This resource is using open-source code maintained in github (see the quick-start-guide section) and available for download from NGC

This repository provides an optimized implementation of the wav2vec 2.0 model, as described in the paper wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations. It is based on the Fairseq codebase published by the authors of the paper. The wav2vec 2.0 model is pre-trained unsupervised on large corpora of speech recordings. Afterward, it can be quickly fine-tuned in a supervised way for speech recognition or serve as an extractor of high-level features and pseudo-phonemes for other applications.

The differences between this wav2vec 2.0 and the reference implementation are:

Support for increased batch size, which does not change batch-dependent constants for negative sampling and loss calculation and improves hardware utilization
Support for the Hourglass Transformer architecture, which in the default setting improves the training speed of the Base model by 1.4x, lowers memory consumption by 38%, and retains accuracy

This model is trained with mixed precision using Tensor Cores on NVIDIA Volta, NVIDIA Turning, and the NVIDIA Ampere GPU architectures. Therefore, researchers can get results up to 1.35x faster than training without Tensor Cores while experiencing the benefits of mixed precision training. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.

Model architecture

The model takes raw waveforms as its input. A fully convolutional feature extractor reduces the resolution of the signal to a single vector roughly every 20 ms. Most of the computation is performed in the transformer encoder part of the model. The outputs of the transformer, and quantized outputs from the feature extractor, serve as inputs to the contrastive loss. During fine-tuning, this loss is replaced with the CTC loss, and quantization is not performed.

\n $\"wav2vec$ \n

\n Figure 1. The architecture of wav2vec 2.0 ([source](https://proceedings.neurips.cc/paper/2020/file/92d1e1eb1cd6f9fba3227870bb6d7f07-Paper.pdf)). The model is composed of a convolutional feature extractor, and a transformer encoder. During fine-tuning, quantization is disabled and contrastive loss is replaced with the CTC loss function.\n

In addition, our model uses the Hourglass Transformer architecture for the encoder. This architecture uses fixed-sized pooling in order to reduce the time dimension T of the signal, and thus, lower the O(T²) cost of the self-attention mechanism.

\n $\"The$ \n

\n Figure 2. The Hourglass Transformer module ([source](https://arxiv.org/abs/2110.13711)). The signal is processed by the initial layers and downsampled. Most of the layers operate on the downsampled signal. Finally, the signal is upsampled for the final layers. The Hourglass Transformer replaced a regular stack of transformer layers, typically improving throughput and lowering memory consumption.\n

Default configuration

The following features were implemented in this model:

general:\n
- multi-GPU and multi-node training
- Hourglass Transformer architecture
- dynamic loss scaling with backoff for tensor cores (mixed precision) training
- mixed-precision training with O2 optimization level, based on float16 or bfloat16
\n
training:\n
- support for variable batch size without changing batch-dependent constants for the loss function
\n
inference:\n
- masking for inference with a larger batch
\n

Our main recipes replicate the Base model described in the wav2vec 2.0 paper, and use Hourglass Transformer with pooling factor 4. Note that Hourglass Transformer can be entirely disabled and this codebase is compatible with Fairseq checkpoints.

Below we present performance numbers for the Hourglass Transformer with different pooling factors (Base model, pre-training, A100 80GB GPU, bfloat16):

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Configuration	Throughput speedup	GPU memory (% of Baseline)
Baseline	1.00	100.00%
Hourglass factor=2	1.25	70.98%
Hourglass factor=3	1.33	64.31%
Hourglass factor=4 (default)	1.37	62.35%
Hourglass factor=5	1.39	60.00%
Hourglass factor=6	1.40	59.61%

Feature support matrix

This model supports the following features:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Feature	wav2vec 2.0
Multi-node training	yes
Automatic mixed precision (AMP)	yes

Features

Automatic Mixed Precision (AMP)\nThis implementation uses automatic mixed-precision training ported from Fairseq.\nIt allows us to use FP16 or BF16 training with FP16 master weights.

Mixed precision training

Mixed precision is the combined use of different numerical precisions in a computational method. Mixed precision training offers significant computational speedup by performing operations in half-precision format while storing minimal information in single-precision to retain as much information as possible in critical parts of the network. Since the introduction of Tensor Cores in NVIDIA Volta, and following with both the NVIDIA Turing and Ampere architectures, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures. Using mixed precision training previously required two steps:

Porting the model to use the FP16 data type where appropriate.
Adding loss scaling to preserve small gradient values.

For information about:

How to train using mixed precision, refer to the Mixed Precision Training paper and Training With Mixed Precision documentation.
Techniques used for mixed precision training, refer to the Mixed-Precision Training of Deep Neural Networks blog.

Enabling mixed precision

For training and inference, mixed precision can be enabled by adding the --fp16 flag or --bf16 flag, depending on the target's lower precision. NVIDIA Ampere and later architectures provide hardware support for bfloat16, which is beneficial for this model, as it skips certain stabilizing FP32 casts. For NVIDIA Volta and NVIDIA Turing architectures, select --fp16.

Enabling TF32

TensorFloat-32 (TF32) is the new math mode in NVIDIA A100 GPUs for handling the matrix math, also called tensor operations. TF32 running on Tensor Cores in A100 GPUs can provide up to 10x speedups compared to single-precision floating-point math (FP32) on NVIDIA Volta GPUs.

TF32 Tensor Cores can speed up networks using FP32, typically with no loss of accuracy. It is more robust than FP16 for models which require a high dynamic range for weights or activations.

