Update app.py

app.py

@@ -1,36 +1,31 @@
 import gradio as gr
-import speech_recognition as sr
+from pyannote.audio import Pipeline
+from pyannote.core import Segment
+from pyannote.audio import Audio
 from pydub import AudioSegment
 
-# Function to transcribe audio to text
-def transcribe_audio(audio_input):
-    recognizer = sr.Recognizer()
-    audio_file = sr.AudioFile(audio_input)
-    with audio_file as source:
-        audio_data = recognizer.record(source)
-    try:
-        text = recognizer.recognize_google(audio_data)
-    except sr.UnknownValueError:
-        text = "Sorry, I couldn't understand the audio."
-    except sr.RequestError:
-        text = "Sorry, there was a problem with the request."
-    return text
-
-# Function to generate a response (you'll need to implement this)
-def generate_response(user_input):
-    # Placeholder for the text response generation and TTS part
-    text_response = f"Responding as Tommy Vercetti: {user_input}"
-    # Generate audio path based on text_response
-    output_path = "response.wav"  # Placeholder path
-    # Implement TTS and save to output_path
-    return text_response, output_path
+api_k = os.getenv("API_KEY")
+# Initialize the pyannote pipeline for speaker diarization
+pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization-3.1",use_auth_token=api_k)
 
 # Function to process the audio input and return both text and audio response
 def respond(audio_input):
-    print(audio_input)
-    user_input = transcribe_audio(audio_input)
-    text_response, output_path = generate_response(user_input)
+    # Save the audio input to a file
+    audio_file = audio_input
+    audio_input.save(audio_file)
+
+    # Perform diarization on the whole file
+    diarization = pipeline(audio_file)
+
+    # Example: Extract and process a specific segment
+    excerpt = Segment(start=2.0, end=5.0)
+    waveform, sample_rate = Audio().crop(audio_file, excerpt)
+
+    # Further processing (placeholder for actual implementation)
+    text_response = f"Processed segment from {excerpt.start} to {excerpt.end} seconds."
+    output_path = "response.wav"  # Placeholder path
 
+    # Generate an audio file as the response (placeholder for TTS implementation)
     audio = AudioSegment.from_wav(output_path)
     duration = len(audio) / 1000