app.py

import torch

import gradio as gr
import yt_dlp as youtube_dl

from transformers import pipeline
from huggingface_hub import model_info
import re
import tempfile
import os

MODEL_NAME = "razhan/whisper-small-ckb"
BATCH_SIZE = 1
FILE_LIMIT_MB = 10
YT_LENGTH_LIMIT_S = 60 * 10

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(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


def _return_yt_html_embed(yt_url):
    if 'youtu.be' in yt_url:
        video_id = yt_url.split('/')[-1].split('?')[0]
    else:
        video_id = yt_url.split("?v=")[-1].split('&')[0]

    HTML_str = (
        f'<center><iframe width="560" height="315" src="https://www.youtube.com/embed/{video_id}" '
        'frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" '
        'allowfullscreen></iframe></center>'
    )
    return HTML_str





def yt_transcribe(yt_url, task="transcribe", max_filesize=75.0, progress=gr.Progress()):
    html_embed_str = _return_yt_html_embed(yt_url)

    with tempfile.TemporaryDirectory() as tmpdirname:
        filepath = os.path.join(tmpdirname, "video.mp4")
        download_yt_audio(yt_url, filepath)
        with open(filepath, "rb") as f:
            inputs = f.read()

    inputs = ffmpeg_read(inputs, pipe.feature_extractor.sampling_rate)
    inputs = {"array": inputs, "sampling_rate": pipe.feature_extractor.sampling_rate}



    start_time = time.time()
    outputs = pipe(inputs, chunk_length_s=30, batch_size=BATCH_SIZE, generate_kwargs={"task": task, "language": "persian"}, return_timestamps=False)
    exec_time = time.time() - start_time
    logging.info(print(f"transcribe: {exec_time} sec."))
    
    return html_embed_str,  txt, exec_time


def download_yt_audio(yt_url, filename, progress=gr.Progress()):
    if '&list' in yt_url:
        yt_url = yt_url.split('&list')[0]
         
    info_loader = youtube_dl.YoutubeDL()

    try:
        info = info_loader.extract_info(yt_url, download=False)
    except youtube_dl.utils.DownloadError as err:
        raise gr.Error(str(err))
    
    file_length = info["duration_string"]
    file_h_m_s = file_length.split(":")
    file_h_m_s = [int(sub_length) for sub_length in file_h_m_s]
    
    if len(file_h_m_s) == 1:
        file_h_m_s.insert(0, 0)
    if len(file_h_m_s) == 2:
        file_h_m_s.insert(0, 0)
    file_length_s = file_h_m_s[0] * 3600 + file_h_m_s[1] * 60 + file_h_m_s[2]
    
    if file_length_s > YT_LENGTH_LIMIT_S:
        yt_length_limit_hms = time.strftime("%HH:%MM:%SS", time.gmtime(YT_LENGTH_LIMIT_S))
        file_length_hms = time.strftime("%HH:%MM:%SS", time.gmtime(file_length_s))
        raise gr.Error(f"Maximum YouTube length is {yt_length_limit_hms}, got {file_length_hms} YouTube video.")
    
    # ydl_opts = {"outtmpl": filename, "format": "worstvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best"}
    ydl_opts = {"outtmpl": filename, "format": "bestaudio/best"}
    
    with youtube_dl.YoutubeDL(ydl_opts) as ydl:
        try:
            ydl.download([yt_url])
        except youtube_dl.utils.ExtractorError as err:
            raise gr.Error(str(err))
    progress(1, desc="Video downloaded from YouTube!")


demo = gr.Blocks()

mf_transcribe = gr.Interface(
    fn=transcribe,
    inputs=[
        gr.Audio(sources="microphone", type="filepath"),
        gr.Audio(sources="upload", type="filepath"),
    ],
    outputs="text",
    theme="huggingface",
    title="Whisper Central Kurdish‌ (Sorani) Demo: Transcribe Audio",
    description=(
        "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the 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",
        gr.Textbox(
                label="Output",
                rtl=True,
                show_copy_button=True,
        ),
        gr.Text(label="Transcription Time")
        ],
    theme="huggingface",
    title="Whisper Central Kurdish‌ (Sorani) Demo: Transcribe YouTube",
    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()