adapt to the new structure

.gitignore

@@ -1,3 +0,0 @@
-manifest*
-mean_std.npz
-thirdparty/

README.md

@@ -1,180 +1,446 @@
 # DeepSpeech2 on PaddlePaddle
 
->TODO: to be updated, since the directory hierarchy was changed.
+*DeepSpeech2 on PaddlePaddle* is an open-source implementation of end-to-end Automatic Speech Recognition (ASR) engine, based on [Baidu's Deep Speech 2 paper](http://proceedings.mlr.press/v48/amodei16.pdf), with [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) platform. Our vision is to empower both industrial application and academic research on speech recognition, via an easy-to-use, efficient and scalable implementation, including training, inference & testing module, distributed [PaddleCloud](https://github.com/PaddlePaddle/cloud) training, and demo deployment. Besides, several pre-trained models for both English and Mandarin are also released.
+
+## Table of Contents
+- [Prerequisites](#prerequisites)
+- [Installation](#installation)
+- [Getting Started](#getting-started)
+- [Data Preparation](#data-preparation)
+- [Training a Model](#training-a-model)
+- [Data Augmentation Pipeline](#data-augmentation-pipeline)
+- [Inference and Evaluation](#inference-and-evaluation)
+- [Distributed Cloud Training](#distributed-cloud-training)
+- [Hyper-parameters Tuning](#hyper-parameters-tuning)
+- [Training for Mandarin Language](#training-for-mandarin-language)
+- [Trying Live Demo with Your Own Voice](#trying-live-demo-with-your-own-voice)
+- [Experiments and Benchmarks](#experiments-and-benchmarks)
+- [Released Models](#released-models)
+- [Questions and Help](#questions-and-help)
+
+## Prerequisites
+- Python 2.7 only supported
+- PaddlePaddle the latest version (please refer to the [Installation Guide](https://github.com/PaddlePaddle/Paddle#installation))
 
 ## Installation
 
+### Basic setup
+
+Please make sure the above [prerequisites](#prerequisites) have been satisfied before moving on.
+
+```bash
+git clone https://github.com/PaddlePaddle/models.git
+cd models/deep_speech_2
+sh setup.sh
 ```
+
+### Decoders setup
+
+```bash
+cd decoders/swig
 sh setup.sh
+cd ../..
 ```
 
-Please replace `$PADDLE_INSTALL_DIR` with your own paddle installation directory.
+These commands will install the decoders that translate the ouptut probability  vectors of DS2 model to text data, incuding CTC greedy decoder, CTC beam search decoder and its batch version. And a detailed usuage about them will be given in the following sections.
+
+## Getting Started
 
-## Usage
+Several shell scripts provided in `./examples` will help us to quickly give it a try, for most major modules, including data preparation, model training, case inference and model evaluation, with a few public dataset (e.g. [LibriSpeech](http://www.openslr.org/12/), [Aishell](http://www.openslr.org/33)). Reading these examples will also help you to understand how to make it work with your own data.
 
-### Preparing Data
+Some of the scripts in `./examples` are configured with 8 GPUs. If you don't have 8 GPUs available, please modify `CUDA_VISIBLE_DEVICES` and `--trainer_count`. If you don't have any GPU available, please set `--use_gpu` to False to use CPUs instead. Besides, if out-of-memory problem occurs, just reduce `--batch_size` to fit.
 
+Let's take a tiny sampled subset of [LibriSpeech dataset](http://www.openslr.org/12/) for instance.
+
+- Go to directory
+
+    ```bash
+    cd examples/tiny
+    ```
+
+    Notice that this is only a toy example with a tiny sampled subset of LibriSpeech. If you would like to try with the complete dataset (would take several days for training), please go to `examples/librispeech` instead.
+- Prepare the data
+
+    ```bash
+    sh run_data.sh
     ```
-cd datasets
-sh run_all.sh
-cd ..
+
+    `run_data.sh` will download dataset, generate manifests, collect normalizer's statistics and build vocabulary. Once the data preparation is done, you will find the data (only part of LibriSpeech) downloaded in `~/.cache/paddle/dataset/speech/libri` and the corresponding manifest files generated in `./data/tiny` as well as a mean stddev file and a vocabulary file. It has to be run for the very first time you run this dataset and is reusable for all further experiments.
+- Train your own ASR model
+
+    ```bash
+    sh run_train.sh
     ```
 
-`sh run_all.sh` prepares all ASR datasets (currently, only LibriSpeech available). After running, we have several summarization manifest files in json-format.
+    `run_train.sh` will start a training job, with training logs printed to stdout and model checkpoint of every pass/epoch saved to `./checkpoints/tiny`. These checkpoints could be used for training resuming, inference, evaluation and deployment.
+- Case inference with an existing model
 
-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.
+    ```bash
+    sh run_infer.sh
+    ```
 
+    `run_infer.sh` will show us some speech-to-text decoding results for several (default: 10) samples with the trained model. The performance might not be good now as the current model is only trained with a toy subset of LibriSpeech. To see the results with a better model, you can download a well-trained (trained for several days, with the complete LibriSpeech) model and do the inference:
 
-More help for arguments:
+    ```bash
+    sh run_infer_golden.sh
+    ```
+- Evaluate an existing model
 
+    ```bash
+    sh run_test.sh
     ```
-python datasets/librispeech/librispeech.py --help
+
+    `run_test.sh` will evaluate the model with Word Error Rate (or Character Error Rate) measurement. Similarly, you can also download a well-trained model and test its performance:
+
+    ```bash
+    sh run_test_golden.sh
     ```
 
-### Preparing for Training
+More detailed information are provided in the following sections. Wish you a happy journey with the *DeepSpeech2 on PaddlePaddle* ASR engine!
+
+
+## Data Preparation
+
+### Generate Manifest
+
+*DeepSpeech2 on PaddlePaddle* accepts a textual **manifest** file as its data set interface. A manifest file summarizes a set of speech data, with each line containing some meta data (e.g. filepath, transcription, duration) of one audio clip, in [JSON](http://www.json.org/) format, such as:
 
 ```
-python tools/compute_mean_std.py
+{"audio_filepath": "/home/work/.cache/paddle/Libri/134686/1089-134686-0001.flac", "duration": 3.275, "text": "stuff it into you his belly counselled him"}
+{"audio_filepath": "/home/work/.cache/paddle/Libri/134686/1089-134686-0007.flac", "duration": 4.275, "text": "a cold lucid indifference reigned in his soul"}
 ```
 
-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
+To use your custom data, you only need to generate such manifest files to summarize the dataset. Given such summarized manifests, training, inference and all other modules can be aware of where to access the audio files, as well as their meta data including the transcription labels.
 
