Update app.py

app.py

@@ -2,38 +2,80 @@ import gradio as gr
 import torchaudio
 from transformers import AutoModelForSpeechSeq2Seq, PreTrainedTokenizerFast
 
+# Load model and tokenizer globally with pinned revision
+model = AutoModelForSpeechSeq2Seq.from_pretrained(
+    'usefulsensors/moonshine-tiny',
+    revision="main",
+    trust_remote_code=True
+)
+tokenizer = PreTrainedTokenizerFast.from_pretrained(
+    'usefulsensors/moonshine-tiny',
+    revision="main"
+)
+
 def transcribe_audio(audio_path):
-    # Load and resample audio
-    audio, sr = torchaudio.load(audio_path)
-    if sr != 16000:
-        audio = torchaudio.functional.resample(audio, sr, 16000)
+    if audio_path is None:
+        return "Please provide an audio input."
     
-    # Get transcription
-    tokens = model(audio)
-    transcription = tokenizer.decode(tokens[0], skip_special_tokens=True)
-    return transcription
-
-# Load model and tokenizer globally
-model = AutoModelForSpeechSeq2Seq.from_pretrained('usefulsensors/moonshine-tiny', trust_remote_code=True)
-tokenizer = PreTrainedTokenizerFast.from_pretrained('usefulsensors/moonshine-tiny')
+    try:
+        # Load and resample audio
+        audio, sr = torchaudio.load(audio_path)
+        if sr != 16000:
+            audio = torchaudio.functional.resample(audio, sr, 16000)
+        
+        # Get transcription
+        tokens = model(audio)
+        transcription = tokenizer.decode(tokens[0], skip_special_tokens=True)
+        return transcription
+    except Exception as e:
+        return f"Error processing audio: {str(e)}"
 
 # Create Gradio interface
-demo = gr.Blocks()
-
-with demo:
+with gr.Blocks() as demo:
     gr.Markdown("## Audio Transcription App")
     
     with gr.Tabs():
         with gr.TabItem("Upload Audio"):
-            audio_file = gr.Audio(source="upload", type="filepath")
-            output_text1 = gr.Textbox(label="Transcription")
-            upload_button = gr.Button("Transcribe")
-            upload_button.click(fn=transcribe_audio, inputs=audio_file, outputs=output_text1)
+            audio_file = gr.Audio(
+                sources=["upload"],
+                type="filepath",
+                label="Upload Audio File"
+            )
+            output_text1 = gr.Textbox(
+                label="Transcription",
+                placeholder="Transcription will appear here..."
+            )
+            upload_button = gr.Button("Transcribe Uploaded Audio")
+            upload_button.click(
+                fn=transcribe_audio,
+                inputs=audio_file,
+                outputs=output_text1
+            )
             
         with gr.TabItem("Record Audio"):
-            audio_mic = gr.Audio(source="microphone", type="filepath")
-            output_text2 = gr.Textbox(label="Transcription")
-            record_button = gr.Button("Transcribe")
-            record_button.click(fn=transcribe_audio, inputs=audio_mic, outputs=output_text2)
+            audio_mic = gr.Audio(
+                sources=["microphone"],
+                type="filepath",
+                label="Record Audio"
+            )
+            output_text2 = gr.Textbox(
+                label="Transcription",
+                placeholder="Transcription will appear here..."
+            )
+            record_button = gr.Button("Transcribe Recorded Audio")
+            record_button.click(
+                fn=transcribe_audio,
+                inputs=audio_mic,
+                outputs=output_text2
+            )
+
+    gr.Markdown("""
+    ### Instructions:
+    1. Choose either 'Upload Audio' or 'Record Audio' tab
+    2. Upload an audio file or record using your microphone
+    3. Click the respective 'Transcribe' button
+    4. Wait for the transcription to appear
+    """)
 
-demo.launch()
+if __name__ == "__main__":
+    demo.launch()