Skip to content

GitLab

"vscode:/vscode.git/clone" did not exist on "01814fe1906ca6554a23a86330e84e7bd0d602c0"
Commit d75c2dc7 authored by burchim's avatar burchim
Browse files

Efficient Conformer

parents
Show whitespace changes
Inline Side-by-side
Showing with 1716 additions and 0 deletions
.gitignore 0 → 100644
View file @ d75c2dc7
__pycache__
*.pyc
.DS_Store
.vscode
.DS_Store
env/*
datasets/*
callbacks/*
\ No newline at end of file
LICENSE 0 → 100644
View file @ d75c2dc7
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
and distribution as defined by Sections 1 through 9 of this document.
"Licensor" shall mean the copyright owner or entity authorized by
the copyright owner that is granting the License.
"Legal Entity" shall mean the union of the acting entity and all
other entities that control, are controlled by, or are under common
control with that entity. For the purposes of this definition,
"control" means (i) the power, direct or indirect, to cause the
direction or management of such entity, whether by contract or
otherwise, or (ii) ownership of fifty percent (50%) or more of the
outstanding shares, or (iii) beneficial ownership of such entity.
"You" (or "Your") shall mean an individual or Legal Entity
exercising permissions granted by this License.
"Source" form shall mean the preferred form for making modifications,
including but not limited to software source code, documentation
source, and configuration files.
"Object" form shall mean any form resulting from mechanical
transformation or translation of a Source form, including but
not limited to compiled object code, generated documentation,
and conversions to other media types.
"Work" shall mean the work of authorship, whether in Source or
Object form, made available under the License, as indicated by a
copyright notice that is included in or attached to the work
(an example is provided in the Appendix below).
"Derivative Works" shall mean any work, whether in Source or Object
form, that is based on (or derived from) the Work and for which the
editorial revisions, annotations, elaborations, or other modifications
represent, as a whole, an original work of authorship. For the purposes
of this License, Derivative Works shall not include works that remain
separable from, or merely link (or bind by name) to the interfaces of,
the Work and Derivative Works thereof.
"Contribution" shall mean any work of authorship, including
the original version of the Work and any modifications or additions
to that Work or Derivative Works thereof, that is intentionally
submitted to Licensor for inclusion in the Work by the copyright owner
or by an individual or Legal Entity authorized to submit on behalf of
the copyright owner. For the purposes of this definition, "submitted"
means any form of electronic, verbal, or written communication sent
to the Licensor or its representatives, including but not limited to
communication on electronic mailing lists, source code control systems,
and issue tracking systems that are managed by, or on behalf of, the
Licensor for the purpose of discussing and improving the Work, but
excluding communication that is conspicuously marked or otherwise
designated in writing by the copyright owner as "Not a Contribution."
"Contributor" shall mean Licensor and any individual or Legal Entity
on behalf of whom a Contribution has been received by Licensor and
subsequently incorporated within the Work.
2. Grant of Copyright License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
copyright license to reproduce, prepare Derivative Works of,
publicly display, publicly perform, sublicense, and distribute the
Work and such Derivative Works in Source or Object form.
3. Grant of Patent License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
(except as stated in this section) patent license to make, have made,
use, offer to sell, sell, import, and otherwise transfer the Work,
where such license applies only to those patent claims licensable
by such Contributor that are necessarily infringed by their
Contribution(s) alone or by combination of their Contribution(s)
with the Work to which such Contribution(s) was submitted. If You
institute patent litigation against any entity (including a
cross-claim or counterclaim in a lawsuit) alleging that the Work
or a Contribution incorporated within the Work constitutes direct
or contributory patent infringement, then any patent licenses
granted to You under this License for that Work shall terminate
as of the date such litigation is filed.
4. Redistribution. You may reproduce and distribute copies of the
Work or Derivative Works thereof in any medium, with or without
modifications, and in Source or Object form, provided that You
meet the following conditions:
(a) You must give any other recipients of the Work or
Derivative Works a copy of this License; and
(b) You must cause any modified files to carry prominent notices
stating that You changed the files; and
(c) You must retain, in the Source form of any Derivative Works
that You distribute, all copyright, patent, trademark, and
attribution notices from the Source form of the Work,
excluding those notices that do not pertain to any part of
the Derivative Works; and
(d) If the Work includes a "NOTICE" text file as part of its
distribution, then any Derivative Works that You distribute must
include a readable copy of the attribution notices contained
within such NOTICE file, excluding those notices that do not
pertain to any part of the Derivative Works, in at least one
of the following places: within a NOTICE text file distributed
as part of the Derivative Works; within the Source form or
documentation, if provided along with the Derivative Works; or,
within a display generated by the Derivative Works, if and
wherever such third-party notices normally appear. The contents
of the NOTICE file are for informational purposes only and
do not modify the License. You may add Your own attribution
notices within Derivative Works that You distribute, alongside
or as an addendum to the NOTICE text from the Work, provided
that such additional attribution notices cannot be construed
as modifying the License.
You may add Your own copyright statement to Your modifications and
may provide additional or different license terms and conditions
for use, reproduction, or distribution of Your modifications, or
for any such Derivative Works as a whole, provided Your use,
reproduction, and distribution of the Work otherwise complies with
the conditions stated in this License.
5. Submission of Contributions. Unless You explicitly state otherwise,
any Contribution intentionally submitted for inclusion in the Work
by You to the Licensor shall be under the terms and conditions of
this License, without any additional terms or conditions.
Notwithstanding the above, nothing herein shall supersede or modify
the terms of any separate license agreement you may have executed
with Licensor regarding such Contributions.
6. Trademarks. This License does not grant permission to use the trade
names, trademarks, service marks, or product names of the Licensor,
except as required for reasonable and customary use in describing the
origin of the Work and reproducing the content of the NOTICE file.
7. Disclaimer of Warranty. Unless required by applicable law or
agreed to in writing, Licensor provides the Work (and each
Contributor provides its Contributions) on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
implied, including, without limitation, any warranties or conditions
of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
PARTICULAR PURPOSE. You are solely responsible for determining the
appropriateness of using or redistributing the Work and assume any
risks associated with Your exercise of permissions under this License.
8. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise,
unless required by applicable law (such as deliberate and grossly
negligent acts) or agreed to in writing, shall any Contributor be
liable to You for damages, including any direct, indirect, special,
incidental, or consequential damages of any character arising as a
result of this License or out of the use or inability to use the
Work (including but not limited to damages for loss of goodwill,
work stoppage, computer failure or malfunction, or any and all
other commercial damages or losses), even if such Contributor
has been advised of the possibility of such damages.
9. Accepting Warranty or Additional Liability. While redistributing
the Work or Derivative Works thereof, You may choose to offer,
and charge a fee for, acceptance of support, warranty, indemnity,
or other liability obligations and/or rights consistent with this
License. However, in accepting such obligations, You may act only
on Your own behalf and on Your sole responsibility, not on behalf
of any other Contributor, and only if You agree to indemnify,
defend, and hold each Contributor harmless for any liability
incurred by, or claims asserted against, such Contributor by reason
of your accepting any such warranty or additional liability.
END OF TERMS AND CONDITIONS
APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
boilerplate notice, with the fields enclosed by brackets "[]"
replaced with your own identifying information. (Don't include
the brackets!) The text should be enclosed in the appropriate
comment syntax for the file format. We also recommend that a
file or class name and description of purpose be included on the
same "printed page" as the copyright notice for easier
identification within third-party archives.
Copyright [yyyy] [name of copyright owner]
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
README.md 0 → 100644
View file @ d75c2dc7
# Efficient Conformer: Progressive Downsampling and Grouped Attention for Automatic Speech Recognition
Official implementation of the Efficient Conformer, progressively downsampled Conformer with grouped attention for Automatic Speech Recognition.
## Efficient Conformer Encoder
Inspired from previous works done in Automatic Speech Recognition and Computer Vision, the Efficient Conformer encoder is composed of three encoder stages where each stage comprises a number of Conformer blocks using grouped attention. The encoded sequence is progressively downsampled and projected to wider feature dimensions, lowering the amount of computation while achieving better performance. Grouped multi-head attention reduce attention complexity by grouping neighbouring time elements along the feature dimension before applying scaled dot-product attention.
<img src="media/EfficientConformer.jpg" width="50%"/>
## Installation
Clone GitHub repository and set up environment
```
git clone https://github.com/burchim/EfficientConformer.git
cd EfficientConformer
pip install -r requirements.txt
```
Install [ctcdecode](https://github.com/parlance/ctcdecode)
## Download LibriSpeech
[Librispeech](https://www.openslr.org/12) is a corpus of approximately 1000 hours of 16kHz read English speech, prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read audiobooks from the LibriVox project, and has been carefully segmented and aligned.
```
cd datasets
./download_LibriSpeech.sh
```
## Running an experiment
You can run an experiment by providing a config file using the '--config_file' flag. Training checkpoints and logs will be saved in the callback folder specified in the config file. Note that '--prepare_dataset' and '--create_tokenizer' flags may be needed for your first experiment.
```
python main.py --config_file configs/config_file.json
```
## Evaluation
Models can be evaluated by selecting a subset validation/test mode and by providing the epoch/name of the checkpoint to load for evaluation with the '--initial_epoch' flag. The '--gready' flag designates whether to use gready search or beam search decoding for evaluation.
```
python main.py --config_file configs/config_file.json --initial_epoch epoch/name --mode validation/test --gready
```
## Options
```
-c / --config_file type=str default="configs/EfficientConformerCTCSmall.json" help="Json configuration file containing model hyperparameters"
-m / --mode type=str default="training" help="Mode : training, validation-clean, test-clean, eval_time-dev-clean, ..."
-d / --distributed action="store_true" help="Distributed data parallelization"
-i / --initial_epoch type=str default=None help="Load model from checkpoint"
--initial_epoch_lm type=str default=None help="Load language model from checkpoint"
--initial_epoch_encoder type=str default=None help="Load model encoder from encoder checkpoint"
