Update app.py

app.py

@@ -1,51 +1,23 @@
 import gradio as gr
-import torch
-from transformers import WhisperForConditionalGeneration, WhisperProcessor, AutoTokenizer, AutoModelForCausalLM, VitsModel
+from transformers import pipeline
 
-# Load the models and tokenizers
-asr_model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny")
-asr_tokenizer = AutoTokenizer.from_pretrained("openai/whisper-tiny")
-asr_processor = WhisperProcessor.from_pretrained("openai/whisper-tiny")
+asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-tiny")
+llm_pipe = pipeline("text-generation", model="microsoft/Phi-3-mini-4k-instruct", trust_remote_code=True)
+tts_pipe = pipeline("text-to-speech", model="kakao-enterprise/vits-ljs")
 
-llm_model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-mini-4k-instruct", trust_remote_code=True)
-llm_tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct")
+def process_audio(audio):
+    # Convert audio to text using ASR pipeline
+    transcribed_text = asr_pipe(audio)[0]['transcription']
 
-tts_model = VitsModel.from_pretrained("kakao-enterprise/vits-ljs")
-tts_tokenizer = AutoTokenizer.from_pretrained("kakao-enterprise/vits-ljs")
+    # Generate text using LLM pipeline
+    generated_text = llm_pipe(transcribed_text)[0]['generated_text']
 
-# Define the function to transcribe audio
-def transcribe_audio(audio_input):
-    processor = WhisperProcessor.from_pretrained("openai/whisper-tiny")
-    model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny")
-    model.config.forced_decoder_ids = None
-    
-    input_features = processor(audio_input, return_tensors="pt").input_features
-    predicted_ids = model.generate(input_features)
-    transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
-    
-    return transcription
+    # Convert generated text to speech using TTS pipeline
+    speech = tts_pipe(generated_text)[0]['audio']
 
-# Define the function to generate text
-def generate_text(transcription):
-    input_ids = llm_tokenizer.encode(transcription, return_tensors="pt")
-    output_ids = llm_model.generate(input_ids)
-    generated_text = llm_tokenizer.decode(output_ids[0], skip_special_tokens=True)
-    return generated_text
+    return speech
 
-def text_to_speech(generated_text):
-    text_input = tts_tokenizer(generated_text, return_tensors="pt")
-    
-    with torch.no_grad():
-        output = tts_model(**text_input).waveform
-        
-    return output
-
-def my_alexa(audio_input):
-    transcription = transcribe_audio(audio_input)
-    generated_text = generate_text(transcription)
-    speech_output = text_to_speech(generated_text)
-    return speech_output
-
-
-gr.Interface(fn=transcribe_audio, inputs=gr.Audio(sources="microphone"), outputs="text", title="MyAlexa AI Assistant",
-             description="An AI Assistant That Can Answer Your Questions!").launch(inbrowser=True, debug=True)
+audio_input = gr.Audio(label="Audio Input", sources="microphone")
+my_alexa_output = gr.Interface(fn=process_audio, inputs=audio_input, outputs="audio", title="MyAlexa AI Assistant", 
+                      description="An AI Assistant That Will Answer Your Questions!")
+my_alexa_output.launch(inbrowser=True, debug=True)