Update README.md (#2)

- up (f7f4a45e269a903a40678d4be1d8ac4e55954cf3)

README.md

@@ -5,15 +5,17 @@ tags:
 license: cc-by-nc-4.0
 ---
 
-# SeamlessM4T
-SeamlessM4T is designed to provide high quality translation, allowing people from different linguistic communities to communicate effortlessly through speech and text. 
+# SeamlessM4T Large
+
+SeamlessM4T is a collection of models designed to provide high quality translation, allowing people from different 
+linguistic communities to communicate effortlessly through speech and text. 
 
 SeamlessM4T covers:
 - 📥 101 languages for speech input
 - ⌨️ 96 Languages for text input/output
 - 🗣️ 35 languages for speech output. 
 
-This unified model enables multiple tasks without relying on multiple separate models:
+This is the "large" variant of the unified model, which enables multiple tasks without relying on multiple separate models:
 - Speech-to-speech translation (S2ST)
 - Speech-to-text translation (S2TT)
 - Text-to-speech translation (T2ST)
@@ -21,37 +23,40 @@ This unified model enables multiple tasks without relying on multiple separate m
 - Automatic speech recognition (ASR)
 
 ## SeamlessM4T models
+
+The SeamlessM4T models come in two checkpoints of different size: 
+
 | Model Name | #params | checkpoint | metrics |
 | - | - | - | - |
-| SeamlessM4T-Large | 2.3B |[🤗 Model card](https://huggingface.co/facebook/seamless-m4t-large) - [checkpoint](https://huggingface.co/facebook/seamless-m4t-large/resolve/main/multitask_unity_large.pt) | [metrics]() |
-| SeamlessM4T-Medium | 1.2B |[🤗 Model card](https://huggingface.co/facebook/seamless-m4t-medium) - [checkpoint](https://huggingface.co/facebook/seamless-m4t-medium/resolve/main/multitask_unity_medium.pt) | [metrics]() |
+| [SeamlessM4T-Medium]((https://huggingface.co/facebook/seamless-m4t-medium)) | 1.2B | [checkpoint](https://huggingface.co/facebook/seamless-m4t-medium/resolve/main/multitask_unity_medium.pt) | [metrics]() |
+| [SeamlessM4T-Large](https://huggingface.co/facebook/seamless-m4t-large) | 2.3B | [checkpoint](https://huggingface.co/facebook/seamless-m4t-large/resolve/main/multitask_unity_large.pt) | [metrics]() |
 
-We provide the extensive evaluation results of seamlessM4T-Large and SeamlessM4T-Medium reported in the paper (as averages) in the `metrics` files above.
+We provide extensive evaluation results of SeamlessM4T-Medium and SeamlessM4T-Large in the SeamlessM4T paper (as averages) in the `metrics` files above.
 
 ## Instructions to run inference with SeamlessM4T models
 
-Install `seamless_communication` by following the instructions mentioned here: [Installation](https://github.com/fairinternal/seamless_communication/tree/main#installation)
-
-Inference calls for the `Translator` object instanciated with a Multitasking UnitY model with the options:
-- `multitask_unity_large`
-- `multitask_unity_medium`
+The SeamlessM4T models are currently available through the `seamless_communication` package. The `seamless_communication` 
+package can be installed by following the instructions outlined here: [Installation](https://github.com/fairinternal/seamless_communication/tree/main#installation).
 
-and a vocoder `vocoder_36langs`
+Once installed, a [`Translator`](https://github.com/fairinternal/seamless_communication/blob/590547965b343b590d15847a0aa25a6779fc3753/src/seamless_communication/models/inference/translator.py#L47) 
+object can be instantiated to perform all five of the spoken langauge tasks. The `Translator` is instantiated with three arguments:
+1. `model_name_or_card`: SeamlessM4T checkpoint. Can be either `multitask_unity_medium` for the medium model, or `multitask_unity_large` for the large model
+2. `vocoder_name_or_card`: vocoder checkpoint (`vocoder_36langs`)
+3. `device`: Torch device
 
 ```python
 import torch
-import torchaudio
 from seamless_communication.models.inference import Translator
 
 
 # Initialize a Translator object with a multitask model, vocoder on the GPU.
-translator = Translator("multitask_unity_large", "vocoder_36langs", torch.device("cuda:0"))
+translator = Translator("multitask_unity_large", vocoder_name_or_card="vocoder_36langs", device=torch.device("cuda:0"))
 ```
 
-Now `predict()` can be used to run inference as many times on any of the supported tasks.
+Once instantiated, the `predict()` method can be used to run inference as many times on any of the supported tasks.
 
