app.py

import gradio as gr
import yt_dlp as youtube_dl
from transformers import pipeline, WhisperForConditionalGeneration, WhisperTokenizer
from transformers.pipelines.audio_utils import ffmpeg_read
import torch
from huggingface_hub import CommitScheduler
import spaces
import tempfile
import os
import json
from datetime import datetime
from pathlib import Path
from uuid import uuid4
from functools import lru_cache

os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"

MODEL_NAME = "dwb2023/whisper-large-v3-quantized"
BATCH_SIZE = 8
YT_LENGTH_LIMIT_S = 4800  # 1 hour 20 minutes

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

# Load the model
model = WhisperForConditionalGeneration.from_pretrained(MODEL_NAME)
tokenizer = WhisperTokenizer.from_pretrained(MODEL_NAME)

# Initialize the pipeline with the quantized model
pipe = pipeline(task="automatic-speech-recognition", model=model, tokenizer=tokenizer, chunk_length_s=30, device=device)

# Define paths and create directory if not exists
JSON_DATASET_DIR = Path("json_dataset")
JSON_DATASET_DIR.mkdir(parents=True, exist_ok=True)
JSON_DATASET_PATH = JSON_DATASET_DIR / f"transcriptions-{uuid4()}.json"

# Initialize CommitScheduler for saving data to Hugging Face Dataset
scheduler = CommitScheduler(
    repo_id="transcript-dataset-repo",
    repo_type="dataset",
    folder_path=JSON_DATASET_DIR,
    path_in_repo="data",
)

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

@spaces.GPU
@lru_cache(maxsize=10)
def transcribe_audio(inputs, task):
    if inputs is None:
        raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")
    text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
    return text

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"]
    if file_length > YT_LENGTH_LIMIT_S:
        yt_length_limit_hms = time.strftime("%H:%M:%S", time.gmtime(YT_LENGTH_LIMIT_S))
        file_length_hms = time.strftime("%H:%M:%S", time.gmtime(file_length))
        raise gr.Error(f"Maximum YouTube length is {yt_length_limit_hms}, got {file_length_hms} YouTube video.")
    ydl_opts = {"outtmpl": filename, "format": "bestaudio/best"}
    with youtube_dl.YoutubeDL(ydl_opts) as ydl:
        ydl.download([yt_url])

@spaces.GPU
@lru_cache(maxsize=10)
def yt_transcribe(yt_url, task):
    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"]
    save_transcription(yt_url, text)
    return text

def save_transcription(yt_url, transcription):
    with scheduler.lock:
        with JSON_DATASET_PATH.open("a") as f:
            json.dump({"url": yt_url, "transcription": transcription, "datetime": datetime.now().isoformat()}, f)
            f.write("\n")

@spaces.GPU
def yt_transcribe2(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

demo = gr.Blocks()

yt_transcribe_interface = gr.Interface(
    fn=yt_transcribe,
    inputs=[
        gr.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"),
        gr.Radio(["transcribe", "translate"], label="Task", value="transcribe")
    ],
    outputs="text",
    title="Whisper Large V3: 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",
)

yt_transcribe = gr.Interface(
    fn=yt_transcribe2,
    inputs=[
        gr.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"),
        gr.Radio(["transcribe", "translate"], label="Task", value="transcribe")
    ],
    outputs=["html", "text"],
    title="Whisper Large V3: 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([yt_transcribe_interface, yt_transcribe], ["YouTube", "YouTube HF"])

demo.queue().launch()