Update app.py

app.py

@@ -1,20 +1,53 @@
-import gradio as gr
+@app.function()
+class InteractiveChat:
+    def __init__(self, model_name="openai/whisper-large", tts_choice="OpenVoice", **kwargs):
+        self.whisper_processor = WhisperProcessor.from_pretrained(model_name)
+        self.whisper_model = WhisperForConditionalGeneration.from_pretrained(model_name)
+        self.zephyr_tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-beta")
+        self.zephyr_model = AutoModelForCausalLM.from_pretrained("HuggingFaceH4/zephyr-7b-beta", device_map="auto")
+        self.zephyr_pipeline = pipeline("text-generation", model=self.zephyr_model, tokenizer=self.zephyr_tokenizer)
+        self.tts_choice = tts_choice
 
-with gr.Blocks() as demo:
-    model_choice = gr.Dropdown(["openai/whisper-large", "HuggingFaceH4/zephyr-7b-beta"], 
-                                label="Choose Model", value="openai/whisper-large")
-    input_data = gr.Audio(source="microphone", type="filepath", label="Speak Your Message")  # Note: type is now "filepath"
-    output_text = gr.Textbox(label="Transcription and Response")
+    def generate_response(self, input_data):
+        input_features = self.whisper_processor(input_data, sampling_rate=16_000, return_tensors="pt").input_features
+        predicted_ids = self.whisper_model.generate(input_features)
+        transcription = self.whisper_processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
 
-    def generate_response(audio_path, model_name):
-        hf_interface = gr.Interface.load(model_name)  # Load the model directly from the Hub
-        if model_name == "openai/whisper-large":
-            transcription = hf_interface(audio_path)  # Handle transcription
-        else:  # Zephyr
-            transcription = hf_interface(audio_path)[0]["generated_text"] # Extract transcription from zephyr
-        response = hf_interface(transcription)[0]["generated_text"]  # Get Zephyr's response 
+        # Use the transcription as input for Zephyr
+        response = self.zephyr_pipeline(transcription, max_length=1000)[0]["generated_text"]
         return transcription, response
-    
-    input_data.change(generate_response, inputs=[input_data, model_choice], outputs=output_text)
 
-demo.launch()
+    def speak(self, text):
+        try:
+            if self.tts_choice == "OpenVoice":
+                model_path = snapshot_download("facebook/mms-tts-eng")
+                pipe = pipeline("text-to-speech", model=model_path)
+                audio_array = pipe(text).audio
+                pygame.mixer.init()
+                sound = pygame.sndarray.make_sound(audio_array)
+                sound.play()
+                pygame.time.delay(int(sound.get_length() * 1000))
+            else:  # gTTS
+                tts = gTTS(text=text, lang='en')
+                tts.save("response.mp3")
+                pygame.mixer.init()
+                pygame.mixer.music.load("response.mp3")
+                pygame.mixer.music.play()
+                while pygame.mixer.music.get_busy():
+                    pygame.time.Clock().tick(10)
+        except Exception as e:
+            print("Error occurred during speech generation:", e)
+
+
+with gr.Blocks() as demo:
+    model_choice = gr.Dropdown(["openai/whisper-large"], label="Whisper Model", value="openai/whisper-large")
+    tts_choice = gr.Radio(["OpenVoice", "gTTS"], label="TTS Engine", value="OpenVoice")
+    input_data = gr.Audio(source="microphone", type="numpy", label="Speak Your Message")
+    output_text = gr.Textbox(label="Transcription and Response")
+
+    model_choice.change(lambda x, y: InteractiveChat(x, y), inputs=[model_choice, tts_choice], outputs=None)
+    tts_choice.change(lambda x, y: InteractiveChat(y, x), inputs=[tts_choice, model_choice], outputs=None)
+    input_data.change(lambda x, model: model.generate_response(x), inputs=[input_data, model_choice],
+                      outputs=output_text)
+    input_data.change(lambda x, model: model.speak(x[1]), inputs=[output_text, model_choice],
+                      outputs=None)  # Speak the response