Training Your Own Model

Prerequisites for training a model

Getting the training code

Clone the latest released stable branch from Github (e.g. 0.9.3, check here):

git clone --branch v0.9.3

If you plan on committing code or you want to report bugs, please use the master branch.

Creating a virtual environment

Throughout the documentation we assume you are using virtualenv to manage your Python environments. This setup is the one used and recommended by the project authors and is the easiest way to make sure you won’t run into environment issues. If you’re using Anaconda, Miniconda or Mamba, first read the instructions at Training from an Anaconda or miniconda environment and then continue from the installation step below.

In creating a virtual environment you will create a directory containing a python3 binary and everything needed to run deepspeech. You can use whatever directory you want. For the purpose of the documentation, we will rely on $HOME/tmp/deepspeech-train-venv. You can create it using this command:

$ python3 -m venv $HOME/tmp/deepspeech-train-venv/

Once this command completes successfully, the environment will be ready to be activated.

Activating the environment

Each time you need to work with DeepSpeech, you have to activate this virtual environment. This is done with this simple command:

$ source $HOME/tmp/deepspeech-train-venv/bin/activate

Installing DeepSpeech Training Code and its dependencies

Install the required dependencies using pip3:

cd DeepSpeech
pip3 install --upgrade pip==20.2.2 wheel==0.34.2 setuptools==49.6.0
pip3 install --upgrade -e .

Remember to re-run the last pip3 install command above when you update the training code (for example by pulling new changes), in order to update any dependencies.

The webrtcvad Python package might require you to ensure you have proper tooling to build Python modules:

sudo apt-get install python3-dev


If you have a capable (NVIDIA, at least 8GB of VRAM) GPU, it is highly recommended to install TensorFlow with GPU support. Training will be significantly faster than using the CPU. To enable GPU support, you can do:

pip3 uninstall tensorflow
pip3 install 'tensorflow-gpu==1.15.4'

Please ensure you have the required CUDA dependency and/or Prerequisites.

It has been reported for some people failure at training:

tensorflow.python.framework.errors_impl.UnknownError: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
     [[{{node tower_0/conv1d/Conv2D}}]]

Setting the TF_FORCE_GPU_ALLOW_GROWTH environment variable to true seems to help in such cases. This could also be due to an incorrect version of libcudnn. Double check your versions with the TensorFlow 1.15 documentation.

Basic Dockerfile for training

We provide Dockerfile.train to automatically set up a basic training environment in Docker. You need to generate the Dockerfile from the template using: This should ensure that you’ll re-use the upstream Python 3 TensorFlow GPU-enabled Docker image.

make Dockerfile.train

If you want to specify a different DeepSpeech repository / branch, you can pass DEEPSPEECH_REPO or DEEPSPEECH_SHA parameters:

make Dockerfile.train DEEPSPEECH_REPO=git://your/fork DEEPSPEECH_SHA=origin/your-branch

Common Voice training data

The Common Voice corpus consists of voice samples that were donated through Mozilla’s Common Voice Initiative. You can download individual CommonVoice v2.0 language data sets from here. After extraction of such a data set, you’ll find the following contents:

  • the *.tsv files output by CorporaCreator for the downloaded language

  • the mp3 audio files they reference in a clips sub-directory.

For bringing this data into a form that DeepSpeech understands, you have to run the CommonVoice v2.0 importer (bin/

bin/ --filter_alphabet path/to/some/alphabet.txt /path/to/extracted/language/archive

Providing a filter alphabet is optional. It will exclude all samples whose transcripts contain characters not in the specified alphabet. Running the importer with -h will show you some additional options.

Once the import is done, the clips sub-directory will contain for each required .mp3 an additional .wav file. It will also add the following .csv files:

  • clips/train.csv

  • clips/dev.csv

  • clips/test.csv

The CSV files comprise of the following fields:

  • wav_filename - path of the sample, either absolute or relative. Here, the importer produces relative paths.

  • wav_filesize - samples size given in bytes, used for sorting the data before training. Expects integer.

  • transcript - transcription target for the sample.