-p / --prepare_dataset action="store_true" help="Prepare dataset before training"
-j / --num_workers type=int default=8 help="Number of data loading workers"
--create_tokenizer action="store_true" help="Create model tokenizer"
--batch_size_eval type=int default=8 help="Evaluation batch size"
--verbose_val action="store_true" help="Evaluation verbose"
--val_steps type=int default=None help="Number of validation steps"
--steps_per_epoch type=int default=None help="Number of steps per epoch"
--world_size type=int default=torch.cuda.device_count() help="Number of available GPUs"
--cpu action="store_true" help="Load model on cpu"
--show_dict action="store_true" help="Show model dict summary"
--swa action="store_true" help="Stochastic weight averaging"
--swa_epochs nargs="+" default=None help="Start epoch / end epoch for swa"
--swa_epochs_list nargs="+" default=None help="List of checkpoints epochs for swa"
--swa_type type=str default="equal" help="Stochastic weight averaging type (equal/exp)"
--parallel action="store_true" help="Parallelize model using data parallelization"
--rnnt_max_consec_dec_steps type=int default=None help="Number of maximum consecutive transducer decoder steps during inference"
--eval_loss action="store_true" help="Compute evaluation loss during evaluation"
--gready action="store_true" help="Proceed to a gready search evaluation"
--saving_period type=int default=1 help="Model saving every 'n' epochs"
--val_period type=int default=1 help="Model validation every 'n' epochs"
--profiler action="store_true" help="Enable eval time profiler"
```
## Monitor training
```
tensorboard --logdir callback_path
```
<img src="media/logs.jpg"/>
## LibriSpeech Performance
| Model | Size | Type | Params (M) | test-clean/test-other gready WER (%)| test-clean/test-other n-gram WER (%) | GPUs |
| :-------------------: |:--------: |:-----:|:----------:|:------:|:------:|:------:|
| [Efficient Conformer](https://drive.google.com/drive/folders/1Dqu1RTHQ8jxGxEPar2-WMjR0hRhmkpoU?usp=sharing) | Small | CTC | 13.2 | 3.6 / 9.0 | 2.7 / 6.7 | 4 x RTX 2080 Ti |
| [Efficient Conformer](https://drive.google.com/drive/folders/1uaDQWdZZEfq8sbq0u8w6hnFpUqEyjH9S?usp=sharing) | Medium | CTC | 31.5 | 3.0 / 7.6 | 2.4 / 5.8 | 4 x RTX 2080 Ti |
| [Efficient Conformer](https://drive.google.com/drive/folders/1NyxiVNsR7qyLGeIYMOchu9JPiTLFlsoj?usp=sharing) | Large | CTC | 125.6 | 2.5 / 5.8 | 2.1 / 4.7 | 4 x RTX 3090 |
## Reference
[Maxime Burchi, Valentin Vielzeuf. Efficient Conformer: Progressive Downsampling and Grouped Attention for Automatic Speech Recognition.](https://hal.archives-ouvertes.fr/hal-03328716)
<br><br>
## Author
* Maxime Burchi [@burchim](https://github.com/burchim)
* Contact: [maxime.burchi@gmail.com](mailto:maxime.burchi@gmail.com)
configs/ConformerCTCLarge.json 0 → 100644
View file @ d75c2dc7
{
"model_name": "Conformer CTC Large",
"model_type": "CTC",
"encoder_params":
{
"arch": "Conformer",
"num_blocks": 18,
"dim_model": 512,
"ff_ratio": 4,
"num_heads": 8,
"kernel_size": 31,
"Pdrop": 0.1,
"relative_pos_enc": true,
"max_pos_encoding": 10000,
"subsampling_module": "Conv2d",
"subsampling_layers": 2,
"subsampling_filters": [512, 512],
"subsampling_kernel_size": 3,
"subsampling_norm": "batch",
"subsampling_act": "swish",
"sample_rate": 16000,
"win_length_ms": 25,
"hop_length_ms": 10,
"n_fft": 512,
"n_mels": 80,
"normalize": false,
"mean": -5.6501,
"std": 4.2280,
"spec_augment": true,
"mF": 2,
"F": 27,
"mT": 10,
"pS": 0.05
},
"tokenizer_params":
{
"tokenizer_path": "datasets/LibriSpeech/LibriSpeech_bpe_256.model",
"vocab_type": "bpe",
"vocab_size": 256
},
"training_params":
{
"epochs": 450,
"batch_size": 16,
"accumulated_steps": 4,
"mixed_precision": true,
"optimizer": "Adam",
"beta1": 0.9,
"beta2": 0.98,
"eps": 1e-9,
"weight_decay": 1e-6,
"lr_schedule": "Transformer",
"schedule_dim": 512,
"warmup_steps": 10000,
"K": 2,
"train_audio_max_length": 256000,
"train_label_max_length": 256000,
"eval_audio_max_length": null,
"eval_label_max_length": null,
"training_dataset": "LibriSpeech",
"training_dataset_path": "datasets/LibriSpeech/",
"evaluation_dataset": "LibriSpeech",
"evaluation_dataset_path": "datasets/LibriSpeech/",
"callback_path": "callbacks/ConformerCTCLarge/"
},
"decoding_params":
{
"beam_size": 16,
"tmp": 1,
"ngram_path": "callbacks/ngram/6gram_256.arpa",
"ngram_alpha": 0.3,
"ngram_beta": 1,
"lm_config": "configs/LM-Transformer.json",
"lm_weight": 1,
"lm_tmp": 1
}
}
\ No newline at end of file
configs/ConformerCTCMedium.json 0 → 100644
View file @ d75c2dc7
{
"model_name": "Conformer CTC Medium",
"model_type": "CTC",
"encoder_params":
{
"arch": "Conformer",
"num_blocks": 18,
"dim_model": 256,
"ff_ratio": 4,
"num_heads": 4,
"kernel_size": 31,
