Update README/model card.

README.md

@@ -1,3 +1,112 @@
 ---
+language:
+- en
+library_name: nemo
+datasets:
+- ljspeech
+thumbnail: null
+tags:
+- text-to-speech
+- speech
+- audio
+- Vocoder
+- GAN
+- pytorch
+- NeMo
+- Riva
 license: cc-by-4.0
 ---
+# NVIDIA Hifigan Vocoder (en-US)
+<style>
+img {
+ display: inline;
+}
+</style>
+| [![Model architecture](https://img.shields.io/badge/Model_Arch-HiFiGAN--GAN-lightgrey#model-badge)](#model-architecture)
+| [![Model size](https://img.shields.io/badge/Params-85M-lightgrey#model-badge)](#model-architecture)
+| [![Language](https://img.shields.io/badge/Language-en--US-lightgrey#model-badge)](#datasets)
+| [![Riva Compatible](https://img.shields.io/badge/NVIDIA%20Riva-compatible-brightgreen#model-badge)](#deployment-with-nvidia-riva) |
+
+HiFiGAN [1] is a generative adversarial network (GAN) model that generates audio from mel spectrograms. The generator uses transposed convolutions to upsample mel spectrograms to audio.
+ 
+## Usage
+
+The model is available for use in the NeMo toolkit [2] and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.
+To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed the latest PyTorch version.
+
+```
+pip install nemo_toolkit['all']
+```
+
+### Automatically instantiate the model
+
+NOTE: In order to generate audio, you also need a spectrogram generator from NeMo. This example uses the FastPitch model.
+
+```python
+# Load FastPitch
+from nemo.collections.tts.models import FastPitchModel
+spec_generator = FastPitchModel.from_pretrained("tts_en_fastpitch")
+
+# Load vocoder
+from nemo.collections.tts.models import HifiGanModel
+model = HifiGanModel.from_pretrained(model_name="tts_hifigan")
+```
+
+### Generate audio
+
+```python
+import soundfile as sf
+parsed = spec_generator.parse("You can type your sentence here to get nemo to produce speech.")
+spectrogram = spec_generator.generate_spectrogram(tokens=parsed)
+audio = model.convert_spectrogram_to_audio(spec=spectrogram)
+```
+
+### Save the generated audio file
+
+```python
+# Save the audio to disk in a file called speech.wav
+sf.write("speech.wav", audio.to('cpu').numpy(), 22050)
+```
+
+### Input
+
+This model accepts batches of mel spectrograms.
+
+### Output
+
+This model outputs audio at 22050Hz.
+
+## Model Architecture
+
+HiFi-GAN [1] consists of one generator and two discriminators: multi-scale and multi-period discriminators. The generator and discriminators are trained adversarially, along with two additional losses for
+improving training stability and model performance.
+
+## Training
+
+The NeMo toolkit [3] was used for training the models for several epochs. These model are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/tts/hifigan.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/tts/conf/hifigan/hifigan.yaml).
+
+### Datasets
+
+This model is trained on LJSpeech sampled at 22050Hz, and has been tested on generating female English voices with an American accent.
+
+## Performance
+
+No performance information is available at this time.
+
+## Limitations
+
+There are no known limitations at this time.
+
+## Deployment with NVIDIA Riva
+
+For the best real-time accuracy, latency, and throughput, deploy the model with [NVIDIA Riva](https://developer.nvidia.com/riva), an accelerated speech AI SDK deployable on-prem, in all clouds, multi-cloud, hybrid, at the edge, and embedded. 
+Additionally, Riva provides: 
+* World-class out-of-the-box accuracy for the most common languages with model checkpoints trained on proprietary data with hundreds of thousands of GPU-compute hours 
+* Best in class accuracy with run-time word boosting (e.g., brand and product names) and customization of acoustic model, language model, and inverse text normalization 
+* Streaming speech recognition, Kubernetes compatible scaling, and Enterprise-grade support 
+Check out [Riva live demo](https://developer.nvidia.com/riva#demos).
+
+## References
+
+- [1] [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://arxiv.org/abs/2010.05646)
+- [2] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)