To use Common Voice data during training, validation and testing, you pass (comma separated combinations of) their filenames into --train_files, --dev_files, --test_files parameters of

If, for example, Common Voice language en was extracted to ../data/CV/en/, could be called like this:

python3 --train_files ../data/CV/en/clips/train.csv --dev_files ../data/CV/en/clips/dev.csv --test_files ../data/CV/en/clips/test.csv

Training a model

The central (Python) script is in the project’s root directory. For its list of command line options, you can call:

python3 --helpfull

To get the output of this in a slightly better-formatted way, you can also look at the flag definitions in Command-line flags for the training scripts.

For executing pre-configured training scenarios, there is a collection of convenience scripts in the bin folder. Most of them are named after the corpora they are configured for. Keep in mind that most speech corpora are very large, on the order of tens of gigabytes, and some aren’t free. Downloading and preprocessing them can take a very long time, and training on them without a fast GPU (GTX 10 series or newer recommended) takes even longer.

If you experience GPU OOM errors while training, try reducing the batch size with the ``–train_batch_size``, ``–dev_batch_size`` and ``–test_batch_size`` parameters.

As a simple first example you can open a terminal, change to the directory of the DeepSpeech checkout, activate the virtualenv created above, and run:


This script will train on a small sample dataset composed of just a single audio file, the sample file for the TIMIT Acoustic-Phonetic Continuous Speech Corpus, which can be overfitted on a GPU in a few minutes for demonstration purposes. From here, you can alter any variables with regards to what dataset is used, how many training iterations are run and the default values of the network parameters.

Feel also free to pass additional (or overriding) parameters to these scripts. Then, just run the script to train the modified network.

Each dataset has a corresponding importer script in bin/ that can be used to download (if it’s freely available) and preprocess the dataset. See bin/ for an example of how to import and preprocess a large dataset for training with DeepSpeech.

Some importers might require additional code to properly handled your locale-specific requirements. Such handling is dealt with --validate_label_locale flag that allows you to source out-of-tree Python script that defines a validate_label function. Please refer to util/ for implementation example of that function. If you don’t provide this argument, the default validate_label function will be used. This one is only intended for English language, so you might have consistency issues in your data for other languages.

For example, in order to use a custom validation function that disallows any sample with “a” in its transcript, and lower cases everything else, you could put the following code in a file called and then use --validate_label_locale

def validate_label(label):
    if 'a' in label: # disallow labels with 'a'
        return None
    return label.lower() # lower case valid labels

If you’ve run the old importers (in util/importers/), they could have removed source files that are needed for the new importers to run. In that case, simply remove the extracted folders and let the importer extract and process the dataset from scratch, and things should work.

Training with automatic mixed precision

Automatic Mixed Precision (AMP) training on GPU for TensorFlow has been recently [introduced](

Mixed precision training makes use of both FP32 and FP16 precisions where appropriate. FP16 operations can leverage the Tensor cores on NVIDIA GPUs (Volta, Turing or newer architectures) for improved throughput. Mixed precision training also often allows larger batch sizes. Automatic mixed precision training can be enabled by including the flag –automatic_mixed_precision at training time:

` python3 --train_files ./train.csv --dev_files ./dev.csv --test_files ./test.csv --automatic_mixed_precision `

On a Volta generation V100 GPU, automatic mixed precision speeds up DeepSpeech training and evaluation by ~30%-40%.


During training of a model so-called checkpoints will get stored on disk. This takes place at a configurable time interval. The purpose of checkpoints is to allow interruption (also in the case of some unexpected failure) and later continuation of training without losing hours of training time. Resuming from checkpoints happens automatically by just (re)starting training with the same --checkpoint_dir of the former run. Alternatively, you can specify more fine grained options with --load_checkpoint_dir and --save_checkpoint_dir, which specify separate locations to use for loading and saving checkpoints respectively. If not specified these flags use the same value as --checkpoint_dir, ie. load from and save to the same directory.

Be aware however that checkpoints are only valid for the same model geometry they had been generated from. In other words: If there are error messages of certain Tensors having incompatible dimensions, this is most likely due to an incompatible model change. One usual way out would be to wipe all checkpoint files in the checkpoint directory or changing it before starting the training.

Exporting a model for inference

If the --export_dir parameter is provided, a model will have been exported to this directory during training. Refer to the usage instructions for information on running a client that can use the exported model.

