JSON format

The JSON format is the default in this repository and if you are training on your own data it is recommended to manipulate it into this format. Note that the data preparation steps are slightly different given the model you have decided to train so please refer to the model configuration page first.

Page contents

Prepare LibriSpeech in JSON format

This page takes LibriSpeech as it is distributed from the https://www.openslr.org website and prepares it into a JSON manifest format.

Quick Start

To run the data preparation steps for LibriSpeech and the base model run the following from the training/ directory:

# Download data to /datasets/LibriSpeech: requires 120GB of disk
./scripts/prepare_librispeech.sh

To run preprocessing for the testing or large configurations, instead run:

SPM_SIZE=1023 CONFIG_NAME=testing-1023sp ./scripts/prepare_librispeech.sh
SPM_SIZE=17407 CONFIG_NAME=large-17407sp ./scripts/prepare_librispeech.sh

Note

If ~/datasets on the host is mounted to /datasets, the downloaded data will be accessible outside the container at ~/datasets/LibriSpeech.

Further detail: prepare_librispeech.sh

The script will:

  1. Download data
  2. Create JSON manifests for each subset of LibriSpeech
  3. Convert the manifests into end-pointed manifests
  4. Create a sentencepiece tokenizer from the train-960h subset
  5. Record log-mel stats for the train-960h subset
  6. Populate the missing fields of a YAML configuration template
  7. Generate an n-gram language model with KenLM from the train-960h subset

1. Data download

Having run the script, the following folders should exist inside the container:

  • /datasets/LibriSpeech
    • train-clean-100/
    • train-clean-360/
    • train-other-500/
    • dev-clean/
    • dev-other/
    • test-clean/
    • test-other/

2. JSON manifests

  • /datasets/LibriSpeech/
    • librispeech-train-clean-100-flac.json
    • librispeech-train-clean-360-flac.json
    • librispeech-train-other-500-flac.json
    • librispeech-train-clean-100-flac.eos.json
    • librispeech-train-clean-360-flac.eos.json
    • librispeech-train-other-500-flac.eos.json
    • librispeech-dev-clean-flac.json
    • librispeech-dev-other-flac.json
    • librispeech-test-clean-flac.json
    • librispeech-test-other-flac.json

3. Sentencepiece tokenizer

  • /datasets/sentencepieces/
    • librispeech8703.model
    • librispeech8703.vocab

4. Log-mel stats

  • /datasets/stats/STATS_SUBDIR:
    • melmeans.pt
    • meln.pt
    • melvars.pt

The STATS_SUBDIR will differ depending on the model since these stats are affected by the feature extraction window size. They are:

  • testing: /datasets/stats/librispeech-winsz0.02
  • {base, large}: /datasets/stats/librispeech-winsz0.025

5. _run.yaml config

In the configs/ directory. Depending on the model you are training you will have one of:

  • testing: configs/testing-1023sp_run.yaml
  • base: configs/base-8703sp_run.yaml
  • large: configs/large-17407sp_run.yaml

_run indicates that this is a complete config, not just a template.

6. N-gram language model

  • /datasets/ngrams/librispeech8703/
    • transcripts.txt
    • ngram.arpa
    • ngram.binary

To train an n-gram on a different dataset, see n-gram docs.

Prepare Other Datasets

Convert your dataset to the JSON format

Options:

  • Adapt the code in caiman_asr_train/data/make_datasets/librispeech.py.
  • If your dataset is in Hugging Face format, you can use the script described here

Generate artifacts needed for training

Suppose you have preprocessed CommonVoice, organized like this:

CommonVoice17.0
|-- common_voice_17.0_dev
|-- common_voice_17.0_dev.json
|-- common_voice_17.0_test
|-- common_voice_17.0_test.json
|-- common_voice_17.0_train
|-- common_voice_17.0_train.json

To generate the training artifacts, run the following:

DATASET_NAME_LOWER_CASE=commonvoice
MAX_DURATION_SECS=20.0
SPM_SIZE=8703
CONFIG_NAME=base-8703sp
DATA_DIR=/datasets/CommonVoice17.0
NGRAM_ORDER=4
TRAIN_MANIFESTS=/datasets/CommonVoice17.0/common_voice_17.0_train.json
./scripts/make_json_artifacts.sh $DATASET_NAME_LOWER_CASE $MAX_DURATION_SECS \
    $SPM_SIZE $CONFIG_NAME $DATA_DIR $NGRAM_ORDER $TRAIN_MANIFESTS

where:

  • DATASET_NAME_LOWER_CASE will determine the name of the generated SENTENCEPIECE and STATS_SUBDIR
  • MAX_DURATION_SECS is number of seconds above which audio clips will be discarded during training
  • SPM_SIZE is the size of the sentencepiece model---in this case, the base model
  • CONFIG_NAME is the name of the template configuration file to read
  • DATA_DIR is the path to your dataset
  • NGRAM_ORDER is the order of the n-gram language model that can be used during beam search
  • TRAIN_MANIFESTS can be a space-separated list

It is advised that you use all of your training data transcripts to build the sentencepiece tokenizer but it is ok to use a subset of the data to calculate the mel stats via the --n_utterances_only flag to caiman_asr_train/data/generate_mel_stats.py.

Before running make_json_artifacts.sh on your custom dataset, you may want to create an EOS version as explained here

Next steps

Having run the data preparation steps, go to the training docs to start training.

See also