*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.
*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, and demo deployment. Besides, several pre-trained models for both English and Mandarin are also released.
- [Training for Mandarin Language](#training-for-mandarin-language)
- [Trying Live Demo with Your Own Voice](#trying-live-demo-with-your-own-voice)
@ -22,13 +21,45 @@
## Installation
For this project was developed in PaddlePaddle V2 API, which is not maintained officially any more, we only support [running it in Docker container](#running-in-docker-container), instead of building environment from source code. And we are going to release the update to the latest Paddle Fluid API very soon, please keep an eye on this project.
To avoid the trouble of environment setup, [running in Docker container](#running-in-docker-container) is highly recommended. Otherwise follow the guidelines below to install the dependencies manually.
### Prerequisites
- Python 2.7 only supported
- PaddlePaddle 1.6 version (Coming soon ...)
### Setup
- Make sure these libraries or tools installed: `pkg-config`, `flac`, `ogg`, `vorbis`, `boost` and `swig`, e.g. installing them via `apt-get`:
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.
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.
Some of the scripts in `./examples` are configured with 8 GPUs. If you don't have 8 GPUs available, please modify `CUDA_VISIBLE_DEVICES`. 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.
@ -45,7 +76,7 @@ Let's take a tiny sampled subset of [LibriSpeech dataset](http://www.openslr.org
sh run_data.sh
```
`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.
`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 `./dataset/librispeech` 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.
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.
@ -206,8 +238,8 @@ A language model is required to improve the decoder's performance. We have prepa
```bash
cd models/lm
sh download_lm_en.sh
sh download_lm_ch.sh
bash download_lm_en.sh
bash download_lm_ch.sh
```
If you wish to train your own better language model, please refer to [KenLM](https://github.com/kpu/kenlm) for tutorials. Here we provide some tips to show how we preparing our English and Mandarin language models. You can take it as a reference when you train your own.
@ -216,7 +248,7 @@ If you wish to train your own better language model, please refer to [KenLM](htt
The English corpus is from the [Common Crawl Repository](http://commoncrawl.org) and you can download it from [statmt](http://data.statmt.org/ngrams/deduped_en). We use part en.00 to train our English language model. There are some preprocessing steps before training:
* Characters not in \[A-Za-z0-9\s'\] (\s represents whitespace characters) are removed and Arabic numbers are converted to English numbers like 1000 to one thousand.
* Characters not in \['A-Za-z0-9\s'\] (\s represents whitespace characters) are removed and Arabic numbers are converted to English numbers like 1000 to one thousand.
* Repeated whitespace characters are squeezed to one and the beginning whitespace characters are removed. Notice that all transcriptions are lowercase, so all characters are converted to lowercase.
* Top 400,000 most frequent words are selected to build the vocabulary and the rest are replaced with 'UNKNOWNWORD'.
@ -239,13 +271,13 @@ An inference module caller `infer.py` is provided to infer, decode and visualize
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`.
@ -264,13 +296,13 @@ To evaluate a model's performance quantitatively, please run:
sudo nvidia-docker run -it -v $(pwd)/DeepSpeech:/DeepSpeech paddlepaddle/deep_speech:latest-gpu /bin/bash
sudo nvidia-docker run -it -v $(pwd)/DeepSpeech:/DeepSpeech hub.baidubce.com/paddlepaddle/deep_speech_fluid:latest-gpu /bin/bash
```
Now go back and start from the [Getting Started](#getting-started) section, you can execute training, inference and hyper-parameters tuning similarly in the Docker container.
## 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:
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
```
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/DeepSpeech/tree/develop/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/DeepSpeech/tree/develop/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.
- Get training logs
Run this to list all the jobs you have submitted, as well as their running status:
```bash
paddlecloud get jobs
```
Run this, the corresponding job's logs will be printed.
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).
