2023-08-09 nightly release (9f5fa84)

examples/asr/librispeech_conformer_rnnt/README.md

@@ -12,7 +12,7 @@ To build TorchAudio from source, refer to the [contributing guidelines](https://
 
 ### Install additional dependencies
 ```bash
-pip install pytorch-lightning sentencepiece
+pip install pytorch-lightning sentencepiece tensorboard
 ```
 
 ## Usage
@@ -27,7 +27,7 @@ pip install pytorch-lightning sentencepiece
 
 Sample SLURM command:
 ```
-srun --cpus-per-task=12 --gpus-per-node=8 -N 4 --ntasks-per-node=8 python train.py --exp_dir ./experiments --librispeech_path ./librispeech/ --global_stats_path ./global_stats.json --sp_model_path ./spm_unigram_1023.model --epochs 160
+srun --cpus-per-task=12 --gpus-per-node=8 -N 4 --ntasks-per-node=8 python train.py --exp-dir ./experiments --librispeech-path ./librispeech/ --global-stats-path ./global_stats.json --sp-model-path ./spm_unigram_1023.model --epochs 160
 ```
 
 ### Evaluation
@@ -36,7 +36,7 @@ srun --cpus-per-task=12 --gpus-per-node=8 -N 4 --ntasks-per-node=8 python train.
 
 Sample SLURM command:
 ```
-srun python eval.py --checkpoint_path ./experiments/checkpoints/epoch=159.ckpt --librispeech_path ./librispeech/ --sp_model_path ./spm_unigram_1023.model --use_cuda
+srun python eval.py --checkpoint-path ./experiments/checkpoints/epoch=159.ckpt --librispeech-path ./librispeech/ --sp-model-path ./spm_unigram_1023.model --use-cuda
 ```
 
 The table below contains WER results for various splits.

examples/asr/librispeech_conformer_rnnt/train.py

@@ -6,7 +6,7 @@
 from lightning import ConformerRNNTModule
 from pytorch_lightning import seed_everything, Trainer
 from pytorch_lightning.callbacks import LearningRateMonitor, ModelCheckpoint
-from pytorch_lightning.plugins import DDPPlugin
+from pytorch_lightning.strategies import DDPStrategy
 from transforms import get_data_module
 
 
@@ -39,9 +39,9 @@ def run_train(args):
         default_root_dir=args.exp_dir,
         max_epochs=args.epochs,
         num_nodes=args.nodes,
-        gpus=args.gpus,
+        devices=args.gpus,
         accelerator="gpu",
-        strategy=DDPPlugin(find_unused_parameters=False),
+        strategy=DDPStrategy(find_unused_parameters=False),
         callbacks=callbacks,
         reload_dataloaders_every_n_epochs=1,
         gradient_clip_val=10.0,

examples/avsr/README.md

@@ -12,7 +12,7 @@
 
 This directory contains the training recipe for real-time audio, visual, and audio-visual speech recognition (ASR, VSR, AV-ASR) models, which is an extension of [Auto-AVSR](https://arxiv.org/abs/2303.14307).
 
-Please refer to [this tutorial]() for real-time AV-ASR inference from microphone and camera.
+Please refer to [this tutorial](https://pytorch.org/audio/main/tutorials/device_avsr.html) for real-time AV-ASR inference from microphone and camera.
 
 ## Preparation