This repository offers a clean code version of the original repository from SeanNaren with classes and modular components (eg trainers, models, loggers...).
At a granular level, synskit is a library that consists of the following components:
| Component | Description |
|---|---|
| asr_deepspeech | Speech Recognition package |
| asr_deepspeech.data | Data related module |
| asr_deepspeech.data.dataset | Build the dataset |
| asr_deepspeech.data.loaders | Load the dataet |
| asr_deepspeech.data.parsers | Parse the dataset |
| asr_deepspeech.data.samplers | Sample the dataset |
| asr_deepspeech.decoders | Decode the generated text |
| asr_deepspeech.loggers | Loggers |
| asr_deepspeech.models | Models architecture |
| asr_deepspeech.modules | Components of the network |
| asr_deepspeech.parsers | Arguments parser |
| asr_deepspeech.test | Test units |
| asr_deepspeech.trainers | Trainers |
We are providing a support for local or docker setup. However we recommend to use docker to avoid any difficulty to run the code. If you decide to run the code locally you will need python3.6 with cuda>=10.1. Several libraries are needed to be installed for training to work. I will assume that everything is being installed in an Anaconda installation on Ubuntu, with Pytorch 1.0. Install PyTorch if you haven't already.
To build the image with docker
docker build . -t jcadic/deepspeech
docker run --gpus all -it --shm-size=70g -v /mnt/.cdata:/mnt/.cdata jcadic/deepspeech bash
sh setup.sh
python -m asr_deepspeech.test
You should be able to get an output like
=1= TEST PASSED : asr_deepspeech
=1= TEST PASSED : asr_deepspeech.data
=1= TEST PASSED : asr_deepspeech.data.dataset
=1= TEST PASSED : asr_deepspeech.data.loaders
=1= TEST PASSED : asr_deepspeech.data.parsers
=1= TEST PASSED : asr_deepspeech.data.samplers
=1= TEST PASSED : asr_deepspeech.decoders
=1= TEST PASSED : asr_deepspeech.loggers
=1= TEST PASSED : asr_deepspeech.models
=1= TEST PASSED : asr_deepspeech.modules
=1= TEST PASSED : asr_deepspeech.parsers
=1= TEST PASSED : asr_deepspeech.test
=1= TEST PASSED : asr_deepspeech.trainers
Currently supports JSUT. Please contact me if you want to download the preprocessed files and jp_labels.json.
wget http://ss-takashi.sakura.ne.jp/corpus/jsut_ver1.1.zip
To create a custom dataset you must create json files containing the necessary information about the dataset. __data__/manifests/{train/val}_jsut.json
{
"UT-PARAPHRASE-sent002-phrase1": {
"audio_filepath": "/mnt/.cdata/ASR/ja/raw/CLEAN/JSUT/jsut_ver1.1/utparaphrase512/wav/UT-PARAPHRASE-sent002-phrase1.wav",
"duration": 2.44,
"text": "専門には、疎いんだから。"
},
"UT-PARAPHRASE-sent002-phrase2": {
"audio_filepath": "/mnt/.cdata/ASR/ja/raw/CLEAN/JSUT/jsut_ver1.1/utparaphrase512/wav/UT-PARAPHRASE-sent002-phrase2.wav",
"duration": 2.82,
"text": "専門には、詳しくないんだから。"
},
...
}
To train on a single gpu
python asr_deepspeech/trainers/__init__.py --labels __data__/labels/labels_jp_500.json --manifest jsut --batch-size 30
To scale to multi-gpu training
python -m multiproc train.py --manifest [manifest_id] --labels [path_to_labels_json]
================ VARS ===================
manifest: clean
distributed: True
train_manifest: __data__/manifests/train_clean.json
val_manifest: __data__/manifests/val_clean.json
model_path: /data/ASRModels/deepspeech_jp_500_clean.pth
continue_from: None
output_file: /data/ASRModels/deepspeech_jp_500_clean.txt
main_proc: True
rank: 0
gpu_rank: 0
world_size: 2
==========================================
...
clean - 0:00:46 >> 2/1000 (1) | Loss 95.1626 | Lr 0.30e-3 | WER/CER 98.06/95.16 - (98.06/[95.16]): 100%|██████████████████████| 18/18 [00:46<00:00, 2.59s/it]
clean - 0:00:47 >> 3/1000 (1) | Loss 96.3579 | Lr 0.29e-3 | WER/CER 97.55/97.55 - (98.06/[95.16]): 100%|██████████████████████| 18/18 [00:47<00:00, 2.61s/it]
clean - 0:00:47 >> 4/1000 (1) | Loss 97.5705 | Lr 0.29e-3 | WER/CER 100.00/100.00 - (98.06/[95.16]): 100%|████████████████████| 18/18 [00:47<00:00, 2.66s/it]
clean - 0:00:48 >> 5/1000 (1) | Loss 97.8628 | Lr 0.29e-3 | WER/CER 98.74/98.74 - (98.06/[95.16]): 100%|██████████████████████| 18/18 [00:50<00:00, 2.78s/it]
clean - 0:00:50 >> 6/1000 (5) | Loss 97.0118 | Lr 0.29e-3 | WER/CER 96.26/93.61 - (96.26/[93.61]): 100%|██████████████████████| 18/18 [00:49<00:00, 2.76s/it]
clean - 0:00:50 >> 7/1000 (5) | Loss 97.2341 | Lr 0.28e-3 | WER/CER 98.35/98.35 - (96.26/[93.61]): 17%|███▊ | 3/18 [00:10<00:55, 3.72s/it]
...
================= 43.52/43.55 =================
----- BEST -----
Ref:や さ し い ほ し は こ た え ま し た
Hyp:や さ し い ほ し は こ た え ま し た
WER:0.0 - CER:0.0
----- LAST -----
Ref:そ れ を 開 き
Hyp:そ れ け
WER:60.0 - CER:60.0
----- WORST -----
Ref:サ ル ト サ ム ラ イ
Hyp:死 る と さ む ら い
WER:100.0 - CER:100.0
CER histogram
|###############################################################################
|█████████████████████████████████████ 144 0-10
|███████████████████████████████████████████████████████ 212 10-20
|█████████████████████████████████████████████████████████████████ 249 20-30
|█████████████████████████████████████████████████████████ 222 30-40
|███████████████████████████████████████████ 168 40-50
|████████████████████████████████████████████████████ 203 50-60
|███████████████████████████████████████████ 167 60-70
|████████████████████████████████ 126 70-80
|████████████████████████████ 110 80-90
|██████ 26 90-100
|████████████████████ 78 100-110
=============================================
Thanks to Egor and Ryan for their contributions!
This is a fork from https://github.com/SeanNaren/deepspeech.pytorch. The code has been improved for the readability only.
For any question please contact me at j.cadic[at]protonmail.ch
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