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Soohwan Kim edited this page Jun 19, 2021 · 1 revision

Get Started

We use Hydra to control all the training configurations. If you are not familiar with Hydra we recommend visiting the Hydra website. Generally, Hydra is an open-source framework that simplifies the development of research applications by providing the ability to create a hierarchical configuration dynamically. If you want to know how we used Hydra, we recommend you to read here.

Supported Datasets

We support LibriSpeech, KsponSpeech, and AISHELL-1.

LibriSpeech is a corpus of approximately 1,000 hours of 16kHz read English speech, prepared by Vassil Panayotov with the assistance of Daniel Povey. The data was derived from reading audiobooks from the LibriVox project, and has been carefully segmented and aligned.

Aishell is an open-source Chinese Mandarin speech corpus published by Beijing Shell Shell Technology Co.,Ltd. 400 people from different accent areas in China were invited to participate in the recording, which was conducted in a quiet indoor environment using high fidelity microphone and downsampled to 16kHz.

KsponSpeech is a large-scale spontaneous speech corpus of Korean. This corpus contains 969 hours of general open-domain dialog utterances, spoken by about 2,000 native Korean speakers in a clean environment. All data were constructed by recording the dialogue of two people freely conversing on a variety of topics and manually transcribing the utterances. To start training, the KsponSpeech dataset must be prepared in advance. To download KsponSpeech, you need permission from AI Hub.

Pre-processed Manifest Files

Dataset Unit Manifest Vocab SP-Model
LibriSpeech character [Link] [Link] -
LibriSpeech subword [Link] [Link] [Link]
AISHELL-1 character [Link] [Link] -
KsponSpeech character [Link] [Link] -
KsponSpeech subword [Link] [Link] [Link]
KsponSpeech grapheme [Link] [Link] -

KsponSpeech needs permission from AI Hub.
Please send e-mail including the approved screenshot to [email protected].

Manifest File

  • Acoustic model manifest file format:
LibriSpeech/test-other/8188/269288/8188-269288-0052.flac        ▁ANNIE ' S ▁MANNER ▁WAS ▁VERY ▁MYSTERIOUS       4039 20 5 531 17 84 2352
LibriSpeech/test-other/8188/269288/8188-269288-0053.flac        ▁ANNIE ▁DID ▁NOT ▁MEAN ▁TO ▁CONFIDE ▁IN ▁ANYONE ▁THAT ▁NIGHT ▁AND ▁THE ▁KIND EST ▁THING ▁WAS ▁TO ▁LEAVE ▁HER ▁A LONE    4039 99 35 251 9 4758 11 2454 16 199 6 4 323 200 255 17 9 370 30 10 492
LibriSpeech/test-other/8188/269288/8188-269288-0054.flac        ▁TIRED ▁OUT ▁LESLIE ▁HER SELF ▁DROPP ED ▁A SLEEP        1493 70 4708 30 115 1231 7 10 1706
LibriSpeech/test-other/8188/269288/8188-269288-0055.flac        ▁ANNIE ▁IS ▁THAT ▁YOU ▁SHE ▁CALL ED ▁OUT        4039 34 16 25 37 208 7 70
LibriSpeech/test-other/8188/269288/8188-269288-0056.flac        ▁THERE ▁WAS ▁NO ▁REPLY ▁BUT ▁THE ▁SOUND ▁OF ▁HURRY ING ▁STEPS ▁CAME ▁QUICK ER ▁AND ▁QUICK ER ▁NOW ▁AND ▁THEN ▁THEY ▁WERE ▁INTERRUPTED ▁BY ▁A ▁GROAN     57 17 56 1368 33 4 489 8 1783 14 1381 133 571 49 6 571 49 82 6 76 45 54 2351 44 10 3154
LibriSpeech/test-other/8188/269288/8188-269288-0057.flac        ▁OH ▁THIS ▁WILL ▁KILL ▁ME ▁MY ▁HEART ▁WILL ▁BREAK ▁THIS ▁WILL ▁KILL ▁ME 299 46 71 669 50 41 235 71 977 46 71 669 50
...
...

