app.py

import torch
import gradio as gr
import yt_dlp as yt
from transformers import pipeline
#from transformers.pipelines.audio_utils import ffmpeg_read
from typing import Tuple
import tempfile
import os
from yt_dlp import YoutubeDL

MODEL_NAME = "openai/whisper-large-v2"
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",
    chunk_length_s=30,
    model=MODEL_NAME,
    device=device,
)

def transcribe(microphone, file_upload, task):
    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):
        raise gr.InterfaceError("You have to either use the microphone or upload an audio file")

    file_size_mb = None

    if file_upload is not None:
        file_size_mb = os.stat(file_upload).st_size / (1024 * 1024)
        if file_size_mb > FILE_LIMIT_MB:
            raise gr.InterfaceError(
                f"File size exceeds file size limit. Got file of size {file_size_mb:.2f}MB for a limit of {FILE_LIMIT_MB}MB."
            )

    file_path = microphone if microphone is not None else file_upload

    with open(file_path, "rb") as f:
        inputs = f.read()

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

    return warn_output + text
    
def download_yt_audio(yt_url, filename):
    ydl_opts = {"outtmpl": filename, "format": "worstvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best"}
    with yt.YoutubeDL(ydl_opts) as ydl:
        try:
            ydl.download([yt_url])
        except yt.utils.ExtractorError as err:
            raise gr.InterfaceError(str(err))

def yt_transcribe(yt_url, task, max_filesize=75.0) -> Tuple[str, str]:
    with YoutubeDL({}) as ydl:
        info_dict = ydl.extract_info(yt_url, download=False)

    video_id = info_dict["id"]
    html_embed_str = f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe> </center>'

    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, pipeline.feature_extractor.sampling_rate)
    #inputs = {"array": inputs, "sampling_rate": pipeline.feature_extractor.sampling_rate}

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

    return html_embed_str, text

demo = gr.Blocks()

mf_transcribe = gr.Interface(
    fn=transcribe,
    inputs=[
        gr.inputs.Audio(source="microphone", type="filepath", optional=True),
        gr.inputs.Audio(source="upload", type="filepath", optional=True),
        gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe"),
    ],
    outputs="text",
    layout="horizontal",
    theme="huggingface",
    title="Whisper Large V2: Transcribe Audio",
    description=(
        "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the"
        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.inputs.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"),
        gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe")
    ],
    outputs=["html", "text"],
    layout="horizontal",
    theme="huggingface",
    title="Whisper Large V2: Transcribe YouTube",
    description=(
        "Transcribe long-form YouTube videos with the click of a button! Demo uses the checkpoint"
        f" [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe video files of"
        " arbitrary length."
    ),
    allow_flagging="never",
)

with demo:
    gr.TabbedInterface([mf_transcribe, yt_transcribe], ["Transcribe Audio", "Transcribe YouTube"])

demo.launch(enable_queue=True)