavs/1221-135766-0002.flac:
+YET THESE THOUGHTS AFFECTED HESTER PRYNNE LESS WITH HOPE THAN APPREHENSION
+
+
+2021-08-20 11:03:19,149 INFO [pretrained.py:341] Decoding Done
+```
+
+### (3) Use HLG decoding + LM rescoring
+
+```bash
+./conformer_ctc/pretrained.py \
+  --checkpoint ./tmp/conformer_ctc/exp/pretraind.pt \
+  --words-file ./tmp/conformer_ctc/data/lang_bpe/words.txt \
+  --HLG ./tmp/conformer_ctc/data/lang_bpe/HLG.pt \
+  --method whole-lattice-rescoring \
+  --G ./tmp/conformer_ctc/data/lm/G_4_gram.pt \
+  --ngram-lm-scale 0.8 \
+  ./tmp/conformer_ctc/test_wavs/1089-134686-0001.flac \
+  ./tmp/conformer_ctc/test_wavs/1221-135766-0001.flac \
+  ./tmp/conformer_ctc/test_wavs/1221-135766-0002.flac
+```
+
+The output is:
+
+```
+2021-08-20 11:12:17,565 INFO [pretrained.py:217] device: cuda:0
+2021-08-20 11:12:17,565 INFO [pretrained.py:219] Creating model
+2021-08-20 11:12:23,728 INFO [pretrained.py:238] Loading HLG from ./tmp/conformer_ctc/data/lang_bpe/HLG.pt
+2021-08-20 11:12:30,035 INFO [pretrained.py:246] Loading G from ./tmp/conformer_ctc/data/lm/G_4_gram.pt
+2021-08-20 11:13:10,779 INFO [pretrained.py:255] Constructing Fbank computer
+2021-08-20 11:13:10,787 INFO [pretrained.py:265] Reading sound files: ['./tmp/conformer_ctc/test_wavs/1089-134686-0001.flac', './tmp/conformer_ctc/test_wavs/1221-135766-0001.flac', './tmp/conformer_ctc/test_wavs/1221-135766-0002.flac']
+2021-08-20 11:13:10,798 INFO [pretrained.py:271] Decoding started
+2021-08-20 11:13:11,085 INFO [pretrained.py:305] Use HLG decoding + LM rescoring
+2021-08-20 11:13:11,736 INFO [pretrained.py:339]
+./tmp/conformer_ctc/test_wavs/1089-134686-0001.flac:
+AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD LIGHT UP HERE AND THERE THE SQUALID QUARTER OF THE BROTHELS
+
+./tmp/conformer_ctc/test_wavs/1221-135766-0001.flac:
+GOD AS A DIRECT CONSEQUENCE OF THE SIN WHICH MAN THUS PUNISHED HAD GIVEN HER A LOVELY CHILD WHOSE PLACE WAS ON THAT SAME DISHONOURED
+BOSOM TO CONNECT HER PARENT FOR EVER WITH THE RACE AND DESCENT OF MORTALS AND TO BE FINALLY A BLESSED SOUL IN HEAVEN
+
+./tmp/conformer_ctc/test_wavs/1221-135766-0002.flac:
+YET THESE THOUGHTS AFFECTED HESTER PRYNNE LESS WITH HOPE THAN APPREHENSION
+
+
+2021-08-20 11:13:11,737 INFO [pretrained.py:341] Decoding Done
+```
+
+### (4) Use HLG decoding + LM rescoring + attention decoder rescoring
+
+```bash
+./conformer_ctc/pretrained.py \
+  --checkpoint ./tmp/conformer_ctc/exp/pretraind.pt \
+  --words-file ./tmp/conformer_ctc/data/lang_bpe/words.txt \
+  --HLG ./tmp/conformer_ctc/data/lang_bpe/HLG.pt \
+  --method attention-decoder \
+  --G ./tmp/conformer_ctc/data/lm/G_4_gram.pt \
+  --ngram-lm-scale 1.3 \
+  --attention-decoder-scale 1.2 \
+  --lattice-score-scale 0.5 \
+  --num-paths 100 \
+  --sos-id 1 \
+  --eos-id 1 \
+  ./tmp/conformer_ctc/test_wavs/1089-134686-0001.flac \
+  ./tmp/conformer_ctc/test_wavs/1221-135766-0001.flac \
+  ./tmp/conformer_ctc/test_wavs/1221-135766-0002.flac
+```
+
+The output is:
+
+```
+2021-08-20 11:19:11,397 INFO [pretrained.py:217] device: cuda:0
+2021-08-20 11:19:11,397 INFO [pretrained.py:219] Creating model
+2021-08-20 11:19:17,354 INFO [pretrained.py:238] Loading HLG from ./tmp/conformer_ctc/data/lang_bpe/HLG.pt
+2021-08-20 11:19:24,615 INFO [pretrained.py:246] Loading G from ./tmp/conformer_ctc/data/lm/G_4_gram.pt
+2021-08-20 11:20:04,576 INFO [pretrained.py:255] Constructing Fbank computer
+2021-08-20 11:20:04,584 INFO [pretrained.py:265] Reading sound files: ['./tmp/conformer_ctc/test_wavs/1089-134686-0001.flac', './tmp/conformer_ctc/test_wavs/1221-135766-0001.flac', './tmp/conformer_ctc/test_wavs/1221-135766-0002.flac']
+2021-08-20 11:20:04,595 INFO [pretrained.py:271] Decoding started
+2021-08-20 11:20:04,854 INFO [pretrained.py:313] Use HLG + LM rescoring + attention decoder rescoring
+2021-08-20 11:20:05,805 INFO [pretrained.py:339]
+./tmp/conformer_ctc/test_wavs/1089-134686-0001.flac:
+AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD LIGHT UP HERE AND THERE THE SQUALID QUARTER OF THE BROTHELS
+
+./tmp/conformer_ctc/test_wavs/1221-135766-0001.flac:
+GOD AS A DIRECT CONSEQUENCE OF THE SIN WHICH MAN THUS PUNISHED HAD GIVEN HER A LOVELY CHILD WHOSE PLACE WAS ON THAT SAME DISHONOURED
+BOSOM TO CONNECT HER PARENT FOR EVER WITH THE RACE AND DESCENT OF MORTALS AND TO BE FINALLY A BLESSED SOUL IN HEAVEN
+
+./tmp/conformer_ctc/test_wavs/1221-135766-0002.flac:
+YET THESE THOUGHTS AFFECTED HESTER PRYNNE LESS WITH HOPE THAN APPREHENSION
+
+
+2021-08-20 11:20:05,805 INFO [pretrained.py:341] Decoding Done
+```
+
+**NOTE**: We provide a colab notebook for demonstration.
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1huyupXAcHsUrKaWfI83iMEJ6J0Nh0213?usp=sharing)
+
+Due to limited memory provided by Colab, you have to upgrade to Colab Pro to
+run `HLG decoding + LM rescoring` and `HLG decoding + LM rescoring + attention decoder rescoring`.
+Otherwise, you can only run `HLG decoding` with Colab.