-```
-python tools/compute_mean_std.py --specgram_type mfcc
+For how to generate such manifest files, please refer to `data/librispeech/librispeech.py`, which will download data and generate manifest files for LibriSpeech dataset.
+
+### Compute Mean & Stddev for Normalizer
+
+To perform z-score normalization (zero-mean, unit stddev) upon audio features, we have to estimate in advance the mean and standard deviation of the features, with some training samples:
+
+```bash
+python tools/compute_mean_std.py \
+--num_samples 2000 \
+--specgram_type linear \
+--manifest_paths data/librispeech/manifest.train \
+--output_path data/librispeech/mean_std.npz
 ```
 
-and specify ```--specgram_type mfcc``` when running train.py, infer.py, evaluator.py or tune.py.
+It will compute the mean and standard deviation of power spectrum feature with 2000 random sampled audio clips listed in `data/librispeech/manifest.train` and save the results to `data/librispeech/mean_std.npz` for further usage.
 
-More help for arguments:
 
+### Build Vocabulary
+
+A vocabulary of possible characters is required to convert the transcription into a list of token indices for training, and in decoding, to convert from a list of indices back to text again. Such a character-based vocabulary can be built with `tools/build_vocab.py`.
+
+```bash
+python tools/build_vocab.py \
+--count_threshold 0 \
+--vocab_path data/librispeech/eng_vocab.txt \
+--manifest_paths data/librispeech/manifest.train
 ```
+
+It will write a vocabuary file `data/librispeeech/eng_vocab.txt` with all transcription text in `data/librispeech/manifest.train`, without vocabulary truncation (`--count_threshold 0`).
+
+### More Help
+
+For more help on arguments:
+
+```bash
+python data/librispeech/librispeech.py --help
 python tools/compute_mean_std.py --help
+python tools/build_vocab.py --help
 ```
 
-### Training
+## Training a model
+
+`train.py` is the main caller of the training module. Examples of usage are shown below.
 
-For GPU Training:
+- Start training from scratch with 8 GPUs:
 
     ```
-CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python train.py
+    CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python train.py --trainer_count 8
     ```
 
-For CPU Training:
+- Start training from scratch with 16 CPUs:
 
     ```
-python train.py --use_gpu False
+    python train.py --use_gpu False --trainer_count 16
     ```
+- Resume training from a checkpoint:
 
-More help for arguments:
-
     ```
+    CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
+    python train.py \
+    --init_model_path CHECKPOINT_PATH_TO_RESUME_FROM
+    ```
+
+For more help on arguments:
+
+```bash
 python train.py --help
 ```
+or refer to `example/librispeech/run_train.sh`.
+
+## Data Augmentation Pipeline
+
+Data augmentation has often been a highly effective technique to boost the deep learning performance. We augment our speech data by synthesizing new audios with small random perturbation (label-invariant transformation) added upon raw audios. You don't have to do the syntheses on your own, as it is already embedded into the data provider and is done on the fly, randomly for each epoch during training.
 
-### Preparing language model
+Six optional augmentation components are provided to be selected, configured and inserted into the processing pipeline.
 
-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
+### Inference
+
+  - Volume Perturbation
+  - Speed Perturbation
+  - Shifting Perturbation
+  - Online Bayesian normalization
+  - Noise Perturbation (need background noise audio files)
+  - Impulse Response (need impulse audio files)
+
+In order to inform the trainer of what augmentation components are needed and what their processing orders are, it is required to prepare in advance a *augmentation configuration file* in [JSON](http://www.json.org/) format. For example:
 
 ```
-cd ./lm
-sh run.sh
-cd ..
+[{
+    "type": "speed",
+    "params": {"min_speed_rate": 0.95,
+               "max_speed_rate": 1.05},
+    "prob": 0.6
+},
+{
+    "type": "shift",
+    "params": {"min_shift_ms": -5,
+               "max_shift_ms": 5},
+    "prob": 0.8
+}]
 ```
 
-### Setup decoders
+When the `--augment_conf_file` argument of `trainer.py` is set to the path of the above example configuration file, every audio clip in every epoch will be processed: with 60% of chance, it will first be speed perturbed with a uniformly random sampled speed-rate between 0.95 and 1.05, and then with 80% of chance it will be shifted in time with a random sampled offset between -5 ms and 5 ms. Finally this newly synthesized audio clip will be feed into the feature extractor for further training.
 
-```shell
-cd models/swig_decoders
-sh setup.sh
-cd ../..
-```
+For other configuration examples, please refer to `conf/augmenatation.config.example`.
 
-These commands will install the decoders that translate the ouptut probability  vectors of DS2 model to text data, incuding CTC greedy decoder, CTC beam search decoder and its batch version.
+Be careful when utilizing the data augmentation technique, as improper augmentation will do harm to the training, due to the enlarged train-test gap.
 
-### Inference
+## Inference and Evaluation
 
-For GPU inference
+### Prepare Language Model
 
+A language model is required to improve the decoder's performance. We have prepared two language models (with lossy compression) for users to download and try. One is for English and the other is for Mandarin. Users can simply run this to download the preprared language models:
+
+```bash
+cd models/lm
+sh download_lm_en.sh
+sh download_lm_ch.sh
 ```
-CUDA_VISIBLE_DEVICES=0 python infer.py
-```
+If you wish to train your own better language model, please refer to [KenLM](https://github.com/kpu/kenlm) for tutorials.
+
+TODO: any other requirements or tips to add?
 
-For CPU inference
+### Speech-to-text Inference
 
+An inference module caller `infer.py` is provided to infer, decode and visualize speech-to-text results for several given audio clips. It might help to have an intuitive and qualitative evaluation of the ASR model's performance.
+
+- Inference with GPU:
+
+    ```bash
+    CUDA_VISIBLE_DEVICES=0 python infer.py --trainer_count 1
     ```
-python infer.py --use_gpu=False
+
+- Inference with CPUs:
+
+    ```bash
+    python infer.py --use_gpu False --trainer_count 12
     ```
 
-More help for arguments:
+We provide two types of CTC decoders: *CTC greedy decoder* and *CTC beam search decoder*. The *CTC greedy decoder* is an implementation of the simple best-path decoding algorithm, selecting at each timestep the most likely token, thus being greedy and locally optimal. The [*CTC beam search decoder*](https://arxiv.org/abs/1408.2873) otherwise utilizes a heuristic breadth-first graph search for reaching a near global optimality; it also requires a pre-trained KenLM language model for better scoring and ranking. The decoder type can be set with argument `--decoding_method`.
+
+For more help on arguments:
 
 ```
 python infer.py --help
 ```
+or refer to `example/librispeech/run_infer.sh`.
+
+### Evaluate a Model
+
+To evaluate a model's performance quantitatively, please run:
 
-### Evaluating
+- Evaluation with GPUs:
 