For more information about the DeepSpeech2 training on PaddleCloud, please refer to
[Train DeepSpeech2 on PaddleCloud](https://github.com/PaddlePaddle/DeepSpeech/tree/develop/cloud).
## Training for Mandarin Language
@ -417,14 +386,13 @@ The key steps of training for Mandarin language are same to that of English lang
## 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.
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_english_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
```
@ -436,7 +404,7 @@ For example, on MAC OS X:
```bash
brew install portaudio
pip install pyaudio
pip install pynput
pip install keyboard
```
Then to start the client, please run this in another console:
@ -452,7 +420,7 @@ Now, in the client console, press the `whitespace` key, hold, and start speaking
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.
Please also refer to `examples/deploy_demo/run_english_demo_server.sh`, which will first download a pre-trained English 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 English to test it. If you would like to try some other models, just update `--model_path` argument in the script.
English | [LibriSpeech Model](https://deepspeech.bj.bcebos.com/eng_models/librispeech_model.tar.gz) | [LibriSpeech Dataset](http://www.openslr.org/12/) | 960 h
English | [BaiduEN8k Model](https://deepspeech.bj.bcebos.com/demo_models/baidu_en8k_model.tar.gz) | Baidu Internal English Dataset | 8628 h
Mandarin | [Aishell Model](https://deepspeech.bj.bcebos.com/mandarin_models/aishell_model.tar.gz) | [Aishell Dataset](http://www.openslr.org/33/) | 151 h
English | [LibriSpeech Model](https://deepspeech.bj.bcebos.com/eng_models/librispeech_model_fluid.tar.gz) | [LibriSpeech Dataset](http://www.openslr.org/12/) | 960 h
English | [BaiduEN8k Model](https://deepspeech.bj.bcebos.com/demo_models/baidu_en8k_model_fluid.tar.gz) | Baidu Internal English Dataset | 8628 h
Mandarin | [Aishell Model](https://deepspeech.bj.bcebos.com/mandarin_models/aishell_model_fluid.tar.gz) | [Aishell Dataset](http://www.openslr.org/33/) | 151 h
We compare the training time with 1, 2, 4, 8, 16 Tesla K40m GPUs (with a subset of LibriSpeech samples whose audio durations are between 6.0 and 7.0 seconds). And it shows that a **near-linear** acceleration with multiple GPUs has been achieved. In the following figure, the time (in seconds) cost for training is printed on the blue bars.
We compare the training time with 1, 2, 4, 8 Tesla V100 GPUs (with a subset of LibriSpeech samples whose audio durations are between 6.0 and 7.0 seconds). And it shows that a **near-linear** acceleration with multiple GPUs has been achieved. In the following figure, the time (in seconds) cost for training is printed on the blue bars.
*DeepSpeech2*是一个采用[PaddlePaddle](https://github.com/PaddlePaddle/Paddle)平台的端到端自动语音识别（ASR）引擎的开源项目，具体原理请参考这篇论文[Baidu's Deep Speech 2 paper](http://proceedings.mlr.press/v48/amodei16.pdf)。
*DeepSpeech2*是一个采用[PaddlePaddle](https://github.com/PaddlePaddle/Paddle)平台的端到端自动语音识别（ASR）引擎的开源项目，具体原理参考这篇论文[Baidu's Deep Speech 2 paper](http://proceedings.mlr.press/v48/amodei16.pdf)。
{"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"}
到目前为止，一个 ASR 模型已经训练完毕，并且用现有的音频文件进行了定性测试（`infer.py`）和定量测试（`test.py`）。但目前还没有用你自己的声音进行测试。`deploy/demo_english_server.py`和`deploy/demo_client.py`能够快速构建一个利用已训练好的模型对ASR引擎进行实时演示的系统，使你能够用自己的语音测试和演示。
English | [LibriSpeech Model](https://deepspeech.bj.bcebos.com/eng_models/librispeech_model.tar.gz) | [LibriSpeech Dataset](http://www.openslr.org/12/) | 960 h
English | [BaiduEN8k Model](https://deepspeech.bj.bcebos.com/demo_models/baidu_en8k_model.tar.gz) | Baidu Internal English Dataset | 8628 h
