---
dataset_info:
- config_name: en2de
  features:
  - name: path
    dtype: string
  - name: sentence
    dtype: float64
  - name: split
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  - name: lang
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  - name: metric_score_xcomet-xl
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  - name: test_tfsmlcv
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  download_size: 569246
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  - name: sentence
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    num_examples: 500
  - name: test_whsptny
    num_bytes: 170804
    num_examples: 500
  download_size: 547013
  dataset_size: 2257484
configs:
- config_name: en2de
  data_files:
  - split: test
    path: en2de/test-*
  - split: test_seamlv2
    path: en2de/test_seamlv2-*
  - split: test_seamlar
    path: en2de/test_seamlar-*
  - split: test_seammid
    path: en2de/test_seammid-*
  - split: test_tfw2vlg
    path: en2de/test_tfw2vlg-*
  - split: test_tfmidmc
    path: en2de/test_tfmidmc-*
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    path: en2de/test_tfsmlmc-*
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    path: en2de/test_tfsmlcv-*
- config_name: es2en
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    path: es2en/test-*
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    path: es2en/test_whsplv3-*
  - split: test_whsplv2
    path: es2en/test_whsplv2-*
  - split: test_whsplar
    path: es2en/test_whsplar-*
  - split: test_whspmid
    path: es2en/test_whspmid-*
  - split: test_whspsml
    path: es2en/test_whspsml-*
  - split: test_whspbas
    path: es2en/test_whspbas-*
  - split: test_whsptny
    path: es2en/test_whsptny-*
license: mit
language:
- de
- es
- en
---


# [SpeechQE: Estimating the Quality of Direct Speech Translation](https://aclanthology.org/2024.emnlp-main.1218)
This is a benchmark and training corpus for the task of quality estimation for speech translation (SpeechQE).

*(We provide test split first, and the training corpus will be provided later. However, if you want those quickly, please do not hesitate to ping me (hjhan@umd.edu)!)

## E2E Model Trained with SpeechQE-CoVoST2

|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|


## 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 with SpeechQE-CoVoST2
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}
}
```