Exporting a model for TFLite

If you want to experiment with the TF Lite engine, you need to export a model that is compatible with it, then use the --export_tflite flags. If you already have a trained model, you can re-export it for TFLite by running again and specifying the same checkpoint_dir that you used for training, as well as passing --export_tflite --export_dir /model/export/destination. If you changed the alphabet you also need to add the --alphabet_config_path my-new-language-alphabet.txt flag.

Making a mmap-able model for inference

The output_graph.pb model file generated in the above step will be loaded in memory to be dealt with when running inference. This will result in extra loading time and memory consumption. One way to avoid this is to directly read data from the disk.

TensorFlow has tooling to achieve this: it requires building the target //tensorflow/contrib/util:convert_graphdef_memmapped_format (binaries are produced by our TaskCluster for some systems including Linux/amd64 and macOS/amd64), use util/ tool to download:

$ python3 util/ --source tensorflow --artifact convert_graphdef_memmapped_format --branch r1.15 --target .

Producing a mmap-able model is as simple as:

$ convert_graphdef_memmapped_format --in_graph=output_graph.pb --out_graph=output_graph.pbmm

Upon sucessfull run, it should report about conversion of a non-zero number of nodes. If it reports converting 0 nodes, something is wrong: make sure your model is a frozen one, and that you have not applied any incompatible changes (this includes quantize_weights).

Continuing training from a release model

There are currently two supported approaches to make use of a pre-trained DeepSpeech model: fine-tuning or transfer-learning. Choosing which one to use is a simple decision, and it depends on your target dataset. Does your data use the same alphabet as the release model? If “Yes”: fine-tune. If “No” use transfer-learning.

If your own data uses the extact same alphabet as the English release model (i.e. a-z plus ) then the release model’s output layer will match your data, and you can just fine-tune the existing parameters. However, if you want to use a new alphabet (e.g. Cyrillic а, б, д), the output layer of a release DeepSpeech model will not match your data. In this case, you should use transfer-learning (i.e. remove the trained model’s output layer, and reinitialize a new output layer that matches your target character set.

N.B. - If you have access to a pre-trained model which uses UTF-8 bytes at the output layer you can always fine-tune, because any alphabet should be encodable as UTF-8.

Fine-Tuning (same alphabet)

If you’d like to use one of the pre-trained models to bootstrap your training process (fine tuning), you can do so by using the --checkpoint_dir flag in Specify the path where you downloaded the checkpoint from the release, and training will resume from the pre-trained model.

For example, if you want to fine tune the entire graph using your own data in my-train.csv, my-dev.csv and my-test.csv, for three epochs, you can something like the following, tuning the hyperparameters as needed:

mkdir fine_tuning_checkpoints
python3 --n_hidden 2048 --checkpoint_dir path/to/checkpoint/folder --epochs 3 --train_files my-train.csv --dev_files my-dev.csv --test_files my_dev.csv --learning_rate 0.0001

Notes about the release checkpoints: the released models were trained with --n_hidden 2048, so you need to use that same value when initializing from the release models. Since v0.6.0, the release models are also trained with --train_cudnn, so you’ll need to specify that as well. If you don’t have a CUDA compatible GPU, then you can workaround it by using the --load_cudnn flag. Use --helpfull to get more information on how the flags work.

You also cannot use `--automatic_mixed_precision` when loading release checkpoints, as they do not use automatic mixed precision training.

If you try to load a release model without following these steps, you’ll get an error similar to this:

E Tried to load a CuDNN RNN checkpoint but there were more missing variables than just the Adam moment tensors.

Transfer-Learning (new alphabet)

If you want to continue training an alphabet-based DeepSpeech model (i.e. not a UTF-8 model) on a new language, or if you just want to add new characters to your custom alphabet, you will probably want to use transfer-learning instead of fine-tuning. If you’re starting with a pre-trained UTF-8 model – even if your data comes from a different language or uses a different alphabet – the model will be able to predict your new transcripts, and you should use fine-tuning instead.

In a nutshell, DeepSpeech’s transfer-learning allows you to remove certain layers from a pre-trained model, initialize new layers for your target data, stitch together the old and new layers, and update all layers via gradient descent. You will remove the pre-trained output layer (and optionally more layers) and reinitialize parameters to fit your target alphabet. The simplest case of transfer-learning is when you remove just the output layer.

