app.py

import torch

import gradio as gr
from transformers import pipeline
from transformers.pipelines.audio_utils import ffmpeg_read

import tempfile
import os

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(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 _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 None#html_embed_str, text


demo = gr.Blocks()

file_transcribe = gr.Interface(
    fn=transcribe,
    inputs=[
        gr.Audio(),
        gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
    ],
    outputs="text",
    #layout="horizontal",
    theme="huggingface",
    title="Whisper Large V3: Transcribe Audio",
    #description=(
    #    "Transcribe long-form microphone or 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."
    #),
    #allow_flagging="never",
)


with demo:
    gr.TabbedInterface([ file_transcribe], [ "Audio file"])

demo.launch(share=True)