Example: Speech to Text

A deep learning-based approach to learning the speech-to-text conversion, built on top of the OpenNMT system.

Given raw audio, we first apply short-time Fourier transform (STFT), then apply Convolutional Neural Networks to get the source features. Based on this source representation, we use an LSTM decoder with attention to produce the text character by character.

Dependencies

  • torchaudio: sudo apt-get install -y sox libsox-dev libsox-fmt-all; pip install git+https://github.com/pytorch/audio
  • librosa: pip install librosa

Quick Start

To get started, we provide a toy speech-to-text example. We assume that the working directory is OpenNMT-py throughout this document.

  1. Download the data.
wget -O data/speech.tgz http://lstm.seas.harvard.edu/latex/speech.tgz; tar zxf data/speech.tgz -C data/
  1. Preprocess the data.
python preprocess.py -data_type audio -src_dir data/speech/an4_dataset -train_src data/speech/src-train.txt -train_tgt data/speech/tgt-train.txt -valid_src data/speech/src-val.txt -valid_tgt data/speech/tgt-val.txt -save_data data/speech/demo
  1. Train the model.
python train.py -model_type audio -data data/speech/demo -save_model demo-model -gpuid 0 -batch_size 16 -max_grad_norm 20 -learning_rate 0.1 -learning_rate_decay 0.98 -train_steps 100000
  1. Translate the speechs.
python translate.py -data_type audio -model demo-model_acc_x_ppl_x_e13.pt -src_dir data/speech/an4_dataset -src data/speech/src-val.txt -output pred.txt -gpu 0 -verbose

Options

  • -src_dir: The directory containing the audio files.
  • -train_tgt: The file storing the tokenized labels, one label per line. It shall look like:
<label0_token0> <label0_token1> ... <label0_tokenN0>
<label1_token0> <label1_token1> ... <label1_tokenN1>
<label2_token0> <label2_token1> ... <label2_tokenN2>
...
  • -train_src: The file storing the paths of the audio files (relative to src_dir).
<speech0_path>
<speech1_path>
<speech2_path>
...
  • sample_rate: Sample rate. Default: 16000.
  • window_size: Window size for spectrogram in seconds. Default: 0.02.
  • window_stride: Window stride for spectrogram in seconds. Default: 0.01.
  • window: Window type for spectrogram generation. Default: hamming.

Acknowledgement

Our preprocessing and CNN encoder is adapted from deepspeech.pytorch.