"Pdrop": 0.1,
"relative_pos_enc": true,
"max_pos_encoding": 10000,
"subsampling_module": "Conv2d",
"subsampling_layers": 2,
"subsampling_filters": [256, 256],
"subsampling_kernel_size": 3,
"subsampling_norm": "batch",
"subsampling_act": "swish",
"sample_rate": 16000,
"win_length_ms": 25,
"hop_length_ms": 10,
"n_fft": 512,
"n_mels": 80,
"normalize": false,
"mean": -5.6501,
"std": 4.2280,
"spec_augment": true,
"mF": 2,
"F": 27,
"mT": 5,
"pS": 0.05
},
"tokenizer_params":
{
"tokenizer_path": "datasets/LibriSpeech/LibriSpeech_bpe_256.model",
"vocab_type": "bpe",
"vocab_size": 256
},
"training_params":
{
"epochs": 450,
"batch_size": 16,
"accumulated_steps": 4,
"mixed_precision": true,
"optimizer": "Adam",
"beta1": 0.9,
"beta2": 0.98,
"eps": 1e-9,
"weight_decay": 1e-6,
"lr_schedule": "Transformer",
"schedule_dim": 256,
"warmup_steps": 10000,
"K": 2,
"train_audio_max_length": 256000,
"train_label_max_length": 256000,
"eval_audio_max_length": null,
"eval_label_max_length": null,
"training_dataset": "LibriSpeech",
"training_dataset_path": "datasets/LibriSpeech/",
"evaluation_dataset": "LibriSpeech",
"evaluation_dataset_path": "datasets/LibriSpeech/",
"callback_path": "callbacks/ConformerCTCMedium/"
},
"decoding_params":
{
"beam_size": 16,
"tmp": 1,
"ngram_path": "callbacks/ngram/6gram_256.arpa",
"ngram_alpha": 0.3,
"ngram_beta": 1,
"lm_config": "configs/LM-Transformer.json",
"lm_weight": 1,
"lm_tmp": 1
}
}
\ No newline at end of file
configs/ConformerCTCSmall.json 0 → 100644
View file @ d75c2dc7
{
"model_name": "Conformer CTC Small",
"model_type": "CTC",
"encoder_params":
{
"arch": "Conformer",
"num_blocks": 16,
"dim_model": 176,
"ff_ratio": 4,
"num_heads": 4,
"kernel_size": 31,
"Pdrop": 0.1,
"relative_pos_enc": true,
"max_pos_encoding": 10000,
"subsampling_module": "Conv2d",
"subsampling_layers": 2,
"subsampling_filters": [176, 176],
"subsampling_kernel_size": 3,
"subsampling_norm": "batch",
"subsampling_act": "swish",
"sample_rate": 16000,
"win_length_ms": 25,
"hop_length_ms": 10,
"n_fft": 512,
"n_mels": 80,
"normalize": false,
"mean": -5.6501,
"std": 4.2280,
"spec_augment": true,
"mF": 2,
"F": 27,
"mT": 5,
"pS": 0.05
},
"tokenizer_params":
{
"tokenizer_path": "datasets/LibriSpeech/LibriSpeech_bpe_256.model",
"vocab_type": "bpe",
"vocab_size": 256
},
"training_params":
{
"epochs": 450,
"batch_size": 32,
"accumulated_steps": 2,
"mixed_precision": true,
"optimizer": "Adam",
"beta1": 0.9,
"beta2": 0.98,
"eps": 1e-9,
"weight_decay": 1e-6,
"lr_schedule": "Transformer",
"schedule_dim": 176,
"warmup_steps": 10000,
"K": 2,
"train_audio_max_length": 256000,
"train_label_max_length": 256000,
"eval_audio_max_length": null,
"eval_label_max_length": null,
"training_dataset": "LibriSpeech",
"training_dataset_path": "datasets/LibriSpeech/",
"evaluation_dataset": "LibriSpeech",
"evaluation_dataset_path": "datasets/LibriSpeech/",
"callback_path": "callbacks/ConformerCTCSmall/"
},
"decoding_params":
{
"beam_size": 16,
"tmp": 1,
"ngram_path": "callbacks/ngram/6gram_256.arpa",
"ngram_alpha": 0.3,
"ngram_beta": 1,
"lm_config": "configs/LM-Transformer.json",
"lm_weight": 1,
"lm_tmp": 1
}
}
\ No newline at end of file
configs/ConformerTransducerLarge.json 0 → 100644
View file @ d75c2dc7
{
"model_name": "Conformer Transducer Large",
"model_type": "Transducer",
"encoder_params":
{
"arch": "Conformer",
"num_blocks": 17,
"dim_model": 512,
"ff_ratio": 4,
"num_heads": 8,
"kernel_size": 31,
"Pdrop": 0.1,
"relative_pos_enc": true,
"max_pos_encoding": 10000,
"subsampling_module": "Conv2d",
"subsampling_layers": 2,
"subsampling_filters": [512, 512],
"subsampling_kernel_size": 3,
"subsampling_norm": "batch",
"subsampling_act": "swish",
"sample_rate": 16000,
"win_length_ms": 25,
"hop_length_ms": 10,
"n_fft": 512,
"n_mels": 80,
"normalize": false,
"mean": -5.6501,
"std": 4.2280,
"spec_augment": true,
"mF": 2,
"F": 27,
"mT": 10,
"pS": 0.05
},
"decoder_params":
{
"arch": "RNN",
"num_layers": 1,
"dim_model": 640,
"vocab_size": 1000
},
"joint_params":
{
"joint_mode": "sum",
"dim_model": 640,
"act": "tanh"
},
"tokenizer_params":
{
"tokenizer_path": "datasets/LibriSpeech/LibriSpeech_bpe_1000.model",
"vocab_type": "bpe",
"vocab_size": 1000
},
"training_params":
{
"epochs": 250,
"batch_size": 8,
"accumulated_steps": 8,
"mixed_precision": true,
"optimizer": "Adam",
"beta1": 0.9,
"beta2": 0.98,
"eps": 1e-9,
"weight_decay": 1e-6,
"lr_schedule": "Transformer",
"schedule_dim": 512,
"warmup_steps": 10000,
"K": 2,
"vn_start_step": 20000,
"vn_std": 0.075,
"train_audio_max_length": 256000,
"train_label_max_length": 90,
"eval_audio_max_length": null,
"eval_label_max_length": null,
"training_dataset": "LibriSpeech",
"training_dataset_path": "datasets/LibriSpeech/",
"evaluation_dataset": "LibriSpeech",
