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Athena is an open-source implementation of end-to-end speech processing engine. Our vision is to empower both industrial application and academic research on end-to-end models for speech processing. To make speech processing available to everyone, we're also releasing example implementation and recipe on some opensource dataset for various tasks (Automatic Speech Recognition, Speech Synthesis, Voice Conversion, Speaker Recognition, etc).

All of our models are implemented in Tensorflow>=2.0.1. For ease of use, we provide Kaldi-free pythonic feature extractor with Athena_transform.

1) Table of Contents

Athena

2) Key Features

Hybrid Attention/CTC based end-to-end ASR
Speech-Transformer
Unsupervised pre-training
Multi-GPU training on one machine or across multiple machines with Horovod
End-to-end Tacotron2 based TTS with support for multi-speaker and GST
Transformer based TTS and FastSpeech
WFST creation and WFST-based decoding
Deployment with Tensorflow C++

3) Installation

3.1) Creating a virtual environment [Optional]

This project has only been tested on Python 3. We highly recommend creating a virtual environment and installing the python requirements there.

# Setting up virtual environment
python -m venv venv_athena
source venv_athena/bin/activate

3.2) Install tensorflow backend

For more information, you can checkout the tensorflow website.

# we highly recommend firstly update pip
pip install --upgrade pip
pip install tensorflow==2.0.1

3.3) Install sph2pipe, spm, kenlm, sclite for ASR Tasks [Optional]

These packages are usually required for ASR tasks, we assume they have been installed when running the recipe for ASR tasks. You can find installation scripts of them in tools/.

3.4) Install horovod for multiple-device training [Optional]

For multiple GPU/CPU training You have to install the horovod, you can find out more information from the horovod website.

3.5) Install pydecoder for WFST decoding [Optional]

For WFST decoding You have to install pydecoder, installation guide for pydecoder can be found athena-decoder website

3.6) Install athena package

git clone https://github.com/athena-team/athena.git
cd athena
pip install -r requirements.txt
python setup.py bdist_wheel sdist
python -m pip install --ignore-installed dist/athena-0.1.0*.whl

Once athena is successfully installed, you should do source tools/env.sh firstly before doing other things.
For installing some other supporting tools, you can check the tools/install*.sh to install kenlm, sph2pipe, spm and ... [Optional]

3.7) Test your installation

On a single cpu/gpu

source tools/env.sh
python examples/translate/spa-eng-example/prepare_data.py examples/translate/spa-eng-example/data/train.csv
python athena/main.py examples/translate/spa-eng-example/transformer.json

On multiple cpu/gpu in one machine (you should make sure your hovorod is successfully installed)

source tools/env.sh
python examples/translate/spa-eng-example/prepare_data.py examples/translate/spa-eng-example/data/train.csv
horovodrun -np 4 -H localhost:4 python athena/horovod_main.py examples/translate/spa-eng-example/transformer.json

Notes

If you see errors such as ERROR: Cannot uninstall 'wrapt' while installing TensorFlow, try updating it using command conda update wrapt. Same for similar dependencies such as entrypoints, llvmlite and so on.
You may want to make sure you have g++ version 7 or above to make sure you can successfully install TensorFlow.

4) Training

We will use ASR task TIMIT as an example to walk you through the whole training process. The recipe for this tutorial can be found at examples/asr/timit/run_101.sh.

4.1) Prepare the data

The data for TIMIT can be found here or here. First, we need to download the data and place it at examples/asr/timit/data/TIMIT. Then we will run the following scripts, which will do some data precessing and generate data csv for train, dev and test set of TIMIT.

mkdir -p examples/asr/timit/data
python examples/asr/timit/local/prepare_data.py examples/asr/timit/data/TIMIT examples/asr/timit/data

Below is an example csv we generated, it contains the absolute path of input audio, its length, its transcript and its speaker

