Update app.py

app.py

@@ -8,6 +8,7 @@ BATCH_SIZE = 8
 
 device = 0 if torch.cuda.is_available() else "cpu"
 
+# Initialize the pipeline
 pipe = pipeline(
     task="automatic-speech-recognition",
     model=MODEL_NAME,
@@ -15,28 +16,20 @@ pipe = pipeline(
     device=device,
 )
 
-
-# Copied from https://github.com/openai/whisper/blob/c09a7ae299c4c34c5839a76380ae407e7d785914/whisper/utils.py#L50
 def format_timestamp(seconds: float, always_include_hours: bool = False, decimal_marker: str = "."):
     if seconds is not None:
         milliseconds = round(seconds * 1000.0)
-
         hours = milliseconds // 3_600_000
         milliseconds -= hours * 3_600_000
-
         minutes = milliseconds // 60_000
         milliseconds -= minutes * 60_000
-
         seconds = milliseconds // 1_000
         milliseconds -= seconds * 1_000
-
         hours_marker = f"{hours:02d}:" if always_include_hours or hours > 0 else ""
         return f"{hours_marker}{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}"
     else:
-        # we have a malformed timestamp so just return it as is
         return seconds
 
-
 def transcribe(file, task, return_timestamps):
     outputs = pipe(file, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=return_timestamps)
     text = outputs["text"]
@@ -49,53 +42,35 @@ def transcribe(file, task, return_timestamps):
         text = "\n".join(str(feature) for feature in timestamps)
     return text
 
-
-demo = gr.Blocks()
-
-mic_transcribe = gr.Interface(
-    fn=transcribe,
-    inputs=[
-        gr.inputs.Audio(source="microphone", type="filepath", optional=True),
-        gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe"),
-        gr.inputs.Checkbox(default=False, label="Return timestamps"),
-    ],
-    outputs="text",
-    layout="horizontal",
-    theme="huggingface",
-    title="Whisper Demo: Transcribe Audio",
-    description=(
-        "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the"
-        f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
-        " of arbitrary length."
-    ),
-    allow_flagging="never",
-)
-
-file_transcribe = gr.Interface(
-    fn=transcribe,
-    inputs=[
-        gr.inputs.Audio(source="upload", optional=True, label="Audio file", type="filepath"),
-        gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe"),
-        gr.inputs.Checkbox(default=False, label="Return timestamps"),
-    ],
-    outputs="text",
-    layout="horizontal",
-    theme="huggingface",
-    title="Whisper Demo: Transcribe Audio",
-    description=(
-        "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the"
-        f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
-        " of arbitrary length."
-    ),
-    examples=[
-        ["./example.flac", "transcribe", False],
-        ["./example.flac", "transcribe", True],
-    ],
-    cache_examples=True,
-    allow_flagging="never",
-)
-
-with demo:
-    gr.TabbedInterface([mic_transcribe, file_transcribe], ["Transcribe Microphone", "Transcribe Audio File"])
-
-demo.launch(enable_queue=True)
+# Use Blocks and modern Gradio components
+with gr.Blocks() as demo:
+    
+    with gr.TabbedInterface(["Transcribe Microphone", "Transcribe Audio File"]) as tabs:
+
+        with gr.TabItem("Transcribe Microphone"):
+            mic_audio = gr.Audio(source="microphone", type="filepath", label="Record Speech")
+            task = gr.Radio(["transcribe", "translate"], label="Task", value="transcribe")
+            return_timestamps = gr.Checkbox(label="Return timestamps")
+            mic_output = gr.Textbox(label="Transcription")
+            mic_button = gr.Button("Transcribe")
+            
+            mic_button.click(
+                fn=transcribe,
+                inputs=[mic_audio, task, return_timestamps],
+                outputs=mic_output,
+            )
+        
+        with gr.TabItem("Transcribe Audio File"):
+            file_audio = gr.Audio(source="upload", type="filepath", label="Upload Audio File")
+            task = gr.Radio(["transcribe", "translate"], label="Task", value="transcribe")
+            return_timestamps = gr.Checkbox(label="Return timestamps")
+            file_output = gr.Textbox(label="Transcription")
+            file_button = gr.Button("Transcribe")
+            
+            file_button.click(
+                fn=transcribe,
+                inputs=[file_audio, task, return_timestamps],
+                outputs=file_output,
+            )
+
+demo.launch(enable_queue=True)