Update app.py

app.py

@@ -5,6 +5,7 @@ from dictionaries import calculate_average, transform_dict
 from icon import generate_icon
 from transformers import pipeline
 from timestamp import format_timestamp
+from youtube import get_youtube_video_id
 
 MODEL_NAME = "openai/whisper-medium"
 BATCH_SIZE = 8
@@ -18,16 +19,39 @@ pipe = pipeline(
     device=device,
 )
 
+#Formating
+title = "Whisper Demo: Transcribe Audio"
+MODEL_NAME1 = "jpdiazpardo/whisper-tiny-metal"
+description = ("Transcribe long-form audio inputs with the click of a button! Demo uses the"
+        f" checkpoint [{MODEL_NAME1}](https://huggingface.co/{MODEL_NAME1}) and 🤗 Transformers to transcribe audio files"
+        " of arbitrary length. Check some of the 'cool' examples below")
+
+examples = [["https://www.youtube.com/watch?v=W72Lnz1n-jw&ab_channel=Whitechapel-Topic",None,None
+            "When a Demon Defiles a Witch.wav",True, True],
+            ["https://www.youtube.com/watch?v=BnO3Io0KOl4&ab_channel=MotionlessInWhite-Topic",None,None,
+             "Immaculate Misconception.wav",True, True]]
+
+linkedin = generate_icon("linkedin")
+github = generate_icon("github")
+article = ("<div style='text-align: center; max-width:800px; margin:10px auto;'>"
+            f"<p>{linkedin} <a href='https://www.linkedin.com/in/juanpablodiazp/' target='_blank'>Juan Pablo Díaz Pardo</a><br>"
+            f"{github} <a href='https://github.com/jpdiazpardo' target='_blank'>jpdiazpardo</a></p>")
+
+title = "Scream: Fine-Tuned Whisper model for automatic gutural speech recognition 🤟🤟🤟"
+
+#-------------------------------------------------------------------------------------------------------------------------------
+
 #Define classifier for sentiment analysis
 classifier = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", top_k=None)
 
-def transcribe(file, task, return_timestamps):
-    outputs = pipe(file, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)
+def transcribe(*args):#file, return_timestamps, *kwargs):
+    '''inputs: file, return_timestamps'''
+    outputs = pipe(args[3], batch_size=BATCH_SIZE, generate_kwargs={"task": 'transcribe'}, return_timestamps=True)
     text = outputs["text"]
     timestamps = outputs["chunks"]
 
     #If return timestamps is True, return html text with timestamps format
-    if return_timestamps==True:
+    if args[4]==True:
       spider_text = [f"{chunk['text']}" for chunk in timestamps] #Text for spider chart without timestamps
       timestamps = [f"[{format_timestamp(chunk['timestamp'][0])} -> {format_timestamp(chunk['timestamp'][1])}] {chunk['text']}" for chunk in timestamps]
 
@@ -36,47 +60,70 @@ def transcribe(file, task, return_timestamps):
       spider_text = timestamps
 
     text = "<br>".join(str(feature) for feature in timestamps)
-    text = f"<h4>Transcription</h4><div style='overflow-y: scroll; height: 250px;'>{text}</div>"
+    text = f"<h4>Transcription</h4><div style='overflow-y: scroll; height: 150px;'>{text}</div>"
 
     spider_text = "\n".join(str(feature) for feature in spider_text)
+    trans_dict=[transform_dict(classifier.predict(t)[0]) for t in spider_text.split("\n")]
+    av_dict = calculate_average(trans_dict)
+    fig = spider_chart(av_dict)
     
