#!/usr/bin/env python3
#
# Copyright      2022  Xiaomi Corp.        (authors: Fangjun Kuang)
#
# See LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# References:
# https://gradio.app/docs/#dropdown

import logging
import os
import time
from datetime import datetime

import gradio as gr
import torchaudio

from model import get_pretrained_model, language_to_models, sample_rate

languages = sorted(language_to_models.keys())


def convert_to_wav(in_filename: str) -> str:
    """Convert the input audio file to a wave file"""
    out_filename = in_filename + ".wav"
    logging.info(f"Converting '{in_filename}' to '{out_filename}'")
    _ = os.system(f"ffmpeg -hide_banner -i '{in_filename}' '{out_filename}'")
    return out_filename


def process(
    in_filename: str,
    language: str,
    repo_id: str,
    decoding_method: str,
    num_active_paths: int,
) -> str:
    logging.info(f"in_filename: {in_filename}")
    logging.info(f"language: {language}")
    logging.info(f"repo_id: {repo_id}")
    logging.info(f"decoding_method: {decoding_method}")
    logging.info(f"num_active_paths: {num_active_paths}")

    filename = convert_to_wav(in_filename)

    now = datetime.now()
    date_time = now.strftime("%Y-%m-%d %H:%M:%S.%f")
    logging.info(f"Started at {date_time}")

    start = time.time()
    wave, wave_sample_rate = torchaudio.load(filename)

    if wave_sample_rate != sample_rate:
        logging.info(
            f"Expected sample rate: {sample_rate}. Given: {wave_sample_rate}. "
            f"Resampling to {sample_rate}."
        )

        wave = torchaudio.functional.resample(
            wave,
            orig_freq=wave_sample_rate,
            new_freq=sample_rate,
        )
    wave = wave[0]  # use only the first channel.

    hyp = get_pretrained_model(repo_id).decode_waves(
        [wave],
        decoding_method=decoding_method,
        num_active_paths=num_active_paths,
    )[0]

    date_time = now.strftime("%Y-%m-%d %H:%M:%S.%f")
    end = time.time()

    duration = wave.shape[0] / sample_rate
    rtf = (end - start) / duration

    logging.info(f"Finished at {date_time} s. Elapsed: {end - start: .3f} s")
    logging.info(f"Duration {duration: .3f} s")
    logging.info(f"RTF {rtf: .3f}")
    logging.info(f"hyp:\n{hyp}")

    return hyp


title = "# Automatic Speech Recognition with Next-gen Kaldi"
description = """
This space shows how to do automatic speech recognition with Next-gen Kaldi.

See more information by visiting the following links:

- <https://github.com/k2-fsa/icefall>
- <https://github.com/k2-fsa/sherpa>
- <https://github.com/k2-fsa/k2>
- <https://github.com/lhotse-speech/lhotse>
"""


def update_model_dropdown(language: str):
    if language in language_to_models:
        choices = language_to_models[language]
        return gr.Dropdown.update(choices=choices, value=choices[0])

    raise ValueError(f"Unsupported language: {language}")


demo = gr.Blocks()

with demo:
    gr.Markdown(title)
    language_choices = list(language_to_models.keys())

    language_radio = gr.Radio(
        label="Language",
        choices=language_choices,
        value=language_choices[0],
    )
    model_dropdown = gr.Dropdown(
        choices=language_to_models[language_choices[0]],
        label="Select a model",
        value=language_to_models[language_choices[0]][0],
    )

    language_radio.change(
        update_model_dropdown,
        inputs=language_radio,
        outputs=model_dropdown,
    )

    decoding_method_radio = gr.Radio(
        label="Decoding method",
        choices=["greedy_search", "modified_beam_search"],
        value="greedy_search",
    )

    num_active_paths_slider = gr.Slider(
        minimum=1,
        value=4,
        step=1,
        label="Number of active paths for modified_beam_search",
    )

    with gr.Tabs():
        with gr.TabItem("Upload from disk"):
            uploaded_file = gr.Audio(
                source="upload",  # Choose between "microphone", "upload"
                type="filepath",
                optional=False,
                label="Upload from disk",
            )
            upload_button = gr.Button("Submit for recognition")
            uploaded_output = gr.Textbox(label="Recognized speech from uploaded file")

        with gr.TabItem("Record from microphone"):
            microphone = gr.Audio(
                source="microphone",  # Choose between "microphone", "upload"
                type="filepath",
                optional=False,
                label="Record from microphone",
            )

            record_button = gr.Button("Submit for recognition")
            recorded_output = gr.Textbox(label="Recognized speech from recordings")

        upload_button.click(
            process,
            inputs=[
                uploaded_file,
                language_radio,
                model_dropdown,
                decoding_method_radio,
                num_active_paths_slider,
            ],
            outputs=uploaded_output,
        )
        record_button.click(
            process,
            inputs=[
                microphone,
                language_radio,
                model_dropdown,
                decoding_method_radio,
                num_active_paths_slider,
            ],
            outputs=recorded_output,
        )
    gr.Markdown(description)

if __name__ == "__main__":
    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"

    logging.basicConfig(format=formatter, level=logging.INFO)

    demo.launch()