--- language: - bn license: cc-by-nc-4.0 task_categories: - automatic-speech-recognition dataset_info: features: - name: audio dtype: audio - name: text dtype: string - name: duration dtype: float64 - name: category dtype: string - name: source dtype: string splits: - name: train num_bytes: 219091915.875 num_examples: 1753 download_size: 214321460 dataset_size: 219091915.875 configs: - config_name: default data_files: - split: train path: data/train-* --- # MegaBNSpeech Test Data To evaluate the performance of the models, we used four test sets. Two of these were developed as part of the MegaBNSpeech corpus, while the remaining two (Fleurs and Common Voice) are commonly used test sets that are widely recognized by the speech community. ## Use dataset library: ```python from datasets import load_dataset dataset = load_dataset("hishab/MegaBNSpeech_Test_Data") ``` ## Reported Word error rate (WER) /character error rate (CER) on four test sets using four ASR systems | Category | Duration (hr) | Hishab BN Fastconformer | Google MMS | OOD-speech | |-------------------- | -------------- | ------------ | ---------- | ----------- | | MegaBNSpeech-YT | 8.1 | 6.4/3.39 | 28.3/18.88 | 51.1/23.49 | | MegaBNSpeech-Tel | 1.9 | ∗40.7/24.38 | ∗59/41.26 | ∗76.8/39.36 | ## Reported Word error rate (WER) /character error rate (CER) on different categories present in Hishab BN FastConformer | Category | Duration (hr) | Hishab BN FastConformer | Google MMS | OOD-speech | |-------------------- | -------------- | ------------ | ---------- | ----------- | | News | 1.21 | 2.5/1.21 | 18.9/10.46 | 52.2/21.65 | | Talkshow | 1.39 | 6/3.29 | 28/18.71 | 48.8/21.5 | | Courses | 3.81 | 6.8/3.79 | 30.8/21.64 | 50.2/23.52 | | Drama | 0.03 | 10.3/7.47 | 37.3/27.43 | 64.3/32.74 | | Science | 0.26 | 5/1.92 | 20.6/11.4 | 45.3/19.93 | | Vlog | 0.18 | 11.3/6.69 | 33/22.9 | 57.9/27.18 | | Recipie | 0.58 | 7.5/3.29 | 26.4/16.6 | 53.3/26.89 | | Waz | 0.49 | 9.6/5.45 | 33.3/23.1 | 57.3/27.46 | | Movie | 0.1 | 8/4.64 | 35.2/23.88 | 64.4/34.96 |