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    Implementation of DeepSpeech2 for PyTorch using PyTorch Lightning. The repo supports training/testing and inference using the DeepSpeech2 model. Optionally a kenlm language model can be used at inference time.


    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 installed.

    Install PyTorch if you haven't already.

    If you want decoding to support beam search with an optional language model, install ctcdecode:

    git clone --recursive
    cd ctcdecode && pip install .

    Finally clone this repo and run this within the repo:

    pip install -r requirements.txt
    pip install -e . # Dev install

    If you plan to use Multi-node training, you'll need etcd. Below is the command to install on Ubuntu.

    sudo apt-get install etcd


    To use the image with a GPU you'll need to have nvidia-docker installed.

    sudo docker run -ti --gpus all -v `pwd`/data:/workspace/data --tmpfs /tmp -p 8888:8888 --net=host --ipc=host seannaren/deepspeech.pytorch:latest # Opens a Jupyter notebook, mounting the /data drive in the container

    Optionally you can use the command line by changing the entrypoint:

    sudo docker run -ti --gpus all -v `pwd`/data:/workspace/data --tmpfs /tmp --entrypoint=/bin/bash --net=host --ipc=host seannaren/deepspeech.pytorch:latest



    Currently supports AN4, TEDLIUM, Voxforge, Common Voice and LibriSpeech. Scripts will setup the dataset and create manifest files used in data-loading. The scripts can be found in the data/ folder. Many of the scripts allow you to download the raw datasets separately if you choose so.

    Training Commands

    cd data/ && python && cd ..
    python +configs=an4
    cd data/ && python && cd ..
    python +configs=librispeech
    Common Voice
    cd data/ && python && cd ..
    python +configs=commonvoice
    cd data/ && python && cd ..
    python +configs=tedlium

    Custom Dataset

    To create a custom dataset you must create a JSON file containing the locations of the training/testing data. This has to be in the format of:


    Where the root_path is the root directory, wav_path is to the audio file, and the transcript_path is to a text file containing the transcript on one line. This can then be used as stated below.

    Note on CSV files ...

    Up until release V2.1, deepspeech.pytorch used CSV manifest files instead of JSON. These manifest files are formatted similarly as a 2 column table:


    Note that this format is incompatible V3.0 onwards.

    Merging multiple manifest files

    To create bigger manifest files (to train/test on multiple datasets at once) we can merge manifest files together like below.

    cd data/
    python manifest_1.json manifest_2.json --out new_manifest_dir

    Modifying Training Configs

    Configuration is done via Hydra.

    Defaults can be seen in Below is how you can override values set already:

    python data.train_path=data/train_manifest.json data.val_path=data/val_manifest.json

    Use python --help for all parameters and options.

    You can also specify a config file to keep parameters stored in a yaml file like so:

    Create folder experiment/ and file experiment/an4.yaml:

      train_path: data/an4_train_manifest.json
      val_path: data/an4_val_manifest.json
    python +experiment=an4

    To see options available, check here.

    Multi-GPU Training

    We support single-machine multi-GPU training via PyTorch Lightning.

    Below is an example command when training on a machine with 4 local GPUs:

    python +configs=an4 trainer.gpus=4

    Multi-Node Training

    Also supported is multi-machine capabilities using TorchElastic. This requires a node to exist as an explicit etcd host (which could be one of the GPU nodes but isn't recommended), a shared mount across your cluster to load/save checkpoints and communication between the nodes.

    Below is an example where we've set one of our GPU nodes as our etcd host however if you're scaling up, it would be suggested to have a separate instance as your etcd instance to your GPU nodes as this will be a single point of failure.

    Assumed below is a shared drive called /share where we save our checkpoints and data to access.

    Run on the etcd host:

    PUBLIC_HOST_NAME= # Change to public host name for all nodes to connect
    etcd --enable-v2 \
         --listen-client-urls http://$PUBLIC_HOST_NAME:4377 \
         --advertise-client-urls http://$PUBLIC_HOST_NAME:4377 \
         --listen-peer-urls http://$PUBLIC_HOST_NAME:4379

    Run on each GPU node:

    python -m torchelastic.distributed.launch \
            --nnodes=2 \
            --nproc_per_node=4 \
            --rdzv_id=123 \
            --rdzv_backend=etcd \
            --rdzv_endpoint=$PUBLIC_HOST_NAME:4377 \
   data.train_path=/share/data/an4_train_manifest.json \
                     data.val_path=/share/data/an4_val_manifest.json model.precision=half \
                     data.num_workers=8 checkpoint.save_folder=/share/checkpoints/ \
                     checkpoint.checkpoint=true checkpoint.load_auto_checkpoint=true checkpointing.save_n_recent_models=3 \
                     data.batch_size=8 trainer.max_epochs=70 \
                     trainer.accelerator=ddp trainer.gpus=4 trainer.num_nodes=2

    Using the load_auto_checkpoint=true flag we can re-continue training from the latest saved checkpoint.

