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 a PyTorch implementation of the HiFi-GAN model described in the paper HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis.\nThe HiFi-GAN model implements a spectrogram inversion model that allows to synthesize speech waveforms from mel-spectrograms. It follows the generative adversarial network (GAN) paradigm, and is composed of a generator and a discriminator. After training, the generator is used for synthesis, and the discriminator is discarded.

Our implementation is based on the one published by the authors of the paper. We modify the original hyperparameters and provide an alternative training recipe, which enables training on larger batches and faster convergence. HiFi-GAN is trained on a publicly available LJ Speech dataset. The samples demonstrate speech synthesized with our publicly available FastPitch and HiFi-GAN checkpoints.

This model is trained with mixed precision using Tensor Cores on NVIDIA Volta and the NVIDIA Ampere GPU architectures and evaluated on Volta, Turing and the NVIDIA Ampere GPU architectures. Therefore, researchers can get results up to 2.5x 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 entire model is composed of a generator and two discriminators. Both discriminators can be further divided into smaller sub-networks, that work at different resolutions.\nThe loss functions take as inputs intermediate feature maps and outputs of those sub-networks.\nAfter training, the generator is used for synthesis, and the discriminators are discarded.\nAll three components are convolutional networks with different architectures.

\n $\"HiFi-GAN$ \n

\n Figure 1. The architecture of HiFi-GAN\n

Default configuration

The following features were implemented in this model:

data-parallel multi-GPU training,
training and inference with mixed precision using Tensor Cores,
gradient accumulation for reproducible results regardless of the number of GPUs.

The training recipe we provide for the model, recreates the v1 model from the HiFi-GAN paper,\nwhich is the largest and has the highest quality of all models described in the paper.\nMixed precision training and memory optimizations allowed us to increase batch size and throughput significantly.\nIn effect, we modify some hyperparameters of the v1 recipe.

Feature support matrix

The following features are supported by this model.

\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	HiFi-GAN
Automatic mixed precision (AMP)	Yes
Distributed data parallel (DDP)	Yes

Features

Automatic Mixed Precision (AMP)\nThis implementation uses native PyTorch AMP\nimplementation of mixed precision training. It allows us to use FP16 training\nwith FP32 master weights by modifying just a few lines of code.

DistributedDataParallel (DDP)\nThe model uses PyTorch Lightning implementation\nof distributed data parallelism at the module level which can run across\nmultiple machines.

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 Volta, and following with both the 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 --amp flag.\nMixed precision is using native PyTorch implementation.

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 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 that 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

Multi-Period Discriminator\nA sub-network that extracts patterns from the data that occur periodically (for example, every N time steps).

Multi-Scale Discriminator\nA sub-network that extracts patterns from the data at different resolutions of the input signal. Lower resolutions are obtained by average-pooling of the signal.

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.