"evaluation_dataset_path": "datasets/LibriSpeech/",
"callback_path": "callbacks/ConformerTransducerLarge/"
},
"decoding_params":
{
"beam_size": 16,
"tmp": 1,
"ngram_path": "callbacks/ngram/6gram_1000.arpa",
"ngram_alpha": 0.3,
"ngram_beta": 1,
"lm_config": "configs/LM-Transformer.json",
"lm_weight": 1,
"lm_tmp": 1
}
}
configs/ConformerTransducerMedium.json 0 → 100644
View file @ d75c2dc7
{
"model_name": "Conformer Transducer Medium",
"model_type": "Transducer",
"encoder_params":
{
"arch": "Conformer",
"num_blocks": 16,
"dim_model": 256,
"ff_ratio": 4,
"num_heads": 4,
"kernel_size": 31,
"Pdrop": 0.1,
"relative_pos_enc": true,
"max_pos_encoding": 10000,
"subsampling_module": "Conv2d",
"subsampling_layers": 2,
"subsampling_filters": [256, 256],
"subsampling_kernel_size": 3,
"subsampling_norm": "batch",
"subsampling_act": "swish",
"sample_rate": 16000,
"win_length_ms": 25,
"hop_length_ms": 10,
"n_fft": 512,
"n_mels": 80,
"normalize": false,
"mean": -5.6501,
"std": 4.2280,
"spec_augment": true,
"mF": 2,
"F": 27,
"mT": 10,
"pS": 0.05
},
"decoder_params":
{
"arch": "RNN",
"num_layers": 1,
"dim_model": 640,
"vocab_size": 1000
},
"joint_params":
{
"joint_mode": "sum",
"dim_model": 640,
"act": "tanh"
},
"tokenizer_params":
{
"tokenizer_path": "datasets/LibriSpeech/LibriSpeech_bpe_1000.model",
"vocab_type": "bpe",
"vocab_size": 1000
},
"training_params":
{
"epochs": 250,
"batch_size": 8,
"accumulated_steps": 8,
"mixed_precision": true,
"optimizer": "Adam",
"beta1": 0.9,
"beta2": 0.98,
"eps": 1e-9,
"weight_decay": 1e-6,
"lr_schedule": "Transformer",
"schedule_dim": 256,
"warmup_steps": 10000,
"K": 2,
"vn_start_step": 20000,
"vn_std": 0.075,
"train_audio_max_length": 256000,
"train_label_max_length": 90,
"eval_audio_max_length": null,
"eval_label_max_length": null,
"training_dataset": "LibriSpeech",
"training_dataset_path": "datasets/LibriSpeech/",
"evaluation_dataset": "LibriSpeech",
"evaluation_dataset_path": "datasets/LibriSpeech/",
"callback_path": "callbacks/ConformerTransducerMedium/"
},
"decoding_params":
{
"beam_size": 16,
"tmp": 1,
"ngram_path": "callbacks/ngram/6gram_1000.arpa",
"ngram_alpha": 0.3,
"ngram_beta": 1,
"lm_config": "configs/LM-Transformer.json",
"lm_weight": 1,
"lm_tmp": 1
}
}
configs/ConformerTransducerSmall.json 0 → 100644
View file @ d75c2dc7
{
"model_name": "Conformer Transducer Small",
"model_type": "Transducer",
"encoder_params":
{
"arch": "Conformer",
"num_blocks": 16,
"dim_model": 144,
"ff_ratio": 4,
"num_heads": 6,
"kernel_size": 31,
"Pdrop": 0.1,
"relative_pos_enc": true,
"max_pos_encoding": 10000,
"subsampling_module": "Conv2d",
"subsampling_layers": 2,
"subsampling_filters": [144, 144],
"subsampling_kernel_size": 3,
"subsampling_norm": "batch",
"subsampling_act": "swish",
"sample_rate": 16000,
"win_length_ms": 25,
"hop_length_ms": 10,
"n_fft": 512,
"n_mels": 80,
"normalize": false,
"mean": -5.6501,
"std": 4.2280,
"spec_augment": true,
"mF": 2,
"F": 27,
"mT": 10,
"pS": 0.05
},
"decoder_params":
{
"arch": "RNN",
"num_layers": 1,
"dim_model": 320,
"vocab_size": 1000
},
"joint_params":
{
"joint_mode": "sum",
"dim_model": 320,
"act": "tanh"
},
"tokenizer_params":
{
"tokenizer_path": "datasets/LibriSpeech/LibriSpeech_bpe_1000.model",
"vocab_type": "bpe",
"vocab_size": 1000
},
"training_params":
{
"epochs": 250,
"batch_size": 16,
"accumulated_steps": 4,
"mixed_precision": true,
"optimizer": "Adam",
"beta1": 0.9,
"beta2": 0.98,
"eps": 1e-9,
"weight_decay": 1e-6,
"lr_schedule": "Transformer",
"schedule_dim": 144,
"warmup_steps": 10000,
"K": 2,
"vn_start_step": 20000,
"vn_std": 0.075,
"train_audio_max_length": 256000,
"train_label_max_length": 90,
"eval_audio_max_length": null,
"eval_label_max_length": null,
"training_dataset": "LibriSpeech",
"training_dataset_path": "datasets/LibriSpeech/",
"evaluation_dataset": "LibriSpeech",
"evaluation_dataset_path": "datasets/LibriSpeech/",
"callback_path": "callbacks/ConformerTransducerSmall/"
},
"decoding_params":
{
"beam_size": 16,
"tmp": 1,
"ngram_path": "callbacks/ngram/6gram_1000.arpa",
"ngram_alpha": 0.3,
"ngram_beta": 1,
"lm_config": "configs/LM-Transformer.json",
"lm_weight": 1,
"lm_tmp": 1
}
}
configs/EfficientConformerCTCLarge.json 0 → 100644
View file @ d75c2dc7
{
"model_name": "Efficient Conformer CTC Large",
"model_type": "CTC",
"encoder_params":
{
"arch": "Conformer",
"num_blocks": 16,
"dim_model": [360, 512, 720],
"ff_ratio": 4,
"num_heads": 8,
"kernel_size": 15,
"Pdrop": 0.1,
"conv_stride": 2,
"att_stride": 1,
"strided_blocks": [4, 10],
"expand_blocks": [4, 10],
"att_group_size": [3, 1, 1],
"relative_pos_enc": true,
"max_pos_encoding": 10000,
"subsampling_module": "Conv2d",
"subsampling_layers": 1,
"subsampling_filters": [360],
"subsampling_kernel_size": 3,
"subsampling_norm": "batch",
"subsampling_act": "swish",
