import gradio as gr
import numpy as np
import torch
from datasets import load_dataset

from transformers import SpeechT5ForTextToSpeech, SpeechT5HifiGan, SpeechT5Processor, pipeline
from transformers import VitsModel, VitsTokenizer

device = "cuda:0" if torch.cuda.is_available() else "cpu"

# load speech translation checkpoint
asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-base", device=device)

# load text-to-speech checkpoint and speaker embeddings
processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")

model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts").to(device)
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan").to(device)


model_mms = VitsModel.from_pretrained("Matthijs/mms-tts-deu").to(device)
tokenizer_mms = VitsTokenizer.from_pretrained("Matthijs/mms-tts-deu")

embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)


def translate(audio):
    outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "transcribe", "language": "de"})
    return outputs["text"]


def synthesise(text):
    inputs = tokenizer_mms(text, return_tensors="pt")
    input_ids = inputs["input_ids"]


    with torch.no_grad():
        outputs = model_mms(input_ids)
    print("mms model", outputs)
    print(outputs.audio[0])
    return outputs.audio[0].cpu()
    inputs = processor(text=text, return_tensors="pt")
    speech = model.generate_speech(inputs["input_ids"].to(device), speaker_embeddings.to(device), vocoder=vocoder)
    print("speecht5 model", speech)
    return speech.cpu()


def speech_to_speech_translation(audio):
    translated_text = translate(audio)
    synthesised_speech = synthesise(translated_text)
    # (((speech["audio"].cpu().numpy()) + 1) / 2.)* 32767
    print(synthesised_speech)
    synthesised_speech_numpy = synthesised_speech.numpy()
    synthesised_speech_numpy += np.min(synthesised_speech_numpy)
    synthesised_speech_numpy /= np.max(synthesised_speech_numpy)
    # synthesised_speech = (synthesised_speech.numpy() * 32767).astype(np.int16)
    synthesised_speech = np.clip((synthesised_speech_numpy*32767) .astype(np.int16), 0, 32767)
    print(synthesised_speech)
    # synthesised_speech = (((synthesised_speech.numpy() + 1) / 2.0) * 32767).astype(np.int16)
    return 16000, synthesised_speech


title = "Cascaded STST"
description = """
Demo for cascaded speech-to-speech translation (STST), mapping from source speech in any language to target speech in German. Demo uses OpenAI's [Whisper Base](https://huggingface.co/openai/whisper-base) model for speech translation, and 
[Massive Multilingual Speech (MMS) TTS](https://huggingface.co/learn/audio-course/chapter6/pre-trained_models#massive-multilingual-speech-mms) model for text-to-speech:

![Cascaded STST](https://huggingface.co/datasets/huggingface-course/audio-course-images/resolve/main/s2st_cascaded.png "Diagram of cascaded speech to speech translation")
"""

demo = gr.Blocks()

mic_translate = gr.Interface(
    fn=speech_to_speech_translation,
    inputs=gr.Audio(source="microphone", type="filepath"),
    outputs=gr.Audio(label="Generated Speech", type="numpy"),
    title=title,
    description=description,
)

file_translate = gr.Interface(
    fn=speech_to_speech_translation,
    inputs=gr.Audio(source="upload", type="filepath"),
    outputs=gr.Audio(label="Generated Speech", type="numpy"),
    examples=[["./example.wav"]],
    title=title,
    description=description,
)

with demo:
    gr.TabbedInterface([mic_translate, file_translate], ["Microphone", "Audio File"])

demo.launch()