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For the most up-to-date performance measurements, go to https://developer.nvidia.com/deep-learning-performance-training-inference.\n\n### Changelog\n\nFebruary 2022\n- Initial release\n\n### Known issues\n- With mixed-precision training on Ampere GPUs, the model might suffer from slower training. Be sure to use the scripts provided in the platform/ directory, and a PyTorch NGC container not older than 21.12-py3.\n- For some mel-spectrogram generator models, the best results require fine-tuning of HiFi-GAN on outputs from those models.\nFor more details, refer to the fine-tuning step of the [Quick Start Guide](#quick-start-guide) section.","hasReleaseNotes","setup","The following section lists the requirements you need to meet in order to start training the HiFi-GAN model.\n\n### Requirements\n\nThis repository contains a Dockerfile that extends the PyTorch 21.12-py3 NGC container and encapsulates some dependencies. Aside from these dependencies, ensure you have the following components:\n- [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker)\n- [PyTorch 21.12-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 HiFi-GAN model on the LJSpeech 1.1 dataset. For the specifics concerning training and inference, refer to the [Advanced](#advanced) section. Pre-trained HiFi-GAN models are available for download on [NGC](https://ngc.nvidia.com/catalog/models?query=HiFi-GAN&quickFilter=models).\n\n1. Clone the repository.\n ```bash\n git clone https://github.com/NVIDIA/DeepLearningExamples.git\n cd DeepLearningExamples/PyTorch/SpeechSynthesis/HiFiGAN\n ```\n\n2. Build and run the HiFi-GAN PyTorch NGC container.\n\n By default the container uses all available GPUs.\n ```bash\n bash scripts/docker/build.sh\n bash scripts/docker/interactive.sh\n ```\n\n3. Download and preprocess the dataset.\n\n ```bash\n bash scripts/download_dataset.sh\n bash scripts/prepare_dataset.sh\n ```\n\n The data is downloaded to the `./data/LJSpeech-1.1` directory (on the host). The\n `./data/LJSpeech-1.1` directory is mounted under the `/workspace/hifigan/data/LJSpeech-1.1`\n location in the NGC container. The complete dataset has the following structure:\n ```bash\n ./data/LJSpeech-1.1\n |-- mels # Mel-spectrograms generated with the `prepare_dataset.sh` script\n |-- metadata.csv # Mapping of waveforms to utterances\n |-- README\n |-- wavs # Raw waveforms\n ```\n\n Apart from generating mel-spectrograms in `data/LJSpeech-1.1/mels` directory, the `scripts/prepare_dataset.sh` script additionally generates LJSpeech-1.1 relevant split metadata files in `data/filelists` directory.\n\n4. Start training.\n ```bash\n NUM_GPUS=8 BATCH_SIZE=16 GRAD_ACCUMULATION=1 bash scripts/train_lj22khz.sh\n ```\n The training produces a HiFi-GAN model capable of generating waveforms from mel-spectrograms.\n It is serialized as a single `.pt` checkpoint file, along with a series of intermediate checkpoints.\n The script is configured for 8x GPU with at least 16GB of memory.\n\n To train with 1x GPU, run:\n ```\n NUM_GPUS=1 BATCH_SIZE=16 GRAD_ACCUMULATION=8 bash scripts/train_lj22khz.sh\n ```\n\n When training with AMP on Ampere GPU architectures, use an additional `--no_amp_grouped_conv` flag to speed up the training:\n ```bash\n AMP=true NUM_GPUS=8 BATCH_SIZE=16 GRAD_ACCUMULATION=1 bash scripts/train_lj22khz.sh --no_amp_grouped_conv\n ```\n The flag will disable mixed-precision training on selected layers. For more details refer to [Known issues](#known-issues).\n\n Consult [Training process](#training-process) and [example configs](#training-performance-benchmark) to adjust to a different configuration or enable Automatic Mixed Precision.\n\n5. (optionally) Fine-tune the model.\n\n Some mel-spectrogram generators are prone to model bias. As the spectrograms differ from the true data on which HiFi-GAN was trained, the quality of the generated audio might suffer. In order to overcome this problem, a HiFi-GAN model can be fine-tuned on the outputs of a particular mel-spectrogram generator in order to adapt to this bias.\n In this section we discuss fine-tuning to [FastPitch](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/SpeechSynthesis/FastPitch) outputs.\n\n Obtain a model for which HiFi-GAN will be fine-tuned. If the FastPitch model was trained using phonemes, additionally download the CMU Dictionary.