+    ```bash
+    CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python test.py --trainer_count 8
     ```
-CUDA_VISIBLE_DEVICES=0 python evaluate.py
+
+- Evaluation with CPUs:
+
+    ```bash
+    python test.py --use_gpu False --trainer_count 12
     ```
 
-More help for arguments:
+The error rate (default: word error rate; can be set with `--error_rate_type`) will be printed.
+
+For more help on arguments:
 
+```bash
+python test.py --help
 ```
-python evaluate.py --help
+or refer to `example/librispeech/run_test.sh`.
+
+## Hyper-parameters Tuning
+
+The hyper-parameters $\alpha$ (coefficient for language model scorer) and $\beta$ (coefficient for word count scorer) for the [*CTC beam search decoder*](https://arxiv.org/abs/1408.2873) often have a significant impact on the decoder's performance. It would be better to re-tune them on a validation set when the acoustic model is renewed.
+
+`tools/tune.py` performs a 2-D grid search over the hyper-parameter $\alpha$ and $\beta$. You must provide the range of $\alpha$ and $\beta$, as well as the number of their attempts.
+
+- Tuning with GPU:
+
+    ```bash
+    CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
+    python tools/tune.py \
+    --trainer_count 8 \
+    --alpha_from 0.1 \
+    --alpha_to 0.36 \
+    --num_alphas 14 \
+    --beta_from 0.05 \
+    --beta_to 1.0 \
+    --num_betas 20
     ```
 
-### Parameters tuning
+- Tuning with CPU:
 
-Usually, the parameters $\alpha$ and $\beta$ for the CTC [prefix beam search](https://arxiv.org/abs/1408.2873) decoder need to be tuned after retraining the acoustic model.
+    ```bash
+    python tools/tune.py --use_gpu False
+    ```
 
-For GPU tuning
+After tuning, you can reset $\alpha$ and $\beta$ in the inference and evaluation modules to see if they really help improve the ASR performance.
 
+```bash
+python tune.py --help
 ```
-CUDA_VISIBLE_DEVICES=0 python tune.py
+or refer to `example/librispeech/run_tune.sh`.
+
+TODO: add figure.
+
+## Distributed Cloud Training
+
+We also provide a cloud training module for users to do the distributed cluster training on [PaddleCloud](https://github.com/PaddlePaddle/cloud), to achieve a much faster training speed with multiple machines. To start with this, please first install PaddleCloud client and register a PaddleCloud account, as described in [PaddleCloud Usage](https://github.com/PaddlePaddle/cloud/blob/develop/doc/usage_cn.md#%E4%B8%8B%E8%BD%BD%E5%B9%B6%E9%85%8D%E7%BD%AEpaddlecloud).
+
+Please take the following steps to submit a training job:
+
+- Go to directory:
+
+    ```bash
+    cd cloud
     ```
+- Upload data:
 
-For CPU tuning
+    Data must be uploaded to PaddleCloud filesystem to be accessed within a cloud job. `pcloud_upload_data.sh` helps do the data packing and uploading:
 
+    ```bash
+    sh pcloud_upload_data.sh
     ```
-python tune.py --use_gpu=False
+
+    Given input manifests, `pcloud_upload_data.sh` will:
+
+    - Extract the audio files listed in the input manifests.
+    - Pack them into a specified number of tar files.
+    - Upload these tar files to PaddleCloud filesystem.
+    - Create cloud manifests by replacing local filesystem paths with PaddleCloud filesystem paths. New manifests will be used to inform the cloud jobs of audio files' location and their meta information.
+
+    It should be done only once for the very first time to do the cloud training. Later, the data is kept persisitent on the cloud filesystem and reusable for further job submissions.
+
+    For argument details please refer to [Train DeepSpeech2 on PaddleCloud](https://github.com/PaddlePaddle/models/tree/develop/deep_speech_2/cloud).
+
+ - Configure training arguments:
+
+    Configure the cloud job parameters in `pcloud_submit.sh` (e.g. `NUM_NODES`, `NUM_GPUS`, `CLOUD_TRAIN_DIR`, `JOB_NAME` etc.) and then configure other hyper-parameters for training in `pcloud_train.sh` (just as what you do for local training).
+
+    For argument details please refer to [Train DeepSpeech2 on PaddleCloud](https://github.com/PaddlePaddle/models/tree/develop/deep_speech_2/cloud).
+
+ - Submit the job:
+
+    By running:
+
+    ```bash
+    sh pcloud_submit.sh
     ```
+    a training job has been submitted to PaddleCloud, with the job name printed to the console.
 
-More help for arguments:
+  - Get training logs
 
+    Run this to list all the jobs you have submitted, as well as their running status:
+
+    ```bash
+    paddlecloud get jobs
     ```
-python tune.py --help
+
+    Run this, the corresponding job's logs will be printed.
+    ```bash
+    paddlecloud logs -n 10000 $REPLACED_WITH_YOUR_ACTUAL_JOB_NAME
     ```
 
-Then reset parameters with the tuning result before inference or evaluating.
+For more information about the usage of PaddleCloud, please refer to [PaddleCloud Usage](https://github.com/PaddlePaddle/cloud/blob/develop/doc/usage_cn.md#提交任务).
 
-### Playing with the ASR Demo
+For more information about the DeepSpeech2 training on PaddleCloud, please refer to
+[Train DeepSpeech2 on PaddleCloud](https://github.com/PaddlePaddle/models/tree/develop/deep_speech_2/cloud).
 
-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).
+## Training for Mandarin Language
 
-For example, on MAC OS X:
+TODO: to be added
+
+## Trying Live Demo with Your Own Voice
 
+Until now, an ASR model is trained and tested qualitatively (`infer.py`) and quantitatively (`test.py`) with existing audio files. But it is not yet tested with your own speech. `deploy/demo_server.py` and `deploy/demo_client.py` helps quickly build up a real-time demo ASR engine with the trained model, enabling you to test and play around with the demo, with your own voice.
+
+To start the demo's server, please run this in one console:
+
+```bash
+CUDA_VISIBLE_DEVICES=0 \
+python deploy/demo_server.py \
+--trainer_count 1 \
+--host_ip localhost \
+--host_port 8086
 ```
+
+For the machine (might not be the same machine) to run the demo's client, please do the following installation before moving on.
+
+For example, on MAC OS X:
+
+```bash
 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:
+Then to start the client, please run this in another console:
 
+```bash
+CUDA_VISIBLE_DEVICES=0 \
+python -u deploy/demo_client.py \
+--host_ip 'localhost' \
+--host_port 8086
 ```
-python demo_client.py
+
+Now, in the client console, press the `whitespace` key, hold, and start speaking. Until finishing your utterance, release the key to let the speech-to-text results shown in the console. To quit the client, just press `ESC` key.
+
+Notice that `deploy/demo_client.py` must be run on a machine with a microphone device, while `deploy/demo_server.py` could be run on one without any audio recording hardware, e.g. any remote server machine. Just be careful to set the `host_ip` and `host_port` argument with the actual accessible IP address and port, if the server and client are running with two separate machines. Nothing should be done if they are running on one single machine.
+
+Please also refer to `examples/mandarin/run_demo_server.sh`, which will first download a pre-trained Mandarin model (trained with 3000 hours of internal speech data) and then start the demo server with the model. With running `examples/mandarin/run_demo_client.sh`, you can speak Mandarin to test it. If you would like to try some other models, just update `--model_path` argument in the script.  
+
+For more help on arguments:
+
+```bash
+python deploy/demo_server.py --help
+python deploy/demo_client.py --help
 ```
-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`.
+## Experiments and Benchmarks
 
+TODO: to be added
 
-## PaddleCloud Training
+## Released Models
 
-If you wish to train DeepSpeech2 on PaddleCloud, please refer to
-[Train DeepSpeech2 on PaddleCloud](https://github.com/PaddlePaddle/models/tree/develop/deep_speech_2/cloud).
+TODO: to be added
+
+## Questions and Help
+
+You are welcome to submit questions and bug reports in [Github Issues](https://github.com/PaddlePaddle/models/issues). You are also welcome to contribute to this project.

data/librispeech/eng_vocab.txt

@@ -1,28 +0,0 @@
-'
- 
-a
-b
-c
-d
-e
-f
-g
-h
-i
-j
-k
-l
-m
-n
-o
-p
-q
-r
-s
-t
-u
-v
-w
-x
-y
-z

data/librispeech/librispeech.py

@@ -19,8 +19,6 @@ import json
 import codecs
 from paddle.v2.dataset.common import md5file
 
