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https://github.com/mostafaelaraby/tensorflow-keyword-spotting
Keyword spotting using various architecture like convolutional vggnet , 1D convolutional network and CTC.
https://github.com/mostafaelaraby/tensorflow-keyword-spotting
speech-recognition tensorflow
Last synced: about 2 months ago
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Keyword spotting using various architecture like convolutional vggnet , 1D convolutional network and CTC.
- Host: GitHub
- URL: https://github.com/mostafaelaraby/tensorflow-keyword-spotting
- Owner: mostafaelaraby
- License: apache-2.0
- Created: 2018-01-24T11:14:40.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2018-02-12T00:13:31.000Z (almost 7 years ago)
- Last Synced: 2023-10-20T21:53:54.818Z (over 1 year ago)
- Topics: speech-recognition, tensorflow
- Language: Python
- Size: 28.3 KB
- Stars: 27
- Watchers: 4
- Forks: 6
- Open Issues: 1
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Metadata Files:
- Readme: Readme.md
- License: LICENSE
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README
# TensorFlow Keyword Spotting
This project used to train a model which will be used to spot a set of specific keywords from input classes list,
it used several techniques like wav data augmentation (time shift addition of background noise , speed and stretching of input frequency) with several type of models like baseline Conv , VGGNET and CTC model
### Prerequisites
You will need python 2.7/3.0 and TensorFlow 1.4
## Getting Started
You will need to write a yaml configuration file same as the configs for different models in example_config folder.
## Parameters
### general_params
- seed used to randomize input data and input batches also used in the augmentation (randomly select the augmentation technique for each wav and its value)
- unknown_percentage percentage of unknowns in the training set (keywords that are not in the classes set)
- silence_percentage percentage of wavs not having a keyword to be spotted just background noise
- validation_percentage percentage of validation set
- testing_percentage percentage of test set in case of not using a testing list and having mode = 'test'
### Paths
used to specify data paths same format of [Speech Commands dataset](http://download.tensorflow.org/data/speech_commands_v0.01.tar.gz) each folder containing a set of wav and the parent folder name specify the keyword ,
and needs to specify model path tmp directory for logs and the test set path that needs prediction and the background noise folder name.
### classes
the keywords that needs to be spotted from the provided dataset any other keywords available in the dataset will be treated as unknown
### wav\_reading_params
used to specify sampling rate (of input wav files ), time shift in millisecond , training clips duration in milliseconds , window size milliseconds , window stride milliseconds and finger print type (mfcc,mel and log_mel) and ctc flag to denote using of ctc or not.
### model
- used to specify model used
Baseline as in [ http://www.isca-speech.org/archive/interspeech_2015/papers/i15_1478.pdf]( http://www.isca-speech.org/archive/interspeech_2015/papers/i15_1478.pdf "Convolutional Neural Networks for Small-footprint Keyword Spotting") -
- VGGNET workigng on 2D fingerprints
- LSTM for CTC models
### model_params
- loss could be crossentropy or ctc for ctc models
- Optimizedr can be SGD , Adam , Adagrad and Momentum
- training_steps number of training list as string 1000,2000
- learning_rate list of learning rate at each starting step from training_step list corresponding for example 0.01,0.001 will train till step 1K with learning 0.01 and from step 1K to 3K using 0.001 learning rate
- save_eval_step_interval interval of train steps to save a checkpoint and evaluate
- dropout keep probability dropout
- batch_size size of training batch
- rnd_mini_batches True to use random batches as the one used in [https://github.com/tensorflow/tensorflow/blob/57b32eabca4597241120cb4aba8308a431853c30/tensorflow/examples/speech_commands/input_data.py#L398](https://github.com/tensorflow/tensorflow/blob/57b32eabca4597241120cb4aba8308a431853c30/tensorflow/examples/speech_commands/input_data.py#L398 "Tensorflow speech tutorial")
False to ensure iterating over the whole dataset
### augmentation
- Ops : operations to be used speed and stretch
- percentage of augmentation for each available class
### mode
test to test your models and train to use all data
## Results
using speech commands test set data
- baseline.yml config test accuracy 90.4%
- vggnet.yml config test accuracy 92.3%
## TODO
- Language model for CTC
- Add Resnet model
## References
- CTC Tensorflow [https://github.com/philipperemy/tensorflow-ctc-speech-recognition](https://github.com/philipperemy/tensorflow-ctc-speech-recognition "CTC tensorflow")
- Speech Recognition Tutorial by tensorflow [https://www.tensorflow.org/versions/master/tutorials/audio_recognition](https://www.tensorflow.org/versions/master/tutorials/audio_recognition "Simple Speech Recognition tensorflow tutorial")