import gradio as gr
from transformers import AutoFeatureExtractor, Wav2Vec2BertModel
import torch

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

device = 0 if torch.cuda.is_available() else "cpu"
pipe = pipeline(
    task="automatic-speech-recognition",
    model=MODEL_NAME,
    chunk_length_s=30,
    device=device,
)

pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language=lang, task="transcribe")

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"

    file = microphone if microphone is not None else file_upload

    text = pipe(file)["text"]

    return warn_output + text


iface = gr.Interface(
    fn=transcribe,
    inputs=[
        gr.inputs.Audio(source="microphone", type="filepath", optional=True),
        gr.inputs.Audio(source="upload", type="filepath", optional=True),
    ],
    outputs="text",
    layout="horizontal",
    theme="huggingface",
    title="Wav2Vec2-Bert 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}) from Whisper Fine Tuning Sprint Event 2022 "
        "and 🤗 Transformers to transcribe audio files of arbitrary length."
    ),
    allow_flagging="never",
)

iface.launch()