-DATA_HOME = os.path.expanduser('~/.cache/paddle/dataset/speech')
-
 URL_ROOT = "http://www.openslr.org/resources/12"
 URL_TEST_CLEAN = URL_ROOT + "/test-clean.tar.gz"
 URL_TEST_OTHER = URL_ROOT + "/test-other.tar.gz"
@@ -41,7 +39,7 @@ MD5_TRAIN_OTHER_500 = "d1a0fd59409feb2c614ce4d30c387708"
 parser = argparse.ArgumentParser(description=__doc__)
 parser.add_argument(
     "--target_dir",
-    default=DATA_HOME + "/Libri",
+    default='~/.cache/paddle/dataset/speech/libri',
     type=str,
     help="Directory to save the dataset. (default: %(default)s)")
 parser.add_argument(
@@ -60,8 +58,7 @@ args = parser.parse_args()
 
 
 def download(url, md5sum, target_dir):
-    """
-    Download file from url to target_dir, and check md5sum.
+    """Download file from url to target_dir, and check md5sum.
     """
     if not os.path.exists(target_dir): os.makedirs(target_dir)
     filepath = os.path.join(target_dir, url.split("/")[-1])
@@ -77,8 +74,7 @@ def download(url, md5sum, target_dir):
 
 
 def unpack(filepath, target_dir):
-    """
-    Unpack the file to the target_dir.
+    """Unpack the file to the target_dir.
     """
     print("Unpacking %s ..." % filepath)
     tar = tarfile.open(filepath)
@@ -87,8 +83,7 @@ def unpack(filepath, target_dir):
 
 
 def create_manifest(data_dir, manifest_path):
-    """
-    Create a manifest json file summarizing the data set, with each line
+    """Create a manifest json file summarizing the data set, with each line
     containing the meta data (i.e. audio filepath, transcription text, audio
     duration) of each audio file within the data set.
     """
@@ -119,8 +114,7 @@ def create_manifest(data_dir, manifest_path):
 
 
 def prepare_dataset(url, md5sum, target_dir, manifest_path):
-    """
-    Download, unpack and create summmary manifest file.
+    """Download, unpack and create summmary manifest file.
     """
     if not os.path.exists(os.path.join(target_dir, "LibriSpeech")):
         # download
@@ -135,6 +129,8 @@ def prepare_dataset(url, md5sum, target_dir, manifest_path):
 
 
 def main():
+    args.target_dir = os.path.expanduser(args.target_dir)
+
     prepare_dataset(
         url=URL_TEST_CLEAN,
         md5sum=MD5_TEST_CLEAN,
@@ -145,12 +141,12 @@ def main():
         md5sum=MD5_DEV_CLEAN,
         target_dir=os.path.join(args.target_dir, "dev-clean"),
         manifest_path=args.manifest_prefix + ".dev-clean")
+    if args.full_download:
         prepare_dataset(
             url=URL_TRAIN_CLEAN_100,
             md5sum=MD5_TRAIN_CLEAN_100,
             target_dir=os.path.join(args.target_dir, "train-clean-100"),
             manifest_path=args.manifest_prefix + ".train-clean-100")
-    if args.full_download:
         prepare_dataset(
             url=URL_TEST_OTHER,
             md5sum=MD5_TEST_OTHER,

decoders/swig/ctc_decoders.cpp

@@ -7,8 +7,8 @@
 #include <map>
 #include <utility>
 
-#include "fst/fstlib.h"
 #include "ThreadPool.h"
+#include "fst/fstlib.h"
 
 #include "decoder_utils.h"
 #include "path_trie.h"

decoders/swig/path_trie.h

@@ -1,12 +1,12 @@
 #ifndef PATH_TRIE_H
 #define PATH_TRIE_H
 #pragma once
+#include <fst/fstlib.h>
 #include <algorithm>
 #include <limits>
 #include <memory>
 #include <utility>
 #include <vector>
-#include <fst/fstlib.h>
 
 using FSTMATCH = fst::SortedMatcher<fst::StdVectorFst>;
 

deploy/demo_server.py

@@ -11,7 +11,7 @@ import wave
 import paddle.v2 as paddle
 import _init_paths
 from data_utils.data import DataGenerator
-from models.model import DeepSpeech2Model
+from model_utils.model import DeepSpeech2Model
 from data_utils.utils import read_manifest
 from utils.utility import add_arguments, print_arguments
 
@@ -46,7 +46,7 @@ add_arg('vocab_path',       str,
         'data/librispeech/eng_vocab.txt',
         "Filepath of vocabulary.")
 add_arg('model_path',       str,
-        './checkpoints/params.latest.tar.gz',
+        './checkpoints/libri/params.latest.tar.gz',
         "If None, the training starts from scratch, "
         "otherwise, it resumes from the pre-trained model.")
 add_arg('lang_model_path',  str,

examples/librispeech/generate.sh

@@ -1,28 +0,0 @@
-#! /usr/bin/bash
-
-pushd ../..
-
-CUDA_VISIBLE_DEVICES=0 \
-python -u infer.py \
---num_samples=10 \
---trainer_count=1 \
---beam_size=500 \
---num_proc_bsearch=12 \
---num_proc_data=12 \
---num_conv_layers=2 \
---num_rnn_layers=3 \
---rnn_layer_size=2048 \
---alpha=2.15 \
---beta=0.35 \
---cutoff_prob=1.0 \
---use_gru=False \
---use_gpu=True \
---share_rnn_weights=True \
---infer_manifest='data/librispeech/manifest.dev-clean' \
---mean_std_path='data/librispeech/mean_std.npz' \
---vocab_path='data/librispeech/eng_vocab.txt' \
---model_path='checkpoints/params.latest.tar.gz' \
---lang_model_path='lm/data/common_crawl_00.prune01111.trie.klm' \
---decoding_method='ctc_beam_search' \
---error_rate_type='wer' \
---specgram_type='linear'

examples/librispeech/run_data.sh

@@ -1,19 +1,31 @@
 #! /usr/bin/bash
 
-pushd ../..
+pushd ../.. > /dev/null
 
 # download data, generate manifests
 python data/librispeech/librispeech.py \
 --manifest_prefix='data/librispeech/manifest' \
---full_download='True' \
---target_dir=$HOME'/.cache/paddle/dataset/speech/Libri'
+--target_dir='~/.cache/paddle/dataset/speech/Libri' \
+--full_download='True'
 
 if [ $? -ne 0 ]; then
     echo "Prepare LibriSpeech failed. Terminated."
     exit 1
 fi
 
-#cat data/librispeech/manifest.train* | shuf > data/librispeech/manifest.train
+cat data/librispeech/manifest.train-* | shuf > data/librispeech/manifest.train
+
+
+# build vocabulary
+python tools/build_vocab.py \
+--count_threshold=0 \
+--vocab_path='data/librispeech/vocab.txt' \
+--manifest_paths='data/librispeech/manifest.train'
+
+if [ $? -ne 0 ]; then
+    echo "Build vocabulary failed. Terminated."
+    exit 1
+fi
 
