multiline on one line.

Author: vincentqb

intermediate_source/speech_command_recognition_with_torchaudio_tutorial.py

@@ -481,39 +481,19 @@ def predict(tensor):
 #
 
 
-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(seconds=1):
 
     from google.colab import output as colab_output
     from base64 import b64decode
     from io import BytesIO
     from pydub import AudioSegment
 
-    display(ipd.Javascript(RECORD))
+
+    RECORD = b"const sleep  = time => new Promise(resolve => setTimeout(resolve, time))\nconst b2text = blob => new Promise(resolve => {\n  const reader = new FileReader()\n  reader.onloadend = e => resolve(e.srcElement.result)\n  reader.readAsDataURL(blob)\n})\nvar record = time => new Promise(async resolve => {\n  stream = await navigator.mediaDevices.getUserMedia({ audio: true })\n  recorder = new MediaRecorder(stream)\n  chunks = []\n  recorder.ondataavailable = e => chunks.push(e.data)\n  recorder.start()\n  await sleep(time)\n  recorder.onstop = async ()=>{\n    blob = new Blob(chunks)\n    text = await b2text(blob)\n    resolve(text)\n  }\n  recorder.stop()\n})"
+    RECORD = RECORD.decode("ascii")
+
     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])