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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 = "dataprizma/whisper-medium-uz"
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 html_embed_str, text
demo = gr.Blocks()
mf_transcribe = gr.Interface(
fn=transcribe,
inputs=[
gr.inputs.Audio(source="microphone", type="filepath", optional=True),
gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe"),
],
outputs="text",
layout="horizontal",
theme="huggingface",
title="Whisper Medium Uzbek: 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",
)
file_transcribe = gr.Interface(
fn=transcribe,
inputs=[
gr.inputs.Audio(source="upload", type="filepath", optional=True, label="Audio file"),
gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe"),
],
outputs="text",
layout="horizontal",
theme="huggingface",
title="Whisper Medium Uzbek: 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",
)
yt_transcribe = gr.Interface(
fn=yt_transcribe,
inputs=[
gr.inputs.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"),
gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe")
],
outputs=["html", "text"],
layout="horizontal",
theme="huggingface",
title="Whisper Medium Uzbek: Transcribe YouTube",
description=(
"Transcribe long-form YouTube videos with the click of a button! Demo uses the OpenAI Whisper 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([mf_transcribe, file_transcribe, yt_transcribe], ["Microphone", "Audio file", "YouTube"])
demo.launch(enable_queue=True)
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