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End-to-end speech recognition using RNN Transducers in Tensorflow 2.0
| Date | Stars |
|---|---|
| 2026-07-24 | 250 |
| 2026-07-25 | 250 |
| 2026-07-28 | 250 |
| 2026-07-30 | 250 |
| 2026-08-06 | 250 |
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# RNN-Transducer Speech Recognition
End-to-end speech recognition using RNN-Transducer in Tensorflow 2.0
## Overview
This speech recognition model is based off Google's [Streaming End-to-end Speech Recognition For Mobile Devices](https://arxiv.org/pdf/1811.06621.pdf) research paper and is implemented in Python 3 using Tensorflow 2.0
## Setup Your Environment
To setup your environment, run the following command:
```
git clone --recurse https://github.com/noahchalifour/rnnt-speech-recognition.git
cd rnnt-speech-recognition
pip install tensorflow==2.2.0 # or tensorflow-gpu==2.2.0 for GPU support
pip install -r requirements.txt
./scripts/build_rnnt.sh # to setup the rnnt loss
```
## Common Voice
You can find and download the Common Voice dataset [here](https://voice.mozilla.org/en/datasets)
### Convert all MP3s to WAVs
Before you can train a model on the Common Voice dataset, you must first convert all the audio mp3 filetypes to wavs. Do so by running the following command:
> **_NOTE:_** Make sure you have `ffmpeg` installed on your computer, as it uses that to convert mp3 to wav
```
./scripts/common_voice_convert.sh <data_dir> <# of threads>
python scripts/remove_missing_samples.py \
--data_dir <data_dir> \
--replace_old
```
### Preprocessing dataset
After converting all the mp3s to wavs you need to preprocess the dataset, you can do so by running the following command:
```
python preprocess_common_voice.py \
--data_dir <data_dir> \
--output_dir <preprocessed_dir>
```
### Training a model
<!-- #### Training on Host -->
To train a simple model, run the following command:
```
python run_rnnt.py \
--mode train \
--data_dir <path to data directory>
```
<!-- #### Training in Docker Container
[View Image](https://hub.docker.com/r/noahchalifour/rnnt-speech-recognition)
You can also train your model in a docker container based on the Tensorflow docker image.
> **_NOTE:_** Specify all your paramters in ALL CAPS as environment variables when training in a docker container.
To run the model using a CPU only, run the following command:
```
docker run -d --name rnnt-speech-recognition \
-v <path to local data>:/rnnt-speech-recognition/data \
-v <path to save model locally>:/rnnt-speech-recognition/model \
-e MODE=train \
-e DATA_DIR=./data \
-e OUTPUT_DIR=./model \
noahchalifour/rnnt-speech-recognition
```
To run the model using a GPU you must run the following command with the added `--cap-add SYS_ADMIN`, and `--gpus <gpus>`:
```
docker run -d --name rnnt-speech-recognition \
--cap-add SYS_ADMIN \
--gpus <gpus> \
-v <path to local data>:/rnnt-speech-recognition/data \
-v <path to save model locally>:/rnnt-speech-recognition/model \
-e MODE=train \
-e DATA_DIR=./data \
-e OUTPUT_DIR=./model \
noahchalifour/rnnt-speech-recognition
``` -->
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:054533e16b17671d, topic:deep-learning, topic:tensorflow
matched fp:054533e16b17671d, topic:speech-recognition, name:speech recognition, desc:speech recognition