File size: 3,438 Bytes
22ee735 bf2e636 22ee735 d1a43a0 22ee735 9adc690 22ee735 9adc690 22ee735 b74bc53 22ee735 98854ce 035a443 bf2e636 98854ce 22ee735 b74bc53 22ee735 3e24523 df102ed 22ee735 b74bc53 22ee735 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 | import os
import gradio as gr
from transformers import pipeline
from pytube import YouTube
from datasets import Dataset, Audio
from moviepy.editor import AudioFileClip
import googletrans
from googletrans import Translator
pipe = pipeline(model="rafat0421/whisper-small-hi")
def download_from_youtube(url):
streams = YouTube(url).streams.filter(only_audio=True, file_extension='mp4') #Downloads the video from the given YouTube URL and returns the path to the audio file.
fpath = streams.first().download()
return fpath
def get_timestamp(seconds):
minutes = int(seconds / 60)
seconds = int(seconds % 60)
return f"{str(minutes).zfill(2)}:{str(seconds).zfill(2)}" #Creates %M:%S timestamp from seconds.
def create_segments(audio_fpath, seconds_max):
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
max_segments = int(seconds_max / 30)
if n_full_segments > max_segments:
n_full_segments = max_segments
len_last_segment = 0
segment_paths = []
segment_start_times = []
segments_available = n_full_segments + 1
for i in range(min(segments_available, max_segments)):
start = i * 30
# 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
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 get_translation(text):
#translator = Translator()
#result = translator.translate(text)
#result = translator.translate(text, src='fi', dest='fr')
#return result
return "Under Development..."
def transcribe(audio, url, seconds_max):
if url:
fpath = download_from_youtube(url)
segment_paths, segment_start_times = create_segments(fpath, seconds_max)
audio_dataset = Dataset.from_dict({"audio": segment_paths}).cast_column("audio", Audio(sampling_rate=16000))
pred = pipe(audio_dataset["audio"])
text = ""
n_segments = len(segment_start_times)
for i, (seconds, output) in enumerate(zip(segment_start_times, pred)):
text += f"[Segment {i+1}/{n_segments}, start time {get_timestamp(seconds)}]\n"
text += f"{output['text']}\n"
text += f"[Translation]\n{get_translation(output['text'])}\n\n"
return text
else:
text = pipe(audio)["text"]
return text
iface = gr.Interface(
fn=transcribe,
inputs=[
gr.Audio(source="microphone", type="filepath", label="Transcribe from Microphone"),
gr.Text(max_lines=1, placeholder="YouTube Link", label="Transcribe from YouTube URL"),
gr.Slider(minimum=30, maximum=600, value=30, step=30, label="Number of seconds to transcribe")
],
outputs="text",
title="Whisper: transcribe Swedish language audio to text",
description="Swedish Text Transcription using Transformers.",
)
iface.launch() |