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Add more doc for the LibriSpeech recipe.

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Fangjun Kuang 2021-08-24 20:09:50 +08:00
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364
docs/source/recipes/librispeech/conformer_ctc.rst
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@ -14,6 +14,12 @@ with the `LibriSpeech <https://www.openslr.org/12>`_ dataset.
We recommend you to use a GPU or several GPUs to run this recipe.
In this tutorial, you will learn:
- (1) How to prepare data for training and decoding
- (2) How to start the training, either with a single GPU or multiple GPUs
- (3) How to do decoding after training, with n-gram LM rescoring and attention decoder rescoring
- (4) How to use a pre-trained model, provided by us
Data preparation
----------------
@ -81,12 +87,12 @@ The following options are used quite often:
- ``--full-libri``
If it's True, the training part uses all the training data, i.e.,
960 hours. Otherwise, the training part uses only 100 hours subset.
960 hours. Otherwise, the training part uses only the subset
``train-clean-100``, which has 100 hours of training data.
.. CAUTION::
The training set is perturbed by two different speeds:
one with a value 0.9 and the other is 1.1.
The training set is perturbed by speed with two factors: 0.9 and 1.1.
If ``--full-libri`` is True, each epoch actually processes
``3x960 == 2880`` hours of data.
@ -143,11 +149,11 @@ The following options are used quite often:
.. CAUTION::
Only multi-GPU single-machine DDP training is implemented at present.
Mult-GPU multi-machine DDP training will be added later.
Multi-GPU multi-machine DDP training will be added later.
- ``--max-duration``
It specifies number of seconds over all utterances in a
It specifies the number of seconds over all utterances in a
batch, before **padding**.
If you encounter CUDA OOM, please reduce it. For instance, if
your are using V100 NVIDIA GPU, we recommend you to set it to ``200``.
@ -157,8 +163,8 @@ The following options are used quite often:
Due to padding, the number of seconds of all utterances in a
batch will usually be larger than ``--max-duration``.
A large value for ``--max-duration`` may cause OOM during training,
while a small value may increase the training time. You have to
A larger value for ``--max-duration`` may cause OOM during training,
while a smaller value may increase the training time. You have to
tune it.
@ -272,6 +278,350 @@ training from epoch 3. Also, it trains for 10 epochs.
Decoding
--------
The decoding part uses checkpoints saved by the training part, so you have
to run the training part first.
.. code-block:: bash
$ cd egs/librispeech/ASR
$ ./conformer_ctc/decode.py --help
shows the options for decoding.
The commonly used options are:
- ``--method``
This specifies the decoding method.
The following command uses attention decoder for rescoring:
.. code-block::
$ cd egs/librispeech/ASR
$ ./conformer_ctc/decode.py --method attention-decoder --max-duration 30 --lattice-score-scale 0.5
- ``--lattice-score-scale``
It is used to scaled down lattice scores so that we can more unique
paths for rescoring.
- ``--max-duration``
It has the same meaning as the one during training. A larger
value may cause OOM.
Pre-trained Model
-----------------
We have uploaded the pre-trained model to
`<https://huggingface.co/pkufool/icefall_asr_librispeech_conformer_ctc>`_.
We describe how to use the pre-trained model to transcribe a sound file or
multiple sound files in the following.
Install kaldifeat
~~~~~~~~~~~~~~~~~
`kaldifeat <https://github.com/csukuangfj/kaldifeat>`_ is used to
extract features for a single sound file or multiple soundfiles
at the same time.
Please refer to `<https://github.com/csukuangfj/kaldifeat>`_ for installation.
Download the pre-trained model
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The following commands describe how to download the pre-trained model:
.. code-block::
$ cd egs/librispeech/ASR
$ mkdir tmp
$ cd tmp
$ git lfs install
$ git clone https://huggingface.co/pkufool/icefall_asr_librispeech_conformer_ctc
.. CAUTION::
You have to use ``git lfs`` to download the pre-trained model.
After downloading, you will have the following files:
.. code-block:: bash
$ cd egs/librispeech/ASR
$ tree tmp
.. code-block:: bash
tmp
`-- icefall_asr_librispeech_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 convenience 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:
.. code-block:: bash
$ soxi tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/*.flac
Input File : 'tmp/icefall_asr_librispeech_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/icefall_asr_librispeech_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/icefall_asr_librispeech_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
Usage
~~~~~
.. code-block::
$ cd egs/librispeech/ASR
$ ./conformer_ctc/pretrained.py --help
displays the help information.
It supports three decoding methods:
- HLG decoding
- HLG + n-gram LM rescoring
- HLG + n-gram LM rescoring + attention decoder rescoring
HLG decoding
^^^^^^^^^^^^
HLG decoding uses the best path of the decoding lattice as the decoding result.
