import gradio as gr
import torch
import numpy as np
from transformers import pipeline, WhisperForConditionalGeneration, WhisperProcessor
from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
from datasets import load_dataset

# Check if a GPU is available and set the device
device = "cuda:0" if torch.cuda.is_available() else "cpu"

# Load the Whisper ASR model
whisper_model_id = "riteshkr/quantized-whisper-large-v3"
whisper_model = WhisperForConditionalGeneration.from_pretrained(whisper_model_id)
whisper_processor = WhisperProcessor.from_pretrained(whisper_model_id)

# Set the language to English using forced_decoder_ids
forced_decoder_ids = whisper_processor.get_decoder_prompt_ids(language="english", task="transcribe")

whisper_pipe = pipeline(
    "automatic-speech-recognition",
    model=whisper_model,
    tokenizer=whisper_processor.tokenizer,
    feature_extractor=whisper_processor.feature_extractor,
    device=0 if torch.cuda.is_available() else -1
)

# Load the SpeechT5 TTS model
tts_processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
tts_model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")

tts_model.to(device)
vocoder.to(device)

# Load speaker embeddings for TTS
embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0).to(device)

# Set target data type and max range for speech
target_dtype = np.int16
max_range = np.iinfo(target_dtype).max

# Define the transcription function (Whisper ASR)
def transcribe_speech(filepath):
    batch_size = 16 if torch.cuda.is_available() else 4
    output = whisper_pipe(
        filepath,
        max_new_tokens=256,
        generate_kwargs={"forced_decoder_ids": forced_decoder_ids},
        chunk_length_s=30,
        batch_size=batch_size,
    )
    return output["text"]

# Define the synthesis function (SpeechT5 TTS)
def synthesise(text):
    inputs = tts_processor(text=text, return_tensors="pt")
    speech = tts_model.generate_speech(
        inputs["input_ids"].to(device), speaker_embeddings, vocoder=vocoder
    )
    return speech.cpu()

# Define the speech-to-speech translation function
def speech_to_speech_translation(audio):
    # Transcribe speech
    translated_text = transcribe_speech(audio)
    # Synthesize speech
    synthesised_speech = synthesise(translated_text)
    # Convert speech to desired format
    synthesised_speech = (synthesised_speech.numpy() * max_range).astype(np.int16)
    return 16000, synthesised_speech

# Define the Gradio interfaces for microphone input and file upload
mic_translate = gr.Interface(
    fn=speech_to_speech_translation,
    inputs=gr.Audio(sources="microphone", type="filepath"),
    outputs=gr.Audio(label="Generated Speech", type="numpy"),
)

file_translate = gr.Interface(
    fn=speech_to_speech_translation,
    inputs=gr.Audio(sources="upload", type="filepath"),
    outputs=gr.Audio(label="Generated Speech", type="numpy"),
)

# Define the Gradio interfaces for transcription
mic_transcribe = gr.Interface(
    fn=transcribe_speech,
    inputs=gr.Audio(sources="microphone", type="filepath"),
    outputs=gr.Textbox(),
)

file_transcribe = gr.Interface(
    fn=transcribe_speech,
    inputs=gr.Audio(sources="upload", type="filepath"),
    outputs=gr.Textbox(),
)

# Create the app using Gradio Blocks with tabbed interfaces
demo = gr.Blocks()

with demo:
    gr.TabbedInterface(
        [
            mic_transcribe, file_transcribe,  # For transcription
            mic_translate, file_translate  # For speech-to-speech translation
        ],
        [
            "Transcribe Microphone", "Transcribe Audio File",
            "Translate Microphone", "Translate Audio File"
        ]
    )

# Launch the app with debugging enabled
if __name__ == "__main__":
    demo.launch(debug=True, share=True)