egs/librispeech/ASR/conformer_ctc/asr_datamodule.py

@@ -0,0 +1 @@
+../tdnn_lstm_ctc/asr_datamodule.py

egs/librispeech/ASR/conformer_ctc/decode.py

@@ -13,14 +13,15 @@ from typing import Dict, List, Optional, Tuple
 import k2
 import torch
 import torch.nn as nn
+from asr_datamodule import LibriSpeechAsrDataModule
 from conformer import Conformer
 
 from icefall.bpe_graph_compiler import BpeCtcTrainingGraphCompiler
 from icefall.checkpoint import average_checkpoints, load_checkpoint
-from icefall.dataset.librispeech import LibriSpeechAsrDataModule
 from icefall.decode import (
     get_lattice,
     nbest_decoding,
+    nbest_oracle,
     one_best_decoding,
     rescore_with_attention_decoder,
     rescore_with_n_best_list,
@@ -56,6 +57,18 @@ def get_parser():
         "consecutive checkpoints before the checkpoint specified by "
         "'--epoch'. ",
     )
+
+    parser.add_argument(
+        "--lattice-score-scale",
+        type=float,
+        default=1.0,
+        help="The scale to be applied to `lattice.scores`."
+        "It's needed if you use any kinds of n-best based rescoring. "
+        "Currently, it is used when the decoding method is: nbest, "
+        "nbest-rescoring, attention-decoder, and nbest-oracle. "
+        "A smaller value results in more unique paths.",
+    )
+
     return parser
 
 
@@ -85,10 +98,14 @@ def get_params() -> AttributeDict:
             #  - nbest-rescoring
             #  - whole-lattice-rescoring
             #  - attention-decoder
+            #  - nbest-oracle
+            #  "method": "nbest",
+            #  "method": "nbest-rescoring",
             #  "method": "whole-lattice-rescoring",
             "method": "attention-decoder",
+            #  "method": "nbest-oracle",
             # num_paths is used when method is "nbest", "nbest-rescoring",
-            # and attention-decoder
+            # attention-decoder, and nbest-oracle
             "num_paths": 100,
         }
     )
@@ -179,6 +196,19 @@ def decode_one_batch(
         subsampling_factor=params.subsampling_factor,
     )
 
+    if params.method == "nbest-oracle":
+        # Note: You can also pass rescored lattices to it.
+        # We choose the HLG decoded lattice for speed reasons
+        # as HLG decoding is faster and the oracle WER
+        # is slightly worse than that of rescored lattices.
+        return nbest_oracle(
+            lattice=lattice,
+            num_paths=params.num_paths,
+            ref_texts=supervisions["text"],
+            lexicon=lexicon,
+            scale=params.lattice_score_scale,
+        )
+
     if params.method in ["1best", "nbest"]:
         if params.method == "1best":
             best_path = one_best_decoding(
@@ -190,8 +220,9 @@ def decode_one_batch(
                 lattice=lattice,
                 num_paths=params.num_paths,
                 use_double_scores=params.use_double_scores,
+                scale=params.lattice_score_scale,
             )
-            key = f"no_rescore-{params.num_paths}"
+            key = f"no_rescore-scale-{params.lattice_score_scale}-{params.num_paths}"  # noqa
 
         hyps = get_texts(best_path)
         hyps = [[lexicon.word_table[i] for i in ids] for ids in hyps]
@@ -212,6 +243,7 @@ def decode_one_batch(
             G=G,
             num_paths=params.num_paths,
             lm_scale_list=lm_scale_list,
+            scale=params.lattice_score_scale,
         )
     elif params.method == "whole-lattice-rescoring":
         best_path_dict = rescore_with_whole_lattice(
@@ -231,6 +263,7 @@ def decode_one_batch(
             memory_key_padding_mask=memory_key_padding_mask,
             sos_id=sos_id,
             eos_id=eos_id,
+            scale=params.lattice_score_scale,
         )
     else:
         assert False, f"Unsupported decoding method: {params.method}"
@@ -284,7 +317,11 @@ def decode_dataset(
     results = []
 
     num_cuts = 0
-    tot_num_cuts = len(dl.dataset.cuts)
+
+    try:
+        num_batches = len(dl)
+    except TypeError:
+        num_batches = "?"
 
     results = defaultdict(list)
     for batch_idx, batch in enumerate(dl):
@@ -313,10 +350,10 @@ def decode_dataset(
         num_cuts += len(batch["supervisions"]["text"])
 
         if batch_idx % 100 == 0:
+            batch_str = f"{batch_idx}/{num_batches}"
+
             logging.info(
-                f"batch {batch_idx}, cuts processed until now is "
+                f"batch {batch_str}, cuts processed until now is {num_cuts}"
-                f"{num_cuts}/{tot_num_cuts} "
-                f"({float(num_cuts)/tot_num_cuts*100:.6f}%)"
             )
     return results
 
@@ -376,7 +413,7 @@ def main():
     params = get_params()
     params.update(vars(args))
 
-    setup_logger(f"{params.exp_dir}/log/log-decode")
+    setup_logger(f"{params.exp_dir}/log-{params.method}/log-decode")
     logging.info("Decoding started")
     logging.info(params)
 
@@ -399,7 +436,9 @@ def main():
     sos_id = graph_compiler.sos_id
     eos_id = graph_compiler.eos_id
 
-    HLG = k2.Fsa.from_dict(torch.load(f"{params.lang_dir}/HLG.pt"))
+    HLG = k2.Fsa.from_dict(
+        torch.load(f"{params.lang_dir}/HLG.pt", map_location="cpu")
+    )
     HLG = HLG.to(device)
     assert HLG.requires_grad is False
 
