Add notes about prototype features in tutorials (#2288)

Summary: Pull Request resolved: https://github.com/pytorch/audio/pull/2288

Reviewed By: hwangjeff

Differential Revision: D35099492

Pulled By: mthrok

fbshipit-source-id: 955c5e617469009ae2600d2764d601d794ee916f

examples/tutorials/asr_inference_with_ctc_decoder_tutorial.py

@@ -11,6 +11,21 @@ using CTC loss.
 
 """
 
+######################################################################
+#
+# .. note::
+#
+#    This tutorial requires torchaudio prototype features.
+#
+#    torchaudio prototype features are available on nightly builds.
+#    Please refer to https://pytorch.org/get-started/locally/
+#    for instructions.
+#
+#    The interfaces of prototype features are unstable and subject to
+#    change. Please refer to `the nightly build documentation
+#    <https://pytorch.org/audio/main/>`__ for the up-to-date
+#    API references.
+#
 
 ######################################################################
 # Overview

examples/tutorials/online_asr_tutorial.py

@@ -10,28 +10,22 @@ to perform online speech recognition.
 """
 
 ######################################################################
-# 1. Overview
-# -----------
 #
-# Performing online speech recognition is composed of the following steps
-#
-# 1. Build the inference pipeline
-#    Emformer RNN-T is composed of three components: feature extractor,
-#    decoder and token processor.
-# 2. Format the waveform into chunks of expected sizes.
-# 3. Pass data through the pipeline.
-
-######################################################################
-# 2. Preparation
-# --------------
+# .. note::
 #
-
-######################################################################
+#    This tutorial requires torchaudio with prototype features and
+#    FFmpeg libraries (>=4.1).
 #
-# .. note::
+#    torchaudio prototype features are available on nightly builds.
+#    Please refer to https://pytorch.org/get-started/locally/
+#    for instructions.
 #
-#    The streaming API requires FFmpeg libraries (>=4.1).
+#    The interfaces of prototype features are unstable and subject to
+#    change. Please refer to `the nightly build documentation
+#    <https://pytorch.org/audio/main/>`__ for the up-to-date
+#    API references.
 #
+#    There are multiple ways to install FFmpeg libraries.
 #    If you are using Anaconda Python distribution,
 #    ``conda install -c anaconda ffmpeg`` will install
 #    the required libraries.
@@ -44,6 +38,23 @@ to perform online speech recognition.
 #       !add-apt-repository -y ppa:savoury1/ffmpeg4
 #       !apt-get -qq install -y ffmpeg
 
+######################################################################
+# 1. Overview
+# -----------
+#
+# Performing online speech recognition is composed of the following steps
+#
+# 1. Build the inference pipeline
+#    Emformer RNN-T is composed of three components: feature extractor,
+#    decoder and token processor.
+# 2. Format the waveform into chunks of expected sizes.
+# 3. Pass data through the pipeline.
+
+######################################################################
+# 2. Preparation
+# --------------
+#
+
 import IPython
 import torch
 import torchaudio

examples/tutorials/streaming_api_tutorial.py

@@ -8,6 +8,35 @@ libavfilter provides.
 
 """
 
+######################################################################
+#
+# .. note::
+#
+#    This tutorial requires torchaudio with prototype features and
+#    FFmpeg libraries (>=4.1).
+#
+#    The torchaudio prototype features are available on nightly builds.
+#    Please refer to https://pytorch.org/get-started/locally/
+#    for instructions.
+#
+#    The interfaces of prototype features are unstable and subject to
+#    change. Please refer to `the nightly build documentation
+#    <https://pytorch.org/audio/main/>`__ for the up-to-date
+#    API references.
+#
+#    There are multiple ways to install FFmpeg libraries.
+#    If you are using Anaconda Python distribution,
+#    ``conda install -c anaconda ffmpeg`` will install
+#    the required libraries.
+#
+#    When running this tutorial in Google Colab, the following
+#    command should do.
+#
+#    .. code::
+#
+#       !add-apt-repository -y ppa:savoury1/ffmpeg4
+#       !apt-get -qq install -y ffmpeg
+
 ######################################################################
 # 1. Overview
 # -----------
@@ -44,24 +73,6 @@ libavfilter provides.
 # --------------
 #
 
-######################################################################
-#
-# .. note::
-#
-#    The streaming API requires FFmpeg libraries (>=4.1).
-#
-#    If you are using Anaconda Python distribution,
-#    ``conda install -c anaconda ffmpeg`` will install
-#    the required libraries.
-#
-#    When running this tutorial in Google Colab, the following
-#    command should do.
-#
-#    .. code::
-#
-#       !add-apt-repository -y ppa:savoury1/ffmpeg4
-#       !apt-get -qq install -y ffmpeg
-
 import IPython
 import matplotlib.pyplot as plt
 import torch