Speech Recognition

This is for speech recognition including models and train, evaluate, inference scripts based tensorflow 2
You can execute script examples on below descriptions with test data
resources/configs directory contains default datasets (LibriSpeech, KsponSpeech, Clovacall) and models (LAS, DeepSpeech2) configs.
resources/sp-models directory contains default sentencepiece tokenizer for each datasets

References

LAS Model

DeepSpeech2 Model

Deep Speech 2: End-to-End Speech Recognition in English and Mandarin

Dataset Format

Dataset File is tsv(tab separated values) format.
The dataset file should have header line.
The 1st column is audio file path relative to directory that contains dataset tsv file.
The 2nd column is recognized text.
Refer to tests/data/dataset.tsv file.

FilePath	Text
audio/001.wav	안녕하세요
audio/002.wav	반갑습니다
audio/003.wav	근데 이름이 어떻게 되세요?
...	...

This is tsv file example.

Train

Example

You can start training by running script like below example.

$ python -m speech_recognition.run.train \
    --data-config resources/configs/libri_config.yml \
    --model-config resources/configs/las_small.yml \
    --sp-model-path resources/sp-models/sp_model_unigram_16K_libri.model \
    --train-dataset-paths tests/data/wav_dataset.tsv \
    --dev-dataset-paths tests/data/wav_dataset.tsv \
    --train-dataset-size 1000 \
    --steps-per-epoch 100 \
    --epochs 10 \
    --batch-size 32 \
    --dev-batch-size 32 \
    --learning-rate 2e-4 \
    --mixed-precision \
    --device CPU

You can also start training with train configuration file using --from-file parameter.

$ python -m speech_recognition.run.train --from-file resources/configs/train_config_sample.yml

And you can override the parameter of file by command line arguments like below. $ python -m speech_recognition.run.train
--from-file resources/configs/train_config_sample.yml
--epochs 1
--batch-size 128
--device GPU