@@ -430,7 +469,7 @@ def main():
                 torch.save(G.as_dict(), params.lm_dir / "G_4_gram.pt")
         else:
             logging.info("Loading pre-compiled G_4_gram.pt")
-            d = torch.load(params.lm_dir / "G_4_gram.pt")
+            d = torch.load(params.lm_dir / "G_4_gram.pt", map_location="cpu")
             G = k2.Fsa.from_dict(d).to(device)
 
         if params.method in ["whole-lattice-rescoring", "attention-decoder"]:

egs/librispeech/ASR/conformer_ctc/pretrained.py

@@ -0,0 +1,350 @@
+#!/usr/bin/env python3
+
+import argparse
+import logging
+import math
+from typing import List
+
+import k2
+import kaldifeat
+import torch
+import torchaudio
+from conformer import Conformer
+from torch.nn.utils.rnn import pad_sequence
+
+from icefall.decode import (
+    get_lattice,
+    one_best_decoding,
+    rescore_with_attention_decoder,
+    rescore_with_whole_lattice,
+)
+from icefall.utils import AttributeDict, get_texts
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--checkpoint",
+        type=str,
+        required=True,
+        help="Path to the checkpoint. "
+        "The checkpoint is assumed to be saved by "
+        "icefall.checkpoint.save_checkpoint().",
+    )
+
+    parser.add_argument(
+        "--words-file",
+        type=str,
+        required=True,
+        help="Path to words.txt",
+    )
+
+    parser.add_argument(
+        "--HLG", type=str, required=True, help="Path to HLG.pt."
+    )
+
+    parser.add_argument(
+        "--method",
+        type=str,
+        default="1best",
+        help="""Decoding method.
+        Possible values are:
+        (1) 1best - Use the best path as decoding output. Only
+            the transformer encoder output is used for decoding.
+            We call it HLG decoding.
+        (2) whole-lattice-rescoring - Use an LM to rescore the
+            decoding lattice and then use 1best to decode the
+            rescored lattice.
+            We call it HLG decoding + n-gram LM rescoring.
+        (3) attention-decoder - Extract n paths from he rescored
+            lattice and use the transformer attention decoder for
+            rescoring.
+            We call it HLG decoding + n-gram LM rescoring + attention
+            decoder rescoring.
+        """,
+    )
+
+    parser.add_argument(
+        "--G",
+        type=str,
+        help="""An LM for rescoring.
+        Used only when method is
+        whole-lattice-rescoring or attention-decoder.
+        It's usually a 4-gram LM.
+        """,
+    )
+
+    parser.add_argument(
+        "--num-paths",
+        type=int,
+        default=100,
+        help="""
+        Used only when method is attention-decoder.
+        It specifies the size of n-best list.""",
+    )
+
+    parser.add_argument(
+        "--ngram-lm-scale",
+        type=float,
+        default=1.3,
+        help="""
+        Used only when method is whole-lattice-rescoring and attention-decoder.
+        It specifies the scale for n-gram LM scores.
+        (Note: You need to tune it on a dataset.)
+        """,
+    )
+
+    parser.add_argument(
+        "--attention-decoder-scale",
+        type=float,
+        default=1.2,
+        help="""
+        Used only when method is attention-decoder.
+        It specifies the scale for attention decoder scores.
+        (Note: You need to tune it on a dataset.)
+        """,
+    )
+
+    parser.add_argument(
+        "--lattice-score-scale",
+        type=float,
+        default=0.5,
+        help="""
+        Used only when method is attention-decoder.
+        It specifies the scale for lattice.scores when
+        extracting n-best lists. A smaller value results in
+        more unique number of paths with the risk of missing
+        the best path.
+        """,
+    )
+
+    parser.add_argument(
+        "--sos-id",
+        type=float,
+        default=1,
+        help="""
+        Used only when method is attention-decoder.
+        It specifies ID for the SOS token.
+        """,
+    )
+
+    parser.add_argument(
+        "--eos-id",
+        type=float,
+        default=1,
+        help="""
+        Used only when method is attention-decoder.
+        It specifies ID for the EOS token.
+        """,
+    )
+
+    parser.add_argument(
+        "sound_files",
+        type=str,
+        nargs="+",
+        help="The input sound file(s) to transcribe. "
+        "Supported formats are those supported by torchaudio.load(). "
+        "For example, wav and flac are supported. "
+        "The sample rate has to be 16kHz.",
+    )
+
+    return parser
+
+
+def get_params() -> AttributeDict:
+    params = AttributeDict(
+        {
+            "feature_dim": 80,
+            "nhead": 8,
+            "num_classes": 5000,
+            "sample_rate": 16000,
+            "attention_dim": 512,
+            "subsampling_factor": 4,
+            "num_decoder_layers": 6,
+            "vgg_frontend": False,
+            "is_espnet_structure": True,
+            "mmi_loss": False,
+            "use_feat_batchnorm": True,
+            "search_beam": 20,
+            "output_beam": 8,
+            "min_active_states": 30,
+            "max_active_states": 10000,
+            "use_double_scores": True,
+        }
+    )
+    return params
+
+
+def read_sound_files(
+    filenames: List[str], expected_sample_rate: float
+) -> List[torch.Tensor]:
+    """Read a list of sound files into a list 1-D float32 torch tensors.
+    Args:
+      filenames:
+        A list of sound filenames.
+      expected_sample_rate:
+        The expected sample rate of the sound files.
+    Returns:
+      Return a list of 1-D float32 torch tensors.
+    """
+    ans = []
+    for f in filenames:
+        wave, sample_rate = torchaudio.load(f)
+        assert sample_rate == expected_sample_rate, (
+            f"expected sample rate: {expected_sample_rate}. "
+            f"Given: {sample_rate}"
+        )
+        # We use only the first channel
+        ans.append(wave[0])
+    return ans
+
+
+def main():
+    parser = get_parser()
+    args = parser.parse_args()
+
+    params = get_params()
+    params.update(vars(args))
+    logging.info(f"{params}")
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+
+    logging.info(f"device: {device}")
+
+    logging.info("Creating model")
+    model = Conformer(
+        num_features=params.feature_dim,
+        nhead=params.nhead,
+        d_model=params.attention_dim,
+        num_classes=params.num_classes,
+        subsampling_factor=params.subsampling_factor,
+        num_decoder_layers=params.num_decoder_layers,
+        vgg_frontend=params.vgg_frontend,
+        is_espnet_structure=params.is_espnet_structure,
+        mmi_loss=params.mmi_loss,
+        use_feat_batchnorm=params.use_feat_batchnorm,
+    )
+
+    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    model.load_state_dict(checkpoint["model"])
+    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()
+
+    if params.method in ["whole-lattice-rescoring", "attention-decoder"]:
+        logging.info(f"Loading G from {params.G}")
+        G = k2.Fsa.from_dict(torch.load(params.G, map_location="cpu"))
+        G = G.to(device)
+        # Add epsilon self-loops to G as we will compose
+        # it with the whole lattice later
+        G = k2.add_epsilon_self_loops(G)
+        G = k2.arc_sort(G)
+        G.lm_scores = G.scores.clone()
+
+    logging.info("Constructing Fbank computer")
+    opts = kaldifeat.FbankOptions()
+    opts.device = device
+    opts.frame_opts.dither = 0
+    opts.frame_opts.snip_edges = False
+    opts.frame_opts.samp_freq = params.sample_rate
+    opts.mel_opts.num_bins = params.feature_dim
+
+    fbank = kaldifeat.Fbank(opts)
+
+    logging.info(f"Reading sound files: {params.sound_files}")
+    waves = read_sound_files(
+        filenames=params.sound_files, expected_sample_rate=params.sample_rate
+    )
+    waves = [w.to(device) for w in waves]
+
+    logging.info(f"Decoding started")
+    features = fbank(waves)
+
+    features = pad_sequence(
+        features, batch_first=True, padding_value=math.log(1e-10)
+    )
+
+    # Note: We don't use key padding mask for attention during decoding
+    with torch.no_grad():
+        nnet_output, memory, memory_key_padding_mask = model(features)
+
+    batch_size = nnet_output.shape[0]
+    supervision_segments = torch.tensor(
+        [[i, 0, nnet_output.shape[1]] for i in range(batch_size)],
+        dtype=torch.int32,
+    )
+
+    lattice = get_lattice(
+        nnet_output=nnet_output,
+        HLG=HLG,
+        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,
+    )
+
+    if params.method == "1best":
+        logging.info("Use HLG decoding")
+        best_path = one_best_decoding(
+            lattice=lattice, use_double_scores=params.use_double_scores
+        )
+    elif params.method == "whole-lattice-rescoring":
+        logging.info("Use HLG decoding + LM rescoring")
+        best_path_dict = rescore_with_whole_lattice(
+            lattice=lattice,
+            G_with_epsilon_loops=G,
+            lm_scale_list=[params.ngram_lm_scale],
+        )
+        best_path = next(iter(best_path_dict.values()))
+    elif params.method == "attention-decoder":
+        logging.info("Use HLG + LM rescoring + attention decoder rescoring")
+        rescored_lattice = rescore_with_whole_lattice(
+            lattice=lattice, G_with_epsilon_loops=G, lm_scale_list=None
+        )
+        best_path_dict = rescore_with_attention_decoder(
+            lattice=rescored_lattice,
+            num_paths=params.num_paths,
+            model=model,
+            memory=memory,
+            memory_key_padding_mask=memory_key_padding_mask,
+            sos_id=params.sos_id,
+            eos_id=params.eos_id,
+            scale=params.lattice_score_scale,
+            ngram_lm_scale=params.ngram_lm_scale,
+            attention_scale=params.attention_decoder_scale,
+        )
+        best_path = next(iter(best_path_dict.values()))
+
+    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]
+
+    s = "\n"
+    for filename, hyp in zip(params.sound_files, hyps):
+        words = " ".join(hyp)
+        s += f"{filename}:\n{words}\n\n"
+    logging.info(s)
+
+    logging.info(f"Decoding Done")
+
+
+if __name__ == "__main__":
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    main()

