Update README.md

README.md

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 ---
 library_name: transformers
-tags: []
+license: mit
+datasets:
+- h-j-han/SpeechQE-CoVoST2
+language:
+- es
+- en
+base_model:
+- Unbabel/TowerInstruct-7B-v0.2
+- openai/whisper-large-v2
 ---
-
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
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-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
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-### Model Sources [optional]
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-
-## Uses
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-<!-- 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
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-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-### Downstream Use [optional]
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-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-### Out-of-Scope Use
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-## Bias, Risks, and Limitations
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-### Recommendations
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-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
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-[More Information Needed]
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-## 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. -->
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-[More Information Needed]
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-### Training Procedure
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-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### Preprocessing [optional]
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-[More Information Needed]
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-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-#### Speeds, Sizes, Times [optional]
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-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-[More Information Needed]
-
+# [SpeechQE: Estimating the Quality of Direct Speech Translation](https://aclanthology.org/2024.emnlp-main.1218)
+This is End-to-End model for the task of quality estimation for speech translation (SpeechQE).
+
+|Task | E2E Model | Trained Domain
+|---|---|---|
+|SpeechQE for English-to-German Speech Translation |[h-j-han/SpeechQE-TowerInstruct-7B-en2de](https://huggingface.co/h-j-han/SpeechQE-TowerInstruct-7B-en2de)| CoVoST2|
+|SpeechQE for Spanish-to-English Speech Translation  |[h-j-han/SpeechQE-TowerInstruct-7B-es2en](https://huggingface.co/h-j-han/SpeechQE-TowerInstruct-7B-es2en)|CoVoST2|
+
+## Architecture and Training
+Our design incorporates a pretrained speech encoder (whisper-large-v2) and a large language model (TowerInstruct-7B-v0.2) to leverage their existing capabilities in extracting high-quality audio features and handling
+translation-related tasks.
+The model is trained with two-phase approach where we first train only an adapter with ASR and ST tasks while freezing textLLM to focus solely on mapping between text and speech modality. 
+Then, we continue training with the SpeechQE task to let the LLM learn the unseen task of QE. In the second phase, the adapter pre-trained in the previous phase is frozen, while text-LLM is trained with LoRA
+
+
+## Setup
+We provide code in Github repo : https://github.com/h-j-han/SpeechQE  
+```bash
+$ git clone https://github.com/h-j-han/SpeechQE.git
+$ cd SpeechQE
+```
+```bash
+$ conda create -n speechqe Python=3.11 pytorch=2.0.1  pytorch-cuda=11.7 torchvision torchaudio -c pytorch -c nvidia
+$ conda activate speechqe
+$ pip install -r requirements.txt
+```
+
+## Download Audio Data
+Download the audio data from Common Voice. Here, we use mozilla-foundation/common_voice_4_0.
+```
+import datasets
+cv4en = datasets.load_dataset(
+    "mozilla-foundation/common_voice_4_0", "es", cache_dir='path/to/cv4/download',
+)
+```
 ## 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. -->
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-[More Information Needed]
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-[More Information Needed]
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-#### Metrics
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-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-[More Information Needed]
-
-### Results
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-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
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-[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]
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-- **Cloud Provider:** [More Information Needed]
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-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
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-[More Information Needed]
-
-### Compute Infrastructure
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-#### Hardware
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-#### Software
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-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
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-**APA:**
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-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-## More Information [optional]
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-## Model Card Authors [optional]
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-
-## Model Card Contact
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-[More Information Needed]
+We provide SpeechQE benchmark: [h-j-han/SpeechQE-CoVoST2](https://huggingface.co/datasets/h-j-han/SpeechQE-CoVoST2).
+BASE_AUDIO_PATH is the path of downloaded Common Voice dataset.
+```bash
+$ python speechqe/score_speechqe.py \
+    --speechqe_model=h-j-han/SpeechQE-TowerInstruct-7B-es2en \
+    --dataset_name=h-j-han/SpeechQE-CoVoST2 \
+    --base_audio_path=$BASE_AUDIO_PATH \
+    --dataset_config_name=es2en \
+    --test_split_name=test \
+```
+
+
+## Reference
+Please find details in this paper :
+```
+@misc{han2024speechqe,
+    title={SpeechQE: Estimating the Quality of Direct Speech Translation},
+    author={HyoJung Han and Kevin Duh and Marine Carpuat},
+    year={2024},
+    eprint={2410.21485},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```