The command to run HLG decoding is:
.. code-block:: bash
$ cd egs/librispeech/ASR
$ ./conformer_ctc/pretrained.py \
--checkpoint ./tmp/icefall_asr_librispeech_conformer_ctc/exp/pretraind.pt \
--words-file ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/words.txt \
--HLG ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/HLG.pt \
./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1089-134686-0001.flac \
./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0001.flac \
./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0002.flac
The output is given below:
.. code-block::
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/icefall_asr_librispeech_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/icefall_asr_librispeech_conformer_ctc/test_wavs/1089-134686-0001.flac', './tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0001.flac', './tmp/icefall_asr_librispeech_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/icefall_asr_librispeech_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/icefall_asr_librispeech_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/icefall_asr_librispeech_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
HLG decoding + LM rescoring
^^^^^^^^^^^^^^^^^^^^^^^^^^^
It uses an n-gram LM to rescore the decoding lattice and the best
path of the rescored lattice is the decoding result.
The command to run HLG decoding + LM rescoring is:
.. code-block:: bash
$ cd egs/librispeech/ASR
$ ./conformer_ctc/pretrained.py \
--checkpoint ./tmp/icefall_asr_librispeech_conformer_ctc/exp/pretraind.pt \
--words-file ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/words.txt \
--HLG ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/HLG.pt \
--method whole-lattice-rescoring \
--G ./tmp/icefall_asr_librispeech_conformer_ctc/data/lm/G_4_gram.pt \
--ngram-lm-scale 0.8 \
./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1089-134686-0001.flac \
./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0001.flac \
./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0002.flac
Its output is:
.. code-block::
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/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/HLG.pt
2021-08-20 11:12:30,035 INFO [pretrained.py:246] Loading G from ./tmp/icefall_asr_librispeech_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/icefall_asr_librispeech_conformer_ctc/test_wavs/1089-134686-0001.flac', './tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0001.flac', './tmp/icefall_asr_librispeech_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/icefall_asr_librispeech_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/icefall_asr_librispeech_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/icefall_asr_librispeech_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
HLG decoding + LM rescoring + attention decoder rescoring
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
It uses an n-gram LM to rescore the decoding lattice, extracts
n paths from the rescored lattice, recores the extracted paths with
an attention decoder. The path with the highest score is the decoding result.
The command to run HLG decoding + LM rescoring + attention decoder rescoring is:
.. code-block:: bash
$ cd egs/librispeech/ASR
$ ./conformer_ctc/pretrained.py \
--checkpoint ./tmp/icefall_asr_librispeech_conformer_ctc/exp/pretraind.pt \
--words-file ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/words.txt \
--HLG ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/HLG.pt \
--method attention-decoder \
--G ./tmp/icefall_asr_librispeech_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/icefall_asr_librispeech_conformer_ctc/test_wavs/1089-134686-0001.flac \
./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0001.flac \
./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0002.flac
The output is below:
.. code-block::
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/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/HLG.pt
2021-08-20 11:19:24,615 INFO [pretrained.py:246] Loading G from ./tmp/icefall_asr_librispeech_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/icefall_asr_librispeech_conformer_ctc/test_wavs/1089-134686-0001.flac', './tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0001.flac', './tmp/icefall_asr_librispeech_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/icefall_asr_librispeech_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/icefall_asr_librispeech_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/icefall_asr_librispeech_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
Colab notebook
--------------
We do provide a colab notebook for this recipe showing how to use a pre-trained model.
|librispeech asr conformer ctc colab notebook|
.. |librispeech asr conformer ctc colab notebook| image:: https://colab.research.google.com/assets/colab-badge.svg
:target: https://colab.research.google.com/drive/1huyupXAcHsUrKaWfI83iMEJ6J0Nh0213?usp=sharing
.. HINT::
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.
**Congratulations!** You have finished the librispeech ASR recipe with
conformer CTC models in ``icefall``.

