# Speech Recognition Pre-Training ## Wav2Vec2 Speech Pre-Training The script [`run_pretraining.py`](./run_pretraining.py) can be used to pre-train a [Wav2Vec2](https://huggingface.co/transformers/model_doc/wav2vec2.html) model from scratch. In the script [`run_pretraining.py`](./run_pretraining.py), a Wav2Vec2 model is pre-trained on audio data alone using [Wav2Vec2's contrastive loss objective](https://arxiv.org/abs/2006.11477). The following examples show how to pre-train `"base"`- and `"large"`-sized Wav2Vec2 models. --- **NOTE 1** Wav2Vec2's pre-training is known to be quite unstable. It is advised to do a couple of test runs with a smaller dataset, *i.e.* `--dataset_config_name clean`, `--dataset_split_name validation` to find good hyper-parameters for `learning_rate`, `batch_size`, `warmup_steps`, and the optimizer. --- --- **NOTE 2** When training a model on large datasets it is recommended to run the data preprocessing in a first run in a **non-distributed** mode via `--preprocessing_only` so that when running the model in **distributed** mode in a second step the preprocessed data can easily be loaded on each distributed device. --- ## Poplar SDK setup To check if your Poplar SDK has already been enabled, run: ```bash echo $POPLAR_SDK_ENABLED ``` If no path is provided, then follow these steps: 1. Navigate to your Poplar SDK root directory 2. Enable the Poplar SDK with: ```bash source enable ``` More detailed instructions on setting up your Poplar environment are available in the [Poplar quick start guide](https://docs.graphcore.ai/projects/poplar-quick-start). ## Environment setup To prepare your environment, follow these steps: 1. Create and activate a Python3 virtual environment: ```bash python3 -m venv source /bin/activate ``` 2. Navigate to the Poplar SDK root directory 3. Install the PopTorch (PyTorch) wheel: ```bash cd pip3 install poptorch...x86_64.whl ``` 4. Navigate to this example's root directory 5. Install the Python requirements: ```bash pip3 install -r requirements.txt ``` 6. Install the latest release of the `optimum-graphcore` package as described in [optimum-graphcore/#install](https://github.com/huggingface/optimum-graphcore/#install). For example, to install from source: ``` pip install git+https://github.com/huggingface/optimum-graphcore.git ``` ## Demo In this demo run we pre-train a `"base-sized"` Wav2Vec2 model simply only on the validation data of [librispeech_asr](https://huggingface.co/datasets/librispeech_asr). ```bash python run_pretraining.py \ --model_name_or_path "facebook/wav2vec2-base" \ --dataset_name "librispeech_asr" \ --dataset_config_name "clean" \ --train_split_name "validation" \ --ipu_config_name "Graphcore/wav2vec2-base-ipu" \ --output_dir "./wav2vec2-pretrained-demo" \ --max_duration_in_seconds 15.6 \ --min_duration_in_seconds 2.0 \ --do_train \ --overwrite_output_dir \ --layerdrop 0.05 \ --per_device_train_batch_size 1 \ --dataloader_mode "async_rebatched" \ --dataloader_num_workers 64 \ --num_train_epochs 1 \ --warmup_steps 1000 \ --weight_decay 0.01 \ --learning_rate 0.001 \ --adam_beta1 0.9 \ --adam_beta2 0.98 \ --adam_epsilon 1e-04 \ --max_gumbel_temperature 2.0 \ --min_gumbel_temperature 0.5 \ --gumbel_temperature_decay 0.999995 \ --logging_steps 10 \ --n_ipu 16 ``` ## Base To pre-train `"base-sized"` Wav2Vec2 model, *e.g.* [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on 100h of training data from the [librispeech_asr](https://huggingface.co/datasets/librispeech_asr), the following command can be run: ```bash python run_pretraining.py \ --model_name_or_path "facebook/wav2vec2-base" \ --dataset_name "librispeech_asr" \ --dataset_config_name "clean" \ --train_split_name "train.100" \ --ipu_config_name "Graphcore/wav2vec2-base-ipu" \ --output_dir "./wav2vec2-pretrained-base" \ --max_duration_in_seconds 15.6 \ --min_duration_in_seconds 2.0 \ --do_train \ --overwrite_output_dir \ --layerdrop 0.05 \ --per_device_train_batch_size 1 \ --dataloader_mode "async_rebatched" \ --dataloader_num_workers 64 \ --num_train_epochs 10 \ --warmup_steps 1000 \ --weight_decay 0.01 \ --learning_rate 0.001 \ --adam_beta1 0.9 \ --adam_beta2 0.98 \ --adam_epsilon 1e-04 \ --max_gumbel_temperature 2.0 \ --min_gumbel_temperature 0.5 \ --gumbel_temperature_decay 0.999995 \ --logging_steps 10 \ --n_ipu 16 ``` If you increase the effective batch size, for example by increasing the `gradient_accumulation_steps`, it is recommended to increase the `learning_rate` to `0.005` for faster convergence. ## Large To pre-train `"large-sized"` Wav2Vec2 model, *e.g.* [facebook/wav2vec2-large](https://huggingface.co/facebook/wav2vec2-large) on 100h of training data from the [librispeech_asr](https://huggingface.co/datasets/librispeech_asr), the following command can be run: ```bash python run_pretraining.py \ --model_name_or_path "facebook/wav2vec2-large-960h" \ --dataset_name "librispeech_asr" \ --dataset_config_name "clean" \ --train_split_name "train.100" \ --ipu_config_name "Graphcore/wav2vec2-large-ipu" \ --output_dir "./wav2vec2-pretrained-large" \ --max_duration_in_seconds 15.6 \ --min_duration_in_seconds 2.0 \ --do_train \ --overwrite_output_dir \ --layerdrop 0.05 \ --per_device_train_batch_size 1 \ --dataloader_num_workers 64 \ --num_train_epochs 10 \ --warmup_steps 1000 \ --weight_decay 0.01 \ --learning_rate 0.001 \ --adam_beta1 0.9 \ --adam_beta2 0.98 \ --adam_epsilon 1e-04 \ --max_gumbel_temperature 2.0 \ --min_gumbel_temperature 0.5 \ --gumbel_temperature_decay 0.999995 \ --logging_steps 10 \ --n_ipu 16 ``` Similarly to the `"base-sized"` model above, be sure to select optimal `learning_rate` given the effective batch size of your configuration. The effective batch size is defined as `gradient_accumulation_steps * per_device_train_batch_size * replication_factor`. The `replication_factor` is calculated as number of IPUs (`n_ipu`) divided by `ipus_per_replica`. See [Graphcore/wav2vec2-large](https://huggingface.co/Graphcore/wav2vec2-large-ipu) for configuration parameters in addition to the command line arguments.