Create app.py

app.py

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+import gradio as gr
+import librosa
+from asr import transcribe, ASR_EXAMPLES, ASR_NOTE
+from lid import identify  # Import language identification model
+
+# Function to detect language and transcribe automatically
+def auto_detect_and_transcribe(audio):
+    detected_lang = identify(audio)  # Identify language from audio
+    if detected_lang in ["swh", "eng"]:  # Ensure it's either Swahili or English
+        return f"[Detected Language: {detected_lang.upper()}]\n\n" + transcribe(audio)
+    return "Error: Unsupported language detected."
+
+# Speech-to-Text Interface with Auto Language Detection
+mms_transcribe = gr.Interface(
+    fn=auto_detect_and_transcribe,
+    inputs=gr.Audio(),
+    outputs="text",
+    examples=ASR_EXAMPLES,
+    title="Speech-to-Text (Automatic Language Detection)",
+    description="Upload or record audio, and the model will detect if it is Swahili or English before transcribing.",
+    article=ASR_NOTE,
+    allow_flagging="never",
+)
+
+# Main Gradio App
+with gr.Blocks() as demo:
+    gr.Markdown("<p align='center' style='font-size: 20px;'>MMS Speech-to-Text</p>")
+    gr.HTML("<center>Convert speech to text while automatically detecting Swahili or English.</center>")
+
+    mms_transcribe.render()
+
+if __name__ == "__main__":
+    demo.queue()
+    demo.launch()
+