We lifted 9 functions out of this paper's own repositories and ran 8 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| lwang114/graphunsupasr | canonical | 8 of 9 |
| Function | Status | Where it lives |
|---|---|---|
| detect_peaks | Ran | lwang114/graphunsupasr/espum/models/utils.py code served (permissive licence) · get_code("d996cf06772c4c8f") |
| get_assignments | Ran | lwang114/graphunsupasr/w2vu_generate.py code served (permissive licence) · get_code("5d7e11b26ae7fca1") |
| get_convolution_index_set | Ran | lwang114/graphunsupasr/kaldi_self_train/st/steps/nnet2/make_multisplice_configs.py code served (permissive licence) · get_code("9d45b973f1eb1b50") |
| get_dataset_itr | Ran | lwang114/graphunsupasr/w2vu_generate.py code served (permissive licence) · get_code("1a82e55d7de6917c") |
| load_lex | Ran | lwang114/graphunsupasr/kaldi_self_train/st/local/unsup_select.py code served (permissive licence) · get_code("801f1e8e0d260afb") |
| load_tra | Ran | lwang114/graphunsupasr/kaldi_self_train/st/local/unsup_select.py code served (permissive licence) · get_code("c79df675e2681009") |
| max_min_norm | Ran | lwang114/graphunsupasr/espum/models/utils.py code served (permissive licence) · get_code("bed7cf8a53b7f8b7") |
| replicate_first_k_frames | Ran | lwang114/graphunsupasr/espum/models/utils.py code served (permissive licence) · get_code("b962727751797747") |
| parse_splice_string | Not yet run | lwang114/graphunsupasr/kaldi_self_train/st/steps/nnet2/make_multisplice_configs.py code served (permissive licence) · get_code("f1c7206adbb45460") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Training unsupervised speech recognition systems presents challenges due to GAN-associated instability, misalignment between speech and text, and significant memory demands. To tackle these challenges, we introduce a novel ASR system, ESPUM. This system harnesses the power of lower-order N-skipgrams (up to N=3) combined with positional unigram statistics gathered from a small batch of samples. Evaluated on the TIMIT benchmark, our model showcases competitive performance in ASR and phoneme segmentation tasks. Access our publicly available code at https://github.com/lwang114/GraphUnsupASR.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2310.02382")
get_code_for_paper("2310.02382")
have("2310.02382")
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