Spaces:
Paused
Paused
File size: 3,259 Bytes
262d511 1221d26 262d511 048700d 262d511 e9c72a6 262d511 |
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 |
import os, sys, re
import shutil
import argparse
import subprocess
import soundfile
from process_audio import segment_audio
from write_srt import write_to_file
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC, Wav2Vec2Tokenizer
import torch
import gradio as gr
model = "facebook/wav2vec2-large-960h-lv60-self"
tokenizer = Wav2Vec2Tokenizer.from_pretrained(model)
asr_model = Wav2Vec2ForCTC.from_pretrained(model)#.to('cuda')
# Line count for SRT file
line_count = 0
def sort_alphanumeric(data):
convert = lambda text: int(text) if text.isdigit() else text.lower()
alphanum_key = lambda key: [convert(c) for c in re.split('([0-9]+)', key)]
return sorted(data, key = alphanum_key)
def transcribe_audio(tokenizer, asr_model, audio_file, file_handle):
# Run Wav2Vec2.0 inference on each audio file generated after VAD segmentation.
global line_count
speech, rate = soundfile.read(audio_file)
input_values = tokenizer(speech, sampling_rate=16000, return_tensors = "pt", padding='longest').input_values
logits = asr_model(input_values).logits
prediction = torch.argmax(logits, dim = -1)
infered_text = tokenizer.batch_decode(prediction)[0].lower()
infered_text = re.sub(r' ', ' ', infered_text)
infered_text = re.sub(r'\bi\s', 'I ', infered_text)
infered_text = re.sub(r'\si$', ' I', infered_text)
infered_text = re.sub(r'i\'', 'I\'', infered_text)
limits = audio_file.split(os.sep)[-1][:-4].split("_")[-1].split("-")
if len(infered_text) > 1:
line_count += 1
write_to_file(file_handle, infered_text, line_count, limits)
def get_subs(input_file):
# Get directory for audio
base_directory = os.getcwd()
audio_directory = os.path.join(base_directory, "audio")
if os.path.isdir(audio_directory):
shutil.rmtree(audio_directory)
os.mkdir(audio_directory)
# Extract audio from video file
video_file = input_file
audio_file = audio_directory+'/temp.wav'
command = ["ffmpeg", "-i", video_file, "-ac", "1", "-ar", "16000","-vn", "-f", "wav", audio_file]
subprocess.run(command)
video_file = input_file.split('/')[-1][:-4]
srt_file_name = os.path.join(video_file + ".srt")
# Split audio file based on VAD silent segments
segment_audio(audio_file)
os.remove(audio_file)
# Output SRT file
file_handle = open(srt_file_name, "a+")
file_handle.seek(0)
for file in sort_alphanumeric(os.listdir(audio_directory)):
audio_segment_path = os.path.join(audio_directory, file)
if audio_segment_path.split(os.sep)[-1] != audio_file.split(os.sep)[-1]:
transcribe_audio(tokenizer, asr_model, audio_segment_path, file_handle)
file_handle.close()
shutil.rmtree(audio_directory)
return srt_file_name
gradio_ui = gr.Interface(
fn=get_subs,
title="Autoblog - Video to Subtitle",
description="Get subtitles (SRT file) for your videos. Inference speed is about 10s/per 1min of video BUT the speed of uploading your video depends on your internet connection.",
inputs=gr.inputs.Video(label="Upload Video File"),
outputs=gr.outputs.File(label="Auto-Transcript")
)
gradio_ui.launch(inline=False)
|