# -*- coding: utf-8 -*-
"""Built_Speech-to-Speech_Translation.ipynb"""

# Automatically generated by Colaboratory.

# Original file is located at
#     https://colab.research.google.com/drive/1AHToRlVpGAy3jQdbTm14tDdTyRPc-oG3

"""Speech Translation to Text Part"""

from huggingface_hub import login
login("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")

import torch
from transformers import pipeline

device = "cuda:0" if torch.cuda.is_available() else "cpu"
pipe = pipeline(
    "automatic-speech-recognition", model="openai/whisper-base", device=device
)

from datasets import load_dataset
dataset = load_dataset("facebook/voxpopuli", "nl", split="validation", streaming=True)

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

"""Text-to-Speech Part"""

from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
model = SpeechT5ForTextToSpeech.from_pretrained("Bolakubus/speecht5_finetuned_voxpopuli_nl")
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")

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

def synthesize(text):
    inputs = processor(text=text, return_tensors="pt")
    speech = model.generate_speech(
        inputs["input_ids"].to(device), speaker_embeddings.to(device), vocoder=vocoder
    )
    return speech.cpu()

"""Creating Speech-to-Speech Translation (STST) Demo"""
import numpy as np

# Normalized Audio array by the dynamic range of the target dtype (int16)
# Next convert from the default NumPy dtype (float64) to the target dtype (int16)
target_dtype = np.int16
max_range = np.iinfo(target_dtype).max

def speech_to_speech_translation(audio):
    translated_text = translate(audio)
    synthesized_speech = synthesize(translated_text)
    synthesized_speech = (synthesized_speech.numpy() * max_range).astype(np.int16)
    return 16000, synthesized_speech

import gradio as gr

demo = gr.Blocks()
description = "Speech-to-Speech Translation En->Nl"
title = "Building Demo for Audio Course"

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

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

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

demo.launch(share=False, debug=False)