For more information, refer to the TensorFloat-32 in the A100 GPU Accelerates AI Training, HPC up to 20x blog post.

TF32 is supported in the NVIDIA Ampere GPU architecture and is enabled by default.

Glossary

Brain Floating Point (bfloat16)\nA 16-bit floating point format that uses an 8-bit exponent, a 7-bit fraction, and a sign bit.\nContrary to float16, which uses a 5-bit exponent, bfloat16 retains the same exponent precision as float32,\nand its robustness with respect to wide ranges of values during training.

Fine-tuning\nTraining an already pretrained model further using a task-specific dataset for subject-specific refinements by adding task-specific layers on top if required.

Hourglass Transformer\nArchitecture proposed in the paper Hierarchical Transformers Are More Efficient Language Models, which improves resource consumption\nof a stack of transformer layers, in many cases retaining the accuracy.

Pre-training\nTraining a model on vast amounts of data on the same (or different) task to build general understandings.

Transformer\nThe paper Attention Is All You Need introduces a novel architecture called transformer that uses an attention mechanism and transforms one sequence into another.

Connectionist Temporal Classification (CTC) Loss\nA loss function introduced in Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks. It calculates the probability of all valid output sequences with repetitions, and allows to train end-to-end ASR models without any prior alignments of transcriptions to audio.

