import torch
import spaces
import gradio as gr
import soundfile as sf
import numpy as np
import pytube as pt
import librosa
from transformers import AutoProcessor, Wav2Vec2BertForCTC

MODEL_NAME = "mikr/w2v-bert-2.0-czech-colab-cv16"


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

processor = AutoProcessor.from_pretrained(MODEL_NAME)
model = Wav2Vec2BertForCTC.from_pretrained(MODEL_NAME).to(device)


@spaces.GPU
def text_from_audio(audio_path):
    a, s = librosa.load(audio_path, sr=16_000)
    input_values = processor(a, sampling_rate=s, return_tensors="pt").input_features

    with torch.no_grad():
        logits = model(input_values.to(device)).logits
   
    predicted_ids = torch.argmax(logits, dim=-1)

    # transcribe speech
    transcription = processor.batch_decode(predicted_ids)
    text = transcription[0]
    return text


def transcribe(microphone, file_upload):
    warn_output = ""
    if (microphone is not None) and (file_upload is not None):
        warn_output = (
            "WARNING: You've uploaded an audio file and used the microphone. "
            "The recorded file from the microphone will be used and the uploaded audio will be discarded.\n"
        )

    elif (microphone is None) and (file_upload is None):
        return "ERROR: You have to either use the microphone or upload an audio file"

    audio_path = microphone if microphone is not None else file_upload

    text = text_from_audio(audio_path)

    return warn_output + text


def _return_yt_html_embed(yt_url):
    video_id = yt_url.split("?v=")[-1]
    HTML_str = (
        f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>'
        " </center>"
    )
    return HTML_str


def yt_transcribe(yt_url):
    yt = pt.YouTube(yt_url)
    html_embed_str = _return_yt_html_embed(yt_url)
    stream = yt.streams.filter(only_audio=True)[0]
    stream.download(filename="audio.mp3")

    text = text_from_audio("audio.mp3")

    return html_embed_str, text


demo = gr.Blocks()

mf_transcribe = gr.Interface(
    fn=transcribe,
    inputs=[
        gr.Audio(sources="microphone", type="filepath"),
        gr.Audio(sources="upload", type="filepath"),
    ],
    outputs="text",
    title="W2V Bert 2.0 Demo: Transcribe Czech Audio",
    description=(
        "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the fine-tuned"
        f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) "
        "and 🤗 Transformers to transcribe audio files of arbitrary length."
    ),
    allow_flagging="never",
)

yt_transcribe = gr.Interface(
    fn=yt_transcribe,
    inputs=[gr.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL")],
    outputs=["html", "text"],
    title="W2V Bert 2.0 Demo: Transcribe Czech YouTube Video",
    description=(
        "Transcribe long-form YouTube videos with the click of a button! Demo uses the the fine-tuned checkpoint:"
        f" [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files of"
        " arbitrary length."
    ),
    allow_flagging="never",
)

with demo:
    gr.TabbedInterface([mf_transcribe, yt_transcribe], ["Transcribe Audio", "Transcribe YouTube"])

demo.launch(server_name="0.0.0.0")