Mandarin | [Aishell Model](https://deepspeech.bj.bcebos.com/mandarin_models/aishell_model.tar.gz) | [Aishell Dataset](http://www.openslr.org/33/) | 151 h
English | [LibriSpeech Model](https://deepspeech.bj.bcebos.com/eng_models/librispeech_model_fluid.tar.gz) | [LibriSpeech Dataset](http://www.openslr.org/12/) | 960 h
English | [BaiduEN8k Model](https://deepspeech.bj.bcebos.com/demo_models/baidu_en8k_model_fluid.tar.gz) | Baidu Internal English Dataset | 8628 h
Mandarin | [Aishell Model](https://deepspeech.bj.bcebos.com/mandarin_models/aishell_model_fluid.tar.gz) | [Aishell Dataset](http://www.openslr.org/33/) | 151 h
>Please make sure [PaddleCloud Client](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) has be installed and current directory is `deep_speech_2/cloud/`
## Step 1: Upload Data
Provided with several input manifests, `pcloud_upload_data.sh` will pack and upload all the containing audio files to PaddleCloud filesystem, and also generate some corresponding manifest files with updated cloud paths.
Please modify the following arguments in `pcloud_upload_data.sh`:
- `IN_MANIFESTS`： Paths (in local filesystem) of manifest files containing the audio files to be uploaded. Multiple paths can be concatenated with a whitespace delimeter.
- `OUT_MANIFESTS`: Paths (in local filesystem) to write the updated output manifest files to. Multiple paths can be concatenated with a whitespace delimeter. The values of `audio_filepath` in the output manifests are updated with cloud filesystem paths.
- `CLOUD_DATA_DIR`: Directory (in PaddleCloud filesystem) to upload the data to. Don't forget to replace `USERNAME` in the default directory and make sure that you have the permission to write it.
- `NUM_SHARDS`: Number of data shards / parts (in tar files) to be generated when packing and uploading data. Smaller `num_shards` requires larger temoporal local disk space for packing data.
By running:
```
sh pcloud_upload_data.sh
```
all the audio files will be uploaded to PaddleCloud filesystem, and you will get modified manifests files in `OUT_MANIFESTS`.
You have to take this step only once, in the very first time you do the cloud training. Later on, the data is persisitent on the cloud filesystem and reusable for further job submissions.
## Step 2: Configure Training
Configure cloud training arguments in `pcloud_submit.sh`, with the following arguments:
- `TRAIN_MANIFEST`: Manifest filepath (in local filesystem) for training. Notice that the`audio_filepath` should be in cloud filesystem, like those generated by `pcloud_upload_data.sh`.
- `DEV_MANIFEST`: Manifest filepath (in local filesystem) for validation.
- `CLOUD_MODEL_DIR`: Directory (in PaddleCloud filesystem) to save the model parameters (checkpoints). Don't forget to replace `USERNAME` in the default directory and make sure that you have the permission to write it.
- `BATCH_SIZE`: Training batch size for a single node.
- `NUM_GPU`: Number of GPUs allocated for a single node.
- `NUM_NODE`: Number of nodes (machines) allocated for this job.
- `IS_LOCAL`: Set to False to enable parameter server, if using multiple nodes.
Configure other training hyper-parameters in `pcloud_train.sh` as you wish, just as what you can do in local training.
By running:
```
sh pcloud_submit.sh
```
you submit a training job to PaddleCloud. And you will see the job name when the submission is done.
## Step 3 Get Job Logs
Run this to list all the jobs you have submitted, as well as their running status:
```
paddlecloud get jobs
```
Run this, the corresponding job's logs will be printed.
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).
Li Fuchen
5 years ago
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