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Aside from these dependencies, ensure you have the following components:\n- [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker)\n- [PyTorch 22.11-py3 NGC container](https://ngc.nvidia.com/registry/nvidia-pytorch) or newer\n- Supported GPUs:\n - [NVIDIA Volta architecture](https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/)\n - [NVIDIA Turing architecture](https://www.nvidia.com/en-us/design-visualization/technologies/turing-architecture/)\n - [NVIDIA Ampere architecture](https://www.nvidia.com/en-us/data-center/nvidia-ampere-gpu-architecture/)\n\nFor more information about how to get started with NGC containers, refer to the following sections from the NVIDIA GPU Cloud Documentation and the Deep Learning Documentation:\n- [Getting Started Using NVIDIA GPU Cloud](https://docs.nvidia.com/ngc/ngc-getting-started-guide/index.html)\n- [Accessing And Pulling From The NGC Container Registry](https://docs.nvidia.com/deeplearning/frameworks/user-guide/index.html#accessing_registry)\n- [Running PyTorch](https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/running.html#running)\n\nFor those unable to use the PyTorch NGC container to set up the required environment or create your own container, refer to the versioned [NVIDIA Container Support Matrix](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html).","hasSetup","quickStartGuide","To train your model using mixed or TF32 precision with Tensor Cores or using FP32, perform the following steps using the default parameters of the wav2vec 2.0 model on the LibriSpeech dataset. For the specifics concerning training and inference, refer to the [Advanced](#advanced) section.\n\n1. Clone the repository.\n ```bash\n git clone https://github.com/NVIDIA/DeepLearningExamples\n cd DeepLearningExamples/PyTorch/SpeechRecognition/wav2vec2\n ```\n\n2. Build the 22.11-py3 PyTorch NGC container and start an interactive session to run training/inference. `DATASET_DIR` on the host will be mounted as `/datasets` inside the container.\n ```bash\n bash scripts/docker/build.sh\n DATASET_DIR=[PATH] bash scripts/docker/run.sh\n ```\n\n3. Download and preprocess the dataset. The dataset size is about 70GB and this step could take up to a few hours to complete.\n ```bash\n bash scripts/download_data.sh\n ```\n\n4. Generate filelists.\n ```bash\n bash scripts/generate_filelists.sh\n ```\n\n5. Start pre-training.\n ```bash\n NUM_GPUS=[NUM] UPDATE_FREQUENCY=[NUM] NUM_CONCAT_BATCHES=[NUM] BF16=[true|false] FP16=[true|false] \\\n bash scripts/pretrain_base.sh\n ```\n Adjust the variables to maintain `NUM_GPUS x NUM_CONCAT_BATCHES x UPDATE_FREQUENCY = 64`.\n For more details, refer to [Adjusting batch size and the number of GPUs](#adjusting-batch-size-and-the-number-of-gpus) and [Adjusting mixed precision](#adjusting-mixed-precision).\n\n For instance:\n ```bash\n # Mixed precision training on 4x A100 40GB\n NUM_GPUS=4 NUM_CONCAT_BATCHES=8 UPDATE_FREQUENCY=2 BF16=true bash scripts/pretrain_base.sh\n ```\n\n6. Start fine-tuning.\n ```bash\n PRETRAINED_MODEL=[PATH] NUM_GPUS=[NUM] UPDATE_FREQUENCY=[NUM] BF16=[true|false] FP16=[true|false] \\\n bash scripts/finetune_base_960h.sh\n ```\n Adjust the variables to maintain `NUM_GPUS x NUM_CONCAT_BATCHES x UPDATE_FREQUENCY = 8`.\n\n7. Start inference/predictions.\n ```bash\n FINETUNED_MODEL=[PATH] BF16=[true|false] FP16=[true|false] BATCH_SIZE=[NUM] bash scripts/inference.sh\n ```\n\nNow that you have your model trained and evaluated, you can choose to compare your training results with our [Training accuracy results](#training-accuracy-results). You can also choose to benchmark your performance to [Training performance benchmark](#training-performance-results) or [Inference performance benchmark](#inference-performance-results). Following the steps in these sections ensures you achieve the same accuracy and performance results as stated in the [Results](#results) section.","hasQuickStartGuide","advanced","The following sections provide greater details of the dataset, running training and inference, and the training results.\n\n### Scripts and sample code\n\nIn the root directory, the most important files are:\n```bash\n.\n|-- common # Generic code for training\n│ |-- fairseq # Parts of https://github.com/facebookresearch/fairseq\n│ |-- ...\n|-- inference.py # Evaluates trained models and measures latency\n|-- scripts\n│ |-- download_wav2vec2_base.sh # Downloads pre-trained models from NGC\n│ |-- finetune_base_960h.sh # Helper script for fine-tuning with train.py\n│ |-- inference.sh # Helper script for inference.py\n│ |-- pretrain_base.sh # Helper script for pre-training with train.py\n│ |-- ...\n|-- train.py # Main pre-training and fine-tuning script\n|-- utils # Misc standalone Python scripts\n|-- wav2vec2 # Code specific to wav2vec 2.0 model\n |-- arg_parser.py\n |-- criterion.py\n |-- logging.py\n |-- model.py\n |-- utils.py\n```\n\n### Parameters\n\nParameters can be set through environment variables.