-    fig = spider_chart(calculate_average([transform_dict(classifier.predict(t)[0]) for t in spider_text.split("\n")]))
-
-    return file, text, fig
-
-
-inputs = [gr.Audio(source="upload", label="Audio file", type="filepath"),
-        gr.Radio(["transcribe"], label="Task", value="transcribe"),
-        gr.Checkbox(value=True, label="Return timestamps")]
-
-outputs = [gr.Audio(label="Processed Audio", type="filepath"),
-        gr.outputs.HTML("text"),
-           gr.Plot(label="fig")]
-
-title = "Whisper Demo: Transcribe Audio"
-
-MODEL_NAME1 = "jpdiazpardo/whisper-tiny-metal"
-
-description = ("Transcribe long-form audio inputs with the click of a button! Demo uses the"
-        f" checkpoint [{MODEL_NAME1}](https://huggingface.co/{MODEL_NAME1}) and 🤗 Transformers to transcribe audio files"
-        " of arbitrary length. Check some of the 'cool' examples below")
-
-examples = [["When a Demon Defiles a Witch.wav","transcribe",True],
-            ["Immaculate Misconception.wav","transcribe", True]]
-
-
-linkedin = generate_icon("linkedin")
-github = generate_icon("github")
-
-article = ("<div style='text-align: center; max-width:800px; margin:10px auto;'>"
-            f"<p>{linkedin} <a href='https://www.linkedin.com/in/juanpablodiazp/' target='_blank'>Juan Pablo Díaz Pardo</a><br>"
-            f"{github} <a href='https://github.com/jpdiazpardo' target='_blank'>jpdiazpardo</a></p>"
-          )
+    return args[3], text, fig, av_dict
 
+def filter(choice):
+    if choice=="YouTube":
+        return yt_link.update(interactive=True), audio_input.update(interactive=False)
+    elif choice == "Upload File":
+        return yt_link.update(value=None,interactive=False), audio_input.update(interactive=True)
+    else:
+        return yt_link.update(interactive=False), audio_input.update(interactive=False)
+
+
+embed_html = '<iframe src="https://www.youtube.com/embed/YOUTUBE_ID'\
+              'title="YouTube video player" frameborder="0" allow="accelerometer;'\
+              'autoplay; clipboard-write; encrypted-media; gyroscope;'\
+              'picture-in-picture" allowfullscreen></iframe>'
+
+def download(link):
+    subprocess.run(['python3', 'youtubetowav.py', link])
+    return thumbnail.update(value=embed_html.replace("YOUTUBE_ID",get_youtube_video_id(link)), visible=True)
+
+def hide_sa(value):
+  if value == True:
+    return sa_plot.update(visible=True), sa_frequency.update(visible=True)
+  else:
+    return sa_plot.update(visible=False), sa_frequency.update(visible=False)
+  
+#Input components        
+yt_link = gr.Textbox(value=None,label="YouTube link", info = "Optional: Copy and paste YouTube URL")
+audio_input = gr.Audio(source="upload", type="filepath", label="Upload audio file for transcription")
+download_button = gr.Button("Download")
+thumbnail = gr.HTML(value=embed_html, visible=False)
+sa_checkbox = gr.Checkbox(value=True, label="Sentiment analysis")
+
+inputs = [yt_link, #0
+          download_button, #1
+          thumbnail, #2
+          audio_input, #3
+          gr.Checkbox(value=True, label="Return timestamps"), #4
+          sa_checkbox] #5
+
+#Ouput components
+audio_out = gr.Audio(label="Processed Audio", type="filepath", info = "Vocals only") 
+sa_plot = gr.Plot(label="Sentiment Analysis")
+sa_frequency = gr.Label(label="Frequency")
+
+outputs = [audio_out, gr.outputs.HTML("text"), sa_plot, sa_frequency]
+
+with gr.Blocks() as demo:
+  download_button.click(download, inputs=[yt_link], outputs=[thumbnail])
+  sa_checkbox.change(hide_sa, inputs=[sa_checkbox], outputs=[sa_plot, sa_frequency])
+
+
+  with gr.Column():
+      gr.Interface(title = title, fn=transcribe, inputs = inputs, outputs = outputs, 
+                   description=description, cache_examples=True, allow_flagging="never", article = article , examples=examples)
+            
+demo.queue(concurrency_count=3)
+demo.launch(debug = True)
 
-title = "Scream: Fine-Tuned Whisper model for automatic gutural speech recognition 🤟🤟🤟"
-
-demo = gr.Interface(title = title, fn=transcribe, inputs = inputs, outputs = outputs, description=description, cache_examples=True, allow_flagging="never", article = article , examples=examples)
 
-demo.queue(concurrency_count=3)
-demo.launch(debug = True)