Add necessary file

app.py

@@ -0,0 +1,97 @@
+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')
+vocab_dict = tokenizer.get_vocab()
+sort_vocab = sorted((value, key) for (key,value) in vocab_dict.items())
+vocab =  ([x[1].replace("|", " ") if x[1] not in tokenizer.all_special_tokens else "_" for x in sort_vocab])
+
+
+# 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_directory = os.path.join(base_directory, "srt")
+    srt_file_name = os.path.join(srt_directory, 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",
+    inputs=gr.inputs.Video(label="Upload Video File"),
+    outputs=gr.outputs.File(label="Auto-Transcript")
+    )
+
+gradio_ui.launch(inline=False)

process_audio.py

@@ -0,0 +1,16 @@
+import auditok
+
+
+def segment_audio(audio_name):
+    audio_regions = auditok.split(audio_name,
+    min_dur=2,     # minimum duration of a valid audio in seconds
+    max_dur=8,       # maximum duration of an audio segment
+    max_silence=0.8, # maximum duration of tolerated continuous silence within an event
+    energy_threshold=55, # threshold of detection
+    sampling_rate=16000
+)
+
+    for i, r in enumerate(audio_regions):
+        filename = r.save(audio_name[:-4]+"_{meta.start:.3f}-{meta.end:.3f}.wav")
+
+

requirements.txt

@@ -0,0 +1,5 @@
+soundfile
+transformers
+torch
+gradio
+auditok

write_srt.py

@@ -0,0 +1,31 @@
+import os
+import datetime
+
+def write_to_file(file_handle, inferred_text, line_count, limits):
+    """Write the inferred text to SRT file
+    Follows a specific format for SRT files
+
+    Args:
+        file_handle : SRT file handle
+        inferred_text : text to be written
+        line_count : subtitle line count 
+        limits : starting and ending times for text
+    """
+
+    sep = ','
+    
+    d = str(datetime.timedelta(seconds=float(limits[0])))
+    try:
+        from_dur = "0" + str(d.split(".")[0]) + sep + str(d.split(".")[-1][:2])
+    except:
+        from_dur = "0" + str(d) + sep + "00"
+        
+    d = str(datetime.timedelta(seconds=float(limits[1])))
+    try:
+        to_dur = "0" + str(d.split(".")[0]) + sep + str(d.split(".")[-1][:2])
+    except:
+        to_dur = "0" + str(d) + sep + "00"
+
+    file_handle.write(str(line_count) + "\n")
+    file_handle.write(from_dur + " --> " + to_dur + "\n")
+    file_handle.write(inferred_text + "\n\n")