\nThe most important available parameters for `scripts/pretrain_base.sh` script are:\n```bash\nOUTPUT_DIR directory for results, logs, and created checkpoints\n (default: \"./results/pretrain_base\")\nNUM_GPUS number of GPUs to use. (default: 8)\nMAX_TOKENS upper limit for the number of tokens in a batch; changing\n this value alters loss function consts (default: 1400000)\nNUM_CONCAT_BATCHES number of sub-batches, each with MAX_TOKENS tokens,\n to make up one large batch (default: 8)\nUPDATE_FREQ number of grad accumulation steps before the update (default: 1)\nMAX_UPDATE training length expressed as the number of updates (default: 400000)\nLEARNING_RATE peak learning rate (default: 0.0005)\nSEED random seed controlling model weights and data shuffling (default: disabled)\nFP16 enables mixed-precision training with float16 (default: false)\nBF16 enabled mixed-precision training with bfloat16 (default: false)\nDATASET_DIR directory with file lists (default: /datasets/LibriSpeech)\nTRAIN_SUBSET base name of the .tsv file list in the DATASET_DIR (default: \"train-full-960\")\nVALID_SUBSET base name of the validation .tsv file list in the DATASET_DIR (default: \"dev-other\")\nSAVE_FREQUENCY frequency of saving checkpoints to disk (default: 1)\nHOURGLASS_CONFIG configuration of Hourglass Transformer; refer to the section\n below for details (default: \"[2,(8,4),2]\")\n```\n\nIn addition, important parameters for `scripts/finetune_base_960h.sh` script are:\n```bash\nPRETRAINED_MODEL a path to a pre-trained model checkpoint for fine-tuning\n (default: \"./results/pretrain_base/wav2vec2_update400000.pt\")\nFREEZE_FINETUNE_UPDATES freeze wav2vec 2.0 encoder for an initial number of steps and train only\n the output linear projection (default: 0)\n```\n\nBelow we present more details on how to set crucial parameters.\n\n#### Adjusting batch size and the number of GPUs\n\nEvery training recipe assumes a constant world size, and variables need to be adjusted to maintain that world size,\nfor example, `NUM_GPUS x NUM_CONCAT_BATCHES x UPDATE_FREQUENCY = 64` for pre-training of the `Base` model:\n* first, set `NUM_GPUS` to the number of available GPUs,\n* then, adjust `NUM_CONCAT_BATCHES` to a high value that does not cause out-of-memory errors\n* finally, adjust the update frequency that controls gradient accumulation, to maintain the effective world size.\n\n`NUM_CONCAT_BATCHES` controls the number of sub-batches that are forwarded through the model, each with `--max_tokens` tokens.\nIn the case of out-of-memory errors, it has to be lowered. With Hourglass Transformer and mixed-precision training,\nthe model should fit within 12GB of GPU memory on the lowest `NUM_CONCAT_BATCHES=1` setting.\n\n#### Adjusting mixed precision\n\nBy default, the model is trained in TF32 (A100 GPUs) or FP32 (V100 and older GPUs).\nMixed-precision training can be performed in float16 or bfloat16 precisions.\nTraining in bfloat16 is more stable and requires less stabilizing casts to FP32; thus, it is a bit faster.\nIt is supported on the hardware level in NVIDIA Ampere and newer architectures.\nScripts `scripts/pretrain_base.sh` and `scripts/finetune_base_960h.sh` provide env vars\nfor setting appropriate casting flags.\nIn order to benefit from mixed-precision training,\nset either `BF16=true` or `FP16=true`, depending on the architecture of the GPU.\n\n#### Adjusting Hourglass Transformer\n\nThe Hourglass Transformer architecture is configurable by four parameters:\n* the number of initial transformer layers,\n* the number of middle transformer layers that process the downsampled signal,\n* downsampling rate,\n* the number of output transformer layers.\n\nThese are expressed in that exact order by a Python list without whitespace. For instance, the default setting is `HOURGLASS_CONFIG=\"[2,(8,4),2]\"`.\nIt uses 12 layers in total (two initial, eight middle with a downsampling rate 4, and two output layers).\n\nDuring fine-tuning, the same architecture as during pre-training has to be set.\n\n### Command-line options\n\nTo view the full list of available options and their descriptions, use the `-h` or `--help` command-line option, for example:\n`python train.py -h`. Most of the command-line options are a subset of those from the original [Fairseq](https://github.com/facebookresearch/fairseq) wav2vec 2.0 codebase.\n\n### Getting the data\n\nThe wav2vec 2.0 model described in the paper was pre-trained on either the LibriSpeech or LibriVox datasets.\nWe publish recipes for training on pre-training and fine-tuning on the LibriSpeech dataset.\nThe `dev-other` subset is used as a validation dataset, and `test-other` is used as a testing dataset.\n\nThe `./scripts/download_ls_dataset.sh [TARGET_DATA_DIR]` script downloads and extracts the LibriSpeech dataset to the directory of choice,\nby default `/datasets/LibriSpeech` if the argument is omitted.\n\nThe `./scripts/generate_ls_filelists.sh [SOURCE_DATA_DIR] [TARGET_FILELISTS_DIR]` script prepares filelists and collect transcriptions.\nAgain, positional arguments are optional and default to `/datasets/LibriSpeech`.\n\n#### Dataset guidelines\n\nLibriSpeech data is kept at the default sampling rate of 16 kHz.\nThe model works with either `.wav` or `.flac` files. Both are lossless, with `.flac` being more efficient in terms of storage but requiring extra computation during training.\nFiles are listed in `.tsv` filelists. The first row is the top-level directory, and subsequent lines listths to files and a number of samples delimited by `tab`:\n```bash\n/datasets/LibriSpeech/test-other\n367/293981/367-293981-0017.flac\\t46560\n367/293981/367-293981-0009.flac\\t52720\n...\n```\n\nThe `.ltr` files, generated alongside `.tsv` filelists, hold character-level transcriptions for filelists with the same basename.\nFilelists and transcription lists should list samples in matching order.\n```bash\nA N D | A | V E R Y | R E S P E C T A B L E | O N E | S A I D | T H E | I N N K E E P E R |\nT H E | O F F I C E R | T U R N E D | T O | H I M | A N D | S A I D | W E L L | H O W | G O E S | I T | G O O D | M A N |\n...\n```\n\nFinally, generate a `dict.ltr.txt` dictionary using training `.ltr` transcripts:\n```bash\npython utils/generate_dictionary.py /my/dataset/path/train.ltr /my/dataset/path/dict.ltr.txt\n```\n\n#### Multi-dataset\n\nIn order to train on multiple datasets, prepare a filelist and transcription list with all files from those datasets.