"sample_rate": 16000,
"win_length_ms": 25,
"hop_length_ms": 10,
"n_fft": 512,
"n_mels": 80,
"normalize": false,
"mean": -5.6501,
"std": 4.2280,
"spec_augment": true,
"mF": 2,
"F": 27,
"mT": 10,
"pS": 0.05
},
"tokenizer_params":
{
"tokenizer_path": "datasets/LibriSpeech/LibriSpeech_bpe_256.model",
"vocab_type": "bpe",
"vocab_size": 256
},
"training_params":
{
"epochs": 450,
"batch_size": 16,
"accumulated_steps": 4,
"mixed_precision": true,
"optimizer": "Adam",
"beta1": 0.9,
"beta2": 0.98,
"eps": 1e-9,
"weight_decay": 1e-6,
"lr_schedule": "Transformer",
"schedule_dim": 720,
"warmup_steps": 10000,
"K": 2,
"train_audio_max_length": 256000,
"train_label_max_length": 256000,
"eval_audio_max_length": null,
"eval_label_max_length": null,
"training_dataset": "LibriSpeech",
"training_dataset_path": "datasets/LibriSpeech/",
"evaluation_dataset": "LibriSpeech",
"evaluation_dataset_path": "datasets/LibriSpeech/",
"callback_path": "callbacks/EfficientConformerCTCLarge/"
},
"decoding_params":
{
"beam_size": 16,
"tmp": 1,
"ngram_path": "callbacks/ngram/6gram_256.arpa",
"ngram_alpha": 0.3,
"ngram_beta": 1,
"lm_config": "configs/LM-Transformer.json",
"lm_weight": 1,
"lm_tmp": 1
}
}
\ No newline at end of file
configs/EfficientConformerCTCMedium.json 0 → 100644
View file @ d75c2dc7
{
"model_name": "Efficient Conformer CTC Medium",
"model_type": "CTC",
"encoder_params":
{
"arch": "Conformer",
"num_blocks": 16,
"dim_model": [180, 256, 360],
"ff_ratio": 4,
"num_heads": 4,
"kernel_size": 15,
"Pdrop": 0.1,
"conv_stride": 2,
"att_stride": 1,
"strided_blocks": [4, 10],
"expand_blocks": [4, 10],
"att_group_size": [3, 1, 1],
"relative_pos_enc": true,
"max_pos_encoding": 10000,
"subsampling_module": "Conv2d",
"subsampling_layers": 1,
"subsampling_filters": [180],
"subsampling_kernel_size": 3,
"subsampling_norm": "batch",
"subsampling_act": "swish",
"sample_rate": 16000,
"win_length_ms": 25,
"hop_length_ms": 10,
"n_fft": 512,
"n_mels": 80,
"normalize": false,
"mean": -5.6501,
"std": 4.2280,
"spec_augment": true,
"mF": 2,
"F": 27,
"mT": 5,
"pS": 0.05
},
"tokenizer_params":
{
"tokenizer_path": "datasets/LibriSpeech/LibriSpeech_bpe_256.model",
"vocab_type": "bpe",
"vocab_size": 256
},
"training_params":
{
"epochs": 450,
"batch_size": 16,
"accumulated_steps": 4,
"mixed_precision": true,
"optimizer": "Adam",
"beta1": 0.9,
"beta2": 0.98,
"eps": 1e-9,
"weight_decay": 1e-6,
"lr_schedule": "Transformer",
"schedule_dim": 360,
"warmup_steps": 10000,
"K": 2,
"train_audio_max_length": 256000,
"train_label_max_length": 256000,
"eval_audio_max_length": null,
"eval_label_max_length": null,
"training_dataset": "LibriSpeech",
"training_dataset_path": "datasets/LibriSpeech/",
"evaluation_dataset": "LibriSpeech",
"evaluation_dataset_path": "datasets/LibriSpeech/",
"callback_path": "callbacks/EfficientConformerCTCMedium/"
},
"decoding_params":
{
"beam_size": 16,
"tmp": 1,
"ngram_path": "callbacks/ngram/6gram_256.arpa",
"ngram_alpha": 0.3,
"ngram_beta": 1,
"lm_config": "configs/LM-Transformer.json",
"lm_weight": 1,
"lm_tmp": 1
}
}
\ No newline at end of file
configs/EfficientConformerCTCSmall.json 0 → 100644
View file @ d75c2dc7
{
"model_name": "Efficient Conformer CTC Small",
"model_type": "CTC",
"encoder_params":
{
"arch": "Conformer",
"num_blocks": 15,
"dim_model": [120, 168, 240],
"ff_ratio": 4,
"num_heads": 4,
"kernel_size": 15,
"Pdrop": 0.1,
"conv_stride": 2,
"att_stride": 1,
"strided_blocks": [4, 9],
"expand_blocks": [4, 9],
"att_group_size": [3, 1, 1],
"relative_pos_enc": true,
"max_pos_encoding": 10000,
"subsampling_module": "Conv2d",
"subsampling_layers": 1,
"subsampling_filters": [120],
"subsampling_kernel_size": 3,
"subsampling_norm": "batch",
"subsampling_act": "swish",
"sample_rate": 16000,
"win_length_ms": 25,
"hop_length_ms": 10,
"n_fft": 512,
"n_mels": 80,
"normalize": false,
"mean": -5.6501,
"std": 4.2280,
"spec_augment": true,
"mF": 2,
"F": 27,
"mT": 5,
"pS": 0.05
},
"tokenizer_params":
{
"tokenizer_path": "datasets/LibriSpeech/LibriSpeech_bpe_256.model",
"vocab_type": "bpe",
"vocab_size": 256
},
"training_params":
{
"epochs": 450,
"batch_size": 32,
"accumulated_steps": 2,
"mixed_precision": true,
"optimizer": "Adam",
"beta1": 0.9,
"beta2": 0.98,
"eps": 1e-9,
"weight_decay": 1e-6,
"lr_schedule": "Transformer",
"schedule_dim": 240,
"warmup_steps": 10000,
"K": 2,
"train_audio_max_length": 256000,
"train_label_max_length": 256000,
"eval_audio_max_length": null,
"eval_label_max_length": null,
"training_dataset": "LibriSpeech",
"training_dataset_path": "datasets/LibriSpeech/",
"evaluation_dataset": "LibriSpeech",
"evaluation_dataset_path": "datasets/LibriSpeech/",
"callback_path": "callbacks/EfficientConformerCTCSmall/"
},
"decoding_params":
{
"beam_size": 16,
"tmp": 1,
"ngram_path": "callbacks/ngram/6gram_256.arpa",
