Update app.py

app.py

@@ -3,6 +3,7 @@ import numpy as np
 import torch
 from transformers import pipeline, AutoModelForSpeechSeq2Seq, AutoProcessor
 
+# Initialize the model and processor
 model_id = 'openai/whisper-large-v3'
 device = "cuda:0" if torch.cuda.is_available() else "cpu"
 torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
@@ -11,19 +12,24 @@ processor = AutoProcessor.from_pretrained(model_id)
 
 pipe_asr = pipeline("automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, max_new_tokens=128, chunk_length_s=15, batch_size=16, torch_dtype=torch_dtype, device=device, return_timestamps=True)
 
-base_audio_drive = "/data/audio"
-
 def transcribe_function(audio):
-    sr, y = audio[0], audio[1]
+    sr, y = audio
     y = y.astype(np.float32) / np.max(np.abs(y))
     result = pipe_asr({"array": y, "sampling_rate": sr}, return_timestamps=False)
     full_text = result.get("text", "")
     return full_text
 
-def gradio_transcribe(audio):
-    sr, y = audio
-    return transcribe_function((sr, y))
+with gr.Blocks() as demo:
+    gr.Markdown("# Voice to Text Transcription\nTranscribe your voice input to text using a pre-trained Whisper model.")
+    
+    with gr.Row():
+        with gr.Column():
+            audio_input = gr.Audio(source="microphone", type="numpy", label="Speak Here")
+            transcribe_button = gr.Button("Transcribe")
+        
+        with gr.Column():
+            output_text = gr.Textbox(label="Transcription")
 
-iface = gr.Interface(fn=gradio_transcribe, inputs=gr.inputs.Audio(source="microphone", type="numpy"), outputs="text", title="Voice to Text Transcription", description="Transcribe your voice input to text using a pre-trained Whisper model.")
+    transcribe_button.click(transcribe_function, inputs=audio_input, outputs=output_text)
 
-iface.launch()
+demo.launch()