A new release with better error rates, largely from the same data as the previous one.
Metrics*:
- Raw acoustic model (without a scorer)
- Czech Commonvoice 6.1 test dataset: WER: 0.405500, CER: 0.106870, loss: 15.227368
- Vystadial 2016 test dataset: WER: 0.506131, CER: 0.195149, loss: 17.695986
- Large Corpus of Czech Parliament Plenary Hearings test dataset: WER: 0.213377, CER: 0.052676, loss: 20.449242
- ParCzech 3.0 test dataset: WER: 0.209651, CER: 0.061622, loss: 28.217770
- With the attached
czech-large-vocab.scorer:
- Czech Commonvoice 6.1 test dataset: WER: 0.152865, CER: 0.067557, loss: 15.227368**
- Vystadial 2016 test dataset: WER: 0.357435, CER: 0.201479, loss: 17.695986
- Large Corpus of Czech Parliament Plenary Hearings test dataset: WER: 0.097380, CER: 0.036706, loss: 20.449242
- ParCzech 3.0 test dataset: WER: 0.101289, CER: 0.045102, loss: 28.217770
Metrics for the quantized model are circa one percent worse.
*Any clips longer than thirty seconds were discarded
**Better than expected results on the common voice set with the language model might possibly be explained by a partial overlap of the test transcriptions and language model sources, namely Wikipedia and Europarl v7.