In DeepSpeech’s implementation of transfer-learning, all removed layers will be contiguous, starting from the output layer. The key flag you will want to experiment with is --drop_source_layers. This flag accepts an integer from 1 to 5 and allows you to specify how many layers you want to remove from the pre-trained model. For example, if you supplied --drop_source_layers 3, you will drop the last three layers of the pre-trained model: the output layer, penultimate layer, and LSTM layer. All dropped layers will be reinintialized, and (crucially) the output layer will be defined to match your supplied target alphabet.

You need to specify the location of the pre-trained model with --load_checkpoint_dir and define where your new model checkpoints will be saved with --save_checkpoint_dir. You need to specify how many layers to remove (aka “drop”) from the pre-trained model: --drop_source_layers. You also need to supply your new alphabet file using the standard --alphabet_config_path (remember, using a new alphabet is the whole reason you want to use transfer-learning).

python3 \
    --drop_source_layers 1 \
    --alphabet_config_path my-new-language-alphabet.txt \
    --save_checkpoint_dir path/to/output-checkpoint/folder \
    --load_checkpoint_dir path/to/release-checkpoint/folder \
    --train_files   my-new-language-train.csv \
    --dev_files   my-new-language-dev.csv \
    --test_files  my-new-language-test.csv

UTF-8 mode

DeepSpeech includes a UTF-8 operating mode which can be useful to model languages with very large alphabets, such as Chinese Mandarin. For details on how it works and how to use it, see CTC beam search decoder.


Augmentation is a useful technique for better generalization of machine learning models. Thus, a pre-processing pipeline with various augmentation techniques on raw pcm and spectrogram has been implemented and can be used while training the model. Following are the available augmentation techniques that can be enabled at training time by using the corresponding flags in the command line.

Each sample of the training data will get treated by every specified augmentation in their given order. However: whether an augmentation will actually get applied to a sample is decided by chance on base of the augmentation’s probability value. For example a value of p=0.1 would apply the according augmentation to just 10% of all samples. This also means that augmentations are not mutually exclusive on a per-sample basis.

The --augment flag uses a common syntax for all augmentation types:

--augment augmentation_type1[param1=value1,param2=value2,...] --augment augmentation_type2[param1=value1,param2=value2,...] ...

For example, for the overlay augmentation:

python3 --augment overlay[p=0.1,source=/path/to/audio.sdb,snr=20.0] ...

In the documentation below, whenever a value is specified as <float-range> or <int-range>, it supports one of the follow formats:

  • <value>: A constant (int or float) value.

  • <value>~<r>: A center value with a randomization radius around it. E.g. 1.2~0.4 will result in picking of a uniformly random value between 0.8 and 1.6 on each sample augmentation.

  • <start>:<end>: The value will range from <start> at the beginning of the training to <end> at the end of the training. E.g. -0.2:1.2 (float) or 2000:4000 (int)

  • <start>:<end>~<r>: Combination of the two previous cases with a ranging center value. E.g. 4-6~2 would at the beginning of the training pick values between 2 and 6 and at the end of the training between 4 and 8.

Ranges specified with integer limits will only assume integer (rounded) values.


When feature caching is enabled, by default the cache has no expiration limit and will be used for the entire training run. This will cause these augmentations to only be performed once during the first epoch and the result will be reused for subsequent epochs. This would not only hinder value ranges from reaching their intended final values, but could also lead to unintended over-fitting. In this case flag --cache_for_epochs N (with N > 1) should be used to periodically invalidate the cache after every N epochs and thus allow samples to be re-augmented in new ways and with current range-values.

Every augmentation targets a certain representation of the sample - in this documentation these representations are referred to as domains. Augmentations are applied in the following order:

  1. sample domain: The sample just got loaded and its waveform is represented as a NumPy array. For implementation reasons these augmentations are the only ones that can be “simulated” through bin/

  2. signal domain: The sample waveform is represented as a tensor.

  3. spectrogram domain: The sample spectrogram is represented as a tensor.

  4. features domain: The sample’s mel spectrogram features are represented as a tensor.

Within a single domain, augmentations are applied in the same order as they appear in the command-line.

