transformers/examples/research_projects/xls_r/README.md

Speech recognition community week - version 2 🤗

Welcome to the 2nd version of the speech recognition community event🎙️ ! The goal of this event is to build robust, real-world speech recognition (ASR) models in as many languages as possible🌏🌍🌎.

If necessary and available, free access to a V100 32 GB GPU will kindly be provided by the OVH team 🚀.

This document summarizes all the relevant information required for the speech community event📋.

Don't forget to sign up [here](TODO: Create google from)🤗.

Table of Contents

• Organization
• Important dates
• How to install pytorch, transformers, datasets
• How to fine-tune a speech recognition model
• Talks
• Project evaluation
• General Tips & Tricks
• FAQ

Organization

Participants are encouraged to leverage pre-trained speech recognition checkpoints, preferably facebook/wav2vec2-large-xlsr-53, to train a speech recognition system in a language of their choice.

Participants can make use of whatever data they think is useful to build a robust speech recognition system for real-world audio data. We strongly recommend making use of Mozilla's diverse Common Voice dataset when training the model. Please do not use the "test" split of the Common Voice datasets for training as we will likely use this split for the final evaluation of your model. We kindly ask you to make sure that the dataset that you are using for training has the appropriate licensing - see here for more information.

During the event, the fine-tuned models will regularly be tested on a development dataset provided by the Hugging Face team and at the end of the event, all models will be tested on a test dataset. For each language, the best performing model will receive a prize 🏆 - more information regarding the testing here and prizes here. We believe that framing the event as a competition is more fun, but at the core, we strongly encourage participants to work together by helping each other to solve bugs, share important findings, etc...🤗

If possible it is encouraged to fine-tune the models on local GPU machines, but if those are not available, the OVH cloud team kindly provides a limited number of GPUs for the event. For more information on how to get access to the GPU - see here.

Please note: All important announcements will be made on discord. Please make sure that you've joined the following discord server: TODO: fill out. Please make sure that you have been added to the Speech Event Organization. You should have received an invite by email. If you didn't receive an invite, please contact the organizers, e.g. Anton, Patrick, or Omar on discord.

Important dates

• 12.01. Official announcement of the community event. Make sure to sign-up in this google form
• 12.01. - 19.01. Participants sign up for the event.
• 19.01. Release of all relevant guides and training scripts.
• 24.01. Start of the community week! OVH & Hugging Face gives access to GPUs.
• 24.01. - 07.02. The OVH & Hugging Face team will be available for any questions, problems the participants might have.
• 07.02. Access to GPU is deactivated and community week officially ends.

How to install relevant libraries

The following libraries are required to fine-tune a speech model with 🤗 Transformers and 🤗 Datasets in PyTorch.

• PyTorch
• Transformers
• Datasets

We recommend installing the above libraries in a virtual environment. If you're unfamiliar with Python virtual environments, check out the user guide. Create a virtual environment with the version of Python you're going to use and activate it.

You should be able to run the command:

python3 -m venv <your-venv-name>

You can activate your venv by running

source ~/<your-venv-name>/bin/activate

To begin with please make sure you have PyTorch and CUDA correctly installed. The following command should return True:

python -c "import torch; print(torch.cuda.is_available())"

If the above command doesn't print True, in a first step, please follow the instructions here to install PyTorch with CUDA.

We strongly recommend making use of the provided PyTorch examples scripts in transformers/examples/pytorch/speech-recognition to train your speech recognition system. In all likelihood, you will adjust one of the example scripts, so we recommend forking and cloning the 🤗 Transformers repository as follows.

1. Fork the repository by clicking on the 'Fork' button on the repository's page. This creates a copy of the code under your GitHub user account.

2. Clone your fork to your local disk, and add the base repository as a remote:

   $ git clone https://github.com/<your Github handle>/transformers.git
   $ cd transformers
   $ git remote add upstream https://github.com/huggingface/transformers.git
   

3. Create a new branch to hold your development changes. This is especially useful to share code changes with your team:

   $ git checkout -b a-descriptive-name-for-my-project
   

4. Set up a PyTorch environment by running the following command your virtual environment:

   $ pip install -e ".[torch-speech]"
   

   (If transformers was already installed in the virtual environment, remove it with pip uninstall transformers before reinstalling it in editable mode with the -e flag.)