"ngram_alpha": 0.3,
"ngram_beta": 1,
"lm_config": "configs/LM-Transformer.json",
"lm_weight": 1,
"lm_tmp": 1
}
}
\ No newline at end of file
configs/EfficientConformerTransducerLarge.json 0 → 100644
View file @ d75c2dc7
{
"model_name": "Efficient Conformer Transducer Large",
"model_type": "Transducer",
"encoder_params":
{
"arch": "Conformer",
"num_blocks": 15,
"dim_model": [360, 512, 720],
"ff_ratio": 4,
"num_heads": 8,
"kernel_size": 15,
"Pdrop": 0.1,
"conv_stride": 2,
"att_stride": 1,
"strided_blocks": [4, 9],
"expand_blocks": [4, 9],
"att_group_size": [3, 1, 1],
"relative_pos_enc": true,
"max_pos_encoding": 10000,
"subsampling_module": "Conv2d",
"subsampling_layers": 1,
"subsampling_filters": [360],
"subsampling_kernel_size": 3,
"subsampling_norm": "batch",
"subsampling_act": "swish",
"sample_rate": 16000,
"win_length_ms": 25,
"hop_length_ms": 10,
"n_fft": 512,
"n_mels": 80,
"normalize": false,
"mean": -5.6501,
"std": 4.2280,
"spec_augment": true,
"mF": 2,
"F": 27,
"mT": 10,
"pS": 0.05
},
"decoder_params":
{
"arch": "RNN",
"num_layers": 1,
"dim_model": 640,
"vocab_size": 1000
},
"joint_params":
{
"joint_mode": "sum",
"dim_model": 640,
"act": "tanh"
},
"tokenizer_params":
{
"tokenizer_path": "datasets/LibriSpeech/LibriSpeech_bpe_1000.model",
"vocab_type": "bpe",
"vocab_size": 1000
},
"training_params":
{
"epochs": 250,
"batch_size": 16,
"accumulated_steps": 4,
"mixed_precision": true,
"vn_start_step": 20000,
"vn_std": 0.075,
"optimizer": "Adam",
"beta1": 0.9,
"beta2": 0.98,
"eps": 1e-9,
"weight_decay": 1e-6,
"lr_schedule": "Transformer",
"schedule_dim": 720,
"warmup_steps": 10000,
"K": 2,
"train_audio_max_length": 256000,
"train_label_max_length": 90,
"eval_audio_max_length": null,
"eval_label_max_length": null,
"training_dataset": "LibriSpeech",
"training_dataset_path": "datasets/LibriSpeech/",
"evaluation_dataset": "LibriSpeech",
"evaluation_dataset_path": "datasets/LibriSpeech/",
"callback_path": "callbacks/EfficientConformerTransducerLarge/"
},
"decoding_params":
{
"beam_size": 16,
"tmp": 1,
"ngram_path": "callbacks/ngram/6gram_1000.arpa",
"ngram_alpha": 0.3,
"ngram_beta": 1,
"lm_config": "configs/LM-Transformer.json",
"lm_weight": 1,
"lm_tmp": 1
}
}
\ No newline at end of file
configs/EfficientConformerTransducerMedium.json 0 → 100644
View file @ d75c2dc7
{
"model_name": "Efficient Conformer Transducer Medium",
"model_type": "Transducer",
"encoder_params":
{
"arch": "Conformer",
"num_blocks": 15,
"dim_model": [180, 256, 360],
"ff_ratio": 4,
"num_heads": 4,
"kernel_size": 15,
"Pdrop": 0.1,
"conv_stride": 2,
"att_stride": 1,
"strided_blocks": [4, 9],
"expand_blocks": [4, 9],
"att_group_size": [3, 1, 1],
"relative_pos_enc": true,
"max_pos_encoding": 10000,
"subsampling_module": "Conv2d",
"subsampling_layers": 1,
"subsampling_filters": [180],
"subsampling_kernel_size": 3,
"subsampling_norm": "batch",
"subsampling_act": "swish",
"sample_rate": 16000,
"win_length_ms": 25,
"hop_length_ms": 10,
"n_fft": 512,
"n_mels": 80,
"normalize": false,
"mean": -5.6501,
"std": 4.2280,
"spec_augment": true,
"mF": 2,
"F": 27,
"mT": 10,
"pS": 0.05
},
"decoder_params":
{
"arch": "RNN",
"num_layers": 1,
"dim_model": 640,
"vocab_size": 1000
},
"joint_params":
{
"joint_mode": "sum",
"dim_model": 640,
"act": "tanh"
},
"tokenizer_params":
{
"tokenizer_path": "datasets/LibriSpeech/LibriSpeech_bpe_1000.model",
"vocab_type": "bpe",
"vocab_size": 1000
},
"training_params":
{
"epochs": 250,
"batch_size": 16,
"accumulated_steps": 4,
"mixed_precision": true,
"vn_start_step": 20000,
"vn_std": 0.075,
"optimizer": "Adam",
"beta1": 0.9,
"beta2": 0.98,
"eps": 1e-9,
"weight_decay": 1e-6,
"lr_schedule": "Transformer",
"schedule_dim": 360,
"warmup_steps": 10000,
"K": 2,
"train_audio_max_length": 256000,
"train_label_max_length": 90,
"eval_audio_max_length": null,
"eval_label_max_length": null,
"training_dataset": "LibriSpeech",
"training_dataset_path": "datasets/LibriSpeech/",
"evaluation_dataset": "LibriSpeech",
"evaluation_dataset_path": "datasets/LibriSpeech/",
"callback_path": "callbacks/EfficientConformerTransducerMedium/"
},
"decoding_params":
{
"beam_size": 16,
"tmp": 1,
"ngram_path": "callbacks/ngram/6gram_1000.arpa",
"ngram_alpha": 0.3,
"ngram_beta": 1,
"lm_config": "configs/LM-Transformer.json",
"lm_weight": 1,
"lm_tmp": 1
}
}
\ No newline at end of file
configs/EfficientConformerTransducerSmall.json 0 → 100644
View file @ d75c2dc7
{
"model_name": "Efficient Conformer Transducer Small",
"model_type": "Transducer",
"encoder_params":
{
"arch": "Conformer",
"num_blocks": 15,
"dim_model": [100, 140, 200],
"ff_ratio": 4,
"num_heads": 4,
"kernel_size": 15,
"Pdrop": 0.1,
"conv_stride": 2,
"att_stride": 1,
"strided_blocks": [4, 9],
"expand_blocks": [4, 9],
"att_group_size": [3, 1, 1],