 
 # compute mean and stddev for normalizer
@@ -30,3 +42,4 @@ fi
 
 
 echo "LibriSpeech Data preparation done."
+exit 0

examples/librispeech/run_infer.sh

@@ -0,0 +1,45 @@
+#! /usr/bin/bash
+
+pushd ../.. > /dev/null
+
+# download language model
+pushd models/lm > /dev/null
+sh download_lm_en.sh
+if [ $? -ne 0 ]; then
+    exit 1
+fi
+popd > /dev/null
+
+
+# infer
+CUDA_VISIBLE_DEVICES=0 \
+python -u infer.py \
+--num_samples=10 \
+--trainer_count=1 \
+--beam_size=500 \
+--num_proc_bsearch=8 \
+--num_conv_layers=2 \
+--num_rnn_layers=3 \
+--rnn_layer_size=2048 \
+--alpha=2.15 \
+--beta=0.35 \
+--cutoff_prob=1.0 \
+--use_gru=False \
+--use_gpu=True \
+--share_rnn_weights=True \
+--infer_manifest='data/librispeech/manifest.test-clean' \
+--mean_std_path='data/librispeech/mean_std.npz' \
+--vocab_path='data/librispeech/vocab.txt' \
+--model_path='checkpoints/libri/params.latest.tar.gz' \
+--lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm' \
+--decoding_method='ctc_beam_search' \
+--error_rate_type='wer' \
+--specgram_type='linear'
+
+if [ $? -ne 0 ]; then
+    echo "Failed in inference!"
+    exit 1
+fi
+
+
+exit 0

examples/librispeech/run_infer_golden.sh

@@ -0,0 +1,54 @@
+#! /usr/bin/bash
+
+pushd ../.. > /dev/null
+
+# download language model
+pushd models/lm > /dev/null
+sh download_lm_en.sh
+if [ $? -ne 0 ]; then
+    exit 1
+fi
+popd > /dev/null
+
+
+# download well-trained model
+pushd models/librispeech > /dev/null
+sh download_model.sh
+if [ $? -ne 0 ]; then
+    exit 1
+fi
+popd > /dev/null
+
+
+# infer
+CUDA_VISIBLE_DEVICES=0 \
+python -u infer.py \
+--num_samples=10 \
+--trainer_count=1 \
+--beam_size=500 \
+--num_proc_bsearch=8 \
+--num_conv_layers=2 \
+--num_rnn_layers=3 \
+--rnn_layer_size=2048 \
+--alpha=2.15 \
+--beta=0.35 \
+--cutoff_prob=1.0 \
+--use_gru=False \
+--use_gpu=True \
+--share_rnn_weights=True \
+--infer_manifest='data/librispeech/manifest.test-clean' \
+--mean_std_path='models/librispeech/mean_std.npz' \
+--vocab_path='models/librispeech/vocab.txt' \
+--model_path='models/librispeech/params.tar.gz' \
+--lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm' \
+--decoding_method='ctc_beam_search' \
+--error_rate_type='wer' \
+--specgram_type='linear'
+
+if [ $? -ne 0 ]; then
+    echo "Failed in inference!"
+    exit 1
+fi
+
+
+exit 0

examples/librispeech/run_test.sh

@@ -1,14 +1,24 @@
 #! /usr/bin/bash
 
-pushd ../..
+pushd ../.. > /dev/null
 
+# download language model
+pushd models/lm > /dev/null
+sh download_lm_en.sh
+if [ $? -ne 0 ]; then
+    exit 1
+fi
+popd > /dev/null
+
+
+# evaluate model
 CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
 python -u test.py \
 --batch_size=128 \
 --trainer_count=8 \
 --beam_size=500 \
---num_proc_bsearch=12 \
---num_proc_data=12 \
+--num_proc_bsearch=8 \
+--num_proc_data=4 \
 --num_conv_layers=2 \
 --num_rnn_layers=3 \
 --rnn_layer_size=2048 \
@@ -20,9 +30,17 @@ python -u test.py \
 --share_rnn_weights=True \
 --test_manifest='data/librispeech/manifest.test-clean' \
 --mean_std_path='data/librispeech/mean_std.npz' \
---vocab_path='data/librispeech/eng_vocab.txt' \
---model_path='checkpoints/params.latest.tar.gz' \
---lang_model_path='lm/data/common_crawl_00.prune01111.trie.klm' \
+--vocab_path='data/librispeech/vocab.txt' \
+--model_path='checkpoints/libri/params.latest.tar.gz' \
+--lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm' \
 --decoding_method='ctc_beam_search' \
 --error_rate_type='wer' \
 --specgram_type='linear'
+
+if [ $? -ne 0 ]; then
+    echo "Failed in evaluation!"
+    exit 1
+fi
+
+
+exit 0

examples/librispeech/run_test_golden.sh

@@ -0,0 +1,55 @@
+#! /usr/bin/bash
+
+pushd ../.. > /dev/null
+
+# download language model
+pushd models/lm > /dev/null
+sh download_lm_en.sh
+if [ $? -ne 0 ]; then
+    exit 1
+fi
+popd > /dev/null
+
+
+# download well-trained model
+pushd models/librispeech > /dev/null
+sh download_model.sh
+if [ $? -ne 0 ]; then
+    exit 1
+fi
+popd > /dev/null
+
+
+# evaluate model
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
+python -u test.py \
+--batch_size=128 \
+--trainer_count=8 \
+--beam_size=500 \
+--num_proc_bsearch=8 \
+--num_proc_data=4 \
+--num_conv_layers=2 \
+--num_rnn_layers=3 \
+--rnn_layer_size=2048 \
+--alpha=0.36 \
+--beta=0.25 \
+--cutoff_prob=0.99 \
+--use_gru=False \
+--use_gpu=True \
+--share_rnn_weights=True \
+--test_manifest='data/tiny/manifest.test-clean' \
+--mean_std_path='models/librispeech/mean_std.npz' \
+--vocab_path='models/librispeech/vocab.txt' \
+--model_path='models/librispeech/params.tar.gz' \
+--lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm' \
+--decoding_method='ctc_beam_search' \
+--error_rate_type='wer' \
+--specgram_type='linear'
+
+if [ $? -ne 0 ]; then
+    echo "Failed in evaluation!"
+    exit 1
+fi
+
+
+exit 0

examples/librispeech/run_train.sh

@@ -1,12 +1,14 @@
 #! /usr/bin/bash
 
-pushd ../..
+pushd ../.. > /dev/null
 
+# train model
+# if you wish to resume from an exists model, uncomment --init_model_path
 CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
 python -u train.py \
---batch_size=256 \
+--batch_size=512 \
 --trainer_count=8 \
---num_passes=200 \
+--num_passes=50 \
 --num_proc_data=12 \
 --num_conv_layers=2 \
 --num_rnn_layers=3 \
@@ -23,8 +25,16 @@ python -u train.py \
 --train_manifest='data/librispeech/manifest.train' \
 --dev_manifest='data/librispeech/manifest.dev' \
 --mean_std_path='data/librispeech/mean_std.npz' \
---vocab_path='data/librispeech/eng_vocab.txt' \
---output_model_dir='./checkpoints' \
+--vocab_path='data/librispeech/vocab.txt' \
+--output_model_dir='./checkpoints/libri' \
 --augment_conf_path='conf/augmentation.config' \
 --specgram_type='linear' \
 --shuffle_method='batch_shuffle_clipped'
+
+if [ $? -ne 0 ]; then
+    echo "Failed in training!"
+    exit 1
+fi
+
+
+exit 0

examples/librispeech/run_tune.sh

@@ -1,7 +1,8 @@
 #! /usr/bin/bash
 
-pushd ../..
+pushd ../.. > /dev/null
 
+# grid-search for hyper-parameters in language model
 CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
 python -u tools/tune.py \
 --num_samples=100 \
@@ -23,8 +24,16 @@ python -u tools/tune.py \
 --share_rnn_weights=True \
 --tune_manifest='data/librispeech/manifest.dev-clean' \
 --mean_std_path='data/librispeech/mean_std.npz' \
---vocab_path='data/librispeech/eng_vocab.txt' \
---model_path='checkpoints/params.latest.tar.gz' \
---lang_model_path='lm/data/common_crawl_00.prune01111.trie.klm' \
+--vocab_path='data/librispeech/vocab.txt' \
+--model_path='checkpoints/libri/params.latest.tar.gz' \
+--lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm' \
 --error_rate_type='wer' \
 --specgram_type='linear'
+
+if [ $? -ne 0 ]; then
+    echo "Failed in tuning!"
+    exit 1
+fi
+
+
+exit 0