   If you have already cloned that repo, you might need to git pull to get the most recent changes in the transformers library.

   Running this command will automatically install pytorch and the most relevant libraries required for fine-tuning a speech recognition system.

Next, you should also install the 🤗 Datasets library. We strongly recommend installing the library from source to profit from the most current additions during the community week.

Simply run the following steps:

$ cd ~/
$ git clone https://github.com/huggingface/datasets.git
$ cd datasets
$ pip install -e ".[streaming]"

If you plan on contributing a specific dataset during the community week, please fork the datasets repository and follow the instructions here.

To verify that all libraries are correctly installed, you can run the following command in a Python shell. It verifies that both transformers and datasets have been correclty installed.

from transformers import AutoModelForCTC, AutoProcessor
from datasets import load_dataset

dummy_dataset = load_dataset("common_voice", "ab", split="test")

model = AutoModelForCTC.from_pretrained("hf-internal-testing/tiny-random-wav2vec2")
model.to("cuda")

processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2")

input_values = processor(dummy_dataset[0]["audio"]["array"], return_tensors="pt", sampling_rate=16_000).input_values
input_values = input_values.to("cuda")

logits = model(input_values).logits

assert logits.shape[-1] == 32

How to finetune a model

In this section, we show you how to fine-tune a pre-trained XLS-R Model on the Common Voice 7 dataset.

We recommend fine-tuning one of the following pre-trained XLS-R checkpoints:

• 300M parameters version
• 1B parameters version
• 2B parameters version

To begin with, please note that to use the Common Voice dataset, you have to accept that your email address and username are shared with the mozilla-foundation. To get access to the dataset please click on "Access repository" here.

Next, we recommended that you get familiar with the XLS-R model and its capabilities. In collaboration with Fairseq's Wav2Vec2 team, we've written "Fine-tuning XLS-R for Multi-Lingual ASR with 🤗 Transformers" which gives an in-detail explanation of how XLS-R functions and how it can be fine-tuned.

The blog can also be opened and directly fine-tuned in a google colab notebook. In this section, we will explain how to fine-tune the model on a local machine.

1. Log in

To begin with you should check that you are correctly logged in and that you have git-lfs installed so that your fine-tuned model can automatically be uploaded.

Run:

huggingface-cli login

to login. It is recommend to login with your personal access token that can be found under your hugging face profile (icon in the top right corner on hf.co, then Settings -> Access Tokens -> User Access Tokens -> New Token (if haven't generated one already)

You can then copy-paste this token to log in locally.

2. Create your model repository

First, let's make sure that git-lfs is correctly installed. To so, simply run:

git-lfs -v

The output should show something like git-lfs/2.13.2 (GitHub; linux amd64; go 1.15.4). If your console states that the git-lfs command was not found, please make sure to install it here or simply via:

sudo apt-get install git-lfs

Now you can create your model repository which will contain all relevant files to reproduce your training. You can either directly create the model repository on the Hub (Settings -> New Model) or via the CLI. Here we choose to use the CLI instead.

Assuming that we want to call our model repository xls-r-ab-test, we can run the following command:

huggingface-cli repo create xls-r-ab-test

You can now see the model on the Hub, e.g. under https://huggingface.co/hf-test/xls-r-ab-test .

Let's clone the repository so that we can define our training script inside.

git lfs install
git clone https://huggingface.co/hf-test/xls-r-ab-test

3. Add your training script and run-command to the repository

We encourage participants to add all relevant files for training directly to the directory so that everything is fully reproducible.

Let's first copy-paste the official training script from our clone of transformers to our just created directory:

cp ~/transformers/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py ./

Next, we'll create a bash file to define the hyper-parameters and configurations for training. More detailed information on different settings (single-GPU vs. multi-GPU) can be found here.

For demonstration purposes, we will use a dummy XLS-R model model_name_or_path="hf-test/xls-r-dummy" on the very low-resource language of "Abkhaz" of Common Voice 7: dataset_config_name="ab" for just a single epoch.