egs/librispeech/ASR/conformer_ctc/train.py

@@ -13,6 +13,7 @@ import torch
 import torch.distributed as dist
 import torch.multiprocessing as mp
 import torch.nn as nn
+from asr_datamodule import LibriSpeechAsrDataModule
 from conformer import Conformer
 from lhotse.utils import fix_random_seed
 from torch.nn.parallel import DistributedDataParallel as DDP
@@ -23,7 +24,6 @@ from transformer import Noam
 from icefall.bpe_graph_compiler import BpeCtcTrainingGraphCompiler
 from icefall.checkpoint import load_checkpoint
 from icefall.checkpoint import save_checkpoint as save_checkpoint_impl
-from icefall.dataset.librispeech import LibriSpeechAsrDataModule
 from icefall.dist import cleanup_dist, setup_dist
 from icefall.lexicon import Lexicon
 from icefall.utils import (
@@ -60,9 +60,6 @@ def get_parser():
         help="Should various information be logged in tensorboard.",
     )
 
-    # TODO: add extra arguments and support DDP training.
-    # Currently, only single GPU training is implemented. Will add
-    # DDP training once single GPU training is finished.
     return parser
 
 
@@ -127,7 +124,7 @@ def get_params() -> AttributeDict:
     """
     params = AttributeDict(
         {
-            "exp_dir": Path("conformer_ctc/exp_new"),
+            "exp_dir": Path("conformer_ctc/exp"),
             "lang_dir": Path("data/lang_bpe"),
             "feature_dim": 80,
             "weight_decay": 1e-6,
@@ -145,7 +142,6 @@ def get_params() -> AttributeDict:
             "beam_size": 10,
             "reduction": "sum",
             "use_double_scores": True,
-            #
             "accum_grad": 1,
             "att_rate": 0.7,
             "attention_dim": 512,

egs/librispeech/ASR/tdnn_lstm_ctc/asr_datamodule.py

@@ -1,14 +1,16 @@
 import argparse
 import logging
+from functools import lru_cache
 from pathlib import Path
 from typing import List, Union
 
-from lhotse import Fbank, FbankConfig, load_manifest
+from lhotse import CutSet, Fbank, FbankConfig, load_manifest
 from lhotse.dataset import (
     BucketingSampler,
     CutConcatenate,
     CutMix,
     K2SpeechRecognitionDataset,
+    PrecomputedFeatures,
     SingleCutSampler,
     SpecAugment,
 )
@@ -19,7 +21,7 @@ from icefall.dataset.datamodule import DataModule
 from icefall.utils import str2bool
 
