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

from transformers import SpeechT5ForTextToSpeech, SpeechT5HifiGan, SpeechT5Processor, pipeline

## Imports for MMS
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)


# For Dutch

##### speecht5 #####
# model_id = 'sanchit-gandhi/speecht5_tts_vox_nl'
# processor = SpeechT5Processor.from_pretrained(model_id)
# model = SpeechT5ForTextToSpeech.from_pretrained(model_id)

# vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan").to(device)


##### mms #####
model = VitsModel.from_pretrained("Matthijs/mms-tts-nld")
tokenizer = VitsTokenizer.from_pretrained("Matthijs/mms-tts-nld")

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







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

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

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

# Original
# 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 synthesise(text):
    inputs = tokenizer(text, return_tensors="pt")
    with torch.no_grad():
        outputs = model(inputs["input_ids"])
    speech = outputs.audio[0]
    
    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 any language to target speech in English. 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")
# """

description = """
# Demo for cascaded speech-to-speech translation (STST), mapping from source speech in any language to target speech in Dutch. 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()