wav2vec 2.0 Recognize Implementation.

Wave2vec is part of fairseq This repository is the result of the issue submitted in the fairseq repository here.

Please first download one of the pre-trained models available from fairseq (see later).

We make use of python:3.7.4-slim-buster as base image in order to let developers to have more flexibility in customize this Dockerfile. For a simplifed install please refer to Alternative Install section. If you go for this container, please install using the provided Dockerfile

docker build -t wav2vec -f Dockerfile .

Before running, please copy the downloaded model (e.g. wav2vec_small_10m.pt) to the data/ folder. Please copy there the wav file to test as well, like data/temp.wav in the following examples. So the data/ folder will now look like this

.
├── dict.ltr.txt
├── temp.wav
└── wav2vec_small_10m.pt

We now run the container as a daemon and the we enter and execute the recognition.

docker run -d -it --rm -v $PWD/data:/app/data --name w2v wav2vec
docker exec -it w2v bash
python examples/wav2vec/recognize.py --wav_path /app/data/temp.wav --w2v_path /app/data/wav2vec_small_10m.pt --target_dict_path /app/data/dict.ltr.txt 

We provide an alternative Dockerfile named wav2letter.Dockerfile that makes use of wav2letter/wav2letter:cpu-latest Docker image as FROM. Here are the commands for build, install and run in this case:

docker build -t wav2vec2 -f wav2letter.Dockerfile .
docker run -d -it --rm -v $PWD/data:/root/data --name w2v2 wav2vec2
docker exec -it w2v2 bash
python examples/wav2vec/recognize.py --wav_path /root/data/temp.wav --w2v_path /root/data/wav2vec_small_10m.pt --target_dict_path /root/data/dict.ltr.txt 

Thanks to all contributors to this repo.

• sooftware
• mychiux413