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

from transformers import SpeechT5ForTextToSpeech, SpeechT5HifiGan, SpeechT5Processor, pipeline, WhisperTokenizer

device = "cuda:0" if torch.cuda.is_available() else "cpu"
#tokenizer = WhisperTokenizer.from_pretrained("GatinhoEducado/whisper-tiny-finetuned-minds14", language="Portuguese", task="translate")
tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-tiny", language="Portuguese", task="translate")
# load speech translation checkpoint
asr_pipe = pipeline(
    "automatic-speech-recognition", 
    model="GatinhoEducado/whisper-tiny-finetuned-minds14",
    device=device,
    tokenizer = tokenizer,
    generate_kwargs = {"language":"<|pt|>",
                     "task": "transcribe",
                     "repetition_penalty":1.5
                     }
)

# load text-to-speech checkpoint and speaker embeddings
processor = SpeechT5Processor.from_pretrained("GatinhoEducado/speechT5_tts-finetuned-cml-tts")

model = SpeechT5ForTextToSpeech.from_pretrained("GatinhoEducado/speechT5_tts-finetuned-cml-tts").to(device)
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan").to(device)

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


def translate(audio):
    #outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "translate"})
    outputs = asr_pipe(audio, max_new_tokens=100)
    return outputs["text"]


def synthesise(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()


def speech_to_speech_translation(audio):
    translated_text = translate(audio)
    synthesised_speech = synthesise(translated_text)
    synthesised_speech = (synthesised_speech.numpy() * 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 English to target Portuguese speech. Demo uses OpenAI's [Whisper Base](https://huggingface.co/openai/whisper-base) model for speech translation, and Microsoft's
[SpeechT5 TTS](https://huggingface.co/microsoft/speecht5_tts) 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()