mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-08-13 12:02:21 +00:00
Merge remote-tracking branch 'upstream/master'
This commit is contained in:
Daniel Povey
commit
24d3a98378
@ -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.
|
||||
[](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
|
||||
|
||||
23
egs/librispeech/ASR/RESULTS.md
Normal file
23
egs/librispeech/ASR/RESULTS.md
Normal file
@ -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|
|
||||
|
||||
351
egs/librispeech/ASR/conformer_ctc/README.md
Normal file
351
egs/librispeech/ASR/conformer_ctc/README.md
Normal file
@ -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.
|
||||
[](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.
|
||||
1
egs/librispeech/ASR/conformer_ctc/asr_datamodule.py
Symbolic link
1
egs/librispeech/ASR/conformer_ctc/asr_datamodule.py
Symbolic link
@ -0,0 +1 @@
|
||||
../tdnn_lstm_ctc/asr_datamodule.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"{num_cuts}/{tot_num_cuts} "
|
||||
f"({float(num_cuts)/tot_num_cuts*100:.6f}%)"
|
||||
f"batch {batch_str}, cuts processed until now is {num_cuts}"
|
||||
)
|
||||
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"]:
|
||||
|
||||
350
egs/librispeech/ASR/conformer_ctc/pretrained.py
Executable file
350
egs/librispeech/ASR/conformer_ctc/pretrained.py
Executable file
@ -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()
|
||||
@ -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,
|
||||
|
||||
@ -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,
|
||||
persistent_workers=True,
|
||||
num_workers=self.args.num_workers,
|
||||
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
|
||||
@ -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"{num_cuts}/{tot_num_cuts} "
|
||||
f"({float(num_cuts)/tot_num_cuts*100:.6f}%)"
|
||||
f"batch {batch_str}, cuts processed until now is {num_cuts}"
|
||||
)
|
||||
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":
|
||||
|
||||
@ -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()
|
||||
|
||||
@ -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)
|
||||
|
||||
@ -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
|
||||
@ -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 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=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 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.
|
||||
@ -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)
|
||||
|
||||
expanded_memory_key_padding_mask = memory_key_padding_mask.index_select(
|
||||
0, 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
|
||||
|
||||
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]
|
||||
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]
|
||||
|
||||
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]
|
||||
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)
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user