wav_filename	wav_length_ms	transcript	speaker
/workspace/athena/examples/asr/timit/data/wav/TRAIN/MCLM0-SI1456.WAV	3065	sil dh iy z eh er er vcl g ae sh vcl b ah vcl b ax sh epi m ey cl k hh ay l ix f ah ng cl sh epi en el th er m el vcl b eh r ix er z sil	MCLM0
/workspace/athena/examples/asr/timit/data/wav/TRAIN/MCLM0-SX286.WAV	3283	sil ih n eh v r ih m ey vcl jh er cl k l ow v er l iy f cl t r ae f ix cl k s ah m cl t ay m z vcl g eh cl s vcl b ae cl t ah cl p sil	MCLM0
/workspace/athena/examples/asr/timit/data/wav/TRAIN/MCLM0-SX196.WAV	1740	sil hh aw vcl d uw ao r sh cl ch er zh epi m ey cl p er l vcl d z sil	MCLM0
/workspace/athena/examples/asr/timit/data/wav/TRAIN/MCLM0-SX106.WAV	2214	sil eh hh y uw vcl jh cl t ae cl p ix sh cl t r ix hh ah ng ix n er hh ah l w ey sil	MCLM0
/workspace/athena/examples/asr/timit/data/wav/TRAIN/MCLM0-SX16.WAV	1926	sil ey r ow l el v w ay er l ey n ih er dh ax w ao l sil	MCLM0
/workspace/athena/examples/asr/timit/data/wav/TRAIN/MCLM0-SI2086.WAV	2745	sil ae vcl b s el uw sh en f ao r hh ix z l ay hh sil	MCLM0
/workspace/athena/examples/asr/timit/data/wav/TRAIN/MCLM0-SX376.WAV	2464	sil w ih m ix n m ey n eh v er vcl b ix cl k ah ng cl k ax m cl p l iy cl l iy cl k w el cl t ax m eh n sil	MCLM0
/workspace/athena/examples/asr/timit/data/wav/TRAIN/MCLM0-SI826.WAV	3596	sil k ao sh en cl k en cl t ih n y uw s ix vcl m ih n ax sh cl t r ey sh en ix z epi n aa vcl r eh cl k m eh n d ix f ax l ae cl t ey dx ng cl k aw z sil	MCLM0

4.2) Setting the Configuration File

All of our training/ inference configurations are written in config.json. Below is an example configuration file with comments to help you understand.

{
  "batch_size":16,
  "num_epochs":20,
  "sorta_epoch":1,  # keep batches sorted for sorta_epoch, this helps with the convergence of models
  "ckpt":"examples/asr/timit/ckpts/mtl_transformer_ctc_sp/",
  "summary_dir":"examples/asr/timit/ckpts/mtl_transformer_ctc_sp/event",

  "solver_gpu":[0],
  "solver_config":{
    "clip_norm":100,  # clip gradients into a norm of 100
    "log_interval":10,  # print logs for log_interval steps
    "enable_tf_function":true  # enable tf_function to make training faster
  },

  "model":"mtl_transformer_ctc",  # the type of model this training uses, it's a multi-task transformer based model
  "num_classes": null,
  "pretrained_model": null,
  "model_config":{
    "model":"speech_transformer",
    "model_config":{
      "return_encoder_output":true,  # whether to return encoder only or encoder + decoder
      "num_filters":256,  # dimension of cnn filter
      "d_model":256,  # dimension of transformer
      "num_heads":8,  # heads of transformer
      "num_encoder_layers":9,
      "num_decoder_layers":3,
      "dff":1024,  # dimension of feed forward layer
      "rate":0.2,  # dropout rate for transformer
      "label_smoothing_rate":0.0,  # label smoothing rate for output logits
      "schedual_sampling_rate":1.0  # scheduled sampling rate for decoder
    },
    "mtl_weight":0.5
  },

  "inference_config":{
    "decoder_type":"beam_search_decoder",  # use beam search instead of argmax
    "beam_size":10,
    "ctc_weight":0.0,  # weight for ctc joint decoding
    "model_avg_num":10  # averaging checkpoints gives better results than using single checkpoint with best loss/ metrics
  },

  "optimizer":"warmup_adam",
  "optimizer_config":{  # configs for warmup optimizer
    "d_model":256,
    "warmup_steps":4000,
    "k":1
  },


  "dataset_builder": "speech_recognition_dataset",
  "num_data_threads": 1,
  "trainset_config":{
    "data_csv": "examples/asr/timit/data/train.csv",
    "audio_config":{"type":"Fbank", "filterbank_channel_count":40},  # config for feature extraction
    "cmvn_file":"examples/asr/timit/data/cmvn",  # mean and variance of FBank
    "text_config": {"type":"eng_vocab", "model":"examples/asr/timit/data/vocab"},  # vocab list
    "speed_permutation": [0.9, 1.0, 1.1],  # use speed perturbation to increase data diversitty
    "input_length_range":[10, 8000]  # range of audio input length
  },
  "devset_config":{
    "data_csv": "examples/asr/timit/data/dev.csv",
    "audio_config":{"type":"Fbank", "filterbank_channel_count":40},
    "cmvn_file":"examples/asr/timit/data/cmvn",
    "text_config": {"type":"eng_vocab", "model":"examples/asr/timit/data/vocab"},
    "input_length_range":[10, 8000]
  },
  "testset_config":{
    "data_csv": "examples/asr/timit/data/test.csv",
    "audio_config":{"type":"Fbank", "filterbank_channel_count":40},
    "cmvn_file":"examples/asr/timit/data/cmvn",
    "text_config": {"type":"eng_vocab", "model":"examples/asr/timit/data/vocab"}
  }
}

To get state-of-the-art models, we usually need to train for more epochs and use ctc joint decoding with language model. These are omitted for to make this tutorial easier to understand.

