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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", default="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(enable_queue=True)
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