add app.py

app.py

@@ -0,0 +1,58 @@
+import gradio as gr
+import numpy as np
+from transformers import pipeline
+
+# Load pidgin model via pipeline
+transcriber = pipeline("automatic-speech-recognition", model="asr-nigerian-pidgin/pidgin-wav2vec2-xlsr53")
+
+# Transcription function
+def transcribe(audio):
+    if audio is None:
+        return "No audio provided."
+    
+    sr, y = audio
+
+    # Convert to mono if stereo
+    if y.ndim > 1:
+        y = y.mean(axis=1)
+        
+    y = y.astype(np.float32)
+    y /= np.max(np.abs(y))
+
+    return transcriber({"sampling_rate": sr, "raw": y})["text"] 
+
+# Define the Gradio UI components
+with gr.Blocks() as demo:
+    gr.Markdown("# 🗣️ Nigerian Pidgin ASR Demo")
+    gr.Markdown("Upload or record audio in Nigerian Pidgin to get transcription.")
+
+    with gr.Row():
+        audio_input = gr.Audio(
+            sources=["microphone", "upload"],
+            type="numpy",
+            label="🎤 Record or Upload Audio",
+            interactive=True,
+            max_length=30,
+        )
+
+    with gr.Row():
+        submit_btn = gr.Button("Submit", variant="primary")
+        clear_btn = gr.Button("Clear", variant="secondary")
+
+    with gr.Row():
+        output_text = gr.Textbox(
+            label="📝 Transcription Output",
+            interactive=False,
+            show_copy_button=True
+        )
+
+    with gr.Row():
+        flag_btn = gr.Button("🚩 Flag this output as incorrect", size="sm")
+        share_btn = gr.Button("🔗 Share", size="sm")
+
+    # 
+    submit_btn.click(fn=transcribe, inputs=audio_input, outputs=output_text)
+    clear_btn.click(fn=lambda: (None, ""), inputs=None, outputs=[audio_input, output_text])
+    flag_btn.click(fn=lambda: "Thank you for your feedback.", inputs=None, outputs=output_text)
+
+demo.launch(share=True)