 
-class AsrDataModule(DataModule):
+class LibriSpeechAsrDataModule(DataModule):
     """
     DataModule for K2 ASR experiments.
     It assumes there is always one train and valid dataloader,
@@ -47,6 +49,13 @@ class AsrDataModule(DataModule):
             "effective batch sizes, sampling strategies, applied data "
             "augmentations, etc.",
         )
+        group.add_argument(
+            "--full-libri",
+            type=str2bool,
+            default=True,
+            help="When enabled, use 960h LibriSpeech. "
+            "Otherwise, use 100h subset.",
+        )
         group.add_argument(
             "--feature-dir",
             type=Path,
@@ -77,7 +86,7 @@ class AsrDataModule(DataModule):
         group.add_argument(
             "--concatenate-cuts",
             type=str2bool,
-            default=True,
+            default=False,
             help="When enabled, utterances (cuts) will be concatenated "
             "to minimize the amount of padding.",
         )
@@ -104,6 +113,29 @@ class AsrDataModule(DataModule):
             "extraction. Will drop existing precomputed feature manifests "
             "if available.",
         )
+        group.add_argument(
+            "--shuffle",
+            type=str2bool,
+            default=True,
+            help="When enabled (=default), the examples will be "
+            "shuffled for each epoch.",
+        )
+        group.add_argument(
+            "--return-cuts",
+            type=str2bool,
+            default=True,
+            help="When enabled, each batch will have the "
+            "field: batch['supervisions']['cut'] with the cuts that "
+            "were used to construct it.",
+        )
+
+        group.add_argument(
+            "--num-workers",
+            type=int,
+            default=2,
+            help="The number of training dataloader workers that "
+            "collect the batches.",
+        )
 
     def train_dataloaders(self) -> DataLoader:
         logging.info("About to get train cuts")
@@ -138,9 +170,9 @@ class AsrDataModule(DataModule):
         ]
 
         train = K2SpeechRecognitionDataset(
-            cuts_train,
             cut_transforms=transforms,
             input_transforms=input_transforms,
+            return_cuts=self.args.return_cuts,
         )
 
         if self.args.on_the_fly_feats:
@@ -154,14 +186,13 @@ class AsrDataModule(DataModule):
             # to be strict (e.g. could be randomized)
             # transforms = [PerturbSpeed(factors=[0.9, 1.1], p=2/3)] + transforms   # noqa
             # Drop feats to be on the safe side.
-            cuts_train = cuts_train.drop_features()
             train = K2SpeechRecognitionDataset(
-                cuts=cuts_train,
                 cut_transforms=transforms,
                 input_strategy=OnTheFlyFeatures(
                     Fbank(FbankConfig(num_mel_bins=80))
                 ),
                 input_transforms=input_transforms,
+                return_cuts=self.args.return_cuts,
             )
 
         if self.args.bucketing_sampler:
@@ -169,44 +200,60 @@ class AsrDataModule(DataModule):
             train_sampler = BucketingSampler(
                 cuts_train,
                 max_duration=self.args.max_duration,
-                shuffle=True,
+                shuffle=self.args.shuffle,
                 num_buckets=self.args.num_buckets,
+                bucket_method="equal_duration",
+                drop_last=True,
             )
         else:
             logging.info("Using SingleCutSampler.")
             train_sampler = SingleCutSampler(
                 cuts_train,
                 max_duration=self.args.max_duration,
-                shuffle=True,
+                shuffle=self.args.shuffle,
             )
         logging.info("About to create train dataloader")
+
         train_dl = DataLoader(
             train,
             sampler=train_sampler,
             batch_size=None,
-            num_workers=4,
+            num_workers=self.args.num_workers,
-            persistent_workers=True,
+            persistent_workers=False,
         )
+
         return train_dl
 
     def valid_dataloaders(self) -> DataLoader:
         logging.info("About to get dev cuts")
         cuts_valid = self.valid_cuts()
 
+        transforms = []
+        if self.args.concatenate_cuts:
+            transforms = [
+                CutConcatenate(
+                    duration_factor=self.args.duration_factor, gap=self.args.gap
+                )
+            ] + transforms
+
         logging.info("About to create dev dataset")
         if self.args.on_the_fly_feats:
-            cuts_valid = cuts_valid.drop_features()
             validate = K2SpeechRecognitionDataset(
-                cuts_valid.drop_features(),
+                cut_transforms=transforms,
                 input_strategy=OnTheFlyFeatures(
                     Fbank(FbankConfig(num_mel_bins=80))
                 ),
+                return_cuts=self.args.return_cuts,
             )
         else:
-            validate = K2SpeechRecognitionDataset(cuts_valid)
+            validate = K2SpeechRecognitionDataset(
+                cut_transforms=transforms,
+                return_cuts=self.args.return_cuts,
+            )
         valid_sampler = SingleCutSampler(
             cuts_valid,
             max_duration=self.args.max_duration,
+            shuffle=False,
         )
         logging.info("About to create dev dataloader")
         valid_dl = DataLoader(
@@ -214,8 +261,9 @@ class AsrDataModule(DataModule):
             sampler=valid_sampler,
             batch_size=None,
             num_workers=2,
-            persistent_workers=True,
+            persistent_workers=False,
         )
+
         return valid_dl
 
     def test_dataloaders(self) -> Union[DataLoader, List[DataLoader]]:
@@ -228,10 +276,12 @@ class AsrDataModule(DataModule):
         for cuts_test in cuts:
             logging.debug("About to create test dataset")
             test = K2SpeechRecognitionDataset(
-                cuts_test,
                 input_strategy=OnTheFlyFeatures(
                     Fbank(FbankConfig(num_mel_bins=80))
-                ),
+                )
+                if self.args.on_the_fly_feats
+                else PrecomputedFeatures(),
+                return_cuts=self.args.return_cuts,
             )
             sampler = SingleCutSampler(
                 cuts_test, max_duration=self.args.max_duration
@@ -246,3 +296,42 @@ class AsrDataModule(DataModule):
             return test_loaders
         else:
             return test_loaders[0]
+
+    @lru_cache()
+    def train_cuts(self) -> CutSet:
+        logging.info("About to get train cuts")
+        cuts_train = load_manifest(
+            self.args.feature_dir / "cuts_train-clean-100.json.gz"
+        )
+        if self.args.full_libri:
+            cuts_train = (
+                cuts_train
+                + load_manifest(
+                    self.args.feature_dir / "cuts_train-clean-360.json.gz"
+                )
+                + load_manifest(
+                    self.args.feature_dir / "cuts_train-other-500.json.gz"
+                )
+            )
+        return cuts_train
+
+    @lru_cache()
+    def valid_cuts(self) -> CutSet:
+        logging.info("About to get dev cuts")
+        cuts_valid = load_manifest(
+            self.args.feature_dir / "cuts_dev-clean.json.gz"
+        ) + load_manifest(self.args.feature_dir / "cuts_dev-other.json.gz")
+        return cuts_valid
+
+    @lru_cache()
+    def test_cuts(self) -> List[CutSet]:
+        test_sets = ["test-clean", "test-other"]
+        cuts = []
+        for test_set in test_sets:
+            logging.debug("About to get test cuts")
+            cuts.append(
+                load_manifest(
+                    self.args.feature_dir / f"cuts_{test_set}.json.gz"
+                )
+            )
+        return cuts

