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https://github.com/juliuskunze/speechless

Speech-to-text based on wav2letter built for transfer learning
https://github.com/juliuskunze/speechless

keras python3 speech-recognition tensorflow

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Speech-to-text based on wav2letter built for transfer learning

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README 

        
# speechless

Speech recognizer based on [wav2letter architecture](https://arxiv.org/pdf/1609.03193v2.pdf) built with [Keras](https://keras.io/).



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](http://www.openslr.org/12) works out of the box, 

while training for the German language requires downloading data manually.



## Installation



Python 3.4+ and [TensorFlow](https://www.tensorflow.org/install/) 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](https://github.com/timediv/tensorflow-with-kenlm) 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



```python

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:

```python

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](https://drive.google.com/drive/folders/0B0Azt-a50ylyal9JVDJnbXJJd2c?resourcekey=0-fVYtlyCyldcgeVdo_-OQ6A&usp=sharing) 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.



```python

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:

```python

# ... 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:



```python

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](https://docs.google.com/presentation/d/1X30IcB-CzCxnGt780ze0qOrbsRtDrxbWrZ_zQ91TOZQ/edit#slide=id.g1b9173e933_0_15) 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.:



```python

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`.