Before starting to train, let's make sure we have installed all the required libraries. You might want to run:

pip install -r ~/transformers/examples/pytorch/speech-recognition/requirements.txt

Alright, finally we can define the training script. We'll simply use some dummy hyper-parameters and configurations for demonstration purposes.

Note that we add the flag --use_auth_token so that datasets requiring access, such as Common Voice 7 can be downloaded. In addition, we add the --push_to_hub flag to make use of the Trainers push_to-hub functionality so that your model will be automatically uploaded to the Hub.

Let's copy the following code snippet in a file called run.sh

echo '''python run_speech_recognition_ctc.py \
	--dataset_name="mozilla-foundation/common_voice_7_0" \
	--model_name_or_path="hf-test/xls-r-dummy" \
	--dataset_config_name="ab" \
	--output_dir="./" \
	--overwrite_output_dir \
	--max_steps="10" \
	--per_device_train_batch_size="2" \
	--learning_rate="3e-4" \
	--save_total_limit="1" \
	--evaluation_strategy="steps" \
	--text_column_name="sentence" \
	--length_column_name="input_length" \
	--save_steps="5" \
	--layerdrop="0.0" \
	--freeze_feature_encoder \
	--gradient_checkpointing \
	--fp16 \
	--group_by_length \
	--push_to_hub \
	--use_auth_token \
	--do_train --do_eval''' > run.sh

4. Start training

Now all that is left to do is to start training the model by executing the run file.

bash run.sh

The training should not take more than a couple of minutes. During the training intermediate saved checkpoints are automatically uploaded to your model repository as can be seen on this commit .

At the end of the training, the Trainer automatically creates a nice model card and all relevant files are uploaded.

5. Tips for real model training

The above steps illustrate how a model can technically be fine-tuned. However as you can see on the model card hf-test/xls-r-ab-test, our demonstration has a very poor performance which is not surprising given that we trained for just 10 steps on a randomly initialized model.

For a real model training, one of the actual pre-trained XLS-R models should be used:

• 300M parameters version
• 1B parameters version
• 2B parameters version

Also, the hyper-parameters should be carefully chosen depending on the dataset. As an example, we will fine-tune the 300M parameters model on Swedish on a single TITAN RTX 24GB GPU.

The model will be called "xls-r-300m-sv". Following the above steps we first create the model:

huggingface-cli repo create xls-r-300m-sv

and then clone it locally:

and we define the following 

hyperparameters for training

```bash

echo '''python run_speech_recognition_ctc.py \
	--dataset_name="mozilla-foundation/common_voice_7_0" \
	--model_name_or_path="facebook/wav2vec2-xls-r-300m" \
	--dataset_config_name="sv-SE" \
	--output_dir="./" \
	--overwrite_output_dir \
	--num_train_epochs="50" \
	--per_device_train_batch_size="8" \
	--per_device_eval_batch_size="8" \
	--gradient_accumulation_steps="4" \
	--learning_rate="7.5e-5" \
	--warmup_steps="2000" \
	--length_column_name="input_length" \
	--evaluation_strategy="steps" \
	--text_column_name="sentence" \
	--chars_to_ignore , ? . ! \- \; \: \" “ % ‘ ” <20> — ’ … – \
	--save_steps="500" \
	--eval_steps="500" \
	--logging_steps="100" \
	--layerdrop="0.0" \
	--activation_dropout="0.1" \
	--save_total_limit="3" \
	--freeze_feature_encoder \
	--feat_proj_dropout="0.0" \
	--mask_time_prob="0.75" \
	--mask_time_length="10" \
	--mask_feature_prob="0.25" \
	--mask_feature_length="64" \
	--gradient_checkpointing \
	--use_auth_token \
	--fp16 \
	--group_by_length \
	--do_train --do_eval \
	--push_to_hub''' > run.sh

The training takes ca. 7 hours and yields a reasonable test word error rate of 27% as can be seen on the automatically generated model card.

The above-chosen hyperparameters probably work quite well on a range of different datasets and languages, but are by no means optimal. It is up to you to find a good set of hyperparameters.