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import os
import gradio as gr
from transformers import pipeline
from pytube import YouTube
from datasets import Dataset, Audio
from moviepy.editor import AudioFileClip
pipe = pipeline(model="Neprox/model")
def download_from_youtube(url):
streams = YouTube(url).streams.filter(only_audio=True, file_extension='mp4')
fpath = streams.first().download()
return fpath
def divide_into_30s_segments(audio_fpath):
if not os.path.exists("segmented_audios"):
os.makedirs("segmented_audios")
sound = AudioFileClip(audio_fpath)
n_full_segments = int(sound.duration / 30)
len_last_segment = sound.duration % 30
segment_paths = []
segment_start_times = []
for i in range(n_full_segments + 1):
# Skip last segment if it is smaller than two seconds
is_last_segment = i == n_full_segments
if is_last_segment and not len_last_segment > 2:
continue
elif is_last_segment:
end = start + len_last_segment
else:
end = (i + 1) * 30
start = i * 30
segment_path = os.path.join("segmented_audios", f"segment_{i}.wav")
segment = sound.subclip(start, end)
segment.write_audiofile(segment_path)
segment_paths.append(segment_path)
segment_start_times.append(start)
return segment_paths, segment_start_times
def transcribe(audio, url):
if url:
fpath = download_from_youtube(url)
segment_paths, segment_start_times = divide_into_30s_segments(fpath)
audio_dataset = Dataset.from_dict({"audio": audio_segment_paths}).cast_column("audio", Audio())
print(audio_dataset)
text = pipe(audio_dataset)
print(type(text))
print(text)
return text
else:
text = pipe(audio)["text"]
return text
iface = gr.Interface(
fn=transcribe,
inputs=[
gr.Audio(source="microphone", type="filepath"),
gr.Text(max_lines=1, placeholder="Enter YouTube Link with Swedish speech to be transcribed")
],
outputs="text",
title="Whisper Small Swedish",
description="Realtime demo for Swedish speech recognition using a fine-tuned Whisper small model.",
)
iface.launch()
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