app.py

import spaces
import torch
import gradio as gr
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer, AutoModel
import numpy as np
from espnet2.bin.tts_inference import Text2Speech
import yaml  # Import yaml for config loading (though not used in the current code, kept for potential future use)
import os  #  Kept for potential future use (e.g., if loading config from files)
import requests  # Corrected: Import the 'requests' library
import nltk  # Import nltk

# Download required NLTK resources
try:
    nltk.data.find('taggers/averaged_perceptron_tagger_eng')
except LookupError:
    nltk.download('averaged_perceptron_tagger_eng')
try:
    nltk.data.find('corpora/cmudict')  # Check for cmudict
except LookupError:
    nltk.download('cmudict')


# Load Whisper model
ASR_MODEL_NAME = "openai/whisper-medium.en"
asr_pipe = pipeline(
    task="automatic-speech-recognition",
    model=ASR_MODEL_NAME,
    chunk_length_s=30,
    device='cuda' if torch.cuda.is_available() else 'cpu', # Use GPU if available
)

all_special_ids = asr_pipe.tokenizer.all_special_ids
transcribe_token_id = all_special_ids[-5]
translate_token_id = all_special_ids[-6]

def _preload_and_load_models():
    global vicuna_tokenizer, vicuna_model
    VICUNA_MODEL_NAME = "EleutherAI/gpt-neo-2.7B"  # Or another model
    vicuna_tokenizer = AutoTokenizer.from_pretrained(VICUNA_MODEL_NAME)
    vicuna_model = AutoModelForCausalLM.from_pretrained(
        VICUNA_MODEL_NAME,
        torch_dtype=torch.float16,
        device_map="auto", # or.to('cuda')
    ) #.to('cuda') # Explicitly move to CUDA after loading

_preload_and_load_models()

tts = Text2Speech.from_pretrained("espnet/kan-bayashi_ljspeech_vits")

@spaces.GPU(required=True)
def process_audio(microphone, state, task="transcribe"):
    if microphone is None:
        return state, state, None
    asr_pipe.model.config.forced_decoder_ids = [
        [2, transcribe_token_id if task == "transcribe" else translate_token_id]
    ]
    text = asr_pipe(microphone)["text"]
    system_prompt = """You are a friendly and enthusiastic tutor for young children (ages 6-9).
        You answer questions clearly and simply, using age-appropriate language.
        You are also a little bit silly and like to make jokes."""
    prompt = f"{system_prompt}\nUser: {text}"
    with torch.no_grad():
        vicuna_input = vicuna_tokenizer(prompt, return_tensors="pt").to('cuda')
        vicuna_output = vicuna_model.generate(**vicuna_input, max_new_tokens=192)
        vicuna_response = vicuna_tokenizer.decode(vicuna_output[0], skip_special_tokens=True) # Access the first sequence [0]
        vicuna_response = vicuna_response.replace(prompt, "").strip()
    updated_state = state + "\nUser: " + text + "\n" + "Tutor: " + vicuna_response  # Include user input in state
    try:
        with torch.no_grad():
            # The espnet TTS model outputs a dictionary
            output = tts(vicuna_response)
            wav = output["wav"]
            sr = tts.fs  # Get the sampling rate from the tts object
            audio_arr = wav.cpu().numpy()

        SAMPLE_RATE = sr
        audio_arr = audio_arr / np.abs(audio_arr).max()  # Normalize to -1 to 1
        audio_output = (SAMPLE_RATE, audio_arr)
        #sf.write('generated_audio.wav', audio_arr, SAMPLE_RATE) # Removed writing to file
    except requests.exceptions.RequestException as e:
        print(f"Error in Hugging Face API request: {e}")
        audio_output = None
    except Exception as e:
        print(f"Error in speech synthesis: {e}")
        audio_output = None
    return updated_state, updated_state, audio_output

with gr.Blocks(title="Whisper, Vicuna, & TTS Demo") as demo:  # Updated title
    gr.Markdown("# Speech-to-Text-to-Speech Demo with Vicuna and Hugging Face TTS") # Updated Markdown
    gr.Markdown("Speak into your microphone, get a transcription, Vicuna will process it, and then you'll hear the result!")
    with gr.Tab("Transcribe & Synthesize"):
        mic_input = gr.Audio(sources="microphone", type="filepath", label="Speak Here")
        transcription_output = gr.Textbox(lines=5, label="Transcription and Vicuna Response")
        audio_output = gr.Audio(label="Synthesized Speech", type="numpy") # Important: type="numpy"
        transcription_state = gr.State(value="")
        mic_input.change(
            fn=process_audio,  # Call the combined function
            inputs=[mic_input, transcription_state, gr.Radio(["transcribe", "translate"], label="Task", value="transcribe")],
            outputs=[transcription_output, transcription_state, audio_output]
        )

demo.launch(share=False)