egs/librispeech/ASR/tdnn_lstm_ctc/decode.py

@@ -10,10 +10,10 @@ from typing import Dict, List, Optional, Tuple
 import k2
 import torch
 import torch.nn as nn
+from asr_datamodule import LibriSpeechAsrDataModule
 from model import TdnnLstm
 
 from icefall.checkpoint import average_checkpoints, load_checkpoint
-from icefall.dataset.librispeech import LibriSpeechAsrDataModule
 from icefall.decode import (
     get_lattice,
     nbest_decoding,
@@ -236,7 +236,11 @@ def decode_dataset(
     results = []
 
     num_cuts = 0
-    tot_num_cuts = len(dl.dataset.cuts)
+
+    try:
+        num_batches = len(dl)
+    except TypeError:
+        num_batches = "?"
 
     results = defaultdict(list)
     for batch_idx, batch in enumerate(dl):
@@ -263,10 +267,10 @@ def decode_dataset(
         num_cuts += len(batch["supervisions"]["text"])
 
         if batch_idx % 100 == 0:
+            batch_str = f"{batch_idx}/{num_batches}"
+
             logging.info(
-                f"batch {batch_idx}, cuts processed until now is "
+                f"batch {batch_str}, cuts processed until now is {num_cuts}"
-                f"{num_cuts}/{tot_num_cuts} "
-                f"({float(num_cuts)/tot_num_cuts*100:.6f}%)"
             )
     return results
 
@@ -328,7 +332,9 @@ def main():
 
     logging.info(f"device: {device}")
 
-    HLG = k2.Fsa.from_dict(torch.load("data/lang_phone/HLG.pt"))
+    HLG = k2.Fsa.from_dict(
+        torch.load("data/lang_phone/HLG.pt", map_location="cpu")
+    )
     HLG = HLG.to(device)
     assert HLG.requires_grad is False
 
@@ -355,7 +361,7 @@ def main():
                 torch.save(G.as_dict(), params.lm_dir / "G_4_gram.pt")
         else:
             logging.info("Loading pre-compiled G_4_gram.pt")
-            d = torch.load(params.lm_dir / "G_4_gram.pt")
+            d = torch.load(params.lm_dir / "G_4_gram.pt", map_location="cpu")
             G = k2.Fsa.from_dict(d).to(device)
 
         if params.method == "whole-lattice-rescoring":

egs/librispeech/ASR/tdnn_lstm_ctc/train.py

@@ -1,7 +1,5 @@
 #!/usr/bin/env python3
 
-# This is just at the very beginning ...
-
 import argparse
 import logging
 from pathlib import Path
@@ -14,16 +12,16 @@ import torch.distributed as dist
 import torch.multiprocessing as mp
 import torch.nn as nn
 import torch.optim as optim
+from asr_datamodule import LibriSpeechAsrDataModule
 from lhotse.utils import fix_random_seed
 from model import TdnnLstm
 from torch.nn.parallel import DistributedDataParallel as DDP
-from torch.nn.utils import clip_grad_value_
+from torch.nn.utils import clip_grad_norm_
 from torch.optim.lr_scheduler import StepLR
 from torch.utils.tensorboard import SummaryWriter
 
 from icefall.checkpoint import load_checkpoint
 from icefall.checkpoint import save_checkpoint as save_checkpoint_impl
-from icefall.dataset.librispeech import LibriSpeechAsrDataModule
 from icefall.dist import cleanup_dist, setup_dist
 from icefall.graph_compiler import CtcTrainingGraphCompiler
 from icefall.lexicon import Lexicon
@@ -61,9 +59,6 @@ def get_parser():
         help="Should various information be logged in tensorboard.",
     )
 
-    # TODO: add extra arguments and support DDP training.
-    # Currently, only single GPU training is implemented. Will add
-    # DDP training once single GPU training is finished.
     return parser
 
 
@@ -406,7 +401,7 @@ def train_one_epoch(
 
         optimizer.zero_grad()
         loss.backward()
-        clip_grad_value_(model.parameters(), 5.0)
+        clip_grad_norm_(model.parameters(), 5.0, 2.0)
         optimizer.step()
 
         loss_cpu = loss.detach().cpu().item()

icefall/checkpoint.py

@@ -91,7 +91,7 @@ def load_checkpoint(
     checkpoint.pop("model")
 
     def load(name, obj):
-        s = checkpoint[name]
+        s = checkpoint.get(name, None)
         if obj and s:
             obj.load_state_dict(s)
             checkpoint.pop(name)

icefall/dataset/librispeech.py

@@ -1,68 +0,0 @@
-import argparse
-import logging
-from functools import lru_cache
-from typing import List
-
-from lhotse import CutSet, load_manifest
-
-from icefall.dataset.asr_datamodule import AsrDataModule
-from icefall.utils import str2bool
-
-
-class LibriSpeechAsrDataModule(AsrDataModule):
-    """
-    LibriSpeech ASR data module. Can be used for 100h subset
-    (``--full-libri false``) or full 960h set.
-    The train and valid cuts for standard Libri splits are
-    concatenated into a single CutSet/DataLoader.
-    """
-
-    @classmethod
-    def add_arguments(cls, parser: argparse.ArgumentParser):
-        super().add_arguments(parser)
-        group = parser.add_argument_group(title="LibriSpeech specific options")
-        group.add_argument(
-            "--full-libri",
-            type=str2bool,
-            default=True,
-            help="When enabled, use 960h LibriSpeech.",
-        )
-
-    @lru_cache()
-    def train_cuts(self) -> CutSet:
-        logging.info("About to get train cuts")
-        cuts_train = load_manifest(
-            self.args.feature_dir / "cuts_train-clean-100.json.gz"
-        )
-        if self.args.full_libri:
-            cuts_train = (
-                cuts_train
-                + load_manifest(
-                    self.args.feature_dir / "cuts_train-clean-360.json.gz"
-                )
-                + load_manifest(
-                    self.args.feature_dir / "cuts_train-other-500.json.gz"
-                )
-            )
-        return cuts_train
-
-    @lru_cache()
-    def valid_cuts(self) -> CutSet:
-        logging.info("About to get dev cuts")
-        cuts_valid = load_manifest(
-            self.args.feature_dir / "cuts_dev-clean.json.gz"
-        ) + load_manifest(self.args.feature_dir / "cuts_dev-other.json.gz")
-        return cuts_valid
-
-    @lru_cache()
-    def test_cuts(self) -> List[CutSet]:
-        test_sets = ["test-clean", "test-other"]
-        cuts = []
-        for test_set in test_sets:
-            logging.debug("About to get test cuts")
-            cuts.append(
-                load_manifest(
-                    self.args.feature_dir / f"cuts_{test_set}.json.gz"
-                )
-            )
-        return cuts

