app.py

import gradio as gr
from asr import transcribe_multiple_files, ASR_LANGUAGES, model
from lid import identify, LID_EXAMPLES
from csv_processor import CSV_FILE_PATH
import logging
import soundfile as sf
import os

# Set up logging
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger(__name__)

def download_csv():
    file_path = CSV_FILE_PATH  # Путь к вашему файлу
    if os.path.exists(file_path):  # Проверяем, существует ли файл
        return file_path  # Если файл существует, возвращаем путь для скачивания
    else:
        logger.error(f"file {file_path} not found!")
        full_path = "/home/user/app/"+ file_path
        exist_full_path = os.path.exists(full_path)
        res = "found" if exist_full_path else "not found"
        logger.error(f"file {exist_full_path} {res}!")
        return None  # Возвращаем None, если файла нет
    
# Prepare language options for Dropdown
language_options = [f"{k} ({v})" for k, v in ASR_LANGUAGES.items()]
bam_val = "bam (Bamanankan)"
bam_index = 0 if bam_val not in language_options else language_options.index(bam_val)

download_interface = gr.Interface(
    fn=download_csv,
    inputs=[],
    outputs=gr.File(label="Download CSV"),
    title="Download CSV file",
    description="Download file audio_plus_hash_uniq_07102024.csv"
)


mms_transcribe = gr.Interface(
    fn=transcribe_multiple_files,
    inputs=[
        gr.File(type="filepath"),  # Allow multiple audio files
        gr.Dropdown(
            choices=language_options,
            label="Language",
            value=language_options[bam_index] if language_options else None,
        ),
        gr.Textbox(label="Optional: Provide your own transcription"),
    ],
    outputs=gr.Textbox(label="Transcriptions", lines=10),
    title="Speech-to-text",
    description="Transcribe multiple audio files in your desired language.",
    allow_flagging="never",
)

mms_identify = gr.Interface(
    fn=identify,
    inputs=[gr.Audio()],
    outputs=gr.Label(num_top_classes=10),
    examples=LID_EXAMPLES,
    title="Language Identification",
    description="Identify the language of input audio.",
    allow_flagging="never",
)

tabbed_interface = gr.TabbedInterface(
    [mms_transcribe, mms_identify, download_interface],
    ["Speech-to-text", "Language Identification", "Download CSV file"],
)

with gr.Blocks() as demo:
    gr.Markdown(
        "<p align='center' style='font-size: 20px;'>MMS: Scaling Speech Technology to 1000+ languages demo. See our <a href='https://ai.facebook.com/blog/multilingual-model-speech-recognition/'>blog post</a> and <a href='https://arxiv.org/abs/2305.13516'>paper</a>.</p>"
    )
    gr.HTML(
        """<center>Click on the appropriate tab to explore Speech-to-text (ASR) and Language identification (LID) demos.</center>"""
    )
    gr.HTML(
        """<center>You can also finetune MMS models on your data using the recipes provided here - <a href='https://huggingface.co/blog/mms_adapters'>ASR</a> <a href='https://github.com/ylacombe/finetune-hf-vits'>TTS</a></center>"""
    )
    gr.HTML(
        """<center><a href="https://huggingface.co/spaces/facebook/MMS?duplicate=true" style="display: inline-block;margin-top: .5em;margin-right: .25em;" target="_blank"><img style="margin-bottom: 0em;display: inline;margin-top: -.25em;" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a> for more control and no queue.</center>"""
    )

    tabbed_interface.render()
    gr.HTML(
        """
        <div class="footer" style="text-align:center">
            <p>
                Model by <a href="https://ai.facebook.com" style="text-decoration: underline;" target="_blank">Meta AI</a> - Gradio Demo by 🤗 Hugging Face
            </p>
        </div>
        """
    )

if __name__ == "__main__":
    mms_transcribe.launch()