Comments (2)
You can take WSJ recipe for reference. Like in stage 0&1, you need to use Kaldi for preparing feature/text files (you can use some of the scripts provided in Espresso for doing that). Stage 2 is creating dictionary and tokenizing text into sequences of characters depending on your language (AFAIK Gujarati can follow the same procedure, possibly with minor modifications). The following stages are training LM and E2E models and then decoding.
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I'm going to close it. If you have further questions you can still raise them here.
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Related Issues (20)
- Stream ASR HOT 1
- TypeError: get_asr_dataset_from_json() got an unexpected keyword argument 'combined' HOT 1
- Different WERs when decoding with different batch size (--max-sentences) HOT 5
- ONNX exportation of speech_lstm based model HOT 1
- Build PyChain with CPU HOT 6
- WER difference when decode with different batch size HOT 5
- SpecAug slows down training time HOT 4
- WSJ Recipe: "wsj_data_prep.sh: Spot check of command line arguments failed" HOT 1
- SpecAugment is not used due to a typo in the prefetch_called assignment HOT 1
- Getting OOM error at the middle of the training in asr_swbd recipe on lstm encoder decoder model HOT 4
- How can I find Kaldi directory? HOT 37
- Google Colab research creation HOT 1
- token_text as outputs HOT 2
- Which is the difference between stage 5 and stage 7 HOT 1
- Error in training stage of run_chain_e2e_bichar.sh: 'odict_items' object is not an iterator HOT 4
- Transformer LM in ASR HOT 2
- 'frame_subsampling_factor' in chain e2e setup HOT 2
- hydra.errors.ConfigCompositionException: Could not override 'task.data'. HOT 1
- Android Espresso not able to test fragement
- SHA hashes in 'main' branch are different from those in the 'origin/main' HOT 2
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