Gsoc Wav2vec2 Save

This repository presents an implementation of the Wav2Vec2 model [1] in TensorFlow 2.0 as a part of Google Summer of Code.

For a quick demo, please check out this. You can find the final report of the project here.

Notebooks

The repository comes with shiny Colab Notebooks. Below you can find a list of them. Spin them up and don't forget to have fun!

Notebook	Description
tensorflow/hub	This notebook gives you a template to fine-tune a pre-trained Wav2Vec2 SavedModel
	This notebook demonstrates conversion of TF Wav2Vec2 model to ONNX and compares the latency of ONNX exported model & TF model on CPU
	This notebook demonstrates Wav2Vec2 evaluation (without any padding) on LibriSpeech data
	This notebook demonstrates Wav2Vec2 SavedModel evaluation (with constant padding upto 246000 length) on LibriSpeech data
	This notebook shows a small demo of how to use Wav2Vec2 for inference for ASR task

Checkpoints

Below is a summary of checkpoints obtained during the project:

To know more about the process of obtaining the first two checkpoints, please check out this section and to know about the process of getting the last checkpoint, please check out this section.

Using this Repository

Install Wav2Vec2 model from this repository using the pip command:

# this will install the wav2vec2 package
pip3 install git+https://github.com/vasudevgupta7/gsoc-wav2vec2@main

You can use the fine-tuned checkpoints (from 🤗 Hub) like this:

from wav2vec2 import Wav2Vec2ForCTC, Wav2Vec2Config

config = Wav2Vec2Config()
model = Wav2Vec2ForCTC(config)
# now use this model like any other TF model

# incase you are interested in already trained model, use `.from_pretrained` method
model_id = "finetuned-wav2vec2-960h"
model = Wav2Vec2ForCTC.from_pretrained(model_id)

Additionally, you can use the SavedModel from TFHub like this:

import tensorflow_hub as Hub

model_url = "https://tfhub.dev/vasudevgupta7/wav2vec2-960h/1"
model = hub.KerasLayer(model_url)

# use this `model`, just like any other TF SavedModel

Please checkout the notebooks referred to in this repository for more information on using the Wav2Vec2 model.

Reproducing this project

Setting Up

# install & setup TensorFlow first
pip3 install tensorflow==2.5
# Only `TF==2.5` is tested for now!

# install other requirements of this project using the following command:
pip3 install -qr requirements.txt
sudo apt-get install libsndfile1-dev

# switch to code directory for further steps
cd src

For using TPUs, it's essential to store model weights and datasets in the GCS bucket so that TPU can access them directly from there. Hence we will create 2 GCS buckets - one for checkpointing and the other for storing LibriSpeech tfrecords.

# these bucket names will be required to run the training script later
export DATA_BUCKET_NAME="gsoc-librispeech-us"
export CKPT_BUCKET_NAME="gsoc-checkpoints-us"

# create GCS buckets
gsutil mb gs://${DATA_BUCKET_NAME}
gsutil mb gs://${CKPT_BUCKET_NAME}

Preparing dataset

Now we will download the LibriSpeech dataset from the official website & convert them into tfrecords using make_tfrecords.py. Finally, we will export all the tfrecords to the GCS bucket.

# possible values are `dev-clean`, `train-clean-100`, `train-clean-360`, `train-other-500`, `test-clean`
# you will have to follow same steps for all the configurations (specified above).
export DATA_SPLIT=dev-clean

wget https://www.openslr.org/resources/12/${DATA_SPLIT}.tar.gz
tar -xf ${DATA_SPLIT}.tar.gz

python3 make_tfrecords.py --data_dir LibriSpeech/${DATA_SPLIT} -d ${DATA_SPLIT} -n 50

# transfer tfrecords to GCS bucket
gsutil cp -r ${DATA_SPLIT} gs://<DATA_BUCKET_NAME>/${DATA_SPLIT}

Now your GCS bucket (DATA_BUCKET_NAME) should look like this:

.
|- ${DATA_SPLIT}
    |- ${DATA_SPLIT}-0.tfrecord
    |- ${DATA_SPLIT}-1.tfrecord
    .
    .

Follow the above steps for all other data splits. You need to change the DATA_SPLIT environment variable.

Model training

Since you have installed everything and GCS buckets are configured, we need to run one command to initiate training.

Note: Following commands assume that you already have exported DATA_BUCKET_NAME & CKPT_BUCKET_NAME environment variables.

The following command will fine-tune the wav2vec2 model on single/multiple GPUs or Colab/Kaggle TPUs:

python3 main.py

For training on Cloud TPUs, run the following command:

# export `TPU_NAME` environment variable first
# this flag will ensure that your VM connects to the specified TPUs & TPUs become visible to TensorFlow
TPU_NAME=<tpu-name> python3 main.py

Running Conversion script

You can convert original PyTorch checkpoints (from Facebook) using the conversion script available in this repository.

python3 convert_torch_to_tf.py \
--hf_model_id facebook/wav2vec2-base \ # HuggingFace Hub ID of the model you want to convert
--with_lm_head # Whether to use `Wav2Vec2ForCTC` or `Wav2Vec2Model` from this repository

Running tests

# first install `torch` & `transformers`
pip3 install torch transformers

# run this from the root of this repository
pytest -sv tests

Acknowledgement

• Sayak Paul, Morgan Roff, Jaeyoun Kim for mentoring me throughout the project.
• TensorFlow team & TRC for providing access to TPUs during my GSoC tenure.

References

[1] Baevski, Alexei, et al. "Wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations." ArXiv:2006.11477 [Cs, Eess], Oct. 2020. arXiv.org, http://arxiv.org/abs/2006.11477.

End Notes

Please create an issue if you encounter any problems while using this project. Don't forget to 🌟 this repository if you like this work.