speechless

Speech recognizer based on wav2letter architecture built with Keras.

Supports CTC loss, KenLM and greedy decoding and transfer learning between different languages. ASG loss is currently not supported.

Training for English with the 1000h LibriSpeech corpus works out of the box, while training for the German language requires downloading data manually.

Installation

Python 3.4+ and TensorFlow are required.

pip3 install [email protected]:JuliusKunze/speechless.git

will install speechless together with minimal requirements.

If you want to use the KenLM decoder, this modified version of TensorFlow needs to be installed first.

You need to have an audio backend available, for example ffmpeg (run brew install ffmpeg on Mac OS).

Training

from speechless.configuration import Configuration

Configuration.minimal_english().train_from_beginning()

will automatically download a small English example corpus (337MB), train a net based on it while giving you updated loss and predictions. If you use a strong consumer-grade GPU, you should observe training predictions become similar to the input after ~12h, e. g.

Expected:  "just thrust and parry and victory to the stronger"
Predicted: "jest thcrus and pary and bettor o the stronter"
Errors: 10 letters (20%), 6 words (67%), loss: 37.19.

All data (corpus, nets, logs) will be stored in ~/speechless-data.

This directory can be changed:

from pathlib import Path

from speechless import configuration
from speechless.configuration import Configuration, DataDirectories

configuration.default_data_directories = DataDirectories(Path("/your/data/path"))

Configuration.minimal_english().train_from_beginning()

To download and train on the full 1000h LibriSpeech corpus, replace mininal_english with english.

main.py contains various other functions that were executed to train and use models.

If you want completely flexible where data is saved and loaded from, you should not use Configuration at all but instead use the code from net, corpus, german_corpus, english_corpus and recording directly.

Loading

By default, all trained models are stored in the ~/speechless-data/nets directory. You use models from here by downloading them into this folder (keep the subfolder from Google Drive). To load a such a model use load_best_english_model or load_best_german_model e. g.

from speechless.configuration import Configuration

wav2letter = Configuration.german().load_best_german_model()

If the language was originally trained with a different character set (e. g. a corpus of another language), specifying the allowed_characters_for_loaded_model parameter of load_model still allows you to use that model for training, thereby allowing transfer learning.

Recording

You can record your own audio with a microphone and get a prediction for it:

# ... after loading a model, see above

from speechless.recording import record_plot_and_save

label = record_plot_and_save()

print(wav2letter.predict(label))

Three seconds of silence will end the recording and silence will be truncated. By default, this will generate a wav-file and a spectrogram plot in ~/speechless-data/recordings.

Testing

Given that you downloaded the German corpus into the corpus directory, you can evaluate the German model on the test set:

german.test_model_grouped_by_loaded_corpus_name(wav2letter)

Testing will write to the standard output and a log to ~/speechless-data/test-results by default.

Plotting

Plotting labeled audio examples from the corpus like this one here can be done with LabeledExamplePlotter.save_spectrogram.

German & Sections

For some German datasets, it is possible to retrieve which word is said at which point of time, allowing to extract labeled sections, e. g.:

from speechless.configuration import Configuration

german = Configuration.german()
wav2letter = german.load_best_german_model()
example = german.corpus.examples[0]
sections = example.sections()
for section in sections:
    print(wav2letter.test_and_predict(section))

If you need to access the section labels only (e. g. for filtering for particular words), use example.positional_label.labels (which is faster because no audio data needs to be sliced). If no positional info is available, sections and positional_label are None.