[release 0.12] Remove examples code using prototype features from release (#2497)

Author: hwangjeff

examples/asr/emformer_rnnt/README.md

@@ -15,7 +15,7 @@ This directory contains sample implementations of training and evaluation pipeli
 ### Pipeline Demo
 
 [`pipeline_demo.py`](./pipeline_demo.py) demonstrates how to use the `EMFORMER_RNNT_BASE_LIBRISPEECH`
-or `EMFORMER_RNNT_BASE_TEDLIUM3` bundle that wraps a pre-trained Emformer RNN-T produced by the corresponding recipe below to perform streaming and full-context ASR on several audio samples.
+bundle that wraps a pre-trained Emformer RNN-T produced by the LibriSpeech recipe below to perform streaming and full-context ASR on several audio samples.
 
 ## Model Types
 
@@ -67,8 +67,6 @@ The table below contains WER results for dev and test subsets of TED-LIUM releas
 | dev         |       0.108  |
 | test        |       0.098  |
 
-[`tedlium3/eval_pipeline.py`](./tedlium3/eval_pipeline.py) evaluates the pre-trained `EMFORMER_RNNT_BASE_TEDLIUM3` bundle on the dev and test sets of TED-LIUM release 3. Running the script should produce WER results that are identical to those in the above table.
-
 ### MuST-C release v2.0
 
 The MuST-C model is configured with a vocabulary size of 500. Consequently, the MuST-C model's last linear layer in the joiner has an output dimension of 501 (500 + 1 to account for the blank symbol). In contrast to those of the datasets for the above two models, MuST-C's transcripts are cased and punctuated; we preserve the casing and punctuation when training the SentencePiece model.

examples/asr/emformer_rnnt/pipeline_demo.py

@@ -13,13 +13,8 @@
 
 import torch
 import torchaudio
-from common import MODEL_TYPE_LIBRISPEECH, MODEL_TYPE_MUSTC, MODEL_TYPE_TEDLIUM3
-from mustc.dataset import MUSTC
+from common import MODEL_TYPE_LIBRISPEECH
 from torchaudio.pipelines import EMFORMER_RNNT_BASE_LIBRISPEECH, RNNTBundle
-from torchaudio.prototype.pipelines import (
-    EMFORMER_RNNT_BASE_MUSTC,
-    EMFORMER_RNNT_BASE_TEDLIUM3,
-)
 
 logger = logging.getLogger(__name__)
 
@@ -35,14 +30,6 @@ class Config:
         partial(torchaudio.datasets.LIBRISPEECH, url="test-clean"),
         EMFORMER_RNNT_BASE_LIBRISPEECH,
     ),
-    MODEL_TYPE_MUSTC: Config(
-        partial(MUSTC, subset="tst-COMMON"),
-        EMFORMER_RNNT_BASE_MUSTC,
-    ),
-    MODEL_TYPE_TEDLIUM3: Config(
-        partial(torchaudio.datasets.TEDLIUM, release="release3", subset="test"),
-        EMFORMER_RNNT_BASE_TEDLIUM3,
-    ),
 }