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# from transformers import pipeline
# import gradio as gr
#
# pipe = pipeline(model="dacavi/whisper-small-hi") # change to "your-username/the-name-you-picked"
# def transcribe(audio):
# text = pipe(audio)["text"]
# return text
#
# iface = gr.Interface(
# fn=transcribe,
# inputs=gr.Audio(sources="microphone", type="filepath"),
# outputs="text",
# title="Whisper Small Hindi",
# description="Realtime demo for Hindi speech recognition using a fine-tuned Whisper small model.",
# )
#
# iface.launch()
import gradio as gr
from transformers import pipeline
from moviepy.editor import VideoFileClip
import tempfile
import os
from pydub import AudioSegment
from huggingface_hub import login
with open("../../token.txt", "r") as file:
token = file.readline().strip()
login(token=token, add_to_git_credential=True)
pipe = pipeline(model="dacavi/whisper-small-hi")
def transcribe_video(video_url):
# Download video and extract audio
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_audio:
# os.system(f"yt-dlp -o {temp_audio.name} -x --audio-format wav {video_url}")
os.system(f"yt-dlp -o audioSample.wav -x --audio-format wav {video_url}")
print("Downloaded audio:", temp_audio.name)
# Transcribe audio
text = pipe("audioSample.wav")["text"]
# Clean up temporary files
os.remove("audioSample.wav")
return text
print(transcribe_video("https://www.youtube.com/watch?v=8FkLRUJj-o0"))
# iface = gr.Interface(
# fn=transcribe_video,
# inputs="text",
# outputs="text",
# live=True,
# title="Video Transcription",
# description="Paste the URL of a video to transcribe the spoken content.",
# )
#
# iface.launch()
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