Add front end

app.py

@@ -1,3 +1,33 @@
 import gradio as gr
+from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
+import torch
 
-gr.load("models/jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn").launch()
+# Load the model and processor
+processor = Wav2Vec2Processor.from_pretrained("jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn")
+model = Wav2Vec2ForCTC.from_pretrained("jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn")
+
+# Function to transcribe the audio
+def transcribe_audio(audio):
+    input_values = processor(audio["array"], sampling_rate=audio["sampling_rate"], return_tensors="pt").input_values
+
+    # Inference
+    with torch.no_grad():
+        logits = model(input_values).logits
+
+    # Decode the transcription
+    predicted_ids = torch.argmax(logits, dim=-1)
+    transcription = processor.batch_decode(predicted_ids)
+
+    return transcription[0]  # Since we're only handling one audio file
+
+# Set up the Gradio interface
+interface = gr.Interface(
+    fn=transcribe_audio,
+    inputs=gr.Audio(source="microphone", type="filepath"),  # Accept audio files
+    outputs="text",
+    title="Chinese Audio Transcription",
+    description="Upload or record an audio file to transcribe it into Chinese."
+)
+
+# Launch the interface
+interface.launch()