examples/mandarin/run_demo_client.sh

@@ -0,0 +1,17 @@
+#! /usr/bin/bash
+
+pushd ../.. > /dev/null
+
+# start demo client
+CUDA_VISIBLE_DEVICES=0 \
+python -u deploy/demo_client.py \
+--host_ip='localhost' \
+--host_port=8086 \
+
+if [ $? -ne 0 ]; then
+    echo "Failed in starting demo client!"
+    exit 1
+fi
+
+
+exit 0

examples/mandarin/run_demo_server.sh

@@ -0,0 +1,53 @@
+#! /usr/bin/bash
+# TODO: replace the model with a mandarin model
+
+pushd ../.. > /dev/null
+
+# download language model
+pushd models/lm > /dev/null
+sh download_lm_en.sh
+if [ $? -ne 0 ]; then
+    exit 1
+fi
+popd > /dev/null
+
+
+# download well-trained model
+pushd models/librispeech > /dev/null
+sh download_model.sh
+if [ $? -ne 0 ]; then
+    exit 1
+fi
+popd > /dev/null
+
+
+# start demo server
+CUDA_VISIBLE_DEVICES=0 \
+python -u deploy/demo_server.py \
+--host_ip='localhost' \
+--host_port=8086 \
+--num_conv_layers=2 \
+--num_rnn_layers=3 \
+--rnn_layer_size=2048 \
+--alpha=0.36 \
+--beta=0.25 \
+--cutoff_prob=0.99 \
+--use_gru=False \
+--use_gpu=True \
+--share_rnn_weights=True \
+--speech_save_dir='demo_cache' \
+--warmup_manifest='data/tiny/manifest.test-clean' \
+--mean_std_path='models/librispeech/mean_std.npz' \
+--vocab_path='models/librispeech/vocab.txt' \
+--model_path='models/librispeech/params.tar.gz' \
+--lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm' \
+--decoding_method='ctc_beam_search' \
+--specgram_type='linear'
+
+if [ $? -ne 0 ]; then
+    echo "Failed in starting demo server!"
+    exit 1
+fi
+
+
+exit 0

examples/tiny/run_data.sh

@@ -0,0 +1,51 @@
+#! /usr/bin/bash
+
+pushd ../.. > /dev/null
+
+# prepare folder
+if [ ! -e data/tiny ]; then
+    mkdir data/tiny
+fi
+
+
+# download data, generate manifests
+python data/librispeech/librispeech.py \
+--manifest_prefix='data/tiny/manifest' \
+--target_dir='~/.cache/paddle/dataset/speech/libri' \
+--full_download='False'
+
+if [ $? -ne 0 ]; then
+    echo "Prepare LibriSpeech failed. Terminated."
+    exit 1
+fi
+
+head -n 64 data/tiny/manifest.dev-clean  > data/tiny/manifest.tiny
+
+
+# build vocabulary
+python tools/build_vocab.py \
+--count_threshold=0 \
+--vocab_path='data/tiny/vocab.txt' \
+--manifest_paths='data/tiny/manifest.dev'
+
+if [ $? -ne 0 ]; then
+    echo "Build vocabulary failed. Terminated."
+    exit 1
+fi
+
+
+# compute mean and stddev for normalizer
+python tools/compute_mean_std.py \
+--manifest_path='data/tiny/manifest.tiny' \
+--num_samples=64 \
+--specgram_type='linear' \
+--output_path='data/tiny/mean_std.npz'
+
+if [ $? -ne 0 ]; then
+    echo "Compute mean and stddev failed. Terminated."
+    exit 1
+fi
+
+
+echo "Tiny data preparation done."
+exit 0

examples/tiny/run_infer.sh

@@ -0,0 +1,45 @@
+#! /usr/bin/bash
+
+pushd ../.. > /dev/null
+
+# download language model
+pushd models/lm > /dev/null
+sh download_lm_en.sh
+if [ $? -ne 0 ]; then
+    exit 1
+fi
+popd > /dev/null
+
+
+# infer
+CUDA_VISIBLE_DEVICES=0 \
+python -u infer.py \
+--num_samples=10 \
+--trainer_count=1 \
+--beam_size=500 \
+--num_proc_bsearch=8 \
+--num_conv_layers=2 \
+--num_rnn_layers=3 \
+--rnn_layer_size=2048 \
+--alpha=0.36 \
+--beta=0.25 \
+--cutoff_prob=0.99 \
+--use_gru=False \
+--use_gpu=True \
+--share_rnn_weights=True \
+--infer_manifest='data/tiny/manifest.tiny' \
+--mean_std_path='data/tiny/mean_std.npz' \
+--vocab_path='data/tiny/vocab.txt' \
+--model_path='checkpoints/tiny/params.pass-19.tar.gz' \
+--lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm' \
+--decoding_method='ctc_beam_search' \
+--error_rate_type='wer' \
+--specgram_type='linear'
+
+if [ $? -ne 0 ]; then
+    echo "Failed in inference!"
+    exit 1
+fi
+
+
+exit 0

examples/tiny/run_infer_golden.sh

@@ -0,0 +1,54 @@
+#! /usr/bin/bash
+
+pushd ../.. > /dev/null
+
+# download language model
+pushd models/lm > /dev/null
+sh download_lm_en.sh
+if [ $? -ne 0 ]; then
+    exit 1
+fi
+popd > /dev/null
+
+
+# download well-trained model
+pushd models/librispeech > /dev/null
+sh download_model.sh
+if [ $? -ne 0 ]; then
+    exit 1
+fi
+popd > /dev/null
+
+
+# infer
+CUDA_VISIBLE_DEVICES=0 \
+python -u infer.py \
+--num_samples=10 \
+--trainer_count=1 \
+--beam_size=500 \
+--num_proc_bsearch=8 \
+--num_conv_layers=2 \
+--num_rnn_layers=3 \
+--rnn_layer_size=2048 \
+--alpha=0.36 \
+--beta=0.25 \
+--cutoff_prob=0.99 \
+--use_gru=False \
+--use_gpu=True \
+--share_rnn_weights=True \
+--infer_manifest='data/tiny/manifest.test-clean' \
+--mean_std_path='models/librispeech/mean_std.npz' \
+--vocab_path='models/librispeech/vocab.txt' \
+--model_path='models/librispeech/params.tar.gz' \
+--lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm' \
+--decoding_method='ctc_beam_search' \
+--error_rate_type='wer' \
+--specgram_type='linear'
+
+if [ $? -ne 0 ]; then
+    echo "Failed in inference!"
+    exit 1
+fi
+
+
+exit 0

