Create app.py

app.py

@@ -0,0 +1,33 @@
+from transformers import pipeline
+from gradio_client import Client
+asr_pipe = pipeline("automatic-speech-recognition", model="Abdullah17/whisper-small-urdu")
+
+def transcribe_the_command(audio):
+      import soundfile as sf
+      sample_rate, audio_data = audio
+      file_name = "recorded_audio.wav"
+      sf.write(file_name, audio_data, sample_rate)
+    # Convert stereo to mono by averaging the two channels
+      print(file_name)
+
+      transcript = asr_pipe(file_name)["text"]
+      client = Client("https://ysharma-explore-llamav2-with-tgi.hf.space/")
+      result = client.predict(
+				"tell me a story",
+				api_name="/chat"
+      )
+    
+      return result
+# get_text_from_voice("urdu.wav")
+import gradio as gr
+
+
+iface = gr.Interface(
+    fn=transcribe_the_command,
+    inputs=gr.inputs.Audio(label="Recorded Audio",source="microphone"),
+    outputs="text",
+    title="Whisper Small Urdu Command",
+    description="Realtime demo for Urdu speech recognition using a fine-tuned Whisper small model and outputting the estimated command on the basis of speech transcript.",
+)
+
+iface.launch()