app.py

#!/usr/bin/env python3
#
# Copyright      2022-2024  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 tempfile
import time
import urllib.request
from datetime import datetime

from examples import examples
import gradio as gr
import soundfile as sf

from model import decode, get_pretrained_model, models


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}\" -ar 16000 -ac 1 \"{out_filename}\" -y"
    )

    return out_filename


def build_html_output(s: str, style: str = "result_item_success"):
    return f"""
    <div class='result'>
        <div class='result_item {style}'>
          {s}
        </div>
    </div>
    """


def submit_fn(
    repo_id: str,
    uploaded_file_path: str,
    microphone_audio_path: str,
    url_text: str,
):
    in_filename = None
    source_description = ""

    if uploaded_file_path:
        in_filename = uploaded_file_path
        source_description = "uploaded file"
        logging.info(f"Processing {source_description}: {in_filename}")
        if not os.path.exists(in_filename):
             return "", [], build_html_output(
                 f"Uploaded file not found: {in_filename}, "
                 "Please first upload a file and then click "
                 'the button "submit for recognition"',
                 "result_item_error",
             )

    elif microphone_audio_path:
        in_filename = microphone_audio_path
        source_description = "microphone recording"
        logging.info(f"Processing {source_description}: {in_filename}")
        if not os.path.exists(in_filename):
             return "", [], build_html_output(
                 f"Microphone recording not found: {in_filename}, "
                 "Please first click 'Record from microphone', speak, "
                 "click 'Stop recording', and then "
                 "click the button 'submit for recognition'",
                 "result_item_error"
             )

    elif url_text and url_text.strip():
        source_description = "URL"
        logging.info(f"Processing {source_description}: {url_text}")
        # Ensure a temporary file is created that persists until process() is done with it.
        # Using delete=False and manual cleanup is safer here.
        temp_file = None
        try:
            with tempfile.NamedTemporaryFile(delete=False, suffix=".tmpaudio") as f:
                temp_file = f.name # Store the name for cleanup
            urllib.request.urlretrieve(url_text, temp_file)
            in_filename = temp_file
            # Process the downloaded file
            eventStr, events, html_info = process(repo_id=repo_id, in_filename=in_filename)
            # Clean up the temporary file after processing
            if temp_file and os.path.exists(temp_file):
                os.unlink(temp_file)
            return eventStr, events, html_info
        except Exception as e:
            logging.error(f"Error processing URL {url_text}: {e}", exc_info=True)
            # Clean up the temporary file in case of error
            if temp_file and os.path.exists(temp_file):
                os.unlink(temp_file)
            return "", [], build_html_output(f"Error processing URL: {str(e)}", "result_item_error")
    else:
        return "", [], build_html_output(
            "Please provide an audio input via upload, microphone, or URL.",
            "result_item_error",
        )

    # Common processing for file-based inputs (upload, mic)
    if in_filename:
        try:
            return process(
                in_filename=in_filename,
                repo_id=repo_id,
            )
        except Exception as e:
            logging.error(f"Error processing {source_description} {in_filename}: {e}", exc_info=True)
            return "", [], build_html_output(f"Error processing {source_description}: {str(e)}", "result_item_error")

    # Should not be reached if logic is correct, but as a fallback:
    return "", [], build_html_output("No valid input provided.", "result_item_error")


def process(
    repo_id: str,
    in_filename: str,
):
    logging.info(f"repo_id: {repo_id}")
    logging.info(f"in_filename: {in_filename}")

    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()

    tagger = get_pretrained_model(repo_id)

    events = decode(tagger, filename)
    
    eventStr = ", ".join([e.name for e in events])
    events = [[e.name, f"{e.prob:.2f}"] for e in events]
    
    date_time = now.strftime("%Y-%m-%d %H:%M:%S.%f")
    end = time.time()

    info = sf.info(filename)
    duration = info.duration

    elapsed = end - start
    rtf = elapsed / duration

    logging.info(f"Finished at {date_time} s. Elapsed: {elapsed: .3f} s")

    info = f"""
    Wave duration  : {duration: .3f} s <br/>
    Processing time: {elapsed: .3f} s <br/>
    RTF: {elapsed: .3f}/{duration: .3f} = {rtf:.3f} <br/>
    """
    if rtf > 1:
        info += (
            "<br/>We are loading the model for the first run. "
            "Please run again to measure the real RTF.<br/>"
        )

    logging.info(info)
    logging.info(f"\nrepo_id: {repo_id}\nDetected events: {events}")

    return eventStr, events, build_html_output(info)


title = "# Audio tagging with [Next-gen Kaldi](https://github.com/k2-fsa) "
description = """
This space shows how to do audio tagging with [Next-gen Kaldi](https://github.com/k2-fsa)

It is running on a machine with 2 vCPUs with 16 GB RAM within a docker container provided by Hugging Face.

See more information by visiting the following links:

- <https://github.com/k2-fsa/sherpa-onnx>

If you want to deploy it locally, please see
<https://k2-fsa.github.io/sherpa/onnx>
"""

# css style is copied from
# https://huggingface.co/spaces/alphacep/asr/blob/main/app.py#L113
css = """
.result {display:flex;flex-direction:column}
.result_item {padding:15px;margin-bottom:8px;border-radius:15px;width:100%}
.result_item_success {background-color:mediumaquamarine;color:white;align-self:start}
.result_item_error {background-color:#ff7070;color:white;align-self:start}
"""


demo = gr.Blocks(css=css)


with demo:
    gr.Markdown(title)
    model_choices = list(models.keys())

    model_dropdown = gr.Dropdown(
        choices=model_choices,
        label="Select a model",
        value=model_choices[0],
    )

    with gr.Tabs():
        with gr.TabItem("Upload from disk"):
            uploaded_file = gr.Audio(
                sources=["upload"],  # Choose between "microphone", "upload"
                type="filepath",
                label="Upload from disk",
            )

        with gr.TabItem("Record from microphone"):
            microphone = gr.Audio(
                sources=["microphone"],  # Choose between "microphone", "upload"
                type="filepath",
                label="Record from microphone",
            )

        with gr.TabItem("From URL"):
            url_textbox = gr.Textbox(
                max_lines=1,
                placeholder="URL to an audio file",
                label="URL",
                interactive=True,
            )

    submit_button = gr.Button("Submit for recognition", variant="primary")

    output_string = gr.Textbox(label="Event (string)", show_label=True, show_copy_button=True)
    output_dataframe = gr.Dataframe(headers=["Event", "Probability"], label="Detected Events")
    output_html_info = gr.HTML(label="Processing Info")

    gr.Examples(
        examples=examples,
        inputs=[
            model_dropdown,
            uploaded_file,
        ],
        outputs=[
            output_string,
            output_dataframe,
            output_html_info,
        ],
        fn=submit_fn,
    )

    # Connect the submit button to the unified function
    submit_button.click(
        fn=submit_fn,
        inputs=[
            model_dropdown,
            uploaded_file,
            microphone,
            url_textbox,
        ],
        outputs=[
            output_string,
            output_dataframe,
            output_html_info,
        ],
    )

    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(inbrowser=True)