examples/tiny/run_test.sh

@@ -0,0 +1,46 @@
+#! /usr/bin/bash
+
+pushd ../.. > /dev/null
+
+# download language model
+pushd models/lm > /dev/null
+sh download_lm_en.sh
+if [ $? -ne 0 ]; then
+    exit 1
+fi
+popd > /dev/null
+
+
+# evaluate model
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
+python -u test.py \
+--batch_size=16 \
+--trainer_count=8 \
+--beam_size=500 \
+--num_proc_bsearch=8 \
+--num_proc_data=4 \
+--num_conv_layers=2 \
+--num_rnn_layers=3 \
+--rnn_layer_size=2048 \
+--alpha=0.36 \
+--beta=0.25 \
+--cutoff_prob=0.99 \
+--use_gru=False \
+--use_gpu=True \
+--share_rnn_weights=True \
+--test_manifest='data/tiny/manifest.tiny' \
+--mean_std_path='data/tiny/mean_std.npz' \
+--vocab_path='data/tiny/vocab.txt' \
+--model_path='checkpoints/params.pass-19.tar.gz' \
+--lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm' \
+--decoding_method='ctc_beam_search' \
+--error_rate_type='wer' \
+--specgram_type='linear'
+
+if [ $? -ne 0 ]; then
+    echo "Failed in evaluation!"
+    exit 1
+fi
+
+
+exit 0

examples/tiny/run_test_golden.sh

@@ -0,0 +1,55 @@
+#! /usr/bin/bash
+
+pushd ../.. > /dev/null
+
+# download language model
+pushd models/lm > /dev/null
+sh download_lm_en.sh
+if [ $? -ne 0 ]; then
+    exit 1
+fi
+popd > /dev/null
+
+
+# download well-trained model
+pushd models/librispeech > /dev/null
+sh download_model.sh
+if [ $? -ne 0 ]; then
+    exit 1
+fi
+popd > /dev/null
+
+
+# evaluate model
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
+python -u test.py \
+--batch_size=128 \
+--trainer_count=8 \
+--beam_size=500 \
+--num_proc_bsearch=8 \
+--num_proc_data=4 \
+--num_conv_layers=2 \
+--num_rnn_layers=3 \
+--rnn_layer_size=2048 \
+--alpha=0.36 \
+--beta=0.25 \
+--cutoff_prob=0.99 \
+--use_gru=False \
+--use_gpu=True \
+--share_rnn_weights=True \
+--test_manifest='data/tiny/manifest.test-clean' \
+--mean_std_path='models/librispeech/mean_std.npz' \
+--vocab_path='models/librispeech/vocab.txt' \
+--model_path='models/librispeech/params.tar.gz' \
+--lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm' \
+--decoding_method='ctc_beam_search' \
+--error_rate_type='wer' \
+--specgram_type='linear'
+
+if [ $? -ne 0 ]; then
+    echo "Failed in evaluation!"
+    exit 1
+fi
+
+
+exit 0

examples/tiny/run_train.sh

@@ -0,0 +1,40 @@
+#! /usr/bin/bash
+
+pushd ../.. > /dev/null
+
+# train model
+# if you wish to resume from an exists model, uncomment --init_model_path
+CUDA_VISIBLE_DEVICES=0,1,2,3 \
+python -u train.py \
+--batch_size=16 \
+--trainer_count=4 \
+--num_passes=20 \
+--num_proc_data=1 \
+--num_conv_layers=2 \
+--num_rnn_layers=3 \
+--rnn_layer_size=2048 \
+--num_iter_print=100 \
+--learning_rate=1e-5 \
+--max_duration=27.0 \
+--min_duration=0.0 \
+--use_sortagrad=True \
+--use_gru=False \
+--use_gpu=True \
+--is_local=True \
+--share_rnn_weights=True \
+--train_manifest='data/tiny/manifest.tiny' \
+--dev_manifest='data/tiny/manifest.tiny' \
+--mean_std_path='data/tiny/mean_std.npz' \
+--vocab_path='data/tiny/vocab.txt' \
+--output_model_dir='./checkpoints/tiny' \
+--augment_conf_path='conf/augmentation.config' \
+--specgram_type='linear' \
+--shuffle_method='batch_shuffle_clipped'
+
+if [ $? -ne 0 ]; then
+    echo "Fail to do inference!"
+    exit 1
+fi
+
+
+exit 0

examples/tiny/run_tune.sh

@@ -0,0 +1,39 @@
+#! /usr/bin/bash
+
+pushd ../.. > /dev/null
+
+# grid-search for hyper-parameters in language model
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
+python -u tools/tune.py \
+--num_samples=100 \
+--trainer_count=8 \
+--beam_size=500 \
+--num_proc_bsearch=12 \
+--num_conv_layers=2 \
+--num_rnn_layers=3 \
+--rnn_layer_size=2048 \
+--num_alphas=14 \
+--num_betas=20 \
+--alpha_from=0.1 \
+--alpha_to=0.36 \
+--beta_from=0.05 \
+--beta_to=1.0 \
+--cutoff_prob=0.99 \
+--use_gru=False \
+--use_gpu=True \
+--share_rnn_weights=True \
+--tune_manifest='data/tiny/manifest.tiny' \
+--mean_std_path='data/tiny/mean_std.npz' \
+--vocab_path='data/tiny/vocab.txt' \
+--model_path='checkpoints/params.pass-9.tar.gz' \
+--lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm' \
+--error_rate_type='wer' \
+--specgram_type='linear'
+
+if [ $? -ne 0 ]; then
+    echo "Failed in tuning!"
+    exit 1
+fi
+
+
+exit 0

infer.py

@@ -7,7 +7,7 @@ import argparse
 import functools
 import paddle.v2 as paddle
 from data_utils.data import DataGenerator
-from models.model import DeepSpeech2Model
+from model_utils.model import DeepSpeech2Model
 from utils.error_rate import wer, cer
 from utils.utility import add_arguments, print_arguments
 
@@ -35,13 +35,13 @@ add_arg('mean_std_path',    str,
         'data/librispeech/mean_std.npz',
         "Filepath of normalizer's mean & std.")
 add_arg('vocab_path',       str,
-        'data/librispeech/eng_vocab.txt',
+        'data/librispeech/vocab.txt',
         "Filepath of vocabulary.")
 add_arg('lang_model_path',  str,
-        'lm/data/common_crawl_00.prune01111.trie.klm',
+        'models/lm/common_crawl_00.prune01111.trie.klm',
         "Filepath for language model.")
 add_arg('model_path',       str,
-        './checkpoints/params.latest.tar.gz',
+        './checkpoints/libri/params.latest.tar.gz',
         "If None, the training starts from scratch, "
         "otherwise, it resumes from the pre-trained model.")
 add_arg('decoding_method',  str,

lm/run.sh

@@ -1,19 +0,0 @@
-echo "Downloading language model ..."
-
-mkdir data
-
-LM=common_crawl_00.prune01111.trie.klm
-MD5="099a601759d467cd0a8523ff939819c5"
-
-wget -c http://paddlepaddle.bj.bcebos.com/model_zoo/speech/$LM -P ./data
-
-echo "Checking md5sum ..."
-md5_tmp=`md5sum ./data/$LM | awk -F[' '] '{print $1}'`
-
-if [ $MD5 != $md5_tmp ]; then
-    echo "Fail to download the language model!"
-    exit 1
-fi
-
-
-

model_utils/decoder.py

@@ -180,6 +180,8 @@ def ctc_beam_search_decoder(probs_seq,
                 prob = prob * ext_scoring_func(result)
             log_prob = log(prob)
             beam_result.append((log_prob, result))
+        else:
+            beam_result.append((float('-inf'), ''))
 