4.3) Data normalization

Data normalization is important for the convergence of neural network models. With the generated csv file, we will compute the cmvn file like this

python athena/cmvn_main.py examples/asr/$dataset_name/configs/mpc.json examples/asr/$dataset_name/data/all.csv

The generated cmvn files will be found at examples/asr/timit/data/cmvn.

4.4) Train a Model

With all the above preparation done, training becomes straight-forward. athena/main.py is the entry point of the training module. Just run:

$ python athena/main.py examples/asr/timit/configs/mtl_transformer_sp_101.json

Please install Horovod and MPI at first, if you want to train model using multi-gpu. See the Horovod page for more instructions.

To run on a machine with 4 GPUs with Athena:

$ horovodrun -np 4 -H localhost:4 python athena/horovod_main.py examples/asr/timit/configs/mtl_transformer_sp_101.json

To run on 4 machines with 4 GPUs each with Athena:

$ horovodrun -np 16 -H server1:4,server2:4,server3:4,server4:4 python athena/horovod_main.py examples/asr/timit/configs/mtl_transformer_sp_101.json

4.5) Evaluate a model

All of our inference related scripts are merged into inference.py. athena/inference.py is the entry point of inference. Just run:

python athena/inference.py examples/asr/timit/configs/mtl_transformer_sp_101.json

A file named inference.log will be generated, which contains the log of decoding. inference.log is very important to get correct scoring results, and it will be overwrited if you run athena/inference.py multiple times.

4.6) Scoring

For scoring, you will need to install sclite first. The results of scoring can be found in score/score_map/inference.log.result.map.sys. The last few lines will look like this

|================================================================|
| Sum/Avg|  192   7215 | 84.4   11.4    4.3    3.2   18.8   99.5 |
|================================================================|
|  Mean  |  1.0   37.6 | 84.7   11.4    3.9    3.3   18.6   99.5 |
|  S.D.  |  0.0   11.7 |  7.7    6.3    4.2    3.6    9.0    7.2 |
| Median |  1.0   36.0 | 85.0   10.8    2.9    2.8   17.5  100.0 |
|----------------------------------------------------------------|

The line with Sum/Avg is usually what you should be looking for if you just want an overall PER result. In this case, 11.4 is the substitution error, 4.3 is the deletion error, 3.2 is the insertion error and 18.8 is the total PER.

5) Decoding with WFST

In Athena, we also provide functionalities for WFST graph creation and WFST based decoding with seq2seq models. To use it, you need to install athena-decoder first.

5.1) WFST graph creation

The graph creation scripts for HKUST and AISHELL can be found in the build graph section of athena-decoder. We support the creation of TLG.fst (for ctc decoding) and LG.fst (for seq2seq model decoding). To build the graph, you need to provide lexicon and vocab of the target dataset.

The graph creation follows the standard procedure of compose -> determinize -> minimize -> arcsort -> remove_disambig. In athena-decoeder, the whole procedure is written in python to make users easier to understand and debug.

5.2) WFST decoding

Once we finished graph creation, the decoding becomes straightforward. We just need to decode with a new json file. Usually the new json file ends with _wfst. The items related to WFST decoding are (taken from examples/asr/aishell/configs/mtl_transformer_sp_wfst.json):

"inference_config":{
    "decoder_type":"wfst_decoder",  # note its different from beam_search_decoder we usually use
    "wfst_graph":"examples/asr/aishell/data/LG.fst",  # path for wfst graph
    "acoustic_scale":10.0,  # scale of acoustic score and language model score
    "max_active":100,  # max active tokens kept alive before path expanding
    "min_active":0,  # min active tokens kept alive before path expanding
    "wfst_beam":100.0,  # a constant to balance the score of beam_cutoff
    "max_seq_len":100  # max length of output seq
  },

Our WFST decoding follows similiar procedure as Kaldi and is composed of four major steps: init_decoding, process_emitting, process_nonemitting and get_best_path. For more detail, please checkout code here.