86
egs/librispeech/ASR/conformer_ctc/decode.py
View File

@ -57,28 +57,63 @@ def get_parser():
parser.add_argument(
"--epoch",
type=int,
default=9,
default=34,
help="It specifies the checkpoint to use for decoding."
"Note: Epoch counts from 0.",
)
parser.add_argument(
"--avg",
type=int,
default=1,
default=20,
help="Number of checkpoints to average. Automatically select "
"consecutive checkpoints before the checkpoint specified by "
"'--epoch'. ",
)
parser.add_argument(
"--method",
type=str,
default="attention-decoder",
help="""Decoding method.
Supported values are:
- (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 with
the highest score is the decoding result.
- (3) nbest-rescoring. Extract n paths from the decoding lattice,
rescore them with an n-gram LM (e.g., a 4-gram LM), the path with
the highest score is the decoding result.
- (4) whole-lattice. Rescore the decoding lattice with an n-gram LM
(e.g., a 4-gram LM), the best path of rescored lattice is the
decoding result.
- (5) attention-decoder. Extract n paths from the LM rescored lattice,
the path with the highest score is the decoding result.
- (6) nbest-oracle. Its WER is the lower bound of any n-best
rescoring method can achieve. Useful for debugging n-best
rescoring method.
""",
)
parser.add_argument(
"--num-paths",
type=int,
default=100,
help="""Number of paths for n-best based decoding method.
Used only when "method" is one of the following values:
nbest, nbest-rescoring, attention-decoder, and nbest-oracle
""",
)
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.",
help="""The scale to be applied to `lattice.scores`.
It's needed if you use any kinds of n-best based rescoring.
Used only when "method" is one of the following values:
nbest, nbest-rescoring, attention-decoder, and nbest-oracle
A smaller value results in more unique paths.
""",
)
return parser
@ -104,21 +139,6 @@ def get_params() -> AttributeDict:
"min_active_states": 30,
"max_active_states": 10000,
"use_double_scores": True,
# Possible values for method:
# - 1best
# - nbest
# - 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",
# attention-decoder, and nbest-oracle
"num_paths": 100,
}
)
return params
@ -129,7 +149,7 @@ def decode_one_batch(
model: nn.Module,
HLG: k2.Fsa,
batch: dict,
lexicon: Lexicon,
word_table: k2.SymbolTable,
sos_id: int,
eos_id: int,
G: Optional[k2.Fsa] = None,
@ -163,8 +183,8 @@ def decode_one_batch(
It is the return value from iterating
`lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation
for the format of the `batch`.
lexicon:
It contains word symbol table.
word_table:
The word symbol table.
sos_id:
The token ID of the SOS.
eos_id:
@ -217,7 +237,7 @@ def decode_one_batch(
lattice=lattice,
num_paths=params.num_paths,
ref_texts=supervisions["text"],
lexicon=lexicon,
word_table=word_table,
scale=params.lattice_score_scale,
)
@ -237,7 +257,7 @@ def decode_one_batch(
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]
hyps = [[word_table[i] for i in ids] for ids in hyps]
return {key: hyps}
assert params.method in [
@ -283,7 +303,7 @@ def decode_one_batch(
ans = dict()
for lm_scale_str, best_path in best_path_dict.items():
hyps = get_texts(best_path)
hyps = [[lexicon.word_table[i] for i in ids] for ids in hyps]
hyps = [[word_table[i] for i in ids] for ids in hyps]
ans[lm_scale_str] = hyps
return ans
@ -293,7 +313,7 @@ def decode_dataset(
params: AttributeDict,
model: nn.Module,
HLG: k2.Fsa,
lexicon: Lexicon,
word_table: k2.SymbolTable,
sos_id: int,
eos_id: int,
G: Optional[k2.Fsa] = None,
@ -309,8 +329,8 @@ def decode_dataset(
The neural model.
HLG:
The decoding graph.
lexicon:
It contains word symbol table.
word_table:
It is the word symbol table.
sos_id:
The token ID for SOS.
eos_id:
@ -344,7 +364,7 @@ def decode_dataset(
model=model,
HLG=HLG,
batch=batch,
lexicon=lexicon,
word_table=word_table,
G=G,
sos_id=sos_id,
eos_id=eos_id,
@ -540,7 +560,7 @@ def main():
params=params,
model=model,
HLG=HLG,
lexicon=lexicon,
word_table=lexicon.word_table,
G=G,
sos_id=sos_id,
eos_id=eos_id,

10
icefall/decode.py
View File

@ -22,8 +22,6 @@ import kaldialign
import torch
import torch.nn as nn
from icefall.lexicon import Lexicon
def _get_random_paths(
lattice: k2.Fsa,
@ -623,7 +621,7 @@ def nbest_oracle(
lattice: k2.Fsa,
num_paths: int,
ref_texts: List[str],
lexicon: Lexicon,
word_table: k2.SymbolTable,
scale: float = 1.0,
) -> Dict[str, List[List[int]]]:
"""Select the best hypothesis given a lattice and a reference transcript.
@ -644,8 +642,8 @@ def nbest_oracle(
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.
word_table:
It is the word symbol table.
scale:
It's the scale applied to the lattice.scores. A smaller value
yields more unique paths.
@ -680,7 +678,7 @@ def nbest_oracle(
best_hyp_words = None
min_error = float("inf")
for hyp_words in hyps:
hyp_words = [lexicon.word_table[i] for i in hyp_words]
hyp_words = [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