Sample domain augmentations

Overlay augmentation --augment overlay[p=<float>,source=<str>,snr=<float-range>,layers=<int-range>]

Layers another audio source (multiple times) onto augmented samples.

  • p: probability value between 0.0 (never) and 1.0 (always) if a given sample gets augmented by this method

  • source: path to the sample collection to use for augmenting (*.sdb or *.csv file). It will be repeated if there are not enough samples left.

  • snr: signal to noise ratio in dB - positive values for lowering volume of the overlay in relation to the sample

  • layers: number of layers added onto the sample (e.g. 10 layers of speech to get “cocktail-party effect”). A layer is just a sample of the same duration as the sample to augment. It gets stitched together from as many source samples as required.

Reverb augmentation --augment reverb[p=<float>,delay=<float-range>,decay=<float-range>]

Adds simplified (no all-pass filters) Schroeder reverberation to the augmented samples.

  • p: probability value between 0.0 (never) and 1.0 (always) if a given sample gets augmented by this method

  • delay: time delay in ms for the first signal reflection - higher values are widening the perceived “room”

  • decay: sound decay in dB per reflection - higher values will result in a less reflective perceived “room”

Resample augmentation --augment resample[p=<float>,rate=<int-range>]

Resamples augmented samples to another sample rate and then resamples back to the original sample rate.

  • p: probability value between 0.0 (never) and 1.0 (always) if a given sample gets augmented by this method

  • rate: sample-rate to re-sample to

Codec augmentation --augment codec[p=<float>,bitrate=<int-range>]

Compresses and then decompresses augmented samples using the lossy Opus audio codec.

  • p: probability value between 0.0 (never) and 1.0 (always) if a given sample gets augmented by this method

  • bitrate: bitrate used during compression

Volume augmentation --augment volume[p=<float>,dbfs=<float-range>]

Measures and levels augmented samples to a target dBFS value.

  • p: probability value between 0.0 (never) and 1.0 (always) if a given sample gets augmented by this method

  • dbfs : target volume in dBFS (default value of 3.0103 will normalize min and max amplitudes to -1.0/1.0)

Spectrogram domain augmentations

Pitch augmentation --augment pitch[p=<float>,pitch=<float-range>]

Scales spectrogram on frequency axis and thus changes pitch.

  • p: probability value between 0.0 (never) and 1.0 (always) if a given sample gets augmented by this method

  • pitch: pitch factor by with the frequency axis is scaled (e.g. a value of 2.0 will raise audio frequency by one octave)

Tempo augmentation --augment tempo[p=<float>,factor=<float-range>]

Scales spectrogram on time axis and thus changes playback tempo.

  • p: probability value between 0.0 (never) and 1.0 (always) if a given sample gets augmented by this method

  • factor: speed factor by which the time axis is stretched or shrunken (e.g. a value of 2.0 will double playback tempo)

Warp augmentation --augment warp[p=<float>,nt=<int-range>,nf=<int-range>,wt=<float-range>,wf=<float-range>]

Applies a non-linear image warp to the spectrogram. This is achieved by randomly shifting a grid of equally distributed warp points along time and frequency axis.

  • p: probability value between 0.0 (never) and 1.0 (always) if a given sample gets augmented by this method

  • nt: number of equally distributed warp grid lines along time axis of the spectrogram (excluding the edges)

  • nf: number of equally distributed warp grid lines along frequency axis of the spectrogram (excluding the edges)

  • wt: standard deviation of the random shift applied to warp points along time axis (0.0 = no warp, 1.0 = half the distance to the neighbour point)

  • wf: standard deviation of the random shift applied to warp points along frequency axis (0.0 = no warp, 1.0 = half the distance to the neighbour point)

Frequency mask augmentation --augment frequency_mask[p=<float>,n=<int-range>,size=<int-range>]

Sets frequency-intervals within the augmented samples to zero (silence) at random frequencies. See the SpecAugment paper for more details -

  • p: probability value between 0.0 (never) and 1.0 (always) if a given sample gets augmented by this method

  • n: number of intervals to mask

  • size: number of frequency bands to mask per interval

Multi domain augmentations

Time mask augmentation --augment time_mask[p=<float>,n=<int-range>,size=<float-range>,domain=<domain>]

Sets time-intervals within the augmented samples to zero (silence) at random positions.

