Update README.md

README.md

@@ -1,3 +1,83 @@
 ---
+language:
+- fr
+- en
+inference: false
+tags:
+- speech-to-speech-translation
+- speechbrain
 license: apache-2.0
+datasets:
+- CVSS
 ---
+
+
+<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/>
+
+# Speech-to-Unit Translation trained on CVSS
+
+This repository provides all the necessary tools for using a a speech-to-unit translation (S2UT) model using a pre-trained Wav2Vec 2.0 encoder and a transformer decoder on the [CVSS](https://arxiv.org/abs/2201.03713) dataset.
+The implementation is based on [Textless Speech-to-Speech Translation](https://arxiv.org/abs/2112.08352) and [Enhanced Direct Speech-to-Speech Translation Using Self-supervised Pre-training and Data Augmentatio](https://arxiv.org/abs/2204.02967) papers.
+
+The pre-trained model take as input waveform and produces discrete self-supervised representations as output. Typically, a vocoder (e.g., HiFiGAN Unit) is utilized on top of the S2UT model to produce waveform.
+To generate the discrete self-supervised representations, we employ a K-means clustering model trained on the 6th layer of HuBERT, with `k=100`.
+
+## Install SpeechBrain
+
+First of all, please install tranformers and SpeechBrain with the following command:
+
+```
+pip install speechbrain transformers==4.28.0
+```
+
+Please notice that we encourage you to read our tutorials and learn more about
+[SpeechBrain](https://speechbrain.github.io).
+
+
+### Perform speech-to-speech translation (S2ST) with S2UT model and the Vocoder
+```python
+import torchaudio
+from speechbrain.pretrained import EncoderDecoderS2UT
+from speechbrain.pretrained import UnitHIFIGAN
+
+# Intialize S2UT (Transformer) and Vocoder (HiFIGAN Unit)
+s2ut = EncoderDecoderS2UT.from_hparams(source="speechbrain/s2st-transformer-fr-en-hubert-l6-k100-cvss", savedir=tmpdir_s2ut)
+hifi_gan_unit = UnitHIFIGAN.from_hparams(source="speechbrain/tts-hifigan-unit-hubert-l6-k100-ljspeech", savedir=tmpdir_vocoder)
+
+# Running the S2UT model
+codes = s2ut.translate_file("speechbrain/s2st-transformer-fr-en-hubert-l6-k100-cvss/example-fr.wav")
+
+# Running Vocoder (units-to-waveform)
+waveforms = hifi_gan.decode_unit(codes)
+
+# Save the waverform
+torchaudio.save('example_TTS.wav',waveforms.squeeze(1), 16000)
+```
+
+### 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