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[Wav2Vec2 Speech Event] Add speech event v2 (#15083)

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This commit is contained in:
Patrick von Platen
2022-01-10 10:46:21 +01:00
committed by GitHub
parent 768e6c1449
commit d72343d2b8
5 changed files with 409 additions and 5 deletions
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2
examples/pytorch/speech-recognition/README.md
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@@ -74,6 +74,7 @@ python run_speech_recognition_ctc.py \
--warmup_steps="500" \
--evaluation_strategy="steps" \
--text_column_name="sentence" \
--length_column_name="input_length" \
--save_steps="400" \
--eval_steps="100" \
--layerdrop="0.0" \
@@ -108,6 +109,7 @@ python -m torch.distributed.launch \
--warmup_steps="500" \
--evaluation_strategy="steps" \
--text_column_name="sentence" \
--length_column_name="input_length" \
--save_steps="400" \
--eval_steps="100" \
--logging_steps="1" \

1
examples/pytorch/speech-recognition/requirements.txt
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@@ -2,3 +2,4 @@ datasets >= 1.13.3
torch >= 1.5
torchaudio
librosa
jiwer

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examples/pytorch/speech-recognition/run_speech_recognition_ctc.py
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@@ -396,7 +396,10 @@ def main():
if training_args.do_train:
raw_datasets["train"] = load_dataset(
data_args.dataset_name, data_args.dataset_config_name, split=data_args.train_split_name
data_args.dataset_name,
data_args.dataset_config_name,
split=data_args.train_split_name,
use_auth_token=data_args.use_auth_token,
)
if data_args.audio_column_name not in raw_datasets["train"].column_names:
@@ -418,7 +421,10 @@ def main():
if training_args.do_eval:
raw_datasets["eval"] = load_dataset(
data_args.dataset_name, data_args.dataset_config_name, split=data_args.eval_split_name
data_args.dataset_name,
data_args.dataset_config_name,
split=data_args.eval_split_name,
use_auth_token=data_args.use_auth_token,
)
if data_args.max_eval_samples is not None:

6
examples/research_projects/wav2vec2/README.md
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@@ -1,12 +1,12 @@
**NOTE**: This example is outdated and is not longer actively maintained. Please
follow the new instructions of fine-tuning Wav2Vec2 [here](https://github.com/huggingface/transformers/blob/master/examples/pytorch/speech-recognition/README.md)
## Fine-tuning Wav2Vec2
The `run_asr.py` script allows one to fine-tune pretrained Wav2Vec2 models that can be found [here](https://huggingface.co/models?search=facebook/wav2vec2).
This finetuning script can also be run as a google colab [TODO: here]( ).
The script is actively maintained by [Patrick von Platen](https://github.com/patrickvonplaten).
Feel free to ask a question on the [Forum](https://discuss.huggingface.co/) or post an issue on [GitHub](https://github.com/huggingface/transformers/issues/new/choose) and adding `@patrickvonplaten` as a tag.
### Fine-Tuning with TIMIT
Let's take a look at the [script](./finetune_base_timit_asr.sh) used to fine-tune [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base)
with the [TIMIT dataset](https://huggingface.co/datasets/timit_asr):

395
examples/research_projects/xls_r/README.md Normal file
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@@ -0,0 +1,395 @@
# 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](https://us.ovhcloud.com/) 🚀.
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](#organization)
- [Important dates](#important-dates)
- [How to install pytorch, transformers, datasets](#how-to-install-relevant-libraries)
- [How to fine-tune a speech recognition model](#how-to-finetune-a-model)
- [Talks](#talks)
- [Project evaluation](#project-evaluation)
- [General Tips & Tricks](#general-tips-and-tricks)
- [FAQ](#faq)
## Organization
Participants are encouraged to leverage pre-trained speech recognition checkpoints,
preferably [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/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](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) 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](TODO: ) 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](TODO: ) and prizes [here](TODO: ). 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](TODO: ).
**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](https://huggingface.co/speech-recognition-community-v2). 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](TODO: )
- **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](https://pytorch.org/)
- [Transformers](https://github.com/huggingface/transformers)
- [Datasets](https://github.com/huggingface/datasets)
We recommend installing the above libraries in a [virtual environment](https://docs.python.org/3/library/venv.html).
If you're unfamiliar with Python virtual environments, check out the [user guide](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/). 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:
```bash
python3 -m venv <your-venv-name>
```
You can activate your venv by running
```bash
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``:
```bash
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](https://pytorch.org/) to install PyTorch with CUDA.
We strongly recommend making use of the provided PyTorch examples scripts in [transformers/examples/pytorch/speech-recognition](https://github.com/huggingface/transformers/tree/master/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](https://github.com/huggingface/transformers) 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:
```bash
$ 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:
```bash
$ git checkout -b a-descriptive-name-for-my-project
```
4. Set up a PyTorch environment by running the following command your virtual environment:
```bash
$ 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](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-create-a-pull-request).
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.
```python
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](https://huggingface.co/docs/transformers/model_doc/xls_r) on the [Common Voice 7 dataset](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
We recommend fine-tuning one of the following pre-trained XLS-R checkpoints:
- [300M parameters version](https://huggingface.co/facebook/wav2vec2-xls-r-300m)
- [1B parameters version](https://huggingface.co/facebook/wav2vec2-xls-r-1b)
- [2B parameters version](https://huggingface.co/facebook/wav2vec2-xls-r-2b)
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](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
Next, we recommended that you get familiar with the XLS-R model and its capabilities.
In collaboration with [Fairseq's Wav2Vec2 team](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec),
we've written ["Fine-tuning XLS-R for Multi-Lingual ASR with 🤗 Transformers"](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) 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:
```bash
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](http://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:
```bash
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](https://git-lfs.github.com/) or simply via:
```bash
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:
```bash
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.
```bash
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:
```bash
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](https://github.com/huggingface/transformers/tree/master/examples/pytorch/speech-recognition#connectionist-temporal-classification).
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](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0): `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:
```bash
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](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) can be downloaded. In addition, we add the `--push_to_hub` flag to make use of the
[Trainers `push_to-hub` functionality](https://huggingface.co/docs/transformers/master/en/main_classes/trainer#transformers.Trainer.push_to_hub) so that your model will be automatically uploaded to the Hub.
Let's copy the following code snippet in a file called `run.sh`
```bash
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
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](https://huggingface.co/hf-test/xls-r-ab-test/commit/0eb19a0fca4d7d163997b59663d98cd856022aa6) .
At the end of the training, the [Trainer](https://huggingface.co/docs/transformers/master/en/main_classes/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](https://huggingface.co/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](https://huggingface.co/facebook/wav2vec2-xls-r-300m)
- [1B parameters version](https://huggingface.co/facebook/wav2vec2-xls-r-1b)
- [2B parameters version](https://huggingface.co/facebook/wav2vec2-xls-r-2b)
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:
```bash
huggingface-cli repo create xls-r-300m-sv
```
and then clone it locally:
```bash
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](https://huggingface.co/hf-test/xls-r-300m-sv).
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.