## Arguments

```text
  --from-file FROM_FILE
                        load configs from file
  --data-config DATA_CONFIG
                        data processing config file
  --model-config MODEL_CONFIG
                        model config file
  --sp-model-path SP_MODEL_PATH
                        sentencepiece model path
  --train-dataset-paths TRAIN_DATASET_PATHS
                        a tsv/tfrecord dataset file or multiple files ex)
                        *.tsv
  --dev-dataset-paths DEV_DATASET_PATHS
                        a tsv/tfrecord dataset file or multiple files ex)
                        *.tsv
  --train-dataset-size TRAIN_DATASET_SIZE
                        the number of training dataset examples
  --output-path OUTPUT_PATH
                        output directory to save log and model checkpoints
  --pretrained-model-path PRETRAINED_MODEL_PATH
                        pretrained model checkpoint
  --epochs EPOCHS
  --steps-per-epoch STEPS_PER_EPOCH
  --learning-rate LEARNING_RATE
  --min-learning-rate MIN_LEARNING_RATE
  --warmup-rate WARMUP_RATE
  --warmup-steps WARMUP_STEPS
  --batch-size BATCH_SIZE
  --dev-batch-size DEV_BATCH_SIZE
  --shuffle-buffer-size SHUFFLE_BUFFER_SIZE
                        shuffle buffer size
  --max-over-policy {filter,slice}
                        policy for sequence whose length is over max
  --use-tfrecord        use tfrecord dataset
  --tensorboard-update-freq TENSORBOARD_UPDATE_FREQ
  --mixed-precision     use mixed precision FP16
  --seed SEED           Set random seed
  --skip-epochs SKIP_EPOCHS
                        skip first N epochs and start N   1 epoch
  --device {CPU,GPU,TPU}
                        device to use (TPU or GPU or CPU)

data-config is config file path for data processing. example config is resources/configs/libri_config.yml.
model-config is config model file path for model initialize. default config is resources/configs/las_small.yml.
sp-model-path is sentencepiece model path to tokenize target text.
pretrained-model-path is pretrained model checkpoint path if you continue to train from pretrained model.
warmup-rate or warmup-steps specify warmup steps. default is zero. warmup-steps is used if both of params provided.
max-over-policy option is for sequences whose length is over than max sequence. You can filter longer example or slice to fit length.
use-tfrecord option should be provided when using TFRecord format dataset.
mixed-precision option is enabling FP16 mixed precision.

Evaluate

Example

You can evaluate your trained model using evaluate.py script. You'll get to know CER or WER as a result of evaluation like below example.

$ python -m speech_recognition.run.evaluate \
    --data-config resources/configs/libri_config.yml \
    --model-config tests/data/model-configs/las_mini_for_test.yml \
    --dataset-paths tests/data/wav_dataset.tsv \
    --model-path tests/data/model-checkpoints/las.ckpt \
    --sp-model-path resources/sp-models/sp_model_unigram_16K_libri.model \
    --device CPU
...
[2021-06-07 13:22:48,599] [ ] Load Tokenizer from resources/sp-models/sp_model_unigram_16K_libri.model
[2021-06-07 13:22:48,626] [ ] Load Data Config from resources/configs/libri_config.yml
[2021-06-07 13:22:48,629] [ ] Load dataset from tests/data/wav_dataset.tsv
2021-06-07 13:22:49.018137: I tensorflow_io/core/kernels/cpu_check.cc:128] Your CPU supports instructions that this TensorFlow IO binary was not compiled to use: AVX2 FMA
[2021-06-07 13:22:49,662] [ ] Use delta and deltas accelerate
[2021-06-07 13:22:53,122] [ ] Load weights of model from tests/data/model-checkpoints/las.ckpt
Model: "las"
...
[2021-06-07 13:22:53,135] [ ] Start Inference
2021-06-07 13:22:53.171394: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)
2021-06-07 13:22:53.188758: I tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 2198835000 Hz
[2021-06-07 13:22:56,352] [ ] Ended Inference
[2021-06-07 13:22:56,589] [ ] Average WER: 2494.6429%
[2021-06-07 13:22:56,589] [ ] Average CER: 7256.3131%

Argument

  --data-config DATA_CONFIG
                        data processing config file
  --model-config MODEL_CONFIG
                        model config file
  --dataset-paths DATASET_PATHS
                        a tsv/tfrecord dataset file or multiple files ex)
                        *.tsv
  --model-path MODEL_PATH
                        pretrained model checkpoint
  --sp-model-path SP_MODEL_PATH
                        sentencepiece model path
  --output-path OUTPUT_PATH
                        output tsv file path to save generated sentences
  --batch-size BATCH_SIZE
  --beam-size BEAM_SIZE
                        not given, use greedy search else beam search with
                        this value as beam size
  --use-tfrecord        use tfrecord dataset
  --mixed-precision     Use mixed precision FP16
  --device DEVICE       device to train

dataset-paths is same as dataset-paths in train script.
If you pass output-path argument, recognized text and real target text, distance metric is exported in tsv format.
You can select your metric of CER or WER by passing metric argument.

Inference

Example

You can infer with trained model to your audio files like below example.

$ python -m speech_recognition.run.inference \
    --data-config resources/configs/libri_config.yml \
    --model-config tests/data/model-configs/las_mini_for_test.yml \
    --audio-files "tests/data/audio_files/*.wav"  \
    --model-path tests/data/model-checkpoints/las.ckpt \
    --sp-model-path resources/sp-models/sp_model_unigram_16K_libri.model \
    --batch-size 3 \
    --device CPU \
    --beam-size 2

...
[2021-06-07 13:28:27,696] [ ] Use delta and deltas accelerate
[2021-06-07 13:28:31,202] Loaded weights of model from tests/data/model-checkpoints/las.ckpt
Model: "las"
(MODEL SUMMARY)
[2021-06-07 13:28:31,204] Start Inference
2021-06-07 13:28:31.238552: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)
2021-06-07 13:28:31.256769: I tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 2198835000 Hz
[2021-06-07 13:28:35,693] Ended Inference, Start to save...
[2021-06-07 13:28:35,694] Saved (audio path,decoded sentence) pairs to output.tsv

Then inferenced files is saved to output path.

Argument

  --data-config DATA_CONFIG
                        data processing config file
  --model-config MODEL_CONFIG
                        model config file
  --audio-files AUDIO_FILES
                        an audio file or glob pattern of multiple files ex)
                        *.pcm
  --model-path MODEL_PATH
                        pretrained model checkpoint
  --output-path OUTPUT_PATH
                        output tsv file path to save generated sentences
  --sp-model-path SP_MODEL_PATH
                        sentencepiece model path
  --batch-size BATCH_SIZE
  --beam-size BEAM_SIZE
                        not given, use greedy search else beam search with
                        this value as beam size
  --mixed-precision     Use mixed precision FP16
  --device DEVICE       device to train

audio-files is audio files glob pattern. i.e) "*.pcm", "data[0-9] .wav"
model-path is tensorflow model checkpoint path.

Make TFRecord

Example

You can convert dataset into TFRecord format like below example.

$ python -m speech_recognition.run.make_tfrecord \
    --data-config resources/configs/libri_config.yml \
    --dataset-paths tests/data/wav_dataset.tsv \
    --sp-model-path resources/sp-models/sp_model_unigram_16K_libri.model \
    --output-dir .

[2021-06-07 13:31:10,444] [ ] Number of Dataset Files: 1
[2021-06-07 13:31:10,445] [ ] Load Config From resources/configs/libri_config.yml
[2021-06-07 13:31:10,447] [ ] Load Tokenizer From resources/sp-models/sp_model_unigram_16K_libri.model
...
2021-06-07 13:31:10.491991: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
[2021-06-07 13:31:10,519] [ ] Start Saving Dataset...
  0%|                                                                                                                                                                                        | 0/1 [00:00<?, ?it/s]2021-06-07 13:31:10.848397: I tensorflow_io/core/kernels/cpu_check.cc:128] Your CPU supports instructions that this TensorFlow IO binary was not compiled to use: AVX2 FMA
2021-06-07 13:31:11.530043: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)
2021-06-07 13:31:11.548833: I tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 2198835000 Hz
100%|█| 1/1 [00:01<00:00,  1.35s/it]
[2021-06-07 13:31:11,867] [ ] Done

Argument

  --data-config DATA_CONFIG
                        data processing config file
  --dataset-paths DATASET_PATHS
                        dataset file path glob pattern
  --output-dir OUTPUT_DIR
                        output directory path, default is input dataset file
                        directoruy
  --sp-model-path SP_MODEL_PATH
                        sentencepiece model path

The arguments is same as train script arguments.
The output TFRecord file contains already pre-processed audio tensors and tokenized tensors, so you can train with only TFRecord file without tsv or audio files.

Name		Name	Last commit message	Last commit date
Latest commit History 133 Commits
.circleci		.circleci
.vscode		.vscode
resources		resources
speech_recognition		speech_recognition
tests		tests
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
pyproject.toml		pyproject.toml
requirements-dev.txt		requirements-dev.txt
requirements.txt		requirements.txt
setup.py		setup.py

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Speech Recognition

References

LAS Model

DeepSpeech2 Model

Dataset Format

Train

Example

Evaluate

Example

Argument

Inference

Example

Argument

Make TFRecord

Example

Argument

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Speech Recognition

References

LAS Model

DeepSpeech2 Model

Dataset Format

Train

Example

Evaluate

Example

Argument

Inference

Example

Argument

Make TFRecord

Example

Argument

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