Updated docs with new checkpoint
@@ -79,7 +79,9 @@ The table below contains description and results of
Deep Speech 2 based models available in OpenSeq2Seq.
WER-512 and WER-2048 is word error rate obtained with beam width of 512 and 2048
-correspondingly. For more details about model descriptions and training setup,
+correspondingly. For beam width of 2048 we also used ``batch_size_per_gpu = 1``
+to eliminate the effect of `cudnn padding issue <https://github.com/NVIDIA/OpenSeq2Seq/issues/69>`_.
+For more details about model descriptions and training setup,
have a look at the `configuration files <https://github.com/NVIDIA/OpenSeq2Seq/blob/master/example_configs/speech2text/>`_.
.. list-table::
@@ -101,13 +103,13 @@ have a look at the `configuration files <https://github.com/NVIDIA/OpenSeq2Seq/b
GRU layers with 800 units.
- `link <https://drive.google.com/file/d/1gfGg3DzXviNhYlIyxl12gWp47R8Uz-Bf/view?usp=sharing>`_
* - `ds2_medium_4gpus.py <https://github.com/NVIDIA/OpenSeq2Seq/blob/master/example_configs/speech2text/ds2_medium_4gpus.py>`_
- - 5.96%
- - 5.50%
+ - 6.12%
+ - 5.49%
- This model was trained for 50 epochs using Adam on the full
LibriSpeech in a few days using Horovod on four GPUs.
- This model has 3 convolutional layers and 3 unidirectional
GRU layers with 1024 units.
- - Coming soon.
+ - `link <https://drive.google.com/file/d/1XpnyZzMaO38RE4dSOJZkcaJ3T8B0lxKe/view?usp=sharing>`_
* - `ds2_small_1gpu.py <https://github.com/NVIDIA/OpenSeq2Seq/blob/master/example_configs/speech2text/ds2_small_1gpu.py>`_
- 11.77%
- 9.32%
