Create app.py

app.py

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+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
+from datasets import load_dataset
+from datasets import load_dataset
+from transformers import pipeline
+import numpy as np
+import gradio as gr
+
+
+
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+asr_model = pipeline(task = "automatic-speech-recognition",
+               model = "distil-whisper/distil-small.en")
+
+
+
+
+
+llm_model = AutoModelForCausalLM.from_pretrained("OuteAI/Lite-Mistral-150M-v2-Instruct").to(device)
+tokenizer = AutoTokenizer.from_pretrained("OuteAI/Lite-Mistral-150M-v2-Instruct")
+
+def generate_response(message: str, temperature: float = 0.3, repetition_penalty: float = 1.2) -> str:
+    # Apply the chat template and convert to PyTorch tensors
+    messages = [
+        {"role": "system",
+         "content": "You are my best close friend. you are full of human emotions and you have feelings. provide short responses."},
+         {"role": "user", "content": message}
+    ]
+    input_ids = tokenizer.apply_chat_template(
+        messages, add_generation_prompt=True, return_tensors="pt"
+    ).to(device)
+
+    # Generate the response
+    output = llm_model.generate(
+        input_ids,
+        max_length = 150,
+        pad_token_id=tokenizer.eos_token_id,
+        temperature=temperature,
+        repetition_penalty=repetition_penalty,
+        do_sample=True
+    ) 
+
+    # Decode the generated output
+    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+    return generated_text.split('\n assistant\n')[1]
+
+
+
+
+
+processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
+tts_model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
+vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
+embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
+speaker_embeddings = torch.tensor(embeddings_dataset[7300]["xvector"]).unsqueeze(0)
+
+def text_to_speech(input_text):
+    inputs = processor(text=input_text, return_tensors="pt")
+    speech = tts_model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder)
+    return speech
+
+
+
+
+
+def spoken_llm(input_voice_file):
+    if input_voice_file is None:
+        gr.Warning("No input audio")
+        return None
+    asr_text = asr_model(input_voice_file)['text']
+    print("\n\nASR\n\n")
+    print(asr_text)
+    llm_response = generate_response(asr_text)
+    print("\n\nLLM\n\n")
+    print(llm_response)
+    audio_out = text_to_speech(llm_response)
+    print("\n\nTTS\n\n")
+    rate = 17000
+    return rate, (audio_out.cpu().numpy().reshape(-1)*2e4).astype(np.int16)
+
+
+
+interface = gr.Interface(fn = spoken_llm, inputs = gr.Audio(sources = "microphone", type = "filepath"), outputs = "audio")
+interface.launch()