\n ```bash\n bash scripts/download_models.sh fastpitch\n bash scripts/download_cmudict.sh\n ```\n\n Generate mel-spectrograms for all utterances in the dataset with the FastPitch model:\n ```bash\n bash scripts/extract_fine_tune_mels.sh\n ```\n\n Mel-spectrograms should now be prepared in the `data/mels-fastpitch-ljs22khz` directory. The fine-tuning script will load an existing HiFi-GAN model and run several epochs of training using spectrograms generated in the last step.\n ```bash\n bash scripts/fine_tune.sh\n ```\n This step will produce another `.pt` HiFi-GAN model checkpoint file fine-tuned to the particular FastPitch model.\n\n5. Start validation/evaluation.\n\n Ensure your training loss values are comparable to those listed in the table in the\n [Results](#results) section. Note the validation loss is evaluated with ground truth durations for letters (not the predicted ones). The loss values are stored in the `./output/nvlog.json` log file, `./output/{train,val,test}` as TensorBoard logs, and printed to the standard output (`stdout`) during training.\n The main reported loss is a weighted sum of losses for mel-, pitch-, and duration- predicting modules.\n\n7. Start inference.\n\n The audio can be synthesized either:\n - from ground truth mel-spectrograms, as a means of assessing the quality of HiFi-GAN, or\n - from an output of a mel-spectrogram generator model like FastPitch or Tacotron 2, as a full text-to-speech pipeline.\n We descibe both scenarios.\n\n **Sythesizing audio from ground truth mel-spectrograms**\n In order to perform inference, pass a `.pt` checkpoint with the `--hifigan` argument to the inference script:\n ```bash\n python inference.py --cuda \\\n --hifigan pretrained_models/hifigan/ \\\n -i phrases/devset10.tsv \\\n -o output/wavs_devset10\n ```\n\n The speech is generated from a file passed with the `-i` argument, with one utterance per line:\n ```bash\n `|`\n ```\n\n **Synthesizing audio from raw text with mel-spectrogram generator**\n The current implementation allows for convenient inference with the FastPitch model. A pre-trained FastPitch model can be downloaded with the `scripts/download_model.sh fastpitch` script. Alternatively, to train FastPitch or Tacotron 2 from scratch, follow the instructions in [NVIDIA/DeepLearningExamples/FastPitch](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/SpeechSynthesis/FastPitch) or [NVIDIA/DeepLearningExamples/Tacotron2](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/SpeechSynthesis/Tacotron2).\n\n Begin with downloading the model and the pronunciation dictionary:\n ```bash\n bash scripts/download_models.sh fastpitch\n bash scripts/download_cmudict.sh\n ```\n To run inference on FastPitch outputs, pass an additional `.pt` checkpoint with the `--fastpitch` flag:\n ```bash\n python inference.py --cuda \\\n --hifigan pretrained_models/hifigan/ \\\n --fastpitch pretrained_models/fastpitch/ \\\n -i phrases/devset10.tsv \\\n -o output/wavs_devset10\n ```\n\nTo run inference in mixed precision, use the `--amp` flag. The output audio will\nbe stored in the path specified by the `-o` argument. Consult the `inference.py` to learn more options, such as setting the batch size.\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 the [Training performance benchmark](#training-performance-results), or the [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.\n\n The audio can be generated by following the [Inference process](#inference-process) section below.\n The synthesized audio should be similar to the samples in the `./audio` directory.","hasQuickStartGuide","advanced","The following sections provide greater details of the dataset, running training and inference, and the training results.\n\n### Parameters\n\nIn this section, we list the most important hyperparameters and command-line arguments,\ntogether with their default values that are used to train HiFi-GAN.\n\n| Flag | Description |\n|:----------------------|:------------------------------------------------------------------------------------|\n| `--epochs` | number of epochs (default: 1000) |\n| `--learning_rate` | learning rate (default: 0.1) |\n| `--batch_size` | actual batch size for a single forward-backward step (default: 16) |\n| `--grad_accumulation` | number of forward-backward steps over which gradients are accumulated (default: 1) |\n| `--amp` | use mixed precision training (default: disabled) |\n\n### Command-line options\n\nTo see the full list of available options and their descriptions, use the `-h` or `--help` command-line option, for example:\n`python train.py -h`.