Update README.md

README.md

@@ -3,197 +3,67 @@ library_name: transformers
 tags: []
 ---
 
-# Model Card for Model ID
+# Model Card for mhubert-base-25hz
 
-<!-- Provide a quick summary of what the model is/does. -->
+This is a version of [Hubert](https://ai.meta.com/blog/hubert-self-supervised-representation-learning-for-speech-recognition-generation-and-compression/) by Meta. This version was introduced in [TWIST](https://arxiv.org/abs/2305.13009) and showed lots of value as a speech tokeniser for training SpeechLMs.
 
+These model weights were converted by [SLP-RL](https://www.cs.huji.ac.il/~adiyoss/slprl/index.html) from the original [Textlesslib release](https://github.com/facebookresearch/textlesslib/tree/main/examples/twist).
 
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+This Hubert model was introduced in [TWIST](https://arxiv.org/abs/2305.13009) we encourage you to look there for the full details. 
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+It was trained on a varied mixture of datasets: Multilingual LS, Vox Populi, Common Voice, Spotify, and Fisher. This Hubert base model was 
+trained for 3 iterations with the default 50Hz features rate. For the 4-th iteration, they add an additional convolutional layer at the CNN
+Encoder with the stride 2, resulting in features of 25Hz.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+We converted the original Fairseq release to Huggingface🤗 using the [conversion script](link), after [adding support](pr_link), and asserted 
+that the results are [identical](test_link).
 
-### Model Sources [optional]
+- **Developed by:** Hassid et. al
+- **Shared by:** [SLP-RL](https://www.cs.huji.ac.il/~adiyoss/slprl/index.html)
+- **Model type:** `transformers.HubertModel`
+- **Languages:** Multi-lingual
+- **License:** MIT, see [textlesslib license](https://github.com/facebookresearch/textlesslib/blob/main/LICENSE) for full details
 
-<!-- Provide the basic links for the model. -->
+### Model Sources
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** https://github.com/facebookresearch/textlesslib/tree/main/examples/twist
+- **Paper:** https://arxiv.org/abs/2305.13009
 
 ## Uses
-
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+This is a base HubertModel and as such is useful as a feature extractor for speech tokenisation for usages such as
+[Spoken Language Modelling](https://arxiv.org/abs/2409.07437) or [Speaking Style Conversion](https://arxiv.org/abs/2212.09730).
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
+This model requires a **new** transformers version `transformers>=??`, so make sure you have it installed. Afterwards it can be used as 
+follows:
 
-#### Factors
+```python
+from transformers import HubertModel
+model = HubertModel.from_pretrained('slprl/mhubert-base-25hz')
+```
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
 
+## Citation
 <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
 **BibTeX:**
 
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
+```
+@article{hassid2024textually,
+  title={Textually pretrained speech language models},
+  author={Hassid, Michael and Remez, Tal and Nguyen, Tu Anh and Gat, Itai and Conneau, Alexis and Kreuk, Felix and Copet, Jade and Defossez, Alexandre and Synnaeve, Gabriel and Dupoux, Emmanuel and others},
+  journal={Advances in Neural Information Processing Systems},
+  volume={36},
+  year={2024}
+}
+```
 
-## Model Card Contact
+## Model Card Authors
 
-[More Information Needed]
+[Gallil Maimon](https://pages.cs.huji.ac.il/gallilmaimon/)