Take AISHELL as an example, with WFST decoding, the command to run becomes python athena/inference.py examples/asr/aishell/configs/mtl_transformer_sp_wfst.json. Some results with AISHELL are listed below: Decoder | CTC Joint Decoding | Error Rate (CER) :-----------: | :------------: | -------: Beam Search | No | 7.98% Beam Search | Yes | 6.82% | WFST | No | 7.21% ** |

Note that beam search without CTC Joint Decoding is considerably worse. But WFST without CTC Joint decoding gives better results than beam search.

** The result using WFST mainly because of using larger beam size

6) Deployment

After training, you can deploy ASR and TTS models on servers using the TensorFlow C++ API. As an example, below are some steps to achieve this functionality with an ASR model.

Install all dependencies, including TensorFlow, Protobuf, absl, Eigen3 and kenlm (optional).
Freeze the model to pb format with athena/deploy_main.py.
Compile the C++ codes.
Load the model and do argmax decoding in C++ codes, see deploy/src/asr.cpp for the entry point.

After compiling, an executable file will be generated and you can run the executable file:

$ ./asr

Detailed implementation is described here.

7) Self-supervised speech representation learning

7.1) MPC

Masked Predictive Coding (MPC) uses masked reconstruction objective to perform predictive coding on transformer based models. It achieved significant improvements on various speech recognition datasets. For more information, please refer to following paper(s).

Improving Transformer-based Speech Recognition Using Unsupervised Pre-training

A Further Study of Unsupervised Pre-training for Transformer Based Speech Recognition

MPC models can be trained by running python athena/main.py examples/asr/*/configs/mpc.json. To use pretrained MPC model in ASR training, simply set the "pretrained_model" section in ASR json config to the checkpoint dir of MPC model and proceed training.

7.2) Speech SimCLR

Speech SimCLR is a new self-supervised objective for speech representation learning. During training, Speech SimCLR applies augmentation on raw speech and its spectrogram. Its objective is the combination of contrastive loss that maximizes agreement between differently augmented samples in the latent space and reconstruction loss of input representation. For more information, please refer to following paper(s).

Speech SimCLR: Combining Contrastive and Reconstruction Objective for Self-supervised Speech Representation Learning

For now, pre-training with Speech SimCLR is only supported for Librispeech. You can run it with python athena/main.py examples/asr/librispeech/configs/speech_simclr.json. For feature extraction, simply run python athena/inference.py examples/asr/librispeech/configs/speech_simclr.json. The pre-trained Speech SimCLR models can be found here.

8) Results

8.1) ASR

Language	Model Name	Training Data	Hours of Speech	Error Rate
English	Transformer	LibriSpeech Dataset	960 h	3.1% (WER)
English	Transformer	Switchboard Dataset	260h	8.6% (WER)
English	Transformer	TIMIT Dataset	3 h	16.8% (PER)
Mandarin	Transformer	HKUST Dataset	151 h	22.75% (CER)
Mandarin	Transformer	AISHELL Dataset	178 h	6.6% (CER)

To compare with other published results, see wer_are_we.md.

9) Directory Structure

Below is the basic directory structure for Athena

|-- Athena
|   |-- data  # - root directory for input-related operations
|   |   |-- datasets  # custom datasets for ASR, TTS and pre-training
|   |-- layers  # some layers
|   |-- models  # some models
|   |-- tools # contains various tools, e.g. decoding tools
|   |-- transform # custom featureizer based on C++
|   |   |-- feats
|   |   |   |-- ops # c++ code on tensorflow ops
|   |-- utils # utils, e.g. checkpoit, learning_rate, metric, etc
|-- deploy  # deployment with Tensorflow C++
|   |-- include
|   |-- src
|-- docker
|-- docs  # docs
|-- examples  # example scripts for ASR, TTS, etc
|   |-- asr  # each subdirectory contains a data preparation scripts and a run script for the task
|   |   |-- aishell
|   |   |-- hkust
|   |   |-- librispeech
|   |   |-- switchboard
|   |   |-- timit
|   |-- translate # examples for translate
|   |   |-- spa-eng-example
|   |-- tts # examples for tts
|   |   |-- data_baker
|   |   |-- libritts
|   |   |-- ljspeech
|   |-- speaker_identification # examples for speaker identification
|   |   |-- voxceleb
|   |-- vc # examples for voice conversion
|   |   |-- vcc2018
|-- tools  # need to source env.sh before training

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