app.py

import torch
import gradio as gr
import yt_dlp as youtube_dl
from transformers import pipeline
from transformers.pipelines.audio_utils import ffmpeg_read
import tempfile
import os
import time

MODEL_NAME = "openai/whisper-large-v3"
BATCH_SIZE = 8
FILE_LIMIT_MB = 1000
YT_LENGTH_LIMIT_S = 3600  # limit to 1 hour YouTube files

device = 0 if torch.cuda.is_available() else "cpu"

pipe = pipeline(
    task="automatic-speech-recognition",
    model=MODEL_NAME,
    chunk_length_s=30,
    device=device,
)

def transcribe(audio, task):
    if audio is None:
        raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")

    text = pipe(audio, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
    return text

def _return_yt_html_embed(yt_url):
    video_id = yt_url.split("?v=")[-1]
    HTML_str = (
        f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>'
        " </center>"
    )
    return HTML_str

def download_yt_audio(yt_url, filename):
    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"}
    
    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))

def yt_transcribe(yt_url, task, max_filesize=75.0):
    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}

    text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]

    return html_embed_str, text

with gr.Blocks(theme="huggingface") as demo:
    gr.Markdown("# Whisper Large V3: Transcribe Audio")
    gr.Markdown(
        "Transcribe long-form audio inputs with the click of a button! Demo uses the OpenAI Whisper"
        f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
        " of arbitrary length."
    )

    with gr.Tabs():
        with gr.TabItem("Microphone"):
            with gr.Row():
                mic_input = gr.Audio(type="filepath", label="Microphone Input")
                # mic_input = gr.Audio(source="microphone", type="filepath", label="Microphone Input")
                mic_task = gr.Radio(["transcribe", "translate"], label="Task", value="transcribe")
            mic_output = gr.Textbox(label="Transcription")
            mic_button = gr.Button("Transcribe")

        with gr.TabItem("Audio file"):
            with gr.Row():
                file_input = gr.Audio(type="filepath", label="Audio file")
                # file_input = gr.Audio(source="upload", type="filepath", label="Audio file")
                file_task = gr.Radio(["transcribe", "translate"], label="Task", value="transcribe")
            file_output = gr.Textbox(label="Transcription")
            file_button = gr.Button("Transcribe")

        with gr.TabItem("YouTube"):
            with gr.Row():
                yt_input = gr.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL")
                yt_task = gr.Radio(["transcribe", "translate"], label="Task", value="transcribe")
            yt_embed = gr.HTML(label="Video")
            yt_output = gr.Textbox(label="Transcription")
            yt_button = gr.Button("Transcribe")

    mic_button.click(transcribe, inputs=[mic_input, mic_task], outputs=mic_output)
    file_button.click(transcribe, inputs=[file_input, file_task], outputs=file_output)
    yt_button.click(yt_transcribe, inputs=[yt_input, yt_task], outputs=[yt_embed, yt_output])

if __name__ == "__main__":
    demo.launch(enable_queue=True)