Delete app.py

app.py

@@ -1,87 +0,0 @@
-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
-
-
-
-
-
-asr_model = pipeline(task = "automatic-speech-recognition",
-               model = "distil-whisper/distil-small.en")
-
-
-
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-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()