DeepSpeech2 on PaddlePaddle

Installation

sh setup.sh
export LD_LIBRARY_PATH=$PADDLE_INSTALL_DIR/Paddle/third_party/install/warpctc/lib:$LD_LIBRARY_PATH

Please replace $PADDLE_INSTALL_DIR with your own paddle installation directory.

Usage

Preparing Data

cd datasets
sh run_all.sh
cd ..

sh run_all.sh prepares all ASR datasets (currently, only LibriSpeech available). After running, we have several summarization manifest files in json-format.

A manifest file summarizes a speech data set, with each line containing the meta data (i.e. audio filepath, transcript text, audio duration) of each audio file within the data set, in json format. Manifest file serves as an interface informing our system of where and what to read the speech samples.

More help for arguments:

python datasets/librispeech/librispeech.py --help

Preparing for Training

python tools/compute_mean_std.py

It will compute mean and stdandard deviation for audio features, and save them to a file with a default name ./mean_std.npz. This file will be used in both training and inferencing. The default feature of audio data is power spectrum, and the mfcc feature is also supported. To train and infer based on mfcc feature, please generate this file by

python tools/compute_mean_std.py --specgram_type mfcc

and specify --specgram_type mfcc when running train.py, infer.py, evaluator.py or tune.py.

More help for arguments:

python tools/compute_mean_std.py --help

Training

For GPU Training:

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python train.py

For CPU Training:

python train.py --use_gpu False

More help for arguments:

python train.py --help

Preparing language model

The following steps, inference, parameters tuning and evaluating, will require a language model during decoding. A compressed language model is provided and can be accessed by

cd ./lm
sh run.sh
cd ..

Inference

For GPU inference

CUDA_VISIBLE_DEVICES=0 python infer.py

For CPU inference

python infer.py --use_gpu=False

More help for arguments:

python infer.py --help

Evaluating

CUDA_VISIBLE_DEVICES=0 python evaluate.py

More help for arguments:

python evaluate.py --help

Parameters tuning

Usually, the parameters \alpha and \beta for the CTC prefix beam search decoder need to be tuned after retraining the acoustic model.

For GPU tuning

CUDA_VISIBLE_DEVICES=0 python tune.py

For CPU tuning

python tune.py --use_gpu=False

More help for arguments:

python tune.py --help

Then reset parameters with the tuning result before inference or evaluating.

Playing with the ASR Demo

A real-time ASR demo is built for users to try out the ASR model with their own voice. Please do the following installation on the machine you'd like to run the demo's client (no need for the machine running the demo's server).

For example, on MAC OS X:

brew install portaudio
pip install pyaudio
pip install pynput

After a model and language model is prepared, we can first start the demo's server:

CUDA_VISIBLE_DEVICES=0 python demo_server.py

And then in another console, start the demo's client:

python demo_client.py

On the client console, press and hold the "white-space" key on the keyboard to start talking, until you finish your speech and then release the "white-space" key. The decoding results (infered transcription) will be displayed.

It could be possible to start the server and the client in two seperate machines, e.g. demo_client.py is usually started in a machine with a microphone hardware, while demo_server.py is usually started in a remote server with powerful GPUs. Please first make sure that these two machines have network access to each other, and then use --host_ip and --host_port to indicate the server machine's actual IP address (instead of the localhost as default) and TCP port, in both demo_server.py and demo_client.py.

PaddleCloud Training

If you wish to train DeepSpeech2 on PaddleCloud, please refer to Train DeepSpeech2 on PaddleCloud.