A mini, simple, and fast end-to-end automatic speech recognition toolkit.

• Minimal Training ⏱
  Self-supervised pre-trained models + minimal fine-tuning.
• Simple and Flexible ⚙️
  Easy to understand and customize.
• Colab Compatible 🧪
  Train your model directly on Google Colab.

• Preprocessing (run_preprocess.py)
  • Find all audio files and transcriptions.
  • Generate vocabularies (character/word/subword/code-switched).
• Training (run_asr.py)
  • Dataset (miniasr/data/dataset.py)
    • Tokenizer for text data (miniasr/data/text.py)
  • DataLoader (miniasr/data/dataloader.py)
  • Model (miniasr/model/base_asr.py)
    • Feature extractor
    • Data augmentation
    • End-to-end CTC ASR
• Testing (run_asr.py)
  • CTC greedy/beam decoding
  • Performance measures: error rates, RTF, latency

• Python 3.6+
• Install sox on your OS
• Install latest s3prl (at least v0.4)

git clone https://github.com/s3prl/s3prl.git
cd s3prl
pip install -e ./
cd ..

• Install via pip:

pip install -e ./

Additional libraries:

• flashlight: to decode with LM and beam search.

You can directly use pre-trained ASR models for any applications. (under construction 🚧)

from miniasr.utils import load_from_checkpoint
from miniasr.data.audio import load_waveform

# Option 1: Loading from a checkpoint
model, args, tokenizer = load_from_checkpoint('path/to/ckpt', 'cuda')
# Option 2: Loading from torch.hub (TODO)
model = torch.hub.load('vectominist/MiniASR', 'ctc_eng').to('cuda')

# Load waveforms and recognize!
waves = [load_waveform('path/to/waveform').to('cuda')]
hyps = model.recognize(waves)

• For already implemented corpora, please see egs/.
• To customize your own dataset, please see miniasr/preprocess.

  --corpus Corpus name.
  --path Path to dataset.
  --set Which subsets to be processed.
  --out Output directory.
  --gen-vocab Specify whether to generate vocabulary files.
  --char-vocab-size Character vocabulary size.
  --word-vocab-size Word vocabulary size.
  --subword-vocab-size Subword vocabulary size.
  --gen-subword Specify whether to generate subword vocabulary.
  --subword-mode {unigram,bpe} Subword training mode.
  --char-coverage Character coverage.
  --seed SEED Set random seed.
  --njobs Number of workers.
  --log-file Logging file.
  --log-level {DEBUG,INFO,WARNING,ERROR,CRITICAL} Logging level.

See examples in egs/.

  --config Training configuration file (.yaml).
  --test Specify testing mode.
  --ckpt Checkpoint for testing.
  --test-name Specify testing results' name.
  --cpu Using CPU only.
  --seed Set random seed.
  --njobs Number of workers.
  --log-file Logging file.
  --log-level {DEBUG,INFO,WARNING,ERROR,CRITICAL} Logging level.

• torch.hub support
• Releasing pre-trained ASR models

• Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks, Graves et al.
• Neural Machine Translation of Rare Words with Subword Units, Sennrich et al.
• HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units, Hsu et al.
• SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition, Park et al.

• PyTorch
• PyTorch Lightning
• S3PRL
• Fairseq
• Flashlight
• SentencePiece
• End-to-end-ASR-Pytorch

@misc{chang2021miniasr,
  title={{MiniASR}},
  author={Chang, Heng-Jui},
  year={2021},
  url={https://github.com/vectominist/MiniASR}
}