Update app.py

app.py

@@ -1,24 +1,20 @@
-import gradio as gr
-import numpy as np
-import sounddevice as sd
 from transformers import pipeline
+asr_pipe = pipeline("automatic-speech-recognition", model="Abdullah17/whisper-small-urdu")
+from difflib import SequenceMatcher
 
-# Load ASR model
-asr_model = "Abdullah17/whisper-small-urdu"
-asr_pipe = pipeline("automatic-speech-recognition", model=asr_model)
-
-# Function to transcribe the command from audio samples
-def transcribe_the_command(audio_samples):
-    transcript = asr_pipe(np.array(audio_samples))[0]["text"]
-    most_similar_command, reply = find_most_similar_command(transcript, commands)
-    return f"Transcript: {transcript}\nMost Similar Command: {most_similar_command}"
-
-# Capture audio samples from the microphone
-def capture_audio(rec_duration=6, sample_rate=16000):
-    audio_data = sd.rec(int(rec_duration * sample_rate), samplerate=sample_rate, channels=1)
-    sd.wait()
-    return audio_data.flatten()
-
+# List of commands
+commands = [
+    "نمائندے ایجنٹ نمائندہ",
+    "  سم  ایکٹیویٹ ",
+    " سم  بلاک بند ",
+    "موبائل پیکیجز انٹرنیٹ پیکیج",
+    " چالان جمع ",
+    " گانا سنانا"
+]
+# replies = [
+# 1,2,
+# ]
+# Function to find the most similar command
 def find_most_similar_command(statement, command_list):
     best_match = None
     highest_similarity = 0
@@ -33,10 +29,28 @@ def find_most_similar_command(statement, command_list):
         i+=1
 
     return best_match,reply
+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"]
+      most_similar_command,reply = find_most_similar_command(transcript, commands)
+      print(f"Given Statement: {transcript}")
+      print(f"Most Similar Command: {most_similar_command}\n")
+      print(reply)
+    
+      return reply
+# get_text_from_voice("urdu.wav")
+import gradio as gr
+
 
 iface = gr.Interface(
     fn=transcribe_the_command,
-    inputs=gr.inputs.Function(capture_audio, label="Recorded Audio"),
+    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.",