app.py

import spaces
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

MODEL_NAME = "openai/whisper-large-v3-turbo"
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,
)


@spaces.GPU
def transcribe(inputs):
    if inputs is None:
        raise gr.Error("未提供音訊檔案！請在提交請求前上傳或錄製一個音訊檔案。")

    text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": "transcribe"}, 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"最大YouTube影片長度為 {yt_length_limit_hms}，但提供的影片長度為 {file_length_hms}。")

    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))

@spaces.GPU
def yt_transcribe(yt_url, 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": "transcribe"}, return_timestamps=True)["text"]

    return html_embed_str, text


demo = gr.Blocks(theme=gr.themes.Ocean())

mf_transcribe = gr.Interface(
    fn=transcribe,
    inputs=gr.Audio(sources="microphone", type="filepath"),
    outputs="text",
    title="清華大學多模態課程＆廖老師嫡傳弟子-第二組 「語音轉文字」模型",
    description=(
        "只需點擊一下按鈕，即可轉錄長篇的麥克風或音訊輸入！此示範使用"
        f"檢查點 [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) 和 🤗 Transformers 來轉錄任意長度的音訊檔案。"
    ),
    allow_flagging="never",
)

file_transcribe = gr.Interface(
    fn=transcribe,
    inputs=gr.Audio(sources="upload", type="filepath", label="音訊檔案"),
    outputs="text",
    title="Whisper Large V3: 音訊轉錄",
    description=(
        "只需點擊一下按鈕，即可轉錄長篇的麥克風或音訊輸入！此示範使用"
        f"檢查點 [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) 和 🤗 Transformers 來轉錄任意長度的音訊檔案。"
    ),
    allow_flagging="never",
)

yt_transcribe_interface = gr.Interface(
    fn=yt_transcribe,
    inputs=gr.Textbox(lines=1, placeholder="在此貼上YouTube影片的URL", label="YouTube URL"),
    outputs=["html", "text"],
    title="Whisper Large V3: YouTube轉錄",
    description=(
        "只需點擊一下按鈕，即可轉錄長篇的YouTube影片！此示範使用"
        f"檢查點 [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) 和 🤗 Transformers 來轉錄任意長度的影片檔案。"
    ),
    allow_flagging="never",
)

with demo:
    gr.TabbedInterface([mf_transcribe, file_transcribe, yt_transcribe_interface], ["麥克風", "音訊檔案", "YouTube"])

demo.queue().launch(ssr_mode=False)