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Summary:
Adds SentencePiece model training script for LibriSpeech Emformer RNN-T example recipe; updates readme with references.

Pull Request resolved: https://github.com/pytorch/audio/pull/2218

Reviewed By: nateanl

Differential Revision: D34177295

Pulled By: hwangjeff

fbshipit-source-id: 9f32805af792fb8c6f834f2812e20104177a6c43

Summary:
We refactored the demo script that can apply RNNT decoding using both `torchaudio.pipelines.EMFORMER_RNNT_BASE_LIBRISPEECH` and `torchaudio.prototype.pipelines.EMFORMER_RNNT_BASE_TEDLIUM3` in both streaming and non-streaming mode. (The first hypothesis prediction is streaming and the second one is non-streaming).

We convert each token id sequence to word pieces and then manually join the word pieces. This allows us to preserve leading whitespaces on output strings and therefore account for word breaks and continuations across token processor invocations, which is particularly useful when performing streaming ASR.

https://user-images.githubusercontent.com/8653221/153627956-f0806f18-3c1c-44df-ac07-ec2def58a0cf.mov

Pull Request resolved: https://github.com/pytorch/audio/pull/2203

Reviewed By: carolineechen

Differential Revision: D34006388

Pulled By: nateanl

fbshipit-source-id: 3d31173ee10cdab8a2f5802570e22b50fcce5632

Summary:
Consolidates LibriSpeech and TED-LIUM Release 3 Emformer RNN-T training recipes in a single directory.

Pull Request resolved: https://github.com/pytorch/audio/pull/2212

Reviewed By: mthrok

Differential Revision: D34120104

Pulled By: hwangjeff

fbshipit-source-id: 29c6e27195d5998f76d67c35b718110e73529456