Training examples

You can simply train with LibriSpeech dataset like below:

  • Example1: Train the conformer-lstm model with filter-bank features on GPU.
$ python ./openspeech_cli/hydra_train.py \
    dataset=librispeech \
    dataset.dataset_download=True \
    dataset.dataset_path=$DATASET_PATH \
    dataset.manifest_file_path=$MANIFEST_FILE_PATH \  
    vocab=libri_subword \
    model=conformer_lstm \
    audio=fbank \
    lr_scheduler=warmup_reduce_lr_on_plateau \
    trainer=gpu \
    criterion=joint_ctc_cross_entropy

You can simply train with KsponSpeech dataset like below:

  • Example2: Train the listen-attend-spell model with mel-spectrogram features On TPU:
$ python ./openspeech_cli/hydra_train.py \
    dataset=ksponspeech \
    dataset.dataset_path=$DATASET_PATH \
    dataset.manifest_file_path=$MANIFEST_FILE_PATH \  
    dataset.test_dataset_path=$TEST_DATASET_PATH \
    dataset.test_manifest_dir=$TEST_MANIFEST_DIR \
    vocab=kspon_character \
    model=listen_attend_spell \
    audio=melspectrogram \
    lr_scheduler=warmup_reduce_lr_on_plateau \
    trainer=tpu \
    criterion=joint_ctc_cross_entropy

You can simply train with AISHELL-1 dataset like below:

  • Example3: Train the quartznet model with mfcc features On GPU with FP16:
$ python ./openspeech_cli/hydra_train.py \
    dataset=aishell \
    dataset.dataset_path=$DATASET_PATH \
    dataset.dataset_download=True \
    dataset.manifest_file_path=$MANIFEST_FILE_PATH \  
    vocab=aishell_character \
    model=quartznet15x5 \
    audio=mfcc \
    lr_scheduler=warmup_reduce_lr_on_plateau \
    trainer=gpu-fp16 \
    criterion=ctc

Evaluation examples

  • Example1: Evaluation the listen_attend_spell model:
$ python ./openspeech_cli/hydra_eval.py \
    audio=melspectrogram \
    eval.model_name=listen_attend_spell \
    eval.dataset_path=$DATASET_PATH \
    eval.checkpoint_path=$CHECKPOINT_PATH \
    eval.manifest_file_path=$MANIFEST_FILE_PATH
  • Example2: Evaluation the listen_attend_spell, conformer_lstm models with ensemble:
$ python ./openspeech_cli/hydra_eval.py \
    audio=melspectrogram \
    eval.model_names=(listen_attend_spell, conformer_lstm) \
    eval.dataset_path=$DATASET_PATH \
    eval.checkpoint_paths=($CHECKPOINT_PATH1, $CHECKPOINT_PATH2) \
    eval.ensemble_weights=(0.3, 0.7) \
    eval.ensemble_method=weighted \
    eval.manifest_file_path=$MANIFEST_FILE_PATH

Language model training example

Language model training requires only data to be prepared in the following format:

openspeech is a framework for making end-to-end speech recognizers.
end to end automatic speech recognition is an emerging paradigm in the field of neural network-based speech recognition that offers multiple benefits.
because of these advantages, many end-to-end speech recognition related open sources have emerged.
...
...

Note that you need to use the same vocabulary as the acoustic model.

  • Example: Train the lstm_lm model:
$ python ./openspeech_cli/hydra_lm_train.py \
    dataset=lm \
    dataset.dataset_path=../../../lm.txt \
    vocab=kspon_character \
    vocab.vocab_path=../../../labels.csv \
    model=lstm_lm \
    lr_scheduler=tri_stage \
    trainer=gpu \
    criterion=perplexity
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