\n\n### Getting the data\n\nThe `./scripts/download_dataset.sh` script will automatically download and extract the dataset to the `./data/LJSpeech-1.1` directory.\nThe `./scripts/prepare_dataset.sh` script will preprocess the dataset by generating split filelists in `./data/filelists` directory and extracting mel-spectrograms into the `./data/LJSpeech-1.1/mels` directory. Data preparation for LJSpeech-1.1 takes around 3 hours on a CPU.\n\n#### Dataset guidelines\n\nThe LJSpeech dataset has 13,100 clips that amount to about 24 hours of speech of a single, female speaker. Since the original dataset does not define a train/dev/test split of the data, we provide a split in the form of three file lists:\n```bash\n./data/filelists\n|-- ljs_audio_train_v3.txt\n|-- ljs_audio_test.txt\n|-- ljs_audio_val.txt\n```\n\nThese files are generated during `./scripts/prepare_dataset.sh` script execution.\n\n#### Multi-dataset\n\nFollow these steps to use datasets different from the default LJSpeech dataset.\n\n1. Prepare a directory with .wav files.\n ```bash\n ./data/my_dataset\n |-- wavs\n ```\n\n2. Prepare filelists with paths to .wav files. They define training/validation split of the data (test is currently unused, but it's a good practice to create it for the final evaluation):\n ```bash\n ./data/filelists\n |-- my-dataset_audio_train.txt\n |-- my-dataset_audio_val.txt\n ```\n Those filelists should list a single wavefile per line as:\n ```bash\n path/to/file001.wav\n path/to/file002.wav\n ...\n ```\n Those paths should be relative to the path provided by the `--dataset-path` option of `train.py`.\n\n3. (Optional) Prepare file lists with paths to pre-calculated pitch when doing fine-tuning:\n ```bash\n ./data/filelists\n |-- my-dataset_audio_pitch_text_train.txt\n |-- my-dataset_audio_pitch_text_val.txt\n ```\n\nIn order to use the prepared dataset, pass the following to the `train.py` script:\n ```bash\n --dataset-path ./data/my_dataset` \\\n --training-files ./data/filelists/my-dataset_audio_text_train.txt \\\n --validation files ./data/filelists/my-dataset_audio_text_val.txt\n ```\n\n### Training process\n\nHiFi-GAN is trained to generate waveforms from input mel-spectrograms. During training and validation, the network processes small, random chunks of the input of fixed length.\n\nThe training can be started with `scripts/train.sh` script. Output models, DLLogger logs and TensorBoard logs will be saved in the `output/` directory.\n\nThe following example output is printed when running the model:\n\n```bash\nDLL 2021-06-30 10:58:05.828323 - epoch 1 | iter 1/24 | d loss 7.966 | g loss 95.839 | mel loss 87.291 | 3092.31 frames/s | took 13.25 s | g lr 3.00e-04 | d lr 3.00e-04\nDLL 2021-06-30 10:58:06.999175 - epoch 1 | iter 2/24 | d loss 7.957 | g loss 96.151 | mel loss 87.627 | 35109.29 frames/s | took 1.17 s | g lr 3.00e-04 | d lr 3.00e-04\nDLL 2021-06-30 10:58:07.945764 - epoch 1 | iter 3/24 | d loss 7.956 | g loss 93.872 | mel loss 88.154 | 43443.33 frames/s | took 0.94 s | g lr 3.00e-04 | d lr 3.00e-04\n```\n\nPerformance is reported in total input mel-spectrogram frames per second and recorded as `train_frames/s` (after each iteration) and `avg_train_frames/s` (averaged over epoch) in the output log file `./output/nvlog.json`.\nThe result is averaged over an entire training epoch and summed over all GPUs that were\nincluded in the training. The metrics are averaged in such a way, that gradient accumulation steps would be transparent to the user.\n\nThe `scripts/train.sh` script is configured for 8x GPU with at least 16GB of memory.\nIn a single accumulated step, there are `batch_size x grad_accumulation x GPUs = 16 x 1 x 8 = 128` examples being processed in parallel. With a smaller number of GPUs, increase gradient accumulation steps to keep the relation satisfied, e.g., through env variables\n```bash\nNUM_GPUS=1 GRAD_ACCUMULATION=8 BATCH_SIZE=16 bash scripts/train.sh\n```\n\nThe script also enables automatic mixed precision training. To train with mixed precision, specify the `AMP` variable\n```bash\nAMP=true bash scripts/train.sh\n```\n\n### Inference process\n\nYou can run inference using the `./inference.py` script. This script takes\nmel-spectrograms as input and runs HiFi-GAN inference to produce audio files.\n\nPre-trained HiFi-GAN models are available for download on [NGC](https://ngc.nvidia.com/catalog/models?query=HiFi-GAN&quickFilter=models).