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README.md
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@@ -8,10 +8,10 @@ We include an `sc09.zip` file that contains:
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- `testing_list.txt` containing the list of testing utterances
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- the original `LICENSE` file
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We also include a `sc09_quantized.zip` file, which contains examples that were used in our MTurk study (details of which can be found in the SaShiMi paper). In particular, we take 50 random examples from each digit class and run each through a round of mu-law quantization followed by dequantization. This mimics the quantization noise that is experienced by samples generated by autoregressive models that are trained with mu-law quantization.
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-
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We split the data into train-val-test for training SaShiMi models and baselines by following the splits provided in `validation_list.txt` and `testing_list.txt`.
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You can use the following BibTeX entries to appropriately cite prior work related to this dataset if you decide to use this in your research:
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```
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@article{goel2022sashimi,
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- `testing_list.txt` containing the list of testing utterances
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- the original `LICENSE` file
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We split the data into train-val-test for training SaShiMi models and baselines by following the splits provided in `validation_list.txt` and `testing_list.txt`.
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+
We also include a `sc09_quantized.zip` file, which contains examples that were used in our MTurk study (details of which can be found in the SaShiMi paper). In particular, we take 50 random examples from each digit class and run each through a round of mu-law quantization followed by dequantization. This mimics the quantization noise that is experienced by samples generated by autoregressive models that are trained with mu-law quantization.
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+
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You can use the following BibTeX entries to appropriately cite prior work related to this dataset if you decide to use this in your research:
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```
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@article{goel2022sashimi,
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