icefall/decode.py

@@ -2,9 +2,42 @@ import logging
 from typing import Dict, List, Optional, Tuple, Union
 
 import k2
+import kaldialign
 import torch
 import torch.nn as nn
 
+from icefall.lexicon import Lexicon
+
+
+def _get_random_paths(
+    lattice: k2.Fsa,
+    num_paths: int,
+    use_double_scores: bool = True,
+    scale: float = 1.0,
+):
+    """
+    Args:
+      lattice:
+        The decoding lattice, returned by :func:`get_lattice`.
+      num_paths:
+        It specifies the size `n` in n-best. Note: Paths are selected randomly
+        and those containing identical word sequences are remove dand only one
+        of them is kept.
+      use_double_scores:
+        True to use double precision floating point in the computation.
+        False to use single precision.
+      scale:
+        It's the scale applied to the lattice.scores. A smaller value
+        yields more unique paths.
+    Returns:
+      Return a k2.RaggedInt with 3 axes [seq][path][arc_pos]
+    """
+    saved_scores = lattice.scores.clone()
+    lattice.scores *= scale
+    path = k2.random_paths(lattice, num_paths=num_paths, use_double_scores=True)
+    lattice.scores = saved_scores
+    return path
+
 
 def _intersect_device(
     a_fsas: k2.Fsa,
@@ -129,7 +162,10 @@ def one_best_decoding(
 
 
 def nbest_decoding(
-    lattice: k2.Fsa, num_paths: int, use_double_scores: bool = True
+    lattice: k2.Fsa,
+    num_paths: int,
+    use_double_scores: bool = True,
+    scale: float = 1.0,
 ) -> k2.Fsa:
     """It implements something like CTC prefix beam search using n-best lists.
 
@@ -152,12 +188,18 @@ def nbest_decoding(
       use_double_scores:
         True to use double precision floating point in the computation.
         False to use single precision.
+      scale:
+        It's the scale applied to the lattice.scores. A smaller value
+        yields more unique paths.
     Returns:
       An FsaVec containing linear FSAs.
     """
-    # First, extract `num_paths` paths for each sequence.
+    path = _get_random_paths(
-    # path is a k2.RaggedInt with axes [seq][path][arc_pos]
+        lattice=lattice,
-    path = k2.random_paths(lattice, num_paths=num_paths, use_double_scores=True)
+        num_paths=num_paths,
+        use_double_scores=use_double_scores,
+        scale=scale,
+    )
 
     # word_seq is a k2.RaggedInt sharing the same shape as `path`
     # but it contains word IDs. Note that it also contains 0s and -1s.
@@ -320,7 +362,11 @@ def compute_am_and_lm_scores(
 
 
 def rescore_with_n_best_list(
-    lattice: k2.Fsa, G: k2.Fsa, num_paths: int, lm_scale_list: List[float]
+    lattice: k2.Fsa,
+    G: k2.Fsa,
+    num_paths: int,
+    lm_scale_list: List[float],
+    scale: float = 1.0,
 ) -> Dict[str, k2.Fsa]:
     """Decode using n-best list with LM rescoring.
 
@@ -342,6 +388,9 @@ def rescore_with_n_best_list(
         It is the size `n` in `n-best` list.
       lm_scale_list:
         A list containing lm_scale values.
+      scale:
+        It's the scale applied to the lattice.scores. A smaller value
+        yields more unique paths.
     Returns:
       A dict of FsaVec, whose key is an lm_scale and the value is the
       best decoding path for each sequence in the lattice.
@@ -356,9 +405,12 @@ def rescore_with_n_best_list(
     assert G.device == device
     assert hasattr(G, "aux_labels") is False
 
-    # First, extract `num_paths` paths for each sequence.
+    path = _get_random_paths(
-    # path is a k2.RaggedInt with axes [seq][path][arc_pos]
+        lattice=lattice,
-    path = k2.random_paths(lattice, num_paths=num_paths, use_double_scores=True)
+        num_paths=num_paths,
+        use_double_scores=True,
+        scale=scale,
+    )
 
     # word_seq is a k2.RaggedInt sharing the same shape as `path`
     # but it contains word IDs. Note that it also contains 0s and -1s.
@@ -376,7 +428,7 @@ def rescore_with_n_best_list(
     #
     # num_repeats is also a k2.RaggedInt with 2 axes containing the
     # multiplicities of each path.
-    # num_repeats.num_elements() == unique_word_seqs.num_elements()
+    # num_repeats.num_elements() == unique_word_seqs.tot_size(1)
     #
     # Since k2.ragged.unique_sequences will reorder paths within a seq,
     # `new2old` is a 1-D torch.Tensor mapping from the output path index
@@ -494,6 +546,8 @@ def rescore_with_whole_lattice(
     del lattice.lm_scores
     assert hasattr(lattice, "lm_scores") is False
 
+    assert hasattr(G_with_epsilon_loops, "lm_scores")
+
     # Now, lattice.scores contains only am_scores
 