  • p: probability value between 0.0 (never) and 1.0 (always) if a given sample gets augmented by this method

  • n: number of intervals to set to zero

  • size: duration of intervals in ms

  • domain: data representation to apply augmentation to - “signal”, “features” or “spectrogram” (default)

Dropout augmentation --augment dropout[p=<float>,rate=<float-range>,domain=<domain>]

Zeros random data points of the targeted data representation.

  • p: probability value between 0.0 (never) and 1.0 (always) if a given sample gets augmented by this method

  • rate: dropout rate ranging from 0.0 for no dropout to 1.0 for 100% dropout

  • domain: data representation to apply augmentation to - “signal”, “features” or “spectrogram” (default)

Add augmentation --augment add[p=<float>,stddev=<float-range>,domain=<domain>]

Adds random values picked from a normal distribution (with a mean of 0.0) to all data points of the targeted data representation.

  • p: probability value between 0.0 (never) and 1.0 (always) if a given sample gets augmented by this method

  • stddev: standard deviation of the normal distribution to pick values from

  • domain: data representation to apply augmentation to - “signal”, “features” (default) or “spectrogram”

Multiply augmentation --augment multiply[p=<float>,stddev=<float-range>,domain=<domain>]

Multiplies all data points of the targeted data representation with random values picked from a normal distribution (with a mean of 1.0).

  • p: probability value between 0.0 (never) and 1.0 (always) if a given sample gets augmented by this method

  • stddev: standard deviation of the normal distribution to pick values from

  • domain: data representation to apply augmentation to - “signal”, “features” (default) or “spectrogram”

Example training with all augmentations:

python -u \
  --train_files "train.sdb" \
  --feature_cache ./feature.cache \
  --cache_for_epochs 10 \
  --epochs 100 \
  --augment overlay[p=0.5,source=noise.sdb,layers=1,snr=50:20~10] \
  --augment reverb[p=0.1,delay=50.0~30.0,decay=10.0:2.0~1.0] \
  --augment resample[p=0.1,rate=12000:8000~4000] \
  --augment codec[p=0.1,bitrate=48000:16000] \
  --augment volume[p=0.1,dbfs=-10:-40] \
  --augment pitch[p=0.1,pitch=1~0.2] \
  --augment tempo[p=0.1,factor=1~0.5] \
  --augment warp[p=0.1,nt=4,nf=1,wt=0.5:1.0,wf=0.1:0.2] \
  --augment frequency_mask[p=0.1,n=1:3,size=1:5] \
  --augment time_mask[p=0.1,domain=signal,n=3:10~2,size=50:100~40] \
  --augment dropout[p=0.1,rate=0.05] \
  --augment add[p=0.1,domain=signal,stddev=0~0.5] \
  --augment multiply[p=0.1,domain=features,stddev=0~0.5] \

The bin/ and bin/ tools also support --augment parameters (for sample domain augmentations) and can be used for experimenting with different configurations or creating augmented data sets.

Example of playing all samples with reverberation and maximized volume:

bin/ --augment reverb[p=0.1,delay=50.0,decay=2.0] --augment volume --random test.sdb

Example simulation of the codec augmentation of a wav-file first at the beginning and then at the end of an epoch:

bin/ --augment codec[p=0.1,bitrate=48000:16000] --clock 0.0 test.wav
bin/ --augment codec[p=0.1,bitrate=48000:16000] --clock 1.0 test.wav

Example of creating a pre-augmented test set:

bin/ \
  --augment overlay[source=noise.sdb,layers=1,snr=20~10] \
  --augment resample[rate=12000:8000~4000] \
  test.sdb test-augmented.sdb

Training from an Anaconda or miniconda environment

Keep in mind that none of the core authors use Anaconda or miniconda, so this setup is not guaranteed to work. If you experience problems, try using a non-conda setup first. We’re happy to accept pull requests fixing any incompatibilities with conda setups, but we will not offer any support ourselves beyond reviewing pull requests.

To prevent common problems, make sure you always use a separate environment when setting things up for training:

(base) $ conda create -n deepspeech python=3.7
(base) $ conda activate deepspeech