\nRefer to `scripts/generate_filelists.sh` for an example of concatenating LibriSpeech training filelists.\n\n### Training process\n\nTraining of wav2vec 2.0 is performed in two stages: unsupervised pre-training and supervised fine-tuning. Both are performed with the `train.py` script.\n\n**Pre-training**\nThe `scripts/pretrain_base.sh` script sets command-line arguments for `train.py`\nand runs a job on a single node that trains the wav2vec 2.0 model from scratch.\nKey variables can be conveniently changed via env variables.\n\n**Fine-tuning**\nThe `scripts/finetune_base_960h.sh` script sets command-line arguments for `train.py`\nand runs a job on a single node that fine-tunes a pre-trained wav2vec 2.0 model.\nKey variables can be conveniently changed via env variables.\nNote that a checkpoint trained with Fairseq can be loaded and fine-tuned\nusing this repository.\n\nApart from the arguments as listed in the [Parameters](#parameters) section, by default both training scripts:\n* Run on eight GPUs with at least 80GB of memory with increased batch size, so that gradient accumulation is not necessary\n* Use TF32 precision (A100 GPU) or FP32 (other GPUs)\n* Use Hourglass Transformer architecture with shortening factor of 4\n* Train on 960 hours of LibriSpeech training data and evaluate on the dev-other subset\n* Remove old checkpoints and preserve milestone checkpoints automatically\n* Maintain a separate checkpoint with the lowest WER on the dev set\n* Create a DLLogger log file and a TensorBoard log\n* Set the remaining parameters according to the recipes published with the original paper\n\nThe current training setup recreates WER [Results](#results) published in the original paper,\nwhile significantly lowering the time and memory required for training.\n\n### Inference process\n\nInference is performed using the `inference.py` script along with parameters defined in `scripts/inference.sh`.\nThe `scripts/inference.sh` script runs the job on a single GPU, taking a fine-tuned wav2vec 2.0 model checkpoint\nand running it on the specified dataset.\nApart from the default arguments as listed in the [Parameters](#parameters) section, by default, the inference script:\n* Evaluates on the LibriSpeech test-other dataset and prints out the final word error rate\n* Uses a batch size of 8\n* Creates a log file with progress and results, which will be stored in the `results` folder\n* Does greedy decoding and optionally saves the transcriptions in the results folder\n* Has the option to save the model output tensors for more complex decoding, for example, beam search\n\nTo view all available options for inference, run `python inference.py --help`","hasAdvanced","performance","The performance measurements in this document were conducted at the time of publication and may not reflect the performance\nachieved from NVIDIA's latest software release. For the most up-to-date performance measurements,\ngo to [NVIDIA Data Center Deep Learning Product Performance](https://developer.nvidia.com/deep-learning-performance-training-inference).\n\n### Benchmarking\n\nThe following section shows how to run benchmarks measuring the model performance in training and inference modes.\n\n#### Training performance benchmark\n\nTo benchmark the training performance with a number of specific configurations, run:\n```bash\nNUM_GPUS=[NUM] UPDATE_FREQ=[NUM] NUM_CONCAT_BATCHES=[NUM] NUM_EPOCHS=[NUM] NUM_WARUP_EPOCHS=[NUM] \\\n BF16=[true|false] FP16=[true|false] bash scripts/pretrain_base_benchmark.sh\n\nNUM_GPUS=[NUM] UPDATE_FREQ=[NUM] NUM_CONCAT_BATCHES=[NUM] NUM_EPOCHS=[NUM] NUM_WARUP_EPOCHS=[NUM] \\\n BF16=[true|false] FP16=[true|false] bash scripts/finetune_base_benchmark.sh\n```\nfor example:\n```bash\nNUM_GPUS=8 UPDATE_FREQ=1 NUM_CONCAT_BATCHES=8 BF16=true bash scripts/pretrain_base_benchmark.sh\nNUM_GPUS=8 UPDATE_FREQ=1 NUM_CONCAT_BATCHES=1 BF16=true bash scripts/finetune_base_benchmark.sh\n```\n\nBy default, these scripts run initially for `NUM_WARMUP_EPOCHS=2`, and collect performance results for another `NUM_EPOCHS=5` on the `train-clean-100` subset of LibriSpeech.\n\n#### Inference performance benchmark\n\nTo benchmark the inference performance on a specific batch size, run:\n\n```bash\nNUM_WARMUP_REPEATS=[NUM] NUM_REPEATS=[NUM] BATCH_SIZE=[NUM] BF16=[true|false] FP16=[true|false] \\\n bash scripts/inference_benchmark.sh\n```\nfor example:\n```bash\nNUM_WARMUP_REPEATS=2 NUM_REPEATS=10 BATCH_SIZE=8 BF16=true bash scripts/inference_benchmark.sh\n```\nBy default, the model will process all samples in the `test-other` subset of LibriSpeech initially `NUM_WARMUP_REPEATS` times for warmup,\nand then `NUM_REPEATS` times recording the measurements. The number of iterations will depend on the batch size.\n\n### Results\n\nThe following sections provide details on how we achieved our performance and accuracy in training and inference.\n\n#### Training accuracy results\n\n##### Training accuracy: NVIDIA DGX A100 (8x A100 80GB)\n\nPre-training results were obtained by running the `scripts/pretrain_base.sh` training script in the PyTorch 22.11-py3 NGC container on NVIDIA A100 (8x A100 80GB) GPUs.\nWe report a median of eight (BF16 mixed precision) and three (TF32) runs.\n\n| GPUs | (Concatenated) batch size / GPU | Accuracy - TF32 | Accuracy - mixed precision | Time to train - TF32 | Time to train - mixed precision | Time to train speedup (TF32 to mixed precision) |\n|--------:|--------------------------------:|-----------------:|----------------------------:|----------------------:|---------------------------------:|------------------------------------------------:|\n| 8 | 8 x 1400k max tokens | 0.619 | 0.633 | 64.9 h | 48.1 h | 1.35 |\n\n