-Given an input audio with `<path_to_input_audio>` or an input text `<input_text>` in `<src_lang>`,
-we can translate into `<tgt_lang>` as follows:
+Given an input audio with `<path_to_input_audio>` or an input text `<input_text>` in `<src_lang>`, we can translate 
+into `<tgt_lang>` as follows.
 
 ### S2ST and T2ST:
 
@@ -61,8 +66,8 @@ translated_text, wav, sr = translator.predict(<path_to_input_audio>, "s2st", <tg
 
 # T2ST
 translated_text, wav, sr = translator.predict(<input_text>, "t2st", <tgt_lang>, src_lang=<src_lang>)
-
 ```
+
 Note that `<src_lang>` must be specified for T2ST.
 
 The generated units are synthesized and the output audio file is saved with:
@@ -92,49 +97,21 @@ transcribed_text, _, _ = translator.predict(<path_to_input_audio>, "asr", <src_l
 translated_text, _, _ = translator.predict(<input_text>, "t2tt", <tgt_lang>, src_lang=<src_lang>)
 
 ```
-Note that `<src_lang>` must be specified for T2TT
-
-
-### Inference using the CLI, from the root directory of the repository:
-
-The model can be specified with e.g., `--model_name multitask_unity_large`:
-
-S2ST:
-```
-python scripts/m4t/predict/predict.py <path_to_input_audio> s2st <tgt_lang> --output_path <path_to_save_audio> --model_name multitask_unity_large
-```
-
-S2TT:
-```
-python scripts/m4t/predict/predict.py <path_to_input_audio> s2tt <tgt_lang>
-```
-
-T2TT:
-```
-python scripts/m4t/predict/predict.py <input_text> t2tt <tgt_lang> --src_lang <src_lang>
-```
-
-T2ST:
-```
-python scripts/m4t/predict/predict.py <input_text> t2st <tgt_lang> --src_lang <src_lang> --output_path <path_to_save_audio>
-```
-
-ASR:
-```
-python scripts/m4t/predict/predict.py <path_to_input_audio> asr <tgt_lang>
-```
+Note that `<src_lang>` must be specified for T2TT.
 
 ## Citation
-If you use SeamlessM4T in your work or any models/datasets/artifacts published in SeamlessM4T, please cite :
+
+If you plan to use SeamlessM4T in your work or any models/datasets/artifacts published in SeamlessM4T, please cite:
 
 ```bibtex
 @article{seamlessm4t2023,
-  title={SeamlessM4T—Massively Multilingual \& Multimodal Machine Translation},
+  title={"SeamlessM4T—Massively Multilingual \& Multimodal Machine Translation"},
   author={{Seamless Communication}, Lo\"{i}c Barrault, Yu-An Chung, Mariano Cora Meglioli, David Dale, Ning Dong, Paul-Ambroise Duquenne, Hady Elsahar, Hongyu Gong, Kevin Heffernan, John Hoffman, Christopher Klaiber, Pengwei Li, Daniel Licht, Jean Maillard, Alice Rakotoarison, Kaushik Ram Sadagopan, Guillaume Wenzek, Ethan Ye,  Bapi Akula, Peng-Jen Chen, Naji El Hachem, Brian Ellis, Gabriel Mejia Gonzalez, Justin Haaheim, Prangthip Hansanti, Russ Howes, Bernie Huang, Min-Jae Hwang, Hirofumi Inaguma, Somya Jain, Elahe Kalbassi, Amanda Kallet, Ilia Kulikov, Janice Lam, Daniel Li, Xutai Ma, Ruslan Mavlyutov, Benjamin Peloquin, Mohamed Ramadan, Abinesh Ramakrishnan, Anna Sun, Kevin Tran, Tuan Tran, Igor Tufanov, Vish Vogeti, Carleigh Wood, Yilin Yang, Bokai Yu, Pierre Andrews, Can Balioglu, Marta R. Costa-juss\`{a} \footnotemark[3], Onur \,{C}elebi,Maha Elbayad,Cynthia Gao, Francisco Guzm\'an, Justine Kao, Ann Lee, Alexandre Mourachko, Juan Pino, Sravya Popuri, Christophe Ropers, Safiyyah Saleem, Holger Schwenk, Paden Tomasello, Changhan Wang, Jeff Wang, Skyler Wang},
   journal={ArXiv},
   year={2023}
 }
 ```
+
 ## License
 
-seamless_communication is CC-BY-NC 4.0 licensed.
+The Seamless Communication code and weights are CC-BY-NC 4.0 licensed.