app.py

import spaces
import torch

import gradio as gr
import yt_dlp as youtube_dl
from pytubefix import YouTube
from pytubefix.cli import on_progress

from transformers import pipeline
from transformers.pipelines.audio_utils import ffmpeg_read

import tempfile
import os

MODEL_NAME = "razhan/whisper-base-hawrami-transcription"
BATCH_SIZE = 1
FILE_LIMIT_MB = 30
YT_LENGTH_LIMIT_S = 60 * 10  # 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, task="transcribe"):
    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 _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))
    # yt = pt.YouTube(yt_url)
    # stream = yt.streams.filter(only_audio=True)[0]
    # stream.download(filename=filename)

# @spaces.GPU
# def yt_transcribe(yt_url, task="transcribe", 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")
#         filepath = os.path.join(tmpdirname, "audio.mp3")
#         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


def yt_transcribe(yt_url, task="transcribe", progress=gr.Progress(), max_filesize=75.0):
    progress(0, desc="Loading audio file...")
    html_embed_str = _return_yt_html_embed(yt_url)
    try:
        # yt = pytube.YouTube(yt_url)
        # stream = yt.streams.filter(only_audio=True)[0]
        yt = YouTube(yt_url, on_progress_callback = on_progress, use_po_token=True)
         
        stream = yt.streams.get_audio_only()
        
    except:
        raise gr.Error("An error occurred while loading the YouTube video. Please try again.")

    if stream.filesize_mb > max_filesize:
        raise gr.Error(f"Maximum YouTube file size is {max_filesize}MB, got {stream.filesize_mb:.2f}MB.")

    # stream.download(filename="audio.mp3")
    stream.download(filename="audio.mp3", mp3=True)

    with open("audio.mp3", "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(theme=gr.themes.Ocean())

mf_transcribe = gr.Interface(
    fn=transcribe,
    inputs=[
        gr.Audio(sources="microphone", type="filepath"),
        gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
    ],
    outputs="text",
    title="Whisper Horami Demo: 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."
    ),
    flagging_mode="never",
)

file_transcribe = gr.Interface(
    fn=transcribe,
    inputs=[
        gr.Audio(sources="upload", type="filepath", label="Audio file"),
        gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
    ],
    outputs="text",
    title="Whisper Horami Demo: 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."
    ),
    flagging_mode="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"),
        # gr.Radio(["transcribe", "translate"], label="Task", value="transcribe")
    ],
    outputs=["html", "text"],
    title="Whisper Horami Demo: Translate 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."
    ),
    flagging_mode="never",
)

with demo:
    # gr.TabbedInterface([mf_transcribe, file_transcribe, yt_transcribe], ["Microphone", "Audio file", "YouTube"])
    gr.TabbedInterface([mf_transcribe, file_transcribe], ["Microphone", "Audio file"])

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

# import spaces
# import torch
# import gradio as gr
# from pytubefix import YouTube
# from pytubefix.cli import on_progress
# from transformers import pipeline
# from transformers.pipelines.audio_utils import ffmpeg_read
# import tempfile
# import os

# MODEL_NAME = "razhan/whisper-base-hawrami-transcription"
# BATCH_SIZE = 1

# 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(inputs, task="transcribe"):
#     if inputs is None:
#         raise gr.Error("Please upload or record an audio file before submitting.")

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

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

# def yt_transcribe(yt_url, task="transcribe", progress=gr.Progress()):
#     progress(0, desc="Loading audio file...")
#     html_embed = _return_yt_html_embed(yt_url)
    
#     try:
#         yt = YouTube(yt_url, on_progress_callback=on_progress, use_po_token=True)
#         stream = yt.streams.get_audio_only()
#     except Exception as e:
#         raise gr.Error(f"Error loading YouTube video: {str(e)}")

#     with tempfile.TemporaryDirectory() as tmpdir:
#         file_path = os.path.join(tmpdir, "audio.mp3")
#         stream.download(filename=file_path)
        
#         with open(file_path, "rb") as f:
#             audio_data = f.read()

#     audio = ffmpeg_read(audio_data, pipe.feature_extractor.sampling_rate)
#     inputs = {"array": audio, "sampling_rate": pipe.feature_extractor.sampling_rate}
    
#     result = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)
#     return html_embed, result["text"]

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

# common_inputs = [
#     gr.Radio(["transcribe", "translate"], label="Task", value="transcribe")
# ]

# mf_transcribe = gr.Interface(
#     fn=transcribe,
#     inputs=[
#         gr.Audio(sources="microphone", type="filepath"),
#         *common_inputs
#     ],
#     outputs="text",
#     title="Whisper Horami: Live Transcription",
#     description="Transcribe audio from your microphone in real-time"
# )

# file_transcribe = gr.Interface(
#     fn=transcribe,
#     inputs=[
#         gr.Audio(sources="upload", type="filepath", label="Audio file"),
#         *common_inputs
#     ],
#     outputs="text",
#     title="Whisper Horami: File Transcription",
#     description="Upload an audio file for transcription"
# )

# yt_interface = gr.Interface(
#     fn=yt_transcribe,
#     inputs=[
#         gr.Textbox(placeholder="YouTube URL", label="Video URL"),
#         *common_inputs
#     ],
#     outputs=["html", "text"],
#     title="Whisper Horami: YouTube Transcription",
#     description="Transcribe audio from YouTube videos"
# )

# with demo:
#     gr.TabbedInterface(
#         [mf_transcribe, file_transcribe],
#         ["Microphone", "Audio File",]
#     )

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