\nThe latest model can be downloaded with:\n```bash\nscripts/download_model.sh hifigan\n```\n\nHaving pre-trained models in place, extract validation mel-spectrograms from the LJSpeech-1.1 test-set, and run inference with:\n```bash\nbash scripts/inference_example.sh\n```\nExamine the `inference_example.sh` script to adjust paths to pre-trained models,\nand call `python inference.py --help` to learn all available options.\nBy default, synthesized audio samples are saved in `./output/audio_*` folders.","hasAdvanced","performance","### Benchmarking\n\nThe following section shows how to run benchmarks measuring the model\nperformance in training and inference mode.\n\n#### Training performance benchmark\n\nTo benchmark the training performance on a specific batch size, run:\n\n* NVIDIA DGX A100 (8x A100 80GB)\n ```bash\n AMP=true NUM_GPUS=1 BS=64 GRAD_ACCUMULATION=8 EPOCHS=10 bash scripts/train.sh\n AMP=true NUM_GPUS=8 BS=64 GRAD_ACCUMULATION=1 EPOCHS=10 bash scripts/train.sh\n NUM_GPUS=1 BS=64 GRAD_ACCUMULATION=8 EPOCHS=10 bash scripts/train.sh\n NUM_GPUS=8 BS=64 GRAD_ACCUMULATION=1 EPOCHS=10 bash scripts/train.sh\n ```\n\n* NVIDIA DGX-1 (8x V100 16GB)\n ```bash\n AMP=true NUM_GPUS=1 BS=64 GRAD_ACCUMULATION=8 EPOCHS=10 bash scripts/train.sh\n AMP=true NUM_GPUS=8 BS=64 GRAD_ACCUMULATION=1 EPOCHS=10 bash scripts/train.sh\n NUM_GPUS=1 BS=64 GRAD_ACCUMULATION=8 EPOCHS=10 bash scripts/train.sh\n NUM_GPUS=8 BS=64 GRAD_ACCUMULATION=1 EPOCHS=10 bash scripts/train.sh\n ```\n\nEach of these scripts runs for 10 epochs and measures the\naverage number of items per second for each epoch. The performance results can be read from\nthe `nvlog.json` files produced by the commands.\n\n#### Inference performance benchmark\n\nTo benchmark the inference performance on a specific batch size, run:\n\n* For FP16\n ```bash\n AMP=true BATCH_SIZE=1 REPEATS=100 bash scripts/inference_benchmark.sh\n ```\n\n* For FP32 or TF32\n ```bash\n BATCH_SIZE=1 REPEATS=100 bash scripts/inference_benchmark.sh\n ```\n\nThe output log files will contain performance numbers for the HiFi-GAN model\n(number of input mel-spectrogram frames per second, reported as `hifigan_frames/s`)\nand FastPitch (number of output mel-spectrogram frames per second as `mel_gen_frames/s`).\nThe `inference.py` script will run a few warm-up iterations before running the benchmark. Inference will be averaged over 100 runs, as set by the `REPEATS` env variable.\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\nOur results were obtained by running the `./platform/DGXA100_HiFi-GAN_{AMP,TF32}_8GPU.sh` training script in the 21.12-py3 NGC container on NVIDIA DGX A100 (8x A100 80GB) GPUs.\nWe present median mel loss values calculated on the validation subset at the end of the training.\n\n| Dataset | Batch size / GPU | Grad accumulation | GPUs | Val mel loss - FP32 | Val mel loss - mixed precision | Time to train - FP32 | Time to train - mixed precision | Time to train speedup (FP32 to mixed precision) |\n|---------|------------------|-------------------|------|-----------------|----------------------------|----------------------|---------------------------------|-------------------------------------------------|\n| LJSpeech-1.1 | 128 | 1 | 1 | 9.65 | 9.68 | 151.4 h | 117.1 h | 1.29x |\n| LJSpeech-1.1 | 16 | 1 | 8 | 9.65 | 9.68 | 32.9 h | 32.8 h | 1.00x |\n\n##### Training accuracy: NVIDIA DGX-1 (8x V100 16GB)\n\nOur results were obtained by running the `./platform/DGX1_HiFi-GAN_{AMP,FP32}_8GPU.sh` training script in the 21.12-py3 NGC container on NVIDIA DGX-1 (8x V100 16GB) GPUs.\nWe present median mel loss values calculated on the validation subset at the end of the training.\n\n| Dataset | Batch size / GPU | Grad accumulation | GPUs | Val mel loss - FP32 | Val mel loss - mixed precision | Time to train - FP32 | Time to train - mixed precision | Time to train speedup (FP32 to mixed precision) |\n|---------|------------------|-------------------|------|-----------------|----------------------------|----------------------|---------------------------------|-------------------------------------------------|\n| LJSpeech-1.1 | 32 | 4 | 1 | 9.65 | 9.73 | 604.6 h | 279.7 h | 2.16x |\n| LJSpeech-1.1 | 64 | 2 | 1 | 9.65 | 9.73 | - | 240.2 h | 2.52x |\n| LJSpeech-1.1 | 16 | 1 | 8 | 9.65 | 9.73 | 88.3 h | 56.7 h | 1.56x |\n\nFine tuning of the model on FastPitch outputs makes about half of the steps of the base model and will prolong the training by 50%.\n\n##### Training stability test\n\nThe training is stable when different random seeds are used. Below we compare loss curves obtained by training HiFi-GAN with different seeds.\n\n