     ## output top beam_size decoding results
     beam_result = sorted(beam_result, key=lambda asd: asd[0], reverse=True)

model_utils/model.py

@@ -8,10 +8,10 @@ import os
 import time
 import gzip
 import paddle.v2 as paddle
-from models.swig_decoders_wrapper import Scorer
-from models.swig_decoders_wrapper import ctc_greedy_decoder
-from models.swig_decoders_wrapper import ctc_beam_search_decoder_batch
-from models.network import deep_speech_v2_network
+from decoders.swig_wrapper import Scorer
+from decoders.swig_wrapper import ctc_greedy_decoder
+from decoders.swig_wrapper import ctc_beam_search_decoder_batch
+from model_utils.network import deep_speech_v2_network
 
 
 class DeepSpeech2Model(object):

model_utils/tests/test_decoders.py

@@ -4,7 +4,7 @@ from __future__ import division
 from __future__ import print_function
 
 import unittest
-from models import decoder
+from model_utils import decoder
 
 
 class TestDecoders(unittest.TestCase):

models/librispeech/download_model.sh

@@ -0,0 +1,20 @@
+#! /usr/bin/bash
+
+source ../../utils/utility.sh
+
+# TODO: add urls
+URL='to-be-added'
+MD5=5b4af224b26c1dc4dd972b7d32f2f52a
+TARGET=./librispeech_model.tar.gz
+
+
+echo "Download LibriSpeech model ..."
+download $URL $MD5 $TARGET
+if [ $? -ne 0 ]; then
+    echo "Fail to download LibriSpeech model!"
+    exit 1
+fi
+tar -zxvf $TARGET
+
+
+exit 0

models/lm/download_lm_en.sh

@@ -0,0 +1,18 @@
+#! /usr/bin/bash
+
+source ../../utils/utility.sh
+
+URL=http://paddlepaddle.bj.bcebos.com/model_zoo/speech/common_crawl_00.prune01111.trie.klm
+MD5="099a601759d467cd0a8523ff939819c5"
+TARGET=./common_crawl_00.prune01111.trie.klm
+
+
+echo "Download language model ..."
+download $URL $MD5 $TARGET
+if [ $? -ne 0 ]; then
+    echo "Fail to download the language model!"
+    exit 1
+fi
+
+
+exit 0

test.py

@@ -7,7 +7,7 @@ import argparse
 import functools
 import paddle.v2 as paddle
 from data_utils.data import DataGenerator
-from models.model import DeepSpeech2Model
+from model_utils.model import DeepSpeech2Model
 from utils.error_rate import wer, cer
 from utils.utility import add_arguments, print_arguments
 
@@ -36,14 +36,14 @@ add_arg('mean_std_path',    str,
         'data/librispeech/mean_std.npz',
         "Filepath of normalizer's mean & std.")
 add_arg('vocab_path',       str,
-        'data/librispeech/eng_vocab.txt',
+        'data/librispeech/vocab.txt',
         "Filepath of vocabulary.")
 add_arg('model_path',       str,
-        './checkpoints/params.latest.tar.gz',
+        './checkpoints/libri/params.latest.tar.gz',
         "If None, the training starts from scratch, "
         "otherwise, it resumes from the pre-trained model.")
 add_arg('lang_model_path',  str,
-        'lm/data/common_crawl_00.prune01111.trie.klm',
+        'models/lm/common_crawl_00.prune01111.trie.klm',
         "Filepath for language model.")
 add_arg('decoding_method',  str,
         'ctc_beam_search',

tools/build_vocab.py

@@ -21,7 +21,7 @@ add_arg = functools.partial(add_arguments, argparser=parser)
 # yapf: disable
 add_arg('count_threshold',  int,    0,  "Truncation threshold for char counts.")
 add_arg('vocab_path',       str,
-        'datasets/vocab/zh_vocab.txt',
+        'data/librispeech/vocab.txt',
         "Filepath to write the vocabulary.")
 add_arg('manifest_paths',   str,
         None,
@@ -34,7 +34,7 @@ args = parser.parse_args()
 
 
 def count_manifest(counter, manifest_path):
-    manifest_jsons = utils.read_manifest(manifest_path)
+    manifest_jsons = read_manifest(manifest_path)
     for line_json in manifest_jsons:
         for char in line_json['text']:
             counter.update(char)

tools/compute_mean_std.py

@@ -20,10 +20,10 @@ add_arg('specgram_type',    str,
         "Audio feature type. Options: linear, mfcc.",
         choices=['linear', 'mfcc'])
 add_arg('manifest_path',    str,
-        'datasets/manifest.train',
+        'data/librispeech/manifest.train',
         "Filepath of manifest to compute normalizer's mean and stddev.")
 add_arg('output_path',    str,
-        'mean_std.npz',
+        'data/librispeech/mean_std.npz',
         "Filepath of write mean and stddev to (.npz).")
 # yapf: disable
 args = parser.parse_args()

tools/tune.py

@@ -9,7 +9,7 @@ import functools
 import paddle.v2 as paddle
 import _init_paths
 from data_utils.data import DataGenerator
-from models.model import DeepSpeech2Model
+from model_utils.model import DeepSpeech2Model
 from utils.error_rate import wer
 from utils.utility import add_arguments, print_arguments
 
@@ -41,13 +41,13 @@ add_arg('mean_std_path',    str,
         'data/librispeech/mean_std.npz',
         "Filepath of normalizer's mean & std.")
 add_arg('vocab_path',       str,
-        'data/librispeech/eng_vocab.txt',
+        'data/librispeech/vocab.txt',
         "Filepath of vocabulary.")
 add_arg('lang_model_path',  str,
-        'lm/data/common_crawl_00.prune01111.trie.klm',
+        'models/lm/common_crawl_00.prune01111.trie.klm',
         "Filepath for language model.")
 add_arg('model_path',       str,
-        './checkpoints/params.latest.tar.gz',
+        './checkpoints/libri/params.latest.tar.gz',
         "If None, the training starts from scratch, "
         "otherwise, it resumes from the pre-trained model.")
 add_arg('error_rate_type',  str,

train.py

@@ -6,7 +6,7 @@ from __future__ import print_function
 import argparse
 import functools
 import paddle.v2 as paddle
-from models.model import DeepSpeech2Model
+from model_utils.model import DeepSpeech2Model
 from data_utils.data import DataGenerator
 from utils.utility import add_arguments, print_arguments
 
@@ -41,14 +41,14 @@ add_arg('mean_std_path',    str,
         'data/librispeech/mean_std.npz',
         "Filepath of normalizer's mean & std.")
 add_arg('vocab_path',       str,
-        'data/librispeech/eng_vocab.txt',
+        'data/librispeech/vocab.txt',
         "Filepath of vocabulary.")
 add_arg('init_model_path',  str,
         None,
         "If None, the training starts from scratch, "
         "otherwise, it resumes from the pre-trained model.")
 add_arg('output_model_dir', str,
-        "./checkpoints",
+        "./checkpoints/libri",
         "Directory for saving checkpoints.")
 add_arg('augment_conf_path',str,
         'conf/augmentation.config',

utils/utility.sh

@@ -0,0 +1,20 @@
+download() {
+    URL=$1
+    MD5=$2
+    TARGET=$3
+
+    if [ -e $TARGET ]; then
+        md5_result=`md5sum $TARGET | awk -F[' '] '{print $1}'`
+        if [ $MD5 == $md5_result ]; then
+            echo "$TARGET already exists, download skipped."
+            return 0
+        fi
+    fi
+
+    wget -c $URL -P `dirname "$TARGET"`
+    md5_result=`md5sum $TARGET | awk -F[' '] '{print $1}'`
+    if [ $MD5 -ne $md5_result ]; then
+        echo "Fail to download the language model!"
+        return 1
+    fi
+}