"relative_pos_enc": true,
"max_pos_encoding": 10000,
"subsampling_module": "Conv2d",
"subsampling_layers": 1,
"subsampling_filters": [100],
"subsampling_kernel_size": 3,
"subsampling_norm": "batch",
"subsampling_act": "swish",
"sample_rate": 16000,
"win_length_ms": 25,
"hop_length_ms": 10,
"n_fft": 512,
"n_mels": 80,
"normalize": false,
"mean": -5.6501,
"std": 4.2280,
"spec_augment": true,
"mF": 2,
"F": 27,
"mT": 10,
"pS": 0.05
},
"decoder_params":
{
"arch": "RNN",
"num_layers": 1,
"dim_model": 320,
"vocab_size": 1000
},
"joint_params":
{
"joint_mode": "sum",
"dim_model": 320,
"act": "tanh"
},
"tokenizer_params":
{
"tokenizer_path": "datasets/LibriSpeech/LibriSpeech_bpe_1000.model",
"vocab_type": "bpe",
"vocab_size": 1000
},
"training_params":
{
"epochs": 250,
"batch_size": 16,
"accumulated_steps": 4,
"mixed_precision": true,
"optimizer": "Adam",
"beta1": 0.9,
"beta2": 0.98,
"eps": 1e-9,
"weight_decay": 1e-6,
"lr_schedule": "Transformer",
"schedule_dim": 200,
"warmup_steps": 10000,
"K": 5,
"vn_start_step": 20000,
"vn_std": 0.075,
"train_audio_max_length": 256000,
"train_label_max_length": 90,
"eval_audio_max_length": null,
"eval_label_max_length": null,
"training_dataset": "LibriSpeech",
"training_dataset_path": "datasets/LibriSpeech/",
"evaluation_dataset": "LibriSpeech",
"evaluation_dataset_path": "datasets/LibriSpeech/",
"callback_path": "callbacks/EfficientConformerTransducerSmall/"
},
"decoding_params":
{
"beam_size": 16,
"tmp": 1,
"ngram_path": "callbacks/ngram/6gram_1000.arpa",
"ngram_alpha": 0.3,
"ngram_beta": 1,
"lm_config": "configs/LM-Transformer.json",
"lm_weight": 1,
"lm_tmp": 1
}
}
\ No newline at end of file
configs/LM-RNN.json 0 → 100644
View file @ d75c2dc7
{
"model_name": "LM RNN",
"model_type": "LM",
"lm_params":
{
"arch": "RNN",
"num_layers": 3,
"vocab_size": 1000,
"dim_model": 4096
},
"tokenizer_params":
{
"tokenizer_path": "datasets/LibriSpeech/LibriSpeech_bpe_1000.model",
"vocab_type": "bpe",
"vocab_size": 1000
},
"training_params":
{
"epochs": 100,
"batch_size": 16,
"accumulated_steps": 4,
"mixed_precision": true,
"optimizer": "Adam",
"beta1": 0.9,
"beta2": 0.98,
"eps": 1e-9,
"weight_decay": 1e-6,
"lr_schedule": "Constant",
"lr_value": 0.001,
"train_label_max_length": 100,
"eval_audio_max_length": null,
"eval_label_max_length": null,
"training_dataset": "LibriSpeechCorpus",
"training_dataset_path": "datasets/librispeech-lm-norm.txt",
"evaluation_dataset": "LibriSpeech",
"evaluation_dataset_path": "datasets/LibriSpeech/",
"lm_mode": true,
"callback_path": "callbacks/LM-RNN/"
},
"decoding_params":
{
"beam_size": 16,
"tmp": 1
}
}
configs/LM-Transformer.json 0 → 100644
View file @ d75c2dc7
{
"model_name": "Transformer XL LM",
"model_type": "LM",
"lm_params":
{
"arch": "Transformer",
"num_blocks": 12,
"dim_model": 768,
"ff_ratio": 4,
"num_heads": 12,
"vocab_size": 1000,
"relative_pos_enc": true,
"max_pos_encoding": 2048,
"Pdrop": 0.1
},
"tokenizer_params":
{
"tokenizer_path": "datasets/LibriSpeech/LibriSpeech_bpe_1000.model",
"vocab_type": "bpe",
"vocab_size": 1000
},
"training_params":
{
"epochs": 100,
"batch_size": 64,
"accumulated_steps": 5,
"mixed_precision": true,
"optimizer": "Adam",
"beta1": 0.9,
"beta2": 0.95,
"eps": 1e-8,
"weight_decay": 0.0,
"lr_schedule": "Cosine",
"warmup_steps": 1000,
"end_step": 300000,
"lr_max": 0.0006,
"lr_min": 0.00006,
"train_label_max_length": 100,
"eval_audio_max_length": null,
"eval_label_max_length": null,
"training_dataset": "LibriSpeechCorpus",
"training_dataset_path": "datasets/librispeech-lm-norm.txt",
"evaluation_dataset": "LibriSpeech",
"evaluation_dataset_path": "datasets/LibriSpeech/",
"lm_mode": true,
"callback_path": "callbacks/LM-Transformer/"
},
"decoding_params":
{
"beam_size": 16,
"tmp": 1
}
}
datasets/download_LibriSpeech.sh 0 → 100755
View file @ d75c2dc7
# Training sets
wget https://www.openslr.org/resources/12/train-clean-100.tar.gz
tar xvzf train-clean-100.tar.gz
wget https://www.openslr.org/resources/12/train-clean-360.tar.gz
tar xvzf train-clean-360.tar.gz
wget https://www.openslr.org/resources/12/train-other-500.tar.gz
tar xvzf train-other-500.tar.gz
# Development sets
wget https://www.openslr.org/resources/12/dev-clean.tar.gz
tar xvzf dev-clean.tar.gz
wget https://www.openslr.org/resources/12/dev-other.tar.gz
tar xvzf dev-other.tar.gz
# Test sets
wget https://www.openslr.org/resources/12/test-clean.tar.gz
tar xvzf test-clean.tar.gz
wget https://www.openslr.org/resources/12/test-other.tar.gz
tar xvzf test-other.tar.gz
datasets/download_LibriSpeech_lm_corpus.sh 0 → 100755
View file @ d75c2dc7
# LM corpus
wget https://www.openslr.org/resources/11/librispeech-lm-norm.txt.gz
gunzip librispeech-lm-norm.txt.gz
\ No newline at end of file
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment