non-colab recording in speech tutorial

index.rst

@@ -121,7 +121,7 @@ Welcome to PyTorch Tutorials
    :header: Speech Command Recognition
    :card_description: Learn how to correctly format an audio dataset and then train/test an audio classifier network on the dataset.
    :image: _static/img/thumbnails/cropped/torchaudio-speech.png
-   :link: intermediate/speech_command_recognition_with_torchaudio.html
+   :link: intermediate/speech_command_recognition_with_torchaudio_tutorial.html
    :tags: Audio
 
 .. Text
@@ -467,8 +467,7 @@ Additional Resources
    :caption: Audio
 
    beginner/audio_preprocessing_tutorial
-   intermediate/speech_command_recognition_with_torchaudio
-
+   intermediate/speech_command_recognition_with_torchaudio_tutorial
 
 .. toctree::
    :maxdepth: 2

intermediate_source/speech_command_recognition_with_torchaudio.py → intermediate_source/speech_command_recognition_with_torchaudio_tutorial.py

@@ -16,26 +16,25 @@
 
 """
 
-# Uncomment the following line to run in Google Colab
+# Uncomment the line corresponding to your "runtime type" to run in Google Colab
 
 # CPU:
-# !pip install torch==1.7.0+cpu torchvision==0.8.1+cpu torchaudio==0.7.0 -f https://download.pytorch.org/whl/torch_stable.html
+# !pip install pydub torch==1.7.0+cpu torchvision==0.8.1+cpu torchaudio==0.7.0 -f https://download.pytorch.org/whl/torch_stable.html
 
 # GPU:
-# !pip install torch==1.7.0+cu101 torchvision==0.8.1+cu101 torchaudio==0.7.0 -f https://download.pytorch.org/whl/torch_stable.html
-
-# For interactive demo at the end:
-# !pip install pydub
+# !pip install pydub torch==1.7.0+cu101 torchvision==0.8.1+cu101 torchaudio==0.7.0 -f https://download.pytorch.org/whl/torch_stable.html
 
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.optim as optim
 import torchaudio
+import sys
 
 import matplotlib.pyplot as plt
 import IPython.display as ipd
-from tqdm.notebook import tqdm
+
+from tqdm import tqdm
 
 
 ######################################################################
@@ -482,39 +481,40 @@ def predict(tensor):
 # will record one second of audio and try to classify it.
 #
 
-from google.colab import output as colab_output
-from base64 import b64decode
-from io import BytesIO
-from pydub import AudioSegment
-
-
-RECORD = """
-const sleep  = time => new Promise(resolve => setTimeout(resolve, time))
-const b2text = blob => new Promise(resolve => {
-  const reader = new FileReader()
-  reader.onloadend = e => resolve(e.srcElement.result)
-  reader.readAsDataURL(blob)
-})
-var record = time => new Promise(async resolve => {
-  stream = await navigator.mediaDevices.getUserMedia({ audio: true })
-  recorder = new MediaRecorder(stream)
-  chunks = []
-  recorder.ondataavailable = e => chunks.push(e.data)
-  recorder.start()
-  await sleep(time)
-  recorder.onstop = async ()=>{
-    blob = new Blob(chunks)
-    text = await b2text(blob)
-    resolve(text)
-  }
-  recorder.stop()
-})
-"""
 
+def record_colab(seconds=1):
+
+    from google.colab import output as colab_output
+    from base64 import b64decode
+    from io import BytesIO
+    from pydub import AudioSegment
+
+    RECORD = (
+        b"const sleep  = time => new Promise(resolve => setTimeout(resolve, time))\n"
+        b"const b2text = blob => new Promise(resolve => {\n"
+        b"  const reader = new FileReader()\n"
+        b"  reader.onloadend = e => resolve(e.srcElement.result)\n"
+        b"  reader.readAsDataURL(blob)\n"
+        b"})\n"
+        b"var record = time => new Promise(async resolve => {\n"
+        b"  stream = await navigator.mediaDevices.getUserMedia({ audio: true })\n"
+        b"  recorder = new MediaRecorder(stream)\n"
+        b"  chunks = []\n"
+        b"  recorder.ondataavailable = e => chunks.push(e.data)\n"
+        b"  recorder.start()\n"
+        b"  await sleep(time)\n"
+        b"  recorder.onstop = async ()=>{\n"
+        b"    blob = new Blob(chunks)\n"
+        b"    text = await b2text(blob)\n"
+        b"    resolve(text)\n"
+        b"  }\n"
+        b"  recorder.stop()\n"
+        b"})"
+    )
+    RECORD = RECORD.decode("ascii")
 
-def record(seconds=1):
-    display(ipd.Javascript(RECORD))
     print(f"Recording started for {seconds} seconds.")
+    display(ipd.Javascript(RECORD))
     s = colab_output.eval_js("record(%d)" % (seconds * 1000))
     print("Recording ended.")
     b = b64decode(s.split(",")[1])
@@ -525,6 +525,32 @@ def record(seconds=1):
     return torchaudio.load(filename)
 
 
+def record_noncolab(seconds=1):
+
+    import sounddevice
+    import scipy.io.wavfile
+
+    sample_rate = 44100
+
+    print(f"Recording started for {seconds} seconds.")
+    myrecording = sounddevice.rec(
+        int(seconds * sample_rate), samplerate=sample_rate, channels=1
+    )
+    sounddevice.wait()
+    print("Recording ended.")
+
+    filename = "_audio.wav"
+    scipy.io.wavfile.write(filename, sample_rate, myrecording)
+    return torchaudio.load(filename)
+
+
+# Detect whether notebook runs in google colab
+if "google.colab" in sys.modules:
+    record = record_colab
+else:
+    record = record_noncolab
+
+
 waveform, sample_rate = record()
 print(f"Predicted: {predict(waveform)}.")
 ipd.Audio(waveform.numpy(), rate=sample_rate)