Tutorial for training transformer for ASR.

PiperOrigin-RevId: 186011568

Author: T2T Team

docs/tutorials/asr_with_transformer.md

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+# Automatic Speech Recognition (ASR) with Transformer
+
+## Data set
+
+This tutorial uses the publicly available
+[Librispeech](http://www.openslr.org/12/) ASR corpus.
+
+## Generate the dataset
+
+To generate the dataset use `t2t-datagen`. You need to create environment
+variables for a data directory `DATA_DIR` where the data is stored and for a
+temporary directory `TMP_DIR` where necessary data is downloaded.
+
+As the audio import in `t2t-datagen` uses `sox` to generate normalized
+waveforms, please install it as appropriate (e.g. `apt-get install sox`).
+
+```
+t2t-datagen --problem=librispeech --data_dir=$DATA_DIR --tmp_dir=$TMP_DIR
+```
+
+You can also use smaller versions of the dataset by replacing `librispeech` with
+`librispeech_clean` or `librispeech_clean_small`
+
+## Training on GPUs
+
+To train a model on GPU set up`OUT_DIR` and run the trainer:
+
+```
+t2t-trainer \
+  --model=transformer \
+  --hparams_set=transformer_librispeech \
+  --problems=librispeech \
+  --train_steps=120000 \
+  --eval_steps=3 \
+  --local_eval_frequency=100 \
+  --data_dir=$DATA_DIR \
+  --output_dir=$OUT_DIR
+```
+
+This model should achieve approximately 22% accuracy per sequence after
+approximately 80,000 steps.
+
+## Training on Cloud TPUs
+
+To train a model on TPU set up `OUT_DIR` and run the trainer:
+
+```
+t2t-trainer \
+  --model=transformer \
+  --hparams_set=transformer_librispeech_tpu \
+  --problems=librispeech \
+  --train_steps=120000 \
+  --eval_steps=3 \
+  --local_eval_frequency=100 \
+  --data_dir=$DATA_DIR \
+  --output_dir=$OUT_DIR \
+  --cloud_tpu \
+  --cloud_delete_on_done
+```
+
+For more information, see [Tensor2Tensor's
+documentation](https://github.com/tensorflow/tensor2tensor/tree/master/docs/cloud_tpu.md)
+for Tensor2Tensor on Cloud TPUs, or the [official Google Cloud Platform
+documentation](https://cloud.google.com/tpu/docs/tutorials/transformer) for
+Cloud TPUs.