Update README.md

README.md

@@ -1,3 +1,97 @@
----
-license: apache-2.0
----
+---
+language: "en"
+inference: false
+tags:
+- Vocoder
+- HiFIGAN
+- speech-synthesis
+- speechbrain
+license: "apache-2.0"
+datasets:
+- LibriTTS
+---
+
+
+<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
+<br/><br/>
+
+# Vocoder with HiFIGAN Unit trained on LibriTTS
+
+This repository provides all the necessary tools for using a [scalable HiFiGAN Unit](https://arxiv.org/abs/2406.10735) vocoder trained with [LibriTTS](https://www.openslr.org/141/). 
+
+The pre-trained model take as input discrete self-supervised representations and produces a waveform as output. This is suitable for a wide range of generative tasks such as speech enhancement, separation, text-to-speech, voice cloning, etc. Please read [DASB - Discrete Audio and Speech Benchmark](https://arxiv.org/abs/2406.14294) for more information.  
+To generate the discrete self-supervised representations, we employ a K-means clustering model trained using `microsoft/wavlm-large` hidden layers ([1, 3, 7, 12, 18, 23]), with k=1000.
+
+## Install SpeechBrain
+
+First of all, please install tranformers and SpeechBrain with the following command:
+
+```
+pip install speechbrain transformers
+```
+
+Please notice that we encourage you to read our tutorials and learn more about
+[SpeechBrain](https://speechbrain.github.io).
+
+### Using the Vocoder with DiscreteSSL
+
+```python
+import torch
+from speechbrain.lobes.models.huggingface_transformers.hubert import (HuBERT)
+
+inputs = torch.rand([3, 2000])
+model_hub = "facebook/hubert-large-ll60k"
+save_path = "savedir"
+ssl_layer_num = [7,23]
+deduplicate =[False, True]
+bpe_tokenizers=[None, None]
+vocoder_repo_id = "speechbrain/hifigan-hubert-k1000-LibriTTS"
+kmeans_dataset = "LibriSpeech"
+num_clusters = 1000
+ssl_model = HuBERT(model_hub, save_path,output_all_hiddens=True)
+model = DiscreteSSL(save_path, ssl_model, vocoder_repo_id=vocoder_repo_id, kmeans_dataset=kmeans_dataset,num_clusters=num_clusters)
+tokens, _, _ = model.encode(inputs,SSL_layers=ssl_layer_num, deduplicates=deduplicate, bpe_tokenizers=bpe_tokenizers)
+sig = model.decode(tokens, ssl_layer_num)
+```
+
+
+
+### Standalone Vocoder Usage
+
+```python
+import torch
+from speechbrain.inference.vocoders import UnitHIFIGAN
+
+hifi_gan_unit = UnitHIFIGAN.from_hparams(source="speechbrain/hifigan-hubert-k1000-LibriTTS", savedir="pretrained_models/vocoder")
+codes = torch.randint(0, 99, (100, 1))
+waveform = hifi_gan_unit.decode_unit(codes)
+
+```
+
+
+### Inference on GPU
+To perform inference on the GPU, add  `run_opts={"device":"cuda"}`  when calling the `from_hparams` method.
+
+
+### Limitations
+The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.
+
+#### Referencing SpeechBrain
+
+```
+@misc{SB2021,
+    author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua },
+    title = {SpeechBrain},
+    year = {2021},
+    publisher = {GitHub},
+    journal = {GitHub repository},
+    howpublished = {\\\\url{https://github.com/speechbrain/speechbrain}},
+  }
+```
+
+#### About SpeechBrain
+SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains.
+
+Website: https://speechbrain.github.io/
+
+GitHub: https://github.com/speechbrain/speechbrain