    Currently it is expected that there is an NFS drive/shared mount across all nodes within the cluster to load the latest checkpoint from.


    There is support for three different types of augmentations: SpecAugment, noise injection and random tempo/gain perturbations.


    Applies simple Spectral Augmentation techniques directly on Mel spectogram features to make the model more robust to variations in input data. To enable SpecAugment, use the --spec-augment flag when training.

    SpecAugment implementation was adapted from this project.

    Noise Injection

    Dynamically adds noise into the training data to increase robustness. To use, first fill a directory up with all the noise files you want to sample from. The dataloader will randomly pick samples from this directory.

    To enable noise injection, use the --noise-dir /path/to/noise/dir/ to specify where your noise files are. There are a few noise parameters to tweak, such as --noise_prob to determine the probability that noise is added, and the --noise-min, --noise-max parameters to determine the minimum and maximum noise to add in training.

    Included is a script to inject noise into an audio file to hear what different noise levels/files would sound like. Useful for curating the noise dataset.

    python --input-path /path/to/input.wav --noise-path /path/to/noise.wav --output-path /path/to/input_injected.wav --noise-level 0.5 # higher levels means more noise

    Tempo/Gain Perturbation

    Applies small changes to the tempo and gain when loading audio to increase robustness. To use, use the --speed-volume-perturb flag when training.


    Typically checkpoints are stored in lightning_logs/ in the current working directory of the script.

    This can be adjusted:

    python checkpoint.file_path=save_dir/

    To load a previously saved checkpoint:

    python trainer.resume_from_checkpoint=lightning_logs/deepspeech_checkpoint_epoch_N_iter_N.ckpt

    This continues from the same training state.


    To evaluate a trained model on a test set (has to be in the same format as the training set):

    python model.model_path=models/deepspeech.pth test_path=/path/to/test_manifest.json

    An example script to output a transcription has been provided:

    python model.model_path=models/deepspeech.pth audio_path=/path/to/audio.wav

    If you used mixed-precision or half precision when training the model, you can use the model.precision=half for a speed/memory benefit.

    Inference Server

    Included is a basic server script that will allow post request to be sent to the server to transcribe files.

    python --host --port 8000 # Run on one window
    curl -X POST -H "Content-type: multipart/form-data" -F "file=@/path/to/input.wav"

    Using an ARPA LM

    We support using kenlm based LMs. Below are instructions on how to take the LibriSpeech LMs found here and tune the model to give you the best parameters when decoding, based on LibriSpeech.

    Tuning the LibriSpeech LMs

    First ensure you've set up the librispeech datasets from the data/ folder. In addition download the latest pre-trained librispeech model from the releases page, as well as the ARPA model you want to tune from here. For the below we use the 3-gram ARPA model (3e-7 prune).

    First we need to generate the acoustic output to be used to evaluate the model on LibriSpeech val.

    python data.test_path=data/librispeech_val_manifest.json model.model_path=librispeech_pretrained_v2.pth save_output=librispeech_val_output.npy

    We use a beam width of 128 which gives reasonable results. We suggest using a CPU intensive node to carry out the grid search.

    python --num-workers 16 --saved-output librispeech_val_output.npy --output-path libri_tune_output.json --lm-alpha-from 0 --lm-alpha-to 5 --lm-beta-from 0 --lm-beta-to 3 --lm-path  --model-path librispeech_pretrained_v2.pth --beam-width 128 --lm-workers 16

    This will run a grid search across the alpha/beta parameters using a beam width of 128. Use the below script to find the best alpha/beta params:

    python --input-path libri_tune_output.json

    Use the alpha/beta parameters when using the beam decoder.

    Building your own LM

    To build your own LM you need to use the KenLM repo found here. Have a read of the documentation to get a sense of how to train your own LM. The above steps once trained can be used to find the appropriate parameters.

    Alternate Decoders

    By default, and use a GreedyDecoder which picks the highest-likelihood output label at each timestep. Repeated and blank symbols are then filtered to give the final output.

    A beam search decoder can optionally be used with the installation of the ctcdecode library as described in the Installation section. The test and transcribe scripts have a lm config. To use the beam decoder, add lm.decoder_type=beam. The beam decoder enables additional decoding parameters:

    • lm.beam_width how many beams to consider at each timestep
    • lm.lm_path optional binary KenLM language model to use for decoding
    • lm.alpha weight for language model
    • lm.beta bonus weight for words

    Time offsets

    Use the offsets=true flag to get positional information of each character in the transcription when using script. The offsets are based on the size of the output tensor, which you need to convert into a format required. For example, based on default parameters you could multiply the offsets by a scalar (duration of file in seconds / size of output) to get the offsets in seconds.

    Pre-trained models

    Pre-trained models can be found under releases here.


    Thanks to Egor and Ryan for their contributions!


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    发行版本 6

    Release V3


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