import torch
import gradio as gr
import numpy as np

from transformers import (
    VitsModel,
    VitsTokenizer,
    pipeline,
)


device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
print(f"Using {device} with fp {torch_dtype}")

# load speech translation checkpoint
asr_pipe = pipeline(  # noqa: F821
    "automatic-speech-recognition",
    model="openai/whisper-medium",
    device=device,
    torch_dtype=torch_dtype,
)

# load text-to-speech checkpoint

model = VitsModel.from_pretrained("facebook/mms-tts-zlm")
tokenizer = VitsTokenizer.from_pretrained("facebook/mms-tts-zlm")


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

    with torch.no_grad():
        outputs = model(input_ids)

    speech = outputs["waveform"]
    return speech


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


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.T


title = "Cascaded STST"
description = """
Demo for cascaded speech-to-speech translation (STST), mapping from source speech in any language to target speech in **Malay**. Demo uses OpenAI's [Whisper Base](https://huggingface.co/openai/whisper-base) model for speech translation, and Facebooks's
[MMS-TTS-ZLM](https://huggingface.co/facebook/mms-tts-zlm) 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="./examples",
    title=title,
    description=description,
    live=True,
)

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

demo.launch()