README.md

metadata

dataset_info:
  - config_name: en2de
    features:
      - name: path
        dtype: string
      - name: sentence
        dtype: float64
      - name: split
        dtype: string
      - name: lang
        dtype: string
      - name: task
        dtype: string
      - name: inst
        dtype: string
      - name: suffix
        dtype: string
      - name: st_system
        dtype: string
      - name: metric_score_xcomet-xl
        dtype: float64
      - name: metric_score_metricx-23-xl
        dtype: float64
    splits:
      - name: test
        num_bytes: 1150690
        num_examples: 3500
      - name: test_seamlv2
        num_bytes: 161689
        num_examples: 500
      - name: test_seamlar
        num_bytes: 161599
        num_examples: 500
      - name: test_seammid
        num_bytes: 161887
        num_examples: 500
      - name: test_tfw2vlg
        num_bytes: 162851
        num_examples: 500
      - name: test_tfmidmc
        num_bytes: 173183
        num_examples: 500
      - name: test_tfsmlmc
        num_bytes: 165835
        num_examples: 500
      - name: test_tfsmlcv
        num_bytes: 163646
        num_examples: 500
    download_size: 569246
    dataset_size: 2301380
  - config_name: es2en
    features:
      - name: path
        dtype: string
      - name: sentence
        dtype: float64
      - name: split
        dtype: string
      - name: lang
        dtype: string
      - name: task
        dtype: string
      - name: inst
        dtype: string
      - name: suffix
        dtype: string
      - name: st_system
        dtype: string
      - name: metric_score_xcomet-xl
        dtype: float64
      - name: metric_score_metricx-23-xl
        dtype: float64
    splits:
      - name: test
        num_bytes: 1128742
        num_examples: 3500
      - name: test_whsplv3
        num_bytes: 160913
        num_examples: 500
      - name: test_whsplv2
        num_bytes: 159492
        num_examples: 500
      - name: test_whsplar
        num_bytes: 157929
        num_examples: 500
      - name: test_whspmid
        num_bytes: 158335
        num_examples: 500
      - name: test_whspsml
        num_bytes: 158008
        num_examples: 500
      - name: test_whspbas
        num_bytes: 163261
        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-*
      - split: test_tfsmlmc
        path: en2de/test_tfsmlmc-*
      - split: test_tfsmlcv
        path: en2de/test_tfsmlcv-*
  - config_name: es2en
    data_files:
      - split: test
        path: es2en/test-*
      - split: test_whsplv3
        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

This is a benchmark and training corpus for the task of quality estimation for speech translation (SpeechQE).

We subsample about 80k segments from the training set and 500 from the dev and test of CoVoST2, then run seven different direct ST models to generate the ST hypotheses. So,test split consists of 3500 instances(500*7). We also provide splits for each translation model. *(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	CoVoST2
SpeechQE for Spanish-to-English Speech Translation	h-j-han/SpeechQE-TowerInstruct-7B-es2en	CoVoST2

Setup

We provide code in Github repo : https://github.com/h-j-han/SpeechQE

$ git clone https://github.com/h-j-han/SpeechQE.git
$ cd SpeechQE

$ 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. BASE_AUDIO_PATH is the path of downloaded Common Voice dataset.

$ 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 EMNLP24 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}
}