     # inv_lattice has word IDs as labels.
@@ -549,14 +603,88 @@ def rescore_with_whole_lattice(
     return ans
 
 
+def nbest_oracle(
+    lattice: k2.Fsa,
+    num_paths: int,
+    ref_texts: List[str],
+    lexicon: Lexicon,
+    scale: float = 1.0,
+) -> Dict[str, List[List[int]]]:
+    """Select the best hypothesis given a lattice and a reference transcript.
+
+    The basic idea is to extract n paths from the given lattice, unique them,
+    and select the one that has the minimum edit distance with the corresponding
+    reference transcript as the decoding output.
+
+    The decoding result returned from this function is the best result that
+    we can obtain using n-best decoding with all kinds of rescoring techniques.
+
+    Args:
+      lattice:
+        An FsaVec. It can be the return value of :func:`get_lattice`.
+        Note: We assume its aux_labels contain word IDs.
+      num_paths:
+        The size of `n` in n-best.
+      ref_texts:
+        A list of reference transcript. Each entry contains space(s)
+        separated words
+      lexicon:
+        It is used to convert word IDs to word symbols.
+      scale:
+        It's the scale applied to the lattice.scores. A smaller value
+        yields more unique paths.
+    Return:
+      Return a dict. Its key contains the information about the parameters
+      when calling this function, while its value contains the decoding output.
+      `len(ans_dict) == len(ref_texts)`
+    """
+    path = _get_random_paths(
+        lattice=lattice,
+        num_paths=num_paths,
+        use_double_scores=True,
+        scale=scale,
+    )
+
+    word_seq = k2.index(lattice.aux_labels, path)
+    word_seq = k2.ragged.remove_values_leq(word_seq, 0)
+    unique_word_seq, _, _ = k2.ragged.unique_sequences(
+        word_seq, need_num_repeats=False, need_new2old_indexes=False
+    )
+    unique_word_ids = k2.ragged.to_list(unique_word_seq)
+    assert len(unique_word_ids) == len(ref_texts)
+    # unique_word_ids[i] contains all hypotheses of the i-th utterance
+
+    results = []
+    for hyps, ref in zip(unique_word_ids, ref_texts):
+        # Note hyps is a list-of-list ints
+        # Each sublist contains a hypothesis
+        ref_words = ref.strip().split()
+        # CAUTION: We don't convert ref_words to ref_words_ids
+        # since there may exist OOV words in ref_words
+        best_hyp_words = None
+        min_error = float("inf")
+        for hyp_words in hyps:
+            hyp_words = [lexicon.word_table[i] for i in hyp_words]
+            this_error = kaldialign.edit_distance(ref_words, hyp_words)["total"]
+            if this_error < min_error:
+                min_error = this_error
+                best_hyp_words = hyp_words
+        results.append(best_hyp_words)
+
+    return {f"nbest_{num_paths}_scale_{scale}_oracle": results}
+
+
 def rescore_with_attention_decoder(
     lattice: k2.Fsa,
     num_paths: int,
     model: nn.Module,
     memory: torch.Tensor,
-    memory_key_padding_mask: torch.Tensor,
+    memory_key_padding_mask: Optional[torch.Tensor],
     sos_id: int,
     eos_id: int,
+    scale: float = 1.0,
+    ngram_lm_scale: Optional[float] = None,
+    attention_scale: Optional[float] = None,
 ) -> Dict[str, k2.Fsa]:
     """This function extracts n paths from the given lattice and uses
     an attention decoder to rescore them. The path with the highest
@@ -580,6 +708,13 @@ def rescore_with_attention_decoder(
         The token ID for SOS.
       eos_id:
         The token ID for EOS.
+      scale:
+        It's the scale applied to the lattice.scores. A smaller value
+        yields more unique paths.
+      ngram_lm_scale:
+        Optional. It specifies the scale for n-gram LM scores.
+      attention_scale:
+        Optional. It specifies the scale for attention decoder scores.
     Returns:
       A dict of FsaVec, whose key contains a string
       ngram_lm_scale_attention_scale and the value is the
@@ -587,7 +722,12 @@ def rescore_with_attention_decoder(
     """
     # First, extract `num_paths` paths for each sequence.
     # path is a k2.RaggedInt with axes [seq][path][arc_pos]
-    path = k2.random_paths(lattice, num_paths=num_paths, use_double_scores=True)
+    path = _get_random_paths(
+        lattice=lattice,
+        num_paths=num_paths,
+        use_double_scores=True,
+        scale=scale,
+    )
 
     # word_seq is a k2.RaggedInt sharing the same shape as `path`
     # but it contains word IDs. Note that it also contains 0s and -1s.
@@ -605,12 +745,12 @@ def rescore_with_attention_decoder(
     #
     # num_repeats is also a k2.RaggedInt with 2 axes containing the
     # multiplicities of each path.
-    # num_repeats.num_elements() == unique_word_seqs.num_elements()
+    # num_repeats.num_elements() == unique_word_seqs.tot_size(1)
     #
     # Since k2.ragged.unique_sequences will reorder paths within a seq,
     # `new2old` is a 1-D torch.Tensor mapping from the output path index
     # to the input path index.
-    # new2old.numel() == unique_word_seqs.tot_size(1)
+    # new2old.numel() == unique_word_seq.tot_size(1)
     unique_word_seq, num_repeats, new2old = k2.ragged.unique_sequences(
         word_seq, need_num_repeats=True, need_new2old_indexes=True
     )
@@ -662,11 +802,13 @@ def rescore_with_attention_decoder(
     path_to_seq_map_long = path_to_seq_map.to(torch.long)
     expanded_memory = memory.index_select(1, path_to_seq_map_long)
 
+    if memory_key_padding_mask is not None:
         expanded_memory_key_padding_mask = memory_key_padding_mask.index_select(
             0, path_to_seq_map_long
         )
+    else:
+        expanded_memory_key_padding_mask = None
 
-    # TODO: pass the sos_token_id and eos_token_id via function arguments
     nll = model.decoder_nll(
         memory=expanded_memory,
         memory_key_padding_mask=expanded_memory_key_padding_mask,
@@ -681,11 +823,17 @@ def rescore_with_attention_decoder(
     assert attention_scores.ndim == 1
     assert attention_scores.numel() == num_word_seqs
 
+    if ngram_lm_scale is None:
         ngram_lm_scale_list = [0.1, 0.3, 0.5, 0.6, 0.7, 0.9, 1.0]
         ngram_lm_scale_list += [1.1, 1.2, 1.3, 1.5, 1.7, 1.9, 2.0]
+    else:
+        ngram_lm_scale_list = [ngram_lm_scale]
 
+    if attention_scale is None:
         attention_scale_list = [0.1, 0.3, 0.5, 0.6, 0.7, 0.9, 1.0]
         attention_scale_list += [1.1, 1.2, 1.3, 1.5, 1.7, 1.9, 2.0]
+    else:
+        attention_scale_list = [attention_scale]
 
     path_2axes = k2.ragged.remove_axis(path, 0)