\n $\"Loss$ \n

\n\nThe training is also stable with respect to different platforms:\n\n

\n $\"Loss$ \n

\n\n#### Training performance results\n\n##### Training performance: NVIDIA DGX A100 (8x A100 80GB)\n\nOur results were obtained by running the `./platform/DGXA100_HiFi-GAN_{AMP,TF32}_8GPU.sh` training script in the 21.12-py3 NGC container on NVIDIA DGX A100 (8x A100 80GB) GPUs. Performance numbers, in input mel-scale spectrogram frames per second, were averaged over an entire training epoch.\n\n| Batch size / GPU | Grad accumulation | GPUs | Throughput - TF32 | Throughput - mixed precision | Throughput speedup (TF32 to mixed precision) | Strong scaling - TF32 | Strong scaling - mixed precision |\n|-----:|-----:|-------:|---------:|----------:|--------:|-----:|------:|\n| 128 | 1 | 1 | 12055.44 | 15578.65 | 1.29 | 1.00 | 1.00 |\n| 32 | 1 | 4 | 36766.63 | 40949.55 | 1.11 | 3.05 | 2.63 |\n| 16 | 1 | 8 | 55372.63 | 55634.66 | 1.00 | 4.59 | 3.57 |\n\n##### Training performance: NVIDIA DGX-1 (8x V100 16GB)\n\nOur results were obtained by running the `./platform/DGX1_HiFi-GAN_{AMP,FP32}_8GPU.sh`\ntraining script in the PyTorch 21.12-py3 NGC container on NVIDIA DGX-1 with\n8x V100 16GB GPUs. Performance numbers, in output mel-scale spectrogram frames per second, were averaged over\nan entire training epoch.\n\n| Batch size / GPU | Grad accumulation | GPUs | Throughput - FP32 | Throughput - mixed precision | Throughput speedup (FP32 to mixed precision) | Strong scaling - FP32 | Strong scaling - mixed precision |\n|-----:|-----:|-------:|----------:|---------:|--------:|-----:|------:|\n| 32 | 4 | 1 | 3017.57 | 6522.37 | 2.16 | 1.00 | 1.00 |\n| 64 | 2 | 1 | - | 7596.32 | 2.52 | - | 1.00 |\n| 32 | 1 | 4 | 12135.04 | 23660.14 | 1.95 | 4.02 | 3.11 |\n| 16 | 1 | 8 | 20659.86 | 32175.41 | 1.56 | 6.85 | 4.24 |\n\n#### Inference performance results\n\nThe following tables show inference statistics for the FastPitch and HiFi-GAN\ntext-to-speech system, gathered from 100 inference runs. Latency is measured from the start of FastPitch inference to\nthe end of HiFi-GAN inference. Throughput is measured\nas the number of generated audio samples per second at 22KHz. RTF is the real-time factor that denotes the number of seconds of speech generated in a second of wall-clock time per input utterance.\n\nOur results were obtained by running the `./scripts/inference_benchmark.sh` script in\nthe PyTorch 21.12-py3 NGC container. The input utterance has 128 characters, synthesized audio has 8.05 s.\n\n##### Inference performance: NVIDIA DGX A100 (1x A100 80GB)\n\nTorchScript + denoising:\n\n|Batch size|Precision|Avg latency (s)|Latency tolerance interval 90% (s)|Latency tolerance interval 95% (s)|Latency tolerance interval 99% (s)|Throughput (samples/sec)|Speed-up with mixed precision|Avg RTF|\n|------|--------|-----------|---------|---------|---------|--------------|-----------|--------|\n| 1 | FP16 | 0.019 | 0.021 | 0.021 | 0.022 | 8906958 | 1.29 | 403.94 |\n| 2 | FP16 | 0.029 | 0.029 | 0.029 | 0.03 | 11780358 | 1.97 | 267.13 |\n| 4 | FP16 | 0.044 | 0.045 | 0.045 | 0.046 | 15248417 | 1.57 | 172.88 |\n| 8 | FP16 | 0.082 | 0.082 | 0.082 | 0.082 | 16597793 | 1.57 | 94.09 |\n| 1 | TF32 | 0.025 | 0.025 | 0.025 | 0.025 | 6915487 | - | 313.63 |\n| 2 | TF32 | 0.057 | 0.057 | 0.057 | 0.057 | 5966172 | - | 135.29 |\n| 4 | TF32 | 0.07 | 0.071 | 0.071 | 0.072 | 9710162 | - | 110.09 |\n| 8 | TF32 | 0.128 | 0.129 | 0.129 | 0.129 | 10578539 | - | 59.97 |\n\nTorchScript, no denoising:\n\n|Batch size|Precision|Avg latency (s)|Latency tolerance interval 90% (s)|Latency tolerance interval 95% (s)|Latency tolerance interval 99% (s)|Throughput (samples/sec)|Speed-up with mixed precision|Avg RTF|\n|------|--------|-----------|---------|---------|---------|--------------|-----------|--------|\n| 1 | FP16 | 0.018 | 0.019 | 0.019 | 0.020 | 9480432 | 1.32 | 429.95 |\n| 2 | FP16 | 0.027 | 0.028 | 0.028 | 0.028 | 12430026 | 2.02 | 281.86 |\n| 4 | FP16 | 0.042 | 0.044 | 0.044 | 0.045 | 15952396 | 1.60 | 180.87 |\n| 8 | FP16 | 0.082 | 0.083 | 0.083 | 0.084 | 16461910 | 1.52 | 93.32 |\n| 1 | TF32 | 0.024 | 0.025 | 0.025 | 0.025 | 7194880 | - | 326.3 |\n| 2 | TF32 | 0.055 | 0.056 | 0.056 | 0.056 | 6141659 | - | 139.27 |\n| 4 | TF32 | 0.068 | 0.068 | 0.069 | 0.069 | 9977333 | - | 113.12 |\n| 8 | TF32 | 0.125 | 0.126 | 0.126 | 0.127 | 10813904 | - | 61.3 |\n\n##### Inference performance: NVIDIA DGX-1 (1x V100 16GB)\n\nTorchScript + denoising:\n\n|Batch size|Precision|Avg latency (s)|Latency tolerance interval 90% (s)|Latency tolerance interval 95% (s)|Latency tolerance interval 99% (s)|Throughput (samples/sec)|Speed-up with mixed precision|Avg RTF|\n|------|--------|-----------|---------|---------|---------|--------------|-----------|--------|\n| 1 | FP16 | 0.033 | 0.034 | 0.035 | 0.035 | 5062222 | 1.82 | 229.58 |\n| 4 | FP16 | 0.083 | 0.085 | 0.085 | 0.085 | 8106313 | 2.29 | 91.91 |\n| 8 | FP16 | 0.142 | 0.144 | 0.144 | 0.145 | 9501139 | 2.54 | 53.86 |\n| 1 | FP32 | 0.061 | 0.062 | 0.062 | 0.062 | 2783145 | - | 126.22 |\n| 4 | FP32 | 0.192 | 0.194 | 0.194 | 0.195 | 3534640 | - | 40.08 |\n| 8 | FP32 | 0.362 | 0.364 | 0.364 | 0.365 | 3747958 | - | 21.25 |\n\nTorchScript, no denoising:\n\n|Batch size|Precision|Avg latency (s)|Latency tolerance interval 90% (s)|Latency tolerance interval 95% (s)|Latency tolerance interval 99% (s)|Throughput (samples/sec)|Speed-up with mixed precision|Avg RTF|\n|------|--------|-----------|---------|---------|---------|--------------|-----------|--------|\n| 1 | FP16 | 0.030 | 0.031 | 0.031 | 0.031 | 5673639 | 2.02 | 257.31 |\n| 4 | FP16 | 0.080 | 0.081 | 0.081 | 0.082 | 8472603 | 2.37 | 96.06 |\n| 8 | FP16 | 0.139 | 0.140 | 0.141 | 0.141 | 9737865 | 2.57 | 55.20 |\n| 1 | FP32 | 0.060 | 0.061 | 0.061 | 0.062 | 2806620 | - | 127.28 |\n| 4 | FP32 | 0.190 | 0.192 | 0.192 | 0.193 | 3571308 | - | 40.49 |\n| 8 | FP32 | 0.358 | 0.360 | 0.361 | 0.361 | 3788591 | - | 21.48 |\n\n##### Inference performance: NVIDIA T4\n\nTorchScript, denoising:\n\n|Batch size|Precision|Avg latency (s)|Latency tolerance interval 90% (s)|Latency tolerance interval 95% (s)|Latency tolerance interval 99% (s)|Throughput (samples/sec)|Speed-up with mixed precision|Avg RTF|\n|------|--------|-----------|---------|---------|---------|--------------|-----------|--------|\n| 1 | FP16 | 0.060 | 0.061 | 0.061 | 0.061 | 2835064 | 2.42 | 128.57 |\n| 4 | FP16 | 0.211 | 0.213 | 0.213 | 0.214 | 3205667 | 2.42 | 36.35 |\n| 8 | FP16 | 0.410 | 0.413 | 0.413 | 0.414 | 3304070 | 2.45 | 18.73 |\n| 1 | FP32 | 0.145 | 0.146 | 0.147 | 0.147 | 1171258 | - | 53.12 |\n| 4 | FP32 | 0.512 | 0.515 | 0.515 | 0.516 | 1324952 | - | 15.02 |\n| 8 | FP32 | 1.006 | 1.011 | 1.012 | 1.013 | 1347688 | - | 7.64 |\n\nTorchScript, no denoising:\n\n|Batch size|Precision|Avg latency (s)|Latency tolerance interval 90% (s)|Latency tolerance interval 95% (s)|Latency tolerance interval 99% (s)|Throughput (samples/sec)|Speed-up with mixed precision|Avg RTF|\n|------|--------|-----------|---------|---------|---------|--------------|-----------|--------|\n| 1 | FP16 | 0.057 | 0.058 | 0.058 | 0.059 | 2969398 | 2.48 | 134.67 |\n| 4 | FP16 | 0.205 | 0.207 | 0.207 | 0.208 | 3299130 | 2.45 | 37.41 |\n| 8 | FP16 | 0.399 | 0.402 | 0.403 | 0.404 | 3389001 | 2.48 | 19.21 |\n| 1 | FP32 | 0.142 | 0.143 | 0.143 | 0.144 | 1195309 | - | 54.21 |\n| 4 | FP32 | 0.504 | 0.507 | 0.507 | 0.509 | 1345995 | - | 15.26 |\n| 8 | FP32 | 0.993 | 0.997 | 0.998 | 0.999 | 1365273 | - | 7.74 |","hasPerformance",[],{"_87":188,"_116":243,"_117":-5,"_125":35,"_124":35,"_126":242,"_224":92},{"_87":29,"_116":29,"_117":-5,"_125":35,"_124":35,"_126":242,"_224":92},{"_87":115,"_116":115,"_117":-5,"_125":35,"_124":35,"_126":241,"_224":92},[],[],"Latest (21.12.1)","nvidia/dle/hifigan_pyt","key","nvidia/dle/resources/hifigan_pyt","resourceId","hifigan_pyt","shortDescription","HiFi-GAN model implements a spectrogram inversion model that allows to synthesize speech waveforms from mel-spectrograms.","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/hifigan-pyt)\n\n\nThis repository provides a PyTorch implementation of the HiFi-GAN model described in the paper [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://arxiv.org/abs/2010.05646).\nThe HiFi-GAN model implements a spectrogram inversion model that allows to synthesize speech waveforms from mel-spectrograms. It follows the generative adversarial network (GAN) paradigm, and is composed of a generator and a discriminator. After training, the generator is used for synthesis, and the discriminator is discarded.\n\nOur implementation is based on the one [published by the authors of the paper](https://github.com/jik876/hifi-gan). We modify the original hyperparameters and provide an alternative training recipe, which enables training on larger batches and faster convergence. HiFi-GAN is trained on a publicly available [LJ Speech dataset](https://keithito.com/LJ-Speech-Dataset/). The samples demonstrate speech synthesized with our publicly available FastPitch and HiFi-GAN checkpoints.\n\nThis model is trained with mixed precision using Tensor Cores on NVIDIA Volta and the NVIDIA Ampere GPU architectures and evaluated on Volta, Turing and the NVIDIA Ampere GPU architectures. Therefore, researchers can get results up to 2.5x 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 entire model is composed of a generator and two discriminators. Both discriminators can be further divided into smaller sub-networks, that work at different resolutions.\nThe loss functions take as inputs intermediate feature maps and outputs of those sub-networks.\nAfter training, the generator is used for synthesis, and the discriminators are discarded.\nAll three components are convolutional networks with different architectures.\n\n