\n $\"Accuracy$ \n

\n\nFine-tuning results were obtained by running the `scripts/finetune_base_960h.sh` training script in the PyTorch 22.11-py3 NGC container on NVIDIA A100 (8x A100 80GB) GPUs.\nWe report a median of eight runs; each resumed from a different pre-training checkpoint.\n\n| GPUs | (Concatenated) batch size / GPU | WER - mixed precision | Time to train - TF32 | Time to train - mid precision | Time to train speedup (TF32 to mixed precision) |\n|--------:|--------------------------------:|-----------------------:|----------------------:|---------------------------------:|------------------------------------------------:|\n| 8 | 1 x 3200k max tokens | 8.878 | 8.2 h | 6.5 h | 1.27 |\n\n

\n $\"Word$ \n

\n\n##### Training stability test\n\nThe wav2vec 2.0 Base model was pre-trained with eight different initial random seeds in bfloat16 precision in the PyTorch 22.11-py3 NGC container on NVIDIA DGX A100 with 8x A100 80GB.\n\nBelow we present accuracy of this model in the self-training task:\n\n| Update | Average | Std | Min | Max | Median |\n|---------:|----------:|------:|------:|------:|---------:|\n| 50k | 0.491 | 0.011 | 0.471 | 0.514 | 0.493 |\n| 100k | 0.537 | 0.009 | 0.518 | 0.550 | 0.539 |\n| 150k | 0.564 | 0.009 | 0.544 | 0.577 | 0.564 |\n| 200k | 0.580 | 0.009 | 0.558 | 0.589 | 0.583 |\n| 250k | 0.599 | 0.008 | 0.586 | 0.607 | 0.602 |\n| 300k | 0.610 | 0.010 | 0.589 | 0.622 | 0.611 |\n| 350k | 0.619 | 0.009 | 0.607 | 0.634 | 0.617 |\n| 400k | 0.629 | 0.007 | 0.614 | 0.636 | 0.633 |\n\nAfterward, each of those runs was fine-tuned on LibriSpeech 960 h dataset with yet another different initial random seed.\nBelow we present the word error rate (WER) on the `dev-other` subset of LibriSpeech:\n\n| Update | Average | Std | Min | Max | Median |\n|---------:|----------:|------:|-------:|-------:|---------:|\n| 50k | 11.198 | 0.303 | 10.564 | 11.628 | 11.234 |\n| 100k | 10.825 | 0.214 | 10.574 | 11.211 | 10.763 |\n| 150k | 10.507 | 0.160 | 10.224 | 10.778 | 10.518 |\n| 200k | 9.567 | 0.186 | 9.235 | 9.836 | 9.530 |\n| 250k | 9.115 | 0.193 | 8.764 | 9.339 | 9.194 |\n| 300k | 8.885 | 0.201 | 8.507 | 9.151 | 8.972 |\n| 320k | 8.827 | 0.188 | 8.440 | 9.043 | 8.878 |\n\n#### Training performance results\n\n##### Training performance: NVIDIA DGX A100 (8x A100 80GB)\n\n**Pre-training**\n\nOur results were obtained by running the `scripts/pretrain_base_benchmark.sh` training script in the PyTorch 22.11-py3 NGC container on NVIDIA A100 (8x A100 80GB) GPUs. Performance numbers in transformer tokens per second were averaged over an entire training epoch.\n\n| GPUs | Concat batches / GPU | Grad accumulation | Throughput - TF32 | Throughput - mixed precision | Throughput speedup (TF32 to mixed precision) | Strong scaling - TF32 | Strong scaling - mixed precision |\n|-------:|-----------------------:|--------------------:|--------------------:|-------------------------------:|-----------------------------------------------:|------------------------:|-----------------------------------:|\n| 1 | 8 | 8 | 28045.27 | 37609.84 | 1.34 | 1.00 | 1.00 |\n| 4 | 8 | 2 | 103842.47 | 138956.38 | 1.34 | 3.70 | 3.69 |\n| 8 | 8 | 1 | 194306.46 | 261881.29 | 1.35 | 6.93 | 6.96 |\n\nTo achieve these same results, follow the steps in the [Quick Start Guide](#quick-start-guide).\n\n**Fine-tuning**\n\nOur results were obtained by running the `scripts/finetune_base_benchmark.sh` training script in the PyTorch 22.11-py3 NGC container on NVIDIA A100 (8x A100 80GB) GPUs. Performance numbers in transformer tokens per second were averaged over an entire training epoch.\n\n| GPUs | Concat batches / GPU | Grad accumulation | Throughput - TF32 | Throughput - mixed precision | Throughput speedup (TF32 to mixed precision) | Strong scaling - TF32 | Strong scaling - mixed precision |\n|-------:|-------------------:|--------------------:|--------------------:|-------------------------------:|-----------------------------------------------:|------------------------:|-----------------------------------:|\n| 1 | 8 | 1 | 34813.46 | 41275.76 | 1.19 | 1.00 | 1.00 |\n| 4 | 2 | 1 | 102326.57 | 132361.62 | 1.29 | 2.94 | 3.21 |\n| 8 | 1 | 1 | 163610.16 | 207200.91 | 1.27 | 4.70 | 5.02 |\n\nTo achieve these same results, follow the steps in the [Quick Start Guide](#quick-start-guide).\n\n#### Inference performance results\n\n##### Inference performance: NVIDIA DGX A100 (1x A100 80GB)\n\nOur results were obtained by running the `scripts/inference_benchmark.sh` inferencing benchmarking script in the PyTorch 22.11-py3 NGC container on the NVIDIA A100 (1x A100 80GB) GPU.\nThe script runs inference on the `test-other` subset of LibriSpeech in variable-length batches.\n\n| | Duration | BF16 Latency (ms) Percentiles | | | | TF32 Latency (ms) Percentiles | | | | BF16/TF32 speedup |\n|-----:|-------:|------:|-------:|-------:|------:|-------:|-------:|-------:|------:|------:|\n| BS | Avg | 90% | 95% | 99% | Avg | 90% | 95% | 99% | Avg | Avg |\n| 1 | 6.54 s | 11.02 | 11.41 | 12.42 | 10.45 | 10.88 | 11.23 | 12.51 | 10.31 | 0.99 |\n| 4 | 6.54 s | 21.74 | 24.12 | 35.80 | 17.69 | 23.17 | 26.85 | 41.62 | 18.42 | 1.04 |\n| 8 | 6.54 s | 40.06 | 48.07 | 74.59 | 28.70 | 46.43 | 54.86 | 88.73 | 31.30 | 1.09 |\n| 16 | 6.54 s | 88.78 | 117.40 | 151.37 | 58.82 | 102.64 | 135.92 | 175.68 | 67.44 | 1.15 |\n\nTo achieve these same results, follow the steps in the [Quick Start Guide](#quick-start-guide).","hasPerformance",[],{"_85":182,"_112":234,"_113":-5,"_121":34,"_120":34,"_122":233,"_217":90},{"_85":28,"_112":28,"_113":-5,"_121":34,"_120":34,"_122":233,"_217":90},[],"Latest (22.11.0)","nvidia/dle/wav2vec2_pyt","key","nvidia/dle/resources/wav2vec2_pyt","resourceId","wav2vec2_pyt","shortDescription","A framework for self-supervised learning of speech representations which masks latent representations of the raw waveform and solves a contrastive task over quantized speech representations.","This resource is using open-source code maintained in github (see the quick-start-guide section) and available for download from [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/wav2vec2-pyt)\n\n\nThis repository provides an optimized implementation of the wav2vec 2.0 model, as described in the paper [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://proceedings.neurips.cc/paper/2020/file/92d1e1eb1cd6f9fba3227870bb6d7f07-Paper.pdf). It is based on the [Fairseq codebase](https://github.com/facebookresearch/fairseq) published by the authors of the paper. The wav2vec 2.0 model is pre-trained unsupervised on large corpora of speech recordings. Afterward, it can be quickly fine-tuned in a supervised way for speech recognition or serve as an extractor of high-level features and pseudo-phonemes for other applications.\n\nThe differences between this wav2vec 2.0 and the reference implementation are:\n* Support for increased batch size, which does not change batch-dependent constants for negative sampling and loss calculation and improves hardware utilization\n* Support for the [Hourglass Transformer](https://arxiv.org/abs/2110.13711) architecture, which in the default setting improves the training speed of the `Base` model by 1.4x, lowers memory consumption by 38%, and retains accuracy\n\nThis model is trained with mixed precision using Tensor Cores on NVIDIA Volta, NVIDIA Turning, and the NVIDIA Ampere GPU architectures. Therefore, researchers can get results up to 1.35x faster than training without Tensor Cores while experiencing the benefits of mixed precision training. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.\n\n### Model architecture\n\nThe model takes raw waveforms as its input. A fully convolutional feature extractor reduces the resolution of the signal to a single vector roughly every 20 ms. Most of the computation is performed in the transformer encoder part of the model. The outputs of the transformer, and quantized outputs from the feature extractor, serve as inputs to the contrastive loss. During fine-tuning, this loss is replaced with the CTC loss, and quantization is not performed.\n\n

\n $\"wav2vec$ \n

\n\nIn addition, our model uses the Hourglass Transformer architecture for the encoder. This architecture uses fixed-sized pooling in order to reduce the time dimension _T_ of the signal, and thus, lower the _O(T²)_ cost of the self-attention mechanism.\n\n

\n $\"The$ \n

\n\n### Default configuration\n\nThe following features were implemented in this model:\n- general:\n - multi-GPU and multi-node training\n - Hourglass Transformer architecture\n - dynamic loss scaling with backoff for tensor cores (mixed precision) training\n - mixed-precision training with `O2` optimization level, based on float16 or bfloat16\n- training:\n - support for variable batch size without changing batch-dependent constants for the loss function\n- inference:\n - masking for inference with a larger batch\n\nOur main recipes replicate the `Base` model described in the wav2vec 2.0 paper, and use Hourglass Transformer with pooling factor 4. Note that Hourglass Transformer can be entirely disabled and this codebase is compatible with Fairseq checkpoints.\n\nBelow we present performance numbers for the Hourglass Transformer with different pooling factors (`Base` model, pre-training, A100 80GB GPU, bfloat16):\n\n| Configuration | Throughput speedup | GPU memory (% of Baseline) |\n|:-------------------|--------------------:|----------------:|\n| Baseline | 1.00 | 100.00% |\n| Hourglass factor=2 | 1.25 | 70.98% |\n| Hourglass factor=3 | 1.33 | 64.31% |\n| **Hourglass factor=4 (default)** | **1.37** | **62.35%** |\n| Hourglass factor=5 | 1.39 | 60.00% |\n| Hourglass factor=6 | 1.40 | 59.61% |\n\n### Feature support matrix\n\nThis model supports the following features:\n\n| Feature | wav2vec 2.0 |\n|---------------------------------|-------------|\n| Multi-node training | yes |\n| Automatic mixed precision (AMP) | yes |\n\n#### Features\n\n**Automatic Mixed Precision (AMP)**\nThis implementation uses automatic mixed-precision training ported from Fairseq.\nIt allows us to use FP16 or BF16 training with FP16 master weights.\n\n### Mixed precision training\n\nMixed precision is the combined use of different numerical precisions in a computational method. [Mixed precision](https://arxiv.org/abs/1710.03740) training offers significant computational speedup by performing operations in half-precision format while storing minimal information in single-precision to retain as much information as possible in critical parts of the network. Since the introduction of [Tensor Cores](https://developer.nvidia.com/tensor-cores) in NVIDIA Volta, and following with both the NVIDIA Turing and Ampere architectures, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures. Using [mixed precision training](https://docs.nvidia.com/deeplearning/performance/mixed-precision-training/index.html) previously required two steps:\n1. Porting the model to use the FP16 data type where appropriate.\n2. Adding loss scaling to preserve small gradient values.\n\nFor information about:\n- How to train using mixed precision, refer to the [Mixed Precision Training](https://arxiv.org/abs/1710.03740) paper and [Training With Mixed Precision](https://docs.nvidia.com/deeplearning/performance/mixed-precision-training/index.html) documentation.\n- Techniques used for mixed precision training, refer to the [Mixed-Precision Training of Deep Neural Networks](https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/) blog.\n\n#### Enabling mixed precision\n\nFor training and inference, mixed precision can be enabled by adding the `--fp16` flag or `--bf16` flag, depending on the target's lower precision. NVIDIA Ampere and later architectures provide hardware support for bfloat16, which is beneficial for this model, as it skips certain stabilizing FP32 casts. For NVIDIA Volta and NVIDIA Turing architectures, select `--fp16`.\n\n#### Enabling TF32\n\nTensorFloat-32 (TF32) is the new math mode in [NVIDIA A100](https://www.nvidia.com/en-us/data-center/a100/) GPUs for handling the matrix math, also called tensor operations. TF32 running on Tensor Cores in A100 GPUs can provide up to 10x speedups compared to single-precision floating-point math (FP32) on NVIDIA Volta GPUs.\n\nTF32 Tensor Cores can speed up networks using FP32, typically with no loss of accuracy. It is more robust than FP16 for models which require a high dynamic range for weights or activations.\n\nFor more information, refer to the [TensorFloat-32 in the A100 GPU Accelerates AI Training, HPC up to 20x](https://blogs.nvidia.com/blog/2020/05/14/tensorfloat-32-precision-format/) blog post.\n\nTF32 is supported in the NVIDIA Ampere GPU architecture and is enabled by default.\n\n### Glossary\n\n**Brain Floating Point (bfloat16)**\nA 16-bit floating point format that uses an 8-bit exponent, a 7-bit fraction, and a sign bit.\nContrary to float16, which uses a 5-bit exponent, bfloat16 retains the same exponent precision as float32,\nand its robustness with respect to wide ranges of values during training.\n\n**Fine-tuning**\nTraining an already pretrained model further using a task-specific dataset for subject-specific refinements by adding task-specific layers on top if required.\n\n**Hourglass Transformer**\nArchitecture proposed in the paper [Hierarchical Transformers Are More Efficient Language Models](https://arxiv.org/abs/2110.13711), which improves resource consumption\nof a stack of transformer layers, in many cases retaining the accuracy.\n\n**Pre-training**\nTraining a model on vast amounts of data on the same (or different) task to build general understandings.\n\n**Transformer**\nThe paper [Attention Is All You Need](https://arxiv.org/abs/1706.03762) introduces a novel architecture called transformer that uses an attention mechanism and transforms one sequence into another.\n\n**Connectionist Temporal Classification (CTC) Loss**\nA loss function introduced in [Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks](https://www.cs.toronto.edu/~graves/icml_2006.pdf). It calculates the probability of all valid output sequences with repetitions, and allows to train end-to-end ASR models without any prior alignments of transcriptions to audio.","displayName","wav2vec 2.0 for PyTorch",["D",1680632354832],"publisher","NVIDIA Deep Learning Examples","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/dle/resources/wav2vec2_pyt","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[274],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_243":275,"_276":277,"_278":279,"_280":87},"Automatic Speech Recognition","value","uscs_automatic_speech_recognition","filter",{"_281":282,"_276":277},"category","field","application",{"_151":1458,"_153":1459},"ngcApps",[1411],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1392":1393,"_1394":1395,"_1396":1397,"_1398":1399,"_1400":294,"_109":318,"_1401":1402,"_1403":59,"_1404":90,"_1405":90,"_1406":1407,"_1408":90,"_1409":1410},"amplitude",{"_1389":1390,"_992":-7,"_1391":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1383":1384,"_1385":1386,"_1387":1388},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1267":399,"_1268":1269,"_1270":1271,"_1272":1273},"announcements",[],"consentState",{"_356":1257,"_1258":90,"_1259":34,"_1260":34,"_1261":1262},"consentConfig",{"_338":339,"_340":341,"_342":343,"_344":345,"_346":347,"_348":349,"_350":351,"_352":353,"_354":355,"_356":357},"showContactAdmin",1783974178520,[],[],[332,333,334,335,336],{"_48":63,"_50":73,"_52":146,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":70,"_52":146,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":64,"_52":146,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":146,"_54":-5,"_56":-5,"_58":59},{"_48":49,"_50":51,"_52":53,"_54":55,"_56":337,"_58":59},6,"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","nv_co","__uxq412__ses.88c1","investor.nvidia.com","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\n","This cookie name is used by DataDog to determine if cookies are supported on the website.dd_cookie_test","AMP_TLDTEST_xxxxxxxx","SESSlsd","This cookie is likely to be a session identifier used to maintain user session state across page requests.","mboxEdgeCluster","This cookie name is associated with Adobe Target which is used to communicate the correct edge cluster to at.js so that Target profiles can stay in sync as users navigate across a site.","_biz_pendingA","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","megaNavCollapsed","_abck","The _abck cookie is associated with Akamai's Bot Manager, which is used to prevent fraud and abuse by safeguarding online interactions and detecting irregularities that could suggest malicious activity, such as bot-driven attacks or hacking attempts.","AMP_TEST","This cookie name AMP_TEST is used for internal analytics by the website operator, necessary for the functionality of the website's chat-box function, and to check whether the cookie storage is working properly.","idccsrf","This cookie name is associated with Salesforce which is used to track CrossSiteRequestForgery validation for certain single sign-on (SSO) flows.","_biz_flagsA","This cookie is used to remember users' settings as well as for authentication and analytics.","ApplicationGatewayAffinity","partners.nvidia.com","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure the user has a consistent experience.","AMP_TEST_xxxxxxxx","bm_lso","This cookie name is associated with Akamai which is used to load the site as quickly as possible and to recognize bots.","SSESS###########","This cookie is likely to be a session cookie used to maintain an anonymized user session by the server in a secure environment, often seen in sites built with the Drupal content management system.","nvdeveloper.nvidia.com","__uxq412__id.88c1","QueueITAccepted-SDFrts345E-V3_{eventId}","TEST_AMCV_COOKIE_WRITE","This cookie is used to test whether cookies are enabled in the browser. It is a temporary cookie used to determine if the browser accepts cookies.","session","This cookie is typically used to maintain an anonymized user session by the server.","XSRF-TOKEN","This cookie is written to help with site security in preventing Cross-Site Request Forgery attacks.This cookie helps with site security in preventing Cross-Site Request Forgery attacks.","AWS ELB application load balancer","This cookie is associated with Akamai. It is used to analyse traffic to determine if it is automated traffic generated by bots or a human user.","bpazaws52gukakzc__ctrl0_ctl78_list_ctl09_systemcaptcha","PHPSESSID","PHP session cookie associated with embedded content from this domain.\n","base.manager.learn.nvidia.com","login_session","bm_ss","This cookie is used to help protect websites from automated bot traffic.","AWS ELB application load balancerUsed to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","_cs_c","The cookie is used by Content Square to save the user consent to be tracked.","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","ASP.NET_SessionId","General purpose platform session cookie, used by sites written with Microsoft .NET based technologies. 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