\n $\"HiFi-GAN$ \n

\n Figure 1. The architecture of HiFi-GAN\n

\n\n### Default configuration\n\nThe following features were implemented in this model:\n* data-parallel multi-GPU training,\n* training and inference with mixed precision using Tensor Cores,\n* gradient accumulation for reproducible results regardless of the number of GPUs.\n\nThe training recipe we provide for the model, recreates the `v1` model from the HiFi-GAN paper,\nwhich is the largest and has the highest quality of all models described in the paper.\nMixed precision training and memory optimizations allowed us to increase batch size and throughput significantly.\nIn effect, we modify some hyperparameters of the `v1` recipe.\n\n### Feature support matrix\n\nThe following features are supported by this model.\n\n| Feature | HiFi-GAN |\n| :-------------------------------|:--------:|\n| Automatic mixed precision (AMP) | Yes |\n| Distributed data parallel (DDP) | Yes |\n\n#### Features\n\n**Automatic Mixed Precision (AMP)**\nThis implementation uses native PyTorch AMP\nimplementation of mixed precision training. It allows us to use FP16 training\nwith FP32 master weights by modifying just a few lines of code.\n\n**DistributedDataParallel (DDP)**\nThe model uses PyTorch Lightning implementation\nof distributed data parallelism at the module level which can run across\nmultiple machines.\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 Volta, and following with both the 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 `--amp` flag.\nMixed precision is using [native PyTorch implementation](https://pytorch.org/blog/accelerating-training-on-nvidia-gpus-with-pytorch-automatic-mixed-precision/).\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 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 that 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**Multi-Period Discriminator**\nA sub-network that extracts patterns from the data that occur periodically (for example, every *N* time steps).\n\n**Multi-Scale Discriminator**\nA sub-network that extracts patterns from the data at different resolutions of the input signal. Lower resolutions are obtained by average-pooling of the signal.\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.","displayName","HiFi-GAN for PyTorch",["D",1680813786469],"publisher","NVIDIA Deep Learning Examples","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/dle/resources/hifigan_pyt","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[283],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_252":284,"_285":286,"_287":288,"_289":89},"Text to Speech","value","uscs_text_to_speech","filter",{"_290":291,"_285":286},"category","field","application",{"_157":1466,"_159":1467},"ngcApps",[1419],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1400":1401,"_1402":1403,"_1404":1405,"_1406":1407,"_1408":303,"_111":327,"_1409":1410,"_1411":60,"_1412":92,"_1413":92,"_1414":1415,"_1416":92,"_1417":1418},"amplitude",{"_1397":1398,"_999":-7,"_1399":35},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1391":1392,"_1393":1394,"_1395":1396},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1274":407,"_1275":1276,"_1277":1278,"_1279":1280},"announcements",[],"consentState",{"_364":1264,"_1265":92,"_1266":35,"_1267":35,"_1268":1269},"consentConfig",{"_346":347,"_348":349,"_350":351,"_352":353,"_354":355,"_356":357,"_358":359,"_360":361,"_362":363,"_364":365},"showContactAdmin",1784224794546,[],[],[341,342,343,344,345],{"_49":64,"_51":75,"_53":70,"_55":-5,"_57":-5,"_59":60},{"_49":64,"_51":71,"_53":70,"_55":-5,"_57":-5,"_59":60},{"_49":64,"_51":65,"_53":70,"_55":-5,"_57":-5,"_59":60},{"_49":61,"_51":62,"_53":54,"_55":56,"_57":58,"_59":60},{"_49":50,"_51":52,"_53":70,"_55":-5,"_57":-5,"_59":60},"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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It is used as a URL shortening service, which allows Twitter to collect data on how links are shared.","snowplow.com",[445],{"_116":446,"_412":443,"_413":35,"_218":447},"sp","Testing","google.com",[450],{"_116":451,"_412":448,"_413":35,"_218":452},"NID","This domain is owned by Google Inc. Although Google is primarily known as a search engine, the company provides a diverse range of products and services. Its main source of revenue however is advertising. Google tracks users extensively both through its own products and sites, and the numerous technologies embedded into many millions of websites around the world. 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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. 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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. 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21.12.1Selected	1	37.87 KB	November 4